diff --git a/config/data_config.json b/config/data_config.json
index 7532ed8..d1de5e8 100644
--- a/config/data_config.json
+++ b/config/data_config.json
@@ -1,5 +1,5 @@
 {
-  "remote.data.address": "tcp://data.tushare.org:8910",
+  "remote.data.address": "tcp://data.quantos.org:8910",
   "remote.data.username": "17621969269",
   "remote.data.password": "eyJhbGciOiJIUzI1NiJ9.eyJjcmVhdGVfdGltZSI6IjE1MTIwMjA0OTQwMzciLCJpc3MiOiJhdXRoMCIsImlkIjoiMTc2MjE5NjkyNjkifQ.WQvI9k6dvXe5zIzQwyuPI4BM0Py1OSYFENIQ3z0RG6c"
 }
diff --git a/doc/base_data.md b/doc/base_data.md
index f036047..ebdd1ac 100644
--- a/doc/base_data.md
+++ b/doc/base_data.md
@@ -40,4 +40,4 @@ df, msg = api.query(
 
 接口还在进一步的丰富过程中。
 
-详细的说明文档，please refer to [tushare](http://tushare.org/pro)
+详细的说明文档，please refer to [quantos](https://www.quantos.org)
diff --git a/doc/source/base_data.rst b/doc/source/base_data.rst
index 8995544..da3c3f4 100644
--- a/doc/source/base_data.rst
+++ b/doc/source/base_data.rst
@@ -63,4 +63,4 @@
 
 接口还在进一步的丰富过程中。
 
-详细的说明文档，please refer to `tushare <http://tushare.org/pro>`__
+详细的说明文档，please refer to `quantos <https://www.quantos.org>`__
diff --git a/doc/source/data_api.rst b/doc/source/data_api.rst
index f745abe..d6d5f66 100644
--- a/doc/source/data_api.rst
+++ b/doc/source/data_api.rst
@@ -20,7 +20,7 @@
 
 .. code:: python
 
-    api = DataApi(addr='tcp://data.tushare.org:8910')
+    api = DataApi(addr='tcp://data.quantos.org:8910')
     api.login("phone", "token") 
 
 调用数据接口
diff --git a/example/WallStreetLectures/ipython/lecture12_DoubleMAStrats.ipynb b/example/WallStreetLectures/ipython/lecture12_DoubleMAStrats.ipynb
index 258b991..377b28d 100644
--- a/example/WallStreetLectures/ipython/lecture12_DoubleMAStrats.ipynb
+++ b/example/WallStreetLectures/ipython/lecture12_DoubleMAStrats.ipynb
@@ -58,7 +58,7 @@
     "token = os.environ.get('QUANTOS_TOKEN')\n",
     "\n",
     "data_config = {\n",
-    "  \"remote.data.address\": \"tcp://data.tushare.org:8910\",\n",
+    "  \"remote.data.address\": \"tcp://data.quantos.org:8910\",\n",
     "  \"remote.data.username\": phone,\n",
     "  \"remote.data.password\": token\n",
     "}\n",
diff --git a/example/WallStreetLectures/ipython/lecture13_CalendarSpread.ipynb b/example/WallStreetLectures/ipython/lecture13_CalendarSpread.ipynb
index 324a462..2bb6868 100644
--- a/example/WallStreetLectures/ipython/lecture13_CalendarSpread.ipynb
+++ b/example/WallStreetLectures/ipython/lecture13_CalendarSpread.ipynb
@@ -167,7 +167,7 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "Begin: DataApi login 17321165656@tcp://data.tushare.org:8910\n",
+      "Begin: DataApi login 17321165656@tcp://data.quantos.org:8910\n",
       "    login success \n",
       "\n"
      ]
@@ -188,7 +188,7 @@
     "token = os.environ.get('QUANTOS_TOKEN')\n",
     "\n",
     "data_config = {\n",
-    "  \"remote.data.address\": \"tcp://data.tushare.org:8910\",\n",
+    "  \"remote.data.address\": \"tcp://data.quantos.org:8910\",\n",
     "  \"remote.data.username\": phone,\n",
     "  \"remote.data.password\": token\n",
     "}\n",
@@ -835,7 +835,7 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "Begin: DataApi login 17321165656@tcp://data.tushare.org:8910\n",
+      "Begin: DataApi login 17321165656@tcp://data.quantos.org:8910\n",
       "    Already login as 17321165656, skip init_from_config\n",
       "on_new_day in trade 20170315\n",
       "\n",
@@ -59733,7 +59733,7 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "Begin: DataApi login 17321165656@tcp://data.tushare.org:8910\n",
+      "Begin: DataApi login 17321165656@tcp://data.quantos.org:8910\n",
       "    Already login as 17321165656, skip init_from_config\n",
       "process trades...\n",
       "get daily stats...\n",
diff --git a/example/WallStreetLectures/ipython/lecture16_correlation.ipynb b/example/WallStreetLectures/ipython/lecture16_correlation.ipynb
new file mode 100644
index 0000000..0d2b820
--- /dev/null
+++ b/example/WallStreetLectures/ipython/lecture16_correlation.ipynb
@@ -0,0 +1,660 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 股票及版块收益率相关系数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 本段代码使用quantOS系统计算申万28个一级行业日收益率的相关系数，以及部分个股间相关系数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 系统设置"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# encoding: utf-8\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import datetime\n",
+    "import seaborn as sns\n",
+    "import matplotlib.mlab as mlab\n",
+    "import scipy.stats as stats\n",
+    "\n",
+    "sns.set_style('darkgrid')\n",
+    "sns.set_context('poster')\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "from jaqs.data import RemoteDataService\n",
+    "import jaqs.util as jutil\n",
+    "from __future__ import print_function, unicode_literals, division, absolute_import\n",
+    "\n",
+    "from jaqs.data import RemoteDataService, DataView"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Begin: DataApi login 17321165656@tcp://data.quantos.org:8910\n",
+      "    login success \n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'0,'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataview_dir_path = '.'\n",
+    "backtest_result_dir_path = '.'\n",
+    "\n",
+    "import os\n",
+    "phone = os.environ.get('QUANTOS_USER')\n",
+    "token = os.environ.get('QUANTOS_TOKEN')\n",
+    "\n",
+    "data_config = {\n",
+    "  \"remote.data.address\": \"tcp://data.quantos.org:8910\",\n",
+    "  \"remote.data.username\": phone,\n",
+    "  \"timeout\": 3600,\n",
+    "  \"remote.data.password\": token\n",
+    "}\n",
+    "\n",
+    "ds = RemoteDataService()\n",
+    "ds.init_from_config(data_config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 设置参数"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "STARTDATE, ENDDATE = 20160401, 20180330"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 计算个股相关系数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. 工商银行与建设银行"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stock_1, _ = ds.daily('601398.SH', STARTDATE, ENDDATE, fields = 'close', adjust_mode = 'post')\n",
+    "stock_2, _ = ds.daily('601939.SH', STARTDATE, ENDDATE, fields = 'close', adjust_mode = 'post')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stock_1['ret'] = stock_1['close'].pct_change()\n",
+    "stock_1 = stock_1.set_index('trade_date')\n",
+    "stock_2['ret'] = stock_2['close'].pct_change()\n",
+    "stock_2 = stock_2.set_index('trade_date')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stock_pair = pd.concat([stock_1['ret'], stock_2['ret']], axis = 1)\n",
+    "stock_pair.columns = ['工商银行', '建设银行']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 相关性矩阵"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>工商银行</th>\n",
+       "      <th>建设银行</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>工商银行</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.847851</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>建设银行</th>\n",
+       "      <td>0.847851</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          工商银行      建设银行\n",
+       "工商银行  1.000000  0.847851\n",
+       "建设银行  0.847851  1.000000"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stock_pair.corr()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 收益率分布"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0,0.5,'601939.SH')"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xe985400>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize = (16, 8))\n",
+    "plt.scatter(stock_pair['工商银行'], stock_pair['建设银行'], s = 30)\n",
+    "ax.set_xlabel('601398.SH')\n",
+    "ax.set_ylabel('601939.SH')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. 工商银行与中国平安"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stock_1, _ = ds.daily('601398.SH', STARTDATE, ENDDATE, fields = 'close', adjust_mode = 'post')\n",
+    "stock_2, _ = ds.daily('601318.SH', STARTDATE, ENDDATE, fields = 'close', adjust_mode = 'post')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stock_1['ret'] = stock_1['close'].pct_change()\n",
+    "stock_1 = stock_1.set_index('trade_date')\n",
+    "stock_2['ret'] = stock_2['close'].pct_change()\n",
+    "stock_2 = stock_2.set_index('trade_date')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stock_pair = pd.concat([stock_1['ret'], stock_2['ret']], axis = 1)\n",
+    "stock_pair.columns = ['工商银行', '中国平安']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 相关性矩阵"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>工商银行</th>\n",
+       "      <th>中国平安</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>工商银行</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.367732</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>中国平安</th>\n",
+       "      <td>0.367732</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          工商银行      中国平安\n",
+       "工商银行  1.000000  0.367732\n",
+       "中国平安  0.367732  1.000000"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stock_pair.corr()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 收益率分布"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0,0.5,'601318.SH')"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xfc62860>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize = (16, 8))\n",
+    "plt.scatter(stock_pair['工商银行'], stock_pair['中国平安'], s = 30)\n",
+    "ax.set_xlabel('601398.SH')\n",
+    "ax.set_ylabel('601318.SH')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. 工商银行与天齐锂业"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stock_1, _ = ds.daily('601398.SH', STARTDATE, ENDDATE, fields = 'close', adjust_mode = 'post')\n",
+    "stock_2, _ = ds.daily('002466.SZ', STARTDATE, ENDDATE, fields = 'close', adjust_mode = 'post')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stock_1['ret'] = stock_1['close'].pct_change()\n",
+    "stock_1 = stock_1.set_index('trade_date')\n",
+    "stock_2['ret'] = stock_2['close'].pct_change()\n",
+    "stock_2 = stock_2.set_index('trade_date')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stock_pair = pd.concat([stock_1['ret'], stock_2['ret']], axis = 1)\n",
+    "stock_pair.columns = ['工商银行', '天齐锂业']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 相关性矩阵"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>工商银行</th>\n",
+       "      <th>天齐锂业</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>工商银行</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.081202</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>天齐锂业</th>\n",
+       "      <td>-0.081202</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          工商银行      天齐锂业\n",
+       "工商银行  1.000000 -0.081202\n",
+       "天齐锂业 -0.081202  1.000000"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stock_pair.corr()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 收益率分布"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0,0.5,'002466.SZ')"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xfc5d748>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize = (16, 8))\n",
+    "plt.scatter(stock_pair['工商银行'], stock_pair['天齐锂业'], s = 30)\n",
+    "ax.set_xlabel('601398.SH')\n",
+    "ax.set_ylabel('002466.SZ')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 计算版块相关系数"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_ret = pd.read_csv('lecture16_industry_daily_ret.csv', index_col = 'trade_date')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_ret_copy = df_ret.loc[20160401:20180330, :]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ret_corr = df_ret_copy.corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0xfd480f0>"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xfd1d7f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize = (20, 18))\n",
+    "sns.heatmap(ret_corr, annot = True, cmap = \"coolwarm\")\n",
+    "# fig.savefig('corrlation_matrix.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/example/WallStreetLectures/ipython/lecture16_industry_daily_ret.csv b/example/WallStreetLectures/ipython/lecture16_industry_daily_ret.csv
new file mode 100644
index 0000000..bb59a7c
--- /dev/null
+++ b/example/WallStreetLectures/ipython/lecture16_industry_daily_ret.csv
@@ -0,0 +1,1519 @@
+trade_date,Appliance,Army,Bank,Car,Chemistry,Clothing,Commercial,Construction Material,Construction decoration,Digging,Electronic,Electronic equipment,Food,Health Care,Housing,IT,Light Industrials,Mechenical equipment,Media,Metal,NLMY,Non bank,Nonferrous Metal,Others,Service,Telecom,Transportation,Utility
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+20120106,4.402619558637389,-90.38733468267392,64.57397345509953,1.8126105908247379,88.46957021920446,82.58253518281171,20.525212030931975,62.138846600861044,-6.99804902875562,116.72752540444245,82.85617452417902,69.72658581930443,89.97078870496591,-27.049508014907858,3.8260988997513032,31.4887839262758,-0.07713371128851865,24.277565643709046,-9.35274227199693,174.00413796715816,-160.78973698801246,126.42032115691613,204.8026806963922,95.86265651959134,-125.873741491613,19.803940984255867,33.38436402464911,68.2632648694908
+20120109,419.7150558336542,348.4793374555832,211.5127175368139,393.48096240809144,464.4360021567084,361.56423181690093,343.2472332306891,474.9091406018683,308.41273546534245,680.4373680474246,436.44019071038736,385.9899928520372,172.77038036301283,324.76328099491485,383.44423141987147,377.0392662241357,398.7843633334362,443.9323755555033,370.37926068170907,288.5650004890932,359.54401191787036,349.9518946550904,527.4745656758557,418.17356649505734,343.2823358043476,344.9717591431199,293.9460575326482,316.8226098851842
+20120110,346.37313905606584,406.0941154204913,96.77265576583876,385.47604319324455,397.39178328822453,373.4838549818999,404.2481322524241,575.4816906308338,473.41297888546546,466.647963918082,406.2380811120057,415.906948526174,344.7848888995403,353.62375109196284,286.69112820585474,362.7848891664065,371.0297150147611,483.0357745908098,371.96490984422377,348.8255722020396,529.5702790403961,383.5901800913897,515.8255002463304,401.796957424361,371.2225182314793,385.935261050079,336.4348616768183,258.3032955715757
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diff --git a/example/WallStreetLectures/ipython/lecture17_portfolioOptimization.ipynb b/example/WallStreetLectures/ipython/lecture17_portfolioOptimization.ipynb
new file mode 100644
index 0000000..0acd35e
--- /dev/null
+++ b/example/WallStreetLectures/ipython/lecture17_portfolioOptimization.ipynb
@@ -0,0 +1,2237 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Markowitz资产组合优化"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 本段代码使用quantOS系统实现了Markowitz资产组合优化，并画出有效前沿。并比较两组股票组合有效前沿的差异"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# encoding: utf-8\n",
+    "import sys\n",
+    "sys.path.insert(0, '/home/lli/JAQS/')\n",
+    "sys.path.append('/store/project/public/py/quantos/jzquant/')\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import datetime\n",
+    "import seaborn as sns\n",
+    "import matplotlib.mlab as mlab\n",
+    "import scipy.stats as stats\n",
+    "import cvxopt as opt\n",
+    "from cvxopt import blas, solvers\n",
+    "\n",
+    "np.random.seed(100)\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from jaqs.data import RemoteDataService\n",
+    "import jaqs.util as jutil\n",
+    "from __future__ import print_function, unicode_literals, division, absolute_import\n",
+    "\n",
+    "from jaqs.data import RemoteDataService, DataView"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Begin: DataApi login 17321165656@tcp://data.quantos.org:8910\n",
+      "    login success \n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "u'0,'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataview_dir_path = '.'\n",
+    "backtest_result_dir_path = '.'\n",
+    "\n",
+    "import os\n",
+    "phone = os.environ.get('QUANTOS_USER')\n",
+    "token = os.environ.get('QUANTOS_TOKEN')\n",
+    "\n",
+    "data_config = {\n",
+    "  \"remote.data.address\": \"tcp://data.quantos.org:8910\",\n",
+    "  \"remote.data.username\": phone,\n",
+    "  \"timeout\": 3600,\n",
+    "  \"remote.data.password\": token\n",
+    "}\n",
+    "\n",
+    "ds = RemoteDataService()\n",
+    "ds.init_from_config(data_config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 获取资产日收益率数据"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "STARTDATE, ENDDATE = 20150101, 20180413"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initialize config success.\n",
+      "Query data...\n",
+      "Query data - query...\n",
+      "NOTE: price adjust method is [post adjust]\n",
+      "220\n",
+      "441\n",
+      "662\n",
+      "220\n",
+      "441\n",
+      "662\n",
+      "Query data - daily fields prepared.\n",
+      "Query instrument info...\n",
+      "Query adj_factor...\n",
+      "Query benchmark...\n",
+      "Query benchmar member info...\n",
+      "Data has been successfully prepared.\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataview_props = {# Start and end date of back-test\n",
+    "                  'start_date': STARTDATE, 'end_date': ENDDATE,\n",
+    "                  # Investment universe and performance benchmark\n",
+    "                  'universe': '000300.SH', 'benchmark': '000300.SH',\n",
+    "                  # Data fields that we need\n",
+    "                  # freq = 1 means we use daily data. Please do not change this.\n",
+    "                  'freq': 1}\n",
+    "\n",
+    "# DataView utilizes RemoteDataService to get various data and store them\n",
+    "dv = DataView()\n",
+    "dv.init_from_config(dataview_props, ds)\n",
+    "dv.prepare_data()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 由四只股票构成第一个股票组合"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Field [daily_ret] is overwritten.\n"
+     ]
+    }
+   ],
+   "source": [
+    "dv.add_formula('daily_ret', 'Return(close_adj, 1)', is_quarterly=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "SYMBOLS = ['601318.SH', '601888.SH', '600887.SH', '002027.SZ']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "daily_ret = dv.get_ts('daily_ret')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "daily_ret = daily_ret.loc[:, SYMBOLS]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 将其中一只股票收益率序列整体增加10 bps，构成第二个股票组合"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "SHIFT = 0.001"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "daily_ret_shift = daily_ret.copy()\n",
+    "daily_ret_shift[SYMBOLS[3]] += SHIFT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "daily_ret = daily_ret.dropna()\n",
+    "daily_ret_shift = daily_ret_shift.dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "return_vec = daily_ret.T.values\n",
+    "return_vec_shift = daily_ret_shift.T.values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 模拟生成资产权重"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rand_weights(n):\n",
+    "    ''' Produces n random weights that sum to 1 '''\n",
+    "    k = np.random.rand(n)\n",
+    "    return k / sum(k)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 生成资产组合"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def random_portfolio(returns):\n",
+    "    ''' \n",
+    "    Returns the mean and standard deviation of returns for a random portfolio\n",
+    "    '''\n",
+    "\n",
+    "    p = np.asmatrix(np.mean(returns, axis=1))\n",
+    "    w = np.asmatrix(rand_weights(returns.shape[0]))\n",
+    "    C = np.asmatrix(np.cov(returns))\n",
+    "    \n",
+    "    mu = w * p.T\n",
+    "    sigma = np.sqrt(w * C * w.T)\n",
+    "    \n",
+    "    # This recursion reduces outliers to keep plots pretty\n",
+    "#     if sigma > 2:\n",
+    "#         return random_portfolio(returns)\n",
+    "    return mu, sigma"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_portfolios = 500\n",
+    "means1, stds1 = np.column_stack([random_portfolio(return_vec) for _ in range(n_portfolios)])\n",
+    "means2, stds2 = np.column_stack([random_portfolio(return_vec_shift) for _ in range(n_portfolios)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 对比以下两图可以发现资产组合的risk-return分布差别较大"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5,1,u'Mean and standard deviation of returns of portfolio1')"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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smS2TdLekSZKWu/saM7tN0j53vzU89X2SvueRNtXu3m5mN0v6iaTRkv7G3XfENVYAAAAAQDLEtkXSUGNNMAAAAADULjNb6+4Fmy2XI87p0AAAAAAADCuEYAAAAABAYhCCAQAAAACJQQgGAAAAACQGIRgAAAAAkBiEYAAAAABAYhCCAQAAAACJQQgGAAAAACQGIRgAAAAAkBiEYAAAAABAYhCCAQAAAACJQQgGAAAAACQGIRgAAAAAkBiEYAAAAABAYhCCAQAAAACJQQgGAAAAACQGIRgAAAAAkBiEYAAAAABAYhCCAQAAAACJQQgGAAAAACQGIRgAAAAAkBiEYAAAAABAYhCCAQAAAACJQQgGAAAAACQGIRgAAAAAkBiEYAAAAABAYhCCAQAAAACJQQgGAAAAACQGIRgAAAAAkBiEYAAAAABAYhCCAQAAAACJQQgGAAAAACQGIRgAAAAAkBiEYAAAAABAYhCCAQAAAACJQQgGAAAAACQGIRgAAAAAkBiEYAAAAABAYhCCAQAAAACJQQgGAAAAACQGIRgAAAAAkBiEYAAAAABAYhCCAQAAAACJQQgGAAAAACQGIRgAAAAAkBiEYAAAAABAYhCCAQAAAACJQQgGAAAAACQGIRgAAAAAkBiEYAAAAABAYhCCAQAAAACJQQgGAAAAACRGrCHYzC41s+1m1m5mXyxyzp+Z2Q4ze8nMvhkeO8HMPPL1dJzjBAAAAAAkQ2wh2MxM0j2SPilpvqR3mNmFeecslHSrpHdLmirprsjdz7m7hV8L4honAAAAACA54qwEL5J00N0fcPddku6U9Pa8cz4s6Wvu/lt3P+zuT8Q4HgAAAABAwsUZgmdIaorc3h4eizpL0ulmts/MmszsPZH7ZoZTpLeZ2fWFLmBmN5pZo5k1tra2Vnb0AAAAAICaU+3GWHWSZkmaK+ldkpab2QRJRxVUkk+SdJ2k28zszPwHu/sd7t7g7g2TJ08ewmEDAAAAAEaiOEPwLgUBN2dOeCzqBUk/dvc2d39c0jZJc9w94+4b3f2Yu/9G0mpJrAsGAAAAAAxKnCF4vaQJZrbEzGZIukHS/Xnn3K+gYdZEM7tAQVDeZmanm9k8MxsdNtO6WNKGGMcKAAAAAEiAurie2N2zZrZM0t2SJkla7u5rzOw2Sfvc/VZJP5Z0uYKKcIukD7v7wbBr9N2STpO0U9It7r41rrECAAAAAJLB3L3aY6iIhoYGb2xsrPYwAAAAAAAxMLO17t4w2OepdmMsAAAAAACGDCEYAAAAAJAYhGAAAAAAQGIQggEAAAAAiUEIBgAAAAAkBiEYAAAAAJAYhGAAAAAAQGIQggEAAAAAiUEIBgAAAAAkBiEYAAAAAJAYhGAAAAAAQGIQggEAAAAAiUEIBgAAAAAkBiEYAAAAAJAYhGAAAAAAQGIQggEAAAAAiUEIBgAAAAAkBiEYAAAAAJAYhGAAAAAAQGIQggEAAAAAiUEIBgAAAAAkBiEYAAAAAJAYhGAAAAAAQGIQggEAAAAAiUEIBgAAAAAkBiEYAAAAAJAYhGAAAAAAQGIQggEAAAAAiUEIBgAAAAAkBiEYAAAAAJAYhGAAAAAAQGIQggEAAAAAiUEIBgAAAAAkBiEYAAAAAJAYhGAAAAAAQGIQggEAAAAAiUEIBgAAAAAkBiEYAAAAAJAYhGAAAAAAQGIQggEAAAAAiUEIBgAAAAAkBiEYAAAAAJAYhGAAAAAAQGIQggEAAAAAiUEIBgAAAAAkBiEYAAAAAJAYhGAAAAAAQGIQggEAAAAAiUEIBgAAAAAkBiEYAAAAAJAYhGAAAAAAQGLEGoLN7FIz225m7Wb2xSLn/JmZ7TCzl8zsm5Hj7zGzPWa238w+Fuc4AQAAAADJEFsINjOTdI+kT0qaL+kdZnZh3jkLJd0q6d2Spkq6Kzw+TtLtkt4u6UJJ/8vMTotrrAAAAACAZIizErxI0kF3f8Ddd0m6U0GojfqwpK+5+2/d/bC7PxEev0LSo+HxzZJ+KGlJjGMFAAAAACRAnCF4hqSmyO3t4bGosySdbmb7zKzJzN5TxmNlZjeaWaOZNba2tlZw6AAAAACAWlTtxlh1kmZJmivpXZKWm9mEUh/s7ne4e4O7N0yePDmuMQIAAAAAakScIXiXgoCbMyc8FvWCpB+7e5u7Py5pW+S8/h4LAAAAAEBZ4gzB6yVNMLMlZjZD0g2S7s87534FDbMmmtkFCsLuNkkPSbrIzF5vZvMlXSvppzGOFQAAAACQAHVxPbG7Z81smaS7JU2StNzd15jZbZL2ufutkn4s6XIFFeEWSR9294OSZGY3S/qJpNGS/sbdd8Q1VgAAAABAMpi7V3sMFdHQ0OCNjY3VHgYAAAAAIAZmttbdGwb7PNVujAUAAAAAwJAhBAMAAAAAEoMQDAAAAABIDEIwAAAAACAxCMEAAAAAgMQgBAMAAAAAEoMQDAAAAABIDEIwAAAAACAxCMEAAAAAgMQgBAMAAAAAEoMQDAAAAABIDEIwAAAAACAxCMEAAAAAgMQgBAMAAAAAEoMQDAAAAABIDEIwAAAAACAxCMEAAAAAgMQgBAMAAAAAEoMQDAAAAABIDEIwAAAAACAxCMEAAAAAgMQgBAMAAAAAEoMQDAAAAABIDEIwAAAAACAxCMEAAAAAgMQgBAMAAAAAEoMQDAAAAABIDEIwAAAAACAxCMEAAAAAgMQgBAMAAAAAEoMQDAAAAABIDEIwAAAAACAxCMEAAAAAgMQgBAMAAAAAEoMQDAAAAABIDEIwAAAAACAxCMEAAAAAgMQgBAMAAAAAEoMQDAAAAABIDEIwAAAAACAxCMEAAAAAgMQgBAMAAAAAEoMQDAAAAABIDEIwAAAAACAxCMEAAAAAgMQgBAMAAAAAEoMQDAAAAABIDEIwAAAAACAxCMEAAAAAgMQgBAMAAAAAEoMQDAAAAABIjFhDsJldambbzazdzL5Y4P6bzMwjX58Oj5+Qd/zpOMcJAAAAAEiG2EKwmZmkeyR9UtJ8Se8wswsLnPpZd7fw67bI8ecixxfENU4AAAAAQHLEWQleJOmguz/g7rsk3Snp7TFeDwAAAACAPsUZgmdIaorc3h4ey/eXZvaSmf3azOZGjs8Mj28zs+sLXcDMbjSzRjNrbG1treDQAQAAAAC1qNqNse6XdLqkqZIekXRXePyogkrySZKuk3SbmZ2Z/2B3v8PdG9y9YfLkyUM0ZAAAAADASBVnCN4laVbk9pzwWDd3f8HdW939sKR/kLQwPJ5x943ufszdfyNptSTWBQMAAAAABiXOELxe0gQzW2JmMyTdoKDy283MXm9mJ5vZCZJuCR8jMzvdzOaZ2eiwmdbFkjbEOFYAAAAAQALEFoLdPStpmaSvS9oi6X53X2Nmt5nZX4WnvU3SM5L2SnpDeL4knSpphaSDkv5F0i3uvjWusQIAAAAAksHcvdpjqIiGhgZvbGys9jAAAAAAADEws7Xu3jDY56l2YywAAAAAAIYMIRgAAAAAkBiEYAAAAABAYhCCAQAAAACJQQgGAAAAACQGIRgAAAAAkBiEYAAAAABAYhCCAQAAAACJQQgGAAAAACQGIRgAAAAAkBiEYAAAAABAYhCCAQAAAACJQQgGAAAAACQGIRgAAAAAkBiEYAAAAABAYhCCAQAAAACJQQgGAAAAACQGIRgAAAAAkBiEYAAAAABAYhCCAQAAAACJQQgGAAAAACQGIRgAAAAAkBiEYAAAAABAYhCCAQAAAACJQQgGAAAAACQGIRgAAAAAkBiEYAAAAABAYhCCAQAAAACJQQgGAAAAACQGIRgAAAAAkBiEYAAAAABAYhCCAQAAAACJUVftAQAAAABAXDJZ16otLdqw+5DOnT5ei+dNUTpl1R4WqogQDAAAAKAmZbKu6+5+XOt2HtDRjozG1Ke16LQJum/ZBQThBGM6NAAAAICatGpLi9btPKAjHRm5pCMdGa3beUCrtrRUe2ioIkIwAAAAgJq0YfchHe3I9Dh2tCOjjbsPVWlEtSGTda3c1KzbV27Vyk3NymS92kMqC9OhAQAAANSkc6eP15j6tI5EgvCY+rTOmT6+iqMa2WphijmVYAAAAAD9GonVv8XzpmjRaRM0tj4tkzQ2DGyL502p9tBGrFqYYk4lGAAAAECfRmr1L50y3bfsAq3a0qKNuw/pHLpDD1pfU8wvP3tqlUZVHkIwAAAAgD5Fq39Sz+rfcA8+6ZTp8rOnDvtxjhS1MMWc6dAAAAAA+jScGkyNxGnZtaQWpphTCQYAAADQp+FS/Rup07JrSS1MMScEAwAAADUmk3Wt2tKiDbsP6dwKhJRc9S8/fA519W8kT8uuJSN9ijkhGAAAAKiiSgfWOKqlw6X6VwtNmVB9hGAAAACgSuIIrHFVS4dD9W+4TMvGyEZjLAAAAKBK4thzdTg1saq0WmjKhOqjEgwAAABUSRzTe2u5WjpcpmVjZCMEAwAAAFUSR2AdLk2s4jIcpmVjZCMEAwAAAFUSR2ClWgr0zdxrY3PphoYGb2xsrPYwAAAAgLLkukMTWIG+mdlad28Y7PNQCQYAAABCld6uqBRM7wWGFiEYAAAAUDzbFQEYfmLdIsnMLjWz7WbWbmZfLHD/TWbmka9PR+57j5ntMbP9ZvaxOMcJAAAAlLtdUSbrWrmpWbev3KqVm5qVydbGMkOg1sVWCTYzk3SPpE9KapS00swedPdH8079rLt/Ie+x4yTdLmmJpDZJvzazn7r7zrjGCwAAgGQrZ7siqsbAyBVnJXiRpIPu/oC775J0p6S3l/jYKyQ96u6/dffNkn6oIBADAAAAschtVxRVbLuicqvGAIaPOEPwDElNkdvbw2P5/tLMXjKzX5vZ3HIea2Y3mlmjmTW2trZWatwAAABIoNx2RWPr0zJJY/vYrqivqjGA4a3ajbHul/R9SUclfVrSXZIuKfXB7n6HpDukYIukOAYIAACAZChnf91c1fhIJAgXqxoDGF7iDMG7JM2K3J4THuvm7i/kvjezf1Cwfjj32CvyHrslnmECAAAAgVK3K8pVjfPXBBeqGgMYXuIMweslTTCzJQoaY90g6UPRE8zs9ZKekXRM0i3hYyTpIUl3hPe3SbpW0gUxjhUAAAAoWTlVYwDDS2wh2N2zZrZM0t2SJkla7u5rzOw2Sfvc/VZJb1MQjEdLekzSsvCx7WZ2s6SfhPf9jbvviGusAAAAQLlKrRoDGF7MvTaW0jY0NHhjY2O1hwEAAAAAiIGZrXX3hsE+T5zdoQEAAAAAGFYIwQAAAACAxCAEAwAAAAASgxAMAAAAAEgMQjAAAAAAIDEIwQAAAACAxIhtn2AAAABUTybrWrWlRRt2H9K508dr8bwpSqes2sMCgKojBAMAANSYTNZ13d2Pa93OAzrakdGY+rQWnTZB9y27gCAMIPGYDg0AAFBjVm1p0bqdB3SkIyOXdKQjo3U7D2jVlpZqDw0Aqo4QDAAAUGM27D6kox2ZHseOdmS0cfehKo0IAIYPQjAAAECNOXf6eI2pT/c4NqY+rXOmj6/SiABg+CAEAwAA1JjF86Zo0WkTNLY+LZM0NlwTvHjelGoPDQCqjsZYAAAANSadMt237AKt2tKijbsP6Ry6QwNAt5JCsJnVS3qbpNnRx7j738czLAAAgMpI6lZB6ZTp8rOn6vKzp1Z7KAAwrJRaCf6JJJP0c0md8Q0HAACgctgqCACQr9QQfImkqe7+UpyDAQAAqKToVkFSz62CqJACQDKVGoLvlXSVpB/FNxQAAIDBi05/3tZ6uOhWQYRgAEimUkPwRZJuNLPtkl5SMDXa3f21sY0MAABA5a3pzZ/+XF+Xkpnk/so5bBUEAMlWagi+KtZRAAAAFFDumt786c/HurIyBU2iMlnXmFEptgoCgIQraZ9gd29292ZJRyV55AsAACA20VDr6rmmt5ANuw/1mv7sCsL0qLRp9snH696l59MUawhksq6Vm5p1+8qtWrmpWZksHx0BDA+lbpH0JklflrRA0lZJp0taJ+n8+IYGAACSrlCo7WtN77nTx2tMfbq7EhzVmXE17T+i1VtbWQ8cM7pyAxjOSqoES1ou6T2SzN3nS1om6XexjQoAAECvhNqovtb0Lp43RYtOm6CxeY89BecWAAAgAElEQVTJyQVoxKvcCj4ADKVSQ/BUBRXgdjMbL+n/SfpAXIMCAACQeoZakzQ2rCgWW9ObTpnuW3aBvvHe1+hti6ZrdF3Pjzo0xRoafVXwAaDaSm2M9TtJsyU9JOmrko5IejqmMQEAAEh6JdSu2tKijbsP6Zx+ukPnHnP52VO1eN4UtbT3npJLU6z4FZqWzh8gAAwX5t5/kwIzO1HSMQWV4+sljZF0n7vvi3d4pWtoaPDGxsZqDwMAAAwjue2VSg3QqAzWBAOIg5mtdfeGQT9PKSE4vOA8SYvc/ftmdpKCfYLbBjuASiEEAwBQWeXszwvk4w8QACqtUiG41O7Qn5D0dkkXS/q+pFdL+oykqwc7AAAAMPx0dGX11uVr9FzrYXVmgv11XzNzIpU8lCw3LZ1O3ACGm1IbY/2VpHdKyv1X73eSLoplRAAAoKoyWdeS5Wu0eW+7OjPBjLGjnVk9uaON7r4jEPv1AkBPpTbGMkkvK9hvXpJOkdQey4gAAEBVrdrSomdbD/c6frQzW3R/XgxPrM0FgN5KrQTfJelLkmRm10r6gaR/jmtQAACgOjJZ18/W7+6uAEeNShvdfUcY9usFgN5KqgS7+2fN7L0KukJfLekr7v69WEcGAACGVK5quLapcN/LMyafwPZCI0xf+/VS0QeQVKU2xrpE0o0K9gquk3SFmX3Z3WfGODYAADCEclXDY13ZHsfTKemsKeP0wE1vYArtCMN+vQDQW6nTob8r6XZJ8xUE4dmS5sQzJAAAUA2FqoaSdM2rp2vFJy5WfV2pHxswXCyeN0WLTpugsfVpmaSx4ZpgKvoAkqzUxlhHJK1292NxDgYAAFRPoarh2Pq0rlk4nQrwCJVOme5bdgH79QJARKkh+F2S7jCznysIxJIkd//3WEYFAACGXK5qmN9JmKrhyMZ+vQDQU6kh+CZJsySdK6krPOaSCMEAANQIqoYAgCQoNQS/W9Ip7n4ozsEAAFDrMlnXqi0t2rD7kM4dhiGzv6rhcB8/AAD9KTUEf0fSpZIeiHEsAADUtNwWRPnTje9bdsGICJKDGX9+eL547mSt3tpKmAYADLlSQ/D5kpaa2XZJL0kySe7ur41tZAAA1JjcFkS5xlNHOjJat/OAVm1pGRHrNQc6/vzwfNyolOrrUurM+Ij8YwAAYGQrda+DqySdJumNkt4c3n5zXIMCAKAWPbXrYK8tiI52ZLRx98hYbVRoC6VSxh8Nzy7paGdWB492dd+OhmkAAOJWUgh29+ZCX3EPDgCAWpHJun759F553vEx9WmdM318VcZUrtwWSlGljL/Y/sNRI+mPAQCAkY1d7wEAGAKrtrSo6cWXeh2fddLYPrcgymRdKzc16/aVW7VyU7My2fwYPXRyWyiNrU/LFOwhXMoWSoXCc76R9McAAMDIVuqaYAAAkKecTskbdh/Sy53ZXsevWjCt6GOGspFWKa9loFso5e8/XGxNMPsRAwCGAiEYAIABKDeg5qqhRyLTgsfWp7VgxolFrzFUjbTKeS39baFUSKHwnOsOzX7EAIChxnRoAAAGIL/ZU3/NnQYylXigjajKVe5rGYhceP745XN1+dlTVV+X6nGbAAwAGCpUggEAKFF0yvC21sNFA2qhKulAphIXqh7HsXa2r7A9ErZuAgCgHIRgAABKkD9luL4uJTPJI32q+guofU0lLrQmN38tbVxrZ4cqbAMAMBwQggEAKEH++txjXVmlTBpdl1JHV3ZQAbWvNbkDaURVrovnTtaodM/nHJU2XTx38qCfu5zmYQAADAVCMAAAJSg0ZTjr0psXTNMZk08YVEDtrwFWuY2oyrV6a6s6unp2ru7oymr11tZBXXcou1sDAFAqGmMBAFCCQnvdjq1P65qF0wfd3GmoGmBJhfcdLrR908ud2UFffygabgEAUC4qwQCAYWm4TaONc33uUK3JLVaZXXrR7FiuT8MtAMBwFGsINrNLJd0jaZKk2939r4ucd7WkFZKudPeHzOwESe2RUza4+4I4xwoAGD6G4zTagXR3LtVQNcAqNu1arliuT8MtAMBwFFsINjNTEIA/KalR0koze9DdH80773hJfyFpdd5TPOfuZ8Y1PgBA5VWqetvfGtlq6au782CfdygaYBWrzG7e2x7L9Ycq3AMAUI44K8GLJB109wckyczulPR2SY/mnfe3kr4q6SMxjgUAELNKVm+TOI02roAd1VdlNo7rD1W4BwCgHHE2xpohqSlye3t4rJuZLZR0prvfX+DxM83sJTPbZmbXF7qAmd1oZo1m1tja2lqxgQNALSvUGKkSKtkEqVATKqbRDl6uMju2Pi1T0Ngr7spsLlwPtnkYAACVUu3GWF+TtKzA8aMKKsnPSWqQ9BMze9Tdn42e5O53SLpDkhoaGirzKQ4Aalica20rWb3tbxrtcGuaNVJQmQUAIN4QvEvSrMjtOeExSZKZ1UlaLOm5YPmwJOlNZnaNu6+QtDE89hszWy1pgaQeIRgAUJ4419pWsglSX2FtODbNGkmGYto1AADDWZzToddLmmBmS8xshqQbJHVPe3b3Lne33Jek/1DQHXqFmZ1uZvPMbLSZXSjpYkkbYhwrACRCnPvRVnqqbbFptOw9CwAABiO2SrC7Z81smaS7FWyRtNzd15jZbZL2ufutfTz81PBxp0naKekWd98a11gBICni3LKm2h2Oa7lpFgAAqJxY1wS7+0pJs/OOfbrIuVdFvv8vSXPjHBsAJFGcW9YM1Tpd9p4FAACDUe3GWACAQSg3eMZVrR3KdbrV2HuWRlwAANQOQjAAjFD9Bc9iwa1SjZGiz9+VyerJHW062pmVVNmGW/mGusMxjbgAAKgthGAAGKH66vS8eN6UWINbfjCsS5s6Mz13qotzne5QdjiOs6M2AAAYenF2hwYAxCSTdf1s/e4e62KlV4Lnw5uatbapLbYOyvkdmvMDsDT063QzWdfKTc26feVWrdzUrEy29O3j+3psnB21AQDA0KMSDADDWKEpzZJ03d2Pa21TW6/zx9SnNX/aOH1+xUYd68r2uO9IBSuzhYKhJI1Km7oyPiTrdKMGM2W5v8fSiAsAgNpCCAaAYapYOFt60Wyt23mgV8gdXZfSotMmSCa1tB/r9Xyj61IVC26FguHY+rQ+9IY5GpVOxb5ON99gpiz399hqNOICAADxIQQDwDBVKJytbWrT/pc6ClZhX3PaBJ0/5yQ9+Ic9vQKyJE0ZP7piwa1YMLz5irOq0ixqMHsH9/fYoW7EBQAA4kUIBoBhqlA4O9aV1ea97b3OTZn05M4Denz7ftXXpZQyKbokdnRdSp97yzkVC27DLRgOZspyKY8dykZcAAAgXoRgAIjJYPeWLRTO8pmk+rqUOjPZ7urvsa6sUhYE346ubHeV9rL5ld+qaLgEw8FMWWa6MwAAyWLupXfPHM4aGhq8sbGx2sMAAEmV2Vs2k3W9/67H9Pj2/SrW6PiiM07W5HGj9cC63YqeYpIumHOSUilTw6yJuumyuaqvq+0NAXJ/dBhIZXowjwUAAEPDzNa6e8Ngn4dKMADEoJxGTcUqxumU6YNvmKMndvRugiUFjag++IY5kqRfbWzuUTG2cHp0R1dW63YeUGNTW8X2CB6oUirjg6meD6YyXY2q9mBnCgAAgIEhBANABeQHmqd2HSypUVN/FeNNe9rVUSAAj0pbjym70em8+dOjy+mUHJdSKuOVqJ5Xaqxxh9Ph8loBAEgiQjCARKpk0MlNW85VbEfXpTTn5LElNWrqr2JcaF3wqLTpo5ec0aMTc7RJ1XOth3X/ut09rlNqp+T+XudAK7mlVMYHs81RpQxVOB0OrxUAgKQiBANInEoHnYc3NfdYt3usK6stzYc1b9o47dh/pM9mS/1tz1PqVkTR6bwrNzX3mh5dX5fS/Gnjyn5tOYOt5BZ7nT9bv7vs6nmchiqcDmZLJwAAMDiEYACJU2z/3Vt+sE7XLJxe9lrVB5/a06txlUuaN/UEfeZN8/pstlSo0ltfl9JzrYe1clOzFs+bUvJWRLkxPrXroGZOHKNnWg53j6szk9W3H31el4X73lbiPSunklvodZpJv3h6b3cH65knlVY9j9NQhdPBbOkEAAAGhxAMIHGK7b97/7rd+tXG5oqtVTXrv9lStNJ7pCOjlAWB9YG8sfT3PPljHJU2RZv/Z12DqmiWEg77OufPLz2z33XLTS++pNknH6+mfqrncRqqcMq2TAAAVA8hGEDi9LX/bqkVzt8/v19ff+gZ3XzFWbr61afop+t396gGp0y6+tWn9DuWdMp079LztfzhrfrVxmY913pYnRkvOpZi8sfYkem9p9JgKpqlhMO+zkmnrN91yy93ZnXVgmlaMOPEqm1VNFThNP/9YFsmAACGDiEYQM0qNoU5v/qa70i4VjV3fqEKZ2fG9a1HnlNjU5vuXXq+Xnf6yVrb1NbdGOu8WRN12fz+w2Ym6/rAt39XdCylBtdCY8w3mIpmKeGwv3P6W7c8pj6tBTNOHPKtiqKGMpxWY1smAABACAYwzJXTxTl67tmnjNM9a7Zr/QsHC05hzgWdn63frV88vbfXPry/eHqvWtof133LLihaOe7MuNbtPKDVW1sHHJzyK7j5Sg2u504fr+NGpXS0s+fryI1gsBXNUsJhOQFyOE8HJpwCAFDbCMEAhq2OrqzeunxN9xTh4+pMsyedoKsWTNOrZpzYI2Dlr4nNrTnNTVHONb96eFOzrjx3WnfQWTxvilraH++u4uYc68p2T0XOBbbfP7+/e6pyTrRSO5Dg1FcFd3RdSgtPPbGkYLh43hTNOvl4bd7b3uN4fV1Kb14wrWjDr3KUEg5LDZBMBwYAANWSqvYAACRLJutaualZt6/cqpWbmpXJb6scOW/J8jXavLe9O3i+3OXavLddX3toqz7+3Sd13d2Pq6Mrq5WbmnXLD9ZpbVObjnRk5ApCbP5TH+vK6u9WbOxxzVwYe/OCab3GkAu4uXM+eskZGpXuGdLKmWJc6LXnqszFmJUWCtMp01ULpin/7I6urM6YfIIuH2BX6DjlAvPHL587LMcHAABqE5VgAEOmnC7Lq7a06NnWw0WfK9c06q3L12jH/iNFpxPna24/1qvRVDplumbh9IJrVHMBN50y3XzFWWpsahvQFN5ir/3epecXXZ8crUaXUmF+1YwT2XYHAACgH1SCAQyZ6PpXV8/ux/k27D6krgIdjqOOdGT0THN70QBcqK7Y0ZXVxt2Heh3PTXkeW5+WSRozKqWZJ43VU7sOdldtcxXhb7z3NfrUlWfpG+99Tb/bJPX32nPrib/x3tfowjNO7jXmXDW6FPmvYewwWmcLAAAwXFAJBjBkStlrNqevbYyiisym1tj6tGaeNFbbWg/32C6oWGU0ukb16V0H9cun96pp/xF9/aGtvSrWlVr7m7+eWFKvinA5ldykrbMtp2kaAABADiEYwIAMJICUstdsTq6q+bvt+9VVLOkW8bo5J+mGN56ui+dO7t5+qJTpy7mAK0n//F/beuwLXM605EJKee251/zkjjYd7cxqVNo086Sxunju5JKvk5TOxuVMrQcAAIgiBAMoW6EAsvDUE/XBN8zRpj3tRUNxOdvi5Kqan/r+k3pg/Z6yxvf6MyZ1h8CBVEbLqViXqpTXnk6Z7l16vpYsX6NnWw+rK+Pasf+IPvDt3xHu8uRvLVWJP1QAAIBkIAQDKFuhAPLYtv363fNtyma9aFWu3Om66ZTprYtm6Fcbm3vtf1vM2Pq0FswY36tS/eeXnllyiCynYl2qUl/76q2tatp/pLsjdq2Fu0pNYY7jDxUAACAZCMEAylYogLjUvfVQLrh9/aFnVJdO9Qg75U7XXTxvil4zc2J3BfW4USmZWcG1wqPrUlp02gRdPHfyoKbKllOxLkcpr72Ww10lpzDH8YcKAACQDIRgAGUrpWnVkY6MvvXIc+rKFK8Ml6JQBfXCMybpT7/5Gz3XelidGVddShpbX6frL5ylT1x2llZvbR3UVNlqNpiq5XBXySnMcf2hAiMLzdEAAANBCAZQVLEPmNEA0lcQrtSU3kIV1Ac/cbEe3tSsv1uxUc3tx9T+cpfuWfO8nmg6oPPnnDToamq1GkzVcrirZJU7aZ2w0RvN0QAAA0UIBtAtGnrPnjZO3370+aIfMHMB5Gfrd+vnT+3psQ1RIZWe0ptOmVIp076XOnSsK1gvnAvbDbMmjthqai2Hu0pXuZPSCRuF0RwNADBQhGAAknpXVerrUurMZLv34c3/gJkLIIvnTVHzoZf12Lb9isZgk3rcjiOEFqssplOpEV1NrdVwV8tVbgy9Wl4/DwCIFyEYqEEDWSeXX1XJVVejin3APG/2RP3u+bbuxliSZCbVp1Pq6MrGFnaKVRYXzBivmy47c8DVVNYZxqOWq9wYerW8fh4AEC9CMFBjBrpOrlBVJV9d2tSZySqTdaVT1n2t3z+/v0cAliR36c0LpumMySfEFnb6qiwOtJrKOsN41WqVG0OPmQUAgIEiBAM15uFNzVrb1NZrnWx/6+QKVVVSJtWlrHu9b2fGddea7Wpsauuu6K3beaC7AVbUmPq0rlk4veJhJ79Ke+/S87V6a2vFKousMwRGBmYWAAAGihAM1JBM1vX5FRt7TWU+UmQac49GWKeM08JTT9T6Fw52V1UWnnqizps9Uf/8yLaCnZ6LVY9Hpa2kikwp047zx3jPmu09xrjotAlaeuFs9d2Wq/Rrs84QGDmYWQAAGAhCMFBDVm1pUUv7sV7HR9eleq2TKzbt9+vvXqTNe9u7qyr/+Otn1ZVX6T3akdHda7YX7MKcTpnOmzlRSy+c3edYS5l2XEqzrse2vai1TW091h7nnqNY0O3oymrJ8jV6tvVwr32MWWcIAABQ2wjBwAjSX+V0w+5D6ijQ0GrK+NG9qrLFpv2mUqaPXz63+7xCodAlPfrci3pyR5vq61IaW5/W0Y6MzCR312Pb9+sPuw72uZa2lGnHpTTryroKTv1ePG9KwZB979Lz9dbla7R5b3v3c+Q/jnWGAAAAtYsQDAwjmazr4U3NevCpPZKkq199ii6bP7VHE6q+KqdnnzJO9XWpHmFxdF1Kn3vLOb2CaKnTfvNDYbQmfLQzuM4NF5+uHfuP6BdP7y15LXIp1y+lWVe+3HNIKhiylz+8Vc+1Hi76uMvPnso6QwAAgBpGCAaqLFfdfWrXQf3yqT3a0ny4O2j+dP1uve70k3s0oSpWOc1kXfes2a7OzCsBOGXSebMm6rL5U3tdb1vr4V6BudC032jzmbvXbNejz73Y4/6XO7MalU7p9Mkn9KpC97WWtpRpx8WadY0Kt17Knx4dfY5iIbuxqa1gI6+6tHVfm3WGlcFWUwAAYDgiBANVlF/dzY9mWZfWNrUVbUIVDZmrtrRo/QsHewTCUemUll44u+Aa2yMdGaUsCJXu6nPaby4USj2rq1LP4FrOWtpSph0XO2fphbO1eW+75hdplJV7jkLjaZg1UU/uaOuuYuecOfmEqk15rsWwyFZTAABguCIEA0MsGni6MtmCgSzqWFdWG8Nw1FfILBSSO7qy2ry3XVeeO01SsMY2er2sS/Vp05+86hRds3B6v+Grv+BazlraUrY36euc3Gu6bP7UgvcXG+tNl81VY1Nb9/swKm06Y/IJeuCmN1QlnNVqWGSrKQAAMFwRgoEhlB946tJWcGpuVK6zc38BtJTpxU/tOtgrcHdkXHMmHV9SMOkvuJa7lrbYtONCldFi4yv2HH2N9d6l52v5w1vV2NSmhlkTddNlc1Vfl+r39cehVsMiW00BAIDhihAMDKH8wNNfAM6t6c2Ft75CZinTi7PZwtfLFDnel0KPqMRa2kpWRguNJ5N1feDbv+t+/nU7D6ixqa1qlddaDYtsNYVKqsUlAwCA6iEEA0OoWLfjXPfnupSpvi6lUyccp/nTxuuaRdO7u0PnzsuF2g1hB+SL507W6q2t2rD7kJZeNFty9djnN/pBMVXkQ2M6VVoVtK+AKqnfD6mlfJCNuzKaPyX8SEdGT+5oq1rltVbDIltNoVJqdckAAKB6CMHAIJVToSgUeCTJ5DJJXVlXV0dGz7S8pOb2Y7rtXYt6PFf+h8HjRqU0Kh10eD7WldXoupTOmzWx6IfDV804UWPzrj+2Pq0FM0oLXIUC6pM72vTV/9yiB9btVkv7MR3rympsgQ+ppX6QjbsyWmhK+NHOrJ7edbAqIbhWw2Ipa76BUtTqkgEAQPUQgoEyRUPv2dPG6duPPl9yheLiuZM1Kt37eFeBvlgHj3Zp+cNb9ak/ntd9LP/D4NHObI9Ad6wrq8e2vaiHNzfrynOm9XrOwQauggG1M6tvPbKtx5TqQh9SS/0gO9DKaKl/jKjklPBKqOWwyFZTqIRaXTIAAKgeQjBQhvxqZn1dsF9tLj71N7V29dbWXnvp9mVtU1uP28WmU0dlXVrxhz0FQ/BgA1exSnahAJn/IbXUD7IDCerlTJcc7JTwOBAWgeJqdckAAKB6CMFAGfKrmccKBNq+ptZu2H2oz+2Q8p03a2KP28VCaL7fPLtPmawXDLeDCVz5AbWv7tb5H1L7+yAbreQuvXC2ZNLmPYXXNucrZ7rkYKeEAxhatbpkAABQPYRgoIhC02tLqcRK0jMt7QVD6LnTx2t0XapgeM534pg63XTZ3B7Hoh8Gj3RklDapUAY9dLQzlvVy+ZXkzkxWd63Z3iuUj65L9fqQ2tcH2cE2vilnuiQfqIGRpZaXDAAAqoMQDBRQLJQtvXB2SZXYn/9hr9peerxXiFs8b4qmjButnW1HCz5u7pTjdawzq4WnTdCXr13Ya+/a3IfBhzc16/MrNmrvwZeV8d4puCPjRdfL9bV2tpR1tdFKcibramxq6zE9fOq40frsW87RZWdP7fHYvj7IrtzUPKjGN+VMl+QDNTDysGQAAFBJhGCggGLTa2XqUUXMXxOc4wrW8+aHuHTK9LlrztFHvvNEwXW02/YdUSbram4/phf/5fcFK6HplEkm7TpwVMV6OY0tEgD72+Ko3GpsuYGy2AfZwTa+Kbe6ywdqAACA5Io1BJvZpZLukTRJ0u3u/tdFzrta0gpJV7r7Q+Gx90j6qqTRkj7r7v8Y51iBqGKhbPOe9h6hb/60cfr8io0FK7vHurIFQ9wlZ03R6LpUwWpyLhj31+X5wT/s6TMAFwuAxbY4+vpDz2jH/iNa29TWPVW71GpsJQLlYBvfUN0FAABAqWILwWZmCgLwJyU1SlppZg+6+6N55x0v6S8krY4cGyfpdklLJLVJ+rWZ/dTdd8Y1XiCqr1BWKPR99N+eUFdeKh1dlyoY4h7Z0qKuTP9rgrMehN1CIbiYs6eN06ffNK9oACy2xdE3Vz1bcJumodqGpBLrdKnuAgAAoBRx7gmySNJBd3/A3XdJulPS2wuc97cKKr5HIseukPSou//W3TdL+qGCQAxURCbrWrmpWbev3KqVm5p7TU3OhbKx9WmZ+q6uXnb2VP3R7ImKZs6USa+dOVHZrPe4Ribr+vyKjeoo0lG5VFe/6hTlZ9yUSZ+68ixdnrcWN+rc6eN13Kjev/bF+nQN1TYkuUru19+zSEsWTdcfnzM16BANAAAAVFic06FnSGqK3N4uqSF6gpktlHSmu3/azD7Sz2Nn5F/AzG6UdKMkzZw5s0LDRq3KNX16atdB/cfTe9W0/0jBdbF9bdMjSf+5Ya8efGqPJOnqV5+iy+ZP1Xc+9Do9vKm5+/ibF5yiex/drpu/v65nY62LZqul/VivsaUtqPxGo3HKpFMnjtHtK7fq7GnjJJM27WnXudPH65J5U3TBnJP0xI4DOtaV1ei6lF47c4Iu66cKunjeFM06+Xht3tve7/vVV/CPy7d/83x3NfhXG5vL6hANAAAAlKLajbG+JmnZQB/s7ndIukOSGhoaBldaQ02LNoTKX4ubW/v68ObmHiEsvzFUJut6/12P6fHt+7vX4/50/W697vSTdd+yC3TludN05bnB1OX/3Li3O6BGr/HgH/aoo0DZdfqEMTp14pgeoXZ0nenuNdt1tDOrXAR0SWNGpfSamRP1Lx+8QKu3tpa1BjadMl21YJq27G3v1cwrZ3RdSm9eME3XLJw+pOtqy9nrFwAAABioOEPwLkmzIrfnhMckSWZWJ2mxpOeC5cOSpDeZ2TXheVfkPXZLjGNFjcsPWPmOdmT04B/29BnCVm1p0RM7DvRoSJX13l2gM1nX53+2sddewLm1uPlrjUfXpfS5a87RZfOn9th/987V23S0M3iOaGA92pnVkzvatHpr64DWwL5qxolFt3nKVX//4V2Lut+3vrZLqqTBdogGAAAAShFnCF4vaYKZLVHQGOsGSR/K3enuXVJ3gUtm9ktJt7n7Q2FjrDvM7PUKGmNdK+mCGMeKGlcoYEXV16X0/9u79+g47jJP+N+nutWy5LscW/Illp1EvsrYISaEJAYjJ0PYBMIwsxAWOBMnwLxknTED7J7ZA5NlYXhndl4GJh5neQk4CcNAmHcukBBveElkHGycmx3b8UV2ZFuWL7ralnWxZHV31W//6Iuru6ta1d1Vff1+zskhVneXqqXCqW8/z+/5GUpZVoljIexI11BKsAVSp0DvPN5n2fIc8Gu4d9Vc9A2Pp1SbW5bVJwx22tLajqsh++FZYyHrydNOWA2haqyrxT3NDWiePz3e/pzpdknpONl/ONcJ0W5ycr5EREREVJo8C8FKKUNEHgawDZEtkrYqpXaLyHcAXFBK/U2a1w6LyGYAv0Bki6T/rpQ649W5UvmzClhmId3A8e4haNG1uTGaAMsapsaPUe3XUoJwwCcJQc0uLNdPrUbL8nq0LK9P2coHAFrbeuOha3nD1LTnW5X0PTPhZDuh1rZe11qT0+1NbP6ebkyIdoPT8yUiIiKi0uTpmmClVCuARUlf+6rNc+9J+vPPAPzMs5OjihILWOZ9cM0MBXJXUXkAACAASURBVJy+NAZltVBWrh3j5uun47WOgYSHawI+rGuaHf/zynnTUGvR8vyX962IhyhzG7Nd6Fq9YHrCumKzG2dPySkcTrSdkFutybqh8PjL7+DN05cQik7EtgvUxbLXL9cmExEREZU3L7dIIsqLibY7AiIB65mNt+Lm62fYHmc8bKQMi1IqMh06doyH192Aan/i/21CusKu9v74n622V7qlcabt5GZz6FK4FroeunMxtn7qZiycWYNqvwZBpAK8vGEqnt90p6fhMFY5N8u0NTkW7r//ysl4AI6JBepksXD+6IamtFs9eSndBwBEREREVPoKPR2aaELp1mc6bV3VDYUHn34D+89ezuh7B/xaQvBr6x5Ome6cXCHNtKJpF7qOdQ/j0Q1Nlu3TXq3LjbFbN3zo/GD88YnOIRbukwMwULi1vk4U09pkIiIiInIfQzAVteSQ6/cJbpo9Bc9tuhMBv2bZuvpGx0V86ef7ceOcKVgVHfQUe55VazEA+DXA6qH6qdUJbcdOA9JE7cZmVsf0+wQh3YBuqIyOZSfTda7mIH/4/BBePNyN0xev4PGX2+OB+EPNDfGfr9Ux7IaRVfmkIGt9nSqWtclERERE5A2GYCpqO471JqzjDekKbT3DuP2vW/HXH1+FI92pQStsAL96uxvAtS1/bl1cZxnIbltch/fdOAu6oRK2JAJS1/EC6QOSVaUVsN5myPzc5Q1T48eMBeGQHjmfvZ0Drgxkymadayx8A8APfncy/rMZDepo6xlGW89w/OdrdY5W4b7KJ/jiB27E5ruWFO2QqWJZm0xERERE3mAIpoLTDYUdx3qxPRpc7101N75+1mq/XQC4cCWITc/ux+LrJiNgMbE5Jhb21jbOTK3gVmm4dXEdRASrFkzDmutn4OC5wcSti5ICol1AAq5tKTQa1FHt1zB7SgBTJlXhzKXRhGM+s/FWPPj0GwlBevWC6XjojkX4we9OxduHx0KGawOZchl0lW57qXRh2u4Dg2IOwDFuVN+JiIiIqDgxBFNBxdp0Xzt1Mb410fMHu/DexXV48PZFODcwZvva8bCB4z3DE36PsaAOn6YlBLJJVRoCfg0/2t2RENAe/+QaHOsZTlv9swpIyVsKjYcNnLt8FcDV+HNGgzr2dQ5gS+s7KVXZg+cGUT9tEsI2A6TMk6Sz2b82l3WuE20vZRemWVElIiIiomLEEEwFtfN4H/Z1DiTszWso4K0zl6HU6ZRpzckmehyIhL3m+dOwqeWmeCAL6QZ+tLsjpT1Y0wSPbmjK+H2kq5aajYcN/OS1zpRAORrU0TN4FdV+wdXwtXc1qeraYK5c9q91ss7VLmAnvzb5Z54uTLOiSkRERETFhiGY8ko3FHa09WL7oUjrswIsW5nHwwYuj4Vy/n61prBnDmRbWttd2wd35/E+nOofSduWbRZrl05+7ltnBqAnbVQc8GvxPYhz2b92oqrsRAE7eUhW58UruBoyODSKiIiIiEoOQzDljW4ofOZHr+H1jkvxyq9E/0muLmoC3HBdLY73DDuq9sZU+zXcs7IejbMmw6dpaJ5v3YLrxjY4ycFRxPq9JAvrCgvqJqFvaDwhCActthKK7UG8YXl9Tut6gfRV2YkCtvm15oo6W5yJiIiIqNQwBFPe7Dzeh7fOXE5ofVaIBMdkhgJePNyLSVVawsTmdKr9Gm5pnInvfvJmV9qDnbwfc3BUKnIO06r9GBoPIxg2UOUThHSVEIxrAj48dt8KbH+7G7880JX2e5hDrpf712YSsCutxTnbddhEREREVJwYgskTVsHhSNeQZbuwAlDj1zCW9JgCEAoblo9Z+XBzA/7uE2scBRQ3hjZZBcdg2MBn1i9C8/xpONo1hGUNU/H0ntMpYbtlWT00EfzmaK/twCkgMeR6uX+tlwE7n9wOrLmswyYiIiKi4sQQTK5LDg6TqjQ0zpqMJXOm2L7GLuSGFaA7CMCaAPe+a65tMLELR7lUNO2CY/P8aQnHbVlebxm2k0NtbGJ1SFeWIdfLacteBux88SKw5rIOm4iIiIiKE0MwuS45OIyFDBzrGcYxB9sZWXG8JlhZh10AnlTznAZHu7BtFWrXNc3GrvZ+25DrVStyOWxn5EVgzXUdNhEREREVH4Zgcp3T7YLcZCjgaPeQZevxxjsWeVLNiwXHHcd6sf3tyLTre1fNnfB1VkHdfB6FWm9b6mt9vQis5dImTkRERETXMAST61bOm+Z4u6BsNM2ZjLOXRhP2060N+KAbyjLsbn+72zIcbdvdAQA5Vzyf/v214P2bo71pq8ylvsa0mIdEeRFYy6FNnIiIiIgSMQRTxiYKQuuXzsGcqdU4OzDmyfe/d9U8vHH6Ukow0TSxDLsAUsKRArDn5EUcOHs5pxCaaQtuKa8xLfYA70VgLYc2cSIiIiJKxBBMGXEShHya4LH7VuD/+uk+6A6KwbMmV+HilZCj718b8GHVgul4dENTSjDZebzPshJ476q56Bsej5+zeY1xriE00xbcUl5jWugAP9GHL14F1lJvEyciIiKiRAzBNCFz+AjrBt7qvBRvRR4N6th/ZiAlCLUsr0fTnKmOhmHdNGcKRs8NJuwHrAng1wRBPbHlOVbZswomdpXAluX18QnN23Z3YM/Jiwnffyyo4/D5QQDIuM030xbcQq8xzaWduZAB3mkVmoH1mmJuXSciIiIqJIZgSis5fPh9gpCeOK95LGTg8PlBbFhen3Dj/ed3LcH3XjqOkxeuIKwrVEVfmzzt+cDZQUyq0gAAV0PGtYFWty/C0e4h6IaCT9PQPD/9jfxElcBYMDJXMwFgUpWGXx/uwQ9+dyrjNt9MW3DdbtnNJOjk2s5cyABf6Cp0qSn21nUiIiKiQmIIprSSw0dyAI7RDWV74/2VP1iKo91DePFwD97pHYZKOsR42IBPE3x+3Q2o8mkJWwWJCFZfP91RFctJIFzXNBtVvsSviQCdl0YnDFh2x8+kBTc+UbqtF9sPRSdKv2viidJ27zeToJNrkMwmwLtVjSzlNvJC4IcGRERERPYYgslSLLxs292RUPmz49M07DjWi32dA/Gp0LEbb00TNM+fjh/87hQMm01/x4I6qnwaHt3QlFUVy+lrdrX3I5g0tToYVtCNxK8lB6yJjp9pC655K6eJJkrbyTTo5BokMw38blYjC91GXmr4oQERERGRPa3QJ0DFJxZeHn12P15NWj9rpTbgw7KGKfhv/3YoZVuk2I33RHsHmwONOdwpXAt3O9p60drWiy2t7Wht64VuStQ72iIBPPk1O4/3JXyfI11DuBpKPMewoeBPqg4nByy7c0o+vhNWx9rXOYAdbb0ZHSdd0LESC5JmmQbJWOB/dEMTNiyvTxtm3fyZxarQtQEfBInrwymVG79rIiIionLFSjClSK4wmtUGfKjyCYJhI75+d/WC6fjey+24cCWY8vyAX4vfeCdX8gBAol83BxqrcDca1PGtF47iwpVgSlURAL75wtGUAD5qUflaOW8aJlVpCUO4JvkFi6+bgs5Lo7Ztvm5W1o50DaX8HMbDBr71wlG0TBAszTKtjuZ7z1s3f2bFslVRqQybytfvulR+HkRERERmDMGUwiq8CIDbb5yFh+5cHF+vGwsjhlJ45KdvWR5rzpQADKVwpGsIC+tq0XnxCq6GDEyq0tA4azI+3Dw3ZeCVVbir9mvoHR5PaLXe1zmAHcd6oYmgb3g85XtXmwJ4zLqm2Qj4E0NwdZUP//7IHdhz8oJtwEoXODMNAivnTUO1X0sJ7b3D4xmt2XQadMznt/H2RYAAx7qHPQ+SbrcwF3rycykNm8rHhwal9PMgIiIiMmMIJgCp2yAlV0trAj48dOfieADZsLwe65pmY+uOdvxi/3nLgVmaAJMnVWHzzw/Eb5IXzZqMe5ob0Dw/cdiVbii0tvXiSNcQljdMTQl3syYHcG5gLOH442ED33zhKP7o3QtS1vkCwJxp1SmBcFd7f8q5hnSFPScvpA1YdoEzHDaw/v/5LfqGxxEMG46CwPqlczBnajXOJr2fYNjIqErqJOgUMqjku/LstVIbNmX3oYFb1dtS+3kQERERxTAEVzjdUNjR1otvvnA0IcgF/JHl4mMhA9V+DbMmRyq6ke2KIu3Q7/n2SxgcC9see970GpxJmrrceWkUzfOnp0xdTg5qqxdMx+MPrMGx7mEsmzsVB89exvdfOZWwDhgA+obGYRgqpeLo0wT3r5mX8D3sBn05adFNDpzL5k7FU7s78Gf/fCChouskCPg0wWMfWYFNP9uf8NpsqqQTVUcLGVSKpYXZLeUwbMrND0XK4edBRERElYmDsSpY7IZ407P7cXZgDONhIz7AKKQrPHznYiycWQMAODcwhs0/P4DPbnsduqGwdUe7bQDWBFgwswYfu3m+o8FNVgOUDp4bhCaCRz54E57+/Wls292REoCBa9srxYYmmd/btl0d+Oy21xEMG2kHfTkNn+ahUJoIDp4bTGlptnuPyVqW1eOWxpmeD3rKdHiW2zIZpFXsymHYlJvDysrh50FERESViZXgCha7IbYLcucGxnDhSjBly6Odx/uwt3PA8pgCwFDAxZFxPPX7U0iOrTUBH5bNnRpvfV45bxoOnR9MG9QOnL2c0JptVhvwoXn+dGxqacLjL7+D779yMt7uPBYycODsZWzd0Z520Fc24TPdtGu/TxDSjXjV3Eq+qqTcWsg9XrZ352vAlJvV23Jrdy8lHEhGRESUG4bgCmRuDU4X5HoGr9reMK9tnIk9FlXVWOi1C60L62rx1O4OHDw3GL9xXlhXaxvU0oVNc4D1aQK/T0M4ab3vWFDH3s4By2NMq/Hj7/7jarQsy7xCaRUuY0K6wo92d2Bv50DaNtN8DHpiUHGPVx9c5HPdtpsfipRbu3up4EAyIiKi3DEEVxjzDZRVgIsJ6QpvnRmACKBMuTJ2w7yuaTZ+/OrptGuCrSytn4KX2voS1wlfvIJFsybbblGUPKQLAK6bEsBf/+GqhC2F7G7w1zbOxL7OgZSK93jIgCaS1Y2jOVyOBnX4NYFuqPiHAMUyJCiboGJXZWL1yZsPLvK5btvtD0UKPbG7EnEgGRERUe4YgitMuj2A/RqgG9equUGLic9VPolvM/Tm1+7GltZ30NrWB0MpnOofgU0BGECkcisiqYOpQkZ8YnRyUFu/dA4aZ03GsZ7hhNeMBnVoWmKAtbvB39TShF/sP58yjXnc4TRmu/BnDpcn+kfw/IGuxPdVJEOCMgkqdlWmZzbeigeffoPVpww5+eAgnwOmWL0tfRxIRkRElDuG4Apj116sCTCjNoALI8G0rw+GDexq748POXrrzGWcvngFYyEDgsiaYBU9nnmOlSbA6gXT8eHmBjx/sCvlsRVzp1kGNZ8muKe5Acd7hhPWF1vd9KW7wX/svhXY9GziNOZaB22gsVAYqyRX+zXc0jgzHv42LK/H+qVzcO+WXZbrn0tt7a1dlSl5XTWrTxNz2raa73XbrN6WNq7zJyIiyh2nQ1cYq4muQCSwDl8No9qf/pK4GjLiA6t2Hu/D/jMD8VZlFf1n2iQ//EmVpSqfhofuXAxAkDzk2VBI+ZrZqvnTHU+htZtG3LI8u2nMO4714rVTF+PheTxs4LVTF7HjWG/8OTuP96Hz4pWU1zbW1Zbc2lu7KpPVuup8TpkuRU4nMcc6GLyeFE7lgdcLERFR7lgJrjCxG6g3T1+KT1GOCYYNLKirwcWRIMaCOgJ+DSHdSAio5vB5pGvIcgDWyHg4JdQGo63HVlsUAcCLh7vxoeaGlK/rhoJhKMyaHIBuXNvHON1N30Tty4fPD0I3FDRNsPN4X9p20O1vd1uG9m27OtDWPRyfbn3V4udwT3NDybWZpltXndxGz+pTek7bVtmiTJng9UJERJQ7huAKE7uBSt5OCIiEmsfuWwFNBEe7hrCsYSqe3nPadojOynnTUOWTlDBtKKR8fVKVhl8f7sHx3sS1vTG7T1zAd39zHJtamhCIVqOTh3hV+zUsqKvB1//DCmgCPPHbEynrLCdqQV2/dA627e7IeW3rm50DeL3jEmoCPtRNDsCnAea5W7Gtm0pNunXVezsHOGU6A5m0rbJFmTLB64WIiCg3DMFlaKJhPD5NsPmuJZahJrZdUOzmqmV5vW3FYf3SObhx9pSUoVW10W2PzpimPS+sq8Xpi1ds254vjASxZccJ/PjV03jza3cj4NdS1qeOhw1cHAniey+/k3Bsc4idaHJqppNV7101N2UNc+xnHHv9aDBx4FYptyemqzKx+pQZbk9FREREVJxEqTSLMUvI2rVr1d69ewt9GgVnVwndePsitPUMW255k0uoCYYN3L91N070jyCsq4Rpwrva++PHPnR+EH//crujY962uA6ff/8NOHR+EI+/3J4wcEoQ2cPYXGWuDfjwD5+6GRuW12NLazu+99I7Ka/58t1L8OiGpgkfT6YbCp/+4at4vWMgZfCVlSqf4IsfuBGb71riaUDkdkWlwY3/j7nx/XmdEBERUTkQkX1KqbW5HoeV4DJjVel87dRF7OscSFhPmzzdeOfxvpT2YvMN9PK5UwGFeJBe1zQbu9r7caRrCF++ewkgwLHu4YQb/eR2PavWaSuvdVzC2+cHsbCuNqWdNDkAA4nrLCdqQc10sqpPEzy87gbsP7s/ZZ9hK2FdocqneR6AzR90BPwa5kytxmP3rUjYN5kKr5Btq06nUxMRERFVGobgMmM1jMdQiAe45PZfp/vCilw7Vk2VFh2apRzfXNu1TtsZDerovHgFi2ZNRqep9bkx2lZtHshlDrFWLairF0yHoRS2tLZjecPUjFtU27qHEXQQgJPPxStWbeJnB8aw6dn9Cds3UWXLtPW/nLACTkREROkwBJcZq0pnMnPl1Om+sOau+bGQkRBCR4M69p8ZSHtz7dMEz2+6M6F1elKVhqBuQLfJl1dDBu5pbkDz/OnxdtJ1TbMTwnlyiE2cAj2EkK7juQNd2PSz/fFK+OoF0/H4A2tSKteZ/Eyr/RruWdmA473DOH1hBFfDClU+wcK6Wqxrmg3Auxtxu72ex8NGWYYcBprsOJ1OXW5YASciIqKJMASXmeRKqKNtjpJulEeDOn6x/3zaIJ1sLGTg7XODaW+uA34NL/zZuoQ1krffeB2+v/MEXjrai/a+EYSNxGnVzfOnp7STTjSgyTwFel/nQEIb82hQx8Fzg9BEEtYApwtadgOOvvvJNdANlRDsz1waxYNPv5FSSXfzRjzdBx3lFnIYaLKXaeu/1/L1YUYlV8CJiIjIGYbgMuPTBM9svBVbd7Rjb+cA3r1wBvZ1DuDguUHLyunS+inwaZIQPgHg7MCY1eHTOtU/4uj8kkPtl/9gKTbftcQy7Fi1KTtZZxm7EbZax5scFCcKWnaTkQFg6452nOgfia9Ttquku3kjHgvlyQEfKL+9exlospf84c2kKg0L62px6Pxg/PF8fZCQzw8zKrUCTkRERM4xBJcZ3VAJFcgDZy/btv8Gwwb+y7++nRKArcRuU1X0361ecXEkmPV5u70Fj13LMJAaFJ0EreTgHbupf/P0JctBXXs7Bzy7EY/9rHYc68U3XziKvqFxjIeNkt6ayQ4DTfYSlwYM4teHe9B5aRSPv9ye94p6Pj/MKLYKOBERERUfhuAyY3WzadX+C0SqmENXwxMe06cJfAKE9Mi61+mTqnDhSmrgXbtoZk7n7uYk3ZXzpmFSlZawdhmIrOVNDop2LeGHz9u3d8d+zlbTrmsCPqxtnJnwe4h93a0bcZ8muHtFA1qW2e/jXA4YaHJj3vP7B787VbCKej4/zOD+zERERDQRhuAyk8nN5t7OAUfH1A2F2BFDusJoSEdtQMNo8FrAnF7jx6aW1H12C2Vd02wE/IkhuNqvYcsDN+OuFYnbCNkF5l8f7sGmlibLUGlXaa7yCdZcPwObWpqwt3PA0Y14LmslC7kFTz4w0Lij0BX1fH6Y4XZXCREREZUfhuAyk8nN5trGmdhz8mLG32MsqGPzhiYYSmFf5wBuaZyJTS1NCPg1x8fwekjOrvb+lCqtTxP4fZLyfdYvnYPGWZNTtm/qvDRqWymz+jlX+QRf/MCN2HzXEsc34hz8lB4DjTsKXVHP94cZxfDhEKeaExERFS+G4DJjdbPZaDMMZ1NLE3786mkMjl1ria6pikyTTrctbk3Ah1ULpmd9g5mP4JdJ5cunCe5pbsDxnuGEtc7pKmXrl87B6gXT8daZyPCtar+Gdy+cEQ/AseM6HeDFwU/2iiHQlLpCV9Qr7cOMSv9wix8AEBFRsWMILjPJ++S+eLgbpy9eiQ/DWb1gOh66czHauoexct40vPbf7sL3d57Avs4B3Nw4A2+euog3Oy/Hj6cJMHWSH8Gwgashw5Wb53wEv0wrX6vmT8+4UiYiaf/sRKHbVKkyFEMIraQPMyr5w61K/wCAiIhKA0NwGUochnMyvtZ1NKjj9Y5LeOvMZQTDRrxK/KHmBjx052IYhsJTu08n7Clc5dPwtx9/F/x+zbWb50PnBz0PfplWvjJ9fvIWTONhI6ub3InCOisq5JZKCqGFVskfblXyBwBERFQ6GILLmNWNmKEQD26jQR1tPcNo6xlGbcCHWZMDCWEMAIJhA+/0jeDRDU0Z3cDYhTfdUPj14Z6ULZbcXp+YaeUr0+e7dZObLnyzokJUmgq9BruQKvkDACIiKh0MwWXM6kbMzmhQh26Mo9qvxUMykN2Nm114e2bjrdi6ox0n+oZTXtNYV+v6+sRMK1+ZPD/dTW4m1dt04bu1rbdiKiqseFM5KfQa7EKq5A8AiIiodHgagkXkgwCeAnAdgC1Kqa8lPf5pAN8G0ACgDcAjSqlXRWQKAHNSOqKUavbyXMtR8o1YwK8hGDZSqrAxwbCBBXU1uDgSzOnGzaodbv+ZAdy/dTdO9I9YDt26p7mhpEKP3U3uuqbZGVdv7cJ3pVRUWPGmclMMa7ALpZI/ACAiotLhWQiWyJSgpwB8CcBeAK0isl0ptcf0tD0AbgMwAODTAJ4A8O7oYyeVUjd5dX6VIPlGbFnDVHzzhaM4OzBm+fyagA+P3bcCmojjGzerCp5leAsZONE/krJtEQDUBnxonj89tzebZ3Y3uW6uh6uUigrXEDrHinnpqNQ12JX8AQAREZUOLyvBawAMKqWeAwAR+SGAjyMSfAEASqmO6GN+AEEAfR6eT0WyuhHb9Oz+hJZnAKj2a1i9YDoMA9h+uAsAsKxhatpj21XwNt6xyHIP3bBFAK7yieMqQbEFAKufrZvV20qpqFRKxTtXrJhTqajUDwCIiKh0eBmC5wPoNP25A8Da5CeJyNcBfAvARQAfMD20UESuAOgF8A2l1D9avPYLAL4AAAsXLnTvzMtYy/J63NI4M6FFun5qNb72H5bjmVc78cWf7otPh37+YBfeu7gO//S52yxvsu0qeFBICW8L62px5tJoSjD+4gduTNhb146TAFAMIdnN6m2lVFQqpeKdK1bMiYiIiNxR8MFYSqm/EpG/BfAZAD9FpII8Fv3fk4gE51+IyB6l1Imk1z4J4EkAWLt2rd1S14qTLgyma+N968xAwvZIhgLeOnMZO471QhNJOZ5VBW80qGP7oW7curgOaxtnwqdpaJ4/DeuaZuPBp99ICbFOAjAwcQAoliqZ29XbUqyoZPphhFs/s2L4EMRLrJgTERERucPLEHweQKPpz4ujX0uhlAoCeEpEtopIIPrno9GHfy8iuwA0Azhh9Xq6Jt1k5l3t/QkBwXzjfOj8YEqLNBDZTumbLxxNGZb1k4ffa1nB0wR48XBPfB/iNdfPwKaWm3Kuak4UALyukjkNWJVSvbWTzYcRbvzMiuVDEC+xYk5ERETkDi9D8EEAM0TkfkQGY30ewOfMTxCRFgCHAFwB8CCAU0qpoIjcAKAKwGkAtwBYB+AvPDzXkjFRGLMKg3tPX8JHt+7GmUujtgHBMKwL6T4Bui9fRTj6uDlcWk2fDulGwj7E5iCaS1VzogDgZZUsOWD5fYKbZk/Bc5vuRMCvpTy/FKu3bsn2w4hcf2Z23/fxl9+B36flrTLsZTW6UtaIExEREXnNsxCslDJE5GEA2xDZImmrUmq3iHwHwAWl1N8AeD+AZwFMBrAPwKeiL18Qfd31AM4C+IpSqt2rcy0VVtWuxrpafKi5AavmT7edzBzUFY73DMe3RrIKJprNjbqKfl+zUVO4NFfwTvaP4LkDXQnPTRdEnQYG3VAwlMKsyQHoxjjGwwZqkwKA0ypZNiElOWCFdIW2nmF8dOtubP+zdWVTaXRDoVp27Vrzv//KSYR1lZfKsNfV6ErvMiAiIiJyi6drgpVSrQAWJX3tq6Z//waAb1i87ncAmrw8t1JkVe1q6xlGW89wPBRuvGMRAn4tpbU5uc6bHExWzZ+OSX7B1fC1Z2qItDcnF4mr/Vo8XJoreK1tvfjN0V5H7ZpOA0Py8wJ+DQtn1uAv71uBlmh1GXBWJcs2pFgFLAA42T/CoURJCtWya/V9AcS35MrHEKl8DK6q5C4DIiIiIrek9nJS0bILY0DiZOY5U6snPFbAr+Fk/wha23qhGwrrmmajusqX8JwqvwaLZcKYM63asgUzFkRrAz4IkFKtNTMHBoXEwGC241gv9nUOxJ83HjbQPzKOQ+cH8cRvT8TPP1Yl+4dP3Ywv370E//Cpm1PCrdPvmWzlvGnw+1JDckhXONo1lPa1VnRDobWtF1ta2+PnXy4yuQa8/L5VFr+v2Ac/XklXBS9n5Xw9ExERUXkq+HRomlishfdU/4hllTdmLKjjWM8wHrtvRcpewJoAVT4NwbABESCkG/jlgS785mhvvIIcstjHtzrp+1X7NTx234qch0I5aZvVDYVv/upoyvsdCxm2ba7pqmTZtuquXzoHN82egrae4YSv11RpGVc4y32AU6FadpO/b0g38MNdpzAWunbteF2RrsTB36pOGAAAH+5JREFUVeV+PRMREVF5YiW4yMVuMh99dj9+eaALId2AJoDV7WXshju2F7C5GvfexXXY+p9uxv1r5qHKp8VbnGPV0O1vd6euJQ4bmDOtOuE4tzTORMuyiQccPbqhKT4My0osMFidf8zO433oGx63fH1IVxlVc+2+p98nCOlG2uqVTxM8t+lOLGuYGq8w1lRpuHnhzIwrnNlWo0uJ02vAy++7+a4luHnhzLxWpAtVBS+kSrieiYiIqPywElzkktcZGipSjb2nuQHHe4bRefEKroaMhDWw6apxbd3DCCZXVqPHtqpiPXbfCmgirlf1nKzhPdI1ZFv1Tj5/J4OX1jXNTmmTDekKP9rdgb2dA2mrVwG/hu1/ti7nCif3es2PQlSkK3FwFa9nIiIiKkUMwUXOctpz2MBNs6fgu59YY3vDbdcabNeyee+quegbHk8JpS3Lrm1t5KaJAoNuKIR1A1U+SWjT9msCnyYJ4dhcQY61jh86PwjDUNA0iU/O3tXen/IBAJC/bXyAymyZLZRCDJGqtMFVvJ6JiIioFDEEFzmrm8zYUKvYXr2Z3HCva5qNhXW1ONk/gpCuUFOlRcLu8nq0LK/HjmO92P52NwDg3lVzXX8/ZnaBwbzO0ByAY+cqIpYV5Njr9p8ZSFgLWuUT3Dh7Cv5gZT2uhuzXUydXr7zY85V7vVI54fVMREREpYghuMiZbzJHgzq06FCr50xDrZwOodENhQeffgNnLo0irCtU+QSLZk3GMxtvhU8T6IbC078/Hb+hzfT4bkluAQciQfbz627A5ruWxJ+TXEFubeuNnHtS0A3pCsd6hjEyHsakKi3lcSC1euXVwJ9KbJn1ghcfULh5LgCK5vy8xOuZiIiIShFDcJEz32T+6mAXXjzcE28FznQf0uRwGdIVOi+NYld7PzYsr8/LPqd2zEHiVP9ISgt4WFfwaVpCsHjkgzcl3Gwf6RpK2SfWrHfoKm6cPQWdF68kBGGrAUZu/Szswloltcy6rZgmEtudi1IKB88NFvz88oHXMxEREZUahuASELvJPNI1ZDnUyukQmomG2GQy5MbNSlxykAj4NYgAyjSwuSbgw4uHu/GD3520DRbLG6ZCE8Bu0HNYV/hw81w0z5+Gw+cHo/sLa2ien3r+bgz8KaawVk4K+WGNk3PZ1zkAAFl/WEVERERE3mIILiG5DqGZ6PVOj+92uEsOEuNhI7qvcWQoVrVfw8K62oQKrmWwmOBb1wR8aJ4/zVHVyo2BP8UU1spJMU0ktjoXq4nmbp5fMbWCExEREZUi7hNcQnLZh1Q3FAxDYdbkAKr9muXrzccHIlsxzZocgKFUwj66bu8NahUkDAUYplLwyNVQylCrWLCIaeseTqgem2W6Z6sbe76mC2vlQjcUWtt6saW1Ha1tvWn3W3brOE72mM4Xq3Op9muo9if+1erW+Zn3Df/eS+/g0Wf347PbXs/65+4mt64FIiIiIq+xElxCsh1Ckzw12acBs6YE8O2PrcJdKyKVqda2XhzpGsLGOxbB0BW+/b/b0Ds8jnMDY9j88wMJlV63K3FWVdfIeUf+dzxsoH8kiIBfs90aye441X4NH25uwEdWz8uoYubGwJ9y3z7GrY6ATI9TTBOJ7c7Fak2wG+dXrN0FbP1Pj9V7IiKi4sIQXGKyGUKz83hfwrZBugFcGAniW9uP4v1LZuPhH7+ZcPO6sK4WF64Ebdc0uh3ukoOEP2lvYCAShBfOrMGFK0HbYGEXSP7uE2uyuuHMdeBPMYU1L7gVyDI9TjFNJLY7F8B6gnmuiqkV3KxYw3kx4AcERERExYchuAIc6Rqy3Bbo3MAY7v7uK7h4ZTxhre3J/hGEk0LoWFDHtt0dACJ7Dbsd7jbesSi+P/GCmTV46venE0J2bcCHv7xvBTRNbINFMYWjYjwft7kVyLI5TjFNJLY7Fy/Or1i7C4o1nBcDfkBARERUfBiCK8DKedPiQ6aSdQ2OpUxTDkX3EDY/XwHYc/IiDpy9jDXXz8AzG2/FrvZ+HD4/BN0woGmCncf7Mg55dlWS1Qumx9tJJ1VFBmMd6R7CqvnTE7ZGsmozzCV8uN22WExhzW1uBbJiDXbFyOvugmyvf/4O7fEDAiIiouLDEFwB1jXNRv3USTh3eSzlMUMhJfDWVGlYNGsyOi+NYiw6/ComVsXY1d6P9UvnYNvujpza/OyqJI8/sAaaCA6fH8SvD/eg89IoHn+5PeF7AEgI0FU+wbRJVbjjputw3+q5aFlW78na1FyCcqmtDUx3vm4FsnJvG3eTl90FubTt8ndojx8QEBERFR+G4DKnGwoPPv0GLl4Zt3y8pkpD46zJOBMNvLGb11ild9vuDuw5eTHhNeYJx7m2+dlVSY51D+PRDU0AgB/87pTl90j+/kFd4cKVIJ472IXnDnbhusmBjAKxk7bFXIJCqa0NnOh8062HjQ1acxL0y71t3G1edRfk0rbL36E9fkBARERUfBiCy1zsxtZqTfAkv+DmhTPjgTf55jV242u+MQauVTHsAuzh84MA4CgEpauS6IbCrw52pUyNjoVwFf13O7FA/Ku3u3DbDbMmDJtO2hZzCQqltjbQyfkmB7Jsg36mwc5coV7eMBWQyBZZpVBdL1a5tu2Wc+t/LvgBARERUfFhCC5zVje2AKAJsPi6Kdj2J+/BrvZ+28A6URUjOcBOqtLwL/vOYetvTyCsq6y3u1nXNBuf3fY69nUOpLzG3EpotbVSMkMB+zoHJgybTtoWcwkKpbY2MJvzzUfQNwft0aCO2GWlFIq+ul7M2LbrHX5AQEREVFwYgsuc3R68hgI6L43iD//X71Naoc0BIl0VwyrAAgrnBq6tPc52u5tYmDLvCwxE9v1dc/0MGIbCke4hNNbV4tSFKynPSzYeNiYMm07aFnMJCnavDekGtrS2F10VM5v3ahWcR4M6fnWwK6v3ZrUmOTlomwe7FXt1vZixbZeIiIgqBUNwmYvd2L55+lLKdOixoI72vhHo0RSRHCCSA4h5KjOQGmBDuoH/tfNkyjmMmrZXsgpCVlUSuwr2PSsb0Dd8FZv/+UB8cvTi6yZj5GoI5y9fRer864hqvzZhUHXStphLULB6rV8T/L+vnERQV6j2a3j3whn4p8/dVhRBOJv3avehy4uHe9A3/HpGFVq71upbF9elbYMv5up6MWPbLhEREVUKhuAyF7uxffzld/D9V06mBGE9aX+k0WiAWL90jqO1neYAu6W1HeHk/ZaiXjVtr+QkCFmFqdqAD42zavFSW2/862MhA2cujeLxB9bA0BWe2tOBt88NJqyB1gS4pXGmo6A6UdtiLkEh+bXjYR1P/PZkPLiPhw283nEJO9p6cffKhgmP57Vs3mssOO/rHEiozo+HjYwrtHat1WsbZ6Ztg2cLb/bYtktERESVQCv0CZD3fJpg811L8J5FdagN+CCIVEatxCqm5gCikDqV2crKedNQU2V9XKfHiImFqdj51kZDuKaJ7TrVH7/WiUPnh1IC8JI5U/DMxltdq2jFgsKjG5qwYXlm2zCZX3tuYCylcm0oYPuhblfO0w2ZvtdYcP5wc2qIN08Vd8JuTbJP0+LXBhD5HWuChOuELbxEREREZIeV4DJjt69rclXvZP8IfnmgK+X1c6ZVY/3SOXjitycyHoq0fukc3LxwJvafGcBYyIAmies1nRwjJt1aYat1qrqhUqZYA5Hvf2ZgDLva+y1bvNnu6T6fJvjI6nn4zdHenIYs2a1Jbp4/DZtabopfG0vmTMGh7kG81RmpEm9qaeLvlIiIiIhsMQSXESf7usZaHVvbelNCSrVfw2P3rYBPk6yGIvk0wTMbb8XWHe3Y2zmAWZMDaD3Wl1UQsgurdutUrSrEMWMZtnhnI5twfe+75uL5g10JHxRoEvl6qXNjyFK6Y8Su5eTf6YGzl7G3c4DToYmIiIjIFkNwGclkexq7gNGyrD7t4+lCjG4oPPj0G/HXTKrSEPBrqA34MgpCE4V5pxXimFjw9mr7nmz3xm1ZVo/bbpgVXz9b7ddwS+PM+O+glLkxZMnJMUpt72UiIiIiKjyG4DKSyb6uEwWMbEJMciCJrc39/LobUOXTHAehiYKN1fAec2hPqDxXafHgnU2LtxPZBrFyn8brxpCliY5RansvlysuMyAiIqJSwhBcRjJtYXYyCTmTEGMVSK6GDFT5NDy6ocnhu8gu2MQC5Y5jvXjhYDd6h66iflo17ls9Dy3L6h23eGdzM59LEOM03tzksm8zuSPbTggiIiKiQmEILiNurMPMhVuBJJfjPP370wnvv38k6LjFO9ubeQaxwin0NU9sSSciIqLSwxBcRgrdXutWIMn2OE7aqNP9fLK9mWcQKxwvr3m2+DrDlnQiIiIqNQzBZaaQ7bVuBZJsj+PkZjzdzyfbm/lCf/gAVHZg8+KaZ4uvc+yEICIiolLDEEyuciuQZHOcXG/Gs3l9cvh85IM35T0kBcMGPrp1N072jyCkK9RUabh54UwGthywxdc5dkIQERFRqWEIpqLgRiUz15vxTF/vVrXQyXu3e45uKNy/dTeO9QzHnzsWMrD/zEDRBrZSqFqzxde5YuiEICIiIsoEQzAVnFthMteb8eTXL2uYCgjwxG9PWIY1N6qFTt57uufsPN6HE/0jKccdCxlFGdhKpc2YLb6Z4ZRzykQpfBBGRETljSGYXJHLTU26MLl+6ZyMjpvrzXjs9euXzpkwrLlRLXQSpNM950jXEMK6SjlulU+KJrCZr42wHqlSx/aQLtY2Y7b4EnmjVD4IIyKi8sYQTDnL5KbGHIiWz50KKODpPacTKm5AJEwePj+Ibbs7CnKz5CSculEtdBKk0z3H6hwA4MbZUwoe2HRDYUdbL775wlH0DY8jGDbg9wlCSaG9GNuM2eJL5A2utyciomLAEEw5c3pTkxyWJZonjNRCJmoCPuiGKtjNkpNw6ka10EmQTvec5HPw+wQ3zZ6C5zbdWdDAFvtd7+scwHjYiH89OQADxdtmzBZfIvdxvT0RERUDhmDKmdObmuSwrCzCLwDURsOkpknBbpbSBU9zNXvjHYsABRzrGc6qWugkSKd7TrFWLGO/a3MANqvyCcK6YpsxUYXhensiIioGDME0oYnW+zq9qbEKy2YC4PYbZ+GhOxfH1wIX6mbJLniua5rt6no2JyF2oucUY8Uy3e+6NuDD5+5cjCqfVjShnYjyg+vtiYioGDAEU1pO1vs6vamxW78aUxPw4aE7F7vabjzRe7ML93bB04v1bE5CbDEG3XTsftfVfg1rrp+BzXctYfAlqkDF2r1CRESVhSGY0nIS+pze1CSHWvOa4FqLgOvlzZKTcG8VPLmezRnz73o0qKPar2HOtGo8dt8KtCyr5w1vBeF2OJSs1D7UIyKi8sMQTGk5DX1Oq5kJ+/BGp0OnW0/r1c1SthVdrmdzhtUeArgdDhERERUnhmBKy+3QZxVq717ZkPN5Zirbim4pr2fLd0WO1R7idjhERERUjBiCKa1SDn3pZBvuS7HCabVfLytylA9cPkBERETFiCGY0irF0OdELuG+lCqcdvv1siJH+cDlA0RERFSMGIJpQqUU+pxyK9wX+9CfdPv1siJHXivXThIiIiIqbQzBVLFyDfelMPQn3X69rMiR18q1k4SIiIhKG0MwUZZKYejPRPv1siJHXivHThIiIiIqbVqhT4Aqk24otLb1YktrO1rbeqEbqtCnlLF0Q3+KRawdtTbgAxAJv9fX1WDrf7q5qCrWRERERET5wkow5V0ptBE7UQpDf9iOSkRERESUiCGY8q4U2oidKMahP3aDutiOSkREREQU4WkIFpEPAngKwHUAtiilvpb0+KcBfBtAA4A2AI8opV6NPvYAgO8BqAbwl0qpJ7w8V8qfYtk7NNfJzsVWZS2XCjsRERERkZc8C8EiIogE4C8B2AugVUS2K6X2mJ62B8BtAAYAfBrAEwDeLSJTAWwBcH/0sd+KyPNKqbNenS/lTzG0EbsVGIupylouFXYiIiIiIi95ORhrDYBBpdRzSqnzAH4I4OPmJyilOpRSPQB0AEEAfdGH7gKwRyn1qlLqGIB/RSQQUxkwD2sSALUFaCM2B0aFxMBYqkphUBcRERERUaF52Q49H0Cn6c8dANYmP0lEvg7gWwAuAvhAmtfOt3jtFwB8AQAWLlzoykmT9/LdRmzV9lwsLdluKoYKOxERERFRsSv4YCyl1F+JyN8C+AyAnyJSQXb62icBPAkAa9euLb09dipYvtqI7dqeN96+qOwCYzEO6iIiIiIiKjZehuDzABpNf14c/VoKpVQQwFMislVEAtHn3ZX02uNenSiVL7t1shCUXWAstkFdRERERETFyMsQfBDADBG5H5HBWJ8H8DnzE0SkBcAhAFcAPAjglFIqKCIvA3hSRN6HyGCsPwbwXg/PlcqUVdvzaFDH9re78czGW7Grvb/kA6NVu3eptnQTEREREXnNsxCslDJE5GEA2xDZImmrUmq3iHwHwAWl1N8AeD+AZwFMBrAPwKeirx0Wkc0AfoHIFkn/XSl1xqtzpfJltU4WAF483IO+4XH85OH3lnRg5LZIRERERESZ8XRNsFKqFcCipK991fTv3wDwDZvX/gzAz7w7O6oEsXWy+zoHMB424l8fDxtFtX1QtnsWc1skIiIiIqLMeLlFElHBxdbJfri5IeWxYtk+KFbNffTZ/fjeS+/g0Wf347PbXoduTDzrjdsiERERERFlhiGYyp5PE3xk9TzUBnwJXy+WadC57Fkca/c2K5b3RURERERUjBiCqSLE2qJjQbjar2HW5AAMpRxVXL2USzXX/L4EQG0ZTLkmIiIiIvJSwfcJJsqHWFv0jrZefOuFo+gdHse5gTFs/vmBgg+Sshre5bSay22RiIiIiIgyw0owVQyfJtA0wYUrQYyHjYxbj72SazXXpwk2LK/HoxuasGF5PQMwEREREVEarARTRUnXelyoacqs5hIRERER5Q9DMFWUXFqPvRSr5nJbIyIiIiIib7EdmioKB0kREREREVU2VoKporD1mIiIiIiosjEEU8Vh6zERERERUeViOzQRERERERFVDIZgIiIiIiIiqhgMwURERERERFQxGIKJiIiIiIioYnAwFhHlTDcUdh7vw5GuIaysoInblfq+iYiIiEoZQzAR5UQ3FD677XUcOHsZY0EdNdG9l3/y8HvLOhBW6vsmIiIiKnVshyainOw83ocDZy9jNKhDARgN6jhw9jJ2Hu8r9Kl5qlLfNxEREVGpYwgmKkG6odDa1ostre1obeuFbqiCncuRriGMBfWEr40FdRztGirQGeVHpb5vIiIiolLHdmiiIpDJ2tJia8NdOW8aagI+jJoCYU3AhxXzpuX9XPKpUt83ERERUaljCCYqsExDrbkNF0hsw92wvD7fp4/1S+dgzfUzUs5//dI5eT+XfKrU901ERERU6hiCiQos01Cbrg23ECHYpwl+8vB7sfN4H452DWFFhUxJrtT3TURERFTqGIKJCizTUFuMbbg+TbBheX1BQnghVer7JiIiIiplHIxFVGCxUGuWLtTG2nBrAz4IgFq24RIREREROcZKMFGBZbq2lG24RERERETZE6UKt7WKm9auXav27t1b6NMgykpsOjRDLRERERGRNRHZp5Ram+txWAkmKgJcW0pERERElB9cE0xEREREREQVgyGYiIiIiIiIKgZDMBEREREREVUMhmAiIiIiIiKqGAzBREREREREVDEYgomIiIiIiKhiMAQTERERERFRxWAIJiIiIiIioorBEExEREREREQVgyGYiIiIiIiIKgZDMBEREREREVUMhmAiIiIiIiKqGAzBREREREREVDEYgomIiIiIiKhiMAQTERERERFRxWAIJiIiIiIioorBEExEREREREQVgyGYiIiIiIiIKgZDMBEREREREVUMhmAiIiIiIiKqGAzBREREREREVDEYgomIiIiIiKhiMAQTERERERFRxWAIJiIiIiIioorBEExEREREREQVgyGYiIiIiIiIKoanIVhEPigiHSIyLCLftnj8T0WkXUSuiMgOEWmMfn2KiCjTP4e9PE8iIiIiIiKqDJ6FYBERAE8B+BKAZQD+SERuT3raGIA/BDAHwNsAvmN67KRSSqL/NHt1nkRERERERFQ5vKwErwEwqJR6Til1HsAPAXzc/ASl1D8qpQ4rpa4A+BcADR6eDxEREREREVU4v4fHng+g0/TnDgBr0zz/TwD8m+nPC0XkCoBeAN9QSv1j8gtE5AsAvhD944iIHM/tlMlF1wG4UOiToKLB64HMeD2QGa8HSsZrgsx4PZDZUjcO4mUIdkxEHgEwF8AXo18aQ6SSfBKR4PwLEdmjlDphfp1S6kkAT+bzXMkZEdmrlEr3oQdVEF4PZMbrgcx4PVAyXhNkxuuBzERkrxvH8bId+jyARtOfF0e/lkBEPgPgAQCfUErpAKCU0pVSR5VS40qp3wPYBYDrgomIiIiIiCgnXobggwBmiMj9IjIfwOcB/NL8BBH5GIA/B/BRpdSY6es3iMhSEamODtNaB+CIh+dKREREREREFcCzEKyUMgA8DOBxAMcB/FIptVtEviMifxF92lcBvBvAQHQrpFi78wIALwAYBPBjAF9RSrV7da7kCbapkxmvBzLj9UBmvB4oGa8JMuP1QGauXA+ilHLjOERERERERERFz8t2aCIiIiIiIqKiwhBMREREREREFYMhmDIiIh8UkQ4RGRaRb1s8/qci0i4iV0Rkh4g0mh77nyJyQUQui8g2ESmKLbooew6uh0+LyGkRuSoi+0XkfabHHhCRbhG5JCL/Ob9nTl7J9poQkRUi8lsRGRGRUyLyQP7PntyWy98R0ccnRx//p/ydNXklx/9m3Cgir0QfaxOR+vyePXkhx2uC95VlZqLrwfS8e6PzpO4yfS2j+0qGYHJMRATAUwC+BGAZgD+KTu82GwPwhwDmAHgbwHeir10L4D8CWA3gRgCrANyXnzMnLzi8HvYAuA3AdAD/AOCJ6GunAtgC4OMAbgfwdRG5Pk+nTh7J5ZoAUAvg7wDMA7ARwJMiMjMf503eyPF6iPkGItskUolz4Xr4ZwAvInJ/8QkAo16fM3krx/sI3leWGYfXA0RkMoD/CtN/G7K5r2QIpkysATColHpOKXUewA8RudjilFL/qJQ6rJS6AuBfADREH+oHEDQ91QDQlYdzJu84uR46lFI9AHREfv990YfuArBHKfWqUuoYgH8FcH/+Tp08kvU1oZTaq5R6QSk1pJR6BUAvgLr8nj65LJe/IyAiqwEsArA9b2dMXsr6ehCRWwFMVkr9TfTviENKqeE8nz+5L5e/I3hfWX4mvB6i/geA7yHxg7CM7ysZgikT8wF0mv7cEf2anT8B8G8AoJTqBPAjAOcAXADwulLqDY/Ok/LD0fUgIl8HEALw9wC+kslrqeTkck2YH78DwBCAU96cJuVJ1teDiGiI3OR81fvTpDzJ5e+HJQDOi8gbIjIoIj8WkSqvT5g8l/U1wfvKsjTh9RD9cPQmpdQvM31tMoZg8oSIPAJgLiKtKxCRmwF8HpGWlbkAbhMRtq1UAKXUXwGoRqR15acFPh0qAumuCRG5AZE9AD+tuIdfRbC5Hv4UwEvRG12qIDbXgx/AewH8ZwCLEbm5faggJ0h5Z3VN8L6yYv09gC+7cSCGYMrEeQCNpj8vjn4tgYh8BsADAD6hlNKjX74VQKtS6lS0reU5RHr2qXQ5uh4AQCkVVEo9BWCJiAQyeS2VlFyuCYjIfADPA/hctJ2JSlsu18MdAP5vEVEAngXwaRH5V69PmDyVy/VwDsAhpdSbSqlLAP4dwAqvT5g8l8s1wfvK8pP2eogOPlsP4GT0vw0fAvBS9MOPjO8rGYIpEwcBzBCR+6M3q58HkNCOICIfA/DnAD6qlBozPXQMwN0isjg60fGjANrydN7kDSfXQ4uIzBaR2mh3wCmlVBDAywDuEJH3icgyAH+MSPih0pb1NSEi1wH43wC+opR6Nf+nTh7I+npQSn1GKSVKKQHwKQA/VUr9cf7fArkol/9m7AKwQETWikgdIusED+X7DZDrcrkmeF9ZftJeD0qpcOy/C9H/Nvz/AO5WSr2ALO4rGYLJMaWUAeBhAI8DOA7gl0qp3SLyHRH5i+jTvgrg3QAGoqPLT0Rf+wqA/w/AG4j8xXUQwM/y/R7IPQ6vh/cDOIzIIItPInIzi+hAk80AfgHgVQB/rZQ6k+e3QC7L5ZoA8DEA7wLw6+jfHUpE7szvOyA35Xg9UJnJ8b8Z44i0Qv87Imv9OgA8k9c3QK7L8ZrgfWWZcXg92L024/tK4ZIrIiIiIiIiqhSsBBMREREREVHFYAgmIiIiIiKiisEQTERERERERBWDIZiIiIiIiIgqBkMwERERERERVQyGYCIiohIjIo+ISK3NY38sIn+f73MiIiIqFQzBREREpecRAJYhmIiIiNJjCCYiIipiIlInIjtEZExEzorI1wGsBNAvIpejz/mkiHSLyGkA7ynk+RIRERU7hmAiIqLi9hEAZwHUIRJwfwrgCIDZSqkZIlIHYAuAjwF4H4CWQp0oERFRKWAIJiIiKm57ACwG8FkAVUqpjqTH3w1gt1LqdaVUN4An832CREREpcRf6BMgIiIie0qpdhFpAXA7gP8pIjusnpbn0yIiIipZrAQTEREVMRF5F4AmAK8D2AXgLgBXACyKPuUtAHeKyHtEpAHA5wpxnkRERKWClWAiIqLiNg+RNb8LARwH8BCAWwC8LCKIrgveDOB5AOMAfg5gUqFOloiIqNiJUuygIiIiIiIiosrAdmgiIiIiIiKqGAzBREREREREVDEYgomIiIiIiKhiMAQTERERERFRxWAIJiIiIiIioorBEExEREREREQVgyGYiIiIiIiIKsb/AY2sGONHRRD/AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fa8070e5f50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize = (16,10))\n",
+    "plt.plot(stds1*np.sqrt(242), means1*242, 'o', markersize=5)\n",
+    "plt.xlabel('std')\n",
+    "plt.ylabel('mean')\n",
+    "ax.set_xlim(0.265, 0.4)\n",
+    "ax.set_ylim(0.25, 0.7)\n",
+    "\n",
+    "plt.title('Mean and standard deviation of returns of portfolio1')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5,1,u'Mean and standard deviation of returns of portfolio2')"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fa814e12610>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize = (16,10))\n",
+    "plt.plot(stds2*np.sqrt(242), means2*242, 'o', markersize=5)\n",
+    "plt.xlabel('std')\n",
+    "plt.ylabel('mean')\n",
+    "ax.set_xlim(0.265, 0.4)\n",
+    "ax.set_ylim(0.25, 0.7)\n",
+    "plt.title('Mean and standard deviation of returns of portfolio2')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 生成有效前沿"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.5534e-03 -1.0025e+00  1e+00  2e-16  3e+00\n",
+      " 1: -1.5540e-03 -1.2468e-02  1e-02  9e-17  3e-02\n",
+      " 2: -1.6103e-03 -2.5646e-03  1e-03  1e-16  2e-03\n",
+      " 3: -2.4050e-03 -2.8075e-03  4e-04  4e-16  4e-04\n",
+      " 4: -2.4341e-03 -2.4398e-03  6e-06  3e-16  6e-06\n",
+      " 5: -2.4361e-03 -2.4361e-03  6e-08  2e-16  6e-08\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.5514e-03 -1.0025e+00  1e+00  2e-16  3e+00\n",
+      " 1: -1.5520e-03 -1.2465e-02  1e-02  6e-17  3e-02\n",
+      " 2: -1.6082e-03 -2.5616e-03  1e-03  8e-17  2e-03\n",
+      " 3: -2.4007e-03 -2.8000e-03  4e-04  2e-16  4e-04\n",
+      " 4: -2.4276e-03 -2.4332e-03  6e-06  2e-18  5e-06\n",
+      " 5: -2.4295e-03 -2.4296e-03  6e-08  1e-16  5e-08\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.5491e-03 -1.0025e+00  1e+00  0e+00  3e+00\n",
+      " 1: -1.5498e-03 -1.2462e-02  1e-02  1e-16  3e-02\n",
+      " 2: -1.6059e-03 -2.5581e-03  1e-03  1e-16  2e-03\n",
+      " 3: -2.3959e-03 -2.7916e-03  4e-04  4e-16  4e-04\n",
+      " 4: -2.4204e-03 -2.4259e-03  5e-06  3e-16  5e-06\n",
+      " 5: -2.4221e-03 -2.4222e-03  5e-08  1e-16  5e-08\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.5466e-03 -1.0025e+00  1e+00  0e+00  3e+00\n",
+      " 1: -1.5473e-03 -1.2459e-02  1e-02  6e-17  3e-02\n",
+      " 2: -1.6032e-03 -2.5543e-03  1e-03  6e-17  2e-03\n",
+      " 3: -2.3904e-03 -2.7823e-03  4e-04  2e-16  3e-04\n",
+      " 4: -2.4123e-03 -2.4176e-03  5e-06  2e-16  5e-06\n",
+      " 5: -2.4139e-03 -2.4139e-03  5e-08  2e-16  5e-08\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.5438e-03 -1.0025e+00  1e+00  2e-16  3e+00\n",
+      " 1: -1.5444e-03 -1.2455e-02  1e-02  8e-17  3e-02\n",
+      " 2: -1.6002e-03 -2.5500e-03  9e-04  7e-17  2e-03\n",
+      " 3: -2.3842e-03 -2.7718e-03  4e-04  1e-15  3e-04\n",
+      " 4: -2.4032e-03 -2.4083e-03  5e-06  3e-16  4e-06\n",
+      " 5: -2.4046e-03 -2.4047e-03  5e-08  1e-16  4e-08\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.5407e-03 -1.0025e+00  1e+00  2e-16  3e+00\n",
+      " 1: -1.5413e-03 -1.2451e-02  1e-02  1e-16  3e-02\n",
+      " 2: -1.5969e-03 -2.5451e-03  9e-04  1e-16  2e-03\n",
+      " 3: -2.3773e-03 -2.7600e-03  4e-04  7e-16  3e-04\n",
+      " 4: -2.3931e-03 -2.3979e-03  5e-06  2e-16  4e-06\n",
+      " 5: -2.3942e-03 -2.3943e-03  5e-08  1e-16  4e-08\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.5371e-03 -1.0024e+00  1e+00  1e-16  3e+00\n",
+      " 1: -1.5378e-03 -1.2446e-02  1e-02  4e-17  3e-02\n",
+      " 2: -1.5931e-03 -2.5397e-03  9e-04  1e-16  2e-03\n",
+      " 3: -2.3694e-03 -2.7469e-03  4e-04  7e-16  2e-04\n",
+      " 4: -2.3816e-03 -2.3862e-03  5e-06  1e-16  3e-06\n",
+      " 5: -2.3826e-03 -2.3826e-03  5e-08  1e-16  3e-08\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.5332e-03 -1.0024e+00  1e+00  4e-16  3e+00\n",
+      " 1: -1.5338e-03 -1.2441e-02  1e-02  1e-16  3e-02\n",
+      " 2: -1.5889e-03 -2.5336e-03  9e-04  5e-17  2e-03\n",
+      " 3: -2.3599e-03 -2.7319e-03  4e-04  4e-16  2e-04\n",
+      " 4: -2.3688e-03 -2.3731e-03  4e-06  2e-16  2e-06\n",
+      " 5: -2.3695e-03 -2.3696e-03  4e-08  1e-16  2e-08\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.5287e-03 -1.0024e+00  1e+00  0e+00  3e+00\n",
+      " 1: -1.5293e-03 -1.2435e-02  1e-02  4e-17  3e-02\n",
+      " 2: -1.5842e-03 -2.5267e-03  9e-04  1e-16  2e-03\n",
+      " 3: -2.3450e-03 -2.7136e-03  4e-04  4e-16  2e-04\n",
+      " 4: -2.3540e-03 -2.3584e-03  4e-06  2e-16  2e-06\n",
+      " 5: -2.3548e-03 -2.3549e-03  4e-08  1e-16  2e-08\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.5237e-03 -1.0024e+00  1e+00  0e+00  3e+00\n",
+      " 1: -1.5243e-03 -1.2429e-02  1e-02  2e-16  3e-02\n",
+      " 2: -1.5789e-03 -2.5190e-03  9e-04  2e-16  2e-03\n",
+      " 3: -2.3282e-03 -2.6931e-03  4e-04  2e-16  1e-04\n",
+      " 4: -2.3374e-03 -2.3419e-03  5e-06  1e-16  2e-06\n",
+      " 5: -2.3384e-03 -2.3384e-03  5e-08  2e-16  2e-08\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.5181e-03 -1.0024e+00  1e+00  2e-16  3e+00\n",
+      " 1: -1.5187e-03 -1.2421e-02  1e-02  1e-16  3e-02\n",
+      " 2: -1.5730e-03 -2.5104e-03  9e-04  1e-16  2e-03\n",
+      " 3: -2.3091e-03 -2.6700e-03  4e-04  4e-16  1e-04\n",
+      " 4: -2.3188e-03 -2.3234e-03  5e-06  2e-16  1e-06\n",
+      " 5: -2.3199e-03 -2.3199e-03  5e-08  1e-16  1e-08\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.5118e-03 -1.0024e+00  1e+00  0e+00  3e+00\n",
+      " 1: -1.5124e-03 -1.2413e-02  1e-02  7e-17  3e-02\n",
+      " 2: -1.5663e-03 -2.5008e-03  9e-04  1e-16  2e-03\n",
+      " 3: -2.2859e-03 -2.6435e-03  4e-04  7e-16  6e-05\n",
+      " 4: -2.2976e-03 -2.3026e-03  5e-06  2e-16  9e-07\n",
+      " 5: -2.2992e-03 -2.2992e-03  5e-08  5e-20  9e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.5048e-03 -1.0024e+00  1e+00  1e-16  3e+00\n",
+      " 1: -1.5054e-03 -1.2403e-02  1e-02  1e-16  3e-02\n",
+      " 2: -1.5589e-03 -2.4900e-03  9e-04  4e-17  2e-03\n",
+      " 3: -2.2603e-03 -2.6138e-03  4e-04  2e-16  2e-05\n",
+      " 4: -2.2740e-03 -2.2793e-03  5e-06  2e-16  4e-07\n",
+      " 5: -2.2759e-03 -2.2759e-03  5e-08  1e-16  4e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.4969e-03 -1.0024e+00  1e+00  2e-16  3e+00\n",
+      " 1: -1.4975e-03 -1.2393e-02  1e-02  7e-17  3e-02\n",
+      " 2: -1.5505e-03 -2.4778e-03  9e-04  6e-17  2e-03\n",
+      " 3: -2.2268e-03 -2.5787e-03  4e-04  2e-16  5e-19\n",
+      " 4: -2.2468e-03 -2.2531e-03  6e-06  2e-16  3e-19\n",
+      " 5: -2.2498e-03 -2.2498e-03  6e-08  3e-16  2e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.4880e-03 -1.0024e+00  1e+00  2e-16  3e+00\n",
+      " 1: -1.4886e-03 -1.2381e-02  1e-02  2e-16  3e-02\n",
+      " 2: -1.5411e-03 -2.4643e-03  9e-04  1e-16  2e-03\n",
+      " 3: -2.1845e-03 -2.5376e-03  4e-04  7e-16  8e-19\n",
+      " 4: -2.2156e-03 -2.2237e-03  8e-06  2e-16  8e-19\n",
+      " 5: -2.2205e-03 -2.2206e-03  8e-08  1e-18  6e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.4780e-03 -1.0024e+00  1e+00  2e-16  3e+00\n",
+      " 1: -1.4786e-03 -1.2368e-02  1e-02  7e-17  3e-02\n",
+      " 2: -1.5305e-03 -2.4490e-03  9e-04  8e-17  2e-03\n",
+      " 3: -2.1396e-03 -2.4926e-03  4e-04  7e-16  3e-19\n",
+      " 4: -2.1805e-03 -2.1908e-03  1e-05  2e-16  2e-19\n",
+      " 5: -2.1876e-03 -2.1877e-03  1e-07  2e-16  2e-19\n",
+      " 6: -2.1877e-03 -2.1877e-03  1e-09  2e-16  3e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.4668e-03 -1.0024e+00  1e+00  1e-16  3e+00\n",
+      " 1: -1.4674e-03 -1.2353e-02  1e-02  1e-16  3e-02\n",
+      " 2: -1.5187e-03 -2.4320e-03  9e-04  1e-16  2e-03\n",
+      " 3: -2.0922e-03 -2.4435e-03  4e-04  4e-16  6e-19\n",
+      " 4: -2.1411e-03 -2.1538e-03  1e-05  1e-16  4e-19\n",
+      " 5: -2.1507e-03 -2.1508e-03  2e-07  1e-16  4e-19\n",
+      " 6: -2.1508e-03 -2.1508e-03  2e-09  1e-16  5e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.4543e-03 -1.0023e+00  1e+00  0e+00  3e+00\n",
+      " 1: -1.4549e-03 -1.2336e-02  1e-02  9e-17  3e-02\n",
+      " 2: -1.5055e-03 -2.4129e-03  9e-04  8e-17  2e-03\n",
+      " 3: -2.0425e-03 -2.3903e-03  3e-04  2e-16  4e-19\n",
+      " 4: -2.0970e-03 -2.1125e-03  2e-05  3e-16  4e-19\n",
+      " 5: -2.1092e-03 -2.1095e-03  2e-07  5e-18  1e-19\n",
+      " 6: -2.1094e-03 -2.1094e-03  2e-09  4e-20  1e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.4402e-03 -1.0023e+00  1e+00  4e-16  3e+00\n",
+      " 1: -1.4408e-03 -1.2317e-02  1e-02  2e-16  3e-02\n",
+      " 2: -1.4906e-03 -2.3915e-03  9e-04  7e-17  2e-03\n",
+      " 3: -1.9905e-03 -2.3327e-03  3e-04  4e-16  6e-19\n",
+      " 4: -2.0481e-03 -2.0665e-03  2e-05  2e-16  5e-19\n",
+      " 5: -2.0626e-03 -2.0631e-03  5e-07  2e-16  1e-19\n",
+      " 6: -2.0630e-03 -2.0630e-03  5e-09  2e-16  2e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.4244e-03 -1.0023e+00  1e+00  1e-16  3e+00\n",
+      " 1: -1.4250e-03 -1.2296e-02  1e-02  4e-17  3e-02\n",
+      " 2: -1.4739e-03 -2.3675e-03  9e-04  6e-17  2e-03\n",
+      " 3: -1.9364e-03 -2.2708e-03  3e-04  2e-16  5e-19\n",
+      " 4: -1.9944e-03 -2.0153e-03  2e-05  1e-16  3e-19\n",
+      " 5: -2.0100e-03 -2.0110e-03  1e-06  1e-16  1e-19\n",
+      " 6: -2.0109e-03 -2.0110e-03  1e-08  1e-16  2e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.4067e-03 -1.0023e+00  1e+00  1e-16  3e+00\n",
+      " 1: -1.4072e-03 -1.2273e-02  1e-02  4e-17  3e-02\n",
+      " 2: -1.4552e-03 -2.3407e-03  9e-04  1e-16  2e-03\n",
+      " 3: -1.8803e-03 -2.2044e-03  3e-04  2e-16  5e-19\n",
+      " 4: -1.9364e-03 -1.9589e-03  2e-05  2e-16  6e-19\n",
+      " 5: -1.9512e-03 -1.9528e-03  2e-06  1e-16  5e-19\n",
+      " 6: -1.9524e-03 -1.9525e-03  1e-07  1e-16  2e-19\n",
+      " 7: -1.9525e-03 -1.9525e-03  3e-09  4e-19  4e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.3868e-03 -1.0022e+00  1e+00  0e+00  3e+00\n",
+      " 1: -1.3873e-03 -1.2246e-02  1e-02  1e-16  3e-02\n",
+      " 2: -1.4343e-03 -2.3106e-03  9e-04  1e-16  2e-03\n",
+      " 3: -1.8223e-03 -2.1336e-03  3e-04  2e-16  8e-19\n",
+      " 4: -1.8748e-03 -1.8975e-03  2e-05  1e-16  2e-19\n",
+      " 5: -1.8882e-03 -1.8894e-03  1e-06  1e-16  3e-19\n",
+      " 6: -1.8887e-03 -1.8888e-03  2e-08  1e-16  1e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.3645e-03 -1.0022e+00  1e+00  2e-16  3e+00\n",
+      " 1: -1.3650e-03 -1.2216e-02  1e-02  4e-17  3e-02\n",
+      " 2: -1.4108e-03 -2.2771e-03  9e-04  2e-16  2e-03\n",
+      " 3: -1.7625e-03 -2.0585e-03  3e-04  1e-16  6e-19\n",
+      " 4: -1.8106e-03 -1.8319e-03  2e-05  1e-16  3e-19\n",
+      " 5: -1.8236e-03 -1.8246e-03  1e-06  1e-16  3e-19\n",
+      " 6: -1.8244e-03 -1.8244e-03  1e-08  2e-16  4e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.3395e-03 -1.0022e+00  1e+00  2e-16  3e+00\n",
+      " 1: -1.3400e-03 -1.2183e-02  1e-02  1e-16  3e-02\n",
+      " 2: -1.3845e-03 -2.2395e-03  9e-04  3e-17  2e-03\n",
+      " 3: -1.7008e-03 -1.9792e-03  3e-04  2e-16  6e-19\n",
+      " 4: -1.7445e-03 -1.7631e-03  2e-05  1e-16  1e-19\n",
+      " 5: -1.7588e-03 -1.7593e-03  5e-07  3e-18  8e-20\n",
+      " 6: -1.7592e-03 -1.7592e-03  5e-09  6e-17  2e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.3114e-03 -1.0021e+00  1e+00  0e+00  3e+00\n",
+      " 1: -1.3119e-03 -1.2146e-02  1e-02  7e-17  3e-02\n",
+      " 2: -1.3550e-03 -2.1975e-03  8e-04  4e-17  2e-03\n",
+      " 3: -1.6372e-03 -1.8959e-03  3e-04  2e-16  6e-19\n",
+      " 4: -1.6755e-03 -1.7014e-03  3e-05  2e-16  3e-19\n",
+      " 5: -1.6912e-03 -1.6923e-03  1e-06  1e-16  3e-19\n",
+      " 6: -1.6921e-03 -1.6921e-03  1e-08  3e-16  2e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.2799e-03 -1.0021e+00  1e+00  0e+00  3e+00\n",
+      " 1: -1.2804e-03 -1.2104e-02  1e-02  2e-16  3e-02\n",
+      " 2: -1.3219e-03 -2.1505e-03  8e-04  8e-17  2e-03\n",
+      " 3: -1.5717e-03 -1.8089e-03  2e-04  1e-16  5e-19\n",
+      " 4: -1.6053e-03 -1.6356e-03  3e-05  1e-16  3e-19\n",
+      " 5: -1.6207e-03 -1.6226e-03  2e-06  1e-16  2e-19\n",
+      " 6: -1.6222e-03 -1.6222e-03  3e-08  2e-16  2e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.2445e-03 -1.0021e+00  1e+00  2e-16  3e+00\n",
+      " 1: -1.2450e-03 -1.2056e-02  1e-02  2e-16  3e-02\n",
+      " 2: -1.2849e-03 -2.0980e-03  8e-04  8e-17  2e-03\n",
+      " 3: -1.5040e-03 -1.7182e-03  2e-04  1e-16  3e-19\n",
+      " 4: -1.5335e-03 -1.5633e-03  3e-05  1e-16  2e-19\n",
+      " 5: -1.5468e-03 -1.5493e-03  2e-06  1e-17  7e-20\n",
+      " 6: -1.5487e-03 -1.5488e-03  1e-07  2e-18  4e-19\n",
+      " 7: -1.5488e-03 -1.5488e-03  1e-09  6e-17  2e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.2048e-03 -1.0020e+00  1e+00  0e+00  3e+00\n",
+      " 1: -1.2053e-03 -1.2004e-02  1e-02  1e-16  3e-02\n",
+      " 2: -1.2433e-03 -2.0394e-03  8e-04  1e-16  2e-03\n",
+      " 3: -1.4337e-03 -1.6242e-03  2e-04  2e-16  3e-19\n",
+      " 4: -1.4594e-03 -1.4854e-03  3e-05  2e-16  3e-19\n",
+      " 5: -1.4691e-03 -1.4717e-03  3e-06  1e-17  3e-19\n",
+      " 6: -1.4705e-03 -1.4708e-03  2e-07  1e-16  2e-19\n",
+      " 7: -1.4706e-03 -1.4707e-03  3e-08  2e-16  1e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.1603e-03 -1.0019e+00  1e+00  0e+00  3e+00\n",
+      " 1: -1.1608e-03 -1.1944e-02  1e-02  1e-16  3e-02\n",
+      " 2: -1.1969e-03 -1.9740e-03  8e-04  6e-17  2e-03\n",
+      " 3: -1.3604e-03 -1.5269e-03  2e-04  1e-16  4e-19\n",
+      " 4: -1.3819e-03 -1.4026e-03  2e-05  1e-16  8e-20\n",
+      " 5: -1.3878e-03 -1.3899e-03  2e-06  1e-16  1e-19\n",
+      " 6: -1.3887e-03 -1.3888e-03  1e-07  1e-16  8e-20\n",
+      " 7: -1.3888e-03 -1.3888e-03  1e-09  6e-17  1e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.1104e-03 -1.0019e+00  1e+00  2e-16  3e+00\n",
+      " 1: -1.1108e-03 -1.1878e-02  1e-02  8e-17  3e-02\n",
+      " 2: -1.1448e-03 -1.9011e-03  8e-04  1e-16  2e-03\n",
+      " 3: -1.2837e-03 -1.4263e-03  1e-04  1e-16  4e-19\n",
+      " 4: -1.3005e-03 -1.3155e-03  2e-05  1e-16  2e-19\n",
+      " 5: -1.3034e-03 -1.3046e-03  1e-06  1e-16  3e-19\n",
+      " 6: -1.3038e-03 -1.3040e-03  2e-07  2e-16  3e-19\n",
+      " 7: -1.3039e-03 -1.3039e-03  2e-08  6e-17  5e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.0544e-03 -1.0018e+00  1e+00  2e-16  3e+00\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 1: -1.0548e-03 -1.1803e-02  1e-02  2e-16  3e-02\n",
+      " 2: -1.0865e-03 -1.8198e-03  7e-04  1e-16  2e-03\n",
+      " 3: -1.2029e-03 -1.3225e-03  1e-04  2e-16  4e-19\n",
+      " 4: -1.2150e-03 -1.2247e-03  1e-05  1e-16  3e-19\n",
+      " 5: -1.2160e-03 -1.2166e-03  7e-07  6e-17  2e-19\n",
+      " 6: -1.2160e-03 -1.2160e-03  2e-08  1e-16  1e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -9.9151e-04 -1.0017e+00  1e+00  0e+00  3e+00\n",
+      " 1: -9.9188e-04 -1.1720e-02  1e-02  1e-16  3e-02\n",
+      " 2: -1.0212e-03 -1.7293e-03  7e-04  1e-16  2e-03\n",
+      " 3: -1.1173e-03 -1.2152e-03  1e-04  1e-16  2e-19\n",
+      " 4: -1.1252e-03 -1.1311e-03  6e-06  6e-17  5e-19\n",
+      " 5: -1.1254e-03 -1.1256e-03  2e-07  8e-17  3e-19\n",
+      " 6: -1.1254e-03 -1.1254e-03  2e-09  1e-16  7e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -9.2098e-04 -1.0016e+00  1e+00  2e-16  3e+00\n",
+      " 1: -9.2133e-04 -1.1626e-02  1e-02  6e-17  3e-02\n",
+      " 2: -9.4813e-04 -1.6285e-03  7e-04  1e-16  2e-03\n",
+      " 3: -1.0262e-03 -1.1040e-03  8e-05  7e-17  3e-19\n",
+      " 4: -1.0309e-03 -1.0344e-03  4e-06  7e-17  4e-19\n",
+      " 5: -1.0309e-03 -1.0310e-03  4e-08  7e-17  4e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -8.4185e-04 -1.0015e+00  1e+00  1e-16  3e+00\n",
+      " 1: -8.4216e-04 -1.1521e-02  1e-02  9e-17  3e-02\n",
+      " 2: -8.6637e-04 -1.5164e-03  7e-04  1e-16  2e-03\n",
+      " 3: -9.2854e-04 -9.8853e-04  6e-05  1e-16  5e-19\n",
+      " 4: -9.3125e-04 -9.3315e-04  2e-06  6e-17  6e-19\n",
+      " 5: -9.3127e-04 -9.3129e-04  2e-08  8e-17  2e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -7.5306e-04 -1.0014e+00  1e+00  0e+00  3e+00\n",
+      " 1: -7.5335e-04 -1.1402e-02  1e-02  8e-17  3e-02\n",
+      " 2: -7.7490e-04 -1.3919e-03  6e-04  1e-16  2e-03\n",
+      " 3: -8.2351e-04 -8.6801e-04  4e-05  7e-17  2e-19\n",
+      " 4: -8.2514e-04 -8.2659e-04  1e-06  7e-17  4e-19\n",
+      " 5: -8.2515e-04 -8.2517e-04  2e-08  6e-17  6e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -6.5345e-04 -1.0013e+00  1e+00  0e+00  3e+00\n",
+      " 1: -6.5371e-04 -1.1270e-02  1e-02  1e-16  3e-02\n",
+      " 2: -6.7259e-04 -1.2537e-03  6e-04  2e-16  1e-03\n",
+      " 3: -7.0999e-04 -7.4202e-04  3e-05  7e-17  1e-06\n",
+      " 4: -7.1115e-04 -7.1231e-04  1e-06  7e-17  3e-08\n",
+      " 5: -7.1116e-04 -7.1117e-04  1e-08  6e-17  3e-10\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -5.4169e-04 -1.0011e+00  1e+00  0e+00  3e+00\n",
+      " 1: -5.4192e-04 -1.1121e-02  1e-02  5e-17  3e-02\n",
+      " 2: -5.5819e-04 -1.1005e-03  5e-04  1e-16  1e-03\n",
+      " 3: -5.8675e-04 -6.0874e-04  2e-05  1e-16  1e-06\n",
+      " 4: -5.8777e-04 -5.8848e-04  7e-07  6e-17  2e-08\n",
+      " 5: -5.8777e-04 -5.8778e-04  7e-09  9e-17  2e-10\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -4.1630e-04 -1.0010e+00  1e+00  0e+00  3e+00\n",
+      " 1: -4.1650e-04 -1.0955e-02  1e-02  2e-16  3e-02\n",
+      " 2: -4.3029e-04 -9.3077e-04  5e-04  5e-17  1e-03\n",
+      " 3: -4.5230e-04 -4.7737e-04  3e-05  2e-16  3e-05\n",
+      " 4: -4.5335e-04 -4.5408e-04  7e-07  8e-17  3e-07\n",
+      " 5: -4.5336e-04 -4.5336e-04  7e-09  3e-17  3e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -2.7562e-04 -1.0008e+00  1e+00  1e-16  3e+00\n",
+      " 1: -2.7579e-04 -1.0768e-02  1e-02  1e-16  3e-02\n",
+      " 2: -2.8735e-04 -7.4302e-04  5e-04  8e-17  1e-03\n",
+      " 3: -3.0508e-04 -3.3370e-04  3e-05  1e-16  5e-05\n",
+      " 4: -3.0613e-04 -3.0686e-04  7e-07  6e-17  6e-07\n",
+      " 5: -3.0613e-04 -3.0614e-04  7e-09  9e-17  6e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.1779e-04 -1.0006e+00  1e+00  0e+00  3e+00\n",
+      " 1: -1.1794e-04 -1.0559e-02  1e-02  2e-16  3e-02\n",
+      " 2: -1.2764e-04 -5.3557e-04  4e-04  8e-17  1e-03\n",
+      " 3: -1.4321e-04 -1.7234e-04  3e-05  2e-16  6e-05\n",
+      " 4: -1.4414e-04 -1.4478e-04  6e-07  6e-17  6e-07\n",
+      " 5: -1.4414e-04 -1.4415e-04  6e-09  1e-16  6e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  5.9284e-05 -1.0003e+00  1e+00  4e-16  3e+00\n",
+      " 1:  5.9157e-05 -1.0324e-02  1e-02  8e-17  3e-02\n",
+      " 2:  5.0775e-05 -3.0666e-04  4e-04  9e-17  9e-04\n",
+      " 3:  3.5428e-05  9.5165e-06  3e-05  1e-16  4e-05\n",
+      " 4:  3.4763e-05  3.4300e-05  5e-07  1e-16  5e-07\n",
+      " 5:  3.4763e-05  3.4758e-05  5e-09  4e-17  5e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  2.5794e-04 -1.0001e+00  1e+00  1e-16  3e+00\n",
+      " 1:  2.5783e-04 -1.0060e-02  1e-02  8e-17  3e-02\n",
+      " 2:  2.5002e-04 -5.4388e-05  3e-04  8e-17  8e-04\n",
+      " 3:  2.3326e-04  2.1585e-04  2e-05  1e-16  1e-05\n",
+      " 4:  2.3296e-04  2.3271e-04  2e-07  1e-16  2e-07\n",
+      " 5:  2.3296e-04  2.3295e-04  2e-09  8e-17  2e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  4.8081e-04 -9.9977e-01  1e+00  0e+00  3e+00\n",
+      " 1:  4.8070e-04 -9.7649e-03  1e-02  8e-17  3e-02\n",
+      " 2:  4.7254e-04  1.5884e-04  3e-04  6e-17  8e-04\n",
+      " 3:  4.5324e-04  4.3815e-04  2e-05  8e-17  2e-18\n",
+      " 4:  4.5307e-04  4.5288e-04  2e-07  1e-16  6e-19\n",
+      " 5:  4.5307e-04  4.5307e-04  2e-09  8e-17  1e-18\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  7.3085e-04 -9.9960e-01  1e+00  2e-16  3e+00\n",
+      " 1:  7.3072e-04 -9.6022e-03  1e-02  3e-17  3e-02\n",
+      " 2:  7.2112e-04  3.3665e-04  4e-04  1e-16  1e-03\n",
+      " 3:  6.9820e-04  6.7939e-04  2e-05  2e-16  9e-19\n",
+      " 4:  6.9802e-04  6.9780e-04  2e-07  7e-17  1e-18\n",
+      " 5:  6.9802e-04  6.9802e-04  2e-09  1e-16  7e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  1.0114e-03 -9.9943e-01  1e+00  0e+00  3e+00\n",
+      " 1:  1.0112e-03 -9.4307e-03  1e-02  6e-17  3e-02\n",
+      " 2:  9.9874e-04  5.3975e-04  5e-04  2e-16  1e-03\n",
+      " 3:  9.7125e-04  9.4825e-04  2e-05  1e-16  2e-18\n",
+      " 4:  9.7106e-04  9.7080e-04  3e-07  1e-16  5e-19\n",
+      " 5:  9.7106e-04  9.7106e-04  3e-09  8e-17  6e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  1.3260e-03 -9.9924e-01  1e+00  1e-16  3e+00\n",
+      " 1:  1.3258e-03 -9.2380e-03  1e-02  2e-16  3e-02\n",
+      " 2:  1.3088e-03  7.7180e-04  5e-04  2e-16  1e-03\n",
+      " 3:  1.2760e-03  1.2484e-03  3e-05  4e-17  2e-18\n",
+      " 4:  1.2758e-03  1.2755e-03  3e-07  9e-17  2e-18\n",
+      " 5:  1.2758e-03  1.2758e-03  3e-09  6e-17  3e-18\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  1.6790e-03 -9.9902e-01  1e+00  0e+00  3e+00\n",
+      " 1:  1.6787e-03 -9.0214e-03  1e-02  9e-17  3e-02\n",
+      " 2:  1.6553e-03  1.0368e-03  6e-04  1e-16  2e-03\n",
+      " 3:  1.6165e-03  1.5842e-03  3e-05  1e-16  2e-18\n",
+      " 4:  1.6163e-03  1.6160e-03  4e-07  6e-17  1e-18\n",
+      " 5:  1.6163e-03  1.6163e-03  4e-09  9e-17  2e-18\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  2.0750e-03 -9.9878e-01  1e+00  0e+00  3e+00\n",
+      " 1:  2.0745e-03 -8.7779e-03  1e-02  2e-16  3e-02\n",
+      " 2:  2.0424e-03  1.3393e-03  7e-04  6e-17  2e-03\n",
+      " 3:  1.9973e-03  1.9602e-03  4e-05  2e-16  7e-19\n",
+      " 4:  1.9971e-03  1.9967e-03  4e-07  1e-16  2e-18\n",
+      " 5:  1.9971e-03  1.9971e-03  4e-09  4e-17  2e-18\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  2.5193e-03 -9.9851e-01  1e+00  3e-16  3e+00\n",
+      " 1:  2.5185e-03 -8.5041e-03  1e-02  1e-16  3e-02\n",
+      " 2:  2.4751e-03  1.6843e-03  8e-04  1e-16  2e-03\n",
+      " 3:  2.4234e-03  2.3817e-03  4e-05  1e-16  3e-18\n",
+      " 4:  2.4232e-03  2.4228e-03  5e-07  4e-17  3e-18\n",
+      " 5:  2.4232e-03  2.4232e-03  5e-09  7e-17  1e-18\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  3.0176e-03 -9.9820e-01  1e+00  0e+00  3e+00\n",
+      " 1:  3.0165e-03 -8.1962e-03  1e-02  1e-16  3e-02\n",
+      " 2:  2.9589e-03  2.0778e-03  9e-04  0e+00  2e-03\n",
+      " 3:  2.9006e-03  2.8544e-03  5e-05  9e-17  2e-18\n",
+      " 4:  2.9003e-03  2.8998e-03  5e-07  1e-16  1e-18\n",
+      " 5:  2.9003e-03  2.9003e-03  5e-09  1e-16  2e-18\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  3.5765e-03 -9.9786e-01  1e+00  1e-16  3e+00\n",
+      " 1:  3.5750e-03 -7.8499e-03  1e-02  2e-16  3e-02\n",
+      " 2:  3.5000e-03  2.5261e-03  1e-03  2e-16  2e-03\n",
+      " 3:  3.4350e-03  3.3849e-03  5e-05  2e-16  2e-18\n",
+      " 4:  3.4347e-03  3.4342e-03  5e-07  1e-16  2e-18\n",
+      " 5:  3.4347e-03  3.4347e-03  5e-09  6e-17  1e-18\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  4.2034e-03 -9.9747e-01  1e+00  0e+00  3e+00\n",
+      " 1:  4.2014e-03 -7.4601e-03  1e-02  7e-17  3e-02\n",
+      " 2:  4.1053e-03  3.0368e-03  1e-03  1e-16  3e-03\n",
+      " 3:  4.0338e-03  3.9803e-03  5e-05  1e-16  3e-18\n",
+      " 4:  4.0335e-03  4.0330e-03  6e-07  1e-16  3e-18\n",
+      " 5:  4.0335e-03  4.0335e-03  6e-09  7e-17  2e-18\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  4.9066e-03 -9.9704e-01  1e+00  0e+00  3e+00\n",
+      " 1:  4.9038e-03 -7.0214e-03  1e-02  2e-16  3e-02\n",
+      " 2:  4.7825e-03  3.6182e-03  1e-03  7e-17  3e-03\n",
+      " 3:  4.7049e-03  4.6488e-03  6e-05  1e-16  3e-18\n",
+      " 4:  4.7047e-03  4.7041e-03  6e-07  1e-16  3e-18\n",
+      " 5:  4.7047e-03  4.7047e-03  6e-09  9e-17  3e-18\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  5.6953e-03 -9.9655e-01  1e+00  0e+00  3e+00\n",
+      " 1:  5.6915e-03 -6.5274e-03  1e-02  8e-17  3e-02\n",
+      " 2:  5.5405e-03  4.2797e-03  1e-03  4e-17  3e-03\n",
+      " 3:  5.4574e-03  5.3992e-03  6e-05  6e-17  1e-18\n",
+      " 4:  5.4571e-03  5.4565e-03  6e-07  1e-16  2e-18\n",
+      " 5:  5.4571e-03  5.4571e-03  6e-09  4e-17  4e-18\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  6.5799e-03 -9.9600e-01  1e+00  2e-16  3e+00\n",
+      " 1:  6.5748e-03 -5.9710e-03  1e-02  8e-17  3e-02\n",
+      " 2:  6.3890e-03  5.0320e-03  1e-03  8e-17  3e-03\n",
+      " 3:  6.3010e-03  6.2417e-03  6e-05  1e-16  3e-18\n",
+      " 4:  6.3008e-03  6.3001e-03  6e-07  1e-16  5e-18\n",
+      " 5:  6.3008e-03  6.3008e-03  6e-09  1e-16  3e-18\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  7.5719e-03 -9.9538e-01  1e+00  0e+00  3e+00\n",
+      " 1:  7.5651e-03 -5.3439e-03  1e-02  2e-16  3e-02\n",
+      " 2:  7.3392e-03  5.8872e-03  1e-03  1e-16  3e-03\n",
+      " 3:  7.2471e-03  7.1875e-03  6e-05  6e-17  6e-18\n",
+      " 4:  7.2468e-03  7.2462e-03  6e-07  1e-16  3e-18\n",
+      " 5:  7.2468e-03  7.2468e-03  6e-09  1e-16  3e-18\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  8.6844e-03 -9.9468e-01  1e+00  0e+00  3e+00\n",
+      " 1:  8.6755e-03 -4.6371e-03  1e-02  2e-16  3e-02\n",
+      " 2:  8.4034e-03  6.8590e-03  2e-03  7e-17  3e-03\n",
+      " 3:  8.3081e-03  8.2491e-03  6e-05  1e-16  5e-18\n",
+      " 4:  8.3079e-03  8.3073e-03  6e-07  1e-16  3e-18\n",
+      " 5:  8.3079e-03  8.3079e-03  6e-09  9e-17  4e-18\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  9.9319e-03 -9.9389e-01  1e+00  0e+00  3e+00\n",
+      " 1:  9.9203e-03 -3.8398e-03  1e-02  2e-16  3e-02\n",
+      " 2:  9.5956e-03  7.9627e-03  2e-03  8e-17  4e-03\n",
+      " 3:  9.4983e-03  9.4407e-03  6e-05  1e-16  1e-17\n",
+      " 4:  9.4981e-03  9.4975e-03  6e-07  5e-17  3e-18\n",
+      " 5:  9.4981e-03  9.4981e-03  6e-09  4e-17  5e-18\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  1.1331e-02 -9.9301e-01  1e+00  0e+00  3e+00\n",
+      " 1:  1.1316e-02 -2.9400e-03  1e-02  1e-16  4e-02\n",
+      " 2:  1.0931e-02  9.2158e-03  2e-03  9e-17  4e-03\n",
+      " 3:  1.0833e-02  1.0778e-02  6e-05  2e-16  3e-18\n",
+      " 4:  1.0833e-02  1.0833e-02  6e-07  1e-16  7e-18\n",
+      " 5:  1.0833e-02  1.0833e-02  6e-09  1e-16  9e-18\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  1.2899e-02 -9.9201e-01  1e+00  0e+00  3e+00\n",
+      " 1:  1.2880e-02 -1.9242e-03  1e-02  9e-17  4e-02\n",
+      " 2:  1.2429e-02  1.0638e-02  2e-03  7e-17  4e-03\n",
+      " 3:  1.2331e-02  1.2279e-02  5e-05  1e-16  6e-18\n",
+      " 4:  1.2331e-02  1.2330e-02  5e-07  3e-17  5e-18\n",
+      " 5:  1.2331e-02  1.2331e-02  5e-09  4e-17  1e-17\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  1.4658e-02 -9.9089e-01  1e+00  0e+00  3e+00\n",
+      " 1:  1.4633e-02 -7.7655e-04  2e-02  1e-16  4e-02\n",
+      " 2:  1.4107e-02  1.2251e-02  2e-03  8e-17  4e-03\n",
+      " 3:  1.4011e-02  1.3960e-02  5e-05  7e-17  5e-06\n",
+      " 4:  1.4011e-02  1.4010e-02  5e-07  6e-17  5e-08\n",
+      " 5:  1.4011e-02  1.4011e-02  5e-09  4e-17  5e-10\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  1.6629e-02 -9.8962e-01  1e+00  0e+00  3e+00\n",
+      " 1:  1.6597e-02  5.2075e-04  2e-02  2e-16  4e-02\n",
+      " 2:  1.5988e-02  1.4080e-02  2e-03  2e-16  4e-03\n",
+      " 3:  1.5896e-02  1.5847e-02  5e-05  1e-16  1e-05\n",
+      " 4:  1.5895e-02  1.5895e-02  5e-07  2e-16  1e-07\n",
+      " 5:  1.5895e-02  1.5895e-02  5e-09  2e-16  1e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  1.8839e-02 -9.8819e-01  1e+00  0e+00  3e+00\n",
+      " 1:  1.8799e-02  1.9882e-03  2e-02  2e-16  4e-02\n",
+      " 2:  1.8098e-02  1.6153e-02  2e-03  8e-17  3e-03\n",
+      " 3:  1.8010e-02  1.7963e-02  5e-05  1e-16  1e-05\n",
+      " 4:  1.8010e-02  1.8010e-02  5e-07  1e-16  1e-07\n",
+      " 5:  1.8010e-02  1.8010e-02  5e-09  3e-17  1e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  2.1316e-02 -9.8658e-01  1e+00  0e+00  3e+00\n",
+      " 1:  2.1265e-02  3.6491e-03  2e-02  1e-16  4e-02\n",
+      " 2:  2.0465e-02  1.8501e-02  2e-03  1e-16  3e-03\n",
+      " 3:  2.0382e-02  2.0338e-02  4e-05  1e-16  2e-05\n",
+      " 4:  2.0382e-02  2.0382e-02  4e-07  1e-16  2e-07\n",
+      " 5:  2.0382e-02  2.0382e-02  4e-09  7e-17  2e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  2.4092e-02 -9.8475e-01  1e+00  3e-16  3e+00\n",
+      " 1:  2.4029e-02  5.5303e-03  2e-02  1e-16  5e-02\n",
+      " 2:  2.3119e-02  2.1158e-02  2e-03  1e-16  3e-03\n",
+      " 3:  2.3044e-02  2.3003e-02  4e-05  4e-17  2e-05\n",
+      " 4:  2.3044e-02  2.3044e-02  4e-07  8e-17  2e-07\n",
+      " 5:  2.3044e-02  2.3044e-02  4e-09  1e-16  2e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  2.7203e-02 -9.8269e-01  1e+00  0e+00  3e+00\n",
+      " 1:  2.7124e-02  7.6625e-03  2e-02  1e-16  5e-02\n",
+      " 2:  2.6097e-02  2.4164e-02  2e-03  6e-17  2e-03\n",
+      " 3:  2.6030e-02  2.5992e-02  4e-05  7e-17  2e-05\n",
+      " 4:  2.6030e-02  2.6030e-02  4e-07  2e-16  2e-07\n",
+      " 5:  2.6030e-02  2.6030e-02  4e-09  1e-16  2e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  3.0690e-02 -9.8036e-01  1e+00  0e+00  3e+00\n",
+      " 1:  3.0592e-02  1.0081e-02  2e-02  8e-17  5e-02\n",
+      " 2:  2.9439e-02  2.7561e-02  2e-03  1e-16  2e-03\n",
+      " 3:  2.9381e-02  2.9347e-02  3e-05  7e-17  2e-05\n",
+      " 4:  2.9381e-02  2.9380e-02  3e-07  2e-16  2e-07\n",
+      " 5:  2.9381e-02  2.9381e-02  3e-09  2e-16  2e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  3.4597e-02 -9.7772e-01  1e+00  0e+00  3e+00\n",
+      " 1:  3.4475e-02  1.2826e-02  2e-02  6e-17  5e-02\n",
+      " 2:  3.3189e-02  3.1397e-02  2e-03  2e-16  1e-03\n",
+      " 3:  3.3140e-02  3.3109e-02  3e-05  1e-16  1e-05\n",
+      " 4:  3.3140e-02  3.3139e-02  3e-07  1e-16  1e-07\n",
+      " 5:  3.3140e-02  3.3140e-02  3e-09  1e-16  1e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  3.8974e-02 -9.7473e-01  1e+00  2e-16  3e+00\n",
+      " 1:  3.8824e-02  1.5943e-02  2e-02  1e-16  6e-02\n",
+      " 2:  3.7398e-02  3.5722e-02  2e-03  1e-16  2e-04\n",
+      " 3:  3.7357e-02  3.7331e-02  3e-05  9e-17  2e-06\n",
+      " 4:  3.7357e-02  3.7357e-02  3e-07  3e-17  2e-08\n",
+      " 5:  3.7357e-02  3.7357e-02  3e-09  5e-17  2e-10\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  4.3877e-02 -9.7134e-01  1e+00  2e-16  3e+00\n",
+      " 1:  4.3694e-02  1.9486e-02  2e-02  8e-17  6e-02\n",
+      " 2:  4.2125e-02  4.0361e-02  2e-03  3e-17  2e-17\n",
+      " 3:  4.2090e-02  4.2063e-02  3e-05  2e-16  9e-18\n",
+      " 4:  4.2090e-02  4.2089e-02  3e-07  1e-16  1e-17\n",
+      " 5:  4.2090e-02  4.2090e-02  3e-09  1e-16  2e-17\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  4.9371e-02 -9.6750e-01  1e+00  2e-16  3e+00\n",
+      " 1:  4.9147e-02  2.3515e-02  3e-02  2e-16  6e-02\n",
+      " 2:  4.7433e-02  4.5509e-02  2e-03  2e-16  2e-17\n",
+      " 3:  4.7399e-02  4.7372e-02  3e-05  8e-17  1e-17\n",
+      " 4:  4.7399e-02  4.7399e-02  3e-07  1e-16  2e-17\n",
+      " 5:  4.7399e-02  4.7399e-02  3e-09  2e-16  3e-17\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  5.5524e-02 -9.6315e-01  1e+00  0e+00  3e+00\n",
+      " 1:  5.5252e-02  2.8100e-02  3e-02  6e-17  7e-02\n",
+      " 2:  5.3388e-02  5.1310e-02  2e-03  4e-17  2e-17\n",
+      " 3:  5.3357e-02  5.3328e-02  3e-05  6e-17  4e-17\n",
+      " 4:  5.3357e-02  5.3356e-02  3e-07  7e-17  3e-17\n",
+      " 5:  5.3357e-02  5.3357e-02  3e-09  1e-16  7e-18\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  6.2415e-02 -9.5821e-01  1e+00  1e-16  3e+00\n",
+      " 1:  6.2087e-02  3.3320e-02  3e-02  1e-16  7e-02\n",
+      " 2:  6.0070e-02  5.7847e-02  2e-03  8e-17  2e-17\n",
+      " 3:  6.0041e-02  6.0011e-02  3e-05  2e-16  3e-17\n",
+      " 4:  6.0041e-02  6.0041e-02  3e-07  2e-16  3e-17\n",
+      " 5:  6.0041e-02  6.0041e-02  3e-09  2e-16  2e-17\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  7.0133e-02 -9.5260e-01  1e+00  4e-16  3e+00\n",
+      " 1:  6.9739e-02  3.9265e-02  3e-02  6e-17  8e-02\n",
+      " 2:  6.7568e-02  6.5212e-02  2e-03  7e-17  2e-17\n",
+      " 3:  6.7541e-02  6.7510e-02  3e-05  8e-17  4e-17\n",
+      " 4:  6.7541e-02  6.7540e-02  3e-07  6e-17  2e-17\n",
+      " 5:  6.7541e-02  6.7541e-02  3e-09  2e-16  3e-17\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  7.8777e-02 -9.4623e-01  1e+00  0e+00  3e+00\n",
+      " 1:  7.8305e-02  4.6040e-02  3e-02  3e-17  8e-02\n",
+      " 2:  7.5982e-02  7.3508e-02  2e-03  1e-16  3e-17\n",
+      " 3:  7.5955e-02  7.5924e-02  3e-05  7e-17  2e-17\n",
+      " 4:  7.5955e-02  7.5955e-02  3e-07  6e-17  5e-17\n",
+      " 5:  7.5955e-02  7.5955e-02  3e-09  1e-16  3e-17\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  8.8456e-02 -9.3899e-01  1e+00  0e+00  3e+00\n",
+      " 1:  8.7895e-02  5.3761e-02  3e-02  1e-16  8e-02\n",
+      " 2:  8.5422e-02  8.2849e-02  3e-03  1e-16  1e-17\n",
+      " 3:  8.5397e-02  8.5365e-02  3e-05  8e-17  5e-17\n",
+      " 4:  8.5397e-02  8.5397e-02  3e-07  1e-16  4e-17\n",
+      " 5:  8.5397e-02  8.5397e-02  3e-09  8e-17  5e-17\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  9.9295e-02 -9.3076e-01  1e+00  2e-16  3e+00\n",
+      " 1:  9.8630e-02  6.2562e-02  4e-02  6e-17  9e-02\n",
+      " 2:  9.6014e-02  9.3363e-02  3e-03  1e-16  4e-17\n",
+      " 3:  9.5990e-02  9.5958e-02  3e-05  6e-17  5e-17\n",
+      " 4:  9.5990e-02  9.5990e-02  3e-07  9e-17  4e-17\n",
+      " 5:  9.5990e-02  9.5990e-02  3e-09  6e-17  4e-17\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  1.1143e-01 -9.2141e-01  1e+00  2e-16  3e+00\n",
+      " 1:  1.1065e-01  7.2596e-02  4e-02  1e-16  9e-02\n",
+      " 2:  1.0790e-01  1.0519e-01  3e-03  6e-17  9e-17\n",
+      " 3:  1.0788e-01  1.0784e-01  3e-05  1e-16  8e-17\n",
+      " 4:  1.0788e-01  1.0788e-01  3e-07  6e-17  5e-17\n",
+      " 5:  1.0788e-01  1.0788e-01  3e-09  1e-16  6e-17\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  1.2502e-01 -9.1076e-01  1e+00  0e+00  3e+00\n",
+      " 1:  1.2411e-01  8.4034e-02  4e-02  1e-16  1e-01\n",
+      " 2:  1.2123e-01  1.1850e-01  3e-03  7e-17  6e-17\n",
+      " 3:  1.2121e-01  1.2118e-01  3e-05  3e-17  5e-17\n",
+      " 4:  1.2121e-01  1.2121e-01  3e-07  7e-17  4e-17\n",
+      " 5:  1.2121e-01  1.2121e-01  3e-09  7e-17  3e-17\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  1.4024e-01 -8.9866e-01  1e+00  2e-16  3e+00\n",
+      " 1:  1.3918e-01  9.7072e-02  4e-02  2e-16  1e-01\n",
+      " 2:  1.3620e-01  1.3346e-01  3e-03  1e-16  3e-17\n",
+      " 3:  1.3618e-01  1.3615e-01  3e-05  1e-16  6e-17\n",
+      " 4:  1.3618e-01  1.3618e-01  3e-07  4e-17  9e-17\n",
+      " 5:  1.3618e-01  1.3618e-01  3e-09  1e-16  1e-16\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  1.5729e-01 -8.8489e-01  1e+00  2e-16  3e+00\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 1:  1.5605e-01  1.1193e-01  4e-02  4e-17  1e-01\n",
+      " 2:  1.5298e-01  1.5027e-01  3e-03  4e-17  1e-16\n",
+      " 3:  1.5297e-01  1.5294e-01  3e-05  1e-16  5e-17\n",
+      " 4:  1.5297e-01  1.5297e-01  3e-07  1e-16  8e-17\n",
+      " 5:  1.5297e-01  1.5297e-01  3e-09  1e-16  9e-17\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  1.7638e-01 -8.6923e-01  1e+00  2e-16  3e+00\n",
+      " 1:  1.7496e-01  1.2885e-01  5e-02  2e-16  1e-01\n",
+      " 2:  1.7182e-01  1.6916e-01  3e-03  1e-16  2e-16\n",
+      " 3:  1.7180e-01  1.7177e-01  3e-05  2e-16  8e-17\n",
+      " 4:  1.7180e-01  1.7180e-01  3e-07  1e-16  9e-17\n",
+      " 5:  1.7180e-01  1.7180e-01  3e-09  9e-17  3e-17\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  1.9775e-01 -8.5142e-01  1e+00  1e-16  3e+00\n",
+      " 1:  1.9613e-01  1.4811e-01  5e-02  1e-16  1e-01\n",
+      " 2:  1.9296e-01  1.9038e-01  3e-03  4e-17  1e-16\n",
+      " 3:  1.9294e-01  1.9291e-01  3e-05  1e-16  6e-17\n",
+      " 4:  1.9294e-01  1.9294e-01  3e-07  8e-17  1e-16\n",
+      " 5:  1.9294e-01  1.9294e-01  3e-09  9e-17  8e-17\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  2.2170e-01 -8.3118e-01  1e+00  0e+00  3e+00\n",
+      " 1:  2.1986e-01  1.7003e-01  5e-02  1e-16  1e-01\n",
+      " 2:  2.1667e-01  2.1420e-01  2e-03  6e-17  9e-17\n",
+      " 3:  2.1666e-01  2.1663e-01  3e-05  7e-17  7e-17\n",
+      " 4:  2.1666e-01  2.1666e-01  3e-07  1e-16  8e-17\n",
+      " 5:  2.1666e-01  2.1666e-01  3e-09  8e-17  1e-16\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  2.4852e-01 -8.0816e-01  1e+00  2e-16  3e+00\n",
+      " 1:  2.4645e-01  1.9494e-01  5e-02  8e-17  1e-01\n",
+      " 2:  2.4328e-01  2.4093e-01  2e-03  8e-17  1e-16\n",
+      " 3:  2.4327e-01  2.4324e-01  2e-05  3e-16  6e-17\n",
+      " 4:  2.4327e-01  2.4327e-01  2e-07  1e-16  1e-16\n",
+      " 5:  2.4327e-01  2.4327e-01  2e-09  1e-16  7e-17\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  2.7857e-01 -7.8201e-01  1e+00  2e-16  3e+00\n",
+      " 1:  2.7626e-01  2.2323e-01  5e-02  4e-17  1e-01\n",
+      " 2:  2.7313e-01  2.7094e-01  2e-03  1e-16  9e-17\n",
+      " 3:  2.7312e-01  2.7310e-01  2e-05  8e-17  4e-17\n",
+      " 4:  2.7312e-01  2.7312e-01  2e-07  8e-17  2e-16\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  3.1224e-01 -7.5232e-01  1e+00  2e-16  3e+00\n",
+      " 1:  3.0968e-01  2.5534e-01  5e-02  4e-17  1e-01\n",
+      " 2:  3.0663e-01  3.0460e-01  2e-03  1e-16  2e-16\n",
+      " 3:  3.0662e-01  3.0660e-01  2e-05  2e-16  1e-16\n",
+      " 4:  3.0662e-01  3.0662e-01  2e-07  2e-16  2e-16\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  3.4996e-01 -7.1861e-01  1e+00  2e-16  3e+00\n",
+      " 1:  3.4716e-01  2.9174e-01  6e-02  6e-17  1e-01\n",
+      " 2:  3.4421e-01  3.4237e-01  2e-03  1e-16  2e-16\n",
+      " 3:  3.4421e-01  3.4419e-01  2e-05  2e-16  2e-16\n",
+      " 4:  3.4421e-01  3.4421e-01  2e-07  2e-16  1e-16\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  3.9225e-01 -6.8037e-01  1e+00  0e+00  3e+00\n",
+      " 1:  3.8920e-01  3.3296e-01  6e-02  1e-16  1e-01\n",
+      " 2:  3.8639e-01  3.8465e-01  2e-03  1e-16  2e-04\n",
+      " 3:  3.8638e-01  3.8636e-01  2e-05  1e-16  2e-06\n",
+      " 4:  3.8638e-01  3.8638e-01  2e-07  7e-17  2e-08\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  4.3965e-01 -6.3702e-01  1e+00  0e+00  3e+00\n",
+      " 1:  4.3635e-01  3.7959e-01  6e-02  1e-16  1e-01\n",
+      " 2:  4.3370e-01  4.3203e-01  2e-03  9e-17  5e-04\n",
+      " 3:  4.3370e-01  4.3368e-01  2e-05  2e-16  6e-06\n",
+      " 4:  4.3370e-01  4.3370e-01  2e-07  2e-16  6e-08\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  4.9279e-01 -5.8791e-01  1e+00  2e-16  3e+00\n",
+      " 1:  4.8926e-01  4.3227e-01  6e-02  1e-16  1e-01\n",
+      " 2:  4.8680e-01  4.8521e-01  2e-03  2e-16  8e-04\n",
+      " 3:  4.8679e-01  4.8678e-01  2e-05  1e-16  8e-06\n",
+      " 4:  4.8679e-01  4.8679e-01  2e-07  2e-16  8e-08\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  5.5237e-01 -5.3230e-01  1e+00  2e-16  3e+00\n",
+      " 1:  5.4863e-01  4.9173e-01  6e-02  7e-17  1e-01\n",
+      " 2:  5.4637e-01  5.4488e-01  1e-03  7e-17  1e-03\n",
+      " 3:  5.4636e-01  5.4635e-01  1e-05  3e-17  1e-05\n",
+      " 4:  5.4636e-01  5.4636e-01  1e-07  1e-16  1e-07\n",
+      " 5:  5.4636e-01  5.4636e-01  1e-09  2e-16  1e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  6.1918e-01 -4.6938e-01  1e+00  2e-16  3e+00\n",
+      " 1:  6.1526e-01  5.5877e-01  6e-02  1e-16  1e-01\n",
+      " 2:  6.1320e-01  6.1183e-01  1e-03  7e-17  1e-03\n",
+      " 3:  6.1320e-01  6.1319e-01  1e-05  7e-17  1e-05\n",
+      " 4:  6.1320e-01  6.1320e-01  1e-07  3e-17  1e-07\n",
+      " 5:  6.1320e-01  6.1320e-01  1e-09  7e-17  1e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  6.9411e-01 -3.9825e-01  1e+00  2e-16  3e+00\n",
+      " 1:  6.9004e-01  6.3428e-01  6e-02  2e-16  1e-01\n",
+      " 2:  6.8820e-01  6.8694e-01  1e-03  6e-17  1e-03\n",
+      " 3:  6.8820e-01  6.8818e-01  1e-05  1e-16  1e-05\n",
+      " 4:  6.8820e-01  6.8820e-01  1e-07  2e-16  1e-07\n",
+      " 5:  6.8820e-01  6.8820e-01  1e-09  2e-16  1e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  7.7816e-01 -3.1789e-01  1e+00  2e-16  3e+00\n",
+      " 1:  7.7396e-01  7.1925e-01  5e-02  1e-16  1e-01\n",
+      " 2:  7.7234e-01  7.7120e-01  1e-03  1e-16  1e-03\n",
+      " 3:  7.7234e-01  7.7233e-01  1e-05  2e-16  1e-05\n",
+      " 4:  7.7234e-01  7.7234e-01  1e-07  1e-16  1e-07\n",
+      " 5:  7.7234e-01  7.7234e-01  1e-09  6e-17  1e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  8.7245e-01 -2.2715e-01  1e+00  0e+00  3e+00\n",
+      " 1:  8.6816e-01  8.1478e-01  5e-02  1e-16  1e-01\n",
+      " 2:  8.6676e-01  8.6573e-01  1e-03  8e-17  1e-03\n",
+      " 3:  8.6676e-01  8.6674e-01  1e-05  2e-16  1e-05\n",
+      " 4:  8.6676e-01  8.6676e-01  1e-07  7e-17  1e-07\n",
+      " 5:  8.6676e-01  8.6676e-01  1e-09  3e-17  1e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  9.7822e-01 -1.2478e-01  1e+00  2e-16  3e+00\n",
+      " 1:  9.7389e-01  9.2210e-01  5e-02  1e-16  1e-01\n",
+      " 2:  9.7269e-01  9.7178e-01  9e-04  1e-16  1e-03\n",
+      " 3:  9.7269e-01  9.7268e-01  9e-06  6e-17  1e-05\n",
+      " 4:  9.7269e-01  9.7269e-01  9e-08  6e-17  1e-07\n",
+      " 5:  9.7269e-01  9.7269e-01  9e-10  3e-17  1e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  1.0969e+00 -9.3384e-03  1e+00  0e+00  3e+00\n",
+      " 1:  1.0926e+00  1.0426e+00  5e-02  6e-17  1e-01\n",
+      " 2:  1.0915e+00  1.0907e+00  8e-04  6e-17  1e-03\n",
+      " 3:  1.0915e+00  1.0915e+00  8e-06  9e-17  1e-05\n",
+      " 4:  1.0915e+00  1.0915e+00  8e-08  4e-17  1e-07\n",
+      " 5:  1.0915e+00  1.0915e+00  8e-10  8e-17  1e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  1.2301e+00  1.2075e-01  1e+00  1e-16  3e+00\n",
+      " 1:  1.2258e+00  1.1778e+00  5e-02  6e-17  1e-01\n",
+      " 2:  1.2249e+00  1.2242e+00  7e-04  3e-17  1e-03\n",
+      " 3:  1.2249e+00  1.2249e+00  7e-06  4e-17  1e-05\n",
+      " 4:  1.2249e+00  1.2249e+00  7e-08  1e-16  1e-07\n",
+      " 5:  1.2249e+00  1.2249e+00  7e-10  8e-17  1e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  1.3795e+00  2.6727e-01  1e+00  1e-16  3e+00\n",
+      " 1:  1.3752e+00  1.3295e+00  5e-02  1e-16  1e-01\n",
+      " 2:  1.3745e+00  1.3739e+00  6e-04  1e-16  1e-03\n",
+      " 3:  1.3745e+00  1.3745e+00  6e-06  2e-16  1e-05\n",
+      " 4:  1.3745e+00  1.3745e+00  6e-08  4e-17  1e-07\n",
+      " 5:  1.3745e+00  1.3745e+00  6e-10  8e-17  1e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.5694e-03 -1.0025e+00  1e+00  4e-16  3e+00\n",
+      " 1: -1.5700e-03 -1.2489e-02  1e-02  6e-17  3e-02\n",
+      " 2: -1.6273e-03 -2.5893e-03  1e-03  8e-17  2e-03\n",
+      " 3: -2.4394e-03 -2.8681e-03  4e-04  4e-16  6e-04\n",
+      " 4: -2.4858e-03 -2.4926e-03  7e-06  2e-16  9e-06\n",
+      " 5: -2.4888e-03 -2.4889e-03  7e-08  2e-16  9e-08\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.8059e-03 -1.0035e+00  1e+00  2e-16  3e+00\n",
+      " 1: -1.8078e-03 -1.3468e-02  1e-02  1e-16  3e-02\n",
+      " 2: -1.9713e-03 -3.5622e-03  2e-03  8e-17  4e-03\n",
+      " 3: -3.3969e-03 -3.9680e-03  6e-04  2e-15  4e-04\n",
+      " 4: -3.4342e-03 -3.4414e-03  7e-06  4e-16  6e-06\n",
+      " 5: -3.4361e-03 -3.4361e-03  7e-08  1e-16  6e-08\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.8039e-03 -1.0035e+00  1e+00  4e-16  3e+00\n",
+      " 1: -1.8059e-03 -1.3465e-02  1e-02  1e-16  3e-02\n",
+      " 2: -1.9691e-03 -3.5588e-03  2e-03  1e-16  4e-03\n",
+      " 3: -3.3913e-03 -3.9600e-03  6e-04  4e-16  4e-04\n",
+      " 4: -3.4277e-03 -3.4348e-03  7e-06  2e-16  5e-06\n",
+      " 5: -3.4295e-03 -3.4296e-03  7e-08  2e-16  5e-08\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.8017e-03 -1.0035e+00  1e+00  0e+00  3e+00\n",
+      " 1: -1.8036e-03 -1.3462e-02  1e-02  1e-16  3e-02\n",
+      " 2: -1.9666e-03 -3.5550e-03  2e-03  1e-16  4e-03\n",
+      " 3: -3.3850e-03 -3.9510e-03  6e-04  2e-16  4e-04\n",
+      " 4: -3.4204e-03 -3.4275e-03  7e-06  5e-19  5e-06\n",
+      " 5: -3.4221e-03 -3.4222e-03  7e-08  7e-20  5e-08\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.7992e-03 -1.0035e+00  1e+00  0e+00  3e+00\n",
+      " 1: -1.8011e-03 -1.3459e-02  1e-02  1e-16  3e-02\n",
+      " 2: -1.9638e-03 -3.5508e-03  2e-03  6e-17  4e-03\n",
+      " 3: -3.3779e-03 -3.9410e-03  6e-04  9e-16  4e-04\n",
+      " 4: -3.4122e-03 -3.4192e-03  7e-06  1e-16  5e-06\n",
+      " 5: -3.4139e-03 -3.4140e-03  7e-08  3e-16  5e-08\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.7964e-03 -1.0035e+00  1e+00  2e-16  3e+00\n",
+      " 1: -1.7983e-03 -1.3455e-02  1e-02  6e-17  3e-02\n",
+      " 2: -1.9607e-03 -3.5460e-03  2e-03  2e-16  4e-03\n",
+      " 3: -3.3700e-03 -3.9299e-03  6e-04  4e-16  4e-04\n",
+      " 4: -3.4029e-03 -3.4099e-03  7e-06  1e-16  5e-06\n",
+      " 5: -3.4046e-03 -3.4047e-03  7e-08  2e-16  5e-08\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.7932e-03 -1.0035e+00  1e+00  0e+00  3e+00\n",
+      " 1: -1.7951e-03 -1.3451e-02  1e-02  1e-16  3e-02\n",
+      " 2: -1.9572e-03 -3.5407e-03  2e-03  1e-16  4e-03\n",
+      " 3: -3.3611e-03 -3.9174e-03  6e-04  4e-16  4e-04\n",
+      " 4: -3.3926e-03 -3.3995e-03  7e-06  1e-16  5e-06\n",
+      " 5: -3.3942e-03 -3.3943e-03  7e-08  2e-16  5e-08\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.7897e-03 -1.0034e+00  1e+00  0e+00  3e+00\n",
+      " 1: -1.7916e-03 -1.3446e-02  1e-02  8e-17  3e-02\n",
+      " 2: -1.9533e-03 -3.5347e-03  2e-03  1e-16  4e-03\n",
+      " 3: -3.3512e-03 -3.9034e-03  6e-04  4e-16  3e-04\n",
+      " 4: -3.3810e-03 -3.3878e-03  7e-06  1e-18  4e-06\n",
+      " 5: -3.3826e-03 -3.3827e-03  7e-08  4e-20  4e-08\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.7857e-03 -1.0034e+00  1e+00  2e-16  3e+00\n",
+      " 1: -1.7876e-03 -1.3441e-02  1e-02  3e-17  3e-02\n",
+      " 2: -1.9489e-03 -3.5280e-03  2e-03  6e-17  4e-03\n",
+      " 3: -3.3400e-03 -3.8877e-03  5e-04  1e-16  3e-04\n",
+      " 4: -3.3680e-03 -3.3747e-03  7e-06  4e-16  4e-06\n",
+      " 5: -3.3695e-03 -3.3696e-03  7e-08  3e-16  4e-08\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.7813e-03 -1.0034e+00  1e+00  0e+00  3e+00\n",
+      " 1: -1.7832e-03 -1.3435e-02  1e-02  8e-17  3e-02\n",
+      " 2: -1.9439e-03 -3.5205e-03  2e-03  1e-16  4e-03\n",
+      " 3: -3.3275e-03 -3.8703e-03  5e-04  4e-16  3e-04\n",
+      " 4: -3.3534e-03 -3.3600e-03  7e-06  2e-16  4e-06\n",
+      " 5: -3.3548e-03 -3.3549e-03  7e-08  3e-16  4e-08\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.7763e-03 -1.0034e+00  1e+00  1e-16  3e+00\n",
+      " 1: -1.7782e-03 -1.3428e-02  1e-02  5e-17  3e-02\n",
+      " 2: -1.9383e-03 -3.5121e-03  2e-03  2e-16  4e-03\n",
+      " 3: -3.3134e-03 -3.8507e-03  5e-04  9e-16  3e-04\n",
+      " 4: -3.3371e-03 -3.3435e-03  6e-06  2e-16  3e-06\n",
+      " 5: -3.3384e-03 -3.3384e-03  6e-08  1e-16  3e-08\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.7707e-03 -1.0034e+00  1e+00  2e-16  3e+00\n",
+      " 1: -1.7726e-03 -1.3421e-02  1e-02  6e-17  3e-02\n",
+      " 2: -1.9321e-03 -3.5026e-03  2e-03  1e-16  4e-03\n",
+      " 3: -3.2975e-03 -3.8289e-03  5e-04  7e-16  2e-04\n",
+      " 4: -3.3187e-03 -3.3250e-03  6e-06  5e-19  3e-06\n",
+      " 5: -3.3199e-03 -3.3200e-03  6e-08  1e-16  3e-08\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.7644e-03 -1.0034e+00  1e+00  0e+00  3e+00\n",
+      " 1: -1.7663e-03 -1.3412e-02  1e-02  6e-17  3e-02\n",
+      " 2: -1.9251e-03 -3.4920e-03  2e-03  6e-17  4e-03\n",
+      " 3: -3.2797e-03 -3.8046e-03  5e-04  1e-16  2e-04\n",
+      " 4: -3.2981e-03 -3.3042e-03  6e-06  1e-16  2e-06\n",
+      " 5: -3.2992e-03 -3.2992e-03  6e-08  2e-16  2e-08\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.7573e-03 -1.0034e+00  1e+00  3e-16  3e+00\n",
+      " 1: -1.7592e-03 -1.3403e-02  1e-02  1e-16  3e-02\n",
+      " 2: -1.9173e-03 -3.4802e-03  2e-03  6e-17  4e-03\n",
+      " 3: -3.2597e-03 -3.7774e-03  5e-04  1e-15  1e-04\n",
+      " 4: -3.2750e-03 -3.2809e-03  6e-06  1e-16  2e-06\n",
+      " 5: -3.2759e-03 -3.2760e-03  6e-08  2e-16  2e-08\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.7494e-03 -1.0034e+00  1e+00  2e-16  3e+00\n",
+      " 1: -1.7513e-03 -1.3392e-02  1e-02  1e-16  3e-02\n",
+      " 2: -1.9085e-03 -3.4669e-03  2e-03  2e-16  4e-03\n",
+      " 3: -3.2356e-03 -3.7466e-03  5e-04  2e-16  9e-05\n",
+      " 4: -3.2490e-03 -3.2548e-03  6e-06  1e-16  1e-06\n",
+      " 5: -3.2498e-03 -3.2499e-03  6e-08  1e-16  1e-08\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.7405e-03 -1.0034e+00  1e+00  2e-16  3e+00\n",
+      " 1: -1.7424e-03 -1.3380e-02  1e-02  1e-16  3e-02\n",
+      " 2: -1.8986e-03 -3.4519e-03  2e-03  8e-17  4e-03\n",
+      " 3: -3.2061e-03 -3.7113e-03  5e-04  4e-16  4e-05\n",
+      " 4: -3.2197e-03 -3.2254e-03  6e-06  4e-19  5e-07\n",
+      " 5: -3.2205e-03 -3.2206e-03  6e-08  1e-16  5e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.7305e-03 -1.0034e+00  1e+00  2e-16  3e+00\n",
+      " 1: -1.7324e-03 -1.3367e-02  1e-02  8e-17  3e-02\n",
+      " 2: -1.8876e-03 -3.4352e-03  2e-03  4e-17  4e-03\n",
+      " 3: -3.1694e-03 -3.6708e-03  5e-04  3e-16  9e-19\n",
+      " 4: -3.1865e-03 -3.1925e-03  6e-06  4e-19  7e-19\n",
+      " 5: -3.1877e-03 -3.1877e-03  6e-08  2e-16  4e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.7194e-03 -1.0034e+00  1e+00  2e-16  3e+00\n",
+      " 1: -1.7212e-03 -1.3352e-02  1e-02  4e-17  3e-02\n",
+      " 2: -1.8752e-03 -3.4165e-03  2e-03  1e-16  4e-03\n",
+      " 3: -3.1146e-03 -3.6210e-03  5e-04  4e-16  1e-18\n",
+      " 4: -3.1484e-03 -3.1555e-03  7e-06  1e-16  6e-19\n",
+      " 5: -3.1508e-03 -3.1508e-03  7e-08  3e-16  1e-18\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.7068e-03 -1.0033e+00  1e+00  0e+00  3e+00\n",
+      " 1: -1.7086e-03 -1.3335e-02  1e-02  9e-17  3e-02\n",
+      " 2: -1.8613e-03 -3.3955e-03  2e-03  9e-17  4e-03\n",
+      " 3: -3.0549e-03 -3.5658e-03  5e-04  2e-16  7e-19\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 4: -3.1055e-03 -3.1140e-03  8e-06  2e-16  8e-19\n",
+      " 5: -3.1094e-03 -3.1095e-03  8e-08  2e-19  9e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.6927e-03 -1.0033e+00  1e+00  2e-16  3e+00\n",
+      " 1: -1.6945e-03 -1.3316e-02  1e-02  1e-16  3e-02\n",
+      " 2: -1.8457e-03 -3.3720e-03  2e-03  9e-17  4e-03\n",
+      " 3: -2.9901e-03 -3.5049e-03  5e-04  1e-16  5e-19\n",
+      " 4: -3.0573e-03 -3.0674e-03  1e-05  2e-16  1e-18\n",
+      " 5: -3.0630e-03 -3.0631e-03  1e-07  1e-16  6e-19\n",
+      " 6: -3.0630e-03 -3.0630e-03  1e-09  2e-16  4e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.6769e-03 -1.0033e+00  1e+00  2e-16  3e+00\n",
+      " 1: -1.6787e-03 -1.3295e-02  1e-02  1e-16  3e-02\n",
+      " 2: -1.8282e-03 -3.3457e-03  2e-03  6e-17  4e-03\n",
+      " 3: -2.9200e-03 -3.4378e-03  5e-04  2e-16  8e-19\n",
+      " 4: -3.0030e-03 -3.0152e-03  1e-05  2e-16  8e-19\n",
+      " 5: -3.0109e-03 -3.0110e-03  1e-07  1e-16  9e-19\n",
+      " 6: -3.0110e-03 -3.0110e-03  1e-09  2e-16  4e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.6592e-03 -1.0033e+00  1e+00  3e-16  3e+00\n",
+      " 1: -1.6610e-03 -1.3272e-02  1e-02  6e-17  3e-02\n",
+      " 2: -1.8086e-03 -3.3163e-03  2e-03  1e-16  4e-03\n",
+      " 3: -2.8446e-03 -3.3639e-03  5e-04  4e-16  4e-19\n",
+      " 4: -2.9418e-03 -2.9566e-03  1e-05  2e-16  1e-18\n",
+      " 5: -2.9524e-03 -2.9526e-03  2e-07  2e-16  6e-19\n",
+      " 6: -2.9525e-03 -2.9525e-03  2e-09  6e-21  9e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.6393e-03 -1.0032e+00  1e+00  0e+00  3e+00\n",
+      " 1: -1.6410e-03 -1.3245e-02  1e-02  2e-16  3e-02\n",
+      " 2: -1.7867e-03 -3.2833e-03  1e-03  1e-16  4e-03\n",
+      " 3: -2.7639e-03 -3.2829e-03  5e-04  2e-16  8e-19\n",
+      " 4: -2.8728e-03 -2.8910e-03  2e-05  1e-16  7e-19\n",
+      " 5: -2.8868e-03 -2.8870e-03  2e-07  1e-16  9e-19\n",
+      " 6: -2.8870e-03 -2.8870e-03  2e-09  2e-16  4e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.6170e-03 -1.0032e+00  1e+00  0e+00  3e+00\n",
+      " 1: -1.6187e-03 -1.3215e-02  1e-02  7e-17  3e-02\n",
+      " 2: -1.7621e-03 -3.2464e-03  1e-03  7e-17  4e-03\n",
+      " 3: -2.6779e-03 -3.1943e-03  5e-04  2e-16  9e-19\n",
+      " 4: -2.7951e-03 -2.8174e-03  2e-05  2e-16  7e-19\n",
+      " 5: -2.8132e-03 -2.8134e-03  3e-07  1e-16  5e-19\n",
+      " 6: -2.8134e-03 -2.8134e-03  3e-09  1e-16  7e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.5919e-03 -1.0032e+00  1e+00  2e-16  3e+00\n",
+      " 1: -1.5936e-03 -1.3182e-02  1e-02  8e-17  3e-02\n",
+      " 2: -1.7345e-03 -3.2052e-03  1e-03  7e-17  4e-03\n",
+      " 3: -2.5868e-03 -3.0978e-03  5e-04  2e-16  1e-18\n",
+      " 4: -2.7078e-03 -2.7350e-03  3e-05  2e-16  4e-19\n",
+      " 5: -2.7305e-03 -2.7309e-03  4e-07  1e-18  4e-19\n",
+      " 6: -2.7309e-03 -2.7309e-03  4e-09  1e-16  8e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.5638e-03 -1.0031e+00  1e+00  1e-16  3e+00\n",
+      " 1: -1.5655e-03 -1.3144e-02  1e-02  1e-16  3e-02\n",
+      " 2: -1.7036e-03 -3.1590e-03  1e-03  3e-17  4e-03\n",
+      " 3: -2.4908e-03 -2.9931e-03  5e-04  1e-16  4e-19\n",
+      " 4: -2.6105e-03 -2.6429e-03  3e-05  1e-16  6e-19\n",
+      " 5: -2.6376e-03 -2.6383e-03  7e-07  1e-16  5e-19\n",
+      " 6: -2.6383e-03 -2.6383e-03  7e-09  5e-20  3e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.5323e-03 -1.0031e+00  1e+00  0e+00  3e+00\n",
+      " 1: -1.5339e-03 -1.3102e-02  1e-02  1e-16  3e-02\n",
+      " 2: -1.6690e-03 -3.1075e-03  1e-03  1e-16  4e-03\n",
+      " 3: -2.3902e-03 -2.8801e-03  5e-04  4e-17  1e-18\n",
+      " 4: -2.5032e-03 -2.5406e-03  4e-05  1e-16  9e-19\n",
+      " 5: -2.5329e-03 -2.5344e-03  2e-06  2e-18  3e-19\n",
+      " 6: -2.5344e-03 -2.5344e-03  2e-08  2e-16  2e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.4969e-03 -1.0031e+00  1e+00  1e-16  3e+00\n",
+      " 1: -1.4985e-03 -1.3055e-02  1e-02  8e-17  3e-02\n",
+      " 2: -1.6303e-03 -3.0499e-03  1e-03  1e-16  3e-03\n",
+      " 3: -2.2854e-03 -2.7585e-03  5e-04  1e-16  6e-19\n",
+      " 4: -2.3868e-03 -2.4278e-03  4e-05  1e-16  5e-19\n",
+      " 5: -2.4148e-03 -2.4181e-03  3e-06  1e-16  5e-19\n",
+      " 6: -2.4177e-03 -2.4178e-03  9e-08  2e-16  3e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.4572e-03 -1.0030e+00  1e+00  2e-16  3e+00\n",
+      " 1: -1.4588e-03 -1.3002e-02  1e-02  1e-16  3e-02\n",
+      " 2: -1.5869e-03 -2.9856e-03  1e-03  6e-17  3e-03\n",
+      " 3: -2.1765e-03 -2.6287e-03  5e-04  3e-16  3e-19\n",
+      " 4: -2.2631e-03 -2.3050e-03  4e-05  3e-16  8e-19\n",
+      " 5: -2.2856e-03 -2.2896e-03  4e-06  1e-16  8e-19\n",
+      " 6: -2.2880e-03 -2.2881e-03  1e-07  2e-16  3e-19\n",
+      " 7: -2.2880e-03 -2.2880e-03  1e-09  2e-16  3e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.4127e-03 -1.0029e+00  1e+00  1e-16  3e+00\n",
+      " 1: -1.4142e-03 -1.2942e-02  1e-02  1e-16  3e-02\n",
+      " 2: -1.5384e-03 -2.9139e-03  1e-03  2e-16  3e-03\n",
+      " 3: -2.0638e-03 -2.4906e-03  4e-04  2e-16  1e-18\n",
+      " 4: -2.1342e-03 -2.1737e-03  4e-05  1e-16  4e-19\n",
+      " 5: -2.1516e-03 -2.1558e-03  4e-06  1e-16  4e-19\n",
+      " 6: -2.1553e-03 -2.1553e-03  5e-08  1e-16  5e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.3627e-03 -1.0029e+00  1e+00  2e-16  3e+00\n",
+      " 1: -1.3642e-03 -1.2875e-02  1e-02  6e-17  3e-02\n",
+      " 2: -1.4840e-03 -2.8339e-03  1e-03  1e-16  3e-03\n",
+      " 3: -1.9476e-03 -2.3448e-03  4e-04  4e-17  7e-19\n",
+      " 4: -2.0024e-03 -2.0364e-03  3e-05  1e-16  3e-19\n",
+      " 5: -2.0175e-03 -2.0218e-03  4e-06  1e-16  2e-19\n",
+      " 6: -2.0210e-03 -2.0211e-03  1e-07  1e-16  4e-19\n",
+      " 7: -2.0211e-03 -2.0211e-03  1e-09  8e-17  2e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.3066e-03 -1.0028e+00  1e+00  0e+00  3e+00\n",
+      " 1: -1.3081e-03 -1.2800e-02  1e-02  2e-16  3e-02\n",
+      " 2: -1.4232e-03 -2.7447e-03  1e-03  8e-17  3e-03\n",
+      " 3: -1.8278e-03 -2.1916e-03  4e-04  1e-16  6e-19\n",
+      " 4: -1.8689e-03 -1.8957e-03  3e-05  3e-17  5e-19\n",
+      " 5: -1.8811e-03 -1.8854e-03  4e-06  1e-16  2e-19\n",
+      " 6: -1.8834e-03 -1.8838e-03  4e-07  2e-16  3e-19\n",
+      " 7: -1.8837e-03 -1.8837e-03  1e-08  1e-16  3e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.2437e-03 -1.0027e+00  1e+00  0e+00  3e+00\n",
+      " 1: -1.2452e-03 -1.2716e-02  1e-02  2e-16  3e-02\n",
+      " 2: -1.3552e-03 -2.6454e-03  1e-03  1e-16  3e-03\n",
+      " 3: -1.7043e-03 -2.0317e-03  3e-04  2e-16  4e-19\n",
+      " 4: -1.7335e-03 -1.7638e-03  3e-05  1e-16  2e-19\n",
+      " 5: -1.7414e-03 -1.7455e-03  4e-06  1e-16  2e-19\n",
+      " 6: -1.7422e-03 -1.7426e-03  4e-07  1e-16  3e-19\n",
+      " 7: -1.7422e-03 -1.7423e-03  3e-08  1e-16  7e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.1731e-03 -1.0026e+00  1e+00  2e-16  3e+00\n",
+      " 1: -1.1745e-03 -1.2622e-02  1e-02  1e-16  3e-02\n",
+      " 2: -1.2791e-03 -2.5349e-03  1e-03  7e-17  3e-03\n",
+      " 3: -1.5768e-03 -1.8656e-03  3e-04  2e-16  6e-19\n",
+      " 4: -1.5968e-03 -1.6256e-03  3e-05  1e-16  4e-19\n",
+      " 5: -1.6009e-03 -1.6034e-03  2e-06  1e-16  4e-19\n",
+      " 6: -1.6010e-03 -1.6011e-03  7e-08  1e-16  2e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.0940e-03 -1.0025e+00  1e+00  2e-16  3e+00\n",
+      " 1: -1.0953e-03 -1.2516e-02  1e-02  1e-16  3e-02\n",
+      " 2: -1.1940e-03 -2.4121e-03  1e-03  1e-16  3e-03\n",
+      " 3: -1.4447e-03 -1.6938e-03  2e-04  1e-16  6e-19\n",
+      " 4: -1.4579e-03 -1.4809e-03  2e-05  9e-17  2e-19\n",
+      " 5: -1.4597e-03 -1.4607e-03  1e-06  2e-17  2e-19\n",
+      " 6: -1.4598e-03 -1.4598e-03  2e-08  1e-16  3e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.0051e-03 -1.0024e+00  1e+00  0e+00  3e+00\n",
+      " 1: -1.0064e-03 -1.2398e-02  1e-02  2e-16  3e-02\n",
+      " 2: -1.0990e-03 -2.2757e-03  1e-03  9e-17  3e-03\n",
+      " 3: -1.3073e-03 -1.5169e-03  2e-04  2e-16  5e-19\n",
+      " 4: -1.3157e-03 -1.3314e-03  2e-05  6e-17  2e-19\n",
+      " 5: -1.3165e-03 -1.3173e-03  8e-07  2e-16  3e-19\n",
+      " 6: -1.3166e-03 -1.3166e-03  1e-08  1e-17  9e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -9.0543e-04 -1.0023e+00  1e+00  0e+00  3e+00\n",
+      " 1: -9.0666e-04 -1.2265e-02  1e-02  7e-17  3e-02\n",
+      " 2: -9.9273e-04 -2.1243e-03  1e-03  7e-17  3e-03\n",
+      " 3: -1.1636e-03 -1.3351e-03  2e-04  8e-17  4e-19\n",
+      " 4: -1.1690e-03 -1.1784e-03  9e-06  1e-16  4e-19\n",
+      " 5: -1.1695e-03 -1.1699e-03  4e-07  1e-16  2e-19\n",
+      " 6: -1.1695e-03 -1.1695e-03  4e-09  1e-16  5e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -7.9359e-04 -1.0021e+00  1e+00  4e-16  3e+00\n",
+      " 1: -7.9476e-04 -1.2115e-02  1e-02  2e-16  3e-02\n",
+      " 2: -8.7410e-04 -1.9566e-03  1e-03  8e-17  3e-03\n",
+      " 3: -1.0125e-03 -1.1484e-03  1e-04  2e-16  4e-19\n",
+      " 4: -1.0163e-03 -1.0231e-03  7e-06  2e-16  4e-19\n",
+      " 5: -1.0166e-03 -1.0168e-03  2e-07  8e-17  3e-19\n",
+      " 6: -1.0166e-03 -1.0166e-03  2e-09  2e-16  4e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -6.6812e-04 -1.0020e+00  1e+00  2e-16  3e+00\n",
+      " 1: -6.6922e-04 -1.1948e-02  1e-02  2e-16  3e-02\n",
+      " 2: -7.4165e-04 -1.7710e-03  1e-03  2e-16  3e-03\n",
+      " 3: -8.5244e-04 -9.5649e-04  1e-04  8e-17  4e-19\n",
+      " 4: -8.5580e-04 -8.6135e-04  6e-06  2e-16  1e-18\n",
+      " 5: -8.5597e-04 -8.5607e-04  1e-07  1e-17  2e-19\n",
+      " 6: -8.5597e-04 -8.5597e-04  1e-09  4e-17  3e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -5.2735e-04 -1.0018e+00  1e+00  1e-16  3e+00\n",
+      " 1: -5.2837e-04 -1.1760e-02  1e-02  8e-17  3e-02\n",
+      " 2: -5.9382e-04 -1.5658e-03  1e-03  9e-17  2e-03\n",
+      " 3: -6.8193e-04 -7.5871e-04  8e-05  1e-16  5e-19\n",
+      " 4: -6.8527e-04 -6.8853e-04  3e-06  4e-17  5e-19\n",
+      " 5: -6.8533e-04 -6.8538e-04  4e-08  2e-16  5e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -3.6941e-04 -1.0016e+00  1e+00  2e-16  3e+00\n",
+      " 1: -3.7036e-04 -1.1550e-02  1e-02  6e-17  3e-02\n",
+      " 2: -4.2889e-04 -1.3393e-03  9e-04  7e-17  2e-03\n",
+      " 3: -4.9898e-04 -5.6033e-04  6e-05  1e-16  2e-05\n",
+      " 4: -5.0245e-04 -5.0486e-04  2e-06  1e-16  2e-07\n",
+      " 5: -5.0248e-04 -5.0251e-04  3e-08  6e-17  2e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.9222e-04 -1.0013e+00  1e+00  4e-16  3e+00\n",
+      " 1: -1.9309e-04 -1.1314e-02  1e-02  2e-16  3e-02\n",
+      " 2: -2.4493e-04 -1.0896e-03  8e-04  2e-16  2e-03\n",
+      " 3: -3.0127e-04 -3.6674e-04  7e-05  1e-16  8e-05\n",
+      " 4: -3.0496e-04 -3.0726e-04  2e-06  9e-17  6e-07\n",
+      " 5: -3.0499e-04 -3.0501e-04  2e-08  8e-17  6e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  6.5666e-06 -1.0011e+00  1e+00  1e-16  3e+00\n",
+      " 1:  5.7762e-06 -1.1050e-02  1e-02  1e-16  3e-02\n",
+      " 2: -3.9823e-05 -8.1483e-04  8e-04  6e-17  2e-03\n",
+      " 3: -8.6634e-05 -1.5368e-04  7e-05  8e-17  1e-04\n",
+      " 4: -9.0209e-05 -9.2258e-05  2e-06  1e-16  7e-07\n",
+      " 5: -9.0223e-05 -9.0244e-05  2e-08  1e-16  7e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  2.2958e-04 -1.0008e+00  1e+00  0e+00  3e+00\n",
+      " 1:  2.2887e-04 -1.0753e-02  1e-02  1e-16  3e-02\n",
+      " 2:  1.8880e-04 -5.1283e-04  7e-04  8e-17  2e-03\n",
+      " 3:  1.4775e-04  8.2885e-05  6e-05  9e-17  1e-04\n",
+      " 4:  1.4466e-04  1.4299e-04  2e-06  1e-16  8e-07\n",
+      " 5:  1.4466e-04  1.4464e-04  2e-08  6e-17  8e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  4.7978e-04 -1.0004e+00  1e+00  0e+00  3e+00\n",
+      " 1:  4.7913e-04 -1.0420e-02  1e-02  6e-17  3e-02\n",
+      " 2:  4.4356e-04 -1.8145e-04  6e-04  1e-16  2e-03\n",
+      " 3:  4.0508e-04  3.4704e-04  6e-05  8e-17  1e-04\n",
+      " 4:  4.0277e-04  4.0155e-04  1e-06  1e-16  8e-07\n",
+      " 5:  4.0277e-04  4.0276e-04  1e-08  8e-17  8e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  7.6047e-04 -1.0001e+00  1e+00  0e+00  3e+00\n",
+      " 1:  7.5987e-04 -1.0048e-02  1e-02  1e-16  3e-02\n",
+      " 2:  7.2740e-04  1.8161e-04  5e-04  2e-16  1e-03\n",
+      " 3:  6.8883e-04  6.4377e-04  5e-05  2e-16  5e-05\n",
+      " 4:  6.8754e-04  6.8680e-04  7e-07  8e-17  5e-07\n",
+      " 5:  6.8754e-04  6.8753e-04  7e-09  9e-17  5e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  1.0753e-03 -9.9964e-01  1e+00  0e+00  3e+00\n",
+      " 1:  1.0748e-03 -9.6302e-03  1e-02  1e-16  3e-02\n",
+      " 2:  1.0436e-03  5.7882e-04  5e-04  6e-17  1e-03\n",
+      " 3:  1.0032e-03  9.7271e-04  3e-05  4e-17  2e-18\n",
+      " 4:  1.0028e-03  1.0024e-03  4e-07  1e-16  3e-18\n",
+      " 5:  1.0028e-03  1.0027e-03  4e-09  7e-17  2e-18\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  1.4286e-03 -9.9917e-01  1e+00  0e+00  3e+00\n",
+      " 1:  1.4280e-03 -9.1622e-03  1e-02  1e-16  3e-02\n",
+      " 2:  1.3960e-03  9.0479e-04  5e-04  1e-16  1e-03\n",
+      " 3:  1.3529e-03  1.3198e-03  3e-05  8e-17  2e-18\n",
+      " 4:  1.3526e-03  1.3522e-03  4e-07  8e-17  9e-19\n",
+      " 5:  1.3526e-03  1.3526e-03  4e-09  1e-16  8e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  1.8248e-03 -9.9879e-01  1e+00  2e-16  3e+00\n",
+      " 1:  1.8242e-03 -8.7849e-03  1e-02  8e-17  3e-02\n",
+      " 2:  1.7892e-03  1.2067e-03  6e-04  1e-16  1e-03\n",
+      " 3:  1.7420e-03  1.7052e-03  4e-05  6e-17  1e-18\n",
+      " 4:  1.7417e-03  1.7412e-03  4e-07  3e-17  2e-18\n",
+      " 5:  1.7417e-03  1.7417e-03  4e-09  6e-17  8e-19\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  2.2693e-03 -9.9851e-01  1e+00  2e-16  3e+00\n",
+      " 1:  2.2687e-03 -8.5120e-03  1e-02  9e-17  3e-02\n",
+      " 2:  2.2277e-03  1.5495e-03  7e-04  3e-17  2e-03\n",
+      " 3:  2.1756e-03  2.1347e-03  4e-05  7e-17  1e-18\n",
+      " 4:  2.1752e-03  2.1747e-03  5e-07  8e-17  2e-18\n",
+      " 5:  2.1752e-03  2.1752e-03  5e-09  2e-16  2e-18\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  2.7680e-03 -9.9821e-01  1e+00  1e-16  3e+00\n",
+      " 1:  2.7671e-03 -8.2050e-03  1e-02  1e-16  3e-02\n",
+      " 2:  2.7170e-03  1.9409e-03  8e-04  7e-17  2e-03\n",
+      " 3:  2.6592e-03  2.6145e-03  4e-05  6e-17  3e-18\n",
+      " 4:  2.6589e-03  2.6584e-03  5e-07  1e-16  3e-18\n",
+      " 5:  2.6589e-03  2.6589e-03  5e-09  1e-16  2e-18\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  3.3272e-03 -9.9786e-01  1e+00  1e-16  3e+00\n",
+      " 1:  3.3261e-03 -7.8597e-03  1e-02  7e-17  3e-02\n",
+      " 2:  3.2631e-03  2.3873e-03  9e-04  4e-17  2e-03\n",
+      " 3:  3.1995e-03  3.1511e-03  5e-05  7e-17  1e-18\n",
+      " 4:  3.1992e-03  3.1986e-03  5e-07  6e-17  4e-18\n",
+      " 5:  3.1992e-03  3.1992e-03  5e-09  9e-17  2e-18\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  3.9546e-03 -9.9748e-01  1e+00  2e-16  3e+00\n",
+      " 1:  3.9531e-03 -7.4710e-03  1e-02  1e-16  3e-02\n",
+      " 2:  3.8731e-03  2.8963e-03  1e-03  4e-17  2e-03\n",
+      " 3:  3.8036e-03  3.7518e-03  5e-05  9e-17  4e-18\n",
+      " 4:  3.8032e-03  3.8026e-03  6e-07  1e-16  2e-18\n",
+      " 5:  3.8032e-03  3.8032e-03  6e-09  2e-16  1e-18\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  4.6582e-03 -9.9704e-01  1e+00  2e-16  3e+00\n",
+      " 1:  4.6562e-03 -7.0336e-03  1e-02  1e-16  3e-02\n",
+      " 2:  4.5547e-03  3.4759e-03  1e-03  8e-17  3e-03\n",
+      " 3:  4.4794e-03  4.4249e-03  5e-05  1e-16  4e-18\n",
+      " 4:  4.4790e-03  4.4785e-03  6e-07  2e-16  3e-18\n",
+      " 5:  4.4790e-03  4.4790e-03  6e-09  1e-16  2e-18\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  5.4474e-03 -9.9656e-01  1e+00  1e-16  3e+00\n",
+      " 1:  5.4446e-03 -6.5410e-03  1e-02  6e-17  3e-02\n",
+      " 2:  5.3167e-03  4.1357e-03  1e-03  1e-16  3e-03\n",
+      " 3:  5.2360e-03  5.1793e-03  6e-05  6e-17  3e-18\n",
+      " 4:  5.2356e-03  5.2350e-03  6e-07  2e-16  3e-18\n",
+      " 5:  5.2356e-03  5.2356e-03  6e-09  1e-16  3e-18\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  6.3325e-03 -9.9601e-01  1e+00  0e+00  3e+00\n",
+      " 1:  6.3286e-03 -5.9861e-03  1e-02  8e-17  3e-02\n",
+      " 2:  6.1689e-03  4.8862e-03  1e-03  2e-16  3e-03\n",
+      " 3:  6.0833e-03  6.0253e-03  6e-05  1e-16  4e-18\n",
+      " 4:  6.0830e-03  6.0824e-03  6e-07  1e-16  3e-18\n",
+      " 5:  6.0830e-03  6.0830e-03  6e-09  3e-17  3e-18\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  7.3251e-03 -9.9539e-01  1e+00  2e-16  3e+00\n",
+      " 1:  7.3198e-03 -5.3608e-03  1e-02  7e-17  3e-02\n",
+      " 2:  7.1225e-03  5.7396e-03  1e-03  2e-16  3e-03\n",
+      " 3:  7.0327e-03  6.9741e-03  6e-05  1e-16  4e-18\n",
+      " 4:  7.0324e-03  7.0318e-03  6e-07  1e-16  2e-18\n",
+      " 5:  7.0324e-03  7.0324e-03  6e-09  3e-17  4e-18\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  8.4382e-03 -9.9469e-01  1e+00  2e-16  3e+00\n",
+      " 1:  8.4311e-03 -4.6558e-03  1e-02  1e-16  3e-02\n",
+      " 2:  8.1898e-03  6.7093e-03  1e-03  6e-17  3e-03\n",
+      " 3:  8.0967e-03  8.0379e-03  6e-05  2e-16  1e-06\n",
+      " 4:  8.0964e-03  8.0958e-03  6e-07  2e-16  1e-08\n",
+      " 5:  8.0964e-03  8.0964e-03  6e-09  2e-16  1e-10\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  9.6864e-03 -9.9391e-01  1e+00  1e-16  3e+00\n",
+      " 1:  9.6769e-03 -3.8606e-03  1e-02  1e-16  3e-02\n",
+      " 2:  9.3849e-03  7.8109e-03  2e-03  1e-16  3e-03\n",
+      " 3:  9.2894e-03  9.2307e-03  6e-05  9e-17  4e-06\n",
+      " 4:  9.2892e-03  9.2886e-03  6e-07  8e-17  4e-08\n",
+      " 5:  9.2892e-03  9.2892e-03  6e-09  6e-17  4e-10\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  1.1086e-02 -9.9303e-01  1e+00  0e+00  3e+00\n",
+      " 1:  1.1073e-02 -2.9631e-03  1e-02  6e-17  4e-02\n",
+      " 2:  1.0723e-02  9.0616e-03  2e-03  0e+00  4e-03\n",
+      " 3:  1.0627e-02  1.0569e-02  6e-05  1e-16  7e-06\n",
+      " 4:  1.0627e-02  1.0626e-02  6e-07  2e-16  7e-08\n",
+      " 5:  1.0627e-02  1.0627e-02  6e-09  1e-16  7e-10\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  1.2655e-02 -9.9203e-01  1e+00  0e+00  3e+00\n",
+      " 1:  1.2639e-02 -1.9498e-03  1e-02  8e-17  4e-02\n",
+      " 2:  1.2223e-02  1.0481e-02  2e-03  8e-17  4e-03\n",
+      " 3:  1.2126e-02  1.2069e-02  6e-05  1e-16  1e-05\n",
+      " 4:  1.2126e-02  1.2126e-02  6e-07  8e-17  1e-07\n",
+      " 5:  1.2126e-02  1.2126e-02  6e-09  1e-16  1e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  1.4415e-02 -9.9091e-01  1e+00  2e-16  3e+00\n",
+      " 1:  1.4393e-02 -8.0487e-04  2e-02  1e-16  4e-02\n",
+      " 2:  1.3903e-02  1.2091e-02  2e-03  1e-16  4e-03\n",
+      " 3:  1.3808e-02  1.3753e-02  6e-05  8e-17  1e-05\n",
+      " 4:  1.3808e-02  1.3808e-02  6e-07  2e-16  1e-07\n",
+      " 5:  1.3808e-02  1.3808e-02  6e-09  1e-16  1e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  1.6387e-02 -9.8964e-01  1e+00  0e+00  3e+00\n",
+      " 1:  1.6359e-02  4.8946e-04  2e-02  1e-16  4e-02\n",
+      " 2:  1.5787e-02  1.3917e-02  2e-03  1e-16  4e-03\n",
+      " 3:  1.5695e-02  1.5642e-02  5e-05  1e-16  2e-05\n",
+      " 4:  1.5695e-02  1.5694e-02  5e-07  2e-16  2e-07\n",
+      " 5:  1.5695e-02  1.5695e-02  5e-09  1e-16  2e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  1.8598e-02 -9.8821e-01  1e+00  0e+00  3e+00\n",
+      " 1:  1.8562e-02  1.9536e-03  2e-02  2e-16  4e-02\n",
+      " 2:  1.7899e-02  1.5987e-02  2e-03  7e-17  3e-03\n",
+      " 3:  1.7811e-02  1.7760e-02  5e-05  2e-16  2e-05\n",
+      " 4:  1.7811e-02  1.7810e-02  5e-07  2e-16  2e-07\n",
+      " 5:  1.7811e-02  1.7811e-02  5e-09  5e-17  2e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  2.1076e-02 -9.8660e-01  1e+00  0e+00  3e+00\n",
+      " 1:  2.1030e-02  3.6111e-03  2e-02  2e-16  4e-02\n",
+      " 2:  2.0267e-02  1.8331e-02  2e-03  2e-16  3e-03\n",
+      " 3:  2.0184e-02  2.0137e-02  5e-05  8e-17  2e-05\n",
+      " 4:  2.0184e-02  2.0184e-02  5e-07  3e-17  2e-07\n",
+      " 5:  2.0184e-02  2.0184e-02  5e-09  9e-17  2e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  2.3854e-02 -9.8478e-01  1e+00  2e-16  3e+00\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 1:  2.3796e-02  5.4885e-03  2e-02  1e-16  5e-02\n",
+      " 2:  2.2923e-02  2.0985e-02  2e-03  7e-17  3e-03\n",
+      " 3:  2.2847e-02  2.2803e-02  4e-05  2e-16  2e-05\n",
+      " 4:  2.2847e-02  2.2847e-02  4e-07  1e-16  2e-07\n",
+      " 5:  2.2847e-02  2.2847e-02  4e-09  1e-16  2e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  2.6967e-02 -9.8272e-01  1e+00  4e-16  3e+00\n",
+      " 1:  2.6893e-02  7.6166e-03  2e-02  2e-16  5e-02\n",
+      " 2:  2.5902e-02  2.3987e-02  2e-03  1e-16  2e-03\n",
+      " 3:  2.5834e-02  2.5794e-02  4e-05  8e-17  2e-05\n",
+      " 4:  2.5834e-02  2.5834e-02  4e-07  6e-17  2e-07\n",
+      " 5:  2.5834e-02  2.5834e-02  4e-09  6e-17  2e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  3.0455e-02 -9.8039e-01  1e+00  2e-16  3e+00\n",
+      " 1:  3.0363e-02  1.0031e-02  2e-02  2e-16  5e-02\n",
+      " 2:  2.9245e-02  2.7380e-02  2e-03  4e-17  2e-03\n",
+      " 3:  2.9186e-02  2.9150e-02  4e-05  4e-17  2e-05\n",
+      " 4:  2.9186e-02  2.9185e-02  4e-07  1e-16  2e-07\n",
+      " 5:  2.9186e-02  2.9186e-02  4e-09  1e-16  2e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  3.4363e-02 -9.7775e-01  1e+00  0e+00  3e+00\n",
+      " 1:  3.4248e-02  1.2771e-02  2e-02  2e-16  5e-02\n",
+      " 2:  3.2996e-02  3.1212e-02  2e-03  8e-17  1e-03\n",
+      " 3:  3.2946e-02  3.2914e-02  3e-05  6e-17  1e-05\n",
+      " 4:  3.2946e-02  3.2945e-02  3e-07  9e-17  1e-07\n",
+      " 5:  3.2946e-02  3.2946e-02  3e-09  1e-16  1e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  3.8742e-02 -9.7477e-01  1e+00  2e-16  3e+00\n",
+      " 1:  3.8599e-02  1.5883e-02  2e-02  1e-16  6e-02\n",
+      " 2:  3.7205e-02  3.5534e-02  2e-03  7e-17  3e-04\n",
+      " 3:  3.7164e-02  3.7137e-02  3e-05  1e-16  3e-06\n",
+      " 4:  3.7164e-02  3.7164e-02  3e-07  2e-16  3e-08\n",
+      " 5:  3.7164e-02  3.7164e-02  3e-09  1e-16  3e-10\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  4.3647e-02 -9.7139e-01  1e+00  2e-16  3e+00\n",
+      " 1:  4.3472e-02  1.9421e-02  2e-02  9e-17  6e-02\n",
+      " 2:  4.1933e-02  4.0203e-02  2e-03  6e-17  2e-17\n",
+      " 3:  4.1897e-02  4.1870e-02  3e-05  1e-16  2e-17\n",
+      " 4:  4.1897e-02  4.1897e-02  3e-07  6e-17  2e-17\n",
+      " 5:  4.1897e-02  4.1897e-02  3e-09  1e-16  2e-17\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  4.9143e-02 -9.6755e-01  1e+00  0e+00  3e+00\n",
+      " 1:  4.8927e-02  2.3445e-02  3e-02  9e-17  6e-02\n",
+      " 2:  4.7241e-02  4.5349e-02  2e-03  6e-17  2e-17\n",
+      " 3:  4.7207e-02  4.7179e-02  3e-05  8e-17  2e-17\n",
+      " 4:  4.7207e-02  4.7207e-02  3e-07  2e-16  3e-17\n",
+      " 5:  4.7207e-02  4.7207e-02  3e-09  1e-16  1e-17\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  5.5298e-02 -9.6321e-01  1e+00  0e+00  3e+00\n",
+      " 1:  5.5035e-02  2.8025e-02  3e-02  2e-16  7e-02\n",
+      " 2:  5.3196e-02  5.1149e-02  2e-03  9e-17  2e-17\n",
+      " 3:  5.3165e-02  5.3136e-02  3e-05  3e-17  3e-17\n",
+      " 4:  5.3165e-02  5.3165e-02  3e-07  5e-17  3e-17\n",
+      " 5:  5.3165e-02  5.3165e-02  3e-09  6e-17  2e-17\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  6.2191e-02 -9.5827e-01  1e+00  0e+00  3e+00\n",
+      " 1:  6.1872e-02  3.3239e-02  3e-02  6e-17  7e-02\n",
+      " 2:  5.9879e-02  5.7684e-02  2e-03  1e-16  3e-17\n",
+      " 3:  5.9850e-02  5.9820e-02  3e-05  3e-17  2e-17\n",
+      " 4:  5.9850e-02  5.9849e-02  3e-07  3e-17  2e-17\n",
+      " 5:  5.9850e-02  5.9850e-02  3e-09  1e-16  2e-17\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  6.9912e-02 -9.5267e-01  1e+00  2e-16  3e+00\n",
+      " 1:  6.9526e-02  3.9178e-02  3e-02  8e-17  8e-02\n",
+      " 2:  6.7378e-02  6.5047e-02  2e-03  8e-17  4e-17\n",
+      " 3:  6.7350e-02  6.7319e-02  3e-05  1e-16  6e-17\n",
+      " 4:  6.7350e-02  6.7350e-02  3e-07  4e-17  3e-17\n",
+      " 5:  6.7350e-02  6.7350e-02  3e-09  2e-16  4e-17\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  7.8557e-02 -9.4630e-01  1e+00  0e+00  3e+00\n",
+      " 1:  7.8095e-02  4.5946e-02  3e-02  1e-16  8e-02\n",
+      " 2:  7.5791e-02  7.3340e-02  2e-03  1e-16  2e-17\n",
+      " 3:  7.5765e-02  7.5734e-02  3e-05  4e-17  2e-17\n",
+      " 4:  7.5765e-02  7.5765e-02  3e-07  2e-16  5e-17\n",
+      " 5:  7.5765e-02  7.5765e-02  3e-09  6e-17  2e-17\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  8.8239e-02 -9.3907e-01  1e+00  2e-16  3e+00\n",
+      " 1:  8.7687e-02  5.3661e-02  3e-02  1e-16  8e-02\n",
+      " 2:  8.5232e-02  8.2679e-02  3e-03  1e-16  6e-17\n",
+      " 3:  8.5207e-02  8.5175e-02  3e-05  1e-16  7e-17\n",
+      " 4:  8.5207e-02  8.5207e-02  3e-07  6e-17  4e-17\n",
+      " 5:  8.5207e-02  8.5207e-02  3e-09  1e-16  5e-17\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  9.9080e-02 -9.3084e-01  1e+00  0e+00  3e+00\n",
+      " 1:  9.8425e-02  6.2456e-02  4e-02  1e-16  9e-02\n",
+      " 2:  9.5825e-02  9.3191e-02  3e-03  8e-17  4e-17\n",
+      " 3:  9.5801e-02  9.5769e-02  3e-05  2e-16  6e-17\n",
+      " 4:  9.5801e-02  9.5800e-02  3e-07  1e-16  6e-17\n",
+      " 5:  9.5801e-02  9.5801e-02  3e-09  6e-17  4e-17\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  1.1122e-01 -9.2149e-01  1e+00  4e-16  3e+00\n",
+      " 1:  1.1045e-01  7.2484e-02  4e-02  2e-16  9e-02\n",
+      " 2:  1.0771e-01  1.0502e-01  3e-03  1e-16  6e-17\n",
+      " 3:  1.0769e-01  1.0765e-01  3e-05  8e-17  4e-17\n",
+      " 4:  1.0769e-01  1.0769e-01  3e-07  5e-17  6e-17\n",
+      " 5:  1.0769e-01  1.0769e-01  3e-09  6e-17  5e-17\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  1.2481e-01 -9.1086e-01  1e+00  0e+00  3e+00\n",
+      " 1:  1.2391e-01  8.3917e-02  4e-02  6e-17  1e-01\n",
+      " 2:  1.2105e-01  1.1832e-01  3e-03  1e-16  4e-17\n",
+      " 3:  1.2102e-01  1.2099e-01  3e-05  1e-16  5e-17\n",
+      " 4:  1.2102e-01  1.2102e-01  3e-07  1e-16  5e-17\n",
+      " 5:  1.2102e-01  1.2102e-01  3e-09  7e-17  2e-17\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  1.4004e-01 -8.9876e-01  1e+00  2e-16  3e+00\n",
+      " 1:  1.3898e-01  9.6948e-02  4e-02  8e-17  1e-01\n",
+      " 2:  1.3601e-01  1.3328e-01  3e-03  1e-16  5e-17\n",
+      " 3:  1.3599e-01  1.3596e-01  3e-05  8e-17  6e-17\n",
+      " 4:  1.3599e-01  1.3599e-01  3e-07  1e-16  7e-17\n",
+      " 5:  1.3599e-01  1.3599e-01  3e-09  6e-17  3e-17\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  1.5708e-01 -8.8499e-01  1e+00  0e+00  3e+00\n",
+      " 1:  1.5586e-01  1.1180e-01  4e-02  6e-17  1e-01\n",
+      " 2:  1.5280e-01  1.5009e-01  3e-03  1e-16  1e-16\n",
+      " 3:  1.5278e-01  1.5275e-01  3e-05  1e-16  6e-17\n",
+      " 4:  1.5278e-01  1.5278e-01  3e-07  6e-17  7e-17\n",
+      " 5:  1.5278e-01  1.5278e-01  3e-09  1e-16  5e-17\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  1.7617e-01 -8.6934e-01  1e+00  1e-16  3e+00\n",
+      " 1:  1.7476e-01  1.2871e-01  5e-02  1e-16  1e-01\n",
+      " 2:  1.7163e-01  1.6898e-01  3e-03  2e-16  6e-17\n",
+      " 3:  1.7162e-01  1.7159e-01  3e-05  2e-16  5e-17\n",
+      " 4:  1.7162e-01  1.7161e-01  3e-07  3e-16  1e-16\n",
+      " 5:  1.7162e-01  1.7162e-01  3e-09  7e-17  1e-16\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  1.9755e-01 -8.5154e-01  1e+00  0e+00  3e+00\n",
+      " 1:  1.9594e-01  1.4797e-01  5e-02  1e-16  1e-01\n",
+      " 2:  1.9277e-01  1.9019e-01  3e-03  1e-16  6e-17\n",
+      " 3:  1.9275e-01  1.9272e-01  3e-05  7e-17  9e-17\n",
+      " 4:  1.9275e-01  1.9275e-01  3e-07  1e-16  3e-17\n",
+      " 5:  1.9275e-01  1.9275e-01  3e-09  1e-16  7e-17\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  2.2150e-01 -8.3129e-01  1e+00  0e+00  3e+00\n",
+      " 1:  2.1967e-01  1.6988e-01  5e-02  5e-17  1e-01\n",
+      " 2:  2.1648e-01  2.1401e-01  2e-03  8e-17  1e-16\n",
+      " 3:  2.1647e-01  2.1644e-01  3e-05  1e-16  5e-17\n",
+      " 4:  2.1647e-01  2.1647e-01  3e-07  1e-16  9e-17\n",
+      " 5:  2.1647e-01  2.1647e-01  3e-09  0e+00  7e-17\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  2.4832e-01 -8.0829e-01  1e+00  4e-16  3e+00\n",
+      " 1:  2.4626e-01  1.9479e-01  5e-02  1e-16  1e-01\n",
+      " 2:  2.4309e-01  2.4075e-01  2e-03  9e-17  1e-16\n",
+      " 3:  2.4308e-01  2.4305e-01  2e-05  4e-17  9e-17\n",
+      " 4:  2.4308e-01  2.4308e-01  2e-07  1e-16  2e-16\n",
+      " 5:  2.4308e-01  2.4308e-01  2e-09  1e-16  9e-17\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  2.7837e-01 -7.8214e-01  1e+00  0e+00  3e+00\n",
+      " 1:  2.7607e-01  2.2308e-01  5e-02  1e-16  1e-01\n",
+      " 2:  2.7294e-01  2.7075e-01  2e-03  4e-17  1e-16\n",
+      " 3:  2.7293e-01  2.7291e-01  2e-05  9e-17  1e-16\n",
+      " 4:  2.7293e-01  2.7293e-01  2e-07  6e-17  1e-16\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  3.1204e-01 -7.5245e-01  1e+00  0e+00  3e+00\n",
+      " 1:  3.0949e-01  2.5518e-01  5e-02  2e-16  1e-01\n",
+      " 2:  3.0644e-01  3.0442e-01  2e-03  5e-17  2e-16\n",
+      " 3:  3.0643e-01  3.0641e-01  2e-05  6e-17  1e-16\n",
+      " 4:  3.0643e-01  3.0643e-01  2e-07  7e-17  1e-16\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  3.4977e-01 -7.1875e-01  1e+00  2e-16  3e+00\n",
+      " 1:  3.4697e-01  2.9158e-01  6e-02  6e-17  1e-01\n",
+      " 2:  3.4403e-01  3.4218e-01  2e-03  2e-16  1e-16\n",
+      " 3:  3.4402e-01  3.4400e-01  2e-05  1e-16  2e-16\n",
+      " 4:  3.4402e-01  3.4402e-01  2e-07  1e-16  2e-16\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  3.9206e-01 -6.8052e-01  1e+00  0e+00  3e+00\n",
+      " 1:  3.8901e-01  3.3280e-01  6e-02  8e-17  1e-01\n",
+      " 2:  3.8620e-01  3.8446e-01  2e-03  1e-16  2e-04\n",
+      " 3:  3.8619e-01  3.8617e-01  2e-05  7e-17  2e-06\n",
+      " 4:  3.8619e-01  3.8619e-01  2e-07  1e-16  2e-08\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  4.3945e-01 -6.3717e-01  1e+00  2e-16  3e+00\n",
+      " 1:  4.3616e-01  3.7942e-01  6e-02  2e-16  1e-01\n",
+      " 2:  4.3352e-01  4.3184e-01  2e-03  3e-17  6e-04\n",
+      " 3:  4.3351e-01  4.3349e-01  2e-05  1e-16  6e-06\n",
+      " 4:  4.3351e-01  4.3351e-01  2e-07  1e-16  6e-08\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  4.9259e-01 -5.8806e-01  1e+00  0e+00  3e+00\n",
+      " 1:  4.8907e-01  4.3210e-01  6e-02  1e-16  1e-01\n",
+      " 2:  4.8661e-01  4.8502e-01  2e-03  1e-16  8e-04\n",
+      " 3:  4.8660e-01  4.8659e-01  2e-05  1e-16  8e-06\n",
+      " 4:  4.8660e-01  4.8660e-01  2e-07  1e-16  8e-08\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  5.5217e-01 -5.3245e-01  1e+00  0e+00  3e+00\n",
+      " 1:  5.4844e-01  4.9156e-01  6e-02  2e-16  1e-01\n",
+      " 2:  5.4618e-01  5.4469e-01  1e-03  6e-17  1e-03\n",
+      " 3:  5.4617e-01  5.4616e-01  1e-05  1e-16  1e-05\n",
+      " 4:  5.4617e-01  5.4617e-01  1e-07  1e-16  1e-07\n",
+      " 5:  5.4617e-01  5.4617e-01  1e-09  7e-17  1e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  6.1899e-01 -4.6954e-01  1e+00  2e-16  3e+00\n",
+      " 1:  6.1507e-01  5.5860e-01  6e-02  1e-16  1e-01\n",
+      " 2:  6.1302e-01  6.1164e-01  1e-03  8e-17  1e-03\n",
+      " 3:  6.1301e-01  6.1300e-01  1e-05  1e-16  1e-05\n",
+      " 4:  6.1301e-01  6.1301e-01  1e-07  2e-16  1e-07\n",
+      " 5:  6.1301e-01  6.1301e-01  1e-09  1e-16  1e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  6.9392e-01 -3.9841e-01  1e+00  0e+00  3e+00\n",
+      " 1:  6.8985e-01  6.3410e-01  6e-02  2e-16  1e-01\n",
+      " 2:  6.8801e-01  6.8675e-01  1e-03  1e-16  1e-03\n",
+      " 3:  6.8801e-01  6.8800e-01  1e-05  2e-16  1e-05\n",
+      " 4:  6.8801e-01  6.8801e-01  1e-07  2e-16  1e-07\n",
+      " 5:  6.8801e-01  6.8801e-01  1e-09  2e-16  1e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  7.7797e-01 -3.1805e-01  1e+00  1e-16  3e+00\n",
+      " 1:  7.7378e-01  7.1907e-01  5e-02  2e-16  1e-01\n",
+      " 2:  7.7216e-01  7.7101e-01  1e-03  2e-16  1e-03\n",
+      " 3:  7.7215e-01  7.7214e-01  1e-05  1e-16  1e-05\n",
+      " 4:  7.7215e-01  7.7215e-01  1e-07  7e-17  1e-07\n",
+      " 5:  7.7215e-01  7.7215e-01  1e-09  3e-17  1e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  8.7226e-01 -2.2732e-01  1e+00  0e+00  3e+00\n",
+      " 1:  8.6798e-01  8.1460e-01  5e-02  2e-16  1e-01\n",
+      " 2:  8.6657e-01  8.6554e-01  1e-03  1e-16  1e-03\n",
+      " 3:  8.6657e-01  8.6656e-01  1e-05  2e-16  1e-05\n",
+      " 4:  8.6657e-01  8.6657e-01  1e-07  6e-17  1e-07\n",
+      " 5:  8.6657e-01  8.6657e-01  1e-09  1e-16  1e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  9.7803e-01 -1.2494e-01  1e+00  2e-16  3e+00\n",
+      " 1:  9.7371e-01  9.2192e-01  5e-02  7e-17  1e-01\n",
+      " 2:  9.7250e-01  9.7159e-01  9e-04  1e-16  1e-03\n",
+      " 3:  9.7250e-01  9.7249e-01  9e-06  4e-17  1e-05\n",
+      " 4:  9.7250e-01  9.7250e-01  9e-08  7e-17  1e-07\n",
+      " 5:  9.7250e-01  9.7250e-01  9e-10  1e-16  1e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  1.0967e+00 -9.5076e-03  1e+00  0e+00  3e+00\n",
+      " 1:  1.0924e+00  1.0424e+00  5e-02  1e-16  1e-01\n",
+      " 2:  1.0914e+00  1.0905e+00  8e-04  9e-17  1e-03\n",
+      " 3:  1.0914e+00  1.0914e+00  8e-06  8e-17  1e-05\n",
+      " 4:  1.0914e+00  1.0914e+00  8e-08  2e-16  1e-07\n",
+      " 5:  1.0914e+00  1.0914e+00  8e-10  7e-17  1e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  1.2299e+00  1.2058e-01  1e+00  1e-16  3e+00\n",
+      " 1:  1.2256e+00  1.1776e+00  5e-02  8e-17  1e-01\n",
+      " 2:  1.2247e+00  1.2240e+00  7e-04  6e-17  1e-03\n",
+      " 3:  1.2247e+00  1.2247e+00  7e-06  2e-16  1e-05\n",
+      " 4:  1.2247e+00  1.2247e+00  7e-08  1e-16  1e-07\n",
+      " 5:  1.2247e+00  1.2247e+00  7e-10  8e-17  1e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0:  1.3793e+00  2.6710e-01  1e+00  2e-16  3e+00\n",
+      " 1:  1.3750e+00  1.3293e+00  5e-02  1e-16  1e-01\n",
+      " 2:  1.3744e+00  1.3737e+00  6e-04  8e-17  1e-03\n",
+      " 3:  1.3744e+00  1.3744e+00  6e-06  1e-16  1e-05\n",
+      " 4:  1.3744e+00  1.3744e+00  6e-08  3e-17  1e-07\n",
+      " 5:  1.3744e+00  1.3744e+00  6e-10  2e-16  1e-09\n",
+      "Optimal solution found.\n",
+      "     pcost       dcost       gap    pres   dres\n",
+      " 0: -1.8218e-03 -1.0035e+00  1e+00  2e-16  3e+00\n",
+      " 1: -1.8237e-03 -1.3489e-02  1e-02  6e-17  3e-02\n",
+      " 2: -1.9890e-03 -3.5891e-03  2e-03  1e-16  4e-03\n",
+      " 3: -3.4416e-03 -4.0323e-03  6e-04  2e-15  5e-04\n",
+      " 4: -3.4862e-03 -3.4938e-03  8e-06  2e-16  7e-06\n",
+      " 5: -3.4884e-03 -3.4884e-03  8e-08  1e-16  7e-08\n",
+      "Optimal solution found.\n"
+     ]
+    }
+   ],
+   "source": [
+    "def optimal_portfolio(returns):\n",
+    "    n = len(returns)\n",
+    "    returns = np.asmatrix(returns)\n",
+    "    \n",
+    "    N = 100\n",
+    "    mus = [10**(5.0 * t/N - 1.0) for t in range(N)]\n",
+    "    \n",
+    "    # Convert to cvxopt matrices\n",
+    "    S = opt.matrix(np.cov(returns))\n",
+    "    pbar = opt.matrix(np.mean(returns, axis=1))\n",
+    "    \n",
+    "    # Create constraint matrices\n",
+    "    G = -opt.matrix(np.eye(n))   # negative n x n identity matrix\n",
+    "    h = opt.matrix(0.0, (n ,1))\n",
+    "    A = opt.matrix(1.0, (1, n))\n",
+    "    b = opt.matrix(1.0)\n",
+    "    \n",
+    "    # Calculate efficient frontier weights using quadratic programming\n",
+    "    portfolios = [solvers.qp(mu*S, -pbar, G, h, A, b)['x'] \n",
+    "                  for mu in mus]\n",
+    "    ## CALCULATE RISKS AND RETURNS FOR FRONTIER\n",
+    "    returns = [blas.dot(pbar, x) for x in portfolios]\n",
+    "    risks = [np.sqrt(blas.dot(x, S*x)) for x in portfolios]\n",
+    "    ## CALCULATE THE 2ND DEGREE POLYNOMIAL OF THE FRONTIER CURVE\n",
+    "    m1 = np.polyfit(returns, risks, 2)\n",
+    "    x1 = np.sqrt(m1[2] / m1[0])\n",
+    "    # CALCULATE THE OPTIMAL PORTFOLIO\n",
+    "    wt = solvers.qp(opt.matrix(x1 * S), -pbar, G, h, A, b)['x']\n",
+    "    return np.asarray(wt), returns, risks\n",
+    "\n",
+    "weights1, returns1, risks1 = optimal_portfolio(return_vec)\n",
+    "weights2, returns2, risks2 = optimal_portfolio(return_vec_shift)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 对比以下两图可以发现资产组合的有效前沿差别较大"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5,1,u'Efficient Frontier of portfolio1')"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fa805f13ad0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize = (16,10))\n",
+    "plt.plot(stds1*np.sqrt(242), means1 * 242, 'o')\n",
+    "plt.ylabel('Mean')\n",
+    "plt.xlabel('Std')\n",
+    "ax.set_xlim(0.265, 0.4)\n",
+    "ax.set_ylim(0.25, 0.7)\n",
+    "plt.plot(np.array(risks1)*np.sqrt(242), np.array(returns1) * 242, 'y-o', color = 'red')\n",
+    "plt.title('Efficient Frontier of portfolio1')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5,1,u'Efficient Frontier of portfolio2')"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fa805c7b250>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize = (16,10))\n",
+    "plt.plot(stds2*np.sqrt(242), means2 * 242, 'o')\n",
+    "plt.ylabel('Mean')\n",
+    "plt.xlabel('Std')\n",
+    "ax.set_xlim(0.265, 0.4)\n",
+    "ax.set_ylim(0.25, 0.7)\n",
+    "\n",
+    "plt.plot(np.array(risks2)*np.sqrt(242), np.array(returns2) * 242, 'y-o', color = 'red')\n",
+    "plt.title('Efficient Frontier of portfolio2')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/example/WallStreetLectures/ipython/lecture18_PortfolioConstruction.ipynb b/example/WallStreetLectures/ipython/lecture18_PortfolioConstruction.ipynb
new file mode 100644
index 0000000..e907171
--- /dev/null
+++ b/example/WallStreetLectures/ipython/lecture18_PortfolioConstruction.ipynb
@@ -0,0 +1,708 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 资产组合构建"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 本段代码利用quantOS系统进行资产分析及组合构建，比较了指数权重、等权重和波动率倒\n",
+    "## 数加权法三种股票组合构建方法"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 系统设置"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# encoding: utf-8\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import datetime\n",
+    "import seaborn as sns\n",
+    "import matplotlib.mlab as mlab\n",
+    "import scipy.stats as stats\n",
+    "from matplotlib.ticker import Formatter\n",
+    "\n",
+    "sns.set_style('darkgrid')\n",
+    "sns.set_context('poster')\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "from jaqs.data import RemoteDataService\n",
+    "import jaqs.util as jutil\n",
+    "from __future__ import print_function, unicode_literals, division, absolute_import\n",
+    "\n",
+    "from jaqs.data import RemoteDataService, DataView"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Begin: DataApi login 17321165656@tcp://data.quantos.org:8910\n",
+      "    login success \n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'0,'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataview_dir_path = '.'\n",
+    "backtest_result_dir_path = '.'\n",
+    "\n",
+    "import os\n",
+    "phone = os.environ.get('QUANTOS_USER')\n",
+    "token = os.environ.get('QUANTOS_TOKEN')\n",
+    "\n",
+    "data_config = {\n",
+    "  \"remote.data.address\": \"tcp://data.quantos.org:8910\",\n",
+    "  \"remote.data.username\": phone,\n",
+    "  \"timeout\": 3600,\n",
+    "  \"remote.data.password\": token\n",
+    "}\n",
+    "\n",
+    "ds = RemoteDataService()\n",
+    "ds.init_from_config(data_config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 设置参数"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "START_DATE, END_DATE = 20140101, 20180420\n",
+    "UNIVERSE = '000905.SH'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 准备DataView"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initialize config success.\n",
+      "Query data...\n",
+      "Query data - query...\n",
+      "NOTE: price adjust method is [post adjust]\n",
+      "112\n",
+      "225\n",
+      "338\n",
+      "451\n",
+      "564\n",
+      "677\n",
+      "790\n",
+      "903\n",
+      "1016\n",
+      "112\n",
+      "225\n",
+      "338\n",
+      "451\n",
+      "564\n",
+      "677\n",
+      "790\n",
+      "903\n",
+      "1016\n",
+      "21\n",
+      "43\n",
+      "65\n",
+      "87\n",
+      "109\n",
+      "131\n",
+      "153\n",
+      "175\n",
+      "197\n",
+      "219\n",
+      "241\n",
+      "263\n",
+      "285\n",
+      "307\n",
+      "329\n",
+      "351\n",
+      "373\n",
+      "395\n",
+      "417\n",
+      "439\n",
+      "461\n",
+      "483\n",
+      "505\n",
+      "527\n",
+      "549\n",
+      "571\n",
+      "593\n",
+      "615\n",
+      "637\n",
+      "659\n",
+      "681\n",
+      "703\n",
+      "725\n",
+      "747\n",
+      "769\n",
+      "791\n",
+      "813\n",
+      "835\n",
+      "857\n",
+      "879\n",
+      "901\n",
+      "923\n",
+      "945\n",
+      "967\n",
+      "989\n",
+      "1011\n",
+      "1033\n",
+      "1055\n",
+      "1077\n",
+      "WARNING: some data is unavailable: \n",
+      "    At fields \n",
+      "Query data - daily fields prepared.\n",
+      "Query instrument info...\n",
+      "Query adj_factor...\n",
+      "Query benchmark...\n",
+      "Query benchmar member info...\n",
+      "Query groups (industry)...\n",
+      "Data has been successfully prepared.\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataview_props = {# Start and end date of back-test\n",
+    "                  'start_date': START_DATE, 'end_date': END_DATE,\n",
+    "                  # Investment universe and performance benchmark\n",
+    "                  'universe': UNIVERSE, 'benchmark': UNIVERSE,\n",
+    "                  # Data fields that we need\n",
+    "                  'fields': 'free_share,total_share,float_share,float_mv,sw1',\n",
+    "                  # freq = 1 means we use daily data. Please do not change this.\n",
+    "                  'freq': 1}\n",
+    "\n",
+    "# DataView utilizes RemoteDataService to get various data and store them\n",
+    "dv = DataView()\n",
+    "dv.init_from_config(dataview_props, ds)\n",
+    "dv.prepare_data()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 计算个股日收益率"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dv.add_formula('daily_ret', 'Return(close_adj, 1)', is_quarterly = False)\n",
+    "daily_ret = dv.get_ts('daily_ret', start_date=dv.extended_start_date_d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "index_member = dv.get_ts('index_member', start_date=dv.extended_start_date_d)\n",
+    "index_member = index_member.astype('bool')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "daily_ret = daily_ret[index_member]\n",
+    "# daily_ret = daily_ret.fillna(0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 计算指数日收益率"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "index_ret = dv.data_benchmark.pct_change()\n",
+    "index_ret.columns = ['index_ret']\n",
+    "index_ret = index_ret.fillna(0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 计算指数周收益率"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "index_ret_copy = index_ret.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "index_ret_copy += 1\n",
+    "index_ret_copy = np.log(index_ret_copy)\n",
+    "index_ret_copy['trade_date'] = index_ret_copy.index\n",
+    "index_ret_copy['NoWeek'] = index_ret_copy['trade_date'].apply(lambda x: str(datetime.datetime.strptime(str(x), '%Y%m%d').date().isocalendar()[1]))\n",
+    "index_ret_copy['NoWeek'] = index_ret_copy['NoWeek'].apply(lambda x: '0' + x if len(x) == 1 else x)\n",
+    "index_ret_copy['year'] = index_ret_copy['trade_date'].apply(lambda x: str(x)[:4])\n",
+    "index_ret_copy['year-week'] = index_ret_copy['year'] + '-'+ index_ret_copy['NoWeek']\n",
+    "del index_ret_copy['NoWeek'], index_ret_copy['year']\n",
+    "weekly_index_ret = index_ret_copy.groupby('year-week').sum()\n",
+    "del weekly_index_ret['trade_date']\n",
+    "weekly_index_ret = np.exp(weekly_index_ret) - 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 计算个股周收益率"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "daily_ret_copy = daily_ret.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "daily_ret_copy += 1\n",
+    "daily_ret_copy = np.log(daily_ret_copy)\n",
+    "daily_ret_copy['trade_date'] = daily_ret_copy.index\n",
+    "daily_ret_copy['NoWeek'] = daily_ret_copy['trade_date'].apply(lambda x: str(datetime.datetime.strptime(str(x), '%Y%m%d').date().isocalendar()[1]))\n",
+    "daily_ret_copy['NoWeek'] = daily_ret_copy['NoWeek'].apply(lambda x: '0' + x if len(x) == 1 else x)\n",
+    "daily_ret_copy['year'] = daily_ret_copy['trade_date'].apply(lambda x: str(x)[:4])\n",
+    "daily_ret_copy['year-week'] = daily_ret_copy['year'] + '-'+ daily_ret_copy['NoWeek']\n",
+    "del daily_ret_copy['NoWeek'], daily_ret_copy['year']\n",
+    "week_day_convert = daily_ret_copy.groupby('year-week')['trade_date'].last()\n",
+    "weekly_ret = daily_ret_copy.groupby('year-week').sum()\n",
+    "del weekly_ret['trade_date']\n",
+    "weekly_ret = np.exp(weekly_ret) - 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weekly_ret = pd.concat([week_day_convert, weekly_ret], axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weekly_ret = weekly_ret.set_index('trade_date')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 等权重组合"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weekly_ret = weekly_ret[index_member]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ew_ret = pd.DataFrame(weekly_ret.mean(axis = 1))\n",
+    "ew_ret.columns = ['ew_ret']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 个股历史波动率倒数加权组合"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\lli\\AppData\\Local\\QuantosFinanceTerminal\\python\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: pd.rolling_std is deprecated for DataFrame and will be removed in a future version, replace with \n",
+      "\tDataFrame.rolling(window=100,center=False).std()\n",
+      "  \n",
+      "C:\\Users\\lli\\AppData\\Local\\QuantosFinanceTerminal\\python\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:907: RuntimeWarning: All-NaN slice encountered\n",
+      "  result = np.apply_along_axis(_nanmedian1d, axis, a, overwrite_input)\n",
+      "C:\\Users\\lli\\AppData\\Local\\QuantosFinanceTerminal\\python\\lib\\site-packages\\jaqs\\data\\py_expression_eval.py:942: RuntimeWarning: invalid value encountered in greater\n",
+      "  mask = diff_abs > z_score * mad\n",
+      "C:\\Users\\lli\\AppData\\Local\\QuantosFinanceTerminal\\python\\lib\\site-packages\\jaqs\\data\\py_expression_eval.py:944: RuntimeWarning: invalid value encountered in sign\n",
+      "  x = x + z_score * mad * np.sign(diff * mask) + mask * median\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 计算个股30天moving window波动率\n",
+    "vol_ret = pd.rolling_std(daily_ret, 100)\n",
+    "dv.append_df(vol_ret, 'volatility_ret', is_quarterly = False)\n",
+    "dv.add_formula('cutoff_volatility_ret', 'Cutoff(volatility_ret)', is_quarterly=False)\n",
+    "vol_ret_cutoff = dv.get_ts('cutoff_volatility_ret', start_date=dv.extended_start_date_d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_start_date = daily_ret.index[101]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vol_ret_cutoff = vol_ret_cutoff.replace(0.0, np.nan)\n",
+    "reverse_vol_ret = 1.0/vol_ret_cutoff\n",
+    "reverse_vol_ret = reverse_vol_ret.fillna(0)\n",
+    "reverse_vol_ret = reverse_vol_ret.replace(np.inf, 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "reverse_vol_ret_weight = reverse_vol_ret.div(reverse_vol_ret.sum(axis = 1), axis = 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reverse_vol_ret_weight_weekly = reverse_vol_ret_weight.loc[weekly_ret.index]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reverse_vol_weekly_ret = pd.DataFrame(reverse_vol_ret_weight_weekly.mul(weekly_ret).sum(axis = 1))\n",
+    "reverse_vol_weekly_ret.columns = ['reverse_vol_ret']\n",
+    "reverse_vol_weekly_ret = reverse_vol_weekly_ret.loc[new_start_date:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 比较三组组合方式"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weekly_index_ret = pd.concat([week_day_convert, weekly_index_ret], axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weekly_index_ret = weekly_index_ret.set_index('trade_date')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weekly_index_ret = weekly_index_ret.loc[new_start_date:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ew_ret = ew_ret.loc[new_start_date:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ret_compare = pd.concat([weekly_index_ret, ew_ret, reverse_vol_weekly_ret], axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "三种组合方式年化收益率：\n",
+      "index_ret          0.154131\n",
+      "ew_ret             0.194628\n",
+      "reverse_vol_ret    0.177556\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"三种组合方式年化收益率：\")\n",
+    "print(ret_compare.mean()*52)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "三种组合方式年化波动率：\n",
+      "index_ret          0.294566\n",
+      "ew_ret             0.301420\n",
+      "reverse_vol_ret    0.286570\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"三种组合方式年化波动率：\")\n",
+    "print(ret_compare.std()*np.sqrt(52))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "三种组合方式年化Sharpe Ratio：\n",
+      "index_ret          0.523247\n",
+      "ew_ret             0.645703\n",
+      "reverse_vol_ret    0.619591\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"三种组合方式年化Sharpe Ratio：\")\n",
+    "print(ret_compare.mean()/ret_compare.std()*np.sqrt(52))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cum_ret_compare = (ret_compare + 1).cumprod()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class MyFormatter(Formatter):\n",
+    "    def __init__(self, dates, fmt='%Y%m'):\n",
+    "        self.dates = dates\n",
+    "        self.fmt = fmt\n",
+    "\n",
+    "    def __call__(self, x, pos=0):\n",
+    "        \"\"\"Return the label for time x at position pos\"\"\"\n",
+    "        ind = int(np.round(x))\n",
+    "        if ind >= len(self.dates) or ind < 0:\n",
+    "            return ''\n",
+    "\n",
+    "        # return self.dates[ind].strftime(self.fmt)\n",
+    "        return pd.to_datetime(self.dates[ind], format=\"%Y%m%d\").strftime(self.fmt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x15d393c8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax1 = plt.subplots(1, 1, figsize=(21, 8))\n",
+    "\n",
+    "idx0 = cum_ret_compare.index\n",
+    "idx = range(len(idx0))\n",
+    "\n",
+    "ax1.plot(idx, cum_ret_compare.index_ret, lw=1.5, color='indianred', label='Index weight')\n",
+    "ax1.plot(idx, cum_ret_compare.ew_ret, lw=1.5, color='royalblue', label='Equal weight')\n",
+    "ax1.plot(idx, cum_ret_compare.reverse_vol_ret, lw=1.5, color='purple', label='Reverse volatility weight')\n",
+    "\n",
+    "# ax1.axhline(0.0, color='k', lw=0.5, ls='--')\n",
+    "\n",
+    "ax1.legend(loc='upper left')\n",
+    "ax1.set_xlabel(\"Date\")\n",
+    "ax1.set_ylabel(\"Return\")\n",
+    "ax1.xaxis.set_major_formatter(MyFormatter(idx0, '%Y%m%d'))\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "# fig.savefig('lgt_brinson_cum.png')\n",
+    "# plt.close()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/example/WallStreetLectures/ipython/lecture19_MutualFunds.ipynb b/example/WallStreetLectures/ipython/lecture19_MutualFunds.ipynb
new file mode 100644
index 0000000..1546205
--- /dev/null
+++ b/example/WallStreetLectures/ipython/lecture19_MutualFunds.ipynb
@@ -0,0 +1,1217 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 公募基金组合"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 本段代码利用quantOS系统进行公募股票和债券基金收益率分析及组合构建，比较了等权重和波动率倒数加权法两种组合构建方法"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 系统设置"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# encoding: utf-8\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import datetime\n",
+    "import seaborn as sns\n",
+    "import matplotlib.mlab as mlab\n",
+    "import scipy.stats as stats\n",
+    "from matplotlib.ticker import Formatter\n",
+    "\n",
+    "sns.set_style('darkgrid')\n",
+    "sns.set_context('poster')\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "from jaqs.data import RemoteDataService, DataApi\n",
+    "import jaqs.util as jutil\n",
+    "from __future__ import print_function, unicode_literals, division, absolute_import\n",
+    "\n",
+    "from jaqs.data import RemoteDataService, DataView"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Begin: DataApi login 17321165656@tcp://data.quantos.org:8910\n",
+      "    login success \n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'0,'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataview_dir_path = '.'\n",
+    "backtest_result_dir_path = '.'\n",
+    "\n",
+    "import os\n",
+    "phone = os.environ.get('QUANTOS_USER')\n",
+    "token = os.environ.get('QUANTOS_TOKEN')\n",
+    "\n",
+    "data_config = {\n",
+    "  \"remote.data.address\": \"tcp://data.quantos.org:8910\",\n",
+    "  \"remote.data.username\": phone,\n",
+    "  \"timeout\": 3600,\n",
+    "  \"remote.data.password\": token\n",
+    "}\n",
+    "\n",
+    "api_uat = DataApi(addr='tcp://data.quantos.org:8910')\n",
+    "api_uat.login(phone, token)\n",
+    "\n",
+    "ds = RemoteDataService()\n",
+    "ds.init_from_config(data_config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 设置参数"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "START_DATE, END_DATE = 20160101, 20180511"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 从数据库中取出符合条件的基金"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_fundlist(api, invest_type, invest_style,launch_date):\n",
+    "    df, msg = api.query(\n",
+    "            view = 'lb.mfInfo',\n",
+    "            fields = 'invest_type,invest_style,status',\n",
+    "            filter = \"\",\n",
+    "            data_format = 'pandas'\n",
+    "    )\n",
+    "    df = df[(df['invest_type'] == invest_type)\n",
+    "           & (df['status'] == 101001000)\n",
+    "           & (df['invest_style'] == invest_style)\n",
+    "           & (df['setup_date'] < launch_date)]\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 取出基金历史净值和指数的历史价格信息，注意需要用复权因子校正后的净值"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_fundnav(api, symbol, start_date, end_date):\n",
+    "    df, msg = api.query(\n",
+    "            view = 'lb.mfNav',\n",
+    "            fields = 'price_date,nav_adjusted',\n",
+    "            filter = 'start_pdate=' + str(start_date) + '&end_pdate=' + str(end_date) + '&symbol=' + symbol,\n",
+    "            data_format = 'pandas'\n",
+    "    )\n",
+    "    \n",
+    "    if df is None:\n",
+    "        print(df, msg)\n",
+    "    df.index = df['price_date'].astype(np.integer)\n",
+    "    df.sort_index(inplace = True)\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 计算每只基金的表现，包括return，volatility，sharpe ratio，beta，最大回撤等"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cal_return(api, symbol, start, end):\n",
+    "\n",
+    "    df_nav = get_fundnav(api, symbol, start, end)\n",
+    "    if len(df_nav) == 0:\n",
+    "        return None, None, None\n",
+    "    strategy_value = pd.DataFrame(df_nav['nav_accumulated']).fillna(method = 'ffill')\n",
+    "    strategy_value.columns = ['strat']\n",
+    "    df_returns = strategy_value.pct_change(periods = 1).fillna(0.0)\n",
+    "    df_returns = df_returns.join((df_returns.loc[:, ['strat']] + 1.0).cumprod(), rsuffix = '_cum')\n",
+    "\n",
+    "    start = pd.to_datetime(start, format = '%Y%m%d')\n",
+    "    end   = pd.to_datetime(end, format = '%Y%m%d')\n",
+    "    years = (end - start).days / 365.0\n",
+    "\n",
+    "    strat_cum = df_returns['strat_cum'].values\n",
+    "\n",
+    "#     max_dd_end = np.argmax(np.maximum.accumulate(strat_cum) - strat_cum)   # end of the period\n",
+    "#     max_dd_start   = np.argmax(strat_cum[:max_dd_end])\n",
+    "#     max_dd = (strat_cum[max_dd_start] - strat_cum[max_dd_end])/strat_cum[max_dd_end]\n",
+    "\n",
+    "    # 计算收益率相关指标\n",
+    "    performance_metrics = dict()\n",
+    "\n",
+    "    performance_metrics['Annual Return (%)'] =\\\n",
+    "        100 * (np.power(df_returns.loc[:, 'strat_cum'].values[-1], 1./years) - 1)\n",
+    "    performance_metrics['Annual volatility (%)'] =\\\n",
+    "        100 * (df_returns.loc[:, 'strat'].std() * np.sqrt(242))\n",
+    "    performance_metrics['Sharpe Ratio'] = (performance_metrics['Annual Return (%)']/performance_metrics['Annual volatility (%)'])\n",
+    "\n",
+    "    # 计算风险相关指标\n",
+    "    risk_metrics = dict()\n",
+    "#     risk_metrics['Maximum Drawdown (%)'] = max_dd * 100\n",
+    "#     risk_metrics['Maximum Drawdown Start'] = df_returns.index[max_dd_end]\n",
+    "#     risk_metrics['Maximum Drawdown End'] = df_returns.index[max_dd_start]\n",
+    "    \n",
+    "    output_returns = df_returns.copy()\n",
+    "    output_returns['symbol'] = symbol\n",
+    "    output_returns = output_returns.reset_index()\n",
+    "    output_returns.columns = ['trade_date', 'strat','strat_cum','symbol']\n",
+    "    \n",
+    "    return performance_metrics, risk_metrics, output_returns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 将上述函数按流程组织在一起，计算出所有符合条件基金的表现"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 剔除指数型基金以及2018年1月1日后发行的股票和债券基金，债券基金选择发行规模最大的1000支作为研究对象\n",
+    "df = get_fundlist(api_uat, '股票型', '股票型', str(20180101))\n",
+    "df_bond = get_fundlist(api_uat, '债券型', '债券型', str(20180101))\n",
+    "df_bond = df_bond.sort_values('issue_totals', ascending = False).head(1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "symbols = df['symbol'].values.tolist()\n",
+    "symbols_bond = df_bond['symbol'].values.tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "indicators = list()\n",
+    "indicators_bond = list()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "daily_returns = pd.DataFrame()\n",
+    "daily_returns_bond = pd.DataFrame()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "for symbol in symbols:\n",
+    "    performance_metrics, risk_metrics, output_returns = cal_return(api_uat, symbol, START_DATE, END_DATE)\n",
+    "    \n",
+    "    if performance_metrics is None:\n",
+    "        continue\n",
+    "    daily_returns = pd.concat([daily_returns, output_returns], axis = 0)\n",
+    "    indicators.append((symbol,\n",
+    "                      performance_metrics['Annual Return (%)'],\n",
+    "                      performance_metrics['Annual volatility (%)'],\n",
+    "                      performance_metrics['Sharpe Ratio'],\n",
+    "                      output_returns['strat_cum'].iat[-1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "for symbol in symbols_bond:\n",
+    "    performance_metrics, risk_metrics, output_returns = cal_return(api_uat, symbol, START_DATE, END_DATE)\n",
+    "    \n",
+    "    if performance_metrics is None:\n",
+    "        continue\n",
+    "    daily_returns_bond = pd.concat([daily_returns_bond, output_returns], axis = 0)\n",
+    "    indicators_bond.append((symbol,\n",
+    "                      performance_metrics['Annual Return (%)'],\n",
+    "                      performance_metrics['Annual volatility (%)'],\n",
+    "                      performance_metrics['Sharpe Ratio'],\n",
+    "                      output_returns['strat_cum'].iat[-1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels = ['symbol', 'AnnualReturn', 'AnnualVolatility', 'SharpeRatio', 'StratCumReturn']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 股票基金收益率描述统计"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>AnnualReturn</th>\n",
+       "      <th>AnnualVolatility</th>\n",
+       "      <th>SharpeRatio</th>\n",
+       "      <th>StratCumReturn</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>307.000000</td>\n",
+       "      <td>307.000000</td>\n",
+       "      <td>307.000000</td>\n",
+       "      <td>307.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>4.364186</td>\n",
+       "      <td>18.482186</td>\n",
+       "      <td>0.303119</td>\n",
+       "      <td>1.118137</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>8.636670</td>\n",
+       "      <td>4.854557</td>\n",
+       "      <td>0.639726</td>\n",
+       "      <td>0.219610</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>-21.810494</td>\n",
+       "      <td>0.386981</td>\n",
+       "      <td>-0.902726</td>\n",
+       "      <td>0.559690</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>-1.415731</td>\n",
+       "      <td>15.445819</td>\n",
+       "      <td>-0.078384</td>\n",
+       "      <td>0.966925</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>3.587610</td>\n",
+       "      <td>19.102425</td>\n",
+       "      <td>0.206999</td>\n",
+       "      <td>1.086700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>10.078499</td>\n",
+       "      <td>21.538705</td>\n",
+       "      <td>0.616507</td>\n",
+       "      <td>1.254229</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>30.874093</td>\n",
+       "      <td>39.064090</td>\n",
+       "      <td>7.204041</td>\n",
+       "      <td>1.886456</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       AnnualReturn  AnnualVolatility  SharpeRatio  StratCumReturn\n",
+       "count    307.000000        307.000000   307.000000      307.000000\n",
+       "mean       4.364186         18.482186     0.303119        1.118137\n",
+       "std        8.636670          4.854557     0.639726        0.219610\n",
+       "min      -21.810494          0.386981    -0.902726        0.559690\n",
+       "25%       -1.415731         15.445819    -0.078384        0.966925\n",
+       "50%        3.587610         19.102425     0.206999        1.086700\n",
+       "75%       10.078499         21.538705     0.616507        1.254229\n",
+       "max       30.874093         39.064090     7.204041        1.886456"
+      ]
+     },
+     "execution_count": 161,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_returns = pd.DataFrame.from_records(indicators, columns = labels)\n",
+    "df_returns.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_returns = pd.merge(left = df_returns, right = df[['symbol', 'issue_totals']], how = 'left', on = 'symbol')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 股票基金年化收益率分布"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1290fdd8>"
+      ]
+     },
+     "execution_count": 163,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x13071550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 画日对数收益率分布直方图\n",
+    "fig = plt.figure(figsize=(18, 18))\n",
+    "v = df_returns['AnnualReturn'].dropna().values\n",
+    "avgRet = np.mean(v)\n",
+    "stdRet = np.std(v)\n",
+    "x = np.linspace(avgRet - 3*stdRet, avgRet + 3*stdRet, 100)\n",
+    "y = mlab.normpdf(x, avgRet, stdRet)\n",
+    "kde = stats.gaussian_kde(v)\n",
+    "\n",
+    "# plot the histogram\n",
+    "plt.subplot(211)\n",
+    "plt.hist(v, 50, weights = np.ones(len(v))/len(v), alpha = 0.4)\n",
+    "plt.axvline(x = avgRet, color = 'red', linestyle = '--', linewidth = 0.8, label = 'Mean Value')\n",
+    "plt.axvline(x = avgRet - 2 * stdRet, color = 'blue', linestyle = '--', linewidth = 0.8, label = '-2 Standard Deviation')\n",
+    "plt.axvline(x = avgRet + 2 * stdRet, color = 'blue', linestyle = '--', linewidth = 0.8, label = '2 Standard Deviation')\n",
+    "plt.ylabel('Percentage', fontsize = 10)\n",
+    "plt.legend(fontsize = 10)\n",
+    "\n",
+    "# plot the kde and normal fit\n",
+    "plt.subplot(212)\n",
+    "plt.plot(x, kde(x), label = 'Kernel Density Estimation')\n",
+    "plt.plot(x, y, color = 'black', linewidth=1, label = 'Normal Fit')\n",
+    "plt.ylabel('Probability', fontsize = 10)\n",
+    "plt.axvline(x = avgRet, color = 'red', linestyle = '--', linewidth = 0.8, label = 'Mean Count')\n",
+    "plt.legend(fontsize = 10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 债券基金收益率描述统计"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>AnnualReturn</th>\n",
+       "      <th>AnnualVolatility</th>\n",
+       "      <th>SharpeRatio</th>\n",
+       "      <th>StratCumReturn</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>998.000000</td>\n",
+       "      <td>998.000000</td>\n",
+       "      <td>998.000000</td>\n",
+       "      <td>998.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1.416466</td>\n",
+       "      <td>2.560495</td>\n",
+       "      <td>1.284039</td>\n",
+       "      <td>1.034567</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>2.282857</td>\n",
+       "      <td>2.557381</td>\n",
+       "      <td>1.541210</td>\n",
+       "      <td>0.052420</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>-20.272923</td>\n",
+       "      <td>0.194170</td>\n",
+       "      <td>-1.540819</td>\n",
+       "      <td>0.586000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.880961</td>\n",
+       "      <td>1.092701</td>\n",
+       "      <td>0.361577</td>\n",
+       "      <td>1.020906</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1.718000</td>\n",
+       "      <td>1.771565</td>\n",
+       "      <td>0.960840</td>\n",
+       "      <td>1.041000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2.469773</td>\n",
+       "      <td>3.171727</td>\n",
+       "      <td>1.872507</td>\n",
+       "      <td>1.059240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>13.288286</td>\n",
+       "      <td>32.461291</td>\n",
+       "      <td>20.173094</td>\n",
+       "      <td>1.342200</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       AnnualReturn  AnnualVolatility  SharpeRatio  StratCumReturn\n",
+       "count    998.000000        998.000000   998.000000      998.000000\n",
+       "mean       1.416466          2.560495     1.284039        1.034567\n",
+       "std        2.282857          2.557381     1.541210        0.052420\n",
+       "min      -20.272923          0.194170    -1.540819        0.586000\n",
+       "25%        0.880961          1.092701     0.361577        1.020906\n",
+       "50%        1.718000          1.771565     0.960840        1.041000\n",
+       "75%        2.469773          3.171727     1.872507        1.059240\n",
+       "max       13.288286         32.461291    20.173094        1.342200"
+      ]
+     },
+     "execution_count": 164,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_returns_bond = pd.DataFrame.from_records(indicators_bond, columns = labels)\n",
+    "df_returns_bond.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_returns_bond = pd.merge(left = df_returns_bond, right = df_bond[['symbol', 'issue_totals']], how = 'left', on = 'symbol')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 债券基金年化收益率分布"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x12f20470>"
+      ]
+     },
+     "execution_count": 166,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x127a3860>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 画日对数收益率分布直方图\n",
+    "fig = plt.figure(figsize=(18, 18))\n",
+    "v = df_returns_bond['AnnualReturn'].dropna().values\n",
+    "avgRet = np.mean(v)\n",
+    "stdRet = np.std(v)\n",
+    "x = np.linspace(avgRet - 3*stdRet, avgRet + 3*stdRet, 100)\n",
+    "y = mlab.normpdf(x, avgRet, stdRet)\n",
+    "kde = stats.gaussian_kde(v)\n",
+    "\n",
+    "# plot the histogram\n",
+    "plt.subplot(211)\n",
+    "plt.hist(v, 50, weights = np.ones(len(v))/len(v), alpha = 0.4)\n",
+    "plt.axvline(x = avgRet, color = 'red', linestyle = '--', linewidth = 0.8, label = 'Mean Value')\n",
+    "plt.axvline(x = avgRet - 2 * stdRet, color = 'blue', linestyle = '--', linewidth = 0.8, label = '-2 Standard Deviation')\n",
+    "plt.axvline(x = avgRet + 2 * stdRet, color = 'blue', linestyle = '--', linewidth = 0.8, label = '2 Standard Deviation')\n",
+    "plt.ylabel('Percentage', fontsize = 10)\n",
+    "plt.legend(fontsize = 10)\n",
+    "\n",
+    "# plot the kde and normal fit\n",
+    "plt.subplot(212)\n",
+    "plt.plot(x, kde(x), label = 'Kernel Density Estimation')\n",
+    "plt.plot(x, y, color = 'black', linewidth=1, label = 'Normal Fit')\n",
+    "plt.ylabel('Probability', fontsize = 10)\n",
+    "plt.axvline(x = avgRet, color = 'red', linestyle = '--', linewidth = 0.8, label = 'Mean Count')\n",
+    "plt.legend(fontsize = 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_daily_returns_bond = daily_returns_bond.pivot_table(index = 'trade_date', columns = 'symbol', values = 'strat')\n",
+    "df_daily_returns_bond = df_daily_returns_bond.fillna(0.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_daily_returns = daily_returns.pivot_table(index = 'trade_date', columns = 'symbol', values = 'strat')\n",
+    "df_daily_returns = df_daily_returns.fillna(0.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 比较同一股票基金在不同年份的表现，检验是否有持续性"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_daily_returns_2016 = df_daily_returns.loc[:20170101]\n",
+    "df_daily_returns_2017 = df_daily_returns.loc[20170101:20180101]\n",
+    "df_daily_returns_2018 = df_daily_returns.loc[20180101:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_daily_returns_2016_cum = pd.DataFrame((df_daily_returns_2016 + 1).cumprod().iloc[-1, :].rank(ascending = False))\n",
+    "df_daily_returns_2016_cum.columns = ['2016rank']\n",
+    "df_daily_returns_2017_cum = pd.DataFrame((df_daily_returns_2017 + 1).cumprod().iloc[-1, :].rank(ascending = False))\n",
+    "df_daily_returns_2017_cum.columns = ['2017rank']\n",
+    "df_daily_returns_2018_cum = pd.DataFrame((df_daily_returns_2018 + 1).cumprod().iloc[-1, :].rank(ascending = False))\n",
+    "df_daily_returns_2018_cum.columns = ['2018rank']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_rank = pd.concat([df_daily_returns_2016_cum, df_daily_returns_2017_cum, df_daily_returns_2018_cum], axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>2016rank</th>\n",
+       "      <th>2017rank</th>\n",
+       "      <th>2018rank</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2016rank</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.082479</td>\n",
+       "      <td>0.144213</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017rank</th>\n",
+       "      <td>0.082479</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.011841</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018rank</th>\n",
+       "      <td>0.144213</td>\n",
+       "      <td>0.011841</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          2016rank  2017rank  2018rank\n",
+       "2016rank  1.000000  0.082479  0.144213\n",
+       "2017rank  0.082479  1.000000  0.011841\n",
+       "2018rank  0.144213  0.011841  1.000000"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_rank.corr()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 比较同一债券基金在不同年份的表现，检验是否有持续性"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_daily_returns_bond_2016 = df_daily_returns_bond.loc[:20170101]\n",
+    "df_daily_returns_bond_2017 = df_daily_returns_bond.loc[20170101:20180101]\n",
+    "df_daily_returns_bond_2018 = df_daily_returns_bond.loc[20180101:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_daily_returns_bond_2016_cum = pd.DataFrame((df_daily_returns_bond_2016 + 1).cumprod().iloc[-1, :].rank(ascending = False))\n",
+    "df_daily_returns_bond_2016_cum.columns = ['2016rank']\n",
+    "df_daily_returns_bond_2017_cum = pd.DataFrame((df_daily_returns_bond_2017 + 1).cumprod().iloc[-1, :].rank(ascending = False))\n",
+    "df_daily_returns_bond_2017_cum.columns = ['2017rank']\n",
+    "df_daily_returns_bond_2018_cum = pd.DataFrame((df_daily_returns_bond_2018 + 1).cumprod().iloc[-1, :].rank(ascending = False))\n",
+    "df_daily_returns_bond_2018_cum.columns = ['2018rank']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_rank_bond = pd.concat([df_daily_returns_bond_2016_cum, df_daily_returns_bond_2017_cum, df_daily_returns_bond_2018_cum], axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>2016rank</th>\n",
+       "      <th>2017rank</th>\n",
+       "      <th>2018rank</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2016rank</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.148519</td>\n",
+       "      <td>-0.023175</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017rank</th>\n",
+       "      <td>0.148519</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.047603</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018rank</th>\n",
+       "      <td>-0.023175</td>\n",
+       "      <td>-0.047603</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          2016rank  2017rank  2018rank\n",
+       "2016rank  1.000000  0.148519 -0.023175\n",
+       "2017rank  0.148519  1.000000 -0.047603\n",
+       "2018rank -0.023175 -0.047603  1.000000"
+      ]
+     },
+     "execution_count": 174,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_rank_bond.corr()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 挑选出在2016年表现最好的三支股票基金和债券基金构建组合"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stock_fund = df_daily_returns_2016_cum.sort_values('2016rank').index[:3].values.tolist()\n",
+    "bond_fund = df_daily_returns_bond_2016_cum.sort_values('2016rank').index[:3].values.tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 193,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "daily_ret_stock = df_daily_returns[stock_fund]\n",
+    "daily_ret_bond = df_daily_returns_bond[bond_fund]\n",
+    "daily_ret_all = pd.concat([daily_ret_stock, daily_ret_bond], axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. 等权重组合基金"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_ew = pd.DataFrame(daily_ret_all.mean(axis = 1))\n",
+    "df_ew.columns = ['daily_ew_ret']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 196,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Annual Return is: 0.1026\n",
+      "Annual Volatility is: 0.0664\n",
+      "Sharpe Ratio is: 1.5452\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Annual Return is: %.4f' % (df_ew.mean() * 242))\n",
+    "print('Annual Volatility is: %.4f' % (df_ew.std() * np.sqrt(242)))\n",
+    "print('Sharpe Ratio is: %.4f' % (df_ew.mean()/df_ew.std() * np.sqrt(242)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. 根据波动率倒数组合基金"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\lli\\AppData\\Local\\QuantosFinanceTerminal\\python\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: pd.rolling_std is deprecated for DataFrame and will be removed in a future version, replace with \n",
+      "\tDataFrame.rolling(window=30,center=False).std()\n",
+      "  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# 计算各支基金过去30个交易日波动率\n",
+    "df_volatility = pd.rolling_std(daily_ret_all, 30)\n",
+    "df_volatility = df_volatility.dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 根据波动率确定各支基金在组合中的权重\n",
+    "df_inverse_volatility = 1.0/df_volatility\n",
+    "df_inverse_volatility = df_inverse_volatility.replace(np.inf, 0.0)\n",
+    "df_weight = df_inverse_volatility.divide(df_inverse_volatility.sum(axis = 1), axis = 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 根据权重组合各支基金\n",
+    "df_rp = pd.DataFrame(df_daily_returns.loc[df_weight.index].mul(df_weight).sum(axis = 1))\n",
+    "df_rp.columns = ['daily_rp_ret']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Annual Return is: 0.0242\n",
+      "Annual Volatility is: 0.0194\n",
+      "Sharpe Ratio is: 1.2487\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Annual Return is: %.4f' % (df_rp.mean() * 242))\n",
+    "print('Annual Volatility is: %.4f' % (df_rp.std() * np.sqrt(242)))\n",
+    "print('Sharpe Ratio is: %.4f' % (df_rp.mean()/df_rp.std() * np.sqrt(242)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class MyFormatter(Formatter):\n",
+    "    def __init__(self, dates, fmt='%Y%m'):\n",
+    "        self.dates = dates\n",
+    "        self.fmt = fmt\n",
+    "\n",
+    "    def __call__(self, x, pos=0):\n",
+    "        \"\"\"Return the label for time x at position pos\"\"\"\n",
+    "        ind = int(np.round(x))\n",
+    "        if ind >= len(self.dates) or ind < 0:\n",
+    "            return ''\n",
+    "\n",
+    "        # return self.dates[ind].strftime(self.fmt)\n",
+    "        return pd.to_datetime(self.dates[ind], format=\"%Y%m%d\").strftime(self.fmt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_ret_compare = pd.concat([df_ew, df_rp], axis = 1)\n",
+    "df_ret_compare = df_ret_compare.dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_cum_ret_compare = (df_ret_compare + 1).cumprod()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x125324a8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax1 = plt.subplots(1, 1, figsize=(21, 10))\n",
+    "\n",
+    "idx0 = df_cum_ret_compare.index\n",
+    "idx = range(len(idx0))\n",
+    "\n",
+    "# ax1.plot(idx, df_cum_ret_compare.daily_issue_ret, lw=1.5, color='indianred', label='Issue value weight')\n",
+    "ax1.plot(idx, df_cum_ret_compare.daily_ew_ret, lw=1.5, color='royalblue', label='Equal weight')\n",
+    "ax1.plot(idx, df_cum_ret_compare.daily_rp_ret, lw=1.5, color='purple', label='Reverse volatility weight')\n",
+    "\n",
+    "# ax1.axhline(0.0, color='k', lw=0.5, ls='--')\n",
+    "\n",
+    "ax1.legend(loc='upper left')\n",
+    "ax1.set_xlabel(\"Date\")\n",
+    "ax1.set_ylabel(\"Return\")\n",
+    "ax1.xaxis.set_major_formatter(MyFormatter(idx0, '%Y%m%d'))\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "# fig.savefig('lgt_brinson_cum.png')\n",
+    "# plt.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 注解：\n",
+    "## 1. 债券基金在2017年表现较差，而股票基金表现更好\n",
+    "## 2. 第二种根据波动率倒数的加权方式更倾向于债券基金，所以在样本内表现不如等权重组合"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/example/WallStreetLectures/ipython/lecture22_BacktestExample.ipynb b/example/WallStreetLectures/ipython/lecture22_BacktestExample.ipynb
new file mode 100644
index 0000000..2599e7d
--- /dev/null
+++ b/example/WallStreetLectures/ipython/lecture22_BacktestExample.ipynb
@@ -0,0 +1,891 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Backtest Example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. 环境设置"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# -*- encoding: utf-8 -*-\n",
+    "import time\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import statsmodels.api as sm\n",
+    "from jaqs.data import DataApi\n",
+    "\n",
+    "from jaqs.data import RemoteDataService\n",
+    "from jaqs.trade import AlphaBacktestInstance\n",
+    "from jaqs.trade import PortfolioManager\n",
+    "import matplotlib.pyplot as plt\n",
+    "#from jaqs.trade import RealTimeTradeApi\n",
+    "%matplotlib inline\n",
+    "import jaqs.util as jutil\n",
+    "import jaqs.trade.analyze as ana\n",
+    "from jaqs.trade import AlphaStrategy\n",
+    "from jaqs.trade import AlphaTradeApi\n",
+    "from jaqs.trade import model\n",
+    "from jaqs.data import DataView"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Begin: DataApi login 17321165656@tcp://data.quantos.org:8910\n",
+      "    login success \n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'0,'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 设置文件存储路径\n",
+    "dataview_dir_path = '../output/backtest/dataview'\n",
+    "backtest_result_dir_path = 'backtest'\n",
+    "backtest_result_folder = '../output/backtest'\n",
+    "# 设置服务器地址、用户名密码\n",
+    "# 例如：\n",
+    "# data_config = {\n",
+    "#   \"remote.data.address\": \"tcp://data.quantos.org:8910\",\n",
+    "#   \"remote.data.username\":  '18688888888',\n",
+    "#   \"remote.data.password\":  '23sdjfk209d0fs9dejkl2j3k4j9d0fsdf'}\n",
+    "\n",
+    "# 如果没有使用quantos金融终端，请自行替换phone,token内容\n",
+    "import os\n",
+    "phone = os.environ.get(\"QUANTOS_USER\")\n",
+    "token = os.environ.get(\"QUANTOS_TOKEN\")\n",
+    "\n",
+    "data_config = {\n",
+    "  \"remote.data.address\": \"tcp://data.quantos.org:8910\",\n",
+    "  \"remote.data.username\":  phone,\n",
+    "  \"remote.data.password\":  token}\n",
+    "trade_config = {\n",
+    "  \"remote.trade.address\": \"tcp://gw.quantos.org:8901\",\n",
+    "  \"remote.trade.username\":  phone,\n",
+    "  \"remote.trade.password\":  token}\n",
+    "# RemoteDataService communicates with a remote server to fetch data\n",
+    "ds = RemoteDataService()\n",
+    "# Use username and password in data_config to login\n",
+    "ds.init_from_config(data_config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 设置参数"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "UNIVERSE = '000300.SH'\n",
+    "START_DATE = 20151207\n",
+    "END_DATE = 20180531"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 准备DataView"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initialize config success.\n",
+      "Query data...\n",
+      "Query data - query...\n",
+      "NOTE: price adjust method is [post adjust]\n",
+      "238\n",
+      "477\n",
+      "238\n",
+      "477\n",
+      "46\n",
+      "93\n",
+      "140\n",
+      "187\n",
+      "234\n",
+      "281\n",
+      "328\n",
+      "375\n",
+      "422\n",
+      "469\n",
+      "516\n",
+      "563\n",
+      "610\n",
+      "WARNING: some data is unavailable: \n",
+      "    At fields \n",
+      "Query data - daily fields prepared.\n",
+      "Query instrument info...\n",
+      "Query adj_factor...\n",
+      "Query benchmark...\n",
+      "Query benchmar member info...\n",
+      "Query groups (industry)...\n",
+      "Data has been successfully prepared.\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataview_props = {# Start and end date of back-test\n",
+    "                  'start_date': START_DATE, 'end_date': END_DATE,\n",
+    "                  # Investment universe and performance benchmark\n",
+    "                  'universe': UNIVERSE, 'benchmark': UNIVERSE,\n",
+    "                  # Data fields that we need\n",
+    "                  'fields': 'pe_ttm,total_mv,sw1',\n",
+    "                  # freq = 1 means we use daily data. Please do not change this.\n",
+    "                  'freq': 1}\n",
+    "\n",
+    "# DataView utilizes RemoteDataService to get various data and store them\n",
+    "dv = DataView()\n",
+    "dv.init_from_config(dataview_props, ds)\n",
+    "dv.prepare_data()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "industry = dv.get_ts('sw1')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "matching = {\n",
+    "    '110000':'NLMY',\n",
+    "    '210000':'Digging',\n",
+    "    '220000':'Chemistry',\n",
+    "    '230000':'Metal',\n",
+    "    '240000':'Nonferrous Metal',\n",
+    "    '270000':'Electronic&IT',\n",
+    "    '280000':'Car',\n",
+    "    '330000':'Appliance',\n",
+    "    '340000':'Food',\n",
+    "    '350000':'Clothing',\n",
+    "    '360000':'Light Industrials',\n",
+    "    '370000':'Health Care',\n",
+    "    '410000':'Utility',\n",
+    "    '420000':'Transportation',\n",
+    "    '430000':'Housing',\n",
+    "    '450000':'Commercial',\n",
+    "    '460000':'Service',\n",
+    "    '480000':'Bank',\n",
+    "    '490000':'Non bank',\n",
+    "    '510000':'Others',\n",
+    "    '610000':'Construction Material',\n",
+    "    '620000':'Construction decoration',\n",
+    "    '630000':'Electronic equipment',\n",
+    "    '640000':'Mechenical equipment',\n",
+    "    '650000':'Army',\n",
+    "    '710000':'Electronic&IT',\n",
+    "    '720000':'Media',\n",
+    "    '730000':'Telecom',\n",
+    "    'nan': 'Unclassified'\n",
+    "     }\n",
+    "\n",
+    "for key, value in matching.items():\n",
+    "    industry = industry.replace(key, value)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def group_sum(df, group_daily):\n",
+    "    groups = np.unique(group_daily.values.flatten())\n",
+    "    mask = groups == 'nan'\n",
+    "    groups = groups[np.logical_not(mask)]\n",
+    "    res = pd.DataFrame(index=df.index, columns=groups, data=np.nan)\n",
+    "    for g in groups:\n",
+    "        mask = group_daily == g\n",
+    "        tmp = df[mask]\n",
+    "        res.loc[:, g] = tmp.sum(axis=1)\n",
+    "    return res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "total_mv = dv.get_ts('total_mv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "total_mv_industry = group_sum(total_mv, industry)\n",
+    "weight_industry = total_mv_industry.div(total_mv_industry.sum(axis = 1), axis = 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 给定某一日期打印指数成份股总市值的行业分布"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Bank                       0.256074\n",
+      "Non bank                   0.127831\n",
+      "Digging                    0.074983\n",
+      "Electronic&IT              0.043884\n",
+      "Utility                    0.043800\n",
+      "Housing                    0.043121\n",
+      "Construction decoration    0.042441\n",
+      "Transportation             0.040842\n",
+      "Media                      0.037435\n",
+      "Health Care                0.035701\n",
+      "Car                        0.035267\n",
+      "Chemistry                  0.034411\n",
+      "Food                       0.029313\n",
+      "Mechenical equipment       0.023761\n",
+      "Nonferrous Metal           0.023391\n",
+      "Army                       0.019155\n",
+      "Electronic equipment       0.015127\n",
+      "Appliance                  0.013135\n",
+      "Metal                      0.012484\n",
+      "Telecom                    0.012411\n",
+      "Commercial                 0.011461\n",
+      "NLMY                       0.006176\n",
+      "Clothing                   0.005745\n",
+      "Service                    0.004544\n",
+      "Construction Material      0.004372\n",
+      "Others                     0.002600\n",
+      "Light Industrials          0.000535\n",
+      "Name: 20151207, dtype: float64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1bc94ef0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "TARGET_DATE = 20151207\n",
+    "plt.figure(figsize = (16,8))\n",
+    "weight_industry.loc[TARGET_DATE].sort_values(ascending = False).plot.bar()\n",
+    "print(weight_industry.loc[TARGET_DATE].sort_values(ascending = False))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 回测"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 中性化函数"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cs_neutralize(input_array, size_array):\n",
+    "    \n",
+    "    var_Y = input_array.copy()\n",
+    "    var_size = size_array.copy()\n",
+    "    \n",
+    "    var_X = var_size\n",
+    "    var_X.columns = ['size']\n",
+    "    var_X_const = sm.add_constant(var_X)\n",
+    "\n",
+    "    regressPanel = pd.concat([var_Y, var_X_const], axis = 1)\n",
+    "    regressPanel = regressPanel.dropna()\n",
+    "    var_Y = regressPanel.iloc[:, 0]\n",
+    "    var_X = regressPanel.iloc[:, 1:3]    \n",
+    "\n",
+    "    # OLS regression\n",
+    "    regressmodel = sm.OLS(var_Y, var_X)\n",
+    "    results = regressmodel.fit()\n",
+    "    return results.resid    \n",
+    "\n",
+    "def group_demean_regress(dv, signal, group, new_name, is_quarterly = False):\n",
+    "    \n",
+    "    df_goal   = dv.get_ts(signal)\n",
+    "    df_output = df_goal.copy()\n",
+    "    \n",
+    "    industry = dv.get_ts(group)\n",
+    "    log_tot_mv = np.log(dv.get_ts('total_mv'))\n",
+    "\n",
+    "    for i in range(len(df_output)):\n",
+    "        df_output.iloc[i, :] = cs_neutralize(df_goal.iloc[i, :], log_tot_mv.iloc[i, :])\n",
+    "\n",
+    "    # insert into dv\n",
+    "    dv.remove_field(new_name)\n",
+    "    dv.append_df(df_output, new_name, is_quarterly = is_quarterly)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Field name [pe_ttm_size] does not exist. Stop remove_field.\n"
+     ]
+    }
+   ],
+   "source": [
+    "group_demean_regress(dv, 'pe_ttm', 'sw1', 'pe_ttm_size', is_quarterly = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Field [pct_pe] is overwritten.\n",
+      "Field [gpct_pe] is overwritten.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 在全指数中排序\n",
+    "dv.add_formula('pct_pe', 'Percentile(pe_ttm)', is_quarterly = False)\n",
+    "# 在行业内排序（行业中性化）\n",
+    "dv.add_formula('gpct_pe', 'GroupPercentile(pe_ttm, sw1)', is_quarterly = False)\n",
+    "# 将pe市值中性化后在全指数中排序（市值中性化）\n",
+    "dv.add_formula('pct_pe_norm', 'Percentile(pe_ttm_size)', is_quarterly = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 策略1： 选择指数成份股中pe最低10%的股票"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def select_func1(context, user_options=None):\n",
+    "    snap = context.snapshot\n",
+    "    score = snap[['pe_ttm', 'pct_pe', 'gpct_pe']]\n",
+    "\n",
+    "    score = score[score['pe_ttm'] >= 5]\n",
+    "    score = score[score['pct_pe'] <= 0.1]\n",
+    "    res = pd.merge(left=snap[['pe_ttm']], right=score[['pct_pe']], how='left', left_index=True, right_index=True)\n",
+    "    res['pct_pe'] = ~res['pct_pe'].isnull()\n",
+    "    res = res[['pct_pe']]\n",
+    "\n",
+    "#     show_selected(res)\n",
+    "\n",
+    "    context.storage['res'] = res\n",
+    "#     print(res)\n",
+    "    \n",
+    "\n",
+    "    return res"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 策略2： 选择行业中pe最低10%的股票（行业中性化）"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def select_func2(context, user_options=None):\n",
+    "    snap = context.snapshot\n",
+    "    score = snap[['pe_ttm', 'pct_pe', 'gpct_pe']]\n",
+    "\n",
+    "    score = score[score['pe_ttm'] >= 5]\n",
+    "    score = score[score['gpct_pe'] <= 0.1]\n",
+    "    res = pd.merge(left=snap[['pe_ttm']], right=score[['pct_pe']], how='left', left_index=True, right_index=True)\n",
+    "    res['pct_pe'] = ~res['pct_pe'].isnull()\n",
+    "    res = res[['pct_pe']]\n",
+    "\n",
+    "#     show_selected(res)\n",
+    "\n",
+    "    context.storage['res'] = res\n",
+    "#     print(res)\n",
+    "    \n",
+    "\n",
+    "    return res"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 策略3： 将pe进行市值中性化处理后选最低10%的股票"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def select_func3(context, user_options=None):\n",
+    "    snap = context.snapshot\n",
+    "    score = snap[['pe_ttm', 'pct_pe', 'gpct_pe', 'pct_pe_norm']]\n",
+    "\n",
+    "    score = score[score['pe_ttm'] >= 5]\n",
+    "    score = score[score['pct_pe_norm'] <= 0.1]\n",
+    "    res = pd.merge(left=snap[['pe_ttm']], right=score[['pct_pe']], how='left', left_index=True, right_index=True)\n",
+    "    res['pct_pe'] = ~res['pct_pe'].isnull()\n",
+    "    res = res[['pct_pe']]\n",
+    "\n",
+    "#     show_selected(res)\n",
+    "\n",
+    "    context.storage['res'] = res\n",
+    "#     print(res)\n",
+    "    \n",
+    "\n",
+    "    return res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AlphaStrategy Initialized.\n",
+      "Run alpha backtest from 20151207 to 20180531\n",
+      "=======new day 20151208\n",
+      "Before 20151208 re-balance: available cash all = 1.0000e+08\n",
+      "=======new day 20151215\n",
+      "Before 20151215 re-balance: available cash all = 1.0239e+08\n",
+      "=======new day 20151222\n",
+      "Before 20151222 re-balance: available cash all = 1.0670e+08\n",
+      "=======new day 20151229\n",
+      "Before 20151229 re-balance: available cash all = 1.0365e+08\n",
+      "=======new day 20160106\n",
+      "Before 20160106 re-balance: available cash all = 9.6038e+07\n",
+      "=======new day 20160113\n",
+      "Before 20160113 re-balance: available cash all = 8.8431e+07\n",
+      "=======new day 20160120\n",
+      "Before 20160120 re-balance: available cash all = 8.7166e+07\n",
+      "=======new day 20160127\n",
+      "Before 20160127 re-balance: available cash all = 8.2564e+07\n",
+      "=======new day 20160203\n",
+      "Before 20160203 re-balance: available cash all = 8.2241e+07\n",
+      "=======new day 20160217\n",
+      "Before 20160217 re-balance: available cash all = 8.4540e+07\n",
+      "=======new day 20160224\n",
+      "Before 20160224 re-balance: available cash all = 8.5355e+07\n",
+      "=======new day 20160302\n",
+      "Before 20160302 re-balance: available cash all = 8.5769e+07\n",
+      "=======new day 20160309\n",
+      "Before 20160309 re-balance: available cash all = 8.4955e+07\n",
+      "=======new day 20160316\n",
+      "Before 20160316 re-balance: available cash all = 8.5305e+07\n",
+      "=======new day 20160323\n",
+      "Before 20160323 re-balance: available cash all = 9.2955e+07\n",
+      "=======new day 20160330\n",
+      "Before 20160330 re-balance: available cash all = 9.2967e+07\n",
+      "=======new day 20160407\n",
+      "Before 20160407 re-balance: available cash all = 9.3675e+07\n",
+      "=======new day 20160414\n",
+      "Before 20160414 re-balance: available cash all = 9.5142e+07\n",
+      "=======new day 20160421\n",
+      "Before 20160421 re-balance: available cash all = 9.0369e+07\n",
+      "=======new day 20160428\n",
+      "Before 20160428 re-balance: available cash all = 9.0021e+07\n",
+      "=======new day 20160506\n",
+      "Before 20160506 re-balance: available cash all = 9.0742e+07\n",
+      "=======new day 20160513\n",
+      "Before 20160513 re-balance: available cash all = 8.8586e+07\n",
+      "=======new day 20160520\n",
+      "Before 20160520 re-balance: available cash all = 8.7366e+07\n",
+      "=======new day 20160527\n",
+      "Before 20160527 re-balance: available cash all = 8.6915e+07\n",
+      "=======new day 20160603\n",
+      "Before 20160603 re-balance: available cash all = 9.1790e+07\n",
+      "=======new day 20160614\n",
+      "Before 20160614 re-balance: available cash all = 8.8495e+07\n",
+      "=======new day 20160621\n",
+      "Before 20160621 re-balance: available cash all = 9.0076e+07\n",
+      "=======new day 20160628\n",
+      "Before 20160628 re-balance: available cash all = 9.1911e+07\n",
+      "=======new day 20160705\n",
+      "Before 20160705 re-balance: available cash all = 9.5223e+07\n",
+      "=======new day 20160712\n",
+      "Before 20160712 re-balance: available cash all = 9.7352e+07\n",
+      "=======new day 20160719\n",
+      "Before 20160719 re-balance: available cash all = 9.7016e+07\n",
+      "=======new day 20160726\n",
+      "Before 20160726 re-balance: available cash all = 9.7379e+07\n",
+      "=======new day 20160802\n",
+      "Before 20160802 re-balance: available cash all = 9.7153e+07\n",
+      "=======new day 20160809\n",
+      "Before 20160809 re-balance: available cash all = 1.0145e+08\n",
+      "=======new day 20160816\n",
+      "Before 20160816 re-balance: available cash all = 1.0667e+08\n",
+      "=======new day 20160823\n",
+      "Before 20160823 re-balance: available cash all = 1.0487e+08\n",
+      "=======new day 20160830\n",
+      "Before 20160830 re-balance: available cash all = 1.0436e+08\n",
+      "=======new day 20160906\n",
+      "Before 20160906 re-balance: available cash all = 1.0548e+08\n",
+      "=======new day 20160913\n",
+      "Before 20160913 re-balance: available cash all = 1.0279e+08\n",
+      "=======new day 20160922\n",
+      "Before 20160922 re-balance: available cash all = 1.0324e+08\n",
+      "=======new day 20160929\n",
+      "Before 20160929 re-balance: available cash all = 1.0081e+08\n",
+      "=======new day 20161013\n",
+      "Before 20161013 re-balance: available cash all = 1.0391e+08\n",
+      "=======new day 20161020\n",
+      "Before 20161020 re-balance: available cash all = 1.0487e+08\n",
+      "=======new day 20161027\n",
+      "Before 20161027 re-balance: available cash all = 1.0508e+08\n",
+      "=======new day 20161103\n",
+      "Before 20161103 re-balance: available cash all = 1.0529e+08\n",
+      "=======new day 20161110\n",
+      "Before 20161110 re-balance: available cash all = 1.0633e+08\n",
+      "=======new day 20161117\n",
+      "Before 20161117 re-balance: available cash all = 1.0817e+08\n",
+      "=======new day 20161124\n",
+      "Before 20161124 re-balance: available cash all = 1.1168e+08\n",
+      "=======new day 20161201\n",
+      "Before 20161201 re-balance: available cash all = 1.1539e+08\n",
+      "=======new day 20161208\n",
+      "Before 20161208 re-balance: available cash all = 1.1343e+08\n",
+      "=======new day 20161215\n",
+      "Before 20161215 re-balance: available cash all = 1.1291e+08\n",
+      "=======new day 20161222\n",
+      "Before 20161222 re-balance: available cash all = 1.1366e+08\n",
+      "=======new day 20161229\n",
+      "Before 20161229 re-balance: available cash all = 1.1250e+08\n",
+      "=======new day 20170106\n",
+      "Before 20170106 re-balance: available cash all = 1.1243e+08\n",
+      "=======new day 20170113\n",
+      "Before 20170113 re-balance: available cash all = 1.1023e+08\n",
+      "=======new day 20170120\n",
+      "Before 20170120 re-balance: available cash all = 1.1240e+08\n",
+      "=======new day 20170203\n",
+      "Before 20170203 re-balance: available cash all = 1.1305e+08\n",
+      "=======new day 20170210\n",
+      "Before 20170210 re-balance: available cash all = 1.1392e+08\n",
+      "=======new day 20170217\n",
+      "Before 20170217 re-balance: available cash all = 1.1425e+08\n",
+      "=======new day 20170224\n",
+      "Before 20170224 re-balance: available cash all = 1.1564e+08\n",
+      "=======new day 20170303\n",
+      "Before 20170303 re-balance: available cash all = 1.1539e+08\n",
+      "=======new day 20170310\n",
+      "Before 20170310 re-balance: available cash all = 1.1555e+08\n",
+      "=======new day 20170317\n",
+      "Before 20170317 re-balance: available cash all = 1.1730e+08\n",
+      "=======new day 20170324\n",
+      "Before 20170324 re-balance: available cash all = 1.1962e+08\n",
+      "=======new day 20170331\n",
+      "Before 20170331 re-balance: available cash all = 1.1661e+08\n",
+      "=======new day 20170411\n",
+      "Before 20170411 re-balance: available cash all = 1.2367e+08\n",
+      "=======new day 20170418\n",
+      "Before 20170418 re-balance: available cash all = 1.2074e+08\n",
+      "=======new day 20170425\n",
+      "Before 20170425 re-balance: available cash all = 1.1873e+08\n",
+      "=======new day 20170503\n",
+      "Before 20170503 re-balance: available cash all = 1.1688e+08\n",
+      "=======new day 20170510\n",
+      "Before 20170510 re-balance: available cash all = 1.1269e+08\n",
+      "=======new day 20170517\n",
+      "Before 20170517 re-balance: available cash all = 1.1465e+08\n",
+      "=======new day 20170524\n",
+      "Before 20170524 re-balance: available cash all = 1.1172e+08\n",
+      "=======new day 20170602\n",
+      "Before 20170602 re-balance: available cash all = 1.1254e+08\n",
+      "=======new day 20170609\n",
+      "Before 20170609 re-balance: available cash all = 1.1588e+08\n",
+      "=======new day 20170616\n",
+      "Before 20170616 re-balance: available cash all = 1.1573e+08\n",
+      "=======new day 20170623\n",
+      "Before 20170623 re-balance: available cash all = 1.1743e+08\n",
+      "=======new day 20170630\n",
+      "Before 20170630 re-balance: available cash all = 1.1914e+08\n",
+      "=======new day 20170707\n",
+      "Before 20170707 re-balance: available cash all = 1.1932e+08\n",
+      "=======new day 20170714\n",
+      "Before 20170714 re-balance: available cash all = 1.1891e+08\n",
+      "=======new day 20170721\n",
+      "Before 20170721 re-balance: available cash all = 1.2178e+08\n",
+      "=======new day 20170728\n",
+      "Before 20170728 re-balance: available cash all = 1.2316e+08\n",
+      "=======new day 20170804\n",
+      "Before 20170804 re-balance: available cash all = 1.2197e+08\n",
+      "=======new day 20170811\n",
+      "Before 20170811 re-balance: available cash all = 1.1947e+08\n",
+      "=======new day 20170818\n",
+      "Before 20170818 re-balance: available cash all = 1.2338e+08\n",
+      "=======new day 20170825\n",
+      "Before 20170825 re-balance: available cash all = 1.2399e+08\n",
+      "=======new day 20170901\n",
+      "Before 20170901 re-balance: available cash all = 1.2509e+08\n",
+      "=======new day 20170908\n",
+      "Before 20170908 re-balance: available cash all = 1.2521e+08\n",
+      "=======new day 20170915\n",
+      "Before 20170915 re-balance: available cash all = 1.2594e+08\n",
+      "=======new day 20170922\n",
+      "Before 20170922 re-balance: available cash all = 1.2425e+08\n",
+      "=======new day 20170929\n",
+      "Before 20170929 re-balance: available cash all = 1.2235e+08\n",
+      "=======new day 20171013\n",
+      "Before 20171013 re-balance: available cash all = 1.2431e+08\n",
+      "=======new day 20171020\n",
+      "Before 20171020 re-balance: available cash all = 1.2275e+08\n",
+      "=======new day 20171027\n",
+      "Before 20171027 re-balance: available cash all = 1.2385e+08\n",
+      "=======new day 20171103\n",
+      "Before 20171103 re-balance: available cash all = 1.2143e+08\n",
+      "=======new day 20171110\n",
+      "Before 20171110 re-balance: available cash all = 1.2246e+08\n",
+      "=======new day 20171117\n",
+      "Before 20171117 re-balance: available cash all = 1.2342e+08\n",
+      "=======new day 20171124\n",
+      "Before 20171124 re-balance: available cash all = 1.2460e+08\n",
+      "=======new day 20171201\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Before 20171201 re-balance: available cash all = 1.2416e+08\n",
+      "=======new day 20171208\n",
+      "Before 20171208 re-balance: available cash all = 1.2290e+08\n",
+      "=======new day 20171215\n",
+      "Before 20171215 re-balance: available cash all = 1.2034e+08\n",
+      "=======new day 20171222\n",
+      "Before 20171222 re-balance: available cash all = 1.1854e+08\n",
+      "=======new day 20171229\n",
+      "Before 20171229 re-balance: available cash all = 1.2099e+08\n",
+      "=======new day 20180108\n",
+      "Before 20180108 re-balance: available cash all = 1.2875e+08\n",
+      "=======new day 20180115\n",
+      "Before 20180115 re-balance: available cash all = 1.2708e+08\n",
+      "=======new day 20180122\n",
+      "Before 20180122 re-balance: available cash all = 1.3377e+08\n",
+      "=======new day 20180129\n",
+      "Before 20180129 re-balance: available cash all = 1.3566e+08\n",
+      "=======new day 20180205\n",
+      "Before 20180205 re-balance: available cash all = 1.3395e+08\n",
+      "=======new day 20180212\n",
+      "Before 20180212 re-balance: available cash all = 1.2040e+08\n",
+      "=======new day 20180226\n",
+      "Before 20180226 re-balance: available cash all = 1.2535e+08\n",
+      "=======new day 20180305\n",
+      "Before 20180305 re-balance: available cash all = 1.2299e+08\n",
+      "=======new day 20180312\n",
+      "Before 20180312 re-balance: available cash all = 1.2547e+08\n",
+      "=======new day 20180319\n",
+      "Before 20180319 re-balance: available cash all = 1.2232e+08\n",
+      "=======new day 20180326\n",
+      "Before 20180326 re-balance: available cash all = 1.1630e+08\n",
+      "=======new day 20180402\n",
+      "Before 20180402 re-balance: available cash all = 1.1848e+08\n",
+      "=======new day 20180411\n",
+      "Before 20180411 re-balance: available cash all = 1.1859e+08\n",
+      "=======new day 20180418\n",
+      "Before 20180418 re-balance: available cash all = 1.1550e+08\n",
+      "=======new day 20180425\n",
+      "Before 20180425 re-balance: available cash all = 1.1512e+08\n",
+      "=======new day 20180504\n",
+      "Before 20180504 re-balance: available cash all = 1.1366e+08\n",
+      "=======new day 20180511\n",
+      "Before 20180511 re-balance: available cash all = 1.1474e+08\n",
+      "=======new day 20180518\n",
+      "Before 20180518 re-balance: available cash all = 1.1647e+08\n",
+      "=======new day 20180525\n",
+      "Before 20180525 re-balance: available cash all = 1.1307e+08\n",
+      "Backtest done. 646 days, 2.66e+03 trades in total. used time: 18.317663s\n",
+      "Backtest results has been successfully saved to:\n",
+      "C:\\Users\\lli\\AppData\\Roaming\\QuantosFinanceTerminal\\workspace\\__lectures\\output\\backtest\n",
+      "process trades...\n",
+      "get daily stats...\n",
+      "calc strategy return...\n",
+      "calc re-balance position\n",
+      "Get stats\n",
+      "Plot strategy PnL...\n",
+      "generate report...\n",
+      "HTML report: C:\\Users\\lli\\AppData\\Roaming\\QuantosFinanceTerminal\\workspace\\__lectures\\output\\backtest\\report.html\n"
+     ]
+    }
+   ],
+   "source": [
+    "backtest_props = {# start and end date of back-test\n",
+    "                  \"start_date\": START_DATE,\n",
+    "                  \"end_date\": END_DATE,\n",
+    "                  # re-balance period length\n",
+    "                  \"period\": \"day\",\n",
+    "                  \"n_periods\": 5,\n",
+    "                  # benchmark and universe\n",
+    "                  \"benchmark\": UNIVERSE,\n",
+    "                  \"universe\": UNIVERSE,\n",
+    "                  # Amount of money at the start of back-test\n",
+    "                  \"init_balance\": 1e8,\n",
+    "                  # Amount of money at the start of back-test\n",
+    "                  \"position_ratio\": 1.0,\n",
+    "                  \"commission_rate\": 15e-4,\n",
+    "                  }\n",
+    "backtest_props.update(data_config)\n",
+    "\n",
+    "my_selector = model.StockSelector()\n",
+    "my_selector.add_filter('selector', select_func3)\n",
+    "\n",
+    "# We use trade_api to send orders\n",
+    "trade_api = AlphaTradeApi()\n",
+    "# This is our strategy\n",
+    "strategy = AlphaStrategy(stock_selector=my_selector,\n",
+    "                         pc_method='index_weight')\n",
+    "# PortfolioManager helps us to manage tasks, orders and calculate positions\n",
+    "pm = PortfolioManager()\n",
+    "# BacktestInstance is in charge of running the back-test\n",
+    "bt = AlphaBacktestInstance()\n",
+    "\n",
+    "# Public variables are stored in context. We can also store anything in it\n",
+    "context = model.Context(dataview=dv, instance=bt, strategy=strategy, trade_api=trade_api, pm=pm)\n",
+    "my_selector.register_context(context)\n",
+    "#signal_model.register_context(context)\n",
+    "\n",
+    "bt.init_from_config(backtest_props)\n",
+    "bt.run_alpha()\n",
+    "\n",
+    "# After finishing back-test, we save trade results into a folder\n",
+    "bt.save_results(folder_path=backtest_result_folder)\n",
+    "\n",
+    "# Analyzer help us calculate various trade statistics according to trade results.\n",
+    "# All the calculation results will be stored as its members.\n",
+    "ta = ana.AlphaAnalyzer()\n",
+    "\n",
+    "ta.initialize(dataview=dv,\n",
+    "              file_folder=backtest_result_folder)\n",
+    "\n",
+    "ta.do_analyze(result_dir=backtest_result_folder,\n",
+    "              selected_sec=[])\n",
+    "\n",
+    "### 注：看报告的两种方法：（1）在本地运行，则直接打开html文件即可。 （2）在服务器上： 文件在：notebook/output/dataview2017之下，点开后，将路径中的”edit\"替换为“files\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/example/WallStreetLectures/ipython/lecture2_DemoStrategy.ipynb b/example/WallStreetLectures/ipython/lecture2_DemoStrategy.ipynb
index 8c0f867..681b58d 100644
--- a/example/WallStreetLectures/ipython/lecture2_DemoStrategy.ipynb
+++ b/example/WallStreetLectures/ipython/lecture2_DemoStrategy.ipynb
@@ -56,7 +56,7 @@
     "# 设置服务器地址、用户名密码\n",
     "# 例如：\n",
     "# data_config = {\n",
-    "#   \"remote.data.address\": \"tcp://data.tushare.org:8910\",\n",
+    "#   \"remote.data.address\": \"tcp://data.quantos.org:8910\",\n",
     "#   \"remote.data.username\":  '18688888888',\n",
     "#   \"remote.data.password\":  '23sdjfk209d0fs9dejkl2j3k4j9d0fsdf'}\n",
     "\n",
diff --git a/example/WallStreetLectures/ipython/lecture8_volatility_calculation.ipynb b/example/WallStreetLectures/ipython/lecture8_volatility_calculation.ipynb
index eb5e5a3..9e2ab89 100644
--- a/example/WallStreetLectures/ipython/lecture8_volatility_calculation.ipynb
+++ b/example/WallStreetLectures/ipython/lecture8_volatility_calculation.ipynb
@@ -49,7 +49,7 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "Begin: DataApi login 18612562791@tcp://data.tushare.org:8910\n",
+      "Begin: DataApi login 18612562791@tcp://data.quantos.org:8910\n",
       "    login success \n",
       "\n"
      ]
@@ -73,7 +73,7 @@
     "\n",
     "data_config = {\n",
     "  \"timeout\": 1800,\n",
-    "  \"remote.data.address\": \"tcp://data.tushare.org:8910\",\n",
+    "  \"remote.data.address\": \"tcp://data.quantos.org:8910\",\n",
     "  \"remote.data.username\":  phone,\n",
     "  \"remote.data.password\":  token}\n",
     "\n",
diff --git a/example/WallStreetLectures/ipython/lecture9_position_management.ipynb b/example/WallStreetLectures/ipython/lecture9_position_management.ipynb
index 060cecc..46e182a 100644
--- a/example/WallStreetLectures/ipython/lecture9_position_management.ipynb
+++ b/example/WallStreetLectures/ipython/lecture9_position_management.ipynb
@@ -52,7 +52,7 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "Begin: DataApi login 18612562791@tcp://data.tushare.org:8910\n",
+      "Begin: DataApi login 18612562791@tcp://data.quantos.org:8910\n",
       "    Already login as 18612562791, skip init_from_config\n"
      ]
     },
@@ -75,7 +75,7 @@
     "\n",
     "data_config = {\n",
     "  \"timeout\": 1800,\n",
-    "  \"remote.data.address\": \"tcp://data.tushare.org:8910\",\n",
+    "  \"remote.data.address\": \"tcp://data.quantos.org:8910\",\n",
     "  \"remote.data.username\":  phone,\n",
     "  \"remote.data.password\":  token}\n",
     "\n",
diff --git a/example/WallStreetLectures/ipython/tk_technical_analysis.ipynb b/example/WallStreetLectures/ipython/tk_technical_analysis.ipynb
index 338ec8c..af5c482 100644
--- a/example/WallStreetLectures/ipython/tk_technical_analysis.ipynb
+++ b/example/WallStreetLectures/ipython/tk_technical_analysis.ipynb
@@ -73,7 +73,7 @@
     "\n",
     "data_config = {\n",
     "  \"timeout\": 1800,\n",
-    "  \"remote.data.address\": \"tcp://data.tushare.org:8910\",\n",
+    "  \"remote.data.address\": \"tcp://data.quantos.org:8910\",\n",
     "  \"remote.data.username\":  phone,\n",
     "  \"remote.data.password\":  token}\n",
     "\n",
@@ -99,7 +99,7 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "Begin: DataApi login 18612562791@tcp://data.tushare.org:8910\n",
+      "Begin: DataApi login 18612562791@tcp://data.quantos.org:8910\n",
       "    login success \n",
       "\n",
       "Initialize config success.\n",
diff --git a/example/alpha/first_example.py b/example/alpha/first_example.py
index e0473e7..197ac76 100644
--- a/example/alpha/first_example.py
+++ b/example/alpha/first_example.py
@@ -18,7 +18,7 @@
 import jaqs.trade.analyze as ana
 
 data_config = {
-    "remote.data.address": "tcp://data.tushare.org:8910",
+    "remote.data.address": "tcp://data.quantos.org:8910",
     "remote.data.username": "YourTelephoneNumber",
     "remote.data.password": "YourToken"
 }
diff --git a/example/eventdriven/DoubleMA.py b/example/eventdriven/DoubleMA.py
index 812eadb..282ffa0 100644
--- a/example/eventdriven/DoubleMA.py
+++ b/example/eventdriven/DoubleMA.py
@@ -14,7 +14,7 @@
 import jaqs.util as jutil
 
 data_config = {
-  "remote.data.address": "tcp://data.tushare.org:8910",
+  "remote.data.address": "tcp://data.quantos.org:8910",
   "remote.data.username": "YourTelephone",
   "remote.data.password": "YourToken"
 }
diff --git a/example/eventdriven/DualThrust.py b/example/eventdriven/DualThrust.py
index d00ee65..faa1e9c 100644
--- a/example/eventdriven/DualThrust.py
+++ b/example/eventdriven/DualThrust.py
@@ -15,6 +15,7 @@
 import jaqs.util as jutil
 
 from config_path import DATA_CONFIG_PATH, TRADE_CONFIG_PATH
+
 data_config = jutil.read_json(DATA_CONFIG_PATH)
 trade_config = jutil.read_json(TRADE_CONFIG_PATH)
 
@@ -28,6 +29,7 @@ def __init__(self):
         self.quote = None
         self.bufferCount = 0
         self.bufferSize = 0
+        self.pre_td = 0
         self.high_list = []
         self.close_list = []
         self.low_list = []
@@ -38,7 +40,7 @@ def __init__(self):
         self.Upper = 0.0
         self.Lower = 0.0
         self.output = False
-        
+
     def init_from_config(self, props):
         super(DualThrust, self).init_from_config(props)
         self.symbol = props.get('symbol')
@@ -55,32 +57,36 @@ def init_from_config(self, props):
         self.tick_size = props.get('tick_size', 1.0)
 
     def initialize(self):
-        self.bufferCount += 1
-        td = self.ctx.trade_date
-        ds = self.ctx.data_api
-        df, msg = ds.daily(symbol=self.symbol, start_date=td, end_date=td)
-
-        self.open_list[0:self.bufferSize - 1] = self.open_list[1:self.bufferSize]
-        self.open_list[-1] = df.open
-        self.high_list[0:self.bufferSize - 1] = self.high_list[1:self.bufferSize]
-        self.high_list[-1] = df.high
-        self.close_list[0:self.bufferSize - 1] = self.close_list[1:self.bufferSize]
-        self.close_list[-1] = df.close
-        self.low_list[0:self.bufferSize - 1] = self.low_list[1:self.bufferSize]
-        self.low_list[-1] = df.low
-
-        HH = max(self.high_list[:-1])
-        HC = max(self.close_list[:-1])
-        LC = min(self.close_list[:-1])
-        LL = min(self.low_list[:-1])
-
-        Range = max(HH - LC, HC - LL)
-        self.Upper = self.open_list[-1] + self.k1 * Range
-        self.Lower = self.open_list[-1] - self.k2 * Range
-        
+        if self.pre_td != 0:
+            self.bufferCount += 1
+            td = self.ctx.trade_date
+            ds = self.ctx.data_api
+            df, msg = ds.daily(symbol=self.symbol, start_date=self.pre_td, end_date=self.pre_td)
+            df_today, msg_today = ds.daily(symbol=self.symbol, fields='open', start_date=td,
+                                           end_date=td)
+
+            self.open_list[0:self.bufferSize - 1] = self.open_list[1:self.bufferSize]
+            self.open_list[-1] = df.open
+            self.high_list[0:self.bufferSize - 1] = self.high_list[1:self.bufferSize]
+            self.high_list[-1] = df.high
+            self.close_list[0:self.bufferSize - 1] = self.close_list[1:self.bufferSize]
+            self.close_list[-1] = df.close
+            self.low_list[0:self.bufferSize - 1] = self.low_list[1:self.bufferSize]
+            self.low_list[-1] = df.low
+
+            HH = max(self.high_list)
+            HC = max(self.close_list)
+            LC = min(self.close_list)
+            LL = min(self.low_list)
+
+            Range = max(HH - LC, HC - LL)
+            self.Upper = df_today.loc[0, 'open'] + self.k1 * Range
+            self.Lower = df_today.loc[0, 'open'] - self.k2 * Range
+        self.pre_td = self.ctx.trade_date
+
     def on_cycle(self):
         pass
-    
+
     def on_tick(self, quote):
         pass
 
@@ -115,7 +121,7 @@ def on_bar(self, quote):
             pass
             # self.cancel_all_orders()
             # self.liquidate(self.quote, 3, tick_size=self.tick_size, pos=self.pos)
-    
+
     def on_trade(self, ind):
         print("\nStrategy on trade: ")
         print(ind)
@@ -126,7 +132,7 @@ def on_order_status(self, ind):
         if self.output:
             print("\nStrategy on order status: ")
             print(ind)
-        
+
     def on_task_rsp(self, rsp):
         if self.output:
             print("\nStrategy on task rsp: ")
@@ -136,7 +142,7 @@ def on_order_rsp(self, rsp):
         if self.output:
             print("\nStrategy on order rsp: ")
             print(rsp)
-    
+
     def on_task_status(self, ind):
         if self.output:
             print("\nStrategy on task ind: ")
@@ -147,7 +153,7 @@ def on_task_status(self, ind):
     "symbol": "rb1710.SHF",
     "start_date": 20170510,
     "end_date": 20170830,
-    "buffersize": 7,
+    "buffersize": 6,
     "k1": 0.2,
     "k2": 0.2,
     "bar_type": "1M",
@@ -176,12 +182,12 @@ def run_strategy():
 
 def analyze():
     ta = ana.EventAnalyzer()
-    
+
     ds = RemoteDataService()
     ds.init_from_config(data_config)
-    
+
     ta.initialize(data_server_=ds, file_folder=result_dir_path)
-    
+
     ta.do_analyze(result_dir=result_dir_path, selected_sec=props['symbol'].split(','))
 
 
diff --git a/example/research/signal_return_ic_analysis.py b/example/research/signal_return_ic_analysis.py
index 57986bd..bd3e36d 100644
--- a/example/research/signal_return_ic_analysis.py
+++ b/example/research/signal_return_ic_analysis.py
@@ -47,7 +47,7 @@ def analyze_signal(dv, signal_name, output_format='pdf'):
     mask_limit_reached = dv.get_ts('limit_reached')
     mask_all = np.logical_or(mask_sus, np.logical_or(mask_index_member, mask_limit_reached))
 
-    signal = dv.get_ts(signal_name).shift(1, axis=0)  # avoid look-ahead bias
+    signal = dv.get_ts(signal_name)  # avoid look-ahead bias
     price = dv.get_ts('close_adj')
     price_bench = dv.data_benchmark
 
diff --git a/jaqs/__init__.py b/jaqs/__init__.py
index c5af303..fb735d7 100644
--- a/jaqs/__init__.py
+++ b/jaqs/__init__.py
@@ -12,5 +12,5 @@
 import os
 
 
-__version__ = '0.6.12'
+__version__ = '0.6.14'
 SOURCE_ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
diff --git a/jaqs/data/dataapi/README.md b/jaqs/data/dataapi/README.md
index 2a8eca4..eccf9db 100644
--- a/jaqs/data/dataapi/README.md
+++ b/jaqs/data/dataapi/README.md
@@ -29,7 +29,7 @@
 ```python
 from DataApi import DataApi  # 这里假设项目目录名为DataApi, 且存放在工作目录下
 
-api = DataApi(addr="tcp://data.tushare.org:8910")
+api = DataApi(addr="tcp://data.quantos.org:8910")
 result, msg = api.login("phone", "token") # 示例账户，用户需要改为自己在www.quantos.org上注册的账户
 print(result)
 print(msg)
diff --git a/jaqs/data/dataapi/data_api.py b/jaqs/data/dataapi/data_api.py
index f24c5b2..f10c171 100644
--- a/jaqs/data/dataapi/data_api.py
+++ b/jaqs/data/dataapi/data_api.py
@@ -61,7 +61,7 @@ class DataApi(object):
 
     """
     
-    def __init__(self, addr="tcp://data.tushare.org:8910", use_jrpc=False):
+    def __init__(self, addr="tcp://data.quantos.org:8910", use_jrpc=False):
         """Create DataApi client.
         
         If use_jrpc, try to load the C version of JsonRpc. If failed, use pure
@@ -96,7 +96,7 @@ def __init__(self, addr="tcp://data.tushare.org:8910", use_jrpc=False):
         self._schema_map = {}
         self._sub_hash = ""
         self._subscribed_set = set()
-        self._timeout = 20
+        self._timeout = 60
     
     def login(self, username, password):
         
diff --git a/jaqs/data/dataapi/utils.py b/jaqs/data/dataapi/utils.py
index 8cd59b5..ac5475d 100644
--- a/jaqs/data/dataapi/utils.py
+++ b/jaqs/data/dataapi/utils.py
@@ -84,9 +84,8 @@ def to_obj(class_name, data):
 
 def to_date_int(date):
     if isinstance(date, basestring):
-        t = dt.datetime.strptime(date, "%Y-%m-%d")
-        date_int = t.year * 10000 + t.month * 100 + t.day
-        return date_int
+        tmp = date.replace('-','')
+        return int(tmp)
     elif isinstance(date, (int, np.integer)):
         return date
     else:
@@ -95,9 +94,14 @@ def to_date_int(date):
 
 def to_time_int(time):
     if isinstance(time, basestring):
-        t = dt.datetime.strptime(time, "%H:%M:%S")
-        time_int = t.hour * 10000 + t.minute * 100 + t.second
-        return time_int
+        if ':' in time:
+            tmp = time.replace(':','')
+            return int(tmp)
+        else:
+            t = dt.datetime.strptime(time, "%H:%M:%S")
+            time_int = t.hour * 10000 + t.minute * 100 + t.second
+            return time_int
+
     elif isinstance(time, (int, np.integer)):
         return time
     else:
diff --git a/jaqs/data/dataservice.py b/jaqs/data/dataservice.py
index a8c7cad..dcf37bf 100644
--- a/jaqs/data/dataservice.py
+++ b/jaqs/data/dataservice.py
@@ -13,6 +13,7 @@
 from builtins import str
 from abc import abstractmethod
 from six import with_metaclass
+
 try:
     basestring
 except NameError:
@@ -81,6 +82,7 @@ class DataService(object):
     subscribe
 
     """
+
     def __init__(self):
         self.ctx = None
     
@@ -250,6 +252,7 @@ class RemoteDataService(with_metaclass(Singleton, DataService)):
     It wraps DataApi and simplify usage.
 
     """
+
     def __init__(self):
         # print("Init RemoteDataService DEBUG")
         super(RemoteDataService, self).__init__()
@@ -260,6 +263,7 @@ def __init__(self):
         self._username = ""
         self._password = ""
         self._timeout = 60
+        self._trade_dates_df = None
         
         self._REPORT_DATE_FIELD_NAME = 'report_date'
         
@@ -268,6 +272,7 @@ def __del__(self):
         self.data_api.close()
 
     '''
+
     def init_from_config(self, props):
         """
         
@@ -283,6 +288,7 @@ def init_from_config(self, props):
         "remote.data.password": "your password"}
 
         """
+
         def get_from_list_of_dict(l, key, default=None):
             res = None
             for dic in l:
@@ -329,6 +335,12 @@ def get_from_list_of_dict(l, key, default=None):
             self.data_api = data_api
             print(INDENT + "login success \n")
         
+        trade_dates_df, err_msg1 = self.query("jz.secTradeCal", fields="trade_date", filter="", orderby="")
+        if not trade_dates_df.empty:
+            self._trade_dates_df = trade_dates_df
+        else:
+            print("No trade date.\n".format(err_msg1))
+
         return err_msg
         
     @property
@@ -790,7 +802,7 @@ def query_index_member(self, index, start_date, end_date):
 
         """
         df_io, err_msg = self._get_index_comp(index, start_date, end_date)
-        return list(np.unique(df_io.loc[:, 'symbol']))
+        return filter(None, list(np.unique(df_io.loc[:, 'symbol'])))
     
     def query_index_member_daily(self, index, start_date, end_date):
         """
@@ -821,6 +833,7 @@ def str2int(s):
                 return s
             else:
                 raise NotImplementedError("type s = {}".format(type(s)))
+
         df_io.loc[:, 'in_date'] = df_io.loc[:, 'in_date'].apply(str2int)
         df_io.loc[:, 'out_date'] = df_io.loc[:, 'out_date'].apply(str2int)
         
@@ -841,6 +854,166 @@ def str2int(s):
         
         return res
 
+    def _get_universe_comp(self, universe, start_date, end_date):
+        """
+        Return all securities that have been in index during start_date and end_date.
+
+        Parameters
+        ----------
+        universe : str
+            separated by ','
+        start_date : int
+        end_date : int
+
+        Returns
+        -------
+        list
+
+        """
+        # Remove mkt suffix
+
+        filter_argument = self._dic2url({'univ_id'   : universe,
+                                         'start_date': start_date,
+                                         'end_date'  : end_date})
+
+        df_io, err_msg = self.query("jz.univMember", fields="",
+                                    filter=filter_argument, orderby="univ_member")
+        self._raise_error_if_msg(err_msg)
+
+        return df_io, err_msg
+
+    def query_universe_member(self, universe, start_date, end_date):
+        """
+        Return list of symbols that have been in index during start_date and end_date.
+
+        Parameters
+        ----------
+        index : str
+            separated by ','
+        start_date : int
+        end_date : int
+
+        Returns
+        -------
+        list
+
+        """
+        df_io, err_msg = self._get_universe_comp(universe, start_date, end_date)
+        return list(np.unique(df_io.loc[:, 'univ_member']))
+
+    def query_universe_member_daily(self, universe, start_date, end_date):
+        """
+        Get universe's members on each day during start_date and end_date.
+
+        Parameters
+        ----------
+        universe : str
+        start_date : int
+        end_date : int
+
+        Returns
+        -------
+        res : pd.DataFrame
+            index dates, columns all securities that have ever been components,
+            values are 0 (not in) or 1 (in)
+
+        """
+        df_io, err_msg = self._get_universe_comp(universe, start_date, end_date)
+        if err_msg != '0,':
+            print(err_msg)
+
+        def str2int(s):
+            if isinstance(s, basestring):
+                return int(s) if s else 99999999
+            elif isinstance(s, (int, np.integer, float, np.float)):
+                return s
+            else:
+                raise NotImplementedError("type s = {}".format(type(s)))
+
+        # df_io.loc[:, 'in_date'] = df_io.loc[:, 'in_date'].apply(str2int)
+        # df_io.loc[:, 'out_date'] = df_io.loc[:, 'out_date'].apply(str2int)
+
+        # df_io.set_index('symbol', inplace=True)
+        dates = self.query_trade_dates(start_date=start_date, end_date=end_date)
+        dates = pd.Series(dates)
+
+        df_io = df_io.rename(columns={'univ_weight': 'weight', 'univ_member':'symbol'})
+        df_io = df_io[['trade_date', 'symbol','weight']]
+
+        dic = dict()
+        gp = df_io.groupby(by='symbol')
+        for sec, df in gp:
+            mask = np.zeros_like(dates, dtype=np.integer)
+            for idx, row in df.iterrows():
+                bool_index = dates.isin(df['trade_date'])
+                mask[bool_index] = 1
+            dic[sec] = mask
+
+        res = pd.DataFrame(index=dates, data=dic)
+        res.index.name = 'trade_date'
+
+        return res
+
+    def query_universe_weights_range(self, universe, start_date, end_date):
+        """
+        Return all securities that have been in index during start_date and end_date.
+
+        Parameters
+        ----------
+        index : str
+            separated by ','
+        trade_date : int
+
+        Returns
+        -------
+        pd.DataFrame
+
+        """
+        df_io, msg = self._get_universe_comp(universe, start_date, end_date)
+        if msg != '0,':
+            print(msg)
+
+        df_io = df_io.rename(columns={'univ_weight': 'weight', 'univ_member':'symbol'})
+        df_io = df_io[['trade_date', 'symbol','weight']]
+        df_io = df_io.astype({'weight': float, 'trade_date': np.integer})
+        df_io.loc[:, 'weight'] = df_io['weight'] # / 100.
+        df_io = df_io.pivot(index='trade_date', columns='symbol', values='weight')
+        df_io = df_io.fillna(0.0)
+        return df_io
+
+    def query_universe_weights_daily(self, universe, start_date, end_date):
+        """
+        Return all securities that have been in index during start_date and end_date.
+
+        Parameters
+        ----------
+        universe : str
+        start_date : int
+        end_date : int
+
+        Returns
+        -------
+        res : pd.DataFrame
+            Index is trade_date, columns are symbols.
+
+        """
+
+        start_dt = jutil.convert_int_to_datetime(start_date)
+        start_dt_extended = start_dt - pd.Timedelta(days=45)
+        start_date_extended = jutil.convert_datetime_to_int(start_dt_extended)
+        trade_dates = self.query_trade_dates(start_date_extended, end_date)
+
+        df_weight_raw = self.query_universe_weights_range(universe, start_date=start_date_extended, end_date=end_date)
+        res = df_weight_raw.reindex(index=trade_dates)
+        res = res.fillna(method='ffill')
+        res = res.loc[res.index >= start_date]
+        res = res.loc[res.index <= end_date]
+
+        mask_col = res.sum(axis=0) > 0
+        res = res.loc[:, mask_col]
+
+        return res
+
     def query_industry_daily(self, symbol, start_date, end_date, type_='SW', level=1):
         """
         Get index components on each day during start_date and end_date.
@@ -866,7 +1039,11 @@ def query_industry_daily(self, symbol, start_date, end_date, type_='SW', level=1
         dic_sec = {sec: df.sort_values(by='in_date', axis=0).reset_index()
                    for sec, df in dic_sec.items()}
 
+        if not dic_sec:
+            return None
+
         df_ann_tmp = pd.concat({sec: df.loc[:, 'in_date'] for sec, df in dic_sec.items()}, axis=1)
+
         df_value_tmp = pd.concat({sec: df.loc[:, 'industry{:d}_code'.format(level)]
                                   for sec, df in dic_sec.items()},
                                  axis=1)
@@ -1027,10 +1204,10 @@ def query_dividend(self, symbol, start_date, end_date):
                                  filter=filter_argument,
                                  data_format='pandas')
         
-        #df = df.set_index('exdiv_date').sort_index(axis=0)
+        # df = df.set_index('exdiv_date').sort_index(axis=0)
         df = df.astype({'cash': float, 'cash_tax': float,
-                        #'bonus_list_date': np.integer,
-                        #'cashpay_date': np.integer,
+                        # 'bonus_list_date': np.integer,
+                        # 'cashpay_date': np.integer,
                         'exdiv_date': np.integer,
                         'publish_date': np.integer,
                         'record_date': np.integer})
@@ -1101,9 +1278,11 @@ def query_trade_dates(self, start_date, end_date):
         filter_argument = self._dic2url({'start_date': start_date,
                                          'end_date': end_date})
     
-        df_raw, err_msg = self.query("jz.secTradeCal", fields="trade_date",
-                                     filter=filter_argument, orderby="")
-        self._raise_error_if_msg(err_msg)
+        # df_raw, err_msg = self.query("jz.secTradeCal", fields="trade_date",
+        #                              filter=filter_argument, orderby="")
+        # self._raise_error_if_msg(err_msg)
+        df_raw = self._trade_dates_df[self._trade_dates_df['trade_date'] >= str(start_date)]
+        df_raw = df_raw[df_raw['trade_date'] <= str(end_date)]
         
         if df_raw.empty:
             return np.array([], dtype=int)
@@ -1171,6 +1350,15 @@ def query_next_trade_date(self, date, n=1):
     
         dates = self.query_trade_dates(date, date_new)
         mask = dates > date
-        res = dates[mask][n-1]
+        res = dates[mask][n - 1]
     
         return int(res)
+
+#
+# # test code
+# if __name__ == "__main__":
+#     data_config = jutil.read_json("D:\\whuang_git\\JAQs\\JAQS\\config\\data_config.json")
+#     ds = RemoteDataService()
+#     ds.init_from_config(data_config)
+#     while True:
+#         ds.query_trade_dates("20180501","20180507")
diff --git a/jaqs/data/dataview.py b/jaqs/data/dataview.py
index 88ad157..bcf725b 100644
--- a/jaqs/data/dataview.py
+++ b/jaqs/data/dataview.py
@@ -5,6 +5,7 @@
 """
 from __future__ import print_function
 import os
+
 try:
     basestring
 except NameError:
@@ -12,19 +13,110 @@
 
 import numpy as np
 import pandas as pd
+import re
 
 import jaqs.util as jutil
 from jaqs.data.align import align
 from jaqs.data.py_expression_eval import Parser
 
 
+class FactorDef:
+    def __init__(self, name, args, body, is_quarterly=False):
+        self.name = name
+        self.body = body
+        self.args = args
+        self.is_quarterly = is_quarterly
+
+
+class FactorFunc:
+    def __init__(self, dv, factor, allow_future_data):
+        self._factor = factor
+        self._dv = dv
+        self._allow_future_data = allow_future_data
+
+    def __call__(self, *args, **kwargs):
+
+        parser = self._dv._create_parser(allow_future_data=self._allow_future_data)
+
+        # print("exec factor: " + self._factor.name + "(" + ','.join(self._factor.args) + ")=" + self._factor.body)
+        expr = parser.parse(self._factor.body)
+
+        var_df_dic = dict()
+        var_list = expr.variables()
+
+        for var in var_list:
+            if var in self._factor.args:
+                i = self._factor.args.index(var)
+                var_df_dic[var] = args[i]
+            elif var in parser.functions:
+                if var in self._dv._import_factors and not self._dv._import_factors[var].args:
+                    var_df_dic[var] = self._dv._get_var(var, self._allow_future_data)
+            else:
+                var_df_dic[var] = self._dv._get_var(var, self._allow_future_data)
+
+        # TODO: send ann_date into expr.evaluate. We assume that ann_date of all fields of a symbol is the same
+        df_ann = self._dv._get_ann_df()
+
+        df_eval = parser.evaluate(var_df_dic, ann_dts=df_ann, trade_dts=self._dv.dates)
+
+        return df_eval
+
+
+
+class ResReturnFunc:
+    """
+    ResReturn(close, m, n)
+    """
+    def __init__(self, dv, allow_future_data):
+        self._dv = dv
+        self._allow_future_data = allow_future_data
+
+    def __call__(self, *args, **kwargs):
+
+        parser = self._dv._create_parser(allow_future_data=self._allow_future_data)
+
+        # print("exec factor: " + self._factor.name + "(" + ','.join(self._factor.args) + ")=" + self._factor.body)
+        field_name = args[0].columns.name
+        formula = "Return({0}, {1}, {2}) - Return({3},{1},{2})".format(
+            field_name, args[1], args[2],
+            "bm_" + field_name
+        )
+        expr = parser.parse(formula)
+
+        var_df_dic = dict()
+        var_list = expr.variables()
+
+        for var in var_list:
+            # if var in self._factor.args:
+            #     i = self._factor.args.index(var)
+            #     var_df_dic[var] = args[i]
+            if var in parser.functions:
+                if var in self._dv._import_factors and not self._dv._import_factors[var].args:
+                    var_df_dic[var] = self._dv._get_var(var, self._allow_future_data)
+            else:
+                var_df_dic[var] = self._dv._get_var(var, self._allow_future_data)
+
+        # TODO: send ann_date into expr.evaluate. We assume that ann_date of all fields of a symbol is the same
+        df_ann = self._dv._get_ann_df()
+
+        df_eval = parser.evaluate(var_df_dic, ann_dts=df_ann, trade_dts=self._dv.dates)
+
+        return df_eval
+
+
+# class LabelDef(FactorDef):
+#     pass
+#
+#
+# class LabelFunc(FactorFunc):
+#     pass
 
 
 class DataView(object):
     """
     Prepare data before research / trade. Support file I/O.
     Support: add field, add formula, save / load.
-    
+
     Attributes
     ----------
     data_api : RemoteDataService
@@ -40,11 +132,13 @@ class DataView(object):
     data_q : pd.DataFrame
         All quarterly frequency data will be merged and stored here.
         index is date, columns is symbol-field MultiIndex
-    
+
     """
+
     def __init__(self):
         self.data_api = None
-        
+        self._factor_df = pd.DataFrame()
+        self._import_factors = {}
         self.universe = ""
         self.symbol = []
         self.benchmark = ""
@@ -56,20 +150,29 @@ def __init__(self):
         self.freq = 1
         self.all_price = True
         self._snapshot = None
+        self.factors = []
+        self.load_factors = []
+        self.labels = []
+        self.load_labels = []
+        self.index_weights = {}
+        self.industry_groups = {}
 
         self.meta_data_list = ['start_date', 'end_date',
                                'extended_start_date_d', 'extended_start_date_q',
                                'freq', 'fields', 'symbol', 'universe', 'benchmark',
-                               'custom_daily_fields', 'custom_quarterly_fields']
+                               'custom_daily_fields', 'custom_quarterly_fields',
+                               'factors', 'load_factors',
+                               'labels', 'load_labels'
+                               ]
 
         self.adjust_mode = 'post'
-        
+
         self.data_d = None
         self.data_q = None
         self._data_benchmark = None
         self._data_inst = None
         # self._data_group = None
-        
+
         common_list = {'symbol', 'start_date', 'end_date'}
         market_bar_list = {'open', 'high', 'low', 'close', 'volume', 'turnover', 'vwap', 'oi'}
         market_tick_list = {'volume', 'oi',
@@ -190,55 +293,145 @@ def __init__(self):
              "spe_bal_netcash_inc", "tot_bal_netcash_inc", "spe_bal_netcash_equ_undir", "tot_bal_netcash_equ_undir",
              "spe_bal_netcash_inc_undir", "tot_bal_netcash_inc_undir"}
         self.fin_indicator = \
-            {"extraordinary","deductedprofit","grossmargin","operateincome","investincome","stmnote_finexp",
-             "stm_is","ebit_daily","ebitda""fcff","fcfe","exinterestdebt_current","exinterestdebt_noncurrent","interestdebt",
-             "netdebt","tangibleasset","workingcapital","networkingcapital","investcapital","retainedearnings","eps_basic_daily", # TODO eps_basic
-             "eps_diluted","eps_diluted2","bps","ocfps","grps","orps","surpluscapitalps","surplusreserveps","undistributedps",
-             "retainedps","cfps","ebitps","fcffps","fcfeps","netprofitmargin","grossprofitmargin","cogstosales",
-             "expensetosales","profittogr","saleexpensetogr","adminexpensetogr","finaexpensetogr","impairtogr_ttm",
-             "gctogr","optogr","ebittogr","roe","roe_deducted","roa2","roa","roic","roe_yearly","roa2_yearly","roe_avg",
-             "operateincometoebt","investincometoebt","nonoperateprofittoebt","taxtoebt","deductedprofittoprofit","salescashintoor",
-             "ocftoor","ocftooperateincome","capitalizedtoda","debttoassets","assetstoequity","dupont_assetstoequity",
-             "catoassets","ncatoassets","tangibleassetstoassets","intdebttototalcap","equitytototalcapital","currentdebttodebt",
-             "longdebtodebt","current","quick","cashratio","ocftoshortdebt","debttoequity","equitytodebt",
-             "equitytointerestdebt","tangibleassettodebt","tangassettointdebt","tangibleassettonetdebt","ocftodebt",
-             "ocftointerestdebt","ocftonetdebt","ebittointerest","longdebttoworkingcapital","ebitdatodebt","turndays",
-             "invturndays","arturndays","invturn","arturn","caturn","faturn","assetsturn","roa_yearly","dupont_roa",
-             "s_stm_bs","prefinexpense_opprofit","nonopprofit","optoebt","noptoebt","ocftoprofit","cashtoliqdebt",
-             "cashtoliqdebtwithinterest","optoliqdebt","optodebt","roic_yearly","tot_faturn","profittoop","qfa_operateincome",
-             "qfa_investincome","qfa_deductedprofit","qfa_eps","qfa_netprofitmargin","qfa_grossprofitmargin","qfa_expensetosales",
-             "qfa_profittogr","qfa_saleexpensetogr","qfa_adminexpensetogr","qfa_finaexpensetogr","qfa_impairtogr_ttm",
-             "qfa_gctogr","qfa_optogr","qfa_roe","qfa_roe_deducted","qfa_roa","qfa_operateincometoebt","qfa_investincometoebt",
-             "qfa_deductedprofittoprofit","qfa_salescashintoor","qfa_ocftosales","qfa_ocftoor","yoyeps_basic","yoyeps_diluted",
-             "yoyocfps","yoyop","yoyebt","yoynetprofit","yoynetprofit_deducted","yoyocf","yoyroe","yoybps","yoyassets",
-             "yoyequity","yoy_tr","yoy_or","qfa_yoygr","qfa_cgrgr","qfa_yoysales","qfa_cgrsales","qfa_yoyop","qfa_cgrop",
-             "qfa_yoyprofit","qfa_cgrprofit","qfa_yoynetprofit","qfa_cgrnetprofit","yoy_equity","rd_expense","waa_roe"}
-        self .custom_daily_fields = []
-        self .custom_quarterly_fields = []
-        
+            {"extraordinary", "deductedprofit", "grossmargin", "operateincome", "investincome", "stmnote_finexp",
+             "stm_is", "ebit_daily", "ebitda""fcff", "fcfe", "exinterestdebt_current", "exinterestdebt_noncurrent",
+             "interestdebt",
+             "netdebt", "tangibleasset", "workingcapital", "networkingcapital", "investcapital", "retainedearnings",
+             "eps_basic_daily",  # TODO eps_basic
+             "eps_diluted", "eps_diluted2", "bps", "ocfps", "grps", "orps", "surpluscapitalps", "surplusreserveps",
+             "undistributedps",
+             "retainedps", "cfps", "ebitps", "fcffps", "fcfeps", "netprofitmargin", "grossprofitmargin", "cogstosales",
+             "expensetosales", "profittogr", "saleexpensetogr", "adminexpensetogr", "finaexpensetogr", "impairtogr_ttm",
+             "gctogr", "optogr", "ebittogr", "roe", "roe_deducted", "roa2", "roa", "roic", "roe_yearly", "roa2_yearly",
+             "roe_avg",
+             "operateincometoebt", "investincometoebt", "nonoperateprofittoebt", "taxtoebt", "deductedprofittoprofit",
+             "salescashintoor",
+             "ocftoor", "ocftooperateincome", "capitalizedtoda", "debttoassets", "assetstoequity",
+             "dupont_assetstoequity",
+             "catoassets", "ncatoassets", "tangibleassetstoassets", "intdebttototalcap", "equitytototalcapital",
+             "currentdebttodebt",
+             "longdebtodebt", "current", "quick", "cashratio", "ocftoshortdebt", "debttoequity", "equitytodebt",
+             "equitytointerestdebt", "tangibleassettodebt", "tangassettointdebt", "tangibleassettonetdebt", "ocftodebt",
+             "ocftointerestdebt", "ocftonetdebt", "ebittointerest", "longdebttoworkingcapital", "ebitdatodebt",
+             "turndays",
+             "invturndays", "arturndays", "invturn", "arturn", "caturn", "faturn", "assetsturn", "roa_yearly",
+             "dupont_roa",
+             "s_stm_bs", "prefinexpense_opprofit", "nonopprofit", "optoebt", "noptoebt", "ocftoprofit", "cashtoliqdebt",
+             "cashtoliqdebtwithinterest", "optoliqdebt", "optodebt", "roic_yearly", "tot_faturn", "profittoop",
+             "qfa_operateincome",
+             "qfa_investincome", "qfa_deductedprofit", "qfa_eps", "qfa_netprofitmargin", "qfa_grossprofitmargin",
+             "qfa_expensetosales",
+             "qfa_profittogr", "qfa_saleexpensetogr", "qfa_adminexpensetogr", "qfa_finaexpensetogr",
+             "qfa_impairtogr_ttm",
+             "qfa_gctogr", "qfa_optogr", "qfa_roe", "qfa_roe_deducted", "qfa_roa", "qfa_operateincometoebt",
+             "qfa_investincometoebt",
+             "qfa_deductedprofittoprofit", "qfa_salescashintoor", "qfa_ocftosales", "qfa_ocftoor", "yoyeps_basic",
+             "yoyeps_diluted",
+             "yoyocfps", "yoyop", "yoyebt", "yoynetprofit", "yoynetprofit_deducted", "yoyocf", "yoyroe", "yoybps",
+             "yoyassets",
+             "yoyequity", "yoy_tr", "yoy_or", "qfa_yoygr", "qfa_cgrgr", "qfa_yoysales", "qfa_cgrsales", "qfa_yoyop",
+             "qfa_cgrop",
+             "qfa_yoyprofit", "qfa_cgrprofit", "qfa_yoynetprofit", "qfa_cgrnetprofit", "yoy_equity", "rd_expense",
+             "waa_roe"}
+
+        self.risk_model_fields = { "expo_value", 
+                            "expo_leverage",
+                            "expo_growth",
+                            "expo_size",
+                            "expo_liquidity",
+                            "expo_shortterm_momentum",
+                            "expo_mediumterm_momentum",
+                            "expo_volatility",
+                            "expo_exchange_rate_sensitivity",
+                            "expo_bshare_market",
+                            "expo_coal_consumable_fuels",
+                            "expo_energy_ex_coal",
+                            "expo_steel",
+                            "expo_chemicals",
+                            "expo_construction_materials",
+                            "expo_metals_mining_ex_steel",
+                            "expo_paper_forest_products",
+                            "expo_commercial_professional_services",
+                            "expo_electrical_equipment",
+                            "expo_construction_engineering",
+                            "expo_transportation_noninfrastructure",
+                            "expo_machinery",
+                            "expo_trading_companies_distributors_conglomerates",
+                            "expo_transportation_infrastructure",
+                            "expo_media",
+                            "expo_retailing",
+                            "expo_textiles_apparel_luxury_goods",
+                            "expo_automobiles",
+                            "expo_household_durables",
+                            "expo_autocomponents",
+                            "expo_consumer_services",
+                            "expo_food_products",
+                            "expo_beverages_tobacco",
+                            "expo_healthcare",
+                            "expo_realestate",
+                            "expo_financials",
+                            "expo_software_services",
+                            "expo_computers_peripherals",
+                            "expo_communications_equipment",
+                            "expo_semiconductors_electronics",
+                            "expo_telecommunication_services",
+                            "expo_utilities"
+                        }
+
+        self.consensus_data = [
+            "est_bps_fy0","est_cfps_fy0","est_dps_fy0","est_ebit_fy0","est_eps_fy0","est_oper_revenue_fy0","est_pb_fy0","est_pe_fy0","est_peg_fy0","est_roe_fy0","net_profit_fy0","rolling_type_fy0",
+            "est_bps_fy1","est_cfps_fy1","est_dps_fy1","est_ebit_fy1","est_eps_fy1","est_oper_revenue_fy1","est_pb_fy1","est_pe_fy1","est_peg_fy1","est_roe_fy1","net_profit_fy1","rolling_type_fy1",
+            "est_bps_fy2","est_cfps_fy2","est_dps_fy2","est_ebit_fy2","est_eps_fy2","est_oper_revenue_fy2","est_pb_fy2","est_pe_fy2","est_peg_fy2","est_roe_fy2","net_profit_fy2","rolling_type_fy2",
+            "est_bps_fy3","est_cfps_fy3","est_dps_fy3","est_ebit_fy3","est_eps_fy3","est_oper_revenue_fy3","est_pb_fy3","est_pe_fy3","est_peg_fy3","est_roe_fy3","net_profit_fy3","rolling_type_fy3",
+            "est_bps_fttm","est_cfps_fttm","est_dps_fttm","est_ebit_fttm","est_eps_fttm","est_oper_revenue_fttm","est_pb_fttm","est_pe_fttm","est_peg_fttm","est_roe_fttm","net_profit_fttm","rolling_type_fttm",
+            "est_bps_yoy","est_cfps_yoy","est_dps_yoy","est_ebit_yoy","est_eps_yoy","est_oper_revenue_yoy","est_pb_yoy","est_pe_yoy","est_peg_yoy","est_roe_yoy","net_profit_yoy","rolling_type_yoy",
+            "est_bps_yoy2","est_cfps_yoy2","est_dps_yoy2","est_ebit_yoy2","est_eps_yoy2","est_oper_revenue_yoy2","est_pb_yoy2","est_pe_yoy2","est_peg_yoy2","est_roe_yoy2","net_profit_yoy2","rolling_type_yoy2",
+            "est_bps_cagr","est_cfps_cagr","est_dps_cagr","est_ebit_cagr","est_eps_cagr","est_oper_revenue_cagr","est_pb_cagr","est_pe_cagr","est_peg_cagr","est_roe_cagr","net_profit_cagr","rolling_type_cagr" ]
+
+        self.stk_rating_data = [
+            "est_price_30d", "est_priceinstnum_30d", "wrating_avg_30d", "wrating_downgrade_30d","wrating_instnum_30d", "wrating_maintain_30d", "wrating_numofbuy_30d", "wrating_numofhold_30d","wrating_numofoutperform_30d", "wrating_numofsell_30d", "wrating_numofunderperform_30d","wrating_upgrade_30d",
+            "est_price_90d", "est_priceinstnum_90d", "wrating_avg_90d", "wrating_downgrade_90d", "wrating_instnum_90d", "wrating_maintain_90d", "wrating_numofbuy_90d","wrating_numofhold_90d", "wrating_numofoutperform_90d", "wrating_numofsell_90d","wrating_numofunderperform_90d", "wrating_upgrade_90d",
+            "est_price_180d", "est_priceinstnum_180d","wrating_avg_180d","wrating_downgrade_180d", "wrating_instnum_180d", "wrating_maintain_180d", "wrating_numofbuy_180d", "wrating_numofhold_180d", "wrating_numofoutperform_180d","wrating_numofsell_180d", "wrating_numofunderperform_180d", "wrating_upgrade_180d"
+        ]
+
+        self.lgt_data = {"lgt_holding", "lgt_holding_ratio"}
+        self.rating_data = {"num_rating"}
+
+        self.default_fields = {
+                          '_daily_adjust_factor', '_limit', 'adjust_factor', 'close',
+                          'close_adj', 'high', 'high_adj', 'index_member', 'index_weight',
+                          'low', 'low_adj', 'open', 'open_adj', 'trade_status', 'vwap',
+                          'vwap_adj',
+                          'sw1', 'sw2', 'sw3', 'sw4', 'zz1', 'zz2' }
+
+        self.custom_daily_fields = []
+        self.custom_quarterly_fields = []
+
         # co nst
-        self .ANN_DATE_FIELD_NAME = 'ann_date'
-        self .REPORT_DATE_FIELD_NAME = 'report_date'
+        self.ANN_DATE_FIELD_NAME = 'ann_date'
+        self.REPORT_DATE_FIELD_NAME = 'report_date'
         self.TRADE_STATUS_FIELD_NAME = 'trade_status'
         self.TRADE_DATE_FIELD_NAME = 'trade_date'
-    
+
+        self.misc_data = [ 'st_flag' ]
+
     # --------------------------------------------------------------------------------------------------------
     # Properties
     @property
     def data_benchmark(self):
         return self._data_benchmark
-    
+
     @property
     def data_inst(self):
         """
-        
+
         Returns
         -------
         pd.DataFrame
 
         """
         return self._data_inst
-    
+
     @data_benchmark.setter
     def data_benchmark(self, df_new):
         if self.data_d is not None and df_new.shape[0] != self.data_d.shape[0]:
@@ -249,7 +442,7 @@ def data_benchmark(self, df_new):
     def dates(self):
         """
         Get daily date array of the underlying data.
-        
+
         Returns
         -------
         res : np.array
@@ -262,7 +455,7 @@ def dates(self):
             res = self.data_api.query_trade_dates(self.extended_start_date_d, self.end_date)
         else:
             raise ValueError("Cannot get dates array when neither of data and data_api exists.")
-    
+
         return res
 
     # --------------------------------------------------------------------------------------------------------
@@ -271,7 +464,7 @@ def _is_quarter_field(self, field_name):
         """
         Check whether a field name is quarterly frequency.
         field_name must be pre-defined or already added.
-        
+
         Parameters
         ----------
         field_name : str
@@ -292,7 +485,7 @@ def _is_daily_field(self, field_name):
         """
         Check whether a field name is daily frequency.
         field_name must be pre-defined or already added.
-        
+
         Parameters
         ----------
         field_name : str
@@ -305,14 +498,20 @@ def _is_daily_field(self, field_name):
         flag = (field_name in self.market_daily_fields
                 or field_name in self.reference_daily_fields
                 or field_name in self.custom_daily_fields
-                or field_name in self.group_fields)
+                or field_name in self.group_fields
+                or field_name in self.risk_model_fields
+                or field_name in self.rating_data
+                or field_name in self.lgt_data
+                or field_name in self.consensus_data
+                or field_name in self.stk_rating_data
+                or field_name in self.misc_data)
         return flag
 
     def _is_predefined_field(self, field_name):
         """
         Check whether a field name can be recognized.
         field_name must be pre-defined or already added.
-        
+
         Parameters
         ----------
         field_name : str
@@ -327,7 +526,7 @@ def _is_predefined_field(self, field_name):
     def _get_fields(self, field_type, fields, complement=False, append=False):
         """
         Get list of fields that are in ref_quarterly_fields.
-        
+
         Parameters
         ----------
         field_type : {'market_daily', 'ref_daily', 'income', 'balance_sheet', 'cash_flow', 'daily', 'quarterly'
@@ -346,35 +545,43 @@ def _get_fields(self, field_type, fields, complement=False, append=False):
                     'balance_sheet': self.fin_stat_balance_sheet,
                     'cash_flow': self.fin_stat_cash_flow,
                     'fin_indicator': self.fin_indicator,
-                    'group': self.group_fields}
+                    'group': self.group_fields,
+                    'risk_model':self.risk_model_fields,
+                    'lgt_data' : self.lgt_data,
+                    'rating_data': self.rating_data,
+                    'consensus_data': self.consensus_data,
+                    'stk_rating_data': self.stk_rating_data,
+                    'misc_data' : self.misc_data
+                    }
         pool_map['daily'] = set.union(pool_map['market_daily'],
                                       pool_map['ref_daily'],
                                       pool_map['group'],
+                                      pool_map['risk_model'],
                                       self.custom_daily_fields)
         pool_map['quarterly'] = set.union(pool_map['income'],
                                           pool_map['balance_sheet'],
                                           pool_map['cash_flow'],
                                           pool_map['fin_indicator'],
                                           self.custom_quarterly_fields)
-        
+
         pool = pool_map.get(field_type, None)
         if pool is None:
             raise NotImplementedError("field_type = {:s}".format(field_type))
-        
+
         s = set.intersection(set(pool), set(fields))
         if not s:
             return []
-        
+
         if complement:
             s = set(fields) - s
-            
+
         if field_type == 'market_daily' and self.all_price:
             # turnover will not be adjusted
             s.update({'open', 'high', 'close', 'low', 'vwap'})
-            
+
         if append:
             s.add('symbol')
-            if field_type == 'market_daily' or field_type == 'ref_daily':
+            if field_type == 'market_daily' or field_type == 'ref_daily' or field_type == "risk_model":
                 s.add('trade_date')
                 if field_type == 'market_daily':
                     s.add(self.TRADE_STATUS_FIELD_NAME)
@@ -384,7 +591,7 @@ def _get_fields(self, field_type, fields, complement=False, append=False):
                   or field_type == 'fin_indicator'):
                 s.add(self.ANN_DATE_FIELD_NAME)
                 s.add(self.REPORT_DATE_FIELD_NAME)
-        
+
         l = list(s)
         return l
 
@@ -394,19 +601,19 @@ def init_from_config(self, props, data_api):
         """
         Initialize various attributes like start/end date, universe/symbol, fields, etc.
         If your want to parse symbol, but use a custom benchmark index, please directly assign self.data_benchmark.
-        
+
         Parameters
         ----------
         props : dict
             start_date, end_date, freq, symbol, fields, etc.
         data_api : BaseDataServer
-        
+
         """
         # data_api.init_from_config(props)
         self.data_api = data_api
-    
+
         sep = ','
-    
+
         # initialize parameters
         self.start_date = props['start_date']
         self.extended_start_date_d = jutil.shift(self.start_date, n_weeks=-8)  # query more data
@@ -414,7 +621,7 @@ def init_from_config(self, props, data_api):
         self.end_date = props['end_date']
         self.all_price = props.get('all_price', True)
         self.freq = props.get('freq', 1)
-    
+
         # get and filter fields
         fields = props.get('fields', [])
         if fields:
@@ -422,51 +629,122 @@ def init_from_config(self, props, data_api):
             self.fields = [field for field in fields if self._is_predefined_field(field)]
             if len(self.fields) < len(fields):
                 print("Field name [{}] not valid, ignore.".format(set.difference(set(fields), set(self.fields))))
-    
+
         # append additional fields
         if self.all_price:
             self.fields.extend(['open_adj', 'high_adj', 'low_adj', 'close_adj',
                                 'open', 'high', 'low', 'close',
                                 'vwap', 'vwap_adj'])
-    
+
+        # always inclde sw1 for industry analysis
+        if 'sw1' not in self.fields:
+            self.fields.extend(['sw1'])
+
+        # TODO: load factors and labels from DataCore server
+        # "factors"       : "vwap=vwap; mom_1_2=mom_m_n(1,);..."
+        # "load_factors"  : vwap, mom_m_n,..."
+        # "labels"        : "vwap=vwap; mom_1_2=mom_m_n(1,);..."
+        # "load_labels"   : "vwap, mom_m_n,..."
+
+        # self._props = props
+        self.factors = props['factors'].split(';') if 'factors' in props else []
+        self.load_factors = props['load_factors'].split(',') if 'load_factors' in props else []
+        self.labels = props['labels'].split(';') if 'labels' in props else []
+        self.load_labels = props['load_labels'].split(',') if 'load_labels' in props else []
+
+        if self.factors or self.load_factors or self.labels or self.load_labels:
+            self._init_factors()
+
         # initialize universe/symbol
         universe = props.get('universe', "")
         symbol = props.get('symbol', "")
         benchmark = props.get('benchmark', '')
-        if symbol and universe:
-            raise ValueError("Please use either [symbol] or [universe].")
-        if not (symbol or universe):
-            raise ValueError("One of [symbol] or [universe] must be provided.")
+        # if symbol and universe:
+        #     raise ValueError("Please use either [symbol] or [universe].")
+        # if not (symbol or universe):
+        #     raise ValueError("One of [symbol] or [universe] must be provided.")
         if universe:
             univ_list = universe.split(',')
             self.universe = univ_list
             symbols_list = []
             for univ in univ_list:
-                symbols_list.extend(data_api.query_index_member(univ, self.extended_start_date_d, self.end_date))
+                if univ.endswith('.UNI'):
+                    symbols_list.extend(data_api.query_universe_member(univ, self.extended_start_date_d, self.end_date))
+                else:
+                    symbols_list.extend(data_api.query_index_member(univ, self.extended_start_date_d, self.end_date))
+
             self.symbol = sorted(list(set(symbols_list)))
-        else:
-            self.symbol = sorted(symbol.split(sep))
+        # else:
+        #    self.symbol = sorted(symbol.split(sep))
+
+        # Merge universe and symbol
+        if symbol:
+            tmp = self.symbol + symbol.split(sep)
+            self.symbol = sorted(list(set(tmp)))
+
         if benchmark:
             self.benchmark = benchmark
         else:
             if self.universe:
                 if len(self.universe) > 1:
                     print("More than one universe are used: {}, "
-                                     "use the first one ({}) as index by default. "
-                                     "If you want to use other benchmark, "
-                                     "please specify benchmark in configs.".format(repr(self.universe), self.universe[0]))
+                          "use the first one ({}) as index by default. "
+                          "If you want to use other benchmark, "
+                          "please specify benchmark in configs.".format(repr(self.universe), self.universe[0]))
                 self.benchmark = self.universe[0]
-    
+
         print("Initialize config success.")
 
+    def _init_factors(self):
+        # append all factors from db
+        factor_df, msg = self.data_api.query(view='jz.factorDef')
+        self._factor_df = factor_df
+        if self._factor_df is None:
+            raise ValueError("no factor_table: " + msg)
+
+        for index, row in self._factor_df.iterrows():
+            factor_id = row['factor_id']
+            factor_body = row['factor_def']
+            factor_args = list(filter(None, row['factor_args'].split(',')))
+            if 'factor_quarterly' in row:
+                factor_quarterly = row['factor_quarterly']
+            else:
+                factor_quarterly = False
+
+            self._import_factors[factor_id] = FactorDef(factor_id, factor_args, factor_body, factor_quarterly)
+
+        factor_label_ids = list(filter(None, list(
+            (set([item.split('(')[0] for item in self.load_factors]))
+                .union(set([item.split('(')[0] for item in self.load_labels]))
+                .union(set([item1.split('(')[0] for item1 in [item.split('=')[-1] for item in self.factors]]))
+                .union(set([item1.split('(')[0] for item1 in [item.split('=')[-1] for item in self.labels]]))
+        )))
+
+        dependencies = []
+        while factor_label_ids:
+            tmp = self._factor_df[self._factor_df['factor_id'].isin(factor_label_ids)]['dependency']
+            factor_label_ids = []
+            for dep in tmp:
+                for d in dep.split(','):
+                    d = d.strip()
+                    if not d: continue
+                    if d in self._import_factors:
+                        factor_label_ids.append(d)
+                    else:
+                        dependencies.append(d)
+
+        for dep in set(dependencies):
+            if dep not in self.fields:
+                self.fields.append(dep)
+
     def distributed_query(self, query_func_name, symbol, start_date, end_date, limit=100000, **kwargs):
         n_symbols = len(symbol.split(','))
         dates = self.data_api.query_trade_dates(start_date, end_date)
         n_days = len(dates)
-        
+
         if n_symbols * n_days > limit:
             n = limit // n_symbols
-            
+
             df_list = []
             i = 0
             pos1, pos2 = n * i, n * (i + 1) - 1
@@ -499,20 +777,20 @@ def prepare_data(self):
         if self.data_q is not None:
             self._prepare_report_date()
         self._align_and_merge_q_into_d()
-        
+
         print("Query instrument info...")
         self._prepare_inst_info()
-    
+
         print("Query adj_factor...")
         self._prepare_adj_factor()
-    
+
         if self.benchmark:
             print("Query benchmark...")
             self._data_benchmark = self._prepare_benchmark()
         if self.universe:
             print("Query benchmar member info...")
             self._prepare_comp_info()
-    
+
         group_fields = self._get_fields('group', self.fields)
         if group_fields:
             print("Query groups (industry)...")
@@ -531,7 +809,7 @@ def _process_index_co(df, index_name):
     def _prepare_daily_quarterly(self, fields):
         """
         Query and process data from data_api.
-        
+
         Parameters
         ----------
         fields : list
@@ -544,11 +822,11 @@ def _prepare_daily_quarterly(self, fields):
         """
         if not fields:
             return None, None
-    
+
         # query data
         print("Query data - query...")
         daily_list, quarterly_list = self._query_data(self.symbol, fields)
-    
+
         def pivot_and_sort(df, index_name):
             df = self._process_index_co(df, index_name)
             df = df.pivot(index=index_name, columns='symbol')
@@ -558,7 +836,7 @@ def pivot_and_sort(df, index_name):
             df = df.sort_index(axis=1, level=col_names)
             df.index.name = index_name
             return df
-    
+
         multi_daily = None
         multi_quarterly = None
         if daily_list:
@@ -572,15 +850,235 @@ def pivot_and_sort(df, index_name):
             multi_quarterly = self._merge_data(quarterly_list_pivot, self.REPORT_DATE_FIELD_NAME)
             multi_quarterly = self._fill_missing_idx_col(multi_quarterly, index=None, symbols=self.symbol)
             print("Query data - quarterly fields prepared.")
-    
+
+        # FIXME: patch for lgt_data and rating_data
+        fields_lgt_ind = self._get_fields('lgt_data', fields, append=True)
+        if fields_lgt_ind:
+            multi_daily = self.query_lgt_data(fields_lgt_ind, multi_daily)
+
+        fields_rating_ind = self._get_fields('rating_data', fields, append=True)
+        if fields_rating_ind:
+            multi_daily = self.query_rating_data(fields_rating_ind, multi_daily)
+
+        tmp_fields = self._get_fields('consensus_data', fields, append=True)
+        if tmp_fields:
+            multi_daily = self.query_consensus_data(tmp_fields, multi_daily)
+
+        tmp_fields = self._get_fields('stk_rating_data', fields, append=True)
+        if tmp_fields:
+            multi_daily = self.query_stk_rating_data(tmp_fields, multi_daily)
+
+        tmp_fields = self._get_fields('misc_data', fields, append=True)
+        if tmp_fields:
+            multi_daily = self.query_misc_data(tmp_fields, multi_daily)
+
         return multi_daily, multi_quarterly
 
+    def query_rating_data(self, fields_rating_ind, daily_df):
+        filter_str = "symbol={0}&start_date={1}&end_date={2}".format(
+            ','.join(self.symbol),
+            self.extended_start_date_q,
+            self.end_date)
+
+        df, msg = self.data_api.query(view='wd.secStockRatingConsus', filter=filter_str)
+
+        df_clean = df[['symbol', 'date', 'wrating_cycle', 'wrating_instnum']]
+        df_clean['wrating_cycle'] = df_clean['wrating_cycle'].str[-4:].astype(int) / 1000 * 30
+        df_clean['date'] = df_clean['date'].astype(int)
+        df_clean = df_clean[df_clean['wrating_cycle'] == 90]
+        df_rating = df_clean.pivot_table(values='wrating_instnum', index='date', columns='symbol').fillna(method='ffill')
+        df_rating = df_rating.replace(np.nan, 0)
+
+        data_d_orig = self.data_d
+        self.data_d = daily_df
+
+        self.append_df(df_rating, 'num_rating', is_quarterly=False)
+
+        daily_df = self.data_d
+        self.data_d = data_d_orig
+        return daily_df
+
+    def query_stk_rating_data(self, fields_rating_ind, daily_df):
+        filter_str = "symbol={0}&start_date={1}&end_date={2}".format(
+            ','.join(self.symbol),
+            self.extended_start_date_d,
+            self.end_date)
+
+        df, msg = self.data_api.query(view='wd.secStockRatingConsus', filter=filter_str)
+        if df is None:
+            raise ValueError("query wd.secStockRatingConsus Error:" + msg)
+
+        df['date'] = df['date'].astype(int)
+
+        cycle_map = {'263001000' : '30d', '263002000': '90d', '263003000': '180d' }
+
+        data_d_orig = self.data_d
+        self.data_d = daily_df
+
+        for cycle, postfix in cycle_map.items():
+            df2 = df[df['wrating_cycle']==cycle].copy()
+            if df2.empty: continue
+            columns = [ (col ,col + '_' + postfix) for col in df2.columns if col not in ['date','symbol', 'wrating_cycle']]
+            df2 = df2.rename(columns=dict(columns))
+            for col in df2.columns :
+                if col in fields_rating_ind and col not in ['date','symbol', 'wrating_cycle']:
+                    tmp = df2.pivot_table(values=col, index='date', columns='symbol').fillna(method='ffill')
+                    self.append_df(tmp, col, is_quarterly=False)
+
+        daily_df = self.data_d
+        self.data_d = data_d_orig
+        return daily_df
+
+    def query_consensus_data(self, fields_ind, daily_df):
+
+        rolling_types = set() #['fy0','fy1','fy2','fy3','fttm','yoy','yoy2','cagr']
+        query_fields = set(['rolling_type'])
+        for f in fields_ind:
+            if f in self.consensus_data:
+                query_fields.add ( '_'.join(f.split('_')[0:-1]))
+                rolling_types.add( f.split('_')[-1] )
+
+
+        nsymbols  = 20
+        data = []
+        for i in range((len(self.symbol) + nsymbols -1) // nsymbols):
+            symbols = self.symbol[i*nsymbols: (i+1)*nsymbols]
+
+            filter_str = "symbol={0}&start_date={1}&end_date={2}&rolling_type={3}".format(
+                ','.join(symbols),
+                self.extended_start_date_d,
+                self.end_date,
+                ','.join(rolling_types).upper()
+            )
+            # filter_str = "symbol={0}&start_date={1}&end_date={2}".format(
+            #     ','.join(symbols),
+            #     self.extended_start_date_d,
+            #     self.end_date
+            # )
+
+            df, msg = self.data_api.query(view='wd.stkConsensusRollingData',
+                                          filter=filter_str,
+                                          fields=','.join(query_fields))
+            if df is None:
+                raise ValueError("query wd.stkConsensusRollingData Error:" + msg)
+            data.append(df)
+
+        df = pd.concat(data)
+        df['date'] = df['date'].astype(int)
+
+        data_d_orig = self.data_d
+        self.data_d = daily_df
+
+        for rolling_type in df['rolling_type'].unique():
+            df2 = df[df['rolling_type']==rolling_type].copy()
+            if df2.empty: continue
+            columns = [ (col ,col + '_' + rolling_type.lower()) for col in df2.columns if col not in ['date','symbol', 'rolling_type']]
+            df2 = df2.rename(columns=dict(columns))
+            df2.index = df2['date']
+            for col in df2.columns:
+                if col in fields_ind and col not in ['date','symbol', 'wrating_cycle']:
+                    tmp = df2.pivot_table(values=col, index='date', columns='symbol').fillna(method='ffill')
+                    self.append_df(tmp, col, is_quarterly=False)
+
+        daily_df = self.data_d
+        self.data_d = data_d_orig
+        return daily_df
+
+    def query_lgt_data(self, fields_lgt_ind, daily_df):
+        filter_str = "symbol={0}&start_date={1}&end_date={2}".format(
+            ','.join(self.symbol),
+            self.extended_start_date_q,
+            self.end_date)
+
+        df, msg = self.data_api.query(view="jz.secMoneyFlowNorth",filter=filter_str, fields=','.join(fields_lgt_ind))
+        if df is None:
+            raise ValueError('query secMoneyFlowNorth error: ' + msg)
+
+        df['trade_date'] = df['trade_date'].astype(np.int32)
+        # 持仓数据
+        holding_dv = df.pivot_table(index='trade_date', columns='symbol', values='hold_stocks')
+
+        # 持仓比例数据
+        holding_ratio = df.pivot_table(index='trade_date', columns='symbol', values='calculate_ratio')
+
+        # 获取除权除息日信息
+        df_dividend, msg = self.data_api.query_dividend(','.join(self.symbol), self.extended_start_date_d, self.end_date)
+        if df_dividend is None:
+            raise ValueError("query_dividend error: " + msg)
+
+        df_dividend = df_dividend[['symbol', 'record_date', 'exdiv_date']]
+        df_dividend['values'] = 1
+        df_dividend = df_dividend.sort_values(['symbol', 'record_date', 'exdiv_date'])
+        df_dividend = df_dividend.dropna()
+        df_regdt = df_dividend.pivot_table(index='record_date', columns='symbol', values='values').replace(np.nan, 0)
+        df_regdt = df_regdt.replace(np.nan, 0)
+
+        data_d_orig = self.data_d
+        self.data_d = daily_df
+
+        # 将除权除息日信息加入DataView
+        self.append_df(df_regdt, '_regdt', is_quarterly=False)
+        regdt = self.get_ts('_regdt').replace(np.nan, 0.0)
+        not_regdt = ~(regdt.astype(bool))
+
+        # 根据股票除权登记日和除权除息日调整lgt_holding
+        self.append_df(holding_dv, 'lgt_holding_origin', is_quarterly=False)
+        lgt_holding = self.get_ts('lgt_holding_origin')
+        adj_lgt_holding = lgt_holding[not_regdt]
+        lgt_holding = adj_lgt_holding.fillna(method='ffill')
+        self.append_df(lgt_holding, 'lgt_holding', is_quarterly=False)
+
+        # # 根据股票除权登记日和除权除息日调整lgt_holding_ratio
+        self.append_df(holding_ratio, 'lgt_holding_ratio_origin', is_quarterly=False)
+        lgt_holding_ratio = self.get_ts('lgt_holding_ratio_origin')
+        adj_lgt_holding_ratio = lgt_holding_ratio[not_regdt]
+        lgt_holding_ratio = adj_lgt_holding_ratio.fillna(method='ffill')
+        self.append_df(lgt_holding_ratio, 'lgt_holding_ratio', is_quarterly=False)
+
+        self.remove_field('_regdt,lgt_holding_origin,lgt_holding_ratio_origin')
+        daily_df = self.data_d
+        self.data_d = data_d_orig
+        return daily_df
+
+    def query_misc_data(self, fields, daily_df):
+
+        data_d_orig = self.data_d
+        self.data_d = daily_df
+
+        if 'st_flag' in fields:
+            df, msg = self.data_api.query(view='wd.stockST')
+            if df is None:
+                raise ValueError("query wd.stockST Error:" + msg)
+
+            st_symbols = df['symbol'].unique()
+            st_daily = self.get_ts('high')
+            for symbol in st_daily.columns:
+                st_daily[symbol] = ''
+                if symbol not in st_symbols: continue
+                tmp = df[df['symbol']==symbol]
+                for i in range(len(tmp)):
+                    x = tmp.iloc[i]
+                    entry_date = x['entry_dt']
+                    remove_dt  = x['remove_dt']
+                    if remove_dt == 0 :
+                        remove_dt = 99999999
+                    else:
+                        remove_dt -= 1
+                    stype = x['stype']
+                    st_daily.loc[pd.IndexSlice[entry_date: remove_dt], [symbol]] += stype
+
+            self.append_df(st_daily, 'st_flag', is_quarterly=False)
+
+        daily_df = self.data_d
+        self.data_d = data_d_orig
+        return daily_df
+
     def _query_data(self, symbol, fields):
         """
         Query data using different APIs, then store them in dict.
         period, start_date and end_date are fixed.
         Keys of dict are securitites.
-        
+
         Parameters
         ----------
         symbol : list of str
@@ -594,11 +1092,11 @@ def _query_data(self, symbol, fields):
         """
         sep = ','
         symbol_str = sep.join(symbol)
-    
+
         if self.freq == 1:
             daily_list = []
             quarterly_list = []
-        
+
             # TODO : use fields = {field: kwargs} to enable params
             fields_market_daily = self._get_fields('market_daily', fields, append=True)
             if fields_market_daily:
@@ -606,65 +1104,83 @@ def _query_data(self, symbol, fields):
                 # no adjust prices and other market daily fields
                 df_daily, msg1 = self.distributed_query('daily', symbol_str,
                                                         start_date=self.extended_start_date_d, end_date=self.end_date,
-                                                        adjust_mode=None, fields=sep.join(fields_market_daily), limit=100000)
-                #df_daily, msg1 = self.data_api.daily(symbol_str, start_date=self.extended_start_date_d, end_date=self.end_date,
+                                                        adjust_mode=None, fields=sep.join(fields_market_daily),
+                                                        limit=100000)
+                # df_daily, msg1 = self.data_api.daily(symbol_str, start_date=self.extended_start_date_d, end_date=self.end_date,
                 #                                     adjust_mode=None, fields=sep.join(fields_market_daily))
-            
+
                 if self.all_price:
                     adj_cols = ['open', 'high', 'low', 'close', 'vwap']
                     # adjusted prices
-                    #df_daily_adjust, msg11 = self.data_api.daily(symbol_str, start_date=self.extended_start_date_d, end_date=self.end_date,
+                    # df_daily_adjust, msg11 = self.data_api.daily(symbol_str, start_date=self.extended_start_date_d, end_date=self.end_date,
                     #                                             adjust_mode=self.adjust_mode, fields=','.join(adj_cols))
                     df_daily_adjust, msg1 = self.distributed_query('daily', symbol_str,
-                                                                   start_date=self.extended_start_date_d, end_date=self.end_date,
-                                                                   adjust_mode=self.adjust_mode, fields=sep.join(fields_market_daily), limit=100000)
-                
+                                                                   start_date=self.extended_start_date_d,
+                                                                   end_date=self.end_date,
+                                                                   adjust_mode=self.adjust_mode,
+                                                                   fields=sep.join(fields_market_daily), limit=100000)
+
                     df_daily = pd.merge(df_daily, df_daily_adjust, how='outer',
                                         on=['symbol', 'trade_date'], suffixes=('', '_adj'))
                 daily_list.append(df_daily.loc[:, fields_market_daily])
-        
+
             fields_ref_daily = self._get_fields('ref_daily', fields, append=True)
             if fields_ref_daily:
                 df_ref_daily, msg2 = self.distributed_query('query_lb_dailyindicator', symbol_str,
-                                                            start_date=self.extended_start_date_d, end_date=self.end_date,
+                                                            start_date=self.extended_start_date_d,
+                                                            end_date=self.end_date,
                                                             fields=sep.join(fields_ref_daily), limit=20000)
                 daily_list.append(df_ref_daily.loc[:, fields_ref_daily])
-        
+
             fields_income = self._get_fields('income', fields, append=True)
             if fields_income:
-                df_income, msg3 = self.data_api.query_lb_fin_stat('income', symbol_str, self.extended_start_date_q, self.end_date,
-                                                                  sep.join(fields_income), drop_dup_cols=['symbol', self.REPORT_DATE_FIELD_NAME])
+                df_income, msg3 = self.data_api.query_lb_fin_stat('income', symbol_str, self.extended_start_date_q,
+                                                                  self.end_date,
+                                                                  sep.join(fields_income),
+                                                                  drop_dup_cols=['symbol', self.REPORT_DATE_FIELD_NAME])
                 quarterly_list.append(df_income.loc[:, fields_income])
-        
+
             fields_balance = self._get_fields('balance_sheet', fields, append=True)
             if fields_balance:
-                df_balance, msg3 = self.data_api.query_lb_fin_stat('balance_sheet', symbol_str, self.extended_start_date_q, self.end_date,
-                                                                   sep.join(fields_balance), drop_dup_cols=['symbol', self.REPORT_DATE_FIELD_NAME])
+                df_balance, msg3 = self.data_api.query_lb_fin_stat('balance_sheet', symbol_str,
+                                                                   self.extended_start_date_q, self.end_date,
+                                                                   sep.join(fields_balance), drop_dup_cols=['symbol',
+                                                                                                            self.REPORT_DATE_FIELD_NAME])
                 quarterly_list.append(df_balance.loc[:, fields_balance])
-        
+
             fields_cf = self._get_fields('cash_flow', fields, append=True)
             if fields_cf:
-                df_cf, msg3 = self.data_api.query_lb_fin_stat('cash_flow', symbol_str, self.extended_start_date_q, self.end_date,
-                                                              sep.join(fields_cf), drop_dup_cols=['symbol', self.REPORT_DATE_FIELD_NAME])
+                df_cf, msg3 = self.data_api.query_lb_fin_stat('cash_flow', symbol_str, self.extended_start_date_q,
+                                                              self.end_date,
+                                                              sep.join(fields_cf),
+                                                              drop_dup_cols=['symbol', self.REPORT_DATE_FIELD_NAME])
                 quarterly_list.append(df_cf.loc[:, fields_cf])
-        
+
             fields_fin_ind = self._get_fields('fin_indicator', fields, append=True)
             if fields_fin_ind:
                 df_fin_ind, msg4 = self.data_api.query_lb_fin_stat('fin_indicator', symbol_str,
                                                                    self.extended_start_date_q, self.end_date,
-                                                                   sep.join(fields_fin_ind), drop_dup_cols=['symbol', self.REPORT_DATE_FIELD_NAME])
+                                                                   sep.join(fields_fin_ind), drop_dup_cols=['symbol',
+                                                                                                            self.REPORT_DATE_FIELD_NAME])
                 quarterly_list.append(df_fin_ind.loc[:, fields_fin_ind])
-    
+
+            fields_risk_model = self._get_fields('risk_model', fields, append=True)    
+            if fields_risk_model:
+                df_risk_model, msg5 = self.data_api.query_risk_model( symbol_str,
+                                                                   self.extended_start_date_q, self.end_date,
+                                                                   sep.join(fields_risk_model))
+                daily_list.append(df_risk_model.loc[:, fields_risk_model])                  
+
         else:
             raise NotImplementedError("freq = {}".format(self.freq))
-    
+
         return daily_list, quarterly_list
 
     @staticmethod
-    def _merge_data(dfs, index_name='trade_date'):
+    def _merge_data(dfs, index_name='trade_date', join='outer', keep_input=True):
         """
         Merge data from different APIs into one DataFrame.
-        
+
         Parameters
         ----------
         dfs : list of pd.DataFrame
@@ -673,29 +1189,67 @@ def _merge_data(dfs, index_name='trade_date'):
         -------
         merge : pd.DataFrame or None
             If dfs is empty, return None
-        
+
         Notes
         -----
         Align on date index, concatenate on columns (symbol and fields)
-        
+
         """
         # dfs = [df for df in dfs if df is not None]
+        new_dfs = []
+        # column level swap: [symbol, field] => [field, symbol]
+        for df in dfs:
+            if keep_input:
+                df_new = df.copy()
+            else:
+                df_new = df
+            df_new.columns = df_new.columns.swaplevel()
+            df_new = df_new.sort_index(axis=1)
+            new_dfs.append(df_new)
+
+        index_set = None
+        for df in new_dfs:
+            if index_set is None:
+                index_set = set(df.index)
+            else:
+                if join == 'inner':
+                    index_set = index_set & set(df.index)
+                else:
+                    index_set = index_set | set(df.index)
+        index_list = list(index_set)
+        index_list.sort()
+            
+        cols = None
+        for df in new_dfs:
+            if cols is None:
+                cols = df.columns
+            else:
+                cols = cols.append(df.columns)
+        
+        merge = pd.DataFrame(data=np.nan, index=index_list, columns=cols)
+        
+        for df in new_dfs:
+            for col in df.columns.levels[0]:
+                if not df[col].empty:
+                    merge[col] = df[col]
+        
+        merge.columns = merge.columns.swaplevel()
+        merge = merge.sort_index(axis=1)
     
-        merge = pd.concat(dfs, axis=1, join='outer')
-    
+        # merge1 = pd.concat(dfs, axis=1, join='outer')
         # drop duplicated columns. ONE LINE EFFICIENT version
         mask_duplicated = merge.columns.duplicated()
         if np.any(mask_duplicated):
             # print("Duplicated columns found. Dropped.")
             merge = merge.loc[:, ~mask_duplicated]
-        
+
             # if merge.isnull().sum().sum() > 0:
             # print "WARNING: nan in final merged data. NO fill"
             # merge.fillna(method='ffill', inplace=True)
-    
+
         merge = merge.sort_index(axis=1, level=['symbol', 'field'])
         merge.index.name = index_name
-    
+
         return merge
 
     def _fill_missing_idx_col(self, df, index=None, symbols=None):
@@ -704,20 +1258,30 @@ def _fill_missing_idx_col(self, df, index=None, symbols=None):
         if symbols is None:
             symbols = self.symbol
         fields = df.columns.levels[1]
-    
-        if len(fields) * len(self.symbol) != len(df.columns) or len(index) != len(df.index):
-            cols_multi = pd.MultiIndex.from_product([symbols, fields], names=['symbol', 'field'])
+
+        if len(fields) * len(self.symbol) != len(df.columns) or set(index) != set(df.index):
+            cols_multi = pd.MultiIndex.from_product([fields, symbols], names=['field', 'symbol'])
             cols_multi = cols_multi.sort_values()
+
             df_final = pd.DataFrame(index=index, columns=cols_multi, data=np.nan)
             df_final.index.name = df.index.name
-        
-            df_final.update(df)
+            
+            df.columns = df.columns.swaplevel()
+            df = df.sort_index(axis=1)
+            
+            for col in df.columns.levels[0]:
+                df_final[col] = df[col]
+            
+            df_final.columns = df_final.columns.swaplevel()
+            df_final = df_final.sort_index(axis=1)
+            # df_final.update(df)
         
             # idx_diff = sorted(set(df_final.index) - set(df.index))
             col_diff = sorted(set(df_final.columns.levels[0].values) - set(df.columns.levels[0].values))
-            print ("WARNING: some data is unavailable: "
+            print("WARNING: some data is unavailable: "
                    # + "\n    At index " + ', '.join(idx_diff)
-                   + "\n    At fields " + ', '.join(col_diff))
+                   + "\n    At fields "
+                   + (', '.join(col_diff) if len(col_diff) < 20 else ', '.join(col_diff[:20]) + " ..."))
             return df_final
         else:
             return df
@@ -727,14 +1291,14 @@ def _align_and_merge_q_into_d(self):
         if data_d is not None and data_q is not None:
             df_ref_ann = data_q.loc[:, pd.IndexSlice[:, self.ANN_DATE_FIELD_NAME]].copy()
             df_ref_ann.columns = df_ref_ann.columns.droplevel(level='field')
-        
+
             dic_expanded = dict()
             for field_name, df in data_q.groupby(level=1, axis=1):  # by column multiindex fields
                 df_expanded = align(df, df_ref_ann, self.dates)
                 dic_expanded[field_name] = df_expanded
             df_quarterly_expanded = pd.concat(dic_expanded.values(), axis=1)
             df_quarterly_expanded.index.name = self.TRADE_DATE_FIELD_NAME
-        
+
             data_d_merge = self._merge_data([data_d, df_quarterly_expanded], index_name=self.TRADE_DATE_FIELD_NAME)
             data_d = data_d_merge.loc[data_d.index, :]
         self.data_d = data_d
@@ -747,22 +1311,51 @@ def _prepare_adj_factor(self):
         symbol_stocks = self.data_inst.loc[mask_stocks].index.values
         symbol_str = ','.join(symbol_stocks)
         df_adj = self.data_api.query_adj_factor_daily(symbol_str,
-                                                      start_date=self.extended_start_date_d, end_date=self.end_date, div=False)
+                                                      start_date=self.extended_start_date_d, end_date=self.end_date,
+                                                      div=False)
         self.append_df(df_adj, 'adjust_factor', is_quarterly=False)
 
     def _prepare_comp_info(self):
         # if a symbol is index member of any one universe, its value of index_member will be 1.0
-        res = dict()
-        for univ in self.universe:
-            df = self.data_api.query_index_member_daily(univ, self.extended_start_date_d, self.end_date)
-            res[univ] = df
-        df_res = pd.concat(res, axis=0)
-        df = df_res.groupby(by='trade_date').apply(lambda df: df.any(axis=0)).astype(float)
-        self.append_df(df, 'index_member', is_quarterly=False)
-    
-        # use weights of the first universe
-        df_weights = self.data_api.query_index_weights_daily(self.universe[0], self.extended_start_date_d, self.end_date)
-        self.append_df(df_weights, 'index_weight', is_quarterly=False)
+
+        #  Backup code
+        #    Multiple universe
+        #     res = dict()
+        #     for univ in self.universe:
+        #         df = self.data_api.query_index_member_daily(univ, self.extended_start_date_d, self.end_date)
+        #         res[univ] = df
+        #     df_res = pd.concat(res, axis=0)
+        #     df = df_res.groupby(by='trade_date').apply(lambda df: df.any(axis=0)).astype(float)
+        #
+        #     # Always include additional symbols
+        #     # for code in self.symbol:
+        #     #     if code not in df.columns:
+        #     #         df[code] = 1.0
+        # else:
+        # tzxu 2018.5.28 only set index_member and index_weight of first universe
+        first_universe = self.universe[0]
+        if first_universe.endswith(".UNI"):
+            df = self.data_api.query_universe_member_daily(first_universe, self.extended_start_date_d, self.end_date)
+            df = df.groupby(by='trade_date').apply(lambda df: df.any(axis=0)).astype(float)
+            self.append_df(df, 'index_member', is_quarterly=False)
+
+            # use weights of the first universe
+            df_weights = self.data_api.query_universe_weights_daily(first_universe, self.extended_start_date_d,
+                                                                    self.end_date)
+            self.append_df(df_weights, 'index_weight', is_quarterly=False)
+        else:
+            df = self.data_api.query_index_member_daily(first_universe, self.extended_start_date_d, self.end_date)
+            df = df.groupby(by='trade_date').apply(lambda df: df.any(axis=0)).astype(float)
+            self.append_df(df, 'index_member', is_quarterly=False)
+
+            # use weights of the first universe
+            df_weights = self.data_api.query_index_weights_daily(first_universe, self.extended_start_date_d,
+                                                                 self.end_date)
+
+            if first_universe not in self.index_weights:
+                self.index_weights[first_universe] = df_weights
+
+            self.append_df(df_weights, 'index_weight', is_quarterly=False)
 
     def _prepare_report_date(self):
         idx = self.data_q.index
@@ -770,9 +1363,9 @@ def _prepare_report_date(self):
         n = len(idx)
         quarter = idx.values // 100 % 100
         df_report_date.loc[:, :] = quarter.reshape(n, -1)
-        
+
         self.append_df(df_report_date, 'quarter', is_quarterly=True)
-    
+
     def _prepare_inst_info(self):
         res = self.data_api.query_inst_info(symbol=','.join(self.symbol),
                                             fields='symbol,inst_type,name,list_date,'
@@ -793,20 +1386,61 @@ def _prepare_group(self, group_fields):
             df = self.data_api.query_industry_daily(symbol=','.join(self.symbol),
                                                     start_date=self.extended_start_date_q, end_date=self.end_date,
                                                     type_=type_, level=level)
-            self.append_df(df, field, is_quarterly=False)
+            if df is not None:
+                self.append_df(df, field, is_quarterly=False)
+                self.industry_groups[field] = df
 
     def _prepare_benchmark(self):
-        df_bench, msg = self.data_api.daily(self.benchmark,
-                                            start_date=self.extended_start_date_d, end_date=self.end_date,
-                                            adjust_mode=self.adjust_mode,
-                                            fields='trade_date,symbol,close,vwap,volume,turnover')
-        # TODO: we want more than just close price of benchmark
-        df_bench = df_bench.set_index('trade_date').loc[:, ['close']]
+        if self.benchmark == 'VW_UNIVERSE':
+            df_close = self.get_ts('close_adj', start_date=self.extended_start_date_d)
+            df_ret   = df_close.pct_change()
+            df_float_mv = self.get_ts('float_mv', start_date=self.extended_start_date_d)
+            df_weight = df_float_mv.div(df_float_mv.sum(axis = 1), axis = 0)
+            df_ret_vw = df_weight.mul(df_ret).sum(axis = 1).replace(np.nan, 0.0)
+            df_price = (1 + df_ret_vw).cumprod()
+            df_price = pd.DataFrame(df_price)
+            df_price.columns = ['close']
+            df_bench = df_price
+        elif self.benchmark == 'EW_UNIVERSE':
+            df_close = self.get_ts('close_adj', start_date=self.extended_start_date_d)
+            df_ret   = df_close.pct_change()
+            df_ret_ew = df_ret.mean(axis = 1).replace(np.nan, 0.0)
+            df_price = (1 + df_ret_ew).cumprod()
+            df_price = pd.DataFrame(df_price)
+            df_price.columns = ['close']
+            df_bench = df_price
+        else:
+            df_bench, msg = self.data_api.daily(self.benchmark,
+                                                start_date=self.extended_start_date_d, end_date=self.end_date,
+                                                adjust_mode=self.adjust_mode,
+                                                fields='trade_date,symbol,open,high,low,close,vwap,volume,turnover')
+            # TODO: we want more than just close price of benchmark
+            df_bench = df_bench.set_index('trade_date').loc[:, ['open','high','low','close']]
+
+            is_index = re.match('399.*.SZ', self.benchmark) or re.match('000.*.SH', self.benchmark)
+            if is_index:
+                # use weights of the first universe
+                if self.benchmark not in self.index_weights:
+                    df_weights = self.data_api.query_index_weights_daily(self.benchmark, self.extended_start_date_d,
+                                                                     self.end_date)
+                    self.index_weights[self.benchmark] = df_weights
+
+            # Add bm_high, bm_low, bm_open, bm_close to each code
+            tmp = self.get_ts('open')
+            for field in ['open','high','low','close']:
+                for symbol in tmp.columns:
+                    tmp[symbol] = df_bench[field]
+                self.append_df(tmp,   'bm_' + field,  is_quarterly=False)
+
+            df_bench = df_bench[['close']]
         return df_bench
 
     # --------------------------------------------------------------------------------------------------------
     # Add/Remove Fields&Formulas
     def _add_field(self, field_name, is_quarterly=None):
+        if field_name in self.fields:
+            return
+
         self.fields.append(field_name)
         if not self._is_predefined_field(field_name):
             if is_quarterly is None:
@@ -819,13 +1453,13 @@ def _add_field(self, field_name, is_quarterly=None):
     def add_field(self, field_name, data_api=None):
         """
         Query and append new field to DataView.
-        
+
         Parameters
         ----------
         data_api : BaseDataServer
         field_name : str
             Must be a known field name (which is given in documents).
-        
+
         Returns
         -------
         bool
@@ -838,7 +1472,7 @@ def add_field(self, field_name, data_api=None):
                 return False
         else:
             self.data_api = data_api
-            
+
         if field_name in self.fields:
             print("Field name [{:s}] already exists.".format(field_name))
             return False
@@ -848,7 +1482,7 @@ def add_field(self, field_name, data_api=None):
             return False
 
         merge_d, merge_q = self._prepare_daily_quarterly([field_name])
-    
+
         if self._is_daily_field(field_name):
             if self.data_d is None:
                 raise ValueError("Please prepare [{:s}] first.".format(field_name))
@@ -859,7 +1493,7 @@ def add_field(self, field_name, data_api=None):
                 raise ValueError("Please prepare [{:s}] first.".format(field_name))
             _, merge = self._prepare_daily_quarterly([field_name])
             is_quarterly = True
-        
+
         merge = merge.loc[:, pd.IndexSlice[:, field_name]]
         merge.columns = merge.columns.droplevel(level=1)
         self.append_df(merge, field_name, is_quarterly=is_quarterly)  # whether contain only trade days is decided by existing data.
@@ -870,13 +1504,60 @@ def add_field(self, field_name, data_api=None):
             df_expanded = align(merge, df_ann, self.dates)
             self.append_df(df_expanded, field_name, is_quarterly=False)
         return True
-    
-    def add_formula(self, field_name, formula, is_quarterly, overwrite=True,
-                    formula_func_name_style='camel', data_api=None,
-                    within_index=True):
-        """
-        Add a new field, which is calculated using existing fields.
-        
+
+    def _create_parser(self, formula_func_name_style='camel', allow_future_data=False):
+        parser = Parser(allow_future_data=allow_future_data)
+        parser.set_capital(formula_func_name_style)
+
+        for key in self._import_factors:
+            factor = self._import_factors[key]
+            parser.register_function(factor.name, FactorFunc(self, factor, allow_future_data))
+
+        parser.register_function("ResReturn", ResReturnFunc(self, allow_future_data))
+        return parser
+
+    def _get_var(self, var, allow_future_data):
+
+        if not allow_future_data:
+            if var in self.labels:
+                raise ValueError("Variable {0} is label while calculating factor!".format(var))
+
+        if var in self._import_factors:
+            if self._is_quarter_field(var):
+                df_var = self.get_ts_quarter(var, start_date=self.extended_start_date_q)
+            elif var in self.fields:
+                df_var = self.get_ts(var, start_date=self.extended_start_date_d, end_date=self.end_date)
+            else:
+                factor_def = self._import_factors[var]
+                df_var = FactorFunc(self, factor_def, allow_future_data)()
+                self.append_df(df_var, var, is_quarterly=factor_def.is_quarterly)
+
+            return df_var
+
+        if self._is_quarter_field(var):
+            return self.get_ts_quarter(var, start_date=self.extended_start_date_q)
+        else:
+            # must use extended date. Default is start_date
+            return self.get_ts(var, start_date=self.extended_start_date_d, end_date=self.end_date)
+
+    def add_factor(self, factor, name=None, is_quarterly=False):  # within_index=True):
+        if not name:
+            name = factor.split('(')[0]
+
+        self.add_formula(field_name=name, formula=factor, is_quarterly=is_quarterly, is_factor=True)
+
+    def add_label(self, factor, name=None, is_quarterly=False):  # within_index=True):
+        if not name:
+            name = factor.split('(')[0]
+
+        self.add_formula(field_name=name, formula=factor, is_quarterly=is_quarterly, is_factor=False)
+
+    def add_formula(self, field_name, formula, is_quarterly, overwrite=True,
+                    formula_func_name_style='camel', data_api=None,
+                    within_index=True, is_factor=True):
+        """
+        Add a new field, which is calculated using existing fields.
+
         Parameters
         ----------
         formula : str or unicode
@@ -891,7 +1572,9 @@ def add_formula(self, field_name, formula, is_quarterly, overwrite=True,
         data_api : RemoteDataService, optional
         within_index : bool
             When do cross-section operatioins, whether just do within index components.
-        
+        is_factor: bool
+            Whether new field is factor or label.
+
         Notes
         -----
         Time cost of this function:
@@ -900,7 +1583,7 @@ def add_formula(self, field_name, formula, is_quarterly, overwrite=True,
         """
         if data_api is not None:
             self.data_api = data_api
-            
+
         if field_name in self.fields:
             if overwrite:
                 self.remove_field(field_name)
@@ -909,15 +1592,17 @@ def add_formula(self, field_name, formula, is_quarterly, overwrite=True,
                 raise ValueError("Add formula failed: name [{:s}] exist. Try another name.".format(field_name))
         elif self._is_predefined_field(field_name):
             raise ValueError("[{:s}] is alread a pre-defined field. Please use another name.".format(field_name))
-        
-        parser = Parser()
-        parser.set_capital(formula_func_name_style)
-        
+
+        parser = self._create_parser(formula_func_name_style, allow_future_data=not is_factor)
         expr = parser.parse(formula)
-        
+
         var_df_dic = dict()
         var_list = expr.variables()
-        
+
+        factors = [var for var in var_list if var in self._import_factors]
+
+        var_list = [var for var in var_list if var not in expr.functions]
+
         # TODO: users do not need to prepare data before add_formula
         if not self.fields:
             self.fields.extend(var_list)
@@ -930,16 +1615,37 @@ def add_formula(self, field_name, formula, is_quarterly, overwrite=True,
                     success = self.add_field(var)
                     if not success:
                         return
-        
+        if is_factor:
+            for var in var_list:
+                if var in self.labels:
+                    raise ValueError("Variable {0} is label while calculating factor!".format(var))
+        else:
+            if field_name not in self.labels:
+                self.labels.append(field_name)
+
         for var in var_list:
             if self._is_quarter_field(var):
                 df_var = self.get_ts_quarter(var, start_date=self.extended_start_date_q)
             else:
                 # must use extended date. Default is start_date
                 df_var = self.get_ts(var, start_date=self.extended_start_date_d, end_date=self.end_date)
-            
+
             var_df_dic[var] = df_var
-        
+
+        # TODO:
+        for factor in factors:
+            if factor in self.fields:
+                df_var = self.get_ts(factor, start_date=self.extended_start_date_d, end_date=self.end_date)
+                var_df_dic[factor] = df_var
+
+            elif not self._import_factors[factor].args:
+                f = parser.functions[factor]
+                df_var = f()
+                self.append_df(df_var, factor, is_quarterly=is_quarterly)
+                var_df_dic[factor] = df_var
+            # else:
+            #     raise ValueError("no arguments for factor: " + factor)
+
         # TODO: send ann_date into expr.evaluate. We assume that ann_date of all fields of a symbol is the same
         df_ann = self._get_ann_df()
         if within_index:
@@ -948,29 +1654,33 @@ def add_formula(self, field_name, formula, is_quarterly, overwrite=True,
         else:
             df_eval = parser.evaluate(var_df_dic, ann_dts=df_ann, trade_dts=self.dates)
 
+        # FIXME: When field_name is factor's id, the factor may be added before!
+        if field_name in self.fields:
+            self.remove_field(field_name)
+
         self.append_df(df_eval, field_name, is_quarterly=is_quarterly)
 
         if is_quarterly:
             df_ann = self._get_ann_df()
             df_expanded = align(df_eval, df_ann, self.dates)
             self.append_df(df_expanded, field_name, is_quarterly=False)
-        
+
     def append_df(self, df, field_name, is_quarterly=False):
         """
         Append DataFrame to existing multi-index DataFrame and add corresponding field name.
-        
+
         Parameters
         ----------
         df : pd.DataFrame or pd.Series
         field_name : str or unicode
         is_quarterly : bool
             Whether df is quarterly data (like quarterly financial statement) or daily data.
-            
+
         Notes
         -----
         append_df does not support overwrite. To overwrite a field, you must first do self.remove_fields(),
         then append_df() again.
-        
+
         """
         df = df.copy()
         if isinstance(df, pd.DataFrame):
@@ -979,26 +1689,35 @@ def append_df(self, df, field_name, is_quarterly=False):
             df = pd.DataFrame(df)
         else:
             raise ValueError("Data to be appended must be pandas format. But we have {}".format(type(df)))
-    
+
         if is_quarterly:
             the_data = self.data_q
         else:
             the_data = self.data_d
-        
-        exist_symbols = the_data.columns.levels[0]
-        if len(df.columns) < len(exist_symbols):
-            df2 = pd.DataFrame(index=df.index, columns=exist_symbols, data=np.nan)
-            df2.update(df)
-            df = df2
-        elif len(df.columns) > len(exist_symbols):
-            df = df.loc[:, exist_symbols]
-        multi_idx = pd.MultiIndex.from_product([exist_symbols, [field_name]])
+
+        # if field_name in the_data.columns.levels[1]:
+        #     raise ValueError("The field already exists in DataView! " + field_name)
+
+        # Copy exists symbols and set multi index
+        df = df.loc[:, the_data.columns.levels[0]]
+        multi_idx = pd.MultiIndex.from_product([[field_name], df.columns])
         df.columns = multi_idx
+        df = df.sort_index(axis=1)
 
+        the_data.columns = the_data.columns.swaplevel()
+        the_data = the_data.sort_index(axis=1)
+
+        if field_name not in self.fields or field_name not in the_data.columns.levels[0]:
+            new_cols = the_data.columns.append(df.columns)
+            the_data = the_data.reindex(columns=new_cols)
+
+        the_data[field_name] = df[field_name]
+        the_data.columns = the_data.columns.swaplevel()
+        the_data = the_data.sort_index(axis=1)
         #the_data = apply_in_subprocess(pd.merge, args=(the_data, df),
         #                            kwargs={'left_index': True, 'right_index': True, 'how': 'left'})  # runs in *only* one process
-        the_data = pd.merge(the_data, df, left_index=True, right_index=True, how='left')
-        the_data = the_data.sort_index(axis=1)
+        #the_data = pd.merge(the_data, df, left_index=True, right_index=True, how='left')
+        #the_data = the_data.sort_index(axis=1)
         #merge = the_data.join(df, how='left')  # left: keep index of existing data unchanged
         #sort_columns(the_data)
     
@@ -1011,13 +1730,13 @@ def append_df(self, df, field_name, is_quarterly=False):
     def remove_field(self, field_names):
         """
         Query and append new field to DataView.
-        
+
         Parameters
         ----------
         field_names : str
             Separated by comma.
             The (custom) field to be removed from dataview.
-        
+
         Returns
         -------
         bool
@@ -1028,13 +1747,13 @@ def remove_field(self, field_names):
             field_names = field_names.split(',')
         else:
             raise ValueError("field_names must be str separated by comma.")
-    
+
         for field_name in field_names:
             # parameter validation
             if field_name not in self.fields:
                 print("Field name [{:s}] does not exist. Stop remove_field.".format(field_name))
                 return
-        
+
             if self._is_daily_field(field_name):
                 is_quarterly = False
             elif self._is_quarter_field(field_name):
@@ -1042,13 +1761,13 @@ def remove_field(self, field_names):
             else:
                 print("Field name [{}] is a pre-defined field, ignore.".format(field_name))
                 return
-        
+
             # remove field data
-            
+
             self.data_d = self.data_d.drop(field_name, axis=1, level=1)
             if is_quarterly:
                 self.data_q = self.data_q.drop(field_name, axis=1, level=1)
-        
+
             # remove fields name from list
             self.fields.remove(field_name)
             if is_quarterly:
@@ -1063,7 +1782,7 @@ def remove_field(self, field_names):
     def get(self, symbol="", start_date=0, end_date=0, fields=""):
         """
         Basic API to get arbitrary data. If nothing fetched, return None.
-        
+
         Parameters
         ----------
         symbol : str, optional
@@ -1082,29 +1801,29 @@ def get(self, symbol="", start_date=0, end_date=0, fields=""):
 
         """
         sep = ','
-    
+
         if not fields:
             fields = slice(None)  # self.fields
         else:
             fields = fields.split(sep)
-    
+
         if not symbol:
             symbol = slice(None)  # this is 3X faster than symbol = self.symbol
         else:
             symbol = symbol.split(sep)
-    
+
         if not start_date:
             start_date = self.start_date
         if not end_date:
             end_date = self.end_date
-    
+
         res = self.data_d.loc[pd.IndexSlice[start_date: end_date], pd.IndexSlice[symbol, fields]]
         return res
-    
+
     def get_snapshot(self, snapshot_date, symbol="", fields=""):
         """
         Get snapshot of given fields and symbol at snapshot_date.
-        
+
         Parameters
         ----------
         snapshot_date : int
@@ -1131,33 +1850,32 @@ def get_snapshot(self, snapshot_date, symbol="", fields=""):
             else:
                 return df
 
-
         res = self.get(symbol=symbol, start_date=snapshot_date, end_date=snapshot_date, fields=fields)
         if res is None:
             print("No data. for date={}, fields={}, symbol={}".format(snapshot_date, fields, symbol))
             return
-    
+
         res = res.stack(level='symbol', dropna=False)
         res.index = res.index.droplevel(level=self.TRADE_DATE_FIELD_NAME)
-    
+
         return res
-    
+
     def _get_ann_df(self):
         """
         Query announcement date of financial statements of all securities.
-        
+
         Returns
         -------
         df_ann : pd.DataFrame or None
             Index is date, column is symbol.
             If no quarterly data available, return None.
-        
+
         """
         if self.data_q is None:
             return None
         df_ann = self.data_q.loc[:, pd.IndexSlice[:, self.ANN_DATE_FIELD_NAME]]
         df_ann.columns = df_ann.columns.droplevel(level='field')
-    
+
         return df_ann
 
     def get_symbol(self, symbol, start_date=0, end_date=0, fields=""):
@@ -1170,28 +1888,33 @@ def get_symbol(self, symbol, start_date=0, end_date=0, fields=""):
         res.columns = res.columns.droplevel(level='symbol')
         return res
 
-    def get_ts_quarter(self, field, symbol="", start_date=0, end_date=0):
+    def get_ts_quarter(self, fields, symbols="", start_date=0, end_date=0):
         # TODO
         sep = ','
-        if not symbol:
-            symbol = self.symbol
+        if not symbols:
+            symbols = self.symbol
         else:
-            symbol = symbol.split(sep)
-    
-        if not start_date:
-            start_date = self.start_date
-        if not end_date:
-            end_date = self.end_date
-    
-        df_ref_quarterly = self.data_q.loc[:, pd.IndexSlice[symbol, field]]
+            symbols = symbols.split(sep)
+
+        if not fields:
+            fields = slice(None)  # self.fields
+        else:
+            fields = fields.split(sep)
+
+        # if not start_date:
+        #     start_date = self.start_date
+        # if not end_date:
+        #     end_date = self.end_date
+
+        df_ref_quarterly = self.data_q.loc[:, pd.IndexSlice[symbols, fields]]
         df_ref_quarterly.columns = df_ref_quarterly.columns.droplevel(level='field')
-    
+
         return df_ref_quarterly
-    
-    def get_ts(self, field, symbol="", start_date=0, end_date=0):
+
+    def get_ts(self, field, symbol="", start_date=0, end_date=0, keep_level=False):
         """
         Get time series data of single field.
-        
+
         Parameters
         ----------
         field : str or unicode
@@ -1215,43 +1938,81 @@ def get_ts(self, field, symbol="", start_date=0, end_date=0):
                                                                                         end_date, field, symbol))
             raise ValueError
             return
-    
-        res.columns = res.columns.droplevel(level='field')
-    
+
+        #if not keep_level and len(res.columns) and len(field.split(',')) == 1:
+        if not keep_level and len(field.split(',')) == 1:
+            res.columns = res.columns.droplevel(level='field')
+            # XXX Save field name for ResReturnFunc
+            res.columns.name = field
+
         return res
-    
+
     # --------------------------------------------------------------------------------------------------------
     # DataView I/O
     @staticmethod
     def _load_h5(fp):
         """Load data and meta_data from hd5 file.
-        
+
         Parameters
         ----------
         fp : str, optional
             File path of pre-stored hd5 file.
-        
+
         """
         h5 = pd.HDFStore(fp)
-        
+
         res = dict()
         for key in h5.keys():
             res[key] = h5.get(key)
-            
+
         h5.close()
-        
+
         return res
 
     def _process_data(self, large_memory=False):
         """
-        Process data for improving performance
+        Process data for improving performance, preload factors
         """
+        for factor in self.factors + self.labels:
+            factor = factor.strip()
+            if not factor: continue
+
+            factor_name = ""
+            factor_expr = ""
+            if "=" in factor:
+                tmp = factor.split('=')
+                factor_name = tmp[0]
+                factor_expr = tmp[1]
+            else:
+                factor_name = factor
+                factor_expr = factor
+
+            factor_id = factor_expr.split('(')[0].strip()
+            if factor_id not in self._import_factors:
+                #print("Can't find factor definitions: " + factor_id)
+                continue
+            if self._import_factors[factor_id].is_quarterly:
+                t = self.get_ts_quarter(factor_name)
+                if t is None or len(t.columns) == 0:
+                    self.add_factor(factor_expr, factor_name, is_quarterly=True)
+            else:
+                t = self.get_ts(factor_name)
+                if t is None or len(t.columns) == 0:
+                    self.add_factor(factor_expr, factor_name, is_quarterly=False)
+
         t = self.get_ts('_daily_adjust_factor')
         if t is None or len(t.columns) == 0:
             a = self.get_ts('adjust_factor')
-            b = (a / a.shift(1)).fillna(1.0)
-            self.append_df(b, '_daily_adjust_factor', is_quarterly=False)
-
+            if len(a.columns):
+                b = (a / a.shift(1)).fillna(1.0)
+                self.append_df(b, '_daily_adjust_factor', is_quarterly=False)
+            else:
+                # add fake _daily_adjust_factor for futures
+                tmp = self.get_ts('close')
+                for c in tmp.columns:
+                    if c:
+                        tmp[c] = 1.0
+                self.append_df(tmp, '_daily_adjust_factor', is_quarterly=False)
 
         t = self.get_ts("_limit")
         if t is None or len(t.columns) == 0:
@@ -1259,47 +2020,69 @@ def _process_data(self, large_memory=False):
             mask = dates < self.start_date
             before_first_day = dates[mask][-1]
 
-            open = self.get_ts('open')
-            preclose = self.get_ts('close', start_date=before_first_day).shift(1)
-            limit = np.abs((open - preclose)/preclose)
+            open = self.get_ts('open_adj')
+            preclose = self.get_ts('close_adj', start_date=before_first_day).shift(1)
+            limit = np.abs((open - preclose) / preclose)
             self.append_df(limit, "_limit", is_quarterly=False)
 
         # Snapshot dict may use large memory.
 
         if large_memory:
-            dates = self.data_d.index.values
-            df = self.data_d.T.unstack()
-            self._snapshot = {}
-            for date in dates:
-                tmp = df[date].copy()
-                del tmp.index.name
-                del tmp.columns.name
-                self._snapshot[date] = tmp
-
+            self.update_snapshot()
+
+    def update_snapshot(self):
+        dates = self.data_d.index.values
+        df = self.data_d.T.unstack()
+        self._snapshot = {}
+        for date in dates:
+            tmp = df[date].copy()
+            del tmp.index.name
+            del tmp.columns.name
+            self._snapshot[date] = tmp
 
     def load_dataview(self, folder_path='.', large_memory=True):
         """
         Load data from local file.
-        
+
         Parameters
         ----------
         folder_path : str or unicode, optional
             Folder path to store hd5 file and meta data.
-            
+
         """
+
         path_meta_data = os.path.join(folder_path, 'meta_data.json')
         path_data = os.path.join(folder_path, 'data.hd5')
         if not (os.path.exists(path_meta_data) and os.path.exists(path_data)):
             raise IOError("There is no data file under directory {}".format(folder_path))
-        
+
         meta_data = jutil.read_json(path_meta_data)
         dic = self._load_h5(path_data)
         self.data_d = dic.get('/data_d', None)
         self.data_q = dic.get('/data_q', None)
         self._data_benchmark = dic.get('/data_benchmark', None)
         self._data_inst = dic.get('/data_inst', None)
+        self._factor_df = dic.get('/factor_df', None)
+
+        for k in dic.keys():
+            if k.startswith('/index_weight/'):
+                self.index_weights[k.split('/')[2]] = dic[k]
+            if k.startswith('/industry_group/'):
+                self.industry_groups[k.split('/')[2]] = dic[k]
+
         self.__dict__.update(meta_data)
 
+        for index, row in self._factor_df.iterrows():
+            factor_id = row['factor_id']
+            factor_body = row['factor_def']
+            factor_args = list(filter(None, row['factor_args'].split(',')))
+            if 'factor_quarterly' in row:
+                factor_quarterly = row['factor_quarterly']
+            else:
+                factor_quarterly = False
+
+            self._import_factors[factor_id] = FactorDef(factor_id, factor_args, factor_body, factor_quarterly)
+
         self._process_data(large_memory)
 
         print("Dataview loaded successfully.")
@@ -1308,7 +2091,7 @@ def save_dataview(self, folder_path):
         """
         Save data and meta_data_to_store to a single hd5 file.
         Store at output/sub_folder
-        
+
         Parameters
         ----------
         folder_path : str or unicode
@@ -1318,25 +2101,36 @@ def save_dataview(self, folder_path):
         abs_folder = os.path.abspath(folder_path)
         meta_path = os.path.join(folder_path, 'meta_data.json')
         data_path = os.path.join(folder_path, 'data.hd5')
-        
-        data_to_store = {'data_d': self.data_d, 'data_q': self.data_q,
-                         'data_benchmark': self.data_benchmark, 'data_inst': self.data_inst}
+
+        data_to_store = {'data_d': self.data_d,
+                         'data_q': self.data_q,
+                         'data_benchmark': self.data_benchmark,
+                         'data_inst': self.data_inst,
+                         'factor_df': self._factor_df
+                         }
+        for symbol in self.index_weights.keys():
+            data_to_store['index_weight/' + symbol] = self.index_weights[symbol]
+
+        for group in self.industry_groups.keys():
+            data_to_store['industry_group/' + group] = self.industry_groups[group]
+
         data_to_store = {k: v for k, v in data_to_store.items() if v is not None}
         meta_data_to_store = {key: self.__dict__[key] for key in self.meta_data_list}
 
+
         print("\nStore data...")
         jutil.save_json(meta_data_to_store, meta_path)
         self._save_h5(data_path, data_to_store)
-        
-        print ("Dataview has been successfully saved to:\n"
-               + abs_folder + "\n\n"
-               + "You can load it with load_dataview('{:s}')".format(abs_folder))
+
+        print("Dataview has been successfully saved to:\n"
+              + abs_folder + "\n\n"
+              + "You can load it with load_dataview('{:s}')".format(abs_folder))
 
     @staticmethod
     def _save_h5(fp, dic):
         """
         Save data in dic to a hd5 file.
-        
+
         Parameters
         ----------
         fp : str
@@ -1346,19 +2140,204 @@ def _save_h5(fp, dic):
         """
         import warnings
         warnings.filterwarnings('ignore', category=pd.io.pytables.PerformanceWarning)
-        
+
         jutil.create_dir(fp)
         h5 = pd.HDFStore(fp, complevel=9, complib='blosc')
         for key, value in dic.items():
             h5[key] = value
         h5.close()
 
+    def dup(self, symbols=None, remove_fields=None, start_date=None, end_date=None, fields=None, with_default_fields=True, large_memory=False):
+        """
+        Duplicate this dataview with less symbols, dates between start_date and end_date and less fields.
+
+        Parameters
+        ----------
+        symbols:       only copy data of symbols in this parameter.
+        remove_fields: only copy fields not in this parameter.
+        start_date:    only copy data starting with this date.
+        end_date:      only copy data ending with this date.
+        fields:        only copy fields in this parameter.
+        """
+        if not start_date:
+            start_date = self.start_date
+        if not end_date:
+            end_date = self.end_date
+
+        extended_start_date_d = jutil.shift(self.start_date, n_weeks=-8)  # query more data
+        extended_start_date_q = jutil.shift(self.start_date, n_weeks=-80)
+
+        if remove_fields and fields:
+            raise ValueError("Shouldn't use both fields and remove_fields")
+
+        if remove_fields:
+            if isinstance(remove_fields, basestring):
+                remove_fields = remove_fields.split(',')
+            exist_fields = self.fields
+            fields = [x for x in exist_fields if x not in remove_fields]
+        elif fields:
+            if isinstance(fields, basestring):
+                fields = fields.split(',')
+        else:
+            fields = slice(None)
+
+        if with_default_fields:
+            if fields != slice(None):
+                fields = list( set(fields) | set(self.default_fields))
+        print("dup fields:", fields)
+
+        if not symbols:
+            symbols = slice(None)
+        elif isinstance(symbols, basestring):
+            symbols = symbols.split(',')
+        elif isinstance(symbols, (list, tuple)):
+            pass
+
+        dv2 = DataView()
+
+        if self.data_benchmark is not None:
+            dv2.data_benchmark = self.data_benchmark.loc[pd.IndexSlice[extended_start_date_d: end_date]]
+
+        if self.data_d is not None:
+            dv2.data_d = self.data_d.loc[pd.IndexSlice[extended_start_date_d: end_date], pd.IndexSlice[symbols, fields]]
+
+        if self.data_q is not None:
+            dv2.data_q = self.data_q.loc[pd.IndexSlice[extended_start_date_d: end_date], pd.IndexSlice[symbols, slice(None)]]
+
+        dv2._data_inst = self.data_inst.copy()
+
+        meta_data = {key: self.__dict__[key] for key in self.meta_data_list}
+        meta_data['start_date'] = start_date
+        meta_data['end_date'] = end_date
+        if symbols != slice(None):
+            meta_data['symbol'] = sorted(set(meta_data['symbol']) & set(symbols))
+
+        if fields != slice(None):
+            meta_data['fields'] = fields
+        dv2.__dict__.update(meta_data)
+
+        dv2.index_weights = {}
+        for k,v in self.index_weights.items():
+            dv2.index_weights[k] = v[ (v.index >= extended_start_date_d) & (v.index <= end_date)]
+
+        dv2.industry_groups = {}
+        for k,v in self.index_weights.items():
+            dv2.industry_groups[k] = v[(v.index >= extended_start_date_d) & (v.index <= end_date)]
+
+        dv2._process_data(large_memory=large_memory)
+        return dv2
+
+    @staticmethod
+    def concat(dv1, dv2):
+        def concat_df(dv1, dv2, name):
+            a = dv1.__dict__[name]
+            b = dv2.__dict__[name]
+            if a is None:
+                return b
+            elif b is None:
+                return a
+            else:
+                a = a[a.index < dv2.start_date]
+                b = b[b.index > dv1.end_date]
+                return pd.concat([a, b])
+
+        dv = DataView()
+        for key in dv.meta_data_list:
+            dv.__dict__[key] = dv1.__dict__[key]
+
+        dv.end_date = dv2.end_date
+        for key in ['fields', 'symbol', 'custom_daily_fields', 'custom_quarterly_fields',
+                    'factors', 'load_factors', 'labels', 'load_labels']:
+            dv.__dict__[key] = list(set(dv1.__dict__[key] + dv2.__dict__[key]))
+
+        dv._data_inst = pd.concat([dv1._data_inst, dv2._data_inst]).drop_duplicates()
+        dv._factor_df = pd.concat([dv1._factor_df, dv2._factor_df])
+
+        if not dv._factor_df.empty:
+            dv._factor_df.drop_duplicates('factor_id')
+            for index, row in dv._factor_df.iterrows():
+                factor_id = row['factor_id']
+                factor_body = row['factor_def']
+                factor_args = list(filter(None, row['factor_args'].split(',')))
+                if 'factor_quarterly' in row:
+                    factor_quarterly = row['factor_quarterly']
+                else:
+                    factor_quarterly = False
+
+                dv._import_factors[factor_id] = FactorDef(factor_id, factor_args, factor_body, factor_quarterly)
+
+        dv.index_weights = {}
+        for k,v in dv1.index_weights.items():
+            if k in dv2.index_weights:
+                df1 = v
+                df2 = dv2.index_weights[k]
+                dv.index_weights[k] = pd.concat( [df1[df1.index < dv2.start_date], df2[df2.index > dv1.end_date]] )
+
+        dv.industry_groups = {}
+        for k,v in dv1.industry_groups.items():
+            if k in dv2.industry_groups:
+                df1 = v
+                df2 = dv2.industry_groups[k]
+                dv.industry_groups[k] = pd.concat( [ df1[df1.index < dv2.start_date], df2[df2.index > dv1.end_date] ] )
+
+        dv.data_d = concat_df(dv1, dv2, 'data_d')
+        dv.data_q = concat_df(dv1, dv2, 'data_q')
+        dv._data_benchmark = concat_df(dv1, dv2, '_data_benchmark')
+
+        dv._process_data(True if dv1._snapshot else False)
+        return dv
+
+    def group_demean(self, signal, group, new_name, is_quarterly=False, method='div'):
+
+        df = self.get_ts(signal + ',' + group + ',index_member')
+        df_all = df.stack(level=0).reset_index()
+
+        ## use only index member for demean
+        df_all_mask = df_all[df_all['index_member'].astype(bool)]
+        if method == 'div':
+            df_all_mask[new_name] = df_all_mask.groupby(['trade_date', group])[signal].apply(lambda x: x / np.nanmedian(x))
+        else:
+            df_all_mask[new_name] = df_all_mask.groupby(['tpdrade_date', group])[signal].apply(lambda x: x - np.nanmedian(x))
+
+        ## convert back to long data
+        df_all_clean = df_all.loc[:, ['trade_date', 'symbol']]\
+            .merge(df_all_mask, on=['trade_date', 'symbol'],how='left')\
+            .pivot_table(values=new_name,
+                         index='trade_date',
+                         columns='symbol',
+                         fill_value=np.nan)
+
+        self.remove_field(new_name)
+        self.append_df(df_all_clean, new_name, is_quarterly=is_quarterly)
+        return self
+
+
+    def to_dataframe(self):
+        df = self.data_d.copy()
+        df.columns = df.columns.swaplevel()
+        return df.stack().reset_index()
+
+    @staticmethod
+    def from_dataframe(df):
+        df = df.sort_values(['trade_date','symbol'])
+        df = df.set_index(['trade_date','symbol']).unstack()
+        df.columns = df.columns.swaplevel()
+        df = df.sort_index(axis=1)
+
+        dv = DataView()
+        dv.data_d = df
+        for field in df.columns.levels[1]:
+            dv.fields.append(field)
+        dv.start_date = df.index.min()
+        dv.end_date = df.index.max()
+        return dv
+
 
 class EventDataView(object):
     """
     Prepare data before research / trade. Support file I/O.
     Support: add field, add formula, save / load.
-    
+
     Attributes
     ----------
     data_api : RemoteDataService
@@ -1374,11 +2353,12 @@ class EventDataView(object):
     data_q : pd.DataFrame
         All quarterly frequency data will be merged and stored here.
         index is date, columns is symbol-field MultiIndex
-    
+
     """
+
     def __init__(self):
         self.data_api = None
-        
+
         self.universe = ""
         self.symbol = []
         self.benchmark = ""
@@ -1388,20 +2368,20 @@ def __init__(self):
         self.fields = []
         self.freq = 1
         self.all_price = True
-        
+
         self.meta_data_list = ['start_date', 'end_date',
                                'extended_start_date_d',
                                'freq', 'fields', 'symbol', 'universe', 'benchmark',
                                'custom_daily_fields']
         self.adjust_mode = 'post'
-        
+
         self.data_d = None
         self.data_q = None
         self._data_benchmark = None
         self._data_inst = None
         self.data_custom = None
         # self._data_group = None
-        
+
         common_list = {'symbol', 'start_date', 'end_date'}
         market_bar_list = {'open', 'high', 'low', 'close', 'volume', 'turnover', 'vwap', 'oi'}
         market_tick_list = {'volume', 'oi',
@@ -1415,40 +2395,40 @@ def __init__(self):
             {'open', 'high', 'low', 'close', 'volume', 'turnover', 'vwap', 'oi', 'trade_status',
              'open_adj', 'high_adj', 'low_adj', 'close_adj', 'vwap_adj', 'index_member', 'index_weight'}
         self.group_fields = {'sw1', 'sw2', 'sw3', 'sw4', 'zz1', 'zz2'}
-        self .custom_daily_fields = []
-        
+        self.custom_daily_fields = []
+
         # const
         self.TRADE_STATUS_FIELD_NAME = 'trade_status'
         self.TRADE_DATE_FIELD_NAME = 'trade_date'
-    
+
     # --------------------------------------------------------------------------------------------------------
     # Properties
     @property
     def data_benchmark(self):
         return self._data_benchmark
-    
+
     @property
     def data_inst(self):
         """
-        
+
         Returns
         -------
         pd.DataFrame
 
         """
         return self._data_inst
-    
+
     @data_benchmark.setter
     def data_benchmark(self, df_new):
         if self.data_d is not None and df_new.shape[0] != self.data_d.shape[0]:
             raise ValueError("You must provide a DataFrame with the same shape of data_benchmark.")
         self._data_benchmark = df_new
-    
+
     @property
     def dates(self):
         """
         Get daily date array of the underlying data.
-        
+
         Returns
         -------
         res : np.array
@@ -1461,16 +2441,16 @@ def dates(self):
             res = self.data_api.query_trade_dates(self.extended_start_date_d, self.end_date)
         else:
             raise ValueError("Cannot get dates array when neither of data and data_api exists.")
-        
+
         return res
-    
+
     # --------------------------------------------------------------------------------------------------------
     # Fields
     def _is_quarter_field(self, field_name):
         """
         Check whether a field name is quarterly frequency.
         field_name must be pre-defined or already added.
-        
+
         Parameters
         ----------
         field_name : str
@@ -1486,12 +2466,12 @@ def _is_quarter_field(self, field_name):
                or field_name in self.fin_indicator
                or field_name in self.custom_quarterly_fields)
         return res
-    
+
     def _is_daily_field(self, field_name):
         """
         Check whether a field name is daily frequency.
         field_name must be pre-defined or already added.
-        
+
         Parameters
         ----------
         field_name : str
@@ -1506,12 +2486,12 @@ def _is_daily_field(self, field_name):
                 or field_name in self.custom_daily_fields
                 or field_name in self.group_fields)
         return flag
-    
+
     def _is_predefined_field(self, field_name):
         """
         Check whether a field name can be recognized.
         field_name must be pre-defined or already added.
-        
+
         Parameters
         ----------
         field_name : str
@@ -1522,11 +2502,11 @@ def _is_predefined_field(self, field_name):
 
         """
         return self._is_quarter_field(field_name) or self._is_daily_field(field_name)
-    
+
     def _get_fields(self, field_type, fields, complement=False, append=False):
         """
         Get list of fields that are in ref_quarterly_fields.
-        
+
         Parameters
         ----------
         field_type : {'market_daily', 'ref_daily', 'income', 'balance_sheet', 'cash_flow', 'daily', 'quarterly'
@@ -1544,22 +2524,22 @@ def _get_fields(self, field_type, fields, complement=False, append=False):
         pool_map['daily'] = set.union(pool_map['market_daily'],
                                       pool_map['group'],
                                       self.custom_daily_fields)
-        
+
         pool = pool_map.get(field_type, None)
         if pool is None:
             raise NotImplementedError("field_type = {:s}".format(field_type))
-        
+
         s = set.intersection(set(pool), set(fields))
         if not s:
             return []
-        
+
         if complement:
             s = set(fields) - s
-        
+
         if field_type == 'market_daily' and self.all_price:
             # turnover will not be adjusted
             s.update({'open', 'high', 'close', 'low', 'vwap'})
-        
+
         if append:
             s.add('symbol')
             if field_type == 'market_daily' or field_type == 'ref_daily':
@@ -1572,29 +2552,29 @@ def _get_fields(self, field_type, fields, complement=False, append=False):
                   or field_type == 'fin_indicator'):
                 s.add(self.ANN_DATE_FIELD_NAME)
                 s.add(self.REPORT_DATE_FIELD_NAME)
-        
+
         l = list(s)
         return l
-    
+
     # --------------------------------------------------------------------------------------------------------
     # Prepare data
     def init_from_config(self, props, data_api):
         """
         Initialize various attributes like start/end date, universe/symbol, fields, etc.
         If your want to parse symbol, but use a custom benchmark index, please directly assign self.data_benchmark.
-        
+
         Parameters
         ----------
         props : dict
             start_date, end_date, freq, symbol, fields, etc.
         data_api : BaseDataServer
-        
+
         """
         # data_api.init_from_config(props)
         self.data_api = data_api
-        
+
         sep = ','
-        
+
         # initialize parameters
         self.start_date = props['start_date']
         self.extended_start_date_d = jutil.shift(self.start_date, n_weeks=-8)  # query more data
@@ -1602,7 +2582,7 @@ def init_from_config(self, props, data_api):
         self.end_date = props['end_date']
         self.all_price = props.get('all_price', True)
         self.freq = props.get('freq', 1)
-        
+
         # get and filter fields
         fields = props.get('fields', [])
         if fields:
@@ -1610,13 +2590,17 @@ def init_from_config(self, props, data_api):
             self.fields = [field for field in fields if self._is_predefined_field(field)]
             if len(self.fields) < len(fields):
                 print("Field name [{}] not valid, ignore.".format(set.difference(set(fields), set(self.fields))))
-        
+
         # append additional fields
         if self.all_price:
             self.fields.extend(['open_adj', 'high_adj', 'low_adj', 'close_adj',
                                 'open', 'high', 'low', 'close',
                                 'vwap', 'vwap_adj'])
-        
+
+        # always inclde sw1 for industry analysis
+        if 'sw1' not in self.fields:
+            self.fields.extend(['sw1'])
+
         # initialize universe/symbol
         universe = props.get('universe', "")
         symbol = props.get('symbol', "")
@@ -1644,17 +2628,17 @@ def init_from_config(self, props, data_api):
                           "If you want to use other benchmark, "
                           "please specify benchmark in configs.".format(repr(self.universe), self.universe[0]))
                 self.benchmark = self.universe[0]
-        
+
         print("Initialize config success.")
 
     def distributed_query(self, query_func_name, symbol, start_date, end_date, limit=100000, **kwargs):
         n_symbols = len(symbol.split(','))
         dates = self.data_api.query_trade_dates(start_date, end_date)
         n_days = len(dates)
-    
+
         if n_symbols * n_days > limit:
             n = limit // n_symbols
-        
+
             df_list = []
             i = 0
             pos1, pos2 = n * i, n * (i + 1) - 1
@@ -1677,34 +2661,34 @@ def distributed_query(self, query_func_name, symbol, start_date, end_date, limit
                                                               start_date=start_date, end_date=end_date,
                                                               **kwargs)
         return df, msg
-    
+
     def prepare_data(self):
         """Prepare data for the FIRST time."""
         # prepare benchmark and group
         print("Query data...")
         data_d = self._prepare_daily_quarterly(self.fields)
         self.data_d = data_d
-        #self._align_and_merge_q_into_d()
-        
+        # self._align_and_merge_q_into_d()
+
         print("Query instrument info...")
         self._prepare_inst_info()
-        
+
         if self.benchmark:
             print("Query benchmark...")
             self._data_benchmark = self._prepare_benchmark()
-        
+
         print("Data has been successfully prepared.")
-    
+
     @staticmethod
     def _process_index_co(df, index_name):
         df = df.astype(dtype={index_name: int})
         df = df.drop_duplicates(subset=['symbol', index_name])
         return df
-    
+
     def _prepare_daily_quarterly(self, fields):
         """
         Query and process data from data_api.
-        
+
         Parameters
         ----------
         fields : list
@@ -1717,11 +2701,11 @@ def _prepare_daily_quarterly(self, fields):
         """
         if not fields:
             return None, None
-        
+
         # query data
         print("Query data - query...")
         daily_list = self._query_data(self.symbol, fields)
-        
+
         def pivot_and_sort(df, index_name):
             df = self._process_index_co(df, index_name)
             df = df.pivot(index=index_name, columns='symbol')
@@ -1731,7 +2715,7 @@ def pivot_and_sort(df, index_name):
             df = df.sort_index(axis=1, level=col_names)
             df.index.name = index_name
             return df
-        
+
         multi_daily = None
         if daily_list:
             daily_list_pivot = [pivot_and_sort(df, self.TRADE_DATE_FIELD_NAME) for df in daily_list]
@@ -1739,15 +2723,15 @@ def pivot_and_sort(df, index_name):
             # use self.dates as index because original data have weekends
             multi_daily = self._fill_missing_idx_col(multi_daily, index=self.dates, symbols=self.symbol)
             print("Query data - daily fields prepared.")
-        
+
         return multi_daily
-    
+
     def _query_data(self, symbol, fields):
         """
         Query data using different APIs, then store them in dict.
         period, start_date and end_date are fixed.
         Keys of dict are securitites.
-        
+
         Parameters
         ----------
         symbol : list of str
@@ -1761,10 +2745,10 @@ def _query_data(self, symbol, fields):
         """
         sep = ','
         symbol_str = sep.join(symbol)
-        
+
         if self.freq == 1:
             daily_list = []
-            
+
             # TODO : use fields = {field: kwargs} to enable params
             fields_market_daily = self._get_fields('market_daily', fields, append=True)
             if fields_market_daily:
@@ -1772,33 +2756,36 @@ def _query_data(self, symbol, fields):
                 # no adjust prices and other market daily fields
                 df_daily, msg1 = self.distributed_query('daily', symbol_str,
                                                         start_date=self.extended_start_date_d, end_date=self.end_date,
-                                                        adjust_mode=None, fields=sep.join(fields_market_daily), limit=100000)
-                #df_daily, msg1 = self.data_api.daily(symbol_str, start_date=self.extended_start_date_d, end_date=self.end_date,
+                                                        adjust_mode=None, fields=sep.join(fields_market_daily),
+                                                        limit=100000)
+                # df_daily, msg1 = self.data_api.daily(symbol_str, start_date=self.extended_start_date_d, end_date=self.end_date,
                 #                                     adjust_mode=None, fields=sep.join(fields_market_daily))
-                
+
                 if self.all_price:
                     adj_cols = ['open', 'high', 'low', 'close', 'vwap']
                     # adjusted prices
-                    #df_daily_adjust, msg11 = self.data_api.daily(symbol_str, start_date=self.extended_start_date_d, end_date=self.end_date,
+                    # df_daily_adjust, msg11 = self.data_api.daily(symbol_str, start_date=self.extended_start_date_d, end_date=self.end_date,
                     #                                             adjust_mode=self.adjust_mode, fields=','.join(adj_cols))
                     df_daily_adjust, msg1 = self.distributed_query('daily', symbol_str,
-                                                                   start_date=self.extended_start_date_d, end_date=self.end_date,
-                                                                   adjust_mode=self.adjust_mode, fields=sep.join(fields_market_daily), limit=100000)
-                    
+                                                                   start_date=self.extended_start_date_d,
+                                                                   end_date=self.end_date,
+                                                                   adjust_mode=self.adjust_mode,
+                                                                   fields=sep.join(fields_market_daily), limit=100000)
+
                     df_daily = pd.merge(df_daily, df_daily_adjust, how='outer',
                                         on=['symbol', 'trade_date'], suffixes=('', '_adj'))
                 daily_list.append(df_daily.loc[:, fields_market_daily])
-        
+
         else:
             raise NotImplementedError("freq = {}".format(self.freq))
-        
+
         return daily_list
-    
+
     @staticmethod
     def _merge_data(dfs, index_name='trade_date'):
         """
         Merge data from different APIs into one DataFrame.
-        
+
         Parameters
         ----------
         dfs : list of pd.DataFrame
@@ -1807,55 +2794,55 @@ def _merge_data(dfs, index_name='trade_date'):
         -------
         merge : pd.DataFrame or None
             If dfs is empty, return None
-        
+
         Notes
         -----
         Align on date index, concatenate on columns (symbol and fields)
-        
+
         """
         # dfs = [df for df in dfs if df is not None]
-        
+
         merge = pd.concat(dfs, axis=1, join='outer')
-        
+
         # drop duplicated columns. ONE LINE EFFICIENT version
         mask_duplicated = merge.columns.duplicated()
         if np.any(mask_duplicated):
             # print("Duplicated columns found. Dropped.")
             merge = merge.loc[:, ~mask_duplicated]
-            
+
             # if merge.isnull().sum().sum() > 0:
             # print "WARNING: nan in final merged data. NO fill"
             # merge.fillna(method='ffill', inplace=True)
-        
+
         merge = merge.sort_index(axis=1, level=['symbol', 'field'])
         merge.index.name = index_name
-        
+
         return merge
-    
+
     def _fill_missing_idx_col(self, df, index=None, symbols=None):
         if index is None:
             index = df.index
         if symbols is None:
             symbols = self.symbol
         fields = df.columns.levels[1]
-        
+
         if len(fields) * len(self.symbol) != len(df.columns) or len(index) != len(df.index):
             cols_multi = pd.MultiIndex.from_product([symbols, fields], names=['symbol', 'field'])
             cols_multi = cols_multi.sort_values()
             df_final = pd.DataFrame(index=index, columns=cols_multi, data=np.nan)
             df_final.index.name = df.index.name
-            
+
             df_final.update(df)
-            
+
             # idx_diff = sorted(set(df_final.index) - set(df.index))
             col_diff = sorted(set(df_final.columns.levels[0].values) - set(df.columns.levels[0].values))
-            print ("WARNING: some data is unavailable: "
-                   # + "\n    At index " + ', '.join(idx_diff)
-                   + "\n    At fields " + ', '.join(col_diff))
+            print("WARNING: some data is unavailable: "
+                  # + "\n    At index " + ', '.join(idx_diff)
+                  + "\n    At fields " + ', '.join(col_diff))
             return df_final
         else:
             return df
-    
+
     def _prepare_adj_factor(self):
         """Query and append daily adjust factor for prices."""
         mask_stocks = self.data_inst['inst_type'] == 1
@@ -1864,9 +2851,10 @@ def _prepare_adj_factor(self):
         symbol_stocks = self.data_inst.loc[mask_stocks].index.values
         symbol_str = ','.join(symbol_stocks)
         df_adj = self.data_api.query_adj_factor_daily(symbol_str,
-                                                      start_date=self.extended_start_date_d, end_date=self.end_date, div=False)
+                                                      start_date=self.extended_start_date_d, end_date=self.end_date,
+                                                      div=False)
         self.append_df(df_adj, 'adjust_factor', is_quarterly=False)
-    
+
     def _prepare_comp_info(self):
         # if a symbol is index member of any one universe, its value of index_member will be 1.0
         res = dict()
@@ -1876,11 +2864,12 @@ def _prepare_comp_info(self):
         df_res = pd.concat(res, axis=0)
         df = df_res.groupby(by='trade_date').apply(lambda df: df.any(axis=0)).astype(float)
         self.append_df(df, 'index_member', is_quarterly=False)
-        
+
         # use weights of the first universe
-        df_weights = self.data_api.query_index_weights_daily(self.universe[0], self.extended_start_date_d, self.end_date)
+        df_weights = self.data_api.query_index_weights_daily(self.universe[0], self.extended_start_date_d,
+                                                             self.end_date)
         self.append_df(df_weights, 'index_weight', is_quarterly=False)
-    
+
     def _prepare_inst_info(self):
         res = self.data_api.query_inst_info(symbol=','.join(self.symbol),
                                             fields='symbol,inst_type,name,list_date,'
@@ -1888,7 +2877,7 @@ def _prepare_inst_info(self):
                                                    'buylot,setlot',
                                             inst_type="")
         self._data_inst = res
-    
+
     def _prepare_group(self, group_fields):
         data_map = {'sw1': ('SW', 1),
                     'sw2': ('SW', 2),
@@ -1901,8 +2890,9 @@ def _prepare_group(self, group_fields):
             df = self.data_api.query_industry_daily(symbol=','.join(self.symbol),
                                                     start_date=self.extended_start_date_q, end_date=self.end_date,
                                                     type_=type_, level=level)
-            self.append_df(df, field, is_quarterly=False)
-    
+            if df is not None:
+                self.append_df(df, field, is_quarterly=False)
+
     def _prepare_benchmark(self):
         df_bench, msg = self.data_api.daily(self.benchmark,
                                             start_date=self.extended_start_date_d, end_date=self.end_date,
@@ -1911,7 +2901,7 @@ def _prepare_benchmark(self):
         # TODO: we want more than just close price of benchmark
         df_bench = df_bench.set_index('trade_date').loc[:, ['close']]
         return df_bench
-    
+
     # --------------------------------------------------------------------------------------------------------
     # Add/Remove Fields&Formulas
     def _add_field(self, field_name, is_quarterly=None):
@@ -1928,13 +2918,13 @@ def _add_symbol(self, symbol_name):
     def add_field(self, field_name, data_api=None):
         """
         Query and append new field to DataView.
-        
+
         Parameters
         ----------
         data_api : BaseDataServer
         field_name : str
             Must be a known field name (which is given in documents).
-        
+
         Returns
         -------
         bool
@@ -1947,17 +2937,17 @@ def add_field(self, field_name, data_api=None):
                 return False
         else:
             self.data_api = data_api
-        
+
         if field_name in self.fields:
             print("Field name [{:s}] already exists.".format(field_name))
             return False
-        
+
         if not self._is_predefined_field(field_name):
             print("Field name [{}] not valid, ignore.".format(field_name))
             return False
-        
+
         merge_d, merge_q = self._prepare_daily_quarterly([field_name])
-        
+
         if self._is_daily_field(field_name):
             if self.data_d is None:
                 raise ValueError("Please prepare [{:s}] first.".format(field_name))
@@ -1968,24 +2958,25 @@ def add_field(self, field_name, data_api=None):
                 raise ValueError("Please prepare [{:s}] first.".format(field_name))
             _, merge = self._prepare_daily_quarterly([field_name])
             is_quarterly = True
-        
+
         merge = merge.loc[:, pd.IndexSlice[:, field_name]]
         merge.columns = merge.columns.droplevel(level=1)
-        self.append_df(merge, field_name, is_quarterly=is_quarterly)  # whether contain only trade days is decided by existing data.
-        
+        self.append_df(merge, field_name,
+                       is_quarterly=is_quarterly)  # whether contain only trade days is decided by existing data.
+
         if is_quarterly:
             df_ann = merge_q.loc[:, pd.IndexSlice[:, self.ANN_DATE_FIELD_NAME]]
             df_ann.columns = df_ann.columns.droplevel(level='field')
             df_expanded = align(merge, df_ann, self.dates)
             self.append_df(df_expanded, field_name, is_quarterly=False)
         return True
-    
+
     def add_formula(self, field_name, formula, is_quarterly, overwrite=True,
                     formula_func_name_style='camel', data_api=None,
-                    within_index=True):
+                    within_index=True, is_factor=True):
         """
         Add a new field, which is calculated using existing fields.
-        
+
         Parameters
         ----------
         formula : str
@@ -2000,7 +2991,7 @@ def add_formula(self, field_name, formula, is_quarterly, overwrite=True,
         data_api : RemoteDataService, optional
         within_index : bool
             When do cross-section operatioins, whether just do within index components.
-        
+
         Notes
         -----
         Time cost of this function:
@@ -2009,7 +3000,7 @@ def add_formula(self, field_name, formula, is_quarterly, overwrite=True,
         """
         if data_api is not None:
             self.data_api = data_api
-        
+
         if field_name in self.fields:
             if overwrite:
                 self.remove_field(field_name)
@@ -2017,15 +3008,15 @@ def add_formula(self, field_name, formula, is_quarterly, overwrite=True,
             else:
                 print("Add formula failed: name [{:s}] exist. Try another name.".format(field_name))
                 return
-        
+
         parser = Parser()
         parser.set_capital(formula_func_name_style)
-        
+
         expr = parser.parse(formula)
-        
+
         var_df_dic = dict()
         var_list = expr.variables()
-        
+
         # TODO: users do not need to prepare data before add_formula
         if not self.fields:
             self.fields.extend(var_list)
@@ -2038,16 +3029,16 @@ def add_formula(self, field_name, formula, is_quarterly, overwrite=True,
                     success = self.add_field(var)
                     if not success:
                         return
-        
+
         for var in var_list:
             if self._is_quarter_field(var):
                 df_var = self.get_ts_quarter(var, start_date=self.extended_start_date_q)
             else:
                 # must use extended date. Default is start_date
                 df_var = self.get_ts(var, start_date=self.extended_start_date_d, end_date=self.end_date)
-            
+
             var_df_dic[var] = df_var
-        
+
         # TODO: send ann_date into expr.evaluate. We assume that ann_date of all fields of a symbol is the same
         df_ann = self._get_ann_df()
         if within_index:
@@ -2055,30 +3046,30 @@ def add_formula(self, field_name, formula, is_quarterly, overwrite=True,
             df_eval = parser.evaluate(var_df_dic, ann_dts=df_ann, trade_dts=self.dates, index_member=df_index_member)
         else:
             df_eval = parser.evaluate(var_df_dic, ann_dts=df_ann, trade_dts=self.dates)
-        
+
         self.append_df(df_eval, field_name, is_quarterly=is_quarterly)
-        
+
         if is_quarterly:
             df_ann = self._get_ann_df()
             df_expanded = align(df_eval, df_ann, self.dates)
             self.append_df(df_expanded, field_name, is_quarterly=False)
-    
+
     def append_df(self, df, field_name, is_quarterly=False):
         """
         Append DataFrame to existing multi-index DataFrame and add corresponding field name.
-        
+
         Parameters
         ----------
         df : pd.DataFrame or pd.Series
         field_name : str
         is_quarterly : bool
             Whether df is quarterly data (like quarterly financial statement) or daily data.
-            
+
         Notes
         -----
         append_df does not support overwrite. To overwrite a field, you must first do self.remove_fields(),
         then append_df() again.
-        
+
         """
         df = df.copy()
         if isinstance(df, pd.DataFrame):
@@ -2087,12 +3078,12 @@ def append_df(self, df, field_name, is_quarterly=False):
             df = pd.DataFrame(df)
         else:
             raise ValueError("Data to be appended must be pandas format. But we have {}".format(type(df)))
-        
+
         if is_quarterly:
             the_data = self.data_q
         else:
             the_data = self.data_d
-        
+
         exist_symbols = the_data.columns.levels[0]
         if len(df.columns) < len(exist_symbols):
             df2 = pd.DataFrame(index=df.index, columns=exist_symbols, data=np.nan)
@@ -2102,14 +3093,14 @@ def append_df(self, df, field_name, is_quarterly=False):
             df = df.loc[:, exist_symbols]
         multi_idx = pd.MultiIndex.from_product([exist_symbols, [field_name]])
         df.columns = multi_idx
-        
-        #the_data = apply_in_subprocess(pd.merge, args=(the_data, df),
+
+        # the_data = apply_in_subprocess(pd.merge, args=(the_data, df),
         #                            kwargs={'left_index': True, 'right_index': True, 'how': 'left'})  # runs in *only* one process
         the_data = pd.merge(the_data, df, left_index=True, right_index=True, how='left')
         the_data = the_data.sort_index(axis=1)
-        #merge = the_data.join(df, how='left')  # left: keep index of existing data unchanged
-        #sort_columns(the_data)
-        
+        # merge = the_data.join(df, how='left')  # left: keep index of existing data unchanged
+        # sort_columns(the_data)
+
         if is_quarterly:
             self.data_q = the_data
         else:
@@ -2119,19 +3110,19 @@ def append_df(self, df, field_name, is_quarterly=False):
     def append_df_symbol(self, df, symbol_name):
         """
         Append DataFrame to existing multi-index DataFrame and add corresponding field name.
-        
+
         Parameters
         ----------
         df : pd.DataFrame or pd.Series
         symbol_name : str
         is_quarterly : bool
             Whether df is quarterly data (like quarterly financial statement) or daily data.
-            
+
         Notes
         -----
         append_df does not support overwrite. To overwrite a field, you must first do self.remove_fields(),
         then append_df() again.
-        
+
         """
         df = df.copy()
         if isinstance(df, pd.DataFrame):
@@ -2140,37 +3131,37 @@ def append_df_symbol(self, df, symbol_name):
             df = pd.DataFrame(df)
         else:
             raise ValueError("Data to be appended must be pandas format. But we have {}".format(type(df)))
-    
+
         the_data = self.data_d
-    
+
         exist_fields = the_data.columns.levels[1]
         if len(set(exist_fields) - set(df.columns)):
-        #if set(df.columns) < set(exist_fields):
+            # if set(df.columns) < set(exist_fields):
             df2 = pd.DataFrame(index=df.index, columns=exist_fields, data=np.nan)
             df2.update(df)
             df = df2
         multi_idx = pd.MultiIndex.from_product([[symbol_name], exist_fields])
         df.columns = multi_idx
-    
-        #the_data = apply_in_subprocess(pd.merge, args=(the_data, df),
+
+        # the_data = apply_in_subprocess(pd.merge, args=(the_data, df),
         #                            kwargs={'left_index': True, 'right_index': True, 'how': 'left'})  # runs in *only* one process
         the_data = pd.merge(the_data, df, left_index=True, right_index=True, how='left')
         the_data = the_data.sort_index(axis=1)
-        #merge = the_data.join(df, how='left')  # left: keep index of existing data unchanged
-        #sort_columns(the_data)
-    
+        # merge = the_data.join(df, how='left')  # left: keep index of existing data unchanged
+        # sort_columns(the_data)
+
         self.data_d = the_data
         self._add_symbol(symbol_name)
 
     def remove_field(self, field_names):
         """
         Query and append new field to DataView.
-        
+
         Parameters
         ----------
         field_names : str or list
             The (custom) field to be removed from dataview.
-        
+
         Returns
         -------
         bool
@@ -2183,13 +3174,13 @@ def remove_field(self, field_names):
             pass
         else:
             raise ValueError("field_names must be str or list of str.")
-        
+
         for field_name in field_names:
             # parameter validation
             if field_name not in self.fields:
                 print("Field name [{:s}] does not exist.".format(field_name))
                 return
-            
+
             if self._is_daily_field(field_name):
                 is_quarterly = False
             elif self._is_quarter_field(field_name):
@@ -2197,13 +3188,13 @@ def remove_field(self, field_names):
             else:
                 print("Field name [{}] is a pre-defined field, ignore.".format(field_name))
                 return
-            
+
             # remove field data
-            
+
             self.data_d = self.data_d.drop(field_name, axis=1, level=1)
             if is_quarterly:
                 self.data_q = self.data_q.drop(field_name, axis=1, level=1)
-            
+
             # remove fields name from list
             self.fields.remove(field_name)
             if is_quarterly:
@@ -2212,13 +3203,13 @@ def remove_field(self, field_names):
             else:
                 if field_name in self.custom_daily_fields:
                     self.custom_daily_fields.remove(field_name)
-    
+
     # --------------------------------------------------------------------------------------------------------
     # Get Data API
     def get(self, symbol="", start_date=0, end_date=0, fields="", data_format='wide'):
         """
         Basic API to get arbitrary data. If nothing fetched, return None.
-        
+
         Parameters
         ----------
         symbol : str, optional
@@ -2239,34 +3230,34 @@ def get(self, symbol="", start_date=0, end_date=0, fields="", data_format='wide'
 
         """
         sep = ','
-        
+
         if not fields:
             fields = slice(None)  # self.fields
         else:
             fields = fields.split(sep)
-        
+
         if not symbol:
             symbol = slice(None)  # this is 3X faster than symbol = self.symbol
         else:
             symbol = symbol.split(sep)
-        
+
         if not start_date:
             start_date = self.start_date
         if not end_date:
             end_date = self.end_date
-        
+
         res = self.data_d.loc[pd.IndexSlice[start_date: end_date], pd.IndexSlice[symbol, fields]]
-        
+
         if data_format == 'wide':
             pass
         else:
             res = res.stack(level='symbol').reset_index()
         return res
-    
+
     def get_snapshot(self, snapshot_date, symbol="", fields=""):
         """
         Get snapshot of given fields and symbol at snapshot_date.
-        
+
         Parameters
         ----------
         snapshot_date : int
@@ -2286,30 +3277,30 @@ def get_snapshot(self, snapshot_date, symbol="", fields=""):
         if res is None:
             print("No data. for date={}, fields={}, symbol={}".format(snapshot_date, fields, symbol))
             return
-        
+
         res = res.stack(level='symbol', dropna=False)
         res.index = res.index.droplevel(level=self.TRADE_DATE_FIELD_NAME)
-        
+
         return res
-    
+
     def _get_ann_df(self):
         """
         Query announcement date of financial statements of all securities.
-        
+
         Returns
         -------
         df_ann : pd.DataFrame or None
             Index is date, column is symbol.
             If no quarterly data available, return None.
-        
+
         """
         if self.data_q is None:
             return None
         df_ann = self.data_q.loc[:, pd.IndexSlice[:, self.ANN_DATE_FIELD_NAME]]
         df_ann.columns = df_ann.columns.droplevel(level='field')
-        
+
         return df_ann
-    
+
     def get_symbol(self, symbol, start_date=0, end_date=0, fields=""):
         res = self.get(symbol, start_date=start_date, end_date=end_date, fields=fields)
         if res is None:
@@ -2323,7 +3314,7 @@ def get_symbol(self, symbol, start_date=0, end_date=0, fields=""):
     def get_ts(self, field, symbol="", start_date=0, end_date=0):
         """
         Get time series data of single field.
-        
+
         Parameters
         ----------
         field : str
@@ -2346,47 +3337,47 @@ def get_ts(self, field, symbol="", start_date=0, end_date=0):
             raise ValueError("No data. for "
                              "start_date={}, end_date={}, field={}, symbol={}".format(start_date, end_date,
                                                                                       field, symbol))
-        
+
         res.columns = res.columns.droplevel(level='field')
         return res
-    
+
     # --------------------------------------------------------------------------------------------------------
     # DataView I/O
     @staticmethod
     def _load_h5(fp):
         """Load data and meta_data from hd5 file.
-        
+
         Parameters
         ----------
         fp : str, optional
             File path of pre-stored hd5 file.
-        
+
         """
         h5 = pd.HDFStore(fp)
-        
+
         res = dict()
         for key in h5.keys():
             res[key] = h5.get(key)
-        
+
         h5.close()
-        
+
         return res
-    
+
     def load_dataview(self, folder_path='.'):
         """
         Load data from local file.
-        
+
         Parameters
         ----------
         folder_path : str, optional
             Folder path to store hd5 file and meta data.
-            
+
         """
         path_meta_data = os.path.join(folder_path, 'meta_data.json')
         path_data = os.path.join(folder_path, 'data.hd5')
         if not (os.path.exists(path_meta_data) and os.path.exists(path_data)):
             raise IOError("There is no data file under directory {}".format(folder_path))
-        
+
         meta_data = jutil.read_json(path_meta_data)
         dic = self._load_h5(path_data)
         self.data_d = dic.get('/data_d', None)
@@ -2395,14 +3386,14 @@ def load_dataview(self, folder_path='.'):
         self._data_inst = dic.get('/data_inst', None)
         self.data_custom = dic.get('/data_custom', None)
         self.__dict__.update(meta_data)
-        
+
         print("Dataview loaded successfully.")
-    
+
     def save_dataview(self, folder_path):
         """
         Save data and meta_data_to_store to a single hd5 file.
         Store at output/sub_folder
-        
+
         Parameters
         ----------
         folder_path : str
@@ -2412,26 +3403,26 @@ def save_dataview(self, folder_path):
         abs_folder = os.path.abspath(folder_path)
         meta_path = os.path.join(folder_path, 'meta_data.json')
         data_path = os.path.join(folder_path, 'data.hd5')
-        
+
         data_to_store = {'data_d': self.data_d, 'data_q': self.data_q,
                          'data_benchmark': self.data_benchmark, 'data_inst': self.data_inst,
                          'data_custom': self.data_custom}
         data_to_store = {k: v for k, v in data_to_store.items() if v is not None}
         meta_data_to_store = {key: self.__dict__[key] for key in self.meta_data_list}
-        
+
         print("\nStore data...")
         jutil.save_json(meta_data_to_store, meta_path)
         self._save_h5(data_path, data_to_store)
-        
-        print ("Dataview has been successfully saved to:\n"
-               + abs_folder + "\n\n"
-               + "You can load it with load_dataview('{:s}')".format(abs_folder))
-    
+
+        print("Dataview has been successfully saved to:\n"
+              + abs_folder + "\n\n"
+              + "You can load it with load_dataview('{:s}')".format(abs_folder))
+
     @staticmethod
     def _save_h5(fp, dic):
         """
         Save data in dic to a hd5 file.
-        
+
         Parameters
         ----------
         fp : str
@@ -2441,10 +3432,13 @@ def _save_h5(fp, dic):
         """
         import warnings
         warnings.filterwarnings('ignore', category=pd.io.pytables.PerformanceWarning)
-        
+
         jutil.create_dir(fp)
         h5 = pd.HDFStore(fp, complevel=9, complib='blosc')
         for key, value in dic.items():
             h5[key] = value
         h5.close()
 
+
+    # --------------------------------------------------------------------------------------------------------
+    # DataView I/O
diff --git a/jaqs/data/py_expression_eval.py b/jaqs/data/py_expression_eval.py
index 5150a91..df11932 100644
--- a/jaqs/data/py_expression_eval.py
+++ b/jaqs/data/py_expression_eval.py
@@ -223,7 +223,7 @@ def variables(self):
             item = self.tokens[i]
             if item.type_ == TVAR and \
                     not item.index_ in vars and \
-                    item.index_ not in self.functions:
+                    True : #item.index_ not in self.functions:
                 vars.append(item.index_)
         return vars
 
@@ -247,7 +247,7 @@ def to_str(self):
 
 
 class Parser(object):
-    def __init__(self):
+    def __init__(self, allow_future_data =False):
         self.success = False
         self.errormsg = ''
         self.expression = ''
@@ -365,6 +365,9 @@ def __init__(self):
             'IsNan': self.is_nan,
             # others
             'If': self.ifFunction,
+            'FillNan': self.fill_nan,
+            'Return_Abs': self.calc_return_abs,
+            'Return_Fwd': self.calc_return_fwd,
             # test
         }
         
@@ -401,6 +404,8 @@ def __init__(self):
         
         self.ann_dts = None
         self.trade_dts = None
+
+        self.allow_future_data = allow_future_data
     
     # -----------------------------------------------------
     # functions
@@ -578,22 +583,30 @@ def sma(df, n, m):
         return r.mean()
     
     def std_dev(self, x, n):
-        return pd.rolling_std(x, n)
+        return x.rolling(n).std()
     
     def ts_sum(self, x, n):
-        return pd.rolling_sum(x, n)
+        return x.rolling(n).sum()
     
     def count_nans(self, x, n):
-        return n - pd.rolling_count(x, n)
+        return n - x.rolling(n).count()
     
     def delay(self, x, n):
+        if not self.allow_future_data and n < 0:
+            raise RuntimeError("Can't use future data")
+
         return x.shift(n)
     
     def delta(self, x, n):
+        if not self.allow_future_data and n < 0:
+            raise RuntimeError("Can't use future data")
+
         return x.diff(n)
     
-    @staticmethod
-    def calc_return(df, forward=1, log=False):
+    def calc_return(self, df, forward=1, log=False):
+        if not self.allow_future_data and forward < 0:
+            raise ValueError("Can't use future data")
+
         if log:
             res = np.log(df).diff(forward)
         else:
@@ -602,22 +615,22 @@ def calc_return(df, forward=1, log=False):
         return res
     
     def ts_mean(self, x, n):
-        return pd.rolling_mean(x, n)
+        return x.rolling(n).mean()
     
     def ts_min(self, x, n):
-        return pd.rolling_min(x, n)
+        return x.rolling(n).min()
     
     def ts_max(self, x, n):
-        return pd.rolling_max(x, n)
+        return x.rolling(n).max()
     
     def ts_kurt(self, x, n):
-        return pd.rolling_kurt(x, n)
+        return x.rolling(n).kurt()
     
     def ts_skew(self, x, n):
-        return pd.rolling_skew(x, n)
+        return x.rolling(n).skew()
     
     def ts_product(self, x, n):
-        return pd.rolling_apply(x, n, np.product)
+        return x.rolling(n).apply(np.product)
     
     @staticmethod
     def ts_rank(df, window):
@@ -648,11 +661,11 @@ def _rank_arr(arr, norm=1.0):
     # Time Series Two Parameters
     def corr(self, x, y, n):
         (x, y) = self._align_bivariate(x, y)
-        return pd.rolling_corr(x, y, n)
+        return x.rolling(n).corr(y)
 
     def cov(self, x, y, n):
         (x, y) = self._align_bivariate(x, y)
-        return pd.rolling_cov(x, y, n)
+        return x.rolling(n).cov(y)
 
     # financial statement data
     @staticmethod
@@ -689,15 +702,40 @@ def decay_linear_array(self, x):
         return np.dot(x, step) / np.sum(step)
     
     def decay_linear(self, x, n):
-        return pd.rolling_apply(x, n, self.decay_linear_array)
+        return x.rolling(n).apply(self.decay_linear_array)
     
     def decay_exp(self, x, f, n):
-        return pd.rolling_apply(x, n, self.decay_exp_array, args=[f])
+        return x.rolling(n).apply(self.decay_exp_array, args=[f])
     
     @staticmethod
     def is_nan(df):
         return df.isnull()
 
+    def fill_nan(self, df, value=None, fmethod = 'ffill'):
+        if value != None:
+            df = df.fillna(value=value)
+            return df
+        else:
+            df = df.fillna(method = fmethod)
+            return df
+
+    def calc_return_abs(self, df, forward=1):
+        if not self.allow_future_data and forward < 0:
+            raise RuntimeError("Can't use future data")
+
+
+        shift = df.shift(forward)
+        res = (df - shift) / abs(shift)
+        return res
+
+    def calc_return_fwd(self, df, forward=1):
+        if not self.allow_future_data and forward > 0:
+            raise RuntimeError("Can't use future data")
+
+        shift = df.shift(-forward)
+        res = (shift - df) / df
+        return res
+
     # -----------------------------------------------------
     # Cross Section functions
     
@@ -1214,6 +1252,7 @@ def evaluate(self, values, ann_dts=None, trade_dts=None, index_member=None):
                 n1 = nstack.pop()
                 f = nstack.pop()
                 if callable(f):
+                    # FIXME: Should set value for factor if it is in list.
                     if type(n1) is list:
                         nstack.append(f(*n1))
                     else:
diff --git a/jaqs/research/signaldigger/digger.py b/jaqs/research/signaldigger/digger.py
index 1955929..6f37262 100644
--- a/jaqs/research/signaldigger/digger.py
+++ b/jaqs/research/signaldigger/digger.py
@@ -141,8 +141,8 @@ def process_signal_before_analysis(self,
         mask_price_return = residual_ret.isnull()
         mask_signal = signal.isnull()
 
-        mask = np.logical_or(mask_signal, mask_price_return)
-        # mask = np.logical_or(mask, mask_signal)
+        mask_tmp = np.logical_or(mask_signal, mask_price_return)
+        mask_all = np.logical_or(mask, mask_tmp)
 
         # if price is not None:
         #     mask_forward = np.logical_or(mask, mask.shift(self.period).fillna(True))
@@ -151,7 +151,7 @@ def process_signal_before_analysis(self,
         # ----------------------------------------------------------------------
         # calculate quantile
         signal_masked = signal.copy()
-        signal_masked = signal_masked[~mask]
+        signal_masked = signal_masked[~mask_all]
         if n_quantiles == 1:
             df_quantile = signal_masked.copy()
             df_quantile.loc[:, :] = 1.0
@@ -165,8 +165,8 @@ def stack_td_symbol(df):
             df.index.names = ['trade_date', 'symbol']
             df.sort_index(axis=0, level=['trade_date', 'symbol'], inplace=True)
             return df
-        
-        mask = stack_td_symbol(mask)
+
+        mask_all = stack_td_symbol(mask_all)
         df_quantile = stack_td_symbol(df_quantile)
         residual_ret = stack_td_symbol(residual_ret)
 
@@ -176,7 +176,7 @@ def stack_td_symbol(df):
         res.columns = ['signal']
         res['return'] = residual_ret
         res['quantile'] = df_quantile
-        res = res.loc[~(mask.iloc[:, 0]), :]
+        res = res.loc[~(mask_all.iloc[:, 0]), :]
         
         print("Nan Data Count (should be zero) : {:d};  " \
               "Percentage of effective data: {:.0f}%".format(res.isnull().sum(axis=0).sum(),
diff --git a/jaqs/research/signaldigger/performance.py b/jaqs/research/signaldigger/performance.py
index 937edb7..13cfc49 100644
--- a/jaqs/research/signaldigger/performance.py
+++ b/jaqs/research/signaldigger/performance.py
@@ -259,15 +259,12 @@ def calc_return_diff_mean_std(q1, q2):
         Difference of mean return and corresponding std.
         
     """
-    assert np.all(q1.index == q2.index)
-    assert np.all(q1.columns == q2.columns)
-    
-    res = pd.DataFrame(index=q1.index, columns=['mean_diff', 'std'])
-    res.loc[:, 'mean_diff'] = q1['mean'] - q2['mean']
-    res.loc[:, 'std'] = np.sqrt(q1['std']**2 + q2['std']**2)
+    res_raw = pd.merge(q1, q2, how='outer', suffixes=('_1','_2'), left_index=True, right_index=True).fillna(0)
+    res_raw['mean_diff'] = res_raw['mean_1'] - res_raw['mean_2']
+    res_raw['std'] = np.sqrt(res_raw['mean_1'] **2 + res_raw['mean_2']**2)
+    res = res_raw[['mean_diff','std']]
     return res
 
-
 '''
 def period2daily(ser, period, do_roll_mean=False):
     if not period > 1:
diff --git a/jaqs/trade/analyze/analyze.py b/jaqs/trade/analyze/analyze.py
index 70146ed..a0e770a 100644
--- a/jaqs/trade/analyze/analyze.py
+++ b/jaqs/trade/analyze/analyze.py
@@ -19,9 +19,11 @@
 except NameError:
     basestring = str
 
+import os
 import numpy as np
 import pandas as pd
 import matplotlib as mpl
+# import seaborn as sns
 mpl.use('Agg')
 import matplotlib.pyplot as plt
 from matplotlib.ticker import Formatter
@@ -29,8 +31,11 @@
 from jaqs.trade.analyze.report import Report
 from jaqs.data import RemoteDataService
 from jaqs.data.basic.instrument import InstManager
+from jaqs.data.py_expression_eval import Parser
 from jaqs.trade import common
+
 import jaqs.util as jutil
+from jaqs.util.profile import prof_sample_begin, prof_sample_end
 
 STATIC_FOLDER = jutil.join_relative_path("trade/analyze/static")
 TO_PCT = 100.0
@@ -53,6 +58,172 @@
                 'lines.linewidth': 2.5,
                 }
 
+class AnalyzeView(object):
+    def __init__(self, data):
+        self._data = data
+
+    @property
+    def data(self):
+        return self._data
+
+    def get_ts(self, field, symbol="", start_date=0, end_date=0, keep_level=False):
+        """
+        Get time series data of single field.
+
+        Parameters
+        ----------
+        field : str or unicode
+            Single field.
+        symbol : str, optional
+            Separated by ',' default "" (all securities).
+        start_date : int, optional
+            Default 0 (self.start_date).
+        end_date : int, optional
+            Default 0 (self.start_date).
+
+        Returns
+        -------
+        res : pd.DataFrame
+            Index is int date, column is symbol.
+
+        """
+        res = self.get(symbol, start_date=start_date, end_date=end_date, fields=field)
+        if res is None:
+            print("No data. for start_date={}, end_date={}, field={}, symbol={}".format(start_date,
+                                                                                        end_date, field, symbol))
+            raise ValueError
+            return
+
+        #if not keep_level and len(res.columns) and len(field.split(',')) == 1:
+        if not keep_level and len(field.split(',')) == 1:
+            res.columns = res.columns.droplevel(level='field')
+            # XXX Save field name for ResReturnFunc
+            #res.columns.name = field
+
+        return res
+
+    def get_snapshot(self, snapshot_date, symbol="", fields=""):
+        """
+        Get snapshot of given fields and symbol at snapshot_date.
+
+        Parameters
+        ----------
+        snapshot_date : int
+            Date of snapshot.
+        symbol : str, optional
+            Separated by ',' default "" (all securities).
+        fields : str, optional
+            Separated by ',' default "" (all fields).
+
+        Returns
+        -------
+        res : pd.DataFrame
+            symbol as index, field as columns
+
+        """
+
+        # if self._snapshot is not None:
+        #     if snapshot_date not in self._snapshot:
+        #         return
+        #
+        #     df = self._snapshot[snapshot_date]
+        #     if fields:
+        #         return df[fields.split(',')]
+        #     else:
+        #         return df
+
+        res = self.get(symbol=symbol, start_date=snapshot_date, end_date=snapshot_date, fields=fields)
+        if res is None:
+            print("No data. for date={}, fields={}, symbol={}".format(snapshot_date, fields, symbol))
+            return
+
+        res = res.stack(level='symbol', dropna=False)
+        res.index = res.index.droplevel(level='trade_date')
+
+        return res
+
+    def get(self, symbol="", start_date=0, end_date=0, fields=""):
+        """
+        Basic API to get arbitrary data. If nothing fetched, return None.
+
+        Parameters
+        ----------
+        symbol : str, optional
+            Separated by ',' default "" (all securities).
+        start_date : int, optional
+            Default 0 (self.start_date).
+        end_date : int, optional
+            Default 0 (self.start_date).
+        fields : str, optional
+            Separated by ',' default "" (all fields).
+
+        Returns
+        -------
+        res : pd.DataFrame or None
+            index is datetimeindex, columns are (symbol, fields) MultiIndex
+
+        """
+        sep = ','
+
+        if self._data is None:
+            return None
+
+        if len(self._data.index) == 0:
+            return None
+
+        if not fields:
+            fields = slice(None)  # self.fields
+        else:
+            fields = fields.split(sep)
+
+        if not symbol:
+            symbol = slice(None)  # this is 3X faster than symbol = self.symbol
+        else:
+            symbol = symbol.split(sep)
+
+        if not start_date:
+            start_date = self._data.index[0]
+        if not end_date:
+            end_date = self._data.index[-1]
+
+        res = self._data.loc[pd.IndexSlice[start_date: end_date], pd.IndexSlice[symbol, fields]]
+        return res
+
+    def add_formula(self, field_name, formula, overwrite=True,
+                    formula_func_name_style='camel'):
+
+        parser = Parser()
+        parser.set_capital(formula_func_name_style)
+
+        expr = parser.parse(formula)
+
+        var_df_dic = dict()
+        var_list = expr.variables()
+
+        for var in var_list:
+            df_var = self.get_ts(var)
+            var_df_dic[var] = df_var
+
+        df_eval = parser.evaluate(var_df_dic)
+        self.add_field(df_eval, field_name)
+
+    def add_field(self, df, field_name=None):
+        """
+        Add fields to self._data.
+        :param df:
+        :param field_names: format:   'open', or 'open,close,high'
+        :return:
+        """
+        if not isinstance(df.columns, pd.core.indexes.multi.MultiIndex):
+            if not field_name:
+                raise ValueError("no field_name provided.")
+
+            df = df.copy()
+            #exist_symbols = self._data.columns.levels[0]
+            exist_symbols = df.columns
+            df.columns = pd.MultiIndex.from_product([exist_symbols, [field_name]])
+
+        self._data = pd.concat([self._data, df], axis=1).sort_index(axis=1)
 
 class TradeRecordEmptyError(Exception):
     def __init__(self, *args):
@@ -108,9 +279,11 @@ class BaseAnalyzer(object):
     risk_metrics : dict
         
     """
-    def __init__(self):
+    def __init__(self, is_alpha_analyzer=True):
         self.file_folder = ""
-        
+
+        self._is_alpha_analyzer = is_alpha_analyzer
+
         self._raw_trades = None
         self._trades = None
         self._configs = None
@@ -127,7 +300,7 @@ def __init__(self):
         self.account = None
         self.daily = None
         self.df_pnl = None
-        
+
         self.adjust_mode = None
         
         self.inst_map = dict()
@@ -136,7 +309,28 @@ def __init__(self):
         self.risk_metrics = dict()
         
         self.report_dic = dict()
-        
+
+        self._holding_data   = None
+        self._portfolio_data = None
+        # self._order_data   = None
+        # self._trade_data   = None
+
+        self._alpha_decay_image = None
+        self._industry_overweight_images = None
+        self._alpha_decomposition_images = None
+        self._alpha_decomposition_industry_images = None
+
+        self._average_industry_overweight = None
+        self._alpha_decay_weight_image = None
+        self._cum_alpha_weight_image = None
+        self._alpha_decomposition = None
+        self._industry_agg = None
+
+        self._sold_alpha_decay = None
+        self._sold_alpha_decay_image = None
+        self._alpha_weight_contribution = None
+        self._alpha_weight_traded_stocks = None
+
     @property
     def trades(self):
         """Read-only attribute"""
@@ -162,6 +356,15 @@ def closes_adj(self):
         """Read-only attribute, close prices of securities in the universe"""
         return self._closes_adj
 
+    @property
+    def holding_data(self):
+        """Read-only attribute, holding data of securities in each day"""
+        return self._holding_data
+
+    @property
+    def portfolio_data(self):
+        return self._portfolio_data
+
     def initialize(self, data_api=None, dataview=None, file_folder='.'):
         """
         Read trading records and configurations from file.
@@ -321,7 +524,9 @@ def _process_trades(self,df):
         # pre-process
         cols_to_drop = ['task_id', 'entrust_no', 'fill_no']
         df = df.drop(cols_to_drop, axis=1)
-        
+
+        perf_tmp = prof_sample_begin("_process_trades")
+
         def _apply(gp_df,inst_map):
             # calculation of non-cumulative fields
             direction = gp_df['entrust_action'].apply(lambda s: 1 if common.ORDER_ACTION.is_positive(s) else -1)
@@ -334,6 +539,7 @@ def _apply(gp_df,inst_map):
             gp_df.loc[:, 'SellVolume'] = (direction - 1) / -2 * fill_size
             
             # Calculation of cumulative fields
+            gp_df.loc[:, 'TurnOver'] = turnover
             gp_df.loc[:, 'CumVolume'] = fill_size.cumsum()
             gp_df.loc[:, 'CumTurnOver'] = turnover.cumsum()
             gp_df.loc[:, 'CumNetTurnOver'] = (turnover * -direction).cumsum()
@@ -346,7 +552,8 @@ def _apply(gp_df,inst_map):
             return gp_df
         gp = df.groupby(by='symbol')
         res = gp.apply(_apply,self.inst_map)
-        
+
+        prof_sample_end(perf_tmp)
         return res
     
     def process_trades(self):
@@ -442,6 +649,17 @@ def _apply(gp_df):
         res = gp.apply(_apply)
     
         self.daily = res
+
+    @staticmethod
+    def _pivot_and_sort(df):
+        data = df.unstack(level=0)
+        data.columns = data.columns.swaplevel()
+        col_names = ['symbol', 'field']
+        data.columns.names = col_names
+        data = data.sort_index(axis=1, level=col_names)
+        #data.sortlevel(axis = 1, inplace=True)
+        data.sort_index(level=0, axis=1, inplace=True)
+        return data
         
     def get_daily(self):
         """
@@ -456,12 +674,12 @@ def get_daily(self):
         trade = self.trades
         
         # pro-process
-        trade_cols = ['trade_date', 'BuyVolume', 'SellVolume', 'commission', 'position', 'AvgPosPrice', 'CumNetTurnOver']
+        trade_cols = ['trade_date', 'BuyVolume', 'SellVolume', 'commission', 'position', 'AvgPosPrice', 'CumNetTurnOver','TurnOver']
     
         trade = trade.loc[:, trade_cols]
         gp = trade.reset_index().groupby(by=['symbol', 'trade_date'])
         func_last = lambda ser: ser.iat[-1]
-        trade = gp.agg({'BuyVolume': np.sum, 'SellVolume': np.sum, 'commission': np.sum,
+        trade = gp.agg({'BuyVolume': np.sum, 'SellVolume': np.sum, 'commission': np.sum, 'TurnOver': np.sum,
                         'position': func_last, 'AvgPosPrice': func_last, 'CumNetTurnOver': func_last})
         trade.index.names = ['symbol', 'trade_date']
 
@@ -473,55 +691,273 @@ def get_daily(self):
         
         # calculate statistics
         close = pd.DataFrame(close.T.stack())
-        close.columns = ['close']
-        close.index.names = ['symbol', 'trade_date']
-        merge = pd.concat([close, trade], axis=1, join='outer')
-        
-        def _apply(gp_df,inst_map):
-            symbol = gp_df.index.levels[0][0]
-            mult = self.inst_map.get(symbol).get("multiplier")
+        close.columns = ['close']     
+        close.index.names = ['symbol', 'trade_date']   
+        adj_close = pd.DataFrame(self.closes_adj.T.stack())
+        adj_close.columns = ['close_adj']
+        adj_close.index.names = ['symbol', 'trade_date']         
+        merge = pd.concat([close, adj_close, trade], axis=1, join='outer')
+        
+        # def _apply(gp_df,inst_map):
+        #     symbol = gp_df.index.levels[0][0]
+        #     mult = self.inst_map.get(symbol).get("multiplier")
+        #     cols_nan_to_zero = ['BuyVolume', 'SellVolume', 'commission']
+        #     cols_nan_fill = ['close', 'position', 'AvgPosPrice', 'CumNetTurnOver']
+        #     # merge: pd.DataFrame
+        #     gp_df.loc[:, cols_nan_fill] = gp_df.loc[:, cols_nan_fill].fillna(method='ffill')
+        #     gp_df.loc[:, cols_nan_fill] = gp_df.loc[:, cols_nan_fill].fillna(0)
+        #
+        #     gp_df.loc[:, cols_nan_to_zero] = gp_df.loc[:, cols_nan_to_zero].fillna(0)
+        #
+        #     mask = gp_df.loc[:, 'AvgPosPrice'] < 1e-5
+        #     gp_df.loc[mask, 'AvgPosPrice'] = gp_df.loc[mask, 'close']
+        #
+        #     gp_df.loc[:, 'CumProfit']     = gp_df.loc[:, 'CumNetTurnOver'] + mult * gp_df.loc[:, 'position'] * gp_df.loc[:, 'close']
+        #     gp_df.loc[:, 'CumProfitComm'] = gp_df['CumProfit'] - gp_df['commission'].cumsum()
+        #
+        #     daily_net_turnover = gp_df['CumNetTurnOver'].diff(1).fillna(gp_df['CumNetTurnOver'].iat[0])
+        #     daily_position_change = gp_df['position'].diff(1).fillna(gp_df['position'].iat[0])
+        #     gp_df['trading_pnl']      = (daily_net_turnover + mult * gp_df['close'] * daily_position_change - gp_df['commission'])
+        #     gp_df['holding_pnl']      = (mult * gp_df['close'].diff(1) * gp_df['position'].shift(1)).fillna(0.0)
+        #     gp_df.loc[:, 'total_pnl'] = gp_df['trading_pnl'] + gp_df['holding_pnl']
+        #     gp_df['trade_shares']     = daily_position_change
+        #
+        #     return gp_df
+
+        # gp = merge.groupby(by='symbol')
+        # res = gp.apply(_apply,self.inst_map)a.columns
+        # self.daily = res
+
+
+        prof_tmp = prof_sample_begin("get_daily")
+
+        def get_ts(dv, field):
+            df = dv.loc[:, pd.IndexSlice[slice(None), field]]
+            df.columns = df.columns.droplevel(1)
+            return df
+
+        def set_ts(dv, field, df):
+            df.columns = pd.MultiIndex.from_product([[field], df.columns])
+            # df = df.sort_index(axis=1)
+
+            dv.columns = dv.columns.swaplevel()
+            #dv = dv.sort_index(axis=1)
+
+            if field not in dv.columns.levels[0]:
+                new_cols = dv.columns.append(df.columns)
+                dv = dv.reindex(columns=new_cols)
+            dv[field] = df[field]
+            dv.columns = dv.columns.swaplevel()
+            #the_data = the_data.sort_index(axis=1)
+
+            return dv
+
+        def build_daily(dv, inst_map):
+
+            if False:
+                mult = get_ts(dv, 'close')
+                for symbol in mult.columns:
+                    mult[symbol] = 1.0#inst_map.get(symbol).get("multiplier")
+            else:
+                mult = 1.0
+
             cols_nan_to_zero = ['BuyVolume', 'SellVolume', 'commission']
             cols_nan_fill = ['close', 'position', 'AvgPosPrice', 'CumNetTurnOver']
-            # merge: pd.DataFrame
-            gp_df.loc[:, cols_nan_fill] = gp_df.loc[:, cols_nan_fill].fillna(method='ffill')
-            gp_df.loc[:, cols_nan_fill] = gp_df.loc[:, cols_nan_fill].fillna(0)
-    
-            gp_df.loc[:, cols_nan_to_zero] = gp_df.loc[:, cols_nan_to_zero].fillna(0)
-    
-            mask = gp_df.loc[:, 'AvgPosPrice'] < 1e-5
-            gp_df.loc[mask, 'AvgPosPrice'] = gp_df.loc[mask, 'close']
-    
-            gp_df.loc[:, 'CumProfit'] = gp_df.loc[:, 'CumNetTurnOver'] + mult * gp_df.loc[:, 'position'] * gp_df.loc[:, 'close']
-            gp_df.loc[:, 'CumProfitComm'] = gp_df['CumProfit'] - gp_df['commission'].cumsum()
-    
-            daily_net_turnover = gp_df['CumNetTurnOver'].diff(1).fillna(gp_df['CumNetTurnOver'].iat[0])
-            daily_position_change = gp_df['position'].diff(1).fillna(gp_df['position'].iat[0])
-            gp_df['trading_pnl'] = (daily_net_turnover + mult * gp_df['close'] * daily_position_change - gp_df['commission'])            
-            gp_df['holding_pnl'] = (mult * gp_df['close'].diff(1) * gp_df['position'].shift(1)).fillna(0.0)
-            gp_df.loc[:, 'total_pnl'] = gp_df['trading_pnl'] + gp_df['holding_pnl']
-            
-            return gp_df
 
-        gp = merge.groupby(by='symbol')
-        res = gp.apply(_apply,self.inst_map)
+            for col in cols_nan_fill:
+                df = get_ts(dv, col)
+                df = df.fillna(method='ffill').fillna(0)
+                dv = set_ts(dv, col, df)
+
+            # # merge: pd.DataFrame
+            # gp_df.loc[:, cols_nan_fill] = gp_df.loc[:, cols_nan_fill].fillna(method='ffill')
+            # gp_df.loc[:, cols_nan_fill] = gp_df.loc[:, cols_nan_fill].fillna(0)
+
+            for col in cols_nan_to_zero:
+                df = get_ts(dv, col)
+                df = df.fillna(0)
+                dv = set_ts(dv, col, df)
+            #gp_df.loc[:, cols_nan_to_zero] = gp_df.loc[:, cols_nan_to_zero].fillna(0)
+
+            df = get_ts(dv, 'AvgPosPrice').copy()
+            mask = df < 1e-5
+            df_close = get_ts(dv, 'close')
+            df[mask] = df_close[mask]
+            dv = set_ts(dv, 'AvgPosPrice', df)
+
+            # mask = gp_df.loc[:, 'AvgPosPrice'] < 1e-5
+            # gp_df.loc[mask, 'AvgPosPrice'] = gp_df.loc[mask, 'close']
+
+            df_turnover    = get_ts(dv, 'CumNetTurnOver')
+            df_position    = get_ts(dv, 'position')
+            df_profit      = df_turnover + mult * df_position * df_close
+            df_commisson   = get_ts(dv, 'commission')
+            df_profit_comm = df_profit - df_commisson.cumsum()
+
+            dv = set_ts(dv, 'CumProfit', df_profit)
+            dv = set_ts(dv, 'CumProfitComm', df_profit_comm)
+
+            # gp_df.loc[:, 'CumProfit'] = gp_df.loc[:, 'CumNetTurnOver'] + mult * gp_df.loc[:, 'position'] * gp_df.loc[:,'close']
+            # gp_df.loc[:, 'CumProfitComm'] = gp_df['CumProfit'] - gp_df['commission'].cumsum()
+
+            daily_net_turnover    = df_turnover.diff(1)#.fillna(df_turnover.iat[0])
+            daily_net_turnover.iloc[0] = df_turnover.iloc[0]
+
+            daily_position_change = df_position.diff(1)#.fillna(gp_df['position'].iat[0])
+            daily_position_change.iloc[0] = df_position.iloc[0]
+
+            # daily_net_turnover = gp_df['CumNetTurnOver'].diff(1).fillna(gp_df['CumNetTurnOver'].iat[0])
+            # daily_position_change = gp_df['position'].diff(1).fillna(gp_df['position'].iat[0])
+
+            df_trading_pnl = (daily_net_turnover + mult * df_close * daily_position_change - df_commisson)
+            df_holding_pnl = (mult * df_close.diff(1) * df_position.shift(1)).fillna(0.0)
+            df_total_pnl = df_trading_pnl + df_holding_pnl
+
+            dv = set_ts(dv, 'trading_pnl',  df_trading_pnl)
+            dv = set_ts(dv, 'holding_pnl',  df_holding_pnl)
+            dv = set_ts(dv, 'total_pnl',    df_total_pnl)
+            dv = set_ts(dv, 'trade_shares', daily_position_change)
+
+
+            # gp_df['trading_pnl'] = (daily_net_turnover + mult * gp_df['close'] * daily_position_change - gp_df['commission'])
+            # gp_df['holding_pnl'] = (mult * gp_df['close'].diff(1) * gp_df['position'].shift(1)).fillna(0.0)
+            # gp_df.loc[:, 'total_pnl'] = gp_df['trading_pnl'] + gp_df['holding_pnl']
+            # gp_df['trade_shares'] = daily_position_change
+            #
+            # return gp_df
+
+            return dv
+
+        dv = merge.copy()
+        dv.index =dv.index.swaplevel()
+        dv = dv.unstack()
+        dv.columns = dv.columns.swaplevel()
+
+        dv = build_daily(dv, self.inst_map)
+
+        # Index(date) : Column(code/field) -> Index(code/date) : Column(field)
+        a = dv.copy()
+        a.columns = a.columns.swaplevel()
+        a = a.stack()
+        a.index = a.index.swaplevel()
+        print(a.columns)
+
+        self.daily = a
+
+        if self._is_alpha_analyzer and self.dataview:
+            self._build_holding_data()
+            self._build_portfolio_data()
+
+        prof_tmp.end()
+
+        self.save_data()
+
+        print(" finished! ")
+
+    def save_data(self):
+        file_path = self.file_folder[0] + "/analyze_data.h5"
+        if self.portfolio_data is not None:
+            self.portfolio_data.to_hdf(file_path, "portfolio_data")
+        if self.holding_data  is not None:
+            self.holding_data._data.to_hdf(file_path, "holding_data")
+
+    def load_data(self):
+
+        file_path = self.file_folder[0] + "/analyze_data.h5"
+        if not os.path.exists(file_path):
+            print("load data error: no analyze_data.h5")
+            return
+
+        self._portfolio_data = pd.read_hdf(file_path, "portfolio_data")
+        self._holding_data = AnalyzeView(pd.read_hdf(file_path, "holding_data"))
+
+
+    def _build_holding_data(self):
+        assert self.dataview, "Should have dataview"
+
+        cols = ['trading_pnl', 'holding_pnl', 'total_pnl', 'commission', 'close','close_adj','position','trade_shares','AvgPosPrice']
+        daily = self.daily.loc[:, cols]
+        daily = daily.rename ( columns={'position': 'holding_shares'})
+
+        tmp = BaseAnalyzer._pivot_and_sort(daily)
+        if len(tmp.index) == 0:
+            return
+
+        holding_data = AnalyzeView(tmp)
+        self._holding_data = holding_data
+
+        start_date = holding_data._data.index[0]
+        end_date   = holding_data._data.index[-1]
+
+        #df_pos = self.get_ts("position")
+        #df_pos_chg = df_pos.diff(1).fillna(0.0)
+        #self.add_analyze_field(df_pos_chg, "trade_shares")
+
+        # Copy base data from dataview, such as OHLC, vwap, volumn.
+        #base_fields = "open,high,low,vwap,turnover,index_weight"
+        # FIXME: tzxu 20180419 no turnover in dataview!
+        base_fields = "open,high,low,vwap,index_weight,turnover2"
+        tmp = self.dataview.get_ts(base_fields, keep_level=True)
+        tmp = tmp.rename (columns={ 'turnover' : 'market_turnover'})
+        holding_data.add_field(tmp)#, base_fields.replace("turnover", "market_turnover"))
+
+        df_close_adj = holding_data.get_ts("close_adj")
+
+        df_rtn = (df_close_adj.diff(1) / df_close_adj).fillna(0.0)
+
+        s_bench_return = (self.data_benchmark.diff(1) / self.data_benchmark).fillna(0.0)
+        s_bench_return = s_bench_return.rename ( columns={'close' : 'benchmark_return'} )
+
+        df_active_return = df_rtn.sub( s_bench_return['benchmark_return'], axis=0 )
+
+        tmp = np.where(holding_data.get_ts("holding_shares") >= 1, 1, 0)
+        df_active_return = df_active_return * tmp
+        df_rtn = df_rtn * tmp
+
+        df_bench_return = df_active_return - df_active_return
+        df_bench_return = df_bench_return.add(s_bench_return['benchmark_return'], axis=0 )
+		
+        holding_data.add_field (df_rtn,           "holding_return")
+        holding_data.add_field (df_active_return, "active_holding_return")
+        holding_data.add_field (df_bench_return,  "benchmark_return")
+
+        df_fillprice   = holding_data.get_ts("AvgPosPrice")
+        df_tradeshares = holding_data.get_ts("trade_shares")
+        df_turnover    =  df_fillprice * df_tradeshares
+        holding_data.add_field(df_turnover, "strategy_turnover")
         
-        self.daily = res
+        df_mktvalue = holding_data.get_ts("holding_shares") * holding_data.get_ts("close")
+        df_mktvalue = df_mktvalue.abs()
+        total_mktvalue = df_mktvalue.apply(lambda x: x.sum(), axis=1)
+        df_weight = df_mktvalue.div(total_mktvalue, axis=0)
+        holding_data.add_field(df_weight, "weight")
 
-    '''
-    def get_daily(self):
-        """Add various statistics to daily DataFrame."""
-        self.daily = self._get_daily(self.closes, self.trades)
-        daily_dic = dict()
-        for sec, df_trade in self.trades.items():
-            df_close = self.closes[sec].rename('close')
-        
-            res = self._get_daily(df_close, df_trade)
-            daily_dic[sec] = res
-    
-        self.daily = daily_dic
-    '''
+        for i in range(5):
+            day = i + 1
+            field_name = 'T+{0}'.format(day )
+
+            field_value = df_weight * df_active_return.rolling(day).sum().shift(-day)
+            #field_value = df_weight * df_active_return.shift(-day)
+            holding_data.add_field(field_value, field_name)
+
+        holding_data._data = holding_data._data.drop(
+            ['AvgPosPrice', 'BuyVolume', 'CumNetTurnOver', 'CumProfit', 'CumProfitComm', 'SellVolume'], axis=1, level=1)
 
-    def get_returns(self, compound_return=False, consider_commission=True):
+    def _build_portfolio_data(self):
+        assert self.dataview, "Should have dataview"
+        assert self._holding_data != None, "Should build holding data firstly"
+
+        df_weight = self._holding_data.get_ts("weight").shift()
+        df = pd.DataFrame()
+
+        df['holding_return']          = (self._holding_data.get_ts("holding_return")        * df_weight).apply(lambda x: x.sum(), axis=1)
+        df['active_holding_return']   = (self._holding_data.get_ts("active_holding_return") * df_weight).apply(lambda x: x.sum(), axis=1)
+        df['holding_pnl']             = (self._holding_data.get_ts("holding_pnl") ).apply(lambda x: x.sum(), axis=1)
+        df['trading_pnl']             = (self._holding_data.get_ts("trading_pnl") ).apply(lambda x: x.sum(), axis=1)
+
+        self._portfolio_data = df
+
+    def get_returns(self, compound_return=False, consider_commission=True, show_turnover_ratio=True):
         """
         Calculate strategy daily return and various metrics indicating strategy's performance.
         
@@ -535,7 +971,7 @@ def get_returns(self, compound_return=False, consider_commission=True):
 
         """
         # only get columns that we need
-        cols = ['trading_pnl', 'holding_pnl', 'total_pnl', 'commission', 'CumProfitComm', 'CumProfit']
+        cols = ['trading_pnl', 'holding_pnl', 'total_pnl', 'commission', 'CumProfitComm', 'CumProfit', 'TurnOver']
         '''
         dic_symbol = {sec: self.inst_map[sec]['multiplier'] * df_daily.loc[:, cols]
                       for sec, df_daily in self.daily.items()}
@@ -567,6 +1003,7 @@ def get_returns(self, compound_return=False, consider_commission=True):
         df_returns = market_values.pct_change(periods=1).fillna(0.0)
         df_cum_returns = (df_returns.loc[:, ['strat', 'bench']] + 1.0).cumprod()
         df_returns = df_returns.join(df_cum_returns, rsuffix='_cum')
+        df_returns.loc[:,'turnover_ratio'] = df_pnl.loc[:,"TurnOver"] / strategy_value
         
         if compound_return:
             df_returns.loc[:, 'active_cum'] = df_returns['strat_cum'] - df_returns['bench_cum'] + 1
@@ -609,11 +1046,25 @@ def get_returns(self, compound_return=False, consider_commission=True):
             100 * (df_returns.loc[:, 'active'].std() * np.sqrt(common.CALENDAR_CONST.TRADE_DAYS_PER_YEAR))
         self.performance_metrics['Sharpe Ratio'] = (self.performance_metrics['Annual Return (%)']
                                                     / self.performance_metrics['Annual Volatility (%)'])
+
+        # self.performance_metrics['Annual Holding Alpha (%)'] = np.mean(self._alpha_decomposition['total_holding_alpha']) * 242/100
+        # self.performance_metrics['Annual Volatility of Holding Alpha (%)'] = np.std(self._alpha_decomposition['total_holding_alpha']) * sqrt(242)/100
+        # self.performance_metrics['Sharpe Ratio of Holding Alpha'] = self.performance_metrics['Annual Holding Alpha (%)']/self.performance_metrics['Annual Volatility of Holding Alpha (%)']
+        #
+        # self.performance_metrics['Annual Industry Holding Alpha (%)'] = np.mean(self._alpha_decomposition['industry_holding_alpha']) * 242/100
+        # self.performance_metrics['Annual Volatility of Industry Holding Alpha (%)'] = np.std(self._alpha_decomposition['industry_holding_alpha']) * sqrt(242)/100
+        # self.performance_metrics['Sharpe Ratio of Industry Holding Alpha'] = self.performance_metrics['Annual Industry Holding Alpha (%)']/self.performance_metrics['Annual Volatility of Industry Holding Alpha (%)']
+        #
+        # self.performance_metrics['Annual Stock Specific Holding Alpha (%)'] = np.mean(self._alpha_decomposition['stock_specific_holding_alpha']) * 242/100
+        # self.performance_metrics['Annual Volatility of Stock Specific Holding Alpha (%)'] = np.std(self._alpha_decomposition['stock_specific_holding_alpha']) * sqrt(242)/100
+        # self.performance_metrics['Sharpe Ratio of Stock Specific Holding Alpha'] = self.performance_metrics['Annual Stock Specific Holding Alpha (%)']/self.performance_metrics['Annual Volatility of Stock Specific Holding Alpha (%)']
+
         self.performance_metrics['Number of Trades']   = len(self.trades.index)
         self.performance_metrics['Total PNL']          = df_pnl.loc[:,'total_pnl'].sum()
         self.performance_metrics['Daily Win Rate(%)']  = win_rate*100
         self.performance_metrics['Daily Lose Rate(%)'] = lose_rate*100
         self.performance_metrics['Commission']         = df_pnl.loc[:,'commission'].sum()
+        self.performance_metrics['Turnover Ratio']         = df_returns.loc[:,'turnover_ratio'].sum() / years
         
         self.risk_metrics['Beta'] = np.corrcoef(df_returns.loc[:, 'bench'], df_returns.loc[:, 'strat'])[0, 1]
         self.risk_metrics['Maximum Drawdown (%)']   = max_dd * TO_PCT
@@ -625,6 +1076,21 @@ def get_returns(self, compound_return=False, consider_commission=True):
         self.performance_metrics_report.append(('Annual Return (%)',        "{:,.2f}".format( self.performance_metrics['Annual Return (%)']))     )
         self.performance_metrics_report.append(('Annual Volatility (%)',    "{:,.2f}".format( self.performance_metrics['Annual Volatility (%)'])) )
         self.performance_metrics_report.append(('Sharpe Ratio',             "{:,.2f}".format( self.performance_metrics['Sharpe Ratio']))          )
+
+        # self.performance_metrics_report.append(('Annual Holding Alpha (%)',        "{:,.2f}".format( self.performance_metrics['Annual Holding Alpha (%)']))     )
+        # self.performance_metrics_report.append(('Annual Volatility of Holding Alpha (%)',    "{:,.2f}".format( self.performance_metrics['Annual Volatility of Holding Alpha (%)'])) )
+        # self.performance_metrics_report.append(('Sharpe Ratio of Holding Alpha',    "{:,.2f}".format( self.performance_metrics['Sharpe Ratio of Holding Alpha'])) )
+        #
+        # self.performance_metrics_report.append(('Annual Industry Holding Alpha (%)',        "{:,.2f}".format( self.performance_metrics['Annual Industry Holding Alpha (%)']))     )
+        # self.performance_metrics_report.append(('Annual Volatility of Industry Holding Alpha (%)',    "{:,.2f}".format( self.performance_metrics['Annual Volatility of Industry Holding Alpha (%)'])) )
+        # self.performance_metrics_report.append(('Sharpe Ratio of Industry Holding Alpha',    "{:,.2f}".format( self.performance_metrics['Sharpe Ratio of Industry Holding Alpha'])) )
+        #
+        # self.performance_metrics_report.append(('Annual Stock Specific Holding Alpha (%)',        "{:,.2f}".format( self.performance_metrics['Annual Stock Specific Holding Alpha (%)']))     )
+        # self.performance_metrics_report.append(('Annual Volatility of Stock Specific Holding Alpha (%)',    "{:,.2f}".format( self.performance_metrics['Annual Volatility of Stock Specific Holding Alpha (%)'])) )
+        # self.performance_metrics_report.append(('Sharpe Ratio of Stock Specific Holding Alpha',    "{:,.2f}".format( self.performance_metrics['Sharpe Ratio of Stock Specific Holding Alpha'])) )
+
+        if show_turnover_ratio:
+            self.performance_metrics_report.append(('Annual Turnover Ratio',               "{:,.2f}".format( self.performance_metrics['Turnover Ratio']))    )
         self.performance_metrics_report.append(('Total PNL',                "{:,.2f}".format( self.performance_metrics['Total PNL']))             )
         self.performance_metrics_report.append(('Commission',               "{:,.2f}".format( self.performance_metrics['Commission']))            )
         self.performance_metrics_report.append(('Number of Trades',         self.performance_metrics['Number of Trades']))
@@ -658,12 +1124,38 @@ def get_returns(self, compound_return=False, consider_commission=True):
         # df_returns = df_returns.join(bt_strat_mv, how='right')
         self.returns = df_returns
     
+    def plot_alpha_decay(self, df, output_folder):
+        fig, ax = plt.subplots(figsize=(16, 8))
+        plt.bar(df.index, df.values)
+        fig.savefig(os.path.join(output_folder, 'alpha_decay.png'), facecolor=fig.get_facecolor(), dpi=fig.get_dpi())
+
+        plt.close(fig)
+
+    def plot_alpha_decay_weight(self, df, output_folder):
+        fig, ax = plt.subplots(figsize=(16, 8))
+        plt.bar(df.index, df.values)
+        fig.savefig(os.path.join(output_folder, 'alpha_decay_weight.png'), facecolor=fig.get_facecolor(), dpi=fig.get_dpi())
+
+        plt.close(fig)
+
+    def plot_cum_alpha_weight(self, df, output_folder):
+        fig, ax = plt.subplots(figsize=(16, 8))
+        ax.plot(range(len(df)), df.mean_weight.cumsum(), lw=1, color='dodgerblue',
+                label='Cumulative weight')
+        ax.plot(range(len(df)), df.alpha_ratio.cumsum(), lw=1, color='darkorchid',
+                label='Cumulative Alpha')
+        ax.legend()
+        ax.grid()
+        fig.savefig(os.path.join(output_folder, 'cum_alpha_weight.png'), facecolor=fig.get_facecolor(), dpi=fig.get_dpi())
+
+        plt.close(fig)
+
     def plot_pnl(self, output_folder):
         """
         Plot 2 graphs:
             1. Percentage return of strategy, benchmark and strategy's excess part.
             2. Daily trading, holding and total PnL.
-        
+
         Parameters
         ----------
         output_folder : str
@@ -672,7 +1164,7 @@ def plot_pnl(self, output_folder):
         """
         old_mpl_rcparams = {k: v for k, v in mpl.rcParams.items()}
         mpl.rcParams.update(MPL_RCPARAMS)
-        
+
         fig1 = plot_portfolio_bench_pnl(self.returns.loc[:, 'strat_cum'],
                                         self.returns.loc[:, 'bench_cum'],
                                         self.returns.loc[:, 'active_cum'],
@@ -680,16 +1172,518 @@ def plot_pnl(self, output_folder):
                                         self.risk_metrics['Maximum Drawdown end'])
         fig1.savefig(os.path.join(output_folder, 'pnl_img.png'), facecolor=fig1.get_facecolor(), dpi=fig1.get_dpi())
         plt.close(fig1)
-        
+
         fig2 = plot_daily_trading_holding_pnl(self.df_pnl['trading_pnl'],
                                               self.df_pnl['holding_pnl'],
                                               self.df_pnl['total_pnl'],
                                               self.df_pnl['total_pnl'].cumsum())
-        fig2.savefig(os.path.join(output_folder, 'pnl_img_trading_holding.png'), facecolor=fig2.get_facecolor(), dpi=fig2.get_dpi())
+        fig2.savefig(os.path.join(output_folder, 'pnl_img_trading_holding.png'), facecolor=fig2.get_facecolor(),
+                     dpi=fig2.get_dpi())
         plt.close(fig2)
-        
+
         mpl.rcParams.update(old_mpl_rcparams)
 
+    def plot_sold_alpha_decay(self, df, output_folder):
+        fig, ax = plt.subplots(figsize=(16, 8))
+        fig, ax = plt.subplots(figsize=(16, 8))
+        plt.bar(df.index, df['alpha'].values)
+
+        fig.savefig(os.path.join(output_folder, self._sold_alpha_decay_image), facecolor=fig.get_facecolor(), dpi=fig.get_dpi())
+
+        plt.close(fig)
+
+    def analyze_alpha_decay(self,result_dir):
+
+        # 个股平均持仓
+        df_weight = self.holding_data.get_ts('weight')
+        df_weight = pd.DataFrame(df_weight.mean(axis=0))
+        df_weight.columns = ['mean_weight']
+        df_weight = pd.merge(left=df_weight, right=self.dataview.data_inst[['name']], left_index=True, right_index=True,
+                             how='left')
+        df_weight = df_weight[df_weight['mean_weight'] > 0]
+        #df_weight.sort_values('mean_weight', ascending=False)
+
+        # 个股Alpha合计
+        raw_alpha = self.holding_data.get_ts('active_holding_return')
+        raw_weight = self.holding_data.get_ts('weight')
+
+        df_alpha = raw_alpha.mul(raw_weight)
+        df_alpha = pd.DataFrame(df_alpha.sum(axis=0))
+        df_alpha.columns = ['alpha']
+        df_alpha = pd.merge(left=df_alpha, right=self.dataview.data_inst[['name']], left_index=True, right_index=True, how='left')
+        df_alpha = df_alpha[df_alpha['alpha'] != 0]
+        df_alpha.sort_values('alpha', ascending=False, inplace=True)
+        df_alpha_weight = pd.concat([df_weight, df_alpha], axis=1)
+
+        # Alpha在个股中分布
+        n_group = 5
+        df_alpha_weight = df_alpha_weight.sort_values('mean_weight', ascending=False)
+        df_alpha_weight['rank'] = range(len(df_alpha_weight))
+        df_alpha_weight['group'] = df_alpha_weight['rank'].apply(lambda x: int(x / n_group))
+        df_alpha_weight['alpha_ratio'] = df_alpha_weight['alpha'] / df_alpha_weight['alpha'].sum()
+
+        df_alpha_group = df_alpha_weight.groupby('group')['alpha_ratio'].sum()
+        df_weight_group = df_alpha_weight.groupby('group')['mean_weight'].sum()
+        df_alpha_weight_group = df_alpha_group / df_weight_group
+
+        #df_alpha_weight_group.plot.bar(figsize=(16, 8), grid=True)
+        self.plot_alpha_decay(df_alpha_group,result_dir)
+        self._alpha_decay_image = "alpha_decay.png"
+
+        self.plot_alpha_decay_weight(df_alpha_weight_group,result_dir)
+        self._alpha_decay_weight_image = "alpha_decay_weight.png"
+
+        self.plot_cum_alpha_weight(df_alpha_weight,result_dir)
+        self._cum_alpha_weight_image = "cum_alpha_weight.png"
+
+    def analyze_alpha_contribution(self, result_dir):
+        """
+        Calculate alpha contribution from industry and specific stocks
+        """
+        if 'sw1' not in self.dataview.data_d.columns.levels[1]:
+            print("Ignore industry overwight analysis for missing sw1 in dataview")
+            return
+
+        if not self.data_api:
+            print("Ignore industry overwight analysis for missing data_api in dataview")
+            return
+
+        # Get the start and end date
+        START_DATE, END_DATE = self.configs['start_date'], self.configs['end_date']
+        # Get stock weight in the portfolio
+        df_weight = self.holding_data.get_ts('weight').loc[START_DATE:END_DATE, :]
+        df_weight = pd.DataFrame(df_weight.mean(axis=0))
+        df_weight.columns = ['mean_weight']
+        df_weight = pd.merge(left=df_weight, right=self.dataview.data_inst[['name']], left_index=True, right_index=True,
+                             how='left')
+        df_weight = df_weight[df_weight['mean_weight'] > 0]
+
+        # Calculate industry weight in the portfolio
+        raw_weight = self.holding_data.get_ts('weight')[df_weight.index].loc[START_DATE:END_DATE, :]
+        index = self.dataview.get_ts('sw1')[df_weight.index].loc[START_DATE:END_DATE, :]
+        index = index.loc[raw_weight.index]
+
+        matching = {
+            '110000': 'NLMY',
+            '210000': 'Digging',
+            '220000': 'Chemistry',
+            '230000': 'Metal',
+            '240000': 'Nonferrous Metal',
+            '270000': 'Electronic',
+            '280000': 'Car',
+            '330000': 'Appliance',
+            '340000': 'Food',
+            '350000': 'Clothing',
+            '360000': 'Light Industrials',
+            '370000': 'Health Care',
+            '410000': 'Utility',
+            '420000': 'Transportation',
+            '430000': 'Housing',
+            '450000': 'Commercial',
+            '460000': 'Service',
+            '480000': 'Bank',
+            '490000': 'Non bank',
+            '510000': 'Others',
+            '610000': 'Construction Material',
+            '620000': 'Construction decoration',
+            '630000': 'Electronic equipment',
+            '640000': 'Mechenical equipment',
+            '650000': 'Army',
+            '710000': 'IT',
+            '720000': 'Media',
+            '730000': 'Telecom',
+            'nan': 'Unclassified'
+        }
+
+        for key, value in matching.items():
+            index = index.replace(key, value)
+
+        def group_sum(df, group_daily):
+            groups = np.unique(group_daily.values.flatten())
+            mask = pd.isnull(groups)
+            groups = groups[np.logical_not(mask)]
+            res = pd.DataFrame(index=df.index, columns=groups, data=np.nan)
+            for g in groups:
+                mask = group_daily == g
+                tmp = df[mask]
+                res.loc[:, g] = tmp.sum(axis=1)
+            return res
+
+        weight_portfolio = group_sum(raw_weight, index)
+
+        # Calculate index weight in the portfolio
+        raw_index_weight = self.dataview.get_ts('index_weight').loc[START_DATE:END_DATE, :]
+        raw_industry = self.dataview.get_ts('sw1').loc[START_DATE:END_DATE, :]
+
+        for key, value in matching.items():
+            raw_industry = raw_industry.replace(key, value)
+
+        weight_index = group_sum(raw_index_weight, raw_industry)
+        weight_overweight = weight_portfolio - weight_index
+
+        # Get the SW industry daily returns
+        df_industry, msg = self.data_api.query(view="jz.industryIndexDaily",
+                                         filter="start_date=%d&end_date=%d" % (START_DATE, END_DATE), fields='preclose')
+
+        mapping = {
+            '801010.SI': 'NLMY',
+            '801020.SI': 'Digging',
+            '801030.SI': 'Chemistry',
+            '801040.SI': 'Metal',
+            '801050.SI': 'Nonferrous Metal',
+            '801080.SI': 'Electronic',
+            '801880.SI': 'Car',
+            '801110.SI': 'Appliance',
+            '801120.SI': 'Food',
+            '801130.SI': 'Clothing',
+            '801140.SI': 'Light Industrials',
+            '801150.SI': 'Health Care',
+            '801160.SI': 'Utility',
+            '801170.SI': 'Transportation',
+            '801180.SI': 'Housing',
+            '801200.SI': 'Commercial',
+            '801210.SI': 'Service',
+            '801780.SI': 'Bank',
+            '801790.SI': 'Non bank',
+            '801230.SI': 'Others',
+            '801710.SI': 'Construction Material',
+            '801720.SI': 'Construction decoration',
+            '801730.SI': 'Electronic equipment',
+            '801890.SI': 'Mechenical equipment',
+            '801740.SI': 'Army',
+            '801750.SI': 'IT',
+            '801760.SI': 'Media',
+            '801770.SI': 'Telecom'
+        }
+
+        for key, value in mapping.items():
+            df_industry = df_industry.replace(key, value)
+
+        # Calculate industry daily return
+        df_industry['ret'] = (df_industry['close'] - df_industry['preclose']) / df_industry['preclose']
+        df_industry['trade_date'] = df_industry['trade_date'].apply(lambda x: int(x))
+        df_industry_ret = df_industry.pivot_table(index='trade_date', columns='symbol', values='ret')
+
+        # Calculate index daily return
+        index_ret = self.dataview.data_benchmark.pct_change()
+        index_ret.columns = ['index_ret']
+        index_ret = index_ret.loc[df_industry_ret.index]
+
+        START_DATE, END_DATE = weight_overweight.index[0], weight_overweight.index[-1]
+        
+        # Calculate index and industry period return from START_DATE to END_DATE
+        df_industry_period_start = df_industry[df_industry['trade_date'] == START_DATE][['close', 'symbol']]
+        df_industry_period_end   = df_industry[df_industry['trade_date'] == END_DATE][['close', 'symbol']]
+        df_industry_period       = pd.merge(left = df_industry_period_start, right = df_industry_period_end,
+                                            how = 'left', on = 'symbol', suffixes=('_start', '_end'))
+        df_industry_period['period_ret'] = (df_industry_period['close_end'] - df_industry_period['close_start'])/df_industry_period['close_start']
+
+        df_index_period = (self.dataview.data_benchmark.loc[END_DATE] - self.dataview.data_benchmark.loc[START_DATE])/self.dataview.data_benchmark.loc[START_DATE]
+        df_industry_period['index_ret'] = df_index_period[0]
+        df_industry_period['period_alpha'] = df_industry_period['period_ret'] - df_industry_period['index_ret']
+        df_industry_period['period_alpha'] *= 100
+        df_industry_period = df_industry_period.set_index('symbol')
+
+        # Calculate industry daily alpha
+        df_industry_alpha = pd.concat([df_industry_ret, index_ret], axis=1)
+        df_industry_alpha = df_industry_alpha.sub(df_industry_alpha['index_ret'], axis=0)
+        del df_industry_alpha['index_ret']
+
+        weight_overweight_shift = weight_overweight.shift(1)
+        df_industry_alpha_stock = weight_overweight_shift.mul(df_industry_alpha)
+        df_industry_alpha_daily = df_industry_alpha_stock.sum(axis=1)
+
+        # Get the total alpha on each day
+        raw_alpha = self.holding_data.get_ts('active_holding_return').loc[START_DATE:END_DATE, :]
+        raw_weight = self.holding_data.get_ts('weight').loc[START_DATE:END_DATE, :]
+        df_alpha = raw_alpha.mul(raw_weight)
+        total_alpha = df_alpha.sum(axis=1)
+
+        df_alpha_all = pd.concat([total_alpha, df_industry_alpha_daily], axis=1)
+        df_alpha_all.columns = ['total_holding_alpha', 'industry_holding_alpha']
+        df_alpha_all['stock_specific_holding_alpha'] = df_alpha_all['total_holding_alpha'] - df_alpha_all['industry_holding_alpha']
+        df_alpha_all *= 10000
+
+        df_alpha_sum = df_alpha_all.sum()/100
+        df_alpha_mean = df_alpha_all.mean() * 242 / 100
+        df_alpha_std  = df_alpha_all.std() * np.sqrt(242) / 100
+        df_alpha_sharpe = df_alpha_mean/df_alpha_std
+        df_alpha_agg = pd.concat([df_alpha_mean, df_alpha_std, df_alpha_sharpe], axis = 1)
+        df_alpha_agg.columns = ['Annual Alpha(%)', 'Annual Alpha Volatility(%)', 'IR']
+        df_alpha_agg = df_alpha_agg.T
+        df_alpha_agg.columns = ['Holding Alpha', 'Industry Holding Alpha', 'Stock Specific Holding Alpha']
+
+        df_industry_alpha_byindustry = df_industry_alpha_stock.sum(axis = 0) * 100
+        df_industry_agg = pd.concat([df_industry_alpha_byindustry, self._average_industry_overweight], axis = 1)
+        df_industry_agg.columns = ['total alpha', 'portfolio', 'index', 'overweight']
+        df_industry_agg = df_industry_agg.sort_values('total alpha', ascending = False)
+        df_industry_agg = pd.concat([df_industry_agg, df_industry_period['period_alpha']], axis = 1)
+        df_industry_agg['selection_alpha'] = df_industry_agg['overweight'] * df_industry_agg['period_alpha']
+        df_industry_agg['timing_alpha'] = df_industry_agg['total alpha'] - df_industry_agg['selection_alpha']
+        df_industry_agg = df_industry_agg[['total alpha', 'selection_alpha', 'timing_alpha', 'period_alpha', 'portfolio', 'index', 'overweight']]
+        df_industry_agg.columns = ['total alpha', 'selection alpha', 'timing alpha', 'period alpha', 'portfolio', 'index', 'overweight']
+
+        # Aggregate the alpha by month
+        df_alpha_all_month_total = df_alpha_all.copy()
+        df_alpha_all_month_total/= 100
+        df_alpha_all_month_total['date'] = df_alpha_all_month_total.index
+        df_alpha_all_month_total['year_month'] = df_alpha_all_month_total['date'].apply(lambda x: str(int(x))[:6])
+        del df_alpha_all_month_total['date']
+        df_alpha_all_month_total = df_alpha_all_month_total.groupby('year_month').sum()
+        df_alpha_all_month_total.columns = ['Total', 'Industry', 'Stock specific']
+
+        df_alpha_all_month_industry = df_industry_alpha_stock.copy()
+        df_alpha_all_month_industry *= 100
+        df_alpha_all_month_industry['date'] = df_alpha_all_month_industry.index
+        df_alpha_all_month_industry['year_month'] = df_alpha_all_month_industry['date'].apply(lambda x: str(int(x))[:6])
+        del df_alpha_all_month_industry['date']
+        df_alpha_all_month_industry = df_alpha_all_month_industry.groupby('year_month').sum()
+
+        self._alpha_decomposition_images = []
+        self._alpha_decomposition_industry_images = []
+
+        image_name1 = 'total_alpha_m'
+        self._alpha_decomposition_images = 'total_alpha_m.png'
+        self.plot_return_heatmap(df_alpha_all_month_total, image_name1, result_dir, (6,12))
+
+        image_name2 = 'industry_alpha_m'
+        self._alpha_decomposition_industry_images = 'industry_alpha_m.png'
+        self.plot_return_heatmap(df_alpha_all_month_industry, image_name2, result_dir, (20,12))
+
+        self._industry_agg = df_industry_agg
+        self._alpha_decomposition = df_alpha_agg
+
+    def analyze_alpha_weight_contribution(self, result_dir):
+        """
+        Get top 40% weights of stocks and calculate its alpaha contribution
+        :return:
+        """
+
+        # Get Top 40% wight stocks
+        mv = self.holding_data.get_ts('holding_shares') * self.holding_data.get_ts('close')
+        total_mv = mv.sum(axis=1)
+
+        for col in mv.columns:
+            mv[col] /= total_mv
+
+        weight_in_day = mv
+
+        weight_in_cycle = weight_in_day.sum(axis=0).sort_values(ascending=False)
+        tmp = weight_in_cycle.cumsum()
+        weight_in_cycle = weight_in_cycle[tmp < tmp[-1] * 0.4]
+        weight_in_cycle /= tmp[-1]
+        symbols = weight_in_cycle.index
+
+        # Get alpha contribution of each stock on whole test cycle
+        active_return = self.holding_data.get_ts('active_holding_return') * weight_in_day
+        alpha_contribution = active_return.loc[:, symbols].sum()
+
+        df_contrib = pd.DataFrame(index=weight_in_cycle.index)
+        df_contrib['symbol'] = weight_in_cycle.index
+        df_contrib['name']   = df_contrib['symbol'].apply(lambda x: self.inst_map[x]['name'])
+        df_contrib['weight']             = weight_in_cycle.apply(lambda x: str(np.round(x * 100, 2)) + "%")
+        df_contrib['alpha_contribution'] = alpha_contribution.apply(lambda x: str(np.round(x * 100, 2)) + "%")
+        df_contrib.reset_index(drop=True, inplace=True)
+
+        holding_shares = self.holding_data.get_ts('holding_shares').sum(axis=0)
+
+        self._alpha_weight_traded_stocks = holding_shares[holding_shares>0].shape[0]
+        self._alpha_weight_contribution = df_contrib
+
+    def analyze_sold_alpha_decay(self, result_dir):
+        """
+        Take sold stocks as a portfolio and calculate next 10 days' pnl.
+        """
+
+        # Get Top 40% wight stocks
+        sold_shares = self.holding_data.get_ts('holding_shares').diff()
+        sold_shares *= np.where(sold_shares < 0, -1, 0)
+
+        mv = sold_shares * self.holding_data.get_ts('close')
+        total_mv = mv.sum(axis=1)
+
+        for col in mv.columns:
+            mv[col] /= total_mv
+
+        weight_in_day = mv
+
+        active_return = self.holding_data.get_ts('active_holding_return')
+
+        sold_pnl = [0 for i in range(10)]
+        for day in range(10):
+            sold_pnl[day] = (active_return.shift(day + 1) * weight_in_day).sum(axis=1).mean()
+
+        df = pd.DataFrame(sold_pnl)
+        df.columns = ['alpha']
+        df.index = [i + 1 for i in range(10)]
+
+        self._sold_alpha_decay_image = "sold_alpha_decay.png"
+        self._sold_alpha_decay = df
+        self.plot_sold_alpha_decay(df, result_dir)
+
+    def plot_return_heatmap(self, df, image_name, output_folder, figsize):
+
+        # plot
+        # df_factor_heatmap_pivot = df_factor_heatmap.pivot('year', 'month', single_factor_name)
+        import seaborn as sns
+        cmap = sns.diverging_palette(220, 10, as_cmap=True)
+        fig, ax = plt.subplots(1, 1, figsize=figsize)
+        ax = sns.heatmap(df, ax=ax, annot=True, center=0,
+                         annot_kws={"size": 10},
+                         fmt="0.2f", linewidths=0.5,
+                         square=False, cbar=True, cmap=cmap)
+        fig.subplots_adjust(hspace=0)
+        plt.yticks(rotation=0)
+        ax.set_ylabel('')
+        ax.set_xlabel('')
+        plt.tick_params(axis='both', which='major', labelsize=16)
+        plt.tight_layout()
+
+        fig.savefig(os.path.join(output_folder, image_name),
+                    facecolor=fig.get_facecolor(),
+                    dpi=fig.get_dpi())
+        plt.close(fig)
+
+    def analyze_industry_overweight(self, result_dir):
+        if 'sw1' not in self.dataview.data_d.columns.levels[1]:
+            print("Ignore industry overwight analysis for missing sw1 in dataview")
+            return
+
+        START_DATE, END_DATE = self.configs['start_date'], self.configs['end_date']
+        # 个股平均持仓
+        df_weight = self.holding_data.get_ts('weight').loc[START_DATE:END_DATE, :]
+        df_weight = pd.DataFrame(df_weight.mean(axis=0))
+        df_weight.columns = ['mean_weight']
+        df_weight = pd.merge(left=df_weight, right=self.dataview.data_inst[['name']], left_index=True, right_index=True,
+                             how='left')
+        df_weight = df_weight[df_weight['mean_weight'] > 0]
+
+        raw_weight = self.holding_data.get_ts('weight')[df_weight.index].loc[START_DATE:END_DATE, :]
+        index = self.dataview.get_ts('sw1')[df_weight.index].loc[START_DATE:END_DATE, :]
+        index = index.loc[raw_weight.index]
+
+        matching = {
+            '110000': 'NLMY',
+            '210000': 'Digging',
+            '220000': 'Chemistry',
+            '230000': 'Metal',
+            '240000': 'Nonferrous Metal',
+            '270000': 'Electronic',
+            '280000': 'Car',
+            '330000': 'Appliance',
+            '340000': 'Food',
+            '350000': 'Clothing',
+            '360000': 'Light Industrials',
+            '370000': 'Health Care',
+            '410000': 'Utility',
+            '420000': 'Transportation',
+            '430000': 'Housing',
+            '450000': 'Commercial',
+            '460000': 'Service',
+            '480000': 'Bank',
+            '490000': 'Non bank',
+            '510000': 'Others',
+            '610000': 'Construction Material',
+            '620000': 'Construction decoration',
+            '630000': 'Electronic equipment',
+            '640000': 'Mechenical equipment',
+            '650000': 'Army',
+            '710000': 'IT',
+            '720000': 'Media',
+            '730000': 'Telecom',
+            'nan': 'Unclassified'
+        }
+
+        for key, value in matching.items():
+            index = index.replace(key, value)
+
+        def group_sum(df, group_daily):
+            groups = np.unique(group_daily.values.flatten())
+            mask = pd.isnull(groups)
+            groups = groups[np.logical_not(mask)]
+            res = pd.DataFrame(index=df.index, columns=groups, data=np.nan)
+            for g in groups:
+                mask = group_daily == g
+                tmp = df[mask]
+                res.loc[:, g] = tmp.sum(axis=1)
+            return res
+
+        weight_industry = group_sum(raw_weight, index)
+
+        ## 指数行业分析
+        raw_index_weight = self.dataview.get_ts('index_weight').loc[START_DATE:END_DATE, :]
+        raw_industry = self.dataview.get_ts('sw1').loc[START_DATE:END_DATE, :]
+
+
+        for key, value in matching.items():
+            raw_industry = raw_industry.replace(key, value)
+
+
+        weight_index = group_sum(raw_index_weight, raw_industry)
+
+        ## 行业超配分析
+        # weight_industry['Army'] = 0.0
+        # weight_industry['Clothing'] = 0.0
+        # weight_industry['Others'] = 0.0
+
+        weight_dif = weight_industry - weight_index
+        index_weight_industry = pd.DataFrame(weight_index.mean(axis=0).sort_values(ascending=False))
+        index_weight_industry.columns = ['index']
+        #print('相对指数超配')
+        self._industry_overweight_images = []
+        for single_industry in weight_dif.columns:
+            image_name = ('iw_' + single_industry + '.png').replace(' ', '_').lower()
+            self._industry_overweight_images.append(image_name)
+            self.plot_industry_weight(weight_dif, single_industry, image_name, result_dir)
+
+        # 行业平均持仓及相对指数超配
+        portfolio_weight_industry = pd.DataFrame(weight_industry.mean(axis=0).sort_values(ascending=False))
+        portfolio_weight_industry.columns = ['portfolio']
+        weight_industry_compare = pd.concat([portfolio_weight_industry, index_weight_industry], axis=1)
+        weight_industry_compare['overweight'] = weight_industry_compare['portfolio'] - weight_industry_compare['index']
+        self._average_industry_overweight = weight_industry_compare.sort_values('overweight', ascending=False)
+
+
+    def plot_industry_weight(self, df, industry_name, image_name, output_folder):
+        idx0 = df.index.astype(str)
+        n = len(idx0)
+        idx = np.arange(n)
+        idx2016 = np.where(idx0 == '20160701')
+        fig, ax3 = plt.subplots(figsize=(16, 8))
+        # ax1 = ax0.twinx()
+
+        bar_width = 0.2
+        profit_color, lose_color = '#D63434', '#2DB635'
+        curve_color = '#174F67'
+        y_label = 'Daily Return'
+        color_arr_raw = np.array([profit_color] * n)
+
+        color_arr = color_arr_raw.copy()
+        # color_arr[holding_pnl_tot.PnL < 0] = lose_color
+        # ax3.bar(idx, df_volume_cyb.turnover_300, width = 1, color='green', label = '300', alpha = 0.4)
+        # ax3.bar(idx, df_volume_cyb.turnover_cyb, width = 1, color='yellow', label = 'cyb', alpha = 0.4)
+
+        # ax3.set(title='Cumulative and daily holding PnL', ylabel=y_label)
+        ax3.xaxis.set_major_formatter(MyFormatter(idx0, '%Y%m%d'))
+        ax3.legend()
+        ax3.axhline(0, color='red', lw=1, ls='--')
+        ax3.plot(idx, df[industry_name], color='blue', label='%s' % industry_name, linewidth=1.5, alpha=0.8)
+
+        # ax3.plot(idx, stock_alpha['active_holding_return'].cumsum(), lw=1, color='red', label = 'alpha')
+        # ax6 = ax3.twinx()
+        # ax3.plot(idx, stock_alpha['weight'], lw = 1, color = 'blue', label = 'weight')
+        # ax3.yaxis.label.set_color(curve_color)
+        ax3.grid()
+        plt.tick_params(axis='both', which='major', labelsize=12)
+        plt.legend(fontsize=14)
+        plt.title('%s' % industry_name, fontsize=16)
+        plt.tight_layout()
+
+        fig.savefig(os.path.join(output_folder, image_name),
+                    facecolor=fig.get_facecolor(),
+                    dpi=fig.get_dpi())
+        plt.close(fig)
+
     def gen_report(self, source_dir, template_fn, out_folder='.', selected=None):
         """
         Generate HTML (and PDF) report of the trade analysis.
@@ -723,7 +1717,22 @@ def gen_report(self, source_dir, template_fn, out_folder='.', selected=None):
         dic['dailypnl_metrics_report'] = self.dailypnl_metrics_report
         dic['dailypnl_top5_metrics_report'] = self.dailypnl_top5_metrics_report
         dic['dailypnl_tail5_metrics_report'] = self.dailypnl_tail5_metrics_report
-        
+        dic['industry_overweight_images'] = self._industry_overweight_images
+        dic['alpha_decomposition_images'] = self._alpha_decomposition_images
+        dic['alpha_decomposition_industry_images'] = self._alpha_decomposition_industry_images
+        dic['alpha_decay_image'] = self._alpha_decay_image
+        dic['alpha_decay_weight_image'] = self._alpha_decay_weight_image
+        dic['cum_alpha_weight_image'] = self._cum_alpha_weight_image
+        dic['average_industry_overweight'] = self._average_industry_overweight
+        dic['alpha_decomposition'] = self._alpha_decomposition
+        dic['industry_agg'] = self._industry_agg
+
+        dic['alpha_weight_contribution']  = self._alpha_weight_contribution
+        dic['alpha_weight_traded_stocks'] = self._alpha_weight_traded_stocks
+
+        dic['sold_alpha_decay_image'] = self._sold_alpha_decay_image
+
+
         self.report_dic.update(dic)
         
         r = Report(self.report_dic, source_dir=source_dir, template_fn=template_fn, out_folder=out_folder)
@@ -755,9 +1764,14 @@ def do_analyze(self, result_dir, selected_sec=None, compound_rtn = False):
         print("process trades...")
         self.process_trades()
         print("get daily stats...")
+
         self.get_daily()
+
         print("calc strategy return...")
+
+        prof_tmp = prof_sample_begin("get_returns")
         self.get_returns(compound_return = compound_rtn, consider_commission=True)
+        prof_tmp.end()
 
         if len(selected_sec) > 0:
             print("Plot single securities PnL")
@@ -775,10 +1789,11 @@ def do_analyze(self, result_dir, selected_sec=None, compound_rtn = False):
                         out_folder=result_dir,
                         selected=selected_sec)
 
+        return self.performance_metrics_report
 
 class EventAnalyzer(BaseAnalyzer):
     def __init__(self):
-        super(EventAnalyzer, self).__init__()
+        super(EventAnalyzer, self).__init__(is_alpha_analyzer = False)
         
         self.metrics = dict()
         self.daily = None
@@ -953,22 +1968,51 @@ def get_rebalance_position(self):
         mask = (self._raw_trades['fill_no'] != '101010') & (self._raw_trades['fill_no'] != '202020')
         trades_rebalance = self._raw_trades.loc[mask]
         rebalance_dates = trades_rebalance['trade_date'].unique()
-        
-        daily_pos_name = self.daily_position.T.copy()
-        daily_pos_name.loc[:, 0] = u'               '
-        for idx, _ in daily_pos_name.iterrows():
-            daily_pos_name.loc[idx, 0] = self.inst_map[idx]['name']
-        
+
+        tmp = self.holding_data.get_ts('holding_shares')
+        for col in tmp.columns:
+            tmp[col] = self.inst_map[col]['name']
+
+        self.holding_data.add_field(tmp, 'name')
+
         dic_pos = OrderedDict()
         for date in rebalance_dates:
-            daily = daily_pos_name.loc[:, [0, date]]
-            daily = daily.loc[daily[date] >= 1]
-            daily = daily.reset_index()
-            daily.index.name = date
-            daily.columns = ['symbol', 'name', 'position']
-            daily.loc[:, 'position'] = daily['position'].astype(np.integer)
-            dic_pos[date] = daily
+            fields = ['name', 'holding_shares','weight', 'T+1','T+2','T+3','T+4','T+5']
+            daily = self.holding_data.data.loc[[date], pd.IndexSlice[:, fields]].T.unstack()
+            daily.columns = daily.columns.droplevel(level='trade_date')
+            daily.columns.name = ""
+            daily['symbol'] = daily.index
+
+            daily.reset_index(drop=True, inplace=True)
+            daily.index.name = ""
+            daily.rename(inplace=True, columns= {'holding_shares': 'position'})
+
+            daily = daily.sort_values('weight', ascending=False)
+            for col in ['weight', 'T+1', 'T+2', 'T+3', 'T+4', 'T+5']:
+                daily[col] = daily[col].apply(lambda x: str(np.round(x * 100, 2)) + "%")
+
+            daily = daily[['symbol', 'name', 'position', 'weight', 'T+1','T+2','T+3','T+4','T+5']]
+
+            dic_pos[date] = daily[(daily['weight'] != "0.0%") & (daily['weight'] != "nan%")]
+            dic_pos[date].index = range(1, len(dic_pos[date]) + 1)
+
         self.rebalance_positions = dic_pos
+
+        # daily_pos_name = self.daily_position.T.copy()
+        # daily_pos_name.loc[:, 0] = u'               '
+        # for idx, _ in daily_pos_name.iterrows():
+        #     daily_pos_name.loc[idx, 0] = self.inst_map[idx]['name']
+        #
+        # dic_pos = OrderedDict()
+        # for date in rebalance_dates:
+        #     daily = daily_pos_name.loc[:, [0, date]]
+        #     daily = daily.loc[daily[date] >= 1]
+        #     daily = daily.reset_index()
+        #     daily.index.name = date
+        #     daily.columns = ['symbol', 'name', 'position']
+        #     daily.loc[:, 'position'] = daily['position'].astype(np.integer)
+        #     dic_pos[date] = daily
+        # self.rebalance_positions = dic_pos
         
     @staticmethod
     def calc_win_ratio(ret_arr):
@@ -997,7 +2041,7 @@ def calc_cum(df):
         '''
         print
         
-    def do_analyze(self, result_dir, selected_sec=None, brinson_group=None, compound_rtn = False):
+    def do_analyze(self, result_dir, selected_sec=None, brinson_group=None, compound_rtn = False, show_turnover_ratio = True):
         if selected_sec is None:
             selected_sec = []
     
@@ -1006,7 +2050,7 @@ def do_analyze(self, result_dir, selected_sec=None, brinson_group=None, compound
         print("get daily stats...")
         self.get_daily()
         print("calc strategy return...")
-        self.get_returns(compound_return = compound_rtn, consider_commission=True)
+        self.get_returns(compound_return = compound_rtn, consider_commission=True, show_turnover_ratio = show_turnover_ratio)
         print("calc re-balance position")
         self.get_rebalance_position()
         print("Get stats")
@@ -1031,9 +2075,20 @@ def do_analyze(self, result_dir, selected_sec=None, brinson_group=None, compound
             if group is None:
                 raise ValueError("group data is None.")
             self.brinson(group, output_folder=result_dir)
-    
-        self.daily_position.to_csv(os.path.join(result_dir, 'daily_position.csv'))
-        self.returns.to_csv(os.path.join(result_dir, 'returns.csv'))
+
+        test_name = self.configs['Name'] if 'Name' in self.configs else "default"
+        self.daily_position.to_csv(os.path.join(result_dir, 'daily_position_%s.csv' % test_name))
+        self.returns.to_csv(os.path.join(result_dir, 'returns_%s.csv' % test_name))
+
+        if self.dataview:
+            print("Analyze alpha data...")
+            self.analyze_alpha_decay(result_dir)
+            self.analyze_industry_overweight(result_dir)
+            self.analyze_alpha_contribution(result_dir)
+            self.analyze_alpha_weight_contribution(result_dir)
+            self.analyze_sold_alpha_decay(result_dir)
+        else:
+            print("Ignore analyzing alpha data")
 
         print("generate report...")
         self.gen_report(source_dir=STATIC_FOLDER, template_fn='report_template.html',
diff --git a/jaqs/trade/analyze/static/report_template.html b/jaqs/trade/analyze/static/report_template.html
index 2b4c759..5f1e0f4 100644
--- a/jaqs/trade/analyze/static/report_template.html
+++ b/jaqs/trade/analyze/static/report_template.html
@@ -172,6 +172,20 @@ <h3>Top 5 Daily PnL</h3>
                 {% endfor %}
             </table>
 
+            {% if alpha_decomposition is not none%}
+              <h2 id="alpha_decomposition">Alpha Decomposition</h2>
+                <div class="card card-body">
+                    {{ alpha_decomposition.to_html(classes="table table-striped", border=0) }}
+                </div>
+            {% endif %}
+
+            {% if industry_agg is not none%}
+              <h2 id="industry_agg">Industry Total Alpha and Overweight</h2>
+                <div class="card card-body">
+                    {{ industry_agg.to_html(classes="table table-striped", border=0) }}
+                </div>
+            {% endif %}
+
             <!-- Trading & Holding PnL -->
             <h2 id="trading_holding">Daily Trading & Holding PnL</h2>
             <p>
@@ -212,7 +226,7 @@ <h2 id="symbol_pnl_table">Daily PnL for Selected Security</h2>
 
             <!-- Position on Re-balance Days -->
             {% if rebalance_positions is not none %}
-            <h2 id="rebalance_positions"> Table of Position on Re-balance Days </h2>
+            <h2 id="rebalance_positions">Table of Position on Re-balance Days </h2>
                 {% for date, df in rebalance_positions.items() %}
                     <p>
                         <button class="btn btn-primary" type="button" data-toggle="collapse" data-target="#collapsePos{{date}}" aria-expanded="false" aria-controls="collapsePos{{date}}">
@@ -229,6 +243,72 @@ <h2 id="rebalance_positions"> Table of Position on Re-balance Days </h2>
                 <p>This data is stored in <code>Analyzer.rebalance_positions</code></p>
             {% endif %}
 
+            {% if alpah_weight_contribution %}
+              <h2 id="alpha_weight_contribution">Alpha Weight Contribution</h2>
+              <p>Table of Alpha contribution on top 40% weight. Total {{ alpha_weight_traded_stocks }} stocks were bought. </p>
+                <div class="card card-body">
+                  {{ alpha_weight_contribution.to_html(classes="table table-striped", border=0) }}
+                </div>
+            {% endif %}
+
+            <!-- Trading & Holding PnL -->
+            {% if alpha_decay_image  %}
+              <h2 id="alpha_decay">Alpha Decay</h2>
+              <p>
+                Sort the stock by average weight from top to bottom, then aggregate the alpha contribution by every 5 stocks.
+              </p>
+
+              <img src="{{alpha_decay_image}}" class="img-fluid" alt="Responsive Image">
+            {% endif %}
+
+            {% if alpha_decay_weight_image  %}
+              <h2 id="alpha_decay_weight">Alpha Decay Scaled by Weight</h2>
+              <p>
+                In each group, scale the alpha contribution by aggregate weight
+              </p>
+
+              <img src="{{alpha_decay_weight_image}}" class="img-fluid" alt="Responsive Image">
+            {% endif %}
+
+            {% if cum_alpha_weight_image  %}
+              <h2 id="cum_alpha_weight">Cumulative Alpha and Weight</h2>
+              <p>
+                Calculate cumulative weight and alpha contribution after sort the stock by average weight from top to bottom.
+              </p>
+
+              <img src="{{cum_alpha_weight_image}}" class="img-fluid" alt="Responsive Image">
+            {% endif %}
+
+            {% if industry_overweight_images %}
+              <h2 id="industry_overweight">Industry Overwight</h2>
+                {% for image in industry_overweight_images %}
+                  <img src="{{image}}" class="img-fluid" alt="Responsive Image">
+                {% endfor %}
+            {% endif %}
+
+            {% if alpha_decomposition_images %}
+              <h2 id="alpha_decomposition_images">Total Alpha Decomposition by Month</h2>
+              <img src="{{alpha_decomposition_images}}" class="img-fluid" alt="Responsive Image">
+            {% endif %}
+
+            {% if alpha_decomposition_industry_images %}
+              <h2 id="alpha_decomposition_industry_images">Industry alpha Decomposition by Month</h2>
+              <img src="{{alpha_decomposition_industry_images}}" class="img-fluid" alt="Responsive Image">
+            {% endif %}
+
+            {% if sold_alpha_decay_image  %}
+              <h2 id="sold_alpha_decay">Sold Alpha Decay</h2>
+
+              <img src="{{sold_alpha_decay_image}}" class="img-fluid" alt="Responsive Image">
+            {% endif %}
+
+
+            <!--{% if average_industry_overweight is not none%}-->
+              <!--<h2 id="average_industry_overweight">Average Industry Overwwight</h2>-->
+                <!--<div class="card card-body">-->
+                    <!--{{ average_industry_overweight.to_html(classes="table table-striped", border=0) }}-->
+                <!--</div>-->
+            <!--{% endif %}-->
 
             <!-- Daily Position -->
             {% if daily_position is not none %}
diff --git a/jaqs/trade/backtest.py b/jaqs/trade/backtest.py
index ad13034..b8f7df1 100644
--- a/jaqs/trade/backtest.py
+++ b/jaqs/trade/backtest.py
@@ -105,7 +105,7 @@ def init_from_config(self, props):
             self.ctx.init_universe(self.ctx.dataview.symbol)
         else:
             raise ValueError("No dataview, no symbol either.")
-        
+
         if 'init_balance' not in props:
             raise ValueError("No [init_balance] provided. Please specify it in props.")
 
@@ -254,6 +254,14 @@ def __init__(self):
 
     def init_from_config(self, props):
         super(AlphaBacktestInstance, self).init_from_config(props)
+        strategy = self.ctx.strategy
+
+        # universe = props.get('universe', "")
+        # symbol = props.get('symbol', "")
+        # if symbol and universe or len(universe.split('.')) > 1:
+        #     if strategy.pc_method in['index_weight', 'equal_index_weight']:
+        #         raise ValueError("{} shouldn't be used if there are both symbol and universe in props", strategy.pc_method)
+
 
     def position_adjust(self):
         """
@@ -351,18 +359,16 @@ def re_balance_plan_before_open(self):
         # only filter index members when universe is defined
         universe_list = self.ctx.universe
         if self.ctx.dataview.universe:
-            col = 'index_member'
-            df_is_member = self.ctx.dataview.get_snapshot(self.ctx.trade_date, fields=col)
+            df_is_member = self.ctx.dataview.get_snapshot(self.ctx.trade_date, fields='index_member')
             df_is_member = df_is_member.fillna(0).astype(bool)
-            dic_index_member = df_is_member.loc[:, col].to_dict()
-            universe_list = [symbol for symbol, value in dic_index_member.items() if value]
+            universe_list = df_is_member[df_is_member['index_member']].index.values
 
         # Step.2 filter out those not listed or already de-listed
         df_inst = self.ctx.dataview.data_inst
         mask = np.logical_and(self.ctx.trade_date > df_inst['list_date'],
                               self.ctx.trade_date < df_inst['delist_date'])
         listing_symbols = df_inst.loc[mask, :].index.values
-        universe_list = [s for s in universe_list if s in listing_symbols]
+        universe_list = np.intersect1d(universe_list, listing_symbols)
         
         # step.3 construct portfolio using models
         self.ctx.strategy.portfolio_construction(universe_list)
@@ -377,7 +383,7 @@ def re_balance_plan_after_open(self):
         Price here must not be adjusted.
 
         """
-        prices = {k: v['close'] for k, v in self.univ_price_dic.items()}
+        prices = {k: v['vwap'] for k, v in self.univ_price_dic.items()}
 
         # suspensions & limit_reaches: list of str
         suspensions = self.get_suspensions()
@@ -416,8 +422,13 @@ def run_alpha(self):
         begin_time = dt.datetime.now()
 
         tapi = self.ctx.trade_api
-        
-        self.ctx.trade_date = self._get_next_trade_date(self.start_date)
+
+        # Keep compatible, the original test starts with next day, not start_date
+        if self.ctx.strategy.period in ['week', 'month']:
+            self.ctx.trade_date = self._get_first_period_day()
+        else:
+            self.ctx.trade_date = self._get_next_trade_date(self.start_date)
+
         self.last_date = self._get_last_trade_date(self.ctx.trade_date)
         self.current_rebalance_date = self.ctx.trade_date
         while True:
@@ -470,7 +481,7 @@ def run_alpha(self):
         print("Backtest done. {0:d} days, {1:.2e} trades in total. used time: {2}s".
               format(len(self.ctx.dataview.dates), len(self.ctx.pm.trades), used_time))
 
-        jutil.prof_print()
+        #jutil.prof_print()
     
     def on_after_market_close(self):
         self.ctx.trade_api.on_after_market_close()
@@ -504,7 +515,119 @@ def _get_last_trade_date(self, date):
             return dates[mask][-1]
         else:
             return self.ctx.data_api.query_last_trade_date(date)
-    
+
+    def _get_first_period_day(self):
+
+        current = self.start_date
+
+        current_date = jutil.convert_int_to_datetime(current)
+        period = self.ctx.strategy.period
+
+        # set current date to first date of current period
+        # set offset to first date of previous period
+        if period == 'day':
+            offset = pd.tseries.offsets.BDay()
+        elif period == 'week':
+            offset = pd.tseries.offsets.Week(weekday=0)
+            current_date -= pd.tseries.offsets.Day(current_date.weekday())
+        elif period == 'month':
+            offset = pd.tseries.offsets.BMonthBegin()
+            current_date -= pd.tseries.offsets.Day(current_date.day - 1)
+        else:
+            raise NotImplementedError("Frequency as {} not support".format(period))
+
+        current_date -= offset * self.ctx.strategy.n_periods;
+
+        current = jutil.convert_datetime_to_int(current_date)
+
+        return self._get_next_period_day(current, self.ctx.strategy.period,
+                                         n=self.ctx.strategy.n_periods,
+                                         extra_offset=self.ctx.strategy.days_delay)
+
+    def _get_next_period_day(self, current, period, n=1, extra_offset=0):
+        """
+        Get the n'th day in next period from current day.
+
+        Parameters
+        ----------
+        current : int
+            Current date in format "%Y%m%d".
+        period : str
+            Interval between current and next. {'day', 'week', 'month'}
+        n : int
+            n times period.
+        extra_offset : int
+            n'th business day after next period.
+
+        Returns
+        -------
+        nxt : int
+
+        """
+        #while True:
+        #for _ in range(4):
+
+        current_date = jutil.convert_int_to_datetime(current)
+        while current <= self.end_date:
+            if period == 'day':
+                offset = pd.tseries.offsets.BDay()  # move to next business day
+                if extra_offset < 0:
+                    raise ValueError("Wrong offset for day period")
+            elif period == 'week':
+                offset = pd.tseries.offsets.Week(weekday=0)  # move to next Monday
+                if extra_offset < -5 :
+                    raise ValueError("Wrong offset for week period")
+            elif period == 'month':
+                offset = pd.tseries.offsets.BMonthBegin()  # move to first business day of next month
+                if extra_offset < -31:
+                    raise  ValueError("Wrong offset for month period")
+            else:
+                raise NotImplementedError("Frequency as {} not support".format(period))
+
+            offset = offset * n
+
+            begin_date = current_date + offset
+            if period == 'day':
+                end_date = begin_date + pd.tseries.offsets.Day()*366
+            elif period == 'week':
+                end_date   = begin_date + pd.tseries.offsets.Day() * 6
+            elif period == 'month':
+                end_date = begin_date + pd.tseries.offsets.BMonthBegin() #- pd.tseries.offsets.BDay()
+
+
+            if extra_offset > 0 :
+                next_date = begin_date + extra_offset * pd.tseries.offsets.BDay()
+                if next_date >= end_date:
+                    next_date = end_date - pd.tseries.offsets.BDay()
+            elif extra_offset < 0:
+                next_date = end_date + extra_offset * pd.tseries.offsets.BDay()
+                if next_date < begin_date:
+                    next_date = begin_date
+            else:
+                next_date = begin_date
+
+            date = next_date
+            while date < end_date:
+                nxt = jutil.convert_datetime_to_int(date)
+                if self._is_trade_date(nxt):
+                    return nxt
+                date += pd.tseries.offsets.BDay()
+
+            date = next_date
+            while date >= begin_date:
+                nxt = jutil.convert_datetime_to_int(date)
+                if self._is_trade_date(nxt):
+                    return nxt
+                date -= pd.tseries.offsets.BDay()
+
+            # no trading day in this period, try next period
+            current_date = end_date - pd.tseries.offsets.BDay()
+            current = jutil.convert_datetime_to_int( current_date)
+            # Check if there are more trade dates
+            self._get_next_trade_date(current)
+
+        raise ValueError("no trading day after {0}".format(current))
+
     def go_next_rebalance_day(self):
         """
         update self.ctx.trade_date and last_date.
@@ -525,18 +648,27 @@ def go_next_rebalance_day(self):
                     return True
             else:
                 # use natural week/month
-                next_period_day = jutil.get_next_period_day(current_date, self.ctx.strategy.period,
+                # next_period_day = jutil.get_next_period_day(current_date, self.ctx.strategy.period,
+                #                                             n=self.ctx.strategy.n_periods,
+                #                                             extra_offset=self.ctx.strategy.days_delay)
+                # # update current_date: next_period_day is a workday, but not necessarily a trade date
+                # if self._is_trade_date(next_period_day):
+                #     current_date = next_period_day
+                # else:
+                #     try:
+                #         current_date = self._get_next_trade_date(next_period_day)
+                #     except IndexError:
+                #         return True
+
+                try:
+                    current_date = self._get_next_period_day(current_date, self.ctx.strategy.period,
                                                             n=self.ctx.strategy.n_periods,
                                                             extra_offset=self.ctx.strategy.days_delay)
-                # update current_date: next_period_day is a workday, but not necessarily a trade date
-                if self._is_trade_date(next_period_day):
-                    current_date = next_period_day
-                else:
-                    try:
-                        current_date = self._get_next_trade_date(next_period_day)
-                    except IndexError:
-                        return True
-                
+                except ValueError:
+                    return True
+                except IndexError:
+                    return True
+
             if current_date > self.end_date:
                 return True
 
@@ -581,7 +713,8 @@ def on_new_day(self, date):
             self.univ_price_dic = self.tmp_univ_price_dic_map[date]
         else:
             # self.univ_price_dic = self.ctx.snapshot.to_dict(orient='index')
-            self.univ_price_dic = self.ctx.snapshot.loc[:, ['close', 'vwap', 'open', 'high', 'low']].to_dict(orient='index')
+            #self.univ_price_dic = self.ctx.snapshot.loc[:, ['close', 'vwap', 'open', 'high', 'low']].to_dict(orient='index')
+            self.univ_price_dic = self.ctx.snapshot.to_dict(orient='index')
             self.tmp_univ_price_dic_map[date] = self.univ_price_dic
 
     def save_results(self, folder_path='.'):
diff --git a/jaqs/trade/common.py b/jaqs/trade/common.py
index 7a75fb5..4b1d9f8 100644
--- a/jaqs/trade/common.py
+++ b/jaqs/trade/common.py
@@ -9,11 +9,11 @@ def __repr__(self):
                                     self._name_)
     
     def __str__(self):
-        return self.__repr__()
+        return self.value
     
     @property
     def full_name(self):
-        return str(self)
+        return self.__repr__()
     
     @classmethod
     def to_enum(cls, key):
diff --git a/jaqs/trade/model.py b/jaqs/trade/model.py
index b610344..c6c55ed 100644
--- a/jaqs/trade/model.py
+++ b/jaqs/trade/model.py
@@ -147,7 +147,7 @@ def dataview(self, value):
             value.register_context(self)
         self._dataview = value
         
-    def init_universe(self, univ):
+    def init_universe(self, symbols):
         """
         univ could be single symbol or securities separated by ,
         
@@ -156,14 +156,14 @@ def init_universe(self, univ):
         univ : str or list
         
         """
-        if isinstance(univ, list):
-            self.universe = univ
-        elif isinstance(univ, basestring):
-            l = univ.split(',')
+        if isinstance(symbols, list):
+            self.universe = symbols
+        elif isinstance(symbols, basestring):
+            l = symbols.split(',')
             l = [x for x in l if x]
             self.universe = l
         else:
-            raise NotImplementedError("type of univ is {}".format(type(univ)))
+            raise NotImplementedError("type of univ is {}".format(type(symbols)))
 
 
 class AlphaContext(Context):
diff --git a/jaqs/trade/strategy.py b/jaqs/trade/strategy.py
index efc8219..0351018 100644
--- a/jaqs/trade/strategy.py
+++ b/jaqs/trade/strategy.py
@@ -9,6 +9,7 @@
 from six import with_metaclass
 
 import numpy as np
+import pandas as pd
 
 from jaqs.data.basic import GoalPosition
 from jaqs.util.sequence import SequenceGenerator
@@ -371,7 +372,12 @@ class AlphaStrategy(Strategy, model.FuncRegisterable):
     # TODO register context
     def __init__(self, signal_model=None, stock_selector=None,
                  cost_model=None, risk_model=None,
-                 pc_method="equal_weight"):
+                 pc_method="equal_weight",
+                 match_method="vwap",
+                 fc_selector=None,
+                 fc_constructor=None,
+                 fc_options=None
+                 ):
         super(AlphaStrategy, self).__init__()
         
         self.period = ""
@@ -389,8 +395,14 @@ def __init__(self, signal_model=None, stock_selector=None,
         self.weights = None
         
         self.pc_method = pc_method
-        
+
         self.goal_positions = None
+        self.match_method = match_method
+
+        self.portfolio_construction = self.forecast_portfolio_construction if pc_method=="forecast" else self.default_portfolio_construction
+        self._fc_selector    = fc_selector if fc_selector else AlphaStrategy.default_forecast_selector
+        self._fc_constructor = fc_constructor if fc_constructor else AlphaStrategy.default_forecast_constructor
+        self._fc_options     = fc_options
 
     def init_from_config(self, props):
         Strategy.init_from_config(self, props)
@@ -402,6 +414,8 @@ def init_from_config(self, props):
         self.position_ratio = props.get('position_ratio', 0.98)
         self.single_symbol_weight_limit = props.get('single_symbol_weight_limit', 1.0)
 
+        self.use_pc_method(name='industry_neutral_equal_weight', func=self.industry_neutral_equal_weight, options=None)
+        self.use_pc_method(name='industry_neutral_index_weight', func=self.industry_neutral_index_weight, options=None)
         self.use_pc_method(name='equal_weight', func=self.equal_weight, options=None)
         self.use_pc_method(name='mc', func=self.optimize_mc, options={'util_func': self.util_net_signal,
                                                                            'constraints': None,
@@ -426,7 +440,11 @@ def _validate_parameters(self):
                                 'index_weight',
                                 'equal_index_weight',
                                 'market_value_weight',
-                                'market_value_sqrt_weight']:
+                                'market_value_sqrt_weight',
+                                'industry_neutral_index_weight',
+                                'industry_neutral_equal_weight']:
+            pass
+        elif self.pc_method in ['forecast']:
             pass
         else:
             raise NotImplementedError("pc_method = {:s}".format(self.pc_method))
@@ -475,8 +493,9 @@ def util_net_signal(self, weights_target):
         cost_coef = 1.0
         net_signal = signal - risk_coef * risk - cost_coef * cost  # - liquid * liq_factor
         return net_signal
-    
-    def portfolio_construction(self, universe_list=None):
+
+
+    def default_portfolio_construction(self, universe_list=None):
         """
         Calculate target weights of each symbol in the strategy universe.
         User should not modify this function arbitrarily.
@@ -495,17 +514,25 @@ def portfolio_construction(self, universe_list=None):
         # Step.1 filter and narrow down universe to sub-universe
         if self.stock_selector is not None:
             selected_list = self.stock_selector.get_selection()
-            universe_list = [s for s in universe_list if s in selected_list]
+            if type(selected_list) is pd.DataFrame:
+                self.ctx.forecast_selected_list = selected_list
+                universe_list = [s for s in universe_list if s in selected_list['symbol']]
+            else:
+                universe_list = [s for s in universe_list if s in selected_list]
+
         sub_univ = sorted(universe_list)
-        
+
         self.ctx.snapshot_sub = self.ctx.snapshot.loc[sub_univ, :]
-        
+
         # Step.2 pick the registered portfolio construction method
+
         rf = self.func_table[self.pc_method]
         func, options = rf.func, rf.options
 
         # Step.3 use the registered method to calculate weights and get weights for all symbols in universe
         weights_sub_universe, msg = func(**options)
+
+        # portfolio balance check
         weights_all_universe = {symbol: weights_sub_universe.get(symbol, 0.0) for symbol in self.ctx.universe}
         if msg:
             print(msg)
@@ -525,11 +552,176 @@ def portfolio_construction(self, universe_list=None):
 
         self.weights = weights_all_universe
 
+    def forecast_portfolio_construction(self, universe_list=None):
+
+        assert callable(self._fc_selector), "fc_selector should be function"
+        assert callable(self._fc_constructor), "fc_constuctor should be function"
+
+        forecast_list = self._fc_selector(self, universe=universe_list)
+
+        options = {}
+        if self._fc_options:
+            options.update(self._fc_options)
+
+        self.weights = self._fc_constructor(self,
+                                            self.weights.copy() if self.weights else [],
+                                            forecast_list,
+                                            **options)
+
+    @staticmethod
+    def default_forecast_selector(self, forecast_field='close_adj', universe=None):
+        forecast = self.ctx.dataview.get_snapshot(self.ctx.trade_date)[[forecast_field]].copy()
+        forecast = forecast.rename( columns={forecast_field: "forecast"})
+        forecast['symbol'] = forecast.index
+        return forecast
+
+    @staticmethod
+    def default_forecast_constructor(self, cur_weights, forecast,
+                                     max_turnover=1,
+                                     alpha_threshold=0.0005,
+                                     turnover_cost_rate=0.001,
+                                     init_size=20):
+
+        forecast = forecast.sort_values(['forecast'], ascending=False)
+        forecast.index = forecast['symbol']
+        if not cur_weights:
+            new_weights = forecast[:init_size].copy()
+            new_weights.loc[:, 'weight'] = 1.0 / len(forecast)
+            new_weights.index = new_weights['symbol']
+            return new_weights[['weight']].T.to_dict(orient='records')[0]
+
+        cur_weights = pd.DataFrame({'symbol': list(cur_weights.keys()), 'weight': list(cur_weights.values())})
+        zero_weights = cur_weights[cur_weights['weight'] == 0].copy()
+        cur_weights = cur_weights[cur_weights['weight'] > 0].copy()
+        cur_weights.index = cur_weights['symbol']
+
+        cur_weights['forecast'] = forecast['forecast']
+        cur_weights['forecast'] = cur_weights['forecast'].fillna(0.0)
+        cur_weights = cur_weights.sort_values(['forecast'])
+        cur_weights['handled'] = False
+
+        turnover = 0.0
+        new_weights = []
+        for i in range(len(forecast)):
+            fc = forecast.iloc[i]
+            replaced = False
+            if fc['symbol'] not in cur_weights['symbol']:
+                for k in range(len(cur_weights)):
+                    tmp = cur_weights.iloc[k]
+                    if fc['forecast'] > tmp['forecast'] + turnover_cost_rate + alpha_threshold and \
+                            tmp['weight'] + turnover <= max_turnover:
+                        new_weights.append({'symbol': fc['symbol'], 'weight': tmp['weight']})
+                        new_weights.append({'symbol': tmp['symbol'], 'weight': 0})
+                        cur_weights['handled'][0] = True
+                        turnover += tmp['weight']
+                        replaced = True
+                        break
+                if not replaced:
+                    break
+            else:
+                tmp = cur_weights.loc[fc['symbol']]
+                cur_weights.loc[fc['symbol'], 'handled'] = True
+                new_weights.append({'symbol': fc['symbol'], 'weight': tmp['weight']})
+
+            cur_weights = cur_weights[cur_weights['handled'] != True].copy()
+            if cur_weights.empty or turnover >= max_turnover:
+                break
+
+        if not cur_weights.empty:
+            for i in range(len(cur_weights)):
+                tmp = cur_weights.iloc[i]
+                new_weights.append({'symbol': tmp['symbol'], 'weight': tmp['weight']})
+
+        new_weights = pd.DataFrame(new_weights)
+        w_sum = new_weights['weight'].sum()
+        if w_sum > 1e-8:  # else all zeros weights
+            new_weights.loc[:, 'weight'] /= w_sum
+
+        # Keep all removed stocks in weight, so when stock can be sold after it is tradable again after suspended.
+        tmp = zero_weights[ ~zero_weights['symbol'].isin(new_weights['symbol']) ]
+        if not tmp.empty:
+            new_weights = pd.concat([new_weights, tmp])
+
+        new_weights.index = new_weights['symbol']
+        del new_weights['symbol']
+
+        return new_weights.T.to_dict(orient='records')[0]
+
     def equal_weight(self):
         # discrete
         weights = {k: 1.0 for k in self.ctx.snapshot_sub.index.values}
         return weights, ''
 
+    def industry_neutral_equal_weight(self):
+        snap = self.ctx.snapshot_sub
+        snap['symbol'] = snap.index
+
+        # calculate weight distribution of all industry
+        # df_weight = self.ctx.dataview.get_snapshot(self.ctx.trade_date)[['total_mv', 'index_member', 'sw1']]
+        # df_weight = df_weight[df_weight['index_member'] == 1]
+        # df_weight['weight'] = df_weight['total_mv']/df_weight['total_mv'].sum()
+        df_weight = self.ctx.dataview.get_snapshot(self.ctx.trade_date)[['index_weight', 'sw1']]
+        df_weight.columns = ['weight', 'sw1']
+
+        df_industry_weight = df_weight.groupby('sw1')['weight'].sum()
+
+        # industries in portfolio
+        industry_list = list(set(snap['sw1'].values.flatten()))
+        df_industry_weight_sub = df_industry_weight.loc[industry_list]
+        df_industry_weight_sub = pd.DataFrame(df_industry_weight_sub)
+        df_industry_weight_sub.columns = ['weight']
+        df_industry_weight_sub['equal_weight'] = pd.DataFrame(snap.groupby('sw1')['sw1'].count()/len(snap))
+
+        df_industry_weight_sub['dif'] = df_industry_weight_sub['equal_weight'] - df_industry_weight_sub['weight']
+
+        df_industry_weight_sub = df_industry_weight_sub.reset_index()
+
+        count_industry = pd.DataFrame(snap.groupby('sw1')['close'].count()).reset_index()
+        count_industry.columns = ['sw1', 'count']
+        count_industry['internal_weight'] = 1.0/count_industry['count']
+
+        snap = pd.merge(left = snap, right = count_industry[['sw1', 'internal_weight']], how = 'left', on = 'sw1')
+        snap = pd.merge(left = snap, right = df_industry_weight_sub[['sw1', 'norm_weight']], how = 'left', on = 'sw1')
+        snap['weight'] = snap['internal_weight'] * snap['norm_weight']
+        df_weight = snap[['symbol','weight']]
+
+        df_weight = df_weight.set_index('symbol')
+        weights = df_weight['weight'].to_dict()
+
+        return weights, ''
+
+    def industry_neutral_index_weight(self):
+        snap = self.ctx.snapshot_sub
+        snap['symbol'] = snap.index
+
+        # calculate weight distribution of all industry
+        df_weight = self.ctx.dataview.get_snapshot(self.ctx.trade_date)[['total_mv', 'index_member', 'sw1']]
+        df_weight = df_weight[df_weight['index_member'] == 1]
+        df_weight['weight'] = df_weight['total_mv']/df_weight['total_mv'].sum()
+        df_industry_weight = df_weight.groupby('sw1')['weight'].sum()
+
+        # industries in portfolio
+        industry_list = list(set(snap['sw1'].values.flatten()))
+        df_industry_weight_sub = df_industry_weight.loc[industry_list]
+        df_industry_weight_sub = pd.DataFrame(df_industry_weight_sub)
+        df_industry_weight_sub.columns = ['weight']
+        df_industry_weight_sub['norm_weight'] = df_industry_weight_sub['weight']/df_industry_weight_sub['weight'].sum()
+        df_industry_weight_sub = df_industry_weight_sub.reset_index()
+
+        count_industry = pd.DataFrame(snap.groupby('sw1')['index_weight'].sum()).reset_index()
+        count_industry.columns = ['sw1', 'agg_index_weight']
+
+        snap = pd.merge(left = snap, right = count_industry[['sw1', 'agg_index_weight']], how = 'left', on = 'sw1')
+        snap['internal_weight'] = snap['index_weight']/snap['agg_index_weight']
+        snap = pd.merge(left = snap, right = df_industry_weight_sub[['sw1', 'norm_weight']], how = 'left', on = 'sw1')
+        snap['weight'] = snap['internal_weight'] * snap['norm_weight']
+        df_weight = snap[['symbol','weight']]
+
+        df_weight = df_weight.set_index('symbol')
+        weights = df_weight['weight'].to_dict()
+
+        return weights, ''
+
     def market_value_weight(self, sqrt=False):
         snap = self.ctx.snapshot_sub
         # TODO: pass options, instead of hard-code 'total_mv', 'float_mv'
@@ -666,7 +858,7 @@ def on_after_rebalance(self, total):
     
     def send_bullets(self):
         # self.ctx.trade_api.goal_portfolio_by_batch_order(self.goal_positions)
-        self.ctx.trade_api.goal_portfolio(self.goal_positions, algo='vwap')
+        self.ctx.trade_api.goal_portfolio(self.goal_positions, algo=self.match_method)
     
     def generate_weights_order(self, weights_dic, turnover, prices, suspensions=None):
         """
diff --git a/jaqs/trade/tradegateway.py b/jaqs/trade/tradegateway.py
index 29a7026..da0cb2f 100644
--- a/jaqs/trade/tradegateway.py
+++ b/jaqs/trade/tradegateway.py
@@ -731,6 +731,8 @@ def goal_portfolio(self, positions, algo="", algo_param={}, userdata=""):
                 order = FixedPriceTypeOrder.new_order(sec, action, 0.0, abs(diff_size), self.ctx.trade_date, 0)
                 if algo == 'vwap':
                     order.price_target = 'vwap'  # TODO
+                elif algo.startswith('limit:'):
+                    order.price_target = algo.split(':')[1].strip()
                 elif algo == '':
                     order.price_target = 'vwap'
                 else:
diff --git a/jaqs/util/numeric.py b/jaqs/util/numeric.py
index 496c53d..74c8553 100644
--- a/jaqs/util/numeric.py
+++ b/jaqs/util/numeric.py
@@ -1,23 +1,39 @@
 # encoding: utf-8
 import numpy as np
+import pandas as pd
 
 
 def quantilize_without_nan(mat, n_quantiles=5, axis=-1):
-    mask = np.isnan(mat)
-    
-    rank = mat.argsort(axis=axis).argsort(axis=axis)  # int
-    
+    mask = pd.isnull(mat)
+    res = mat.copy()
+
+    rank = res.argsort(axis=axis).argsort(axis=axis)
     count = np.sum(~mask, axis=axis)  # int
+
     divisor = count * 1. / n_quantiles  # float
-    shape = list(mat.shape)
+    shape = list(res.shape)
     shape[axis] = 1
     divisor = divisor.reshape(*shape)
-    
     res = np.floor(rank / divisor) + 1.0
+
     res[mask] = np.nan
-    
+
     return res
 
+    # res[~mask] = pd.qcut(mat[~mask], n_quantiles, labels = False) + 1
+    # rank = mat.argsort(axis=axis).argsort(axis=axis)  # int
+    #
+    # count = np.sum(~mask, axis=axis)  # int
+    # divisor = count * 1. / n_quantiles  # float
+    # shape = list(mat.shape)
+    # shape[axis] = 1
+    # divisor = divisor.reshape(*shape)
+    #
+    # res = np.floor(rank / divisor) + 1.0
+    # res[mask] = np.nan
+    
+    #return res
+
 
 # Boolean, unsigned integer, signed integer, float, complex.
 _NUMERIC_KINDS = set('buifc')
diff --git a/test/test_dataview.py b/test/test_dataview.py
index f9c16ae..0fa20b9 100644
--- a/test/test_dataview.py
+++ b/test/test_dataview.py
@@ -24,7 +24,7 @@ def test_write():
 
     dv.init_from_config(props, data_api=ds)
     dv.prepare_data()
-    assert dv.data_d.shape == (281, 48)
+    assert dv.data_d.shape == (281, 54)
     assert dv.dates.shape == (281, )
     # TODO
     """