Quant-Trade-Strategies

Welcome to the Quantitative Trading Strategies repository! This collection features a variety of Python-based trading strategies for analyzing financial markets. Each strategy is accompanied by thorough documentation and organized code, allowing for easy understanding, customization, and implementation.

Table of Contents

• Overview
• Getting Started
• Directory Structure
• Usage
• Strategies
• ML Prediction Models
• Options Strategies
• Backtesting
• Results
• Dependencies
• Contributing
• License
• Acknowledgments

1. Overview

• This repository aims to explore and implement quantitative trading strategies for financial markets.
• Strategies are designed to leverage historical market data to make informed trading decisions.
• Implemented in Python, the code is well-structured and documented for users to easily comprehend and adapt.

2. Getting Started

• To get started, clone this repository to your local machine.

   git clone https://github.com/AnujRaskatla/Quant-Trade-Strategies.git

• Install dependencies by running pip install -r requirements.txt.

• Follow the instructions in the README files of individual strategy directories.

3. Directory Structure

• data/: Contains sample datasets and instructions on obtaining market data.
• strategies/: Each subdirectory represents a different trading strategy.
• backtesting/: Includes scripts for evaluating strategy performance.

4. Usage

1. Replace your_data.csv in the script with the actual file path or URL of your historical stock price data.
2. The data should contain at least the following columns:

Date:                       Date of the stock price data.
Open, High, Low, Close:     Stock price details.
Volume:                     Trading volume.
Ensure that the 'Date' column is in datetime format. If not, convert it using pd.to_datetime().

3. Run the script: your_strategy_script.py
4. View the model evaluation results and visualization in the console and generated plot.

5. Strategies

• MeanReversion/: Implements a mean-reversion trading strategy.

  • README.md: Detailed explanation of the strategy, parameters, and logic.
  • mean_reversion.py: Python script containing the strategy code.

• TrendFollowing/: Implements a trend-following trading strategy.

  • README.md: Comprehensive overview of the strategy.
  • trend_following.py: Python script for the strategy.

• SMA (Simple Moving Average): based on SMA crossovers, aiming to generate buy and sell signals.

  • README.md: Comprehensive overview of the strategy.
  • sma_script.py: Python script containing the strategy.

• Pair Trading/: based on z-score, aiming to generate buy and sell signals after threshold.

  • README.md: Comprehensive overview of the strategy.
  • pt.py: Python script containing the strategy.

• MACD x RSI/: Used indicators - MACD and RSI to generate buy and sell signals.

  • README.md: Comprehensive overview of the strategy.
  • macd.py: Python script containing the strategy.

6. ML Prediction Models

• Random Forest Regressor Strategy/: utilizes a Random Forest Regressor to predict stock prices based on historical data
  • README.md: Comprehensive overview of the strategy.
  • ML_script.py: Python script containing the strategy.

7. Options Strategies

• Covered Call Strategy/: involves owning shares of a particular stock and selling call options against those shares.
  • README.md: Comprehensive overview of the strategy.
  • CC_script.py: Python script containing the strategy.

8. Backtesting

• The backtesting/ directory contains scripts for evaluating the performance of each strategy.
• Detailed instructions on running backtests are provided in the README.md.

9. Results

• Visualizations and performance metrics for each strategy are available in this section.
• Backtest results demonstrate the effectiveness of the implemented strategies.

10. Dependencies:

• Key dependencies include pandas, numpy, and matplotlib.

pip install pandas numpy matplotlib

• View the requirements.txt file for a complete list with version information.

11. Contributing:

• Contributions are welcome! Please follow the guidelines outlined in the CONTRIBUTING.md file.
• Submit issues, feature requests, or pull requests to contribute to the project's development.

12. License:

• This project is licensed under the MIT License. See the LICENSE.md file for details.

13. Acknowledgments:

• Gratitude to the open-source community and contributors.
• Acknowledgments to external libraries and data sources used in the project.