Fyers Trading Bot

A robust, modular, and scalable Python-based automatic intraday trading application using the Fyers API for Indian stock markets.

🎯 Features

• Automated Trading: Place trades automatically based on technical strategies
• Backtesting Engine: Test strategies on historical data with comprehensive performance metrics
• Multiple Strategies: Moving Average Crossover, VWAP Bounce, RSI Price Action
• Risk Management: Stop-loss, take-profit, position sizing, and daily loss limits
• Real-time Monitoring: Live data streaming and trade execution
• Comprehensive Logging: Detailed logs for trades, errors, and performance
• Modular Architecture: Clean separation of concerns with configurable components

📦 Installation

Prerequisites

• Python 3.9+
• Fyers API credentials
• pip package manager

Setup

1. Clone the repository

   git clone <repository-url>
   cd fyers-trading-bot

2. Install dependencies

   pip install -r requirements.txt

3. Configure environment variables

   cp env_template.txt .env

   Edit .env file with your Fyers API credentials:

   FYERS_APP_ID=your_app_id_here
   FYERS_APP_SECRET=your_app_secret_here
   FYERS_ACCESS_TOKEN=your_access_token_here

4. Configure settings

   Edit trading_app/config/settings.yaml to customize:

   • Trading parameters (capital, risk limits)
   • Strategy configurations
   • Risk management settings
   • Logging preferences

🔧 Configuration

Fyers API Setup

1. Create Fyers App

   • Visit Fyers Developer Portal
   • Create a new app and get your App ID and App Secret

2. Generate Access Token

   • Use the Fyers API to generate an access token
   • Add the token to your .env file

Strategy Configuration

The application includes three pre-built strategies:

1. Moving Average Crossover

• Parameters: short_window, long_window, ma_type (SMA/EMA)
• Logic: Buy when short MA crosses above long MA, sell on reverse

2. VWAP Bounce

• Parameters: vwap_period, bounce_threshold, rsi_period
• Logic: Buy when price bounces from below VWAP with RSI confirmation

3. RSI Price Action

• Parameters: rsi_period, rsi_oversold, rsi_overbought
• Logic: Buy on RSI oversold with price action confirmation

🚀 Usage

Command Line Interface

1. Test Connection

python -m trading_app.main --mode test

2. View Available Strategies

python -m trading_app.main --mode strategies

3. View Configured Symbols

python -m trading_app.main --mode symbols

4. Run Backtest

python -m trading_app.main --mode backtest \
    --symbol NIFTY \
    --strategy ma_crossover \
    --start-date 2024-01-01 \
    --end-date 2024-01-31 \
    --timeframe 5min

5. Live Trading

python -m trading_app.main --mode live \
    --symbols NIFTY BANKNIFTY \
    --strategies ma_crossover vwap_bounce

6. View Performance

python -m trading_app.main --mode performance

Python API

from trading_app.main import TradingApp

# Initialize app
app = TradingApp()

# Run backtest
results = app.run_backtest(
    symbol="NIFTY",
    strategy_name="ma_crossover",
    start_date="2024-01-01",
    end_date="2024-01-31"
)

# Start live trading
app.run_live_trading(
    symbols=["NIFTY", "BANKNIFTY"],
    strategies=["ma_crossover", "vwap_bounce"]
)

📊 Backtesting Results

The backtesting engine provides comprehensive performance metrics:

• Total Return: Overall percentage gain/loss
• Sharpe Ratio: Risk-adjusted return measure
• Maximum Drawdown: Largest peak-to-trough decline
• Win Rate: Percentage of profitable trades
• Profit Factor: Ratio of gross profit to gross loss
• Trade Analysis: Entry/exit points, P&L, reasons

Sample Backtest Report

==================================================
BACKTEST REPORT
==================================================
Symbol: NIFTY
Strategy: ma_crossover
Period: 2024-01-01 to 2024-01-31
Timeframe: 5min

PERFORMANCE METRICS:
Initial Capital: ₹50,000.00
Final Capital: ₹52,450.00
Total Return: 4.90%
Sharpe Ratio: 1.25
Max Drawdown: -2.10%
Win Rate: 65.20%
Total Trades: 23
Average Trade: ₹106.52
Profit Factor: 1.85

TRADE SUMMARY:
Winning Trades: 15
Losing Trades: 8
Best Trade: ₹450.00
Worst Trade: ₹-180.00
==================================================

🏗️ Architecture

trading_app/
├── config/
│   └── settings.yaml          # Configuration file
├── data/
│   └── data_loader.py         # Market data management
├── fyers_api/
│   └── fyers_connector.py     # Fyers API integration
├── strategies/
│   ├── base_strategy.py       # Base strategy class
│   ├── ma_crossover.py        # MA Crossover strategy
│   ├── vwap_bounce.py         # VWAP Bounce strategy
│   └── rsi_price_action.py    # RSI Price Action strategy
├── backtest/
│   └── backtest_engine.py     # Backtesting engine
├── execution/
│   └── trade_manager.py       # Live trading execution
├── utils/
│   ├── logger.py              # Logging utilities
│   └── helpers.py             # Helper functions
├── logs/                      # Log files
└── main.py                    # Main application

🔒 Risk Management

The application includes comprehensive risk management features:

• Position Sizing: Dynamic calculation based on risk percentage
• Stop Loss: Automatic stop-loss placement
• Take Profit: Target-based profit taking
• Trailing Stop: Dynamic stop-loss adjustment
• Daily Loss Limits: Maximum daily loss protection
• Maximum Positions: Limit on concurrent positions
• Time-based Exits: Automatic exit at market close

📝 Logging

The application provides detailed logging in multiple files:

• trading_app.log: General application logs
• errors.log: Error and exception logs
• trades.log: Trade-specific logs
• backtest_results_*.json: Backtest results

🧪 Testing

Run tests to verify functionality:

# Install test dependencies
pip install pytest pytest-cov

# Run tests
pytest trading_app/tests/ -v

# Run with coverage
pytest trading_app/tests/ --cov=trading_app --cov-report=html

📈 Performance Monitoring

Real-time Monitoring

• Live trade execution status
• Current positions and P&L
• Strategy performance metrics
• Risk limit monitoring

Performance Metrics

• Total return and Sharpe ratio
• Win rate and profit factor
• Maximum drawdown analysis
• Trade-by-trade breakdown

🔧 Customization

Adding New Strategies

1. Create a new strategy class inheriting from BaseStrategy
2. Implement required methods: generate_signals(), should_buy(), should_sell()
3. Add strategy to the factory in strategies/__init__.py
4. Configure parameters in settings.yaml

Example Custom Strategy

from .base_strategy import BaseStrategy

class CustomStrategy(BaseStrategy):
    def generate_signals(self, df):
        # Your signal generation logic
        return df
    
    def should_buy(self, df, index):
        # Your buy condition
        return condition
    
    def should_sell(self, df, index):
        # Your sell condition
        return condition

⚠️ Disclaimer

This software is for educational and research purposes only. Trading involves substantial risk of loss and is not suitable for all investors. Past performance does not guarantee future results.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Submit a pull request

📞 Support

For issues and questions:

• Create an issue on GitHub
• Check the documentation
• Review the logs for error details

🔄 Updates

Stay updated with the latest features:

• Monitor the repository for updates
• Review changelog for new features
• Test new versions in a safe environment

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Happy Trading! 📈