Algorithmic Trading System - NIFTY 50 Stocks

An automated trading system that implements technical analysis and machine learning strategies on NIFTY 50 stocks. The system features real-time data processing, backtesting, automated trade execution, and integration with Google Sheets for reporting.

Demo Videos

• Algorithmic Trading System Explained
• Live Trading & Google Sheets Integration

Project Structure

algo-trading-assignment/
│
├── 📁 src/                          # Main source code
│   ├── 📁 data/                     # Data handling modules
│   │   ├── __init__.py
│   │   ├── data_fetcher.py          # API connections & data fetching
│   │   ├── data_processor.py        # Data cleaning & preprocessing
│   │   └── indicators.py            # Technical indicators (RSI, MA, MACD)
│   │
│   ├── 📁 strategy/                 # Trading strategy logic
│   │   ├── __init__.py
│   │   ├── base_strategy.py         # Abstract base strategy class
│   │   ├── rsi_ma_strategy.py       # RSI + Moving Average strategy
│   │   └── backtester.py            # Backtesting engine
│   │
│   ├── 📁 ml/                       # Machine Learning components
│   │   ├── __init__.py
│   │   ├── feature_engineer.py      # Feature creation & selection
│   │   ├── model_trainer.py         # ML model training
│   │   └── predictor.py             # Prediction engine
│   │
│   ├── 📁 automation/               # Automation & integration
│   │   ├── __init__.py
│   │   ├── sheets_manager.py        # Google Sheets operations
│   │   ├── telegram_bot.py          # Telegram notifications (bonus)
│   │   └── scheduler.py             # Task scheduling
│   │
│   └── 📁 utils/                    # Utility functions
│       ├── __init__.py
│       ├── logger.py                # Logging configuration
│       ├── config.py                # Configuration management
│       └── helpers.py               # Common helper functions
│
├── 📁 config/                       # Configuration files
│   ├── config.yaml                  # Main configuration
│   ├── api_keys.env.example         # Environment variables template
│   └── stocks_list.json             # NIFTY 50 stocks configuration
│
├── 📁 data/                         # Data storage
│   ├── raw/                         # Raw data from APIs
│   ├── processed/                   # Cleaned/processed data
│   └── backtest_results/            # Backtesting outputs
│
├── 📁 models/                       # Saved ML models
│   ├── decision_tree_model.pkl
│   └── model_metrics.json
│
├── 📁 logs/                         # Log files
│   ├── trading.log
│   ├── errors.log
│   └── api_calls.log
│
├── 📁 notebooks/                    # Jupyter notebooks (optional)
│   ├── data_exploration.ipynb
│   ├── strategy_analysis.ipynb
│   └── ml_model_development.ipynb
│
├── 📁 tests/                        # Unit tests
│   ├── __init__.py
│   ├── test_data_fetcher.py
│   ├── test_strategy.py
│   └── test_ml_model.py
│
├── main.py                          # Main execution script
├── requirements.txt                 # Python dependencies
├── README.md                                           
└── .gitignore                       # Git ignore rules                    

System Architecture

Key Features

• Multi-source Data Integration: Real-time and historical data from Alpha Vantage and Yahoo Finance
• Technical Indicators: RSI, Moving Averages, MACD, Bollinger Bands
• Strategy Engine: Customizable trading strategies with rule-based logic
• Backtesting Framework: Historical strategy performance evaluation
• Machine Learning Integration: Predictive models for trade signal enhancement
• Automation: Scheduled execution and reporting
• Google Sheets Integration: Real-time trade logging and performance tracking
• Telegram Notifications: Instant alerts for trade signals and system events

⚙ System Requirements

• Python 3.9+
• 8GB RAM (recommended)
• 1GB free disk space
• Google Cloud Platform account (for Sheets API)
• Alpha Vantage API key (free tier available)

Installation

# Clone repository
git clone https://github.com/fa-code2/algo-trading-ml-assignment
cd algo-trading-assignment

# Create virtual environment
python -m venv trading-env
source trading-env/bin/activate  # Linux/Mac
trading-env\Scripts\activate    # Windows

# Install dependencies
pip install -r requirements.txt

Configuration

1. Set up configuration files:

   cp config/config.yaml.example config/config.yaml
   cp config/api_keys.env.example config/api_keys.env

2. Edit configuration files:

   • config/config.yaml: Add your Google Sheet URL and trading parameters
   • config/api_keys.env: Add your Alpha Vantage API key and Telegram bot token

3. Google Sheets API Setup:

   • Enable Google Sheets API in Google Cloud Console
   • Download credentials JSON file and save as config/credentials.json

Expected Output

=============================================================
TRADING SYSTEM EXECUTION SUMMARY
=============================================================

 Data Fetched for 3 stocks:
   • RELIANCE.NS
   • TCS.NS
   • INFY.NS

 Backtest Results:
   • RELIANCE.NS: 12.34% return, 65.2% win rate
   • TCS.NS: 8.76% return, 58.1% win rate
   • INFY.NS: 15.23% return, 71.4% win rate

 ML Predictions (Accuracy):
   • RELIANCE.NS: 67.8%
   • TCS.NS: 62.3%
   • INFY.NS: 69.1%

 Current Signals:
   • RELIANCE.NS: BUY at ₹2,456.75
   • TCS.NS: No active signals

 Google Sheets updated with all results
=================================

Running the System

Backtesting a Strategy

python main.py --mode backtest --strategy rsi_ma --symbols TCS,INFY --from 2023-01-01 --to 2023-12-31

Live Trading Mode

python main.py --mode live --strategy ml_enhanced --symbols NIFTY50

Generate Trading Signals

python main.py --mode signals --strategy rsi_ma --symbols RELIANCE

Command Line Options

Option	Description	Default
--mode	Operation mode (backtest, live, etc.)	backtest
--strategy	Trading strategy to use	rsi_ma
--symbols	Comma-separated stock symbols	NIFTY50
--from	Start date (YYYY-MM-DD)	30 days ago
--to	End date (YYYY-MM-DD)	Current date
--output	Output format (console, gsheets)	console

Machine Learning Pipeline

1. Feature Engineering:
   • Technical indicators (RSI, MACD, Bollinger Bands)
   • Price derivatives
   • Volume analysis
   • Statistical features

Google Sheets Integration

The system automatically logs all trades to Google Sheets with the following structure:

Timestamp	Symbol	Action	Price	Quantity	Strategy	Confidence	Indicators
2023-08-04 10:25	TCS.NS	BUY	3450	10	ML	0.87	RSI=28, MA_CROSS
2023-08-04 14:30	RELIANCE.NS	SELL	1450	15	RSI_MA	-	RSI=72, MA_CROSS

Testing

Run the test suite with:

#Run Test to check Full System
python test_system.py

Test Coverage:

• Data fetching and processing
• Indicator calculations
• Strategy logic
• Backtesting engine
• Google Sheets integration

Security Practices

• Sensitive configuration excluded via .gitignore
• API keys stored in environment variables
• Encrypted credentials handling
• Pre-commit hooks for secret detection
• Limited permission Google Cloud credentials

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/your-feature)
3. Commit your changes (git commit -am 'Add some feature')
4. Push to the branch (git push origin feature/your-feature)
5. Open a pull request

Acknowledgments

This algorithmic trading system was developed as part of a 2nd round internship test assignment by H TO H. We acknowledge their comprehensive requirements and evaluation criteria that guided the development of this full-featured trading platform. Special thanks to:

H TO H for providing the internship opportunity and detailed project specifications The evaluation team for setting challenging requirements that pushed for a robust solution The open-source community for the excellent libraries that made this system possible Financial data providers for making market data accessible for testing and development

Disclaimer

This software is for educational and research purposes only. Trading stocks involves risk, and past performance does not guarantee future results. The authors are not responsible for any financial losses incurred through the use of this software. Always consult with a financial advisor before making investment decisions.

License

This project is licensed under the MIT License

Contact

For inquiries or support:

• Email: faaa367810@gmail.com
• LinkedIn: linkedin.com/in/fa-aslam/