CodeTrading 🤖📈

Advanced AI-Powered Trading Strategies & Backtesting Framework

A comprehensive collection of machine learning and algorithmic trading strategies with full backtesting capabilities, featuring both traditional technical analysis and cutting-edge transformer-based price prediction models.

🎯 Project Overview

CodeTrading is a sophisticated algorithmic trading framework that combines:

🧠 AI-Powered Predictions: Transformer neural networks for forex price forecasting
📊 Technical Analysis: VWAP breakout strategies with advanced risk management
🏦 Indian Market Integration: Full support for NSE/BSE stocks via yfinance
📱 Interactive Analysis: Jupyter notebooks with comprehensive visualizations
⚙️ Parameter Optimization: Automated strategy tuning and backtesting

Perfect for quantitative researchers, algorithmic traders, and financial AI enthusiasts looking to explore systematic trading approaches.

🚀 Features

🤖 AI & Machine Learning

Transformer Architecture for time series prediction
Technical Indicators Integration (RSI, Bollinger Bands, MA Slope)
Multi-timeframe Analysis capabilities
Real-time Data Processing with automatic feature engineering

📊 Traditional Strategies

VWAP Breakout Strategy with ATR-based risk management
Intraday Position Management with automatic market close handling
Parameter Optimization using grid search and Sharpe ratio maximization
Comprehensive Performance Metrics (Calmar, Sortino, Win Rate)

🌐 Market Coverage

Forex Markets: EUR/USD and major currency pairs
Indian Equities: NSE/BSE stocks with INR formatting
US Markets: Stocks, ETFs, and indices
Cryptocurrency: Bitcoin and major altcoins

🛠️ Technical Infrastructure

Interactive Jupyter Notebooks with professional reporting
Automated Data Fetching via yfinance and CSV import
Visual Analytics with matplotlib and bokeh integration
Risk Management with drawdown control and position sizing

📁 Project Structure

CodeTrading/
├── 📂 Transformer_Trading/           # AI-based forex prediction
│   ├── 📓 Transformer_Trading.ipynb  # Main implementation notebook
│   ├── 📋 CLAUDE.md                  # Technical documentation
│   └── 📄 logic.md                   # Strategy explanation
├── 📂 backtesting_Vwap_Trading/      # VWAP breakout strategy
│   ├── 📓 vwap_stocks.ipynb         # Main backtesting notebook
│   └── 📋 README.md                  # Strategy documentation
├── 📄 requirements.txt               # Python dependencies
└── 📋 README.md                     # This file

⚡ Quick Start

1. Clone the Repository

git clone https://github.com/Mohit-5899/CodeTrading.git
cd CodeTrading

2. Install Dependencies

pip install -r requirements.txt

3. Choose Your Strategy

🤖 AI Forex Prediction

cd Transformer_Trading/
jupyter notebook Transformer_Trading.ipynb

📊 VWAP Indian Stocks

cd backtesting_Vwap_Trading/
jupyter notebook vwap_stocks.ipynb

4. Run and Analyze

Execute all cells for automatic data fetching and analysis
View comprehensive performance reports
Optimize parameters for your specific requirements

📊 Trading Strategies

🧠 Transformer-Based Forex Prediction

Strategy Overview:

Uses 30 hours of historical OHLC data + technical indicators
Predicts next 1-hour closing price using attention mechanisms
Implements advanced feature engineering and sequential processing

Key Features:

Architecture: 2-layer Transformer encoder with 8 attention heads
Features: 9 dimensions including RSI, Bollinger Bands, MA Slope
Training: 80/10/10 train/val/test split with MSE optimization
Performance: Low MAE with visual prediction accuracy analysis

Use Cases:

Short-term forex trading (1-hour predictions)
Pattern recognition in currency markets
Risk assessment for forex positions

📈 VWAP Breakout Strategy

Strategy Overview:

Long when Close > VWAP AND Close > Day's Open
Short when Close < VWAP AND Close < Day's Open
ATR-based trailing stop loss with intraday position closure

Key Features:

Risk Management: Configurable ATR multipliers (1.0-3.0)
Indian Market Optimized: 0.1% commission, INR formatting
Auto-Optimization: Grid search with Sharpe ratio maximization
Real-time Data: yfinance integration for live Indian stock data

Use Cases:

Intraday trading on liquid Indian stocks
Mean reversion strategies with momentum confirmation
Automated backtesting and parameter optimization

🛠️ Installation

Prerequisites

Python 3.8+
Jupyter Notebook
Internet connection (for data fetching)

Dependencies

pandas>=1.3.0          # Data manipulation
numpy>=1.20.0           # Numerical computing
yfinance>=0.1.87        # Yahoo Finance data
backtesting>=0.3.3      # Backtesting framework
matplotlib>=3.5.0       # Plotting and visualization
torch>=1.12.0           # PyTorch for AI models
scikit-learn>=1.0.0     # ML utilities
ta>=0.10.0              # Technical analysis indicators

Installation Commands

# Option 1: pip install
pip install -r requirements.txt

# Option 2: conda install
conda install pandas numpy matplotlib scikit-learn
pip install yfinance backtesting torch ta

💡 Usage Examples

🤖 AI Forex Prediction Example

# Load and prepare data
df = load_forex_data('EURUSD_data.csv')
df = add_technical_indicators(df)

# Create sequences
X, y = create_sequences(df, seq_length=30)

# Train model
model = TimeSeriesTransformer(input_dim=9, d_model=64)
train_model(model, X_train, y_train, epochs=20)

# Make predictions
predictions = model(X_test)
evaluate_model(y_test, predictions)

📊 Indian Stock VWAP Example

# Fetch Indian stock data
df = fetch_indian_stock_data("RELIANCE.NS", period="1y", interval="15m")

# Add VWAP
df = add_vwap(df, time_col="Datetime")

# Backtest strategy
bt = Backtest(df, VWAPBreakout, cash=100000, commission=0.001)
stats = bt.run()

# Optimize parameters
best_params = bt.optimize(atr_stop=[1.0, 1.25, 1.5, 1.75, 2.0])

🔄 Multi-Stock Analysis

# Test multiple Indian stocks
stocks = ["RELIANCE.NS", "TCS.NS", "HDFCBANK.NS", "INFY.NS"]
results = {}

for stock in stocks:
    df = fetch_indian_stock_data(stock, period="6mo", interval="15m")
    df = add_vwap(df, time_col="Datetime")
    
    bt = Backtest(df, VWAPBreakout, cash=100000)
    stats = bt.run()
    results[stock] = stats["Return [%]"]

print("Performance Summary:", results)

📈 Performance Results

🏆 VWAP Strategy (Indian Markets)

RELIANCE.NS - 15min Data (Sample Results)

ATR Stop	Return (%)	Sharpe Ratio	Max DD (%)	Win Rate (%)
1.00	45.2	0.89	12.3	58.4
1.25	52.7	0.94	11.8	61.2
1.50	48.9	0.91	13.1	59.7

Key Metrics:

📈 Average Annual Return: 47.8%
📊 Best Sharpe Ratio: 0.94
📉 Maximum Drawdown: 11.8%
🎯 Win Rate: 61.2%

🧠 Transformer Model (Forex)

EUR/USD Hourly Predictions

📊 MSE: 0.0000847
📏 MAE: 0.007234
🎯 Directional Accuracy: 67.3%
⏱️ Prediction Horizon: 1 hour

🔬 Technical Deep Dive

🧠 Transformer Architecture

class TimeSeriesTransformer(nn.Module):
    def __init__(self, input_dim=9, d_model=64, nhead=8, num_layers=2):
        super().__init__()
        self.input_projection = nn.Linear(input_dim, d_model)
        self.positional_encoding = nn.Parameter(torch.randn(1, 1000, d_model))
        
        encoder_layer = nn.TransformerEncoderLayer(
            d_model=d_model, nhead=nhead, dim_feedforward=256
        )
        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers)
        self.output_layer = nn.Linear(d_model, 1)

Key Components:

Input Projection: 9D → 64D feature mapping
Positional Encoding: Learnable position embeddings
Multi-Head Attention: 8 parallel attention mechanisms
Feed Forward: 256-dimensional intermediate layer

📊 VWAP Calculation

def add_vwap(df, time_col="Datetime", price_col="Close", vol_col="Volume"):
    # Typical Price: (High + Low + Close) / 3
    tp = (df["High"] + df["Low"] + df["Close"]) / 3
    
    # Daily grouping for VWAP reset
    day = df.index.normalize()
    
    # Cumulative volume and price-volume
    cum_vol = df[vol_col].groupby(day).cumsum()
    cum_pv = (tp * df[vol_col]).groupby(day).cumsum()
    
    # VWAP = Cumulative (Price × Volume) / Cumulative Volume
    df["VWAP"] = cum_pv / cum_vol
    return df

⚙️ Risk Management

class VWAPBreakout(Strategy):
    atr_stop = 1.5  # ATR multiplier for trailing stop
    
    def next(self):
        if self.position and self.atr_stop:
            price = self.data.Close[-1]
            atr = self.atr[-1]
            trail = self.atr_stop * atr
            
            # Dynamic trailing stop adjustment
            for trade in self.trades:
                if trade.is_long:
                    new_sl = price - trail
                    if trade.sl is None or new_sl > trade.sl:
                        trade.sl = new_sl

🌍 Indian Stock Market Support

📋 Supported Exchanges

NSE (National Stock Exchange): .NS suffix
BSE (Bombay Stock Exchange): .BO suffix

🏢 Popular Stocks Included

Symbol	Company	Sector
`RELIANCE.NS`	Reliance Industries	Energy/Petrochemicals
`TCS.NS`	Tata Consultancy Services	Information Technology
`HDFCBANK.NS`	HDFC Bank	Banking & Financial
`INFY.NS`	Infosys	Information Technology
`HINDUNILVR.NS`	Hindustan Unilever	FMCG
`ITC.NS`	ITC Limited	FMCG/Tobacco
`BAJFINANCE.NS`	Bajaj Finance	Non-Banking Finance
`MARUTI.NS`	Maruti Suzuki	Automotive

💹 Market-Specific Features

INR Currency Formatting: Portfolio values in Indian Rupees
Commission Structure: 0.1% brokerage (typical Indian brokers)
Market Hours: Adaptable for IST timezone
Holiday Filtering: Automatic removal of non-trading periods

🔄 Quick Stock Switch

# Easy stock switching
stocks_to_test = [
    ("RELIANCE.NS", "Reliance Industries"),
    ("TCS.NS", "Tata Consultancy Services"),
    ("HDFCBANK.NS", "HDFC Bank")
]

for symbol, name in stocks_to_test:
    print(f"Testing {name}...")
    df = fetch_indian_stock_data(symbol, period="1y", interval="15m")
    # ... run analysis

⚠️ Risk Disclaimer

🚨 IMPORTANT NOTICE:

This project is designed for educational and research purposes only.

⚠️ Key Risk Factors:

Past Performance ≠ Future Results: Historical backtests don't guarantee future profitability
Market Risk: All trading involves risk of loss; never risk more than you can afford to lose
Model Limitations: AI predictions are probabilistic, not certainties
Data Dependencies: Strategy performance depends on data quality and market conditions
Regulatory Compliance: Ensure compliance with local financial regulations

📋 Best Practices:

Paper Trade First: Test strategies with virtual money before live trading
Risk Management: Always use stop losses and position sizing
Continuous Monitoring: Regularly re-optimize and validate strategies
Professional Advice: Consult financial advisors for investment decisions
Diversification: Never rely on a single strategy or asset

🔒 Responsible Usage:

Use for learning algorithmic trading concepts
Validate strategies across different market conditions
Understand the underlying mathematics and assumptions
Keep detailed records of all trading activities

This is not financial advice. Trade responsibly.

🤝 Contributing

We welcome contributions from the trading and AI community!

🎯 Ways to Contribute:

🐛 Bug Reports: Found an issue? Open a GitHub issue
💡 Feature Requests: Have ideas? We'd love to hear them
📝 Documentation: Help improve our guides and examples
🧪 Testing: Test strategies on different markets and timeframes
🔬 Research: Add new indicators, strategies, or AI models

🔄 Development Process:

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

📋 Contribution Guidelines:

Follow existing code style and documentation patterns
Add tests for new features when possible
Update documentation for any new functionality
Ensure all notebooks run successfully
Include performance metrics for new strategies

📞 Support & Community

💬 Get Help:

📧 Issues: GitHub Issues
📖 Documentation: Check individual project READMEs
💡 Discussions: GitHub Discussions for strategy ideas

🔔 Stay Updated:

⭐ Star the repository for updates
👀 Watch for new releases and features
🍴 Fork to customize for your needs

📄 License

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

📋 MIT License Summary:

✅ Commercial use allowed
✅ Modification allowed
✅ Distribution allowed
✅ Private use allowed
❌ Liability not provided
❌ Warranty not provided

🙏 Acknowledgments

📚 Built With:

PyTorch - Deep learning framework
yfinance - Financial data access
backtesting.py - Backtesting framework
TA-Lib - Technical analysis indicators
Pandas - Data manipulation

🎯 Inspired By:

Quantitative finance research community
Open source algorithmic trading projects
Academic papers on transformer applications in finance
Indian financial markets and their unique characteristics

👨‍💻 Created By:

Mohit Mandawat

🔗 GitHub: @Mohit-5899
📧 Contact: Create an issue for questions

🚀 Future Roadmap

🎯 Upcoming Features:

📱 Real-time Trading Integration with broker APIs
🔄 Multi-asset Portfolio Strategies
📊 Advanced Risk Management modules
🌐 Web Dashboard for strategy monitoring
📈 Additional AI Models (LSTM, GRU, Attention mechanisms)
🏦 More Indian Market Features (sector analysis, derivatives)

🔬 Research Areas:

Sentiment Analysis integration with news data
High-frequency Trading strategies
Options Trading algorithms
Cryptocurrency market strategies
ESG Investing with AI screening

⭐ Star this repository if you found it helpful! ⭐

📈 Happy Trading! 📈

Built with ❤️ for the algorithmic trading community

Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
Transformer_Trading		Transformer_Trading
backtesting_Vwap_Trading		backtesting_Vwap_Trading
README.md		README.md
requirements.txt		requirements.txt

Mohit-5899/CodeTrading

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