🚀 Advanced Trading Strategy Platform

A comprehensive, full-stack trading strategy development and backtesting platform with real-time monitoring capabilities. Build, test, and deploy trading strategies without coding using our visual strategy builder.

📋 Table of Contents

• Features
• Architecture
• Installation
• Quick Start
• Strategy Builder Guide
• Performance Analytics
• Real-time Monitoring
• API Documentation
• Configuration
• Contributing
• License

✨ Features

🏗️ Visual Strategy Builder

• Drag-and-drop interface for creating trading strategies
• No coding required - build complex strategies visually
• 20+ technical indicators (RSI, MACD, Bollinger Bands, Moving Averages)
• Custom conditions and logic (AND, OR, crossovers, thresholds)
• Risk management tools (stop loss, take profit, position sizing)

📊 Advanced Performance Analytics

• Comprehensive backtesting on historical data
• Enhanced trading metrics including:
  • Average trade duration
  • Largest win/loss percentages
  • Portfolio turnover rate
  • Risk-adjusted returns (Sharpe, Sortino, Calmar ratios)
• Risk analysis with drawdown monitoring and VaR calculations
• Visual performance reports with heatmaps and distribution analysis

📈 Real-time Strategy Deployment

• Live strategy monitoring with WebSocket connections
• Real-time signal generation and trade execution simulation
• Performance tracking with live P&L updates
• Strategy health monitoring and alerts

🗃️ Enhanced Data Management

• Multi-source data integration (Yahoo Finance, Alpha Vantage, Polygon.io)
• Bulk data download with parallel processing
• Data quality analysis and validation
• CSV upload support for custom datasets
• Advanced caching for optimal performance

🏛️ Architecture

├── frontend/                 # React.js frontend application
│   ├── src/
│   │   ├── components/      # React components
│   │   │   ├── StrategyBuilder/     # Visual strategy builder
│   │   │   ├── PerformanceAnalytics/ # Analytics dashboard
│   │   │   ├── RealtimeDashboard/   # Real-time monitoring
│   │   │   └── BulkDownload/        # Data management
│   │   ├── services/        # API and WebSocket services
│   │   └── utils/           # Utility functions
│   └── public/              # Static assets
│
├── backend/                 # FastAPI backend application
│   ├── app/
│   │   ├── routers/         # API route handlers
│   │   │   ├── strategy.py          # Strategy management
│   │   │   ├── realtime_strategy.py # Real-time operations
│   │   │   ├── enhanced_data.py     # Data management
│   │   │   └── websocket.py         # WebSocket handling
│   │   ├── services/        # Business logic services
│   │   │   ├── strategy_engine.py           # Strategy execution
│   │   │   ├── realtime_strategy_engine.py  # Real-time engine
│   │   │   ├── performance_analytics.py     # Analytics engine
│   │   │   └── technical_indicators.py      # Technical analysis
│   │   └── models/          # Data models
│   └── requirements.txt     # Python dependencies
│
└── docs/                    # Documentation

🚀 Installation

Prerequisites

• Node.js (v16+)
• Python (3.8+)
• Git

Backend Setup

1. Clone the repository

git clone https://github.com/yourusername/trading-strategy-platform.git
cd trading-strategy-platform

2. Set up Python environment

cd backend
python -m venv venv

# On Windows
venv\Scripts\activate

# On macOS/Linux
source venv/bin/activate

3. Install Python dependencies

pip install -r requirements.txt

4. Start the backend server

python -m uvicorn app.main:app --reload --port 8000

Frontend Setup

1. Install Node.js dependencies

cd frontend
npm install

2. Start the development server

npm start

3. Access the application

• Frontend: http://localhost:3000
• Backend API: http://localhost:8000
• API Documentation: http://localhost:8000/docs

🎯 Quick Start

1. Create Your First Strategy

1. Navigate to Strategy Builder in the web interface

2. Select technical indicators from the left panel:

   • Add RSI indicator
   • Set period to 14

3. Create entry condition:

   • RSI < 30 (oversold condition)
   • Connect to BUY action

4. Create exit condition:

   • RSI > 70 (overbought condition)
   • Connect to SELL action

5. Set risk management:

   • Stop loss: 2%
   • Take profit: 4%

6. You can also use example strategies like RSI or SMI crossover

7. Save your strategy

2. Backtest Your Strategy

1. Select symbol (e.g., AAPL, BTC-USD)
2. Choose date range (e.g., 2022-01-01 to 2024-01-01)
3. Click "Run Backtest"
4. Review performance metrics:
   • Total return, Sharpe ratio, maximum drawdown
   • Trade analysis and risk metrics
   • Visual performance charts

3. Deploy to Real-time

1. Click "Deploy Live"
2. Configure real-time parameters:
   • Position size, maximum positions
   • Risk limits
3. Monitor in Real-time Dashboard:
   • Live signals and trade execution
   • Performance tracking
   • Strategy health monitoring

🏗️ Strategy Builder Guide

Technical Indicators Available

Indicator	Description	Parameters
RSI	Relative Strength Index	Period (default: 14)
SMA	Simple Moving Average	Period (default: 20)
EMA	Exponential Moving Average	Period (default: 20)
MACD	Moving Average Convergence Divergence	Fast: 12, Slow: 26, Signal: 9
Bollinger Bands	Volatility-based bands	Period: 20, Std Dev: 2
Stochastic	Momentum oscillator	%K: 14, %D: 3
Williams %R	Momentum indicator	Period: 14
ATR	Average True Range	Period: 14
Volume	Trading volume analysis	-
Price Action	OHLC price data	-

Condition Operators

• Comparison: >, <, >=, <=, ==, !=
• Logic: AND, OR, NOT
• Crossover: crosses_above, crosses_below
• Threshold: above, below, between

Example Strategies

RSI Mean Reversion

Entry: RSI(14) < 40
Exit: RSI(14) > 60
Stop Loss: 2%
Take Profit: 4%

Moving Average Crossover

Entry: SMA(10) crosses above SMA(20)
Exit: SMA(10) crosses below SMA(20)
Stop Loss: 3%

📊 Performance Analytics

Enhanced Trading Metrics

New Metrics (v2.0)

• Average Trade Duration: Mean time positions are held
• Largest Win/Loss: Best and worst individual trade performance
• Turnover Rate: Annual portfolio turnover percentage
• Risk-Adjusted Returns: Sharpe, Sortino, and Calmar ratios

Traditional Metrics

• Total Return: Overall strategy performance
• Win Rate: Percentage of profitable trades
• Profit Factor: Gross profit ÷ gross loss
• Maximum Drawdown: Largest peak-to-trough decline
• Volatility: Standard deviation of returns

Risk Analysis

Value at Risk (VaR)

• 95% VaR: Maximum expected loss 95% of the time
• 99% VaR: Maximum expected loss 99% of the time
• Historical simulation and parametric methods

Drawdown Analysis

• Maximum drawdown: Worst loss from peak
• Drawdown duration: Time to recover from losses
• Underwater curve: Visual representation of drawdown periods

Performance Visualizations

1. Portfolio Value Timeline: Track growth over time
2. Return Distribution Heatmap: Monthly performance patterns
3. Drawdown Analysis Chart: Visualize risk periods
4. P&L Distribution: Histogram of trade returns
5. Trade Duration Analysis: Performance by holding period
6. Rolling Risk Metrics: Dynamic risk measurement

🔴 Real-time Monitoring

Features

• Live Strategy Execution: Strategies run continuously on market data
• WebSocket Connections: Real-time updates for all connected clients
• Signal Generation: Live buy/sell signals based on market conditions
• Trade Simulation: Paper trading with realistic execution
• Performance Tracking: Real-time P&L and portfolio updates

WebSocket Events

// Connect to WebSocket
const ws = new WebSocket('ws://localhost:8000/ws');

// Subscribe to strategy updates
ws.send(JSON.stringify({
  type: 'subscribe_strategy',
  strategy_id: 'your_strategy_id'
}));

// Handle real-time events
ws.onmessage = (event) => {
  const data = JSON.parse(event.data);
  
  switch(data.type) {
    case 'signal_generated':
      // Handle new trading signals
      console.log('New signal:', data.data);
      break;
      
    case 'trade_executed':
      // Handle trade executions
      console.log('Trade executed:', data.data);
      break;
      
    case 'performance_update':
      // Handle performance metrics updates
      console.log('Performance update:', data.data);
      break;
  }
};

Strategy Deployment

1. Deploy from Backtest: One-click deployment from successful backtests
2. Configure Parameters: Set position sizing and risk limits
3. Monitor Health: Real-time strategy status and error handling
4. Stop/Start Control: Manual control over strategy execution

📡 API Documentation

Core Endpoints

Strategy Management

# Create strategy
POST /api/strategy/create
{
  "name": "RSI Strategy",
  "rules": [...],
  "risk_management": {...}
}

# Test strategy
POST /api/strategy/test?symbol=AAPL&start_date=2023-01-01
{...strategy_config...}

# Run backtest
POST /api/strategy/backtest
{...strategy_config...}

Real-time Operations

# Start real-time strategy
POST /api/realtime/start-realtime-strategy
{
  "strategy_name": "Live RSI Strategy",
  "symbol": "AAPL",
  "initial_capital": 100000
}

# Get active strategies
GET /api/realtime/active-realtime-strategies

# Stop strategy
POST /api/realtime/stop-realtime-strategy/{strategy_id}

Data Management

# Bulk download
POST /api/enhanced-data/bulk-download
{
  "symbols": ["AAPL", "GOOGL", "MSFT"],
  "period": "1y",
  "interval": "1d"
}

# Get job status
GET /api/enhanced-data/jobs/{job_id}

# Data quality report
GET /api/enhanced-data/data/analyze/{symbol}

Authentication (Optional)

For production deployment, implement authentication:

# Add to backend/app/middleware/auth.py
from fastapi import HTTPException, Depends
from fastapi.security import HTTPBearer

security = HTTPBearer()

async def verify_token(token: str = Depends(security)):
    # Implement your authentication logic
    if not is_valid_token(token):
        raise HTTPException(status_code=401, detail="Invalid token")
    return get_user_from_token(token)

⚙️ Configuration

Environment Variables

Create .env files for configuration:

Features

REACT_APP_ENABLE_REAL_TIME=true REACT_APP_ENABLE_BULK_DOWNLOAD=true REACT_APP_MAX_SYMBOLS_PER_STRATEGY=10

### Data Source Configuration

Configure multiple data sources in `backend/app/config/data_sources.py`:

```python
DATA_SOURCES = {
    "yahoo_finance": {
        "name": "Yahoo Finance",
        "free": True,
        "rate_limit": 2000,  # requests per hour
        "intervals": ["1m", "5m", "15m", "30m", "1h", "1d", "1wk", "1mo"]
    },
    "alpha_vantage": {
        "name": "Alpha Vantage",
        "requires_api_key": True,
        "rate_limit": 500,  # requests per day
        "intervals": ["1min", "5min", "15min", "30min", "60min", "daily"]
    }
}

Production Deployment

Backend (FastAPI)

# Install production server
pip install gunicorn

# Start with Gunicorn
gunicorn app.main:app -w 4 -k uvicorn.workers.UvicornWorker --bind 0.0.0.0:8000

Frontend (React)

# Build production bundle
npm run build

# Serve with nginx or host on Vercel/Netlify
npm install -g serve
serve -s build -l 3000

📚 Advanced Usage

Custom Indicators

Add custom technical indicators:

# backend/app/services/custom_indicators.py
def custom_momentum(prices: List[float], period: int = 10) -> List[float]:
    """Custom momentum indicator implementation"""
    momentum = []
    for i in range(len(prices)):
        if i >= period:
            mom = (prices[i] - prices[i - period]) / prices[i - period] * 100
            momentum.append(mom)
        else:
            momentum.append(0)
    return momentum

Strategy Templates

Create reusable strategy templates:

# backend/app/templates/strategy_templates.py
RSI_MEAN_REVERSION = {
    "name": "RSI Mean Reversion",
    "description": "Buy oversold, sell overbought conditions",
    "rules": [
        {
            "name": "Buy Oversold",
            "signal_type": "buy",
            "conditions": [
                {
                    "indicator": "RSI",
                    "parameters": {"period": 14},
                    "operator": "<",
                    "value": 30
                }
            ]
        }
    ],
    "risk_management": {
        "stop_loss_percent": 2.0,
        "take_profit_percent": 4.0
    }
}

Performance Optimization

Backend Optimizations

• Async processing for data downloads
• Caching for frequently accessed data
• Database indexing for query optimization
• Connection pooling for external APIs

Frontend Optimizations

• React.memo for component optimization
• useMemo/useCallback for expensive calculations
• Lazy loading for large datasets
• WebSocket connection management

🐛 Troubleshooting

Common Issues

WebSocket Connection Failed

# Check if backend is running
curl http://localhost:8000/health

# Verify WebSocket endpoint
wscat -c ws://localhost:8000/ws

Data Download Errors

# Check API limits
curl http://localhost:8000/api/enhanced-data/data-sources

# Verify symbol format
curl "http://localhost:8000/api/data/market-data?symbol=AAPL&period=1mo"

Strategy Test Failures

# Validate strategy configuration
curl -X POST http://localhost:8000/api/strategy/validate \
  -H "Content-Type: application/json" \
  -d '{"name": "Test", "rules": [...]}'

Debug Mode

Enable debug logging:

# backend/app/config/logging.py
import logging

logging.basicConfig(
    level=logging.DEBUG,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)

🤝 Contributing

Development Setup

1. Fork the repository
2. Create feature branch

git checkout -b feature/amazing-feature

3. Make changes and test

# Backend tests
cd backend && pytest

# Frontend tests
cd frontend && npm test

4. Commit changes

git commit -m "Add amazing feature"

5. Push to branch

git push origin feature/amazing-feature

6. Open Pull Request

Code Style

Python (Backend)

• Black for code formatting
• flake8 for linting
• mypy for type checking

black app/
flake8 app/
mypy app/

TypeScript (Frontend)

• Prettier for code formatting
• ESLint for linting

npm run format
npm run lint

Contribution Guidelines

• Write tests for new features
• Update documentation for API changes
• Follow existing code patterns
• Add type hints for Python code
• Use meaningful commit messages

📄 License

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

🙏 Acknowledgments

• Technical Analysis Library - TA-Lib for technical indicators
• Financial Data - Yahoo Finance, Alpha Vantage APIs
• UI Components - Material-UI for React components
• WebSocket - FastAPI WebSocket support
• Visualization - Recharts for performance charts

📞 Support

• Documentation: Full Documentation
• Email: pratyushyadav0106@gmail.com

🔮 Roadmap

v2.1 (Next Release)

• Options trading support
• Portfolio optimization algorithms
• Machine learning indicators
• Advanced order types (limit, stop-limit)

v2.2 (Future)

• Multi-asset strategies (stocks + crypto + forex)
• Social trading features
• Mobile app (React Native)
• Cloud deployment templates
• AI-powered strategy generation
• Institutional features (prime brokerage integration)
• Advanced risk management (portfolio-level risk)
• Real broker integration (Interactive Brokers, TD Ameritrade)

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