TradingAI 🤖 📈

An AI-powered trading analysis system that combines technical analysis with LLM-based decision making. The system analyzes market conditions, technical indicators, and uses GPT-4 to generate trading decisions.

Overview 🌟

TradingAI helps traders by:

• Analyzing market conditions using EMA-based scoring
• Identifying stocks in Stage 2 uptrend
• Providing AI-powered trading decisions with entry/exit points
• Automating data collection and analysis

Features

Data Management 📊

• Historical data fetching from Zerodha
• Daily EOD data updates
• Data validation and cleaning
• Efficient PostgreSQL storage

Technical Analysis 📈

• Moving Averages (SMA, EMA)
• MACD Indicator
• Bollinger Bands
• Volume Analysis
• Market Breadth

AI Analysis 🧠

• Market Condition Assessment (6-point scoring)
• Pattern Recognition
• Trading Signal Generation
• Risk Assessment
• Stop Loss Calculation

API Interface 🔌

• RESTful API endpoints
• Real-time analysis
• Historical data access
• Batch operations

Installation 🚀

1. Clone the repository:

git clone https://github.com/mitesh-sharma/tradingai.git cd tradingai

2. Create and activate virtual environment: python -m venv venv source venv/bin/activate

3. Install dependencies: pip install -e ".[dev]"

4. Set up environment variables: cp .env.example .env

Edit .env with your credentials

5. Set up database: createdb tradingai

Configuration ⚙️

Required environment variables in .env: Database POSTGRES_SERVER=localhost POSTGRES_USER=postgres POSTGRES_PASSWORD=your_password POSTGRES_DB=tradingai API Security API_KEY=your_api_key Zerodha ZERODHA_API_KEY=your_zerodha_api_key ZERODHA_API_SECRET=your_zerodha_secret ZERODHA_USER_ID=your_zerodha_user_id OpenAI OPENAI_API_KEY=your_openai_key LLM_MODEL_NAME=gpt-4

Usage 📡

1. Start the server: uvicorn src.tradingai.main:app --reload

2. Fetch historical data: curl -X POST http://localhost:8000/api/v1/stock/stocks/historical
   -H "Content-Type: application/json"
   -H "X-API-Key: your-secret-key"
   -d '{ "symbols": ["ZOTA"], "from_date": "2023-02-22", "to_date": "2024-02-22" }'

3. Get analysis with LLM decision: curl http://localhost:8000/api/v1/stock/analyze/ZOTA/with-decision
   -H "X-API-Key: your-secret-key"

API Endpoints 🛣️

• POST /api/v1/stock/stocks/historical: Fetch historical data
• GET /api/v1/stock/analyze/{symbol}: Get technical analysis
• GET /api/v1/stock/analyze/{symbol}/with-decision: Get analysis with LLM trading decision
• GET /api/v1/stock/symbols: List all available symbols
• POST /api/v1/stock/daily-update: Trigger daily data update

Technical Details 🔧

Market Analysis

• Uses 6-point EMA scoring system
• Analyzes market breadth
• Provides market context and direction

Stock Analysis

• 30-week SMA trend analysis
• MACD confirmation
• Bollinger Band correction opportunities
• Volume trend analysis

LLM Integration

• Uses GPT-4 for decision making
• Structured prompts for consistent analysis
• Includes market context in decisions
• Provides detailed reasoning

Contributing 🤝

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

Author 👨‍💻

Mitesh Sharma

• GitHub: @mitesh-sharma

Acknowledgments 🙏

• Zerodha for their trading API

• OpenAI for GPT-4 API

• FastAPI for the web framework

• SQLAlchemy for database ORM

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This project was developed with the assistance of AI language models (Claude and GPT-4) to demonstrate the potential of AI-powered development while maintaining high code quality and security standards.