TradingBot

TradingBot is an AI-powered automated trading system for the Indian stock market, designed to work with brokers like Zerodha and Groww. It consists of a C# API backend and a Python-based signal engine, connected via Docker, and is highly configurable and extendable.

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🚀 Features

• 📈 AI-driven signal generation (Python, OpenAI API)
• 💼 Execute buy/sell orders via broker APIs (e.g., Kite Connect)
• 🔁 Configurable modes: Auto trade / Signal-only
• ⚙️ Trading strategy options: intraday, long-term, swing, options, etc.
• 🔒 Risk management: daily limits, trade caps, risk/reward ratio
• 🔌 Modular architecture for plug-in strategy and AI service integration
• 🧪 Local testing and Docker support for development

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🗂️ Project Structure

TradingBot/
├── TradingBot.Api/           # C# ASP.NET Core backend (REST API)
├── TradingBot.SignalEngine/  # Python signal engine (AI, ML logic)
├── TradingBot.Tests/         # C# Unit Tests
├── docker-compose.yml        # Orchestrates multi-container dev environment
├── docs/                     # Project documentation
└── README.md                 # This file

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🛠️ Quick Start

Prerequisites

• Docker and Docker Compose
• .NET 8.0 SDK (for local development)
• Python 3.11+ (for local development)

1. Clone and Setup

git clone <repository-url>
cd TradingBot

2. Environment Setup

Create a .env file in the root directory:

# Optional: OpenAI API Key for AI strategy
OPENAI_API_KEY=your_openai_api_key_here

# Optional: Database connection (if using external DB)
DB_CONNECTION_STRING=your_db_connection_string

3. Run with Docker

# Build and start all services
docker-compose up --build

# Or run in background
docker-compose up -d --build

4. Access Services

• API Documentation: http://localhost:5000/swagger
• Signal Engine: http://localhost:8000/docs
• API Health: http://localhost:5000/health
• Signal Engine Health: http://localhost:8000/health

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📖 API Usage

Get Trading Configuration

curl http://localhost:5000/api/trade/config

Generate Trading Signals

curl -X POST http://localhost:5000/api/trade/signal \
  -H "Content-Type: application/json" \
  -d '{
    "symbols": ["AAPL", "MSFT"],
    "strategy": "moving_average",
    "tradeMode": "paper"
  }'

Execute Trade

curl -X POST http://localhost:5000/api/trade/execute \
  -H "Content-Type: application/json" \
  -d '{
    "symbol": "AAPL",
    "quantity": 10,
    "price": 150.00,
    "side": "buy",
    "orderType": "market",
    "tradeType": "intraday"
  }'

Get Portfolio

curl http://localhost:5000/api/portfolio

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🧠 Signal Engine Strategies

The Python signal engine supports multiple trading strategies:

1. Moving Average Strategy

• Uses SMA crossover with RSI confirmation
• Volume-based validation
• Configurable periods and thresholds

2. Basic Strategy

• Simple price action analysis
• Volume confirmation
• RSI overbought/oversold signals

3. AI Strategy

• OpenAI GPT integration for advanced analysis
• Technical indicator synthesis
• Fallback to technical analysis

Using Different Strategies

# Moving Average Strategy
curl -X POST http://localhost:8000/signal \
  -H "Content-Type: application/json" \
  -d '{"symbol": "AAPL", "strategy": "moving_average"}'

# AI Strategy (requires OpenAI API key)
curl -X POST http://localhost:8000/signal \
  -H "Content-Type: application/json" \
  -d '{"symbol": "AAPL", "strategy": "ai"}'

# Basic Strategy
curl -X POST http://localhost:8000/signal \
  -H "Content-Type: application/json" \
  -d '{"symbol": "AAPL", "strategy": "basic"}'

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⚙️ Configuration

Trading Settings

Edit TradingBot.Api/appsettings.Development.json:

{
  "TradeSettings": {
    "EnableAutoBuy": true,
    "EnableAutoSell": true,
    "EnableSignalOnly": false,
    "MaxDailyBuyAmount": 10000.00,
    "MaxDailySellAmount": 10000.00,
    "MaxDailyTrades": 10,
    "TradeTypesEnabled": ["Intraday", "Swing", "LongTerm"]
  }
}

Strategy Configuration

Each strategy can be configured with custom parameters. See individual strategy files in TradingBot.SignalEngine/strategies/.

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🔧 Development

Local Development Setup

1. C# API Development:

   cd TradingBot.Api
   dotnet restore
   dotnet run

2. Python Signal Engine Development:

   cd TradingBot.SignalEngine
   pip install -r requirements.txt
   uvicorn main:app --reload --host 0.0.0.0 --port 8000

Adding New Strategies

1. Create a new strategy class in TradingBot.SignalEngine/strategies/
2. Extend BaseStrategy class
3. Implement generate_signal() method
4. Register in main.py STRATEGIES dictionary

Example:

from .base import BaseStrategy, TradeSignal

class MyCustomStrategy(BaseStrategy):
    def generate_signal(self, df: pd.DataFrame, symbol: str) -> TradeSignal:
        # Your custom logic here
        return TradeSignal(...)

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🧪 Testing

Run C# Tests

cd TradingBot.Tests
dotnet test

Test Signal Engine

# Test health endpoint
curl http://localhost:8000/health

# Test signal generation
curl -X POST http://localhost:8000/signal \
  -H "Content-Type: application/json" \
  -d '{"symbol": "AAPL"}'

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📊 Monitoring

Health Checks

• API: GET /health
• Signal Engine: GET /health

Logs

# View all logs
docker-compose logs

# View specific service logs
docker-compose logs tradingbot-api
docker-compose logs signal-engine

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🚀 Deployment

Production Deployment

1. Azure Container Registry:

   # Build and push images
   docker build -t yourregistry.azurecr.io/tradingbot-api .
   docker build -t yourregistry.azurecr.io/tradingbot-signal-engine .
   docker push yourregistry.azurecr.io/tradingbot-api
   docker push yourregistry.azurecr.io/tradingbot-signal-engine

2. Azure Kubernetes Service (AKS):

   • Deploy using provided Kubernetes manifests
   • Configure secrets for API keys
   • Set up monitoring and logging

Environment Variables

Variable	Description	Required
OPENAI_API_KEY	OpenAI API key for AI strategy	No
DB_CONNECTION_STRING	Database connection string	Yes (prod)
BROKER_API_KEY	Broker API credentials	Yes (live)

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🤝 Contributing

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

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📄 License

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

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🆘 Support

• Documentation: Check the docs/ folder
• Issues: Create an issue on GitHub
• Discussions: Use GitHub Discussions

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🗺️ Roadmap

See docs/roadmap.md for detailed roadmap and upcoming features.