# Quantitative Trading Platform

A quantitative trading platform with modular architecture for data acquisition, strategy execution, risk management, and UI/UX.

## Architecture

See `component_diagram.md` and `architecture_diagram.txt` in the parent directory (`/home/thanthos/clawd/`) for detailed system design.

## UI/UX Module

### Dashboard
Simple dashboard displaying key trading metrics:
- PnL (Profit and Loss)
- Drawdown
- Win Rate

## Data Layer Module

### Moomoo API Connector
A robust Python client for moomoo API with comprehensive error handling, rate limiting, and data normalization.

**Features:**
- Historical OHLCV data retrieval
- Real-time WebSocket subscription (mock)
- Instrument metadata
- Rate limiting (token bucket algorithm)
- Circuit breaker pattern for fault tolerance
- Caching with TTL
- Mock mode for development

**Usage:**
```python
from data.connectors import create_moomoo_client, Interval
from datetime import datetime, timedelta

client = create_moomoo_client(mock_mode=True)
data = client.get_ohlcv(
    symbol="AAPL",
    interval=Interval.DAY_1,
    start_date=datetime.now() - timedelta(days=30),
    end_date=datetime.now()
)
```

See `examples/demo_moomoo.py` for complete demonstration.

## Development

### Prerequisites
- Python 3.8+
- Node.js (for frontend, optional)
- Docker (optional)
- Redis (optional, for caching)

### Setup
1. Clone the repository
2. Install dependencies: `pip install -r requirements.txt`
3. Run the dashboard: `python app.py`
4. Test data connector: `python examples/demo_moomoo.py`

### Data Layer Development
The data connector uses a modular design with base classes for easy extension:
- `DataConnector` abstract base class
- `MoomooClient` implementation with mock mode
- Rate limiting and circuit breaker patterns
- Caching support (in-memory or Redis)

To extend with real API credentials:
1. Set environment variables: `MOOMOO_API_KEY`, `MOOMOO_SESSION_TOKEN`
2. Set `mock_mode=False` when creating client
3. Implement actual API calls in `_make_request()` method

### UI/UX Development
The dashboard is built with Flask (backend) and Bootstrap/Chart.js (frontend).
- Backend: `app.py` provides REST API endpoints
- Frontend: `templates/dashboard.html` with interactive charts

## License

Proprietary