JamesTang
994fb0fab5
- Complete clickable mockup showing full platform UI/UX - Dashboard with real-time metrics and charts - Data management interface with API connectors - Strategy engine configuration and backtesting - Risk management controls and monitoring - Navigation between all platform sections - Dark theme with professional trading aesthetic - Responsive design for mobile/desktop - Interactive charts using Chart.js with mock data - Reusable HTML/CSS/JavaScript structure - Updated README with prototype documentation
98 lines
2.9 KiB
Markdown
98 lines
2.9 KiB
Markdown
# Quantitative Trading Platform
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A quantitative trading platform with modular architecture for data acquisition, strategy execution, risk management, and UI/UX.
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## Architecture
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See `component_diagram.md` and `architecture_diagram.txt` in the parent directory (`/home/thanthos/clawd/`) for detailed system design.
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## UI/UX Module
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### Dashboard
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Simple dashboard displaying key trading metrics:
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- PnL (Profit and Loss)
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- Drawdown
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- Win Rate
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## Data Layer Module
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### Moomoo API Connector
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A robust Python client for moomoo API with comprehensive error handling, rate limiting, and data normalization.
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**Features:**
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- Historical OHLCV data retrieval
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- Real-time WebSocket subscription (mock)
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- Instrument metadata
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- Rate limiting (token bucket algorithm)
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- Circuit breaker pattern for fault tolerance
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- Caching with TTL
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- Mock mode for development
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**Usage:**
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```python
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from data.connectors import create_moomoo_client, Interval
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from datetime import datetime, timedelta
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client = create_moomoo_client(mock_mode=True)
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data = client.get_ohlcv(
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symbol="AAPL",
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interval=Interval.DAY_1,
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start_date=datetime.now() - timedelta(days=30),
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end_date=datetime.now()
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)
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```
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See `examples/demo_moomoo.py` for complete demonstration.
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## Development
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### Prerequisites
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- Python 3.8+
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- Node.js (for frontend, optional)
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- Docker (optional)
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- Redis (optional, for caching)
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### Setup
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1. Clone the repository
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2. Install dependencies: `pip install -r requirements.txt`
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3. Run the dashboard: `python app.py`
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4. Test data connector: `python examples/demo_moomoo.py`
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### Data Layer Development
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The data connector uses a modular design with base classes for easy extension:
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- `DataConnector` abstract base class
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- `MoomooClient` implementation with mock mode
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- Rate limiting and circuit breaker patterns
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- Caching support (in-memory or Redis)
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To extend with real API credentials:
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1. Set environment variables: `MOOMOO_API_KEY`, `MOOMOO_SESSION_TOKEN`
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2. Set `mock_mode=False` when creating client
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3. Implement actual API calls in `_make_request()` method
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### UI/UX Development
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The dashboard is built with Flask (backend) and Bootstrap/Chart.js (frontend).
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- Backend: `app.py` provides REST API endpoints
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- Frontend: `templates/dashboard.html` with interactive charts
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## Interactive Prototype
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A complete clickable mockup of the full platform is available in the `prototype/` directory. This showcases the complete user interface with all major sections:
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**Features demonstrated:**
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- Dashboard with real-time metrics
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- Data management interface
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- Strategy engine configuration
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- Risk management controls
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- Navigation between all platform sections
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**To use the prototype:**
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1. Open `prototype/index.html` in a web browser
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2. Click sidebar navigation to explore different sections
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3. Interact with charts, forms, and controls
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The prototype uses HTML/CSS/JavaScript with Bootstrap and Chart.js, and can be reused as the foundation for the actual web application.
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## License
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Proprietary |