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JamesTang 6e86da6733 Implement moomoo API connector for OHLCV data

- Added DataConnector base class with OHLCV, InstrumentInfo, Interval
- Implemented MoomooClient with rate limiting, circuit breaker, caching
- Mock mode generates realistic data for development
- Real-time WebSocket subscription support (mock)
- Added examples/demo_moomoo.py showcasing functionality
- Updated requirements.txt with requests, websocket-client, redis, python-dotenv
- Updated README.md with Data Layer documentation
- Added .env.example for configuration

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data

Implement moomoo API connector for OHLCV data

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examples

Implement moomoo API connector for OHLCV data

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templates

Initial commit: UI/UX dashboard skeleton

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.env.example

Implement moomoo API connector for OHLCV data

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.gitignore

Initial commit: UI/UX dashboard skeleton

2026-02-04 10:41:02 +08:00

app.py

Initial commit: UI/UX dashboard skeleton

2026-02-04 10:41:02 +08:00

README.md

Implement moomoo API connector for OHLCV data

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requirements.txt

Implement moomoo API connector for OHLCV data

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README.md

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:

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

Clone the repository
Install dependencies: pip install -r requirements.txt
Run the dashboard: python app.py
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:

Set environment variables: MOOMOO_API_KEY, MOOMOO_SESSION_TOKEN
Set mock_mode=False when creating client
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