File size: 2,252 Bytes
590a501 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | # Personal Trading System V1
A personal futures trading system with quantitative strategy support, built with Python (FastAPI) backend and Vue 3 frontend.
## Features
- **Market Data Simulation**: 12 futures contracts with realistic price movements
- **Order Management**: Market / Limit / Stop orders
- **Position Tracking**: Real-time P&L and margin calculation
- **Quantitative Strategies**: MA Crossover, Bollinger Bands, Dual Thrust
- **Risk Management**: Position limits, risk usage monitoring
- **Real-time Updates**: WebSocket streaming for live market data
- **Dark Theme UI**: Modern dashboard with ECharts candlestick charts
## Quick Start
### Backend
```bash
cd backend
pip install -r requirements.txt
uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload
```
### Frontend
```bash
cd frontend
npm install
npm run dev
```
Open http://localhost:5173 in your browser.
## Unified Platform (with ML-Alpha-Research-System)
This folder is integrated into the parent repo as the **web visualization + online launcher** layer.
### One-command launch (from repo root)
```bash
python scripts/launch_platform.py --enable-research
# or start all embedded modules after backend is up:
python scripts/launch_platform.py --start-all-modules
```
### Service control panel
- **Web UI**: http://localhost:5173/services — start/stop modules by feature
- **API**: `GET/POST /api/platform/services/*`
| Module ID | Description |
|-----------|-------------|
| `trading_api` | FastAPI backend (always running with uvicorn) |
| `market_data` | Simulated futures WebSocket + K-lines |
| `futures_strategies` | MA / Bollinger / DualThrust engine |
| `qlib_research` | Factor registry, operator builder, A-share strategies |
| `frontend_ui` | Vue dev server (optional subprocess launch) |
### Factor research bridge
After starting `qlib_research`, use:
- **Web UI**: http://localhost:5173/research
- **API**: `/api/research/factors`, `/operators`, `/strategies`
```bash
cd backend
python3 -m pytest tests/ -v # 17 tests
ruff check app/ tests/ # linter
```
## Tech Stack
- **Backend**: Python 3.12, FastAPI, SQLAlchemy, NumPy, Pandas
- **Frontend**: Vue 3, Element Plus, ECharts, Pinia, Vue Router
- **Database**: SQLite (async)
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