OxQuant - Next-Generation AI Quantitative Trading Platform

🚀 Overview

OxQuant is a cutting-edge AI-powered quantitative trading platform that combines traditional quantitative finance with modern machine learning techniques. The platform enables systematic trading strategy development, backtesting, risk management, and live trading across multiple asset classes.

✨ Features

AI-Driven Strategy Development: Leverage LLMs for strategy ideation and code generation
Multi-Asset Support: Equities, Futures, Options, Crypto, Forex
High-Performance Backtesting: Comprehensive backtesting engine with Walk Forward Analysis
Risk Management Suite: Advanced portfolio optimization, position limits, and drawdown control
RESTful API: Complete CRUD APIs for strategies, portfolios, and backtesting
Live Trading Integration: Connect to major brokers and exchanges
Research Environment: Jupyter notebooks with pre-built templates
Collaboration Tools: Team workflow management and version control

📊 Core Engine Enhancements

Order Management

Order status tracking (PENDING, FILLED, PARTIALLY_FILLED, CANCELLED, REJECTED)
Automatic unique order ID generation
Real-time remaining quantity calculation

Position Management

Asset class categorization
Direction tracking (LONG/SHORT/FLAT)
Unrealized P&L calculation with percentage metrics

Portfolio Management

Initial capital tracking
Total return calculation
Risk exposure metrics
Multi-position management

Risk Management

Position size limits
Portfolio concentration limits
Maximum drawdown protection
Daily loss limits

Strategy Framework

Signal type management (LONG/SHORT/FLAT/EXIT)
Strategy context management
Signal filtering mechanism
Performance metrics calculation

🏗️ Architecture

┌─────────────────────────────────────────────────────────────┐
│                     OxQuant Platform                        │
├─────────────────────────────────────────────────────────────┤
│  Frontend Layer          │  API Gateway                    │
│  • Web Dashboard         │  • REST/WebSocket APIs          │
│  • Strategy Studio       │  • Authentication               │
│  • Research Notebooks    │  • Rate Limiting                │
├─────────────────────────────────────────────────────────────┤
│  Core Engine Layer       │  Data Layer                     │
│  • Strategy Engine       │  • Market Data Pipeline         │
│  • Backtesting Engine    │  • Feature Store                │
│  • Risk Engine           │  • Model Registry               │
│  • Execution Engine      │  • Results Database             │
├─────────────────────────────────────────────────────────────┤
│  AI/ML Layer             │  Infrastructure Layer           │
│  • LLM Integration       │  • Container Orchestration      │
│  • Feature Engineering   │  • Monitoring & Alerting        │
│  • Model Training        │  • CI/CD Pipeline               │
│  • Model Serving         │  • Security & Compliance        │
└─────────────────────────────────────────────────────────────┘

🚀 Quick Start

Prerequisites

Python 3.11+
(可选) Docker & Docker Compose
Git

一键启动（推荐）

# 克隆项目
git clone https://github.com/candy-frankie/OxQuant.git
cd OxQuant

# 安装依赖
./start.sh --install

# 本地启动API服务
./start.sh --local

Windows用户：

start.bat --install
start.bat --local

部署方式对比

方式	命令	说明
本地启动	`./start.sh --local`	最简单，直接运行（推荐）
Docker简化版	`./start.sh --docker`	轻量容器，SQLite数据库
Docker完整版	`./start.sh --docker-full`	完整服务，PostgreSQL+Redis
Jupyter	`./start.sh --jupyter`	启动研究环境
运行测试	`./start.sh --test`	执行测试用例

手动安装

# 安装依赖
pip install -r requirements.slim.txt

# 设置环境变量
export ENVIRONMENT=development
export DATABASE_URL=sqlite:///./data/oxquant.db

# 启动API服务
uvicorn src.api.main:app --host 0.0.0.0 --port 8000

Access Services

API Documentation: http://localhost:8000/docs
Jupyter Notebooks: http://localhost:8888 (password: oxquant)
PostgreSQL (完整版): localhost:5432
Redis (完整版): localhost:6379

📁 Project Structure

oxquant/
├── docs/                    # Documentation
├── src/                    # Source code
│   ├── api/               # FastAPI backend
│   │   ├── routers/       # API endpoints
│   │   │   ├── auth.py    # Authentication routes
│   │   │   ├── backtesting.py  # Backtesting routes
│   │   │   ├── portfolio.py    # Portfolio management
│   │   │   ├── data.py         # Market data routes
│   │   │   └── strategies.py   # Strategy management
│   │   ├── schemas/       # Pydantic models
│   │   ├── utils/         # Utility functions
│   │   └── main.py        # Application entry
│   ├── core/              # Core trading engine
│   │   ├── engine.py      # Order/Position/Portfolio/Strategy classes
│   │   ├── backtesting.py # Backtest engine and Walk Forward analyzer
│   │   ├── config.py      # Configuration management
│   │   ├── database.py    # Database connections
│   │   └── models.py      # SQLAlchemy models
│   ├── strategies/        # Trading strategies
│   │   └── examples.py    # Strategy implementations
│   └── data/              # Data pipelines
├── notebooks/             # Research notebooks
├── tests/                 # Test suite
├── deployment/            # Docker, Kubernetes configs
└── tools/                 # Development utilities

🛠️ Technology Stack

Backend: Python 3.11+, FastAPI, PostgreSQL, Redis
Frontend: React 18, TypeScript, Tailwind CSS
Data: Dask, Polars, TimescaleDB
ML: PyTorch, Scikit-learn, XGBoost, LangChain
Infra: Docker, Kubernetes, Terraform, Prometheus
Brokers: Interactive Brokers, Alpaca, Binance, etc.

📈 Available Strategies

Strategy	Description	Parameters
`ma_crossover`	Moving Average Crossover	short_window, long_window
`mean_reversion`	Bollinger Bands Mean Reversion	window, num_std
`macd`	MACD Crossover	fast_period, slow_period, signal_period
`rsi`	RSI Overbought/Oversold	window, oversold, overbought
`atr_exit`	ATR Trailing Stop	atr_window, atr_multiplier

Example: Run Multiple Strategies

from src.strategies.examples import (
    MovingAverageCrossover,
    MeanReversion,
    MACDStrategy,
    RSIStrategy,
    ATRExitStrategy,
    create_strategy
)
import pandas as pd
import numpy as np

# Generate sample data
np.random.seed(42)
dates = pd.date_range('2023-01-01', '2023-12-31', freq='D')
prices = 100 + np.cumsum(np.random.randn(len(dates)) * 0.5)
data = pd.DataFrame({'close': prices}, index=dates)

# Test all strategies
strategies = [
    MovingAverageCrossover(10, 30),
    MeanReversion(20, 2.0),
    MACDStrategy(12, 26, 9),
    RSIStrategy(14, 30, 70),
    ATRExitStrategy(14, 1.5)
]

for strategy in strategies:
    result = strategy.generate_signals(data)
    print(f"\n{strategy.name}:")
    print(f"  Total Return: {result.metrics['total_return']:.2%}")
    print(f"  Sharpe Ratio: {result.metrics['sharpe_ratio']:.2f}")
    print(f"  Max Drawdown: {result.metrics['max_drawdown']:.2%}")
    print(f"  Win Rate: {result.metrics['win_rate']:.1%}")
    print(f"  Total Trades: {result.metrics['total_trades']}")

🌐 API Endpoints

Authentication

POST /api/v1/auth/register - Register new user
POST /api/v1/auth/token - Get access token
POST /api/v1/auth/refresh - Refresh token
GET /api/v1/auth/me - Get current user

Strategies

GET /api/v1/strategies - List strategies
POST /api/v1/strategies - Create strategy
GET /api/v1/strategies/{id} - Get strategy
PUT /api/v1/strategies/{id} - Update strategy
DELETE /api/v1/strategies/{id} - Delete strategy

Backtesting

POST /api/v1/backtesting/run - Run backtest
GET /api/v1/backtesting/results - List results
POST /api/v1/backtesting/optimize - Optimize parameters
POST /api/v1/backtesting/walkforward - Walk Forward Analysis

Portfolio

GET /api/v1/portfolio - List portfolios
POST /api/v1/portfolio - Create portfolio
GET /api/v1/portfolio/{id} - Get portfolio
GET /api/v1/portfolio/{id}/positions - Get positions
GET /api/v1/portfolio/{id}/trades - Get trades

Market Data

GET /api/v1/data/symbols - Get available symbols
GET /api/v1/data/prices/{symbol} - Get price history
GET /api/v1/data/prices/{symbol}/latest - Get latest price
GET /api/v1/data/market/indices - Get market indices
GET /api/v1/data/market/news - Get market news

🔧 Development

Running Tests

pytest tests/

Code Formatting

black src/
isort src/
flake8 src/

Database Migrations

# Initialize database
python -c "from src.core.database import init_db; import asyncio; asyncio.run(init_db())"

🤝 Contributing

Please read CONTRIBUTING.md for details on our code of conduct and the process for submitting pull requests.

📄 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

📞 Support

Issues: GitHub Issues
Email: 644743502@qq.com

🚀 Roadmap

Phase 1: Core architecture and basic backtesting
Phase 1.5: API development and strategy library expansion
Phase 2: AI integration and strategy generation
Phase 3: Multi-broker execution and risk management
Phase 4: Enterprise features and scaling

📊 Performance Metrics

The backtesting engine calculates the following metrics:

Metric	Description
`total_return`	Total portfolio return
`annual_return`	Annualized return
`cagr`	Compound annual growth rate
`volatility`	Annualized volatility
`sharpe_ratio`	Risk-adjusted return (with risk-free rate)
`sortino_ratio`	Downside risk-adjusted return
`calmar_ratio`	Return-to-max-drawdown ratio
`max_drawdown`	Maximum peak-to-trough decline
`win_rate`	Percentage of profitable trades
`profit_factor`	Gross profits / gross losses
`expectancy`	Expected value per trade
`risk_reward_ratio`	Average win / average loss
`avg_trade_duration_days`	Average trade holding period

📈 A股市场交易完整链路

OxQuant提供完整的A股交易链路，从数据获取到实盘交易：

数据获取 → 因子挖掘 → 多因子模型 → 策略开发 → 回测分析 → 模拟交易 → 实盘交易 → 风控监控
     │           │            │            │            │            │            │            │
  AKShare    QLib思路    IC加权      策略基类    A股专用     实时模拟    同花顺/QMT   VaR/CVaR
  Tushare    12+因子     回归方法    信号生成    交易规则    历史回放    券商接口    风险告警

1. 数据获取

支持AKShare和Tushare两种数据源：

from src.data.data_providers import data_manager, DataProviderType

# 获取股票价格数据
data = data_manager.get_price_data(
    symbol="000001",
    start_date="20230101",
    end_date="20231231",
    provider=DataProviderType.AKSHARE
)

# 获取指数成分股
universe = data_manager.get_universe("000300")  # 沪深300

2. 因子挖掘（基于QLib框架）

from src.factors.factor_engine import factor_engine, FactorAnalyzer

# 计算所有因子
factors = factor_engine.compute_all_factors(data)

# 因子分析
analyzer = FactorAnalyzer()
ic = analyzer.calculate_ic(factors['momentum_20'], data['close'].pct_change().shift(-1))
ir = analyzer.calculate_ic_ir(factors['momentum_20'], data['close'].pct_change().shift(-1))

print(f"IC: {ic:.4f}, IR: {ir:.4f}")

支持的因子类型：

因子名称	类型	描述
momentum_20/60/120	动量	多周期动量因子
rsi_14	动量	相对强弱指数
macd	动量	MACD指标
pe/pb/ps	估值	市盈率/市净率/市销率
volatility_20/60	波动率	多周期波动率
volume_20/turnover_20	流动性	均量和换手率
beta/alpha	风险	风险因子

3. 多因子模型

from src.factors.multi_factor_model import (
    MultiFactorModel, FactorCombinationMethod, SignalGenerator
)

# 创建多因子模型
model = MultiFactorModel(
    factors=factors_df,
    returns=returns_series,
    method=FactorCombinationMethod.IC_WEIGHTED
)
model.fit()

# 生成交易信号
generator = SignalGenerator(model)
signals = generator.generate_signals(factors_df, top_n=10)

4. A股专用回测

from src.core.a_stock_backtester import AStockBacktestEngine

backtester = AStockBacktestEngine(
    initial_capital=1000000,
    commission=0.0003,
    slippage=0.0001,
    stamp_tax=0.001
)

result = backtester.run(
    symbols=universe[:20],
    start_date="20230101",
    end_date="20231231",
    strategy_type="multi_factor"
)

print(result.metrics)

5. 模拟交易

from src.trading.simulation import SimulationEngine, SimulationMode

engine = SimulationEngine(
    initial_capital=100000,
    mode=SimulationMode.REALTIME_SIM
)
engine.set_strategy(MyStrategy())
engine.start()

6. 实盘交易

from src.trading.live_trading import LiveTradingEngine, BrokerType

engine = LiveTradingEngine(broker_type=BrokerType.EASYTRADER)
engine.connect(user="your_user", password="your_password")
engine.set_strategy(MyStrategy())
engine.start()

7. 风险管理

from src.risk.risk_management import RiskManager

risk_manager = RiskManager(
    max_position_size_pct=0.1,
    max_drawdown_pct=0.1,
    max_daily_loss_pct=0.05
)

result = risk_manager.check_all(
    order_value=150000,
    portfolio_value=1000000
)

🔧 A股市场配置

交易规则

最小交易单位: 100股（1手）
涨跌幅限制: ±10%（ST股±5%）
T+1交易: 当日买入，次日卖出
印花税: 卖出时收取0.1%
佣金: 双向收取，约0.03%-0.05%
最低佣金: 5元/笔

交易时间

上午: 09:30 - 11:30
下午: 13:00 - 15:00
集合竞价: 09:15 - 09:25

Name		Name	Last commit message	Last commit date
Latest commit History 9 Commits
deployment/postgres		deployment/postgres
src		src
tests/backtesting		tests/backtesting
.env.example		.env.example
.gitignore		.gitignore
CONTRIBUTING.md		CONTRIBUTING.md
Dockerfile.api		Dockerfile.api
Dockerfile.slim		Dockerfile.slim
Dockerfile.web		Dockerfile.web
LICENSE		LICENSE
README.md		README.md
docker-compose.slim.yml		docker-compose.slim.yml
docker-compose.yml		docker-compose.yml
pyproject.toml		pyproject.toml
requirements.slim.txt		requirements.slim.txt
requirements.txt		requirements.txt
setup.py		setup.py
setup_github.sh		setup_github.sh
start.bat		start.bat
verify_project.py		verify_project.py

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OxQuant - Next-Generation AI Quantitative Trading Platform

🚀 Overview

✨ Features

📊 Core Engine Enhancements

Order Management

Position Management

Portfolio Management

Risk Management

Strategy Framework

🏗️ Architecture

🚀 Quick Start

Prerequisites

一键启动（推荐）

部署方式对比

手动安装

Access Services

📁 Project Structure

🛠️ Technology Stack

📈 Available Strategies

Example: Run Multiple Strategies

🌐 API Endpoints

Authentication

Strategies

Backtesting

Portfolio

Market Data

🔧 Development

Running Tests

Code Formatting

Database Migrations

🤝 Contributing

📄 License

📞 Support

🚀 Roadmap

📊 Performance Metrics

📈 A股市场交易完整链路

1. 数据获取

2. 因子挖掘（基于QLib框架）

3. 多因子模型

4. A股专用回测

5. 模拟交易

6. 实盘交易

7. 风险管理

🔧 A股市场配置

交易规则

交易时间

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