Quant Research Platform
Advanced quantitative research platform for professional quant developers and researchers.
Features
1. Multi-Factor Research
- 100+ Alpha Factors: Technical, fundamental, sentiment, alternative data
- Factor Mining: Automated factor discovery and evaluation
- Factor Analysis: IC, IR, IC decay, turnover analysis
- Factor Combination: Intelligent factor weighting and combination
2. Backtesting Engine
- Historical Backtest: Full historical simulation
- Walk-Forward: Out-of-sample validation
- Monte Carlo: Probabilistic performance estimation
- Transaction Cost: Realistic cost modeling
3. Portfolio Optimization
- Mean-Variance: Classic Markowitz optimization
- Risk Parity: Equal risk contribution
- Black-Litterman: Bayesian prior integration
- ACL: Academic factor model optimization
- Kelly Criterion: Optimal leverage calculation
4. Risk Management
- VaR/CVaR: Value at Risk analysis
- Stress Testing: Scenario-based analysis
- Factor Exposure: Style exposure monitoring
- Drawdown Control: Dynamic position sizing
5. Strategy Development
- Momentum Strategies: Trend following, breakout
- Mean Reversion: Statistical arbitrage
- Statistical Models: Pairs trading, cointegration
- ML Strategies: XGBoost, LightGBM, LSTM
Installation
pip install pandas numpy scikit-learn xgboost lightgbm scipy statsmodels
pip install akshare tushare
Usage
Factor Research
from quant_research import FactorResearch
research = FactorResearch()
# Add factors
research.add_factor('momentum_20d', compute_momentum_20d)
research.add_factor('volatility_60d', compute_volatility_60d)
research.add_factor('roe', compute_roe)
# Analyze factor performance
ic_analysis = research.analyze_ic(
factors=['momentum_20d', 'volatility_60d'],
lookback=252
)
print(f"IC: {ic_analysis['ic_mean']:.3f}")
print(f"IR: {ic_analysis['ir']:.3f}")
Backtest
from quant_research import BacktestEngine
bt = BacktestEngine(
start_date='2020-01-01',
end_date='2024-12-31',
initial_capital=1000000
)
# Add strategy
bt.add_strategy(MomentumStrategy(n=20, holding_period=60))
# Run backtest
results = bt.run()
print(f"Annual Return: {results['annual_return']:.2%}")
print(f"Sharpe Ratio: {results['sharpe_ratio']:.2f}")
print(f"Max Drawdown: {results['max_drawdown']:.2%}")
Portfolio Optimization
from quant_research import PortfolioOptimizer
opt = PortfolioOptimizer(returns_df)
# Mean-variance optimization
weights = opt.mean_variance(target_return=0.15)
# Risk parity
weights = opt.risk_parity()
# Black-Litterman
weights = opt.black_litterman(market_cap_weights, views)
Factor Library
Technical Factors
| Factor | Description |
|---|
returns_5d/20d/60d | Cumulative returns |
volatility_20d/60d | Rolling volatility |
momentum_20d/60d | Price momentum |
volume_ratio | Relative volume |
turnover_rate | Turnover rate |
rsi_14d | RSI indicator |
Fundamental Factors
| Factor | Description |
|---|
pe_ttm | P/E ratio |
pb | P/B ratio |
roe | Return on equity |
roa | Return on assets |
debt_ratio | Debt to assets |
gross_margin | Gross margin |
Sentiment Factors
| Factor | Description |
|---|
news_sentiment | News sentiment score |
social_buzz | Social media mentions |
analyst_rating | Analyst consensus |
API Reference
FactorResearch
| Method | Description |
|---|
add_factor(name, func) | Add custom factor |
analyze_ic(factors) | IC/IR analysis |
factor_correlation() | Correlation matrix |
optimal_weights() | Factor combination |
BacktestEngine
| Method | Description |
|---|
add_strategy(strategy) | Add trading strategy |
run() | Execute backtest |
get_metrics() | Performance metrics |
get_trades() | Trade log |
PortfolioOptimizer
| Method | Description |
|---|
mean_variance() | Markowitz optimization |
risk_parity() | Risk parity weights |
black_litterman() | Bayesian optimization |
kelly() | Kelly criterion |
Performance Metrics
- Annual Return
- Sharpe Ratio
- Sortino Ratio
- Calmar Ratio
- Max Drawdown
- Win Rate
- Profit Factor
- Trade Count
Use Cases
- Factor Discovery: Find new alpha factors
- Strategy Development: Build and test strategies
- Portfolio Construction: Optimize asset allocation
- Risk Management: Monitor and control risk
- Research Automation: Systematic research workflow
Advanced Features
Machine Learning Integration
from quant_research.ml import FactorModel
model = FactorModel(algorithm='xgboost')
model.train(factors, returns)
model.predict()
# Feature importance
importance = model.feature_importance()
Alternative Data
from quant_research import AlternativeData
alt = AlternativeData()
# Satellite imagery
sat = alt.satellite_data(company_name)
# Web traffic
traffic = alt.web_traffic(url)
# Supply chain
supply = alt.supply_chain(company_name)
Links
