Skill: jason-aka-chen/quant-research-platform (version 1.0.0)

--- name: quant-research-platform description: Advanced quantitative research platform for multi-factor analysis, factor mining, backtesting, and portfolio optimization. Includes 100+ alpha factors, IC/IR analysis, factor correlation, and intelligent factor combination. tags: - quant - trading - factor - research - backtest - portfolio version: 1.0.0 author: chenq --- # 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 ```bash pip install pandas numpy scikit-learn xgboost lightgbm scipy statsmodels pip install akshare tushare ``` ## Usage ### Factor Research ```python 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 ```python 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 ```python 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 ```python from quant_research.ml import FactorModel model = FactorModel(algorithm='xgboost') model.train(factors, returns) model.predict() # Feature importance importance = model.feature_importance() ``` ### Alternative Data ```python 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 - [QuantConnect](https://www.quantconnect.com) - [Zipline](https://zipline.io) - [Alphalens](https://quantopian.github.io/alphalens) - [TA-Lib](https://ta-lib.org)


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