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#

FactorDescriptionreturns_5d/20d/60dCumulative returnsvolatility_20d/60dRolling volatilitymomentum_20d/60dPrice momentumvolume_ratioRelative volumeturnover_rateTurnover ratersi_14dRSI indicator

Fundamental Factors#

FactorDescriptionpe_ttmP/E ratiopbP/B ratioroeReturn on equityroaReturn on assetsdebt_ratioDebt to assetsgross_marginGross margin

Sentiment Factors#

FactorDescriptionnews_sentimentNews sentiment scoresocial_buzzSocial media mentionsanalyst_ratingAnalyst consensus

API Reference#

FactorResearch#

MethodDescriptionadd_factor(name, func)Add custom factoranalyze_ic(factors)IC/IR analysisfactor_correlation()Correlation matrixoptimal_weights()Factor combination

BacktestEngine#

MethodDescriptionadd_strategy(strategy)Add trading strategyrun()Execute backtestget_metrics()Performance metricsget_trades()Trade log

PortfolioOptimizer#

MethodDescriptionmean_variance()Markowitz optimizationrisk_parity()Risk parity weightsblack_litterman()Bayesian optimizationkelly()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
• Zipline
• Alphalens
• TA-Lib