Quantitative Analysis Skill
Execute structured quantitative analysis workflows with statistical validation.
Workflow Steps
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Plan — Define statistical modeling objectives, metrics, and assumptions
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Data Validation — Use data_validator_cli.py for statistical validity (outliers, gaps, splits)
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Risk Metrics — Use risk_metrics_cli.py for VaR/CVaR/Sharpe/Sortino/Drawdown (minimum 90 days)
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Momentum Analysis — Use momentum_cli.py for confluence analysis
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Volatility Metrics — Use volatility_cli.py for regime analysis
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Correlation Analysis — Use correlation_cli.py for diversification and covariance matrices
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Factor Analysis — Use factors_cli.py for Fama-French 3-factor, Carhart 4-factor models
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Strategy Validation — Use backtester_cli.py with transaction costs and realistic slippage
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Portfolio Optimization — Use optimizer_cli.py for mean-variance, risk parity, max Sharpe, Black-Litterman
CLI Commands
Risk metrics
uv run python src/analysis/risk_metrics_cli.py TICKER --days 252 --benchmark SPY
Momentum confluence
uv run python src/utils/momentum_cli.py TICKER --days 90
Volatility regime
uv run python src/utils/volatility_cli.py TICKER --days 90
Correlation matrix
uv run python src/analysis/correlation_cli.py TICKER1 TICKER2 --days 90
Factor analysis
uv run python src/analysis/factors_cli.py TICKER --days 252 --benchmark SPY
Backtesting
uv run python src/strategies/backtester_cli.py TICKER --days 252 --strategy rsi
Portfolio optimization
uv run python src/strategies/optimizer_cli.py TICKERS --days 252 --method max_sharpe
Requirements
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Start with clear statistical plan and obtain consent before execution
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Validate all assumptions against compliance policies
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Apply robust methods with proper confidence intervals
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All market data must be timestamped and verified against current date
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Minimum 90 days of data for robust statistics