Quantitative Analysis Skill

Execute structured quantitative analysis workflows with statistical validation.

Workflow Steps

• Plan — Define statistical modeling objectives, metrics, and assumptions

• Data Validation — Use data_validator_cli.py for statistical validity (outliers, gaps, splits)

• Risk Metrics — Use risk_metrics_cli.py for VaR/CVaR/Sharpe/Sortino/Drawdown (minimum 90 days)

• Momentum Analysis — Use momentum_cli.py for confluence analysis

• Volatility Metrics — Use volatility_cli.py for regime analysis

• Correlation Analysis — Use correlation_cli.py for diversification and covariance matrices

• Factor Analysis — Use factors_cli.py for Fama-French 3-factor, Carhart 4-factor models

• Strategy Validation — Use backtester_cli.py with transaction costs and realistic slippage

• 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

• Start with clear statistical plan and obtain consent before execution

• Validate all assumptions against compliance policies

• Apply robust methods with proper confidence intervals

• All market data must be timestamped and verified against current date

• Minimum 90 days of data for robust statistics