Quantitative Analysis Skill

Workflow Steps

1. Plan — Define statistical modeling objectives, metrics, and assumptions
2. Data Validation — Use

   data_validator_cli.py

   for statistical validity (outliers, gaps, splits)
3. Risk Metrics — Use

   risk_metrics_cli.py

   for VaR/CVaR/Sharpe/Sortino/Drawdown (minimum 90 days)
4. Momentum Analysis — Use

   momentum_cli.py

   for confluence analysis
5. Volatility Metrics — Use

   volatility_cli.py

   for regime analysis
6. Correlation Analysis — Use

   correlation_cli.py

   for diversification and covariance matrices
7. Factor Analysis — Use

   factors_cli.py

   for Fama-French 3-factor, Carhart 4-factor models
8. Strategy Validation — Use

   backtester_cli.py

   with transaction costs and realistic slippage
9. 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