Quantitative Analysis Skill

Execute structured quantitative analysis workflows with statistical validation.

Capability probe

Before collecting external fundamentals or filings, follow the shared paid MCP capability probe. This workflow wants financial-datasets for normalized statements and filing data. If it is absent, state whether primary-source WebSearch can support the requested model with extra validation; otherwise stop and name the missing MCP and setup action.

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