fin-guru-quant-analysis

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

CLI Commands

# Risk metrics
uv run python -m src.analysis.risk_metrics_cli TICKER --days 252 --benchmark SPY --output json

# Momentum confluence
uv run python -m src.utils.momentum_cli TICKER --days 90 --output json

# Volatility regime
uv run python -m src.utils.volatility_cli TICKER --days 90 --output json

# Correlation matrix
uv run python -m src.analysis.correlation_cli TICKER1 TICKER2 --days 90 --output json

# Factor analysis
uv run python -m src.analysis.factors_cli TICKER --days 252 --benchmark SPY --output json

# Backtesting
uv run python -m src.strategies.backtester_cli TICKER --days 252 --strategy rsi --output json

# Portfolio optimization
uv run python -m src.strategies.optimizer_cli TICKERS --days 252 --method max_sharpe --output json

Output

Return the fin-guru-output-contract skill. In the Numbers table, give each metric its lookback window, such as Sharpe (252d). When fewer than 90 days of data exist, return the Blocked block from that skill instead of a statistic.

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