---
name: Quantitative Analysis
slug: quantitative-analysis
category: Data
description: Quantitative Analysis performs structured statistics on returns, correlations, factors, risk, and portfolio construction. Use it when you need validated market analysis, backtests, or optimization with enough data to support the model.
github: "https://github.com/AojdevStudio/Finance-Guru/tree/main/.claude/skills/fin-guru-quant-analysis"
language: Python
stars: 318
forks: 109
install: "npx degit https://github.com/AojdevStudio/Finance-Guru/tree/main/.claude/skills/fin-guru-quant-analysis ~/.claude/skills/fin-guru-quant-analysis"
installs_to: ~/.claude/skills/fin-guru-quant-analysis
source_path: .claude/skills/fin-guru-quant-analysis/SKILL.md
collection_size: 17
category_size: 710
collection_url: "https://dirskills.com/collections/AojdevStudio/Finance-Guru"
added: 2026-09-03T06:03:34.144Z
last_synced: 2026-09-03T06:03:34.144Z
canonical_url: "https://dirskills.com/skills/quantitative-analysis"
---

# Quantitative Analysis

Quantitative Analysis performs structured statistics on returns, correlations, factors, risk, and portfolio construction. Use it when you need validated market analysis, backtests, or optimization with enough data to support the model.

**Install:**

```bash
npx degit https://github.com/AojdevStudio/Finance-Guru/tree/main/.claude/skills/fin-guru-quant-analysis ~/.claude/skills/fin-guru-quant-analysis
```

## README

# 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](../_shared/PaidMcpCapabilityProbe.md)**. 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

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

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

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

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

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

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

# Portfolio optimization
uv run python -m src.strategies.optimizer_cli 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