fin-guru-quant-analysis
GitHub提供机构级量化分析工作流,涵盖风险、动量、波动率、相关性、因子分析及组合优化。通过CLI执行统计建模与回测,支持VaR/Sharpe等指标计算及Black-Litterman等策略优化。
Trigger Scenarios
Install
npx skills add AojdevStudio/Finance-Guru --skill fin-guru-quant-analysis -g -y
SKILL.md
Frontmatter
{
"name": "fin-guru-quant-analysis",
"description": "Perform quantitative analysis of returns, correlations, risk factors, and portfolio optimization. Statistical modeling with institutional-grade rigor."
}
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
- Plan — Define statistical modeling objectives, metrics, and assumptions
- Data Validation — Use
data_validator_cli.pyfor statistical validity (outliers, gaps, splits) - Risk Metrics — Use
risk_metrics_cli.pyfor VaR/CVaR/Sharpe/Sortino/Drawdown (minimum 90 days) - Momentum Analysis — Use
momentum_cli.pyfor confluence analysis - Volatility Metrics — Use
volatility_cli.pyfor regime analysis - Correlation Analysis — Use
correlation_cli.pyfor diversification and covariance matrices - Factor Analysis — Use
factors_cli.pyfor Fama-French 3-factor, Carhart 4-factor models - Strategy Validation — Use
backtester_cli.pywith transaction costs and realistic slippage - Portfolio Optimization — Use
optimizer_cli.pyfor 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
# 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
Version History
-
ac43b09
Current 2026-09-23 02:03
新增付费MCP能力探测流程以规范数据源依赖;将CLI命令统一重构为模块运行模式(python -m),并更新文档以适配新的实例目录结构。
- d13f5ab 2026-08-20 11:51


