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    quantitative-stock-analyzer

    by Zicheng Liao

    Professional-grade quantitative factor analysis, portfolio optimization, and statistical risk decomposition for stocks.

    Updated May 2026
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    One-time purchase

    $5

    · or 25 credits

    One-time purchase

    30-day refund guarantee

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    Included in download

    • Calculate Sharpe and Sortino ratios for a list of tickers over 5 years.
    • Identify high-momentum stocks with low beta relative to the S&P 500.
    • terminal automation included
    • Includes example output and usage patterns
    • Instant install

    See it in action

    A real example of what this skill takes in and produces.

    Sample output

    Optimized Portfolio (Max Sharpe):

    • AAPL: 25.0%
    • NVDA: 15.4%
    • JPM: 21.2%
    • XLV: 25.0%
    • Cash: 13.4%

    Metrics: Expected Return: 24.8% Volatility: 16.2% Sharpe Ratio: 1.41 VaR (95%): -2.1% Max Drawdown: -14.3% Most Correlated Pair: AAPL/MSFT (0.84)

    About This Skill

    Advanced Quantitative Stock and Portfolio Analysis

    Transform your AI agent into a digital quant desk. This skill provides the mathematical backbone for deep financial analysis, moving beyond simple price checks to rigorous statistical evaluation of stocks and portfolios.

    What it does

    The Quantitative Stock Analyzer implements industry-standard models to evaluate market assets. It automates complex calculations including momentum and volatility factor scoring, Fama-French style risk decomposition, and hierarchical correlation clustering. It doesn't just fetch data; it interprets the underlying statistical structure of your investments.

    Why use this skill

    Standard LLM prompts struggle with the precision required for portfolio optimization and risk metrics like VaR or CVaR. This skill solves that by providing specific algorithmic implementations for:

    • Portfolio Optimization: Solving for maximum Sharpe ratio or minimum variance using SLSQP optimization.
    • Risk Metrics: Calculating sophisticated indicators like Conditional Value at Risk and Maximum Drawdown.
    • Factor Analysis: Identifying stocks based on multi-factor models (Value, Quality, Momentum).
    • Correlation Insights: Detecting hidden dependencies between assets through clustering.

    Supported Tools

    Built on the robust Python scientific stack: Pandas for data manipulation, NumPy for linear algebra, SciPy for optimization, and yfinance for reliable market data ingestion.

    Use Cases

    • Calculate Sharpe and Sortino ratios for a list of tickers over 5 years.
    • Identify high-momentum stocks with low beta relative to the S&P 500.
    • Find the least correlated assets in a group to maximize diversification.
    • Generate an efficient frontier and suggest optimal portfolio weights.
    • Measure portfolio risk using Value at Risk (VaR) and Expected Shortfall.

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    Permissions

    Terminal / Shell

    Allowed Hosts

    mba.tuck.dartmouth.edu
    hq.sinajs.cn

    File Scopes

    quantitative-stock-analyzer/**

    Frequently Asked Questions

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