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    Algorithmic Backtest Overfitting & Alpha Sentry

    1

    Quantitative backtest overfitting auditor. Evaluates trading models using Deflated Sharpe Ratio (DSR), Monte Carlo stress testing, and code leakage detection.

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    Algorithmic Backtest Overfitting & Alpha Sentry

    Algorithmic Backtest Overfitting & Alpha Sentry

    Example session with this skill installed

    Audit our momentum strategy: Nominal Sharpe is 2.42 across 320 parameter iterations over 18 months, with return skewness -1.42 and kurtosis 6.84. Check code for look-ahead leaks.

    • Read your context and instructions
    • Compiled the algorithmic backtest overfitting

    Quantitative Alpha Audit Dossier revealing Deflated Sharpe Ratio of 0.314 (68.6% probability of overfitting) and identifying bar-close execution timing leakage on line 49.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Algorithmic Backtest Overfitting & Alpha Sentry

    The Algorithmic Backtest Overfitting & Alpha Sentry is an institutional-grade quantitative validation engine designed to audit algorithmic trading strategies, differentiate true statistical alpha from curve-fitted noise, and prevent live capital allocations to overfitted backtests.

    Key Capabilities

    • Deflated Sharpe Ratio (DSR) Calculation: Adjusts nominal backtest Sharpe ratios for non-normality (skewness, kurtosis) and multiple hypothesis testing ($N$ trials).
    • White's Reality Check & Data Snooping Defense: Calculates expected maximum performance under the null hypothesis of pure random chance.
    • 5,000-Path Monte Carlo Stress Testing: Simulates stationarity-preserving synthetic market regimes to determine true 95th/99th percentile worst-case drawdowns.
    • Automated Data Leakage Static Audit: Scans Python/Pandas backtesting scripts for forward-lookahead leaks, execution timing mismatches, and global normalization biases.
    • Survivorship Bias Detection: Verifies whether strategy universe uses point-in-time constituent lists.
    • 100% Offline & Private: Evaluates proprietary trading models, algorithms, and backtest CSV data strictly in local memory with zero external transmission.

    Find more skills like this on the Claude Skills Marketplace, the Codex Skills Marketplace and the Gemini Skills Marketplace, or browse All Claude Skills.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

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    2. 2

      Sign in to Agensi

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    3. 3

      Ask for what you need

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    100% offline statistical validation. Zero external network requests or proprietary strategy leaks.

    Listed7 days ago

    What's inside

    Frequently Asked Questions

    Yes. The engine computes Marcos López de Prado's Deflated Sharpe Ratio (DSR) to calculate the exact mathematical probability that a backtest is overfitted noise.

    It inspects Python, Pandas, VectorBT, Backtrader, QuantConnect, and custom backtest code for look-ahead bias, timing mismatches, and future leakage.

    Never. The audit runs 100% locally on your machine. Your proprietary trading algorithms, parameters, and weights are never sent to external servers.