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    leakage aware backtest auditor

    1

    Audit backtests for data leakage, verify point-in-time feature availability, and calculate cost-adjusted returns.

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    leakage aware backtest auditor

    leakage aware backtest auditor

    Example session with this skill installed

    Audit this backtest JSON for the period 2020-2024. Check for leakage in the sentiment feature and apply a 5bps cost deduction per rebalance. Data: input_2024_strat.json

    • Read your context and instructions
    • Compiled the leakage aware backtest
    • Generated the document

    Audit complete for strategy ID: STRAT-2024-001. I have processed the return series and verified feature availability timestamps against the provided data snapshot. Leakage was detected in the 'Sentiment_Score' feature during the 2021-Q2 window due to revision vintages appearing before publication dates.

    Backtest Audit Report

    MetricValueStatusNotes
    Audit Window2020-01-01 to 2024-01-01PASSChronological separation maintained.
    Leakage CheckSentiment_Score (2021-Q2)FAILPoint-in-time timestamp mismatch.
    Gross Return42.15%-Cumulative across period.
    Net Return36.82%-Adjusted for 5bps cost/rebalance.
    Max Drawdown-12.4%-Net compounded basis.
    Survivorship BiasIndeterminateWARNMissing delisting history in universe.

    Analysis

    The strategy shows high sensitivity to rebalance frequency. The 5bps cost deduction reduced the Sharpe ratio from 1.8 to 1.4. The leakage in the sentiment feature (v2.1) suggests the backtest used revised data not available until 48 hours post-event.

    Next steps

    1. Re-run the backtest using the Sentiment_Score_Lagged field to resolve the 2021-Q2 leakage.
    2. Provide universe membership history to clear the survivorship bias warning.
    3. Invoke the helper with the --output report.json flag to save the scored series for further walk-forward analysis.

    leakage-aware-backtest-auditor.pdf

    PDF · document

    Generated

    Example file from a real run - the skill writes it into your workspace.

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

    What you get

    Detect look-ahead bias by verifying point-in-time data availability.Calculate net returns using fractional period-cost deductions.Validate chronological train/test separation in walk-forward windows.Identify survivorship bias by auditing universe membership history.

    About this skill

    The problem

    Standard backtests often suffer from look-ahead bias and unrealistic cost assumptions. Most tools fail to account for the actual publication time of data, leading to inflated performance that vanishes in live trading.

    What it does

    • Audits feature availability by mapping every data point to its earliest possible publication timestamp.
    • Enforces strict chronological separation between training and test sets to prevent data leakage.
    • Calculates cost-adjusted returns by applying fractional deductions to period-specific performance.
    • Generates sensitivity tables across independent walk-forward windows and parameter variants.
    • Identifies missing evidence of survivorship bias and corporate-action handling.

    Frameworks & tools

    Python 3 for execution, JSON for data contracts, and local CLI scripts for analysis.

    Why this beats prompting it yourself

    General LLMs struggle with the rigorous, multi-step verification required to prove train/test isolation. This skill follows a specific audit protocol that treats source references as metadata rather than proof, forcing an evidence-based evaluation of strategy robustness.

    Use cases

    • Auditing a new quantitative strategy before committing capital.
    • Identifying look-ahead bias in third-party backtest reports.
    • Standardizing performance reporting across multiple walk-forward windows.
    • Stress-testing return series against varying slippage and commission tiers.

    Known limitations

    Does not execute raw strategy code or fetch market data. Requires pre-computed return series and data availability timestamps as input.

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
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      Download the ZIP

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      Unzip into your skills folder

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    3. 3

      Ask your agent to use it

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    Trust & safety

    Security scanned

    Verified clean 13 days ago

    • Passed all security checks, Safe to install

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    Alpaca

    Listed13 days ago

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