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Use when developing trading algorithms, validating strategies, or building backtesting infrastructure.","ComeOnOliver\u002Fskillshub","ComeOnOliver","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F43215937?v=4","01M6E5JNFECJAAVFE0W7SMWWD2","Haoran Shu","01M6E5JNNDQS7AJZDYFQ8TPZCK",67,"MIT","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Faiskillstore\u002Fmarketplace\u002Fsickn33\u002Fbacktesting-frameworks\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32],"backtesting frameworks","frameworks","backtesting","infrastructure","use","look","trading","build","algorithms","transaction","strategies","bias","systems","with","2026-11-10 10:12:30",false,"---\nname: backtesting-frameworks\ndescription: Build robust backtesting systems for trading strategies with proper handling of look-ahead bias, survivorship bias, and transaction costs. Use when developing trading algorithms, validating strategies, or building backtesting infrastructure.\n---\n\n# Backtesting Frameworks\n\nBuild robust, production-grade backtesting systems that avoid common pitfalls and produce reliable strategy performance estimates.\n\n## Use this skill when\n\n- Developing trading strategy backtests\n- Building backtesting infrastructure\n- Validating strategy performance and robustness\n- Avoiding common backtesting biases\n- Implementing walk-forward analysis\n\n## Do not use this skill when\n\n- You need live trading execution or investment advice\n- Historical data quality is unknown or incomplete\n- The task is only a quick performance summary\n\n## Instructions\n\n- Define hypothesis, universe, timeframe, and evaluation criteria.\n- Build point-in-time data pipelines and realistic cost models.\n- Implement event-driven simulation and execution logic.\n- Use train\u002Fvalidation\u002Ftest splits and walk-forward testing.\n- If detailed 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