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backtesting-frameworks

Design backtests that evaluate trading strategies against historical data. Account for trading costs, avoid using future information or excluding delisted securities, and compare strategies using separate development and evaluation periods.

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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.

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适用场景

  • Build a backtesting system for trading strategies
  • Check for bias in historical performance estimates
  • Compare strategies after accounting for trading costs
  • Evaluate strategies across rolling time periods

包含的文件(2)

  • SKILL.md3.4 KB
  • references/details.md17.8 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Backtesting Frameworks

Build robust, production-grade backtesting systems that avoid common pitfalls and produce reliable strategy performance estimates.

When to Use This Skill

  • Developing trading strategy backtests
  • Building backtesting infrastructure
  • Validating strategy performance
  • Avoiding common backtesting biases
  • Implementing walk-forward analysis
  • Comparing strategy alternatives

Core Concepts

1. Backtesting Biases

BiasDescriptionMitigation
Look-aheadUsing future informationPoint-in-time data
SurvivorshipOnly testing on survivorsUse delisted securities
OverfittingCurve-fitting to historyOut-of-sample testing
SelectionCherry-picking strategiesPre-registration
TransactionIgnoring trading costsRealistic cost models

2. Proper Backtest Structure

Historical Data
      │
      ▼
┌─────────────────────────────────────────┐
│              Training Set               │
│  (Strategy Development & Optimization)  │
└─────────────────────────────────────────┘
      │
      ▼
┌─────────────────────────────────────────┐
│             Validation Set              │
│  (Parameter Selection, No Peeking)      │
└─────────────────────────────────────────┘
      │
      ▼
┌─────────────────────────────────────────┐
│               Test Set                  │
│  (Final Performance Evaluation)         │
└─────────────────────────────────────────┘

3. Walk-Forward Analysis

Window 1: [Train──────][Test]
Window 2:     [Train──────][Test]
Window 3:         [Train──────][Test]
Window 4:             [Train──────][Test]
                                     ─────▶ Time

Detailed worked examples and patterns

Detailed sections (starting with ## Implementation Patterns) live in references/details.md. Read that file when the navigation summary above is insufficient.

Best Practices

Do's

  • Use point-in-time data - Avoid look-ahead bias
  • Include transaction costs - Realistic estimates
  • Test out-of-sample - Always reserve data
  • Use walk-forward - Not just train/test
  • Monte Carlo analysis - Understand uncertainty

Don'ts

  • Don't overfit - Limit parameters
  • Don't ignore survivorship - Include delisted
  • Don't use adjusted data carelessly - Understand adjustments
  • Don't optimize on full history - Reserve test set
  • Don't ignore capacity - Market impact matters

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