wshobson/agents

backtesting-frameworks

Build robust backtesting systems for trading strategies with proper handling of look-ahead bias, survivorship bias, and transaction costs.

Vedi sorgente

Come usare

  1. Copia il comando e controlla il pacchetto prima di installarlo nel tuo ambiente agente.
  2. Controlla strumenti, credenziali e ambienti richiesti nel documento originale.
  3. Descrivi input e risultato desiderato; confronta l’output con le fonti.
npx skills add https://github.com/wshobson/agents --skill backtesting-frameworks

Prima di iniziare

  • L’installazione non include abbonamenti, quote API o accesso ai file.
  • Stelle e installazioni indicano attività, non un test indipendente di qualità o sicurezza.
Documento Skill originale

Contenuto dal repository con titoli, esempi, codice, tabelle, link e immagini preservati.

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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dallo stesso repository

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