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backtesting-frameworks

8.7

by wshobson

100Favorites
394Upvotes
0Downvotes

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.

backtesting

8.7

Rating

0

Installs

Data & Analytics

Category

Quick Review

Excellent backtesting skill with comprehensive, production-ready implementations. Provides four distinct patterns (event-driven, vectorized, walk-forward, Monte Carlo) with complete working code that handles critical biases and edge cases. The description clearly articulates when to use the skill, and the content delivers deep domain knowledge including proper train/test/validation splits, transaction costs, slippage modeling, and robust performance metrics. Structure is logical with clear progression from concepts to patterns to best practices. The skill is highly novel—building bias-free backtesting infrastructure with walk-forward optimization and Monte Carlo analysis would require significant token expenditure and specialized knowledge from a CLI agent alone. Minor room for improvement: could add a quick-start example at the top and perhaps reference additional files for extensions (though current single-file approach works well given scope). Overall, this is a high-quality, immediately actionable skill that meaningfully reduces complexity for financial strategy validation.

LLM Signals

Description coverage9
Task knowledge10
Structure9
Novelty8

GitHub Signals

26,432
2,921
268
15
Last commit 3 days ago

Publisher

wshobson

wshobson

Skill Author

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