How does this skill help avoid backtesting bias?

It provides specific mitigation strategies and code patterns for common errors like look-ahead bias (using future data accidentally) and survivorship bias (ignoring delisted assets).

What metrics are included in the performance evaluation?

The framework covers essential quantitative metrics including Sharpe Ratio, Maximum Drawdown, Annualized Return, Volatility, and Win Rate.

Can I implement realistic trading costs with this skill?

Absolutely. The implementation patterns include dedicated models for commissions, slippage, and bid-ask spreads to ensure backtest results reflect real-world trading.

Does it support both event-driven and vectorized backtesting?

Yes, the skill includes implementation patterns for both high-fidelity event-driven systems for execution simulation and high-speed vectorized analysis for rapid testing.

Quantitative Backtesting Frameworks

Name: Quantitative Backtesting Frameworks
Author: drgaciw

bydrgaciw

数据科学与机器学习

Builds robust, production-grade backtesting systems for trading strategies while mitigating common biases and handling transaction costs.

关于

This skill provides comprehensive guidance and implementation patterns for developing reliable backtesting infrastructure for quantitative trading. It helps developers avoid critical pitfalls like look-ahead and survivorship bias while implementing professional features such as event-driven architectures, vectorized performance testing, and walk-forward optimization. By providing standardized structures for data handling and performance evaluation, it ensures that trading strategies are validated against realistic market conditions with statistically sound methodologies before live deployment.

主要功能

Event-driven backtesting patterns for high-fidelity strategy simulation
Vectorized backtesting implementations for rapid strategy iteration
Mitigation strategies for look-ahead, survivorship, and selection biases
Realistic transaction cost modeling including slippage and commissions
0 GitHub stars
Walk-forward optimization and out-of-sample validation frameworks

使用场景

Validating algorithmic trading strategies against historical market data
Building custom, modular backtesting engines for proprietary trading
Optimizing strategy parameters using statistically sound validation methods

关于

主要功能

Event-driven backtesting patterns for high-fidelity strategy simulation
Vectorized backtesting implementations for rapid strategy iteration
Mitigation strategies for look-ahead, survivorship, and selection biases
Realistic transaction cost modeling including slippage and commissions
0 GitHub stars
Walk-forward optimization and out-of-sample validation frameworks

使用场景

Validating algorithmic trading strategies against historical market data
Building custom, modular backtesting engines for proprietary trading
Optimizing strategy parameters using statistically sound validation methods