Backtesting Without Cheating: Bitemporal Joins, As-Of Correctness, and the Sahm Rule

📰 Medium · Python

Learn to avoid look-ahead bias in backtesting using bitemporal joins and as-of correctness

intermediate Published 20 Jun 2026

Action Steps

Identify potential sources of look-ahead bias in your backtesting pipeline
Apply bitemporal joins to ensure as-of correctness in your data
Use the Sahm Rule to measure and mitigate look-ahead bias
Implement a backtesting framework that incorporates these techniques to prevent cheating
Test and validate your backtesting results using historical data

Who Needs to Know This

Quantitative analysts and data scientists on a team can benefit from this knowledge to ensure accurate backtesting results, and it's relevant for developers working on trading or investment platforms

Key Insight

💡 Look-ahead bias can significantly impact backtesting results, and using bitemporal joins and as-of correctness can help prevent it

Key Takeaways

Learn to avoid look-ahead bias in backtesting using bitemporal joins and as-of correctness

Full Article

Most backtest had access to a number it shouldn’t have known. Here’s how that look-ahead bias gets in, how to measure it, and how to build… Continue reading on Medium »

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