# Point-in-Time Articoli collegati

Your Backtest Is Lying: Why You Must Use Point-in-Time Data

This article warns against a common pitfall in backtesting trading strategies: look-ahead bias caused by using revised historical data. It illustrates this with a hypothetical Bitcoin strategy based on exchange outflows from Binance. The strategy is built on the premise that sustained outflows (when the 5-day moving average of BTC balance falls below the 14-day average) are bullish, while inflows signal a sell-off. An initial backtest using standard, revised data shows the strategy performing comparably to a simple buy-and-hold approach. However, the author argues these results are misleading because the data has been updated with information that wasn't available in real-time. This data mutation creates an unfair advantage in the backtest. To demonstrate, the test is rerun using Point-in-Time (PiT) data—an immutable, append-only record that reflects only what was known on any given day. The results are significantly worse, as the PiT-based strategy misses key profitable moves. The key takeaway is that accurate backtesting requires immutable Point-in-Time data to avoid look-ahead bias and replay history honestly.

insights.glassnode03/13 14:12