Rolling Profit Factor Windows for Tracking Strategy Stability
Summary
The article explains why a lifetime Profit Factor can hide changes in strategy performance and presents a framework for calculating it over rolling windows of completed trades. Each window contains a fixed number of consecutive trades; as it advances, the calculation removes the outgoing trade and adds the incoming one. Maintaining running gross-profit and gross-loss totals reduces the computation to a single pass. The framework also describes special handling for all-winning, all-losing, and zero-result windows, plus charting and summary components.
The discussion explains how to read the resulting series as evidence of changing local performance, while stressing that Profit Factor estimates historical edge rather than guaranteeing future results. Fixed trade counts keep sample sizes consistent, but the calendar span of each window varies, and overlapping windows are statistically dependent. The article provides design details for a modular MQL5 dashboard and a separate verification suite, but does not report a live strategy evaluation. It recommends using rolling Profit Factor with complementary measures such as expectancy and drawdown.
Key ideas
- A single lifetime Profit Factor hides the timing and clustering of a strategy’s gains and losses.
- Rolling windows with a fixed trade count show how local Profit Factor changes across completed trades.
- Updating running gross-profit and gross-loss totals makes rolling computation linear in the number of trades.
- Fixed trade-count windows vary in calendar duration, and neighboring overlapping windows are not independent.
- Profit Factor is a historical estimate of edge and should be considered alongside expectancy and drawdown.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.