Which method of optimization involves testing on small subsequent portions of data?

Walk Forward optimization is a method that involves evaluating a trading strategy by testing it on small, subsequent portions of data, effectively using a rolling window approach. This technique allows for the real-time adjustment of the parameters of a trading strategy based on the most recent data, as well as assessing how a strategy performs across different segments of data that simulate live trading conditions.

The main advantage of Walk Forward optimization lies in its ability to reduce the risk of overfitting that can occur when a strategy is optimized on a single large dataset. By constantly testing the strategy on a smaller, "out-of-sample" segment of data after optimizing it on a previous segment, practitioners can better understand how the strategy might behave in future market conditions, enhancing its robustness and adaptability. This methodology is particularly valuable in environments that are constantly changing, as it reflects a trader’s ongoing adjustments based on new information.

The combination of optimizing and re-evaluating in smaller increments over time aligns well with the dynamic nature of the markets, making Walk Forward optimization a preferred choice among traders aiming for long-term success and consistent performance.