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Walk Forward Optimization is an optimization process that addresses the issue of curve fitting in strategy development. Walk Forward Optimization segregates the data series into multiple segments, and each segment is divided into an in-sample (IS) portion and an out-of-sample (OOS) portion.
Parameter optimization for the strategy is performed using the IS portion of the first segment. The same parameters are then used to back test the strategy on the OOS portion of the same segment. The process is repeated for the remaining segments.
The OOS performance results from each of the segments are considered "real" instead of "curve-fit" because the parameters that produced the OOS results were generated from IS data.
<br>== Walk Forward Optimization (not anchored) ==
Suppose a data series has 220 bars, and it is divided into five segments of 100 bars per segment.
!width="25%"|OOS
|-
|Segment 1
|align="center"|1-70
|align="center"|71-100
|align="center"|100
|-
|Segment 2
|align="center"|31-100
|align="center"|101-130
|align="center"|100
|-
|Segment 3
|align="center"|61-130
|align="center"|131-160
|align="center"|100
|-
|Segment 4
|align="center"|91-160
|align="center"|161-190
|align="center"|100
|-
|Segment 5
|align="center"|121-190
|align="center"|191-220
!width="25%"|OOS
|-
|Segment 1
|align="center"|1-70
|align="center"|71-100
|align="center"|100
|-
|Segment 2
|align="center"|1-100
|align="center"|101-130
|align="center"|130
|-
|Segment 3
|align="center"|1-130
|align="center"|131-160
|align="center"|160
|-
|Segment 4
|align="center"|1-160
|align="center"|161-190
|align="center"|190
|-
|Segment 5
|align="center"|1-190
|align="center"|191-220
