The problems:
- Noise dominates. In a three-month out-of-sample window, the difference between a real edge of 0.05R and zero can easily be hidden by variance.
- Cost assumptions decide the result. A change of 0.1 pip in assumed spread can flip a window from positive to negative.
- Parameter stability matters more than performance. If optimal parameters jump around from window to window, that tells you more than the P&L does.
- Long in-sample windows (12 months) and shorter out-of-sample (3 months), rolled forward.
- Costs set slightly worse than my live average.
- I track how often the out-of-sample result is positive, not the total.
- I look at the parameter surface: a smooth plateau is good, a single sharp peak is a warning.
How do others judge tiny-edge systems in walk-forward? Any statistical tests you find useful for this?