Walk-forward testing in plain terms

[Shadow Trader]

Optimising a strategy on all available history and then trading it live asks for disappointment. Walk-forward testing offers a more realistic check.

The method splits history into segments. The strategy is optimised on the first segment, then tested on the next segment without changes. The process then slides forward: optimise on a new window, test on the following one, and so on.

The combined out-of-sample results show how the optimisation process performs when facing the unknown, which resembles real trading far better than a single in-sample fit.

If out-of-sample performance is much worse than in-sample, overfitting is likely.

Several platforms support it, and basic versions can be done manually by dividing data into chunks.

It takes more effort than a simple backtest, but it screens out a large share of strategies that look brilliant only because of curve fitting.

Has walk-forward testing been used here, and how did the results compare with the single-pass backtest?