Walk-Forward Analysis
Walk-forward analysis is a backtesting methodology where the strategy is repeatedly optimised on an in-sample window, tested on the immediately following out-of-sample window, and the windows are rolled forward through history. The aggregate of all out-of-sample results is the realistic performance estimate.
How it works
- Split history into many overlapping windows (e.g. 2 years training + 6 months test).
- Optimise parameters on the training window.
- Apply those parameters to the test window — record the result.
- Roll forward 6 months, repeat.
- The concatenation of all test windows is the walk-forward equity curve.
Why it matters
A normal backtest is a one-shot optimisation — you only know how the strategy did with hindsight-tuned parameters. Walk-forward simulates the realistic process: you didn't know the future when you set the parameters.
What to look for
- Walk-forward efficiency (WFE) — out-of-sample return / in-sample return. WFE > 60% is acceptable; > 80% is excellent.
- Stability of parameters across windows — if optimal length jumps from 9 to 47 every six months, the strategy isn't robust.
Related Terms
Backtesting
Backtesting runs a strategy on historical data to estimate how it would have performed before risking real money.
Overfitting
Overfitting is when a strategy is tuned so tightly to historical data that it fails on live markets. The #1 killer of backtested strategies.
Sharpe Ratio
The Sharpe ratio measures excess return per unit of volatility — the most-cited risk-adjusted performance metric in finance.
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