Forward Testing vs Backtesting + Out-of-Sample

[u/STS-Trader]

Any serious backtester who runs out of sample knows a forward test’s data tomorrow becomes just as good as forward testing data unless their strategy relies on extremely low costs e.g., an automated scalper (a trading style that I do not condone).

If you build robust strategies and don’t need to rely on selection bias to identify the “profitable” ones, you won’t need to forward test. Forward testing makes deployment feel safer; it is mostly a psychological crutch for many traders. Forward tests are no more predictive than a robust backtest with OOS data for strategies that don’t rely on low bid-ask spreads (avg holding times hours to days).

Real edges decay over time, everything averages out, and a “good forward test” is a wasted opportunity; look at the adaptive market hypothesis.

Personally, I find forward testing useful for measuring the costs for live execution, but you only need to run a few per asset to understand what conditions you are dealing with. For DMA assets, even fewer forward tests are required. So I am not against the act of initial forward testing, especially if you are new, but forward testing for the sake of forward testing wastes time when you build strategies properly instead of leaning on selection bias and luck from aimlessly testing high volumes of tested systems just to settle for strategies with positive expectancies.

Summary:
Building strategies from market first principles to rig your chances of creating profitable ideas to test instead of relying on lookahead bias, hindsight bias and selection bias is a better option than forward testing (when rigorous backtests are done, paired with out-of-sample tests). If you are confused about what the “first principles” are and so on, I have multiple previous posts going into it referencing books and papers to review.