Backtests assume every order fills. In live trading, some are rejected, and the rejections aren't random. They happen more when price is moving in the direction of the order, which is exactly when the trade would have been profitable.
Modeling this requires a rule: if price moves in the order's favor by more than a threshold during the delay, the order is rejected with some probability. A simple version rejects orders when the market has moved 0.3 pips in the trade's favor during the round trip.
The effect on results is a reduction in the best trades. The winners that depend on speed disappear. The losers, which often come from entries into a market that is moving against the order, still fill.
That skews the distribution toward worse outcomes. A strategy with a profit factor of 1.6 in an idealized test might fall to 1.2 under realistic rejection.
Calibrating the rule requires live data: the fraction of orders rejected and the conditions under which it happens. Logs from a few weeks of live or small-size trading provide enough.
An edge that survives this test is far more likely to survive the real thing.
What fraction of orders gets rejected on the live account, and does it correlate with price movement?
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Simulating last-look rejections
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