How do you backtest a binary signal strategy?

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Backtesting a binary signal strategy means testing the trigger rule, holding logic, re-entry policy, and trading costs together. If you only measure whether the signal would have been directionally right, you usually miss the real strategy behavior. Start from the decision rule: A binary signal is not just a label like buy or do not buy. It is a full decision policy: what threshold fires the trade, how long the position stays on, what cancels it, and whether the strategy can re-enter immediately after an exit. Why binary systems are easy to flatter: Binary signals often look cleaner than continuous ones because they hide everything below the threshold. That can make the historical curve look disciplined while quietly pushing all the sensitivity into one overfit trigger level. What the backtest has to include: A real binary-signal backtest needs both model and policy details. Otherwise you are only testing whether a score sometimes lines up with history, not whether the strategy is tradeable. What Alphora's framing suggests: Alphora's public research loop already pushes toward point-in-time evidence, verified semantics, and forward validation. Binary signal strategies need exactly that discipline, because a tiny policy change around thresholding or holding can completely rewrite the curve.