What the test found
Grouping hundreds of detected setups by the ratio of target-distance to stop-distance, the pattern held in both data halves: the sub-ratio group, risking more than the structure offered, dragged expectancy negative; the healthy-ratio group carried the profits. No other single attribute we tested, including our own scoring, showed that consistency, an honest result we publish on the tools themselves via the reward-filter badge.
Why geometry beats cleverness
The filter works because it is arithmetic, not prediction: with roughly comparable hit rates, the trade paying less than it risks needs an outsized win rate that setups rarely deliver, the break-even math in action. Structural targets closer than structural stops usually mean the trade is late, the move's easy distance already spent, geometry diagnosing staleness better than any indicator.
Using it daily
Before any entry: measure honestly, entry to structural target versus entry to invalidation, using real levels, not hopes. Below one-to-one, skip without ceremony, whatever the pattern's beauty; the fix is usually patience for a better entry price, which repairs both numbers at once. It is the fastest pre-trade filter in existence, and per our own data, the most defensible.
FAQ
What ratio did your testing favour?
Setups with target-distance at least equal to stop-distance, with comfort beginning around one-to-one and above. Below that line, aggregate expectancy went negative.
Why did confluence scores fail validation?
Scores counted aligned conditions, but condition-count and outcome proved to be different things out-of-sample. We report scores as counts, never as probabilities, for exactly this reason.
Does the filter guarantee profitable trades?
No filter does. It removes a class of structurally disadvantaged trades; edge still depends on location, execution and discipline.
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