What this shows: for every setup that has ever produced an FF (original Fail + recovery Fail), the next N filled outcomes for that same setup are aggregated.
Each FF event is treated independently. NoFill / Expired rows roll forward (the model didn't fire, so no trade).
Score = (Post-FF WR − Baseline WR) × √Occurrences — penalises tiny samples.
Live Near-FF Alerts
0 activeRP Live failed + recovery MAE ≥ the rider trigger % of its own SL.Trigger %:Risk $/acct:FF Rider:
Min Score:Min FF:Max Riders:Loss Cap $:
Polling…
FF Rider Outcomes
—Real fills and P&L of rider brackets — measured, not simulated.
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Models with FF
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Total FF Events
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Total Occurrences
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Avg Lift (sample-weighted)
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Model
Dir
FF
Occ (W/L)
Post-FF WR
Baseline WR
Lift
Cum R
Avg R
Avg MFE
Avg MAE
Score
Cum-R curve (1→N)
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