PARTIAL · weak real edge
Hypothesis
The internal scoring function of a legacy strategy (V14 QUANT, stored in 14.6M signalsnapshots) may be overfit. Higher quality scores may not correlate positively with realized P&L.
Method
Random sample 5000 snapshots. For each: fetch forward 1m klines, simulate entry at the snapshot's own entryTop/Bot zone, walk forward bar-by-bar checking SL/TP/timeout. Compute realized pnlPct per trade. Then rank-correlate features vs outcome.
Results — baseline n=3396 entered trades
| Baseline WR | 53.1% |
| Baseline avgPnl per trade (gross) | +0.305% |
| Low quality (Q1, < threshold) | WR 60.1%, avg +0.521% |
| High quality (Q5) | WR 50.6%, avg +0.273% |
| timing="MISSED" subset | WR 63.5%, avg +1.048% |
| Walk-forward decay TRAIN→TEST | −18% per month → −5% per month |
Math — counterintuitive correlation
$$ \text{Corr}(\text{quality}, r_{\text{realized}}) = -0.12 \quad (p < 0.01) $$
Real but small. Inverting the strategy's own scoring captures a +3-6%/month edge at 5× leverage. Strong decay TRAIN→TEST suggests the edge is regime-dependent. Worth deploying as overlay filter, not standalone.
Even "your own" scoring system can be wrong. Test the inverse. Overfit detection often hides in plain sight.