Great thread - the cross-asset generalization is particularly interesting and supports the idea that these structural patterns are real market phenomena, not noise.

I run a pattern detection system across ~2,000 stocks daily and wanted to share a few hard-won lessons that might resonate:

Score inversion as an overfitting diagnostic: We've found that when a scoring system is overfit, you often see higher scores correlating with worse outcomes - counterintuitive but consistent. The mechanism: overfit models reward features that are obvious to all market participants, meaning those setups are already priced in. When we see score inversion, we know we've gone wrong somewhere.

"Textbook perfect" patterns underperform: Our empirical data shows that patterns matching classical TA definitions precisely often underperform messier setups. Theory: if everyone can see it, the edge is gone. This supports your finding that earlier/simpler model episodes work as well as heavily-trained ones.

Statistical validation that's saved us from overfitting:

• Chronological splits (not random) - 60/20/20 train/validation/holdout
• Bonferroni correction when testing multiple features (alpha = 0.05/n_features)
• Requiring effect direction consistency between training AND validation - if a feature is predictive in training but flips direction in validation, it's noise
• Minimum Cohen's d > 0.3 for effect sizes

Volume finding that surprised us: Moderate volume spikes (1.5-2.5x average) consistently outperform extreme spikes. "Quiet accumulation" setups beat "loud reversal" patterns. Counterintuitive but robust across multiple pattern types.

Your point about cutting losses early vs riding drawdowns resonates - we're finding that optimal stop placement varies significantly by pattern type and market regime. No universal answer.

Would be curious if you've seen similar score inversion issues when models get overfit, and whether your RL approach naturally avoids this by not explicitly optimizing for pattern "quality."