KILLED · sample bias
Hypothesis
Borrowing from Perelman's reduced-length functional from Ricci flow theory, "smooth" price approaches to a signal trigger (low geometric path energy) should correlate with higher follow-through than chaotic approaches.
Math — Perelman L-distance, discretized for price action
$$ L(\gamma) = \int_{0}^{\bar{\tau}} \sqrt{\tau}\,\left(R + |\dot{\gamma}|^{2}\right)\,d\tau $$
Discretized for log-price path on 240×1m bars before signal trigger:
$$ L_{\text{price}} = \sum_{k=1}^{N} \sqrt{\tau_k} \cdot \left( \sigma^2_{k} + r_{k}^{2} \right) \cdot \Delta\tau $$
Where τ_k = k/N is normalized time position, σ²_k is local realized variance in 10-bar window centered on k, and r_k is the log-return at bar k.
Results — appeared promising on curated sample
| Q1 (lowest L) WR on pump-only sample | 76.1%, avg +4.37% |
| Q5 (highest L) WR on pump-only sample | 52.1%, avg +2.53% |
| Walk-forward TEST > TRAIN (suspicious) | +5.52% vs +4.67% |
| DD reduction vs no-filter | −72% |
Reality check — 5000 random "smart-money interesting" moments
| RAVEN filter (pre-condition) pass rate | 0.50% (25/5000) |
| Of those, true positives | 24%, not 49% as CV claimed |
| GEOFLOW filter applied on top | 100% removed TPs, kept all FPs |
| Final P&L | −$2.24 / $100 over 12 trades |
L-distance discriminates within pump-only labeled set, but the relationship inverts on the real-world distribution. FPs in the smart-money universe have lower L than TPs. The earlier "Sharpe 18" was a sample-bias artifact. Curated samples deceive.
A filter that works on labeled positives may not generalize. Always validate on the realistic operating distribution, including false positives.