Lab · ML Experiments

ML — Pattern Discovery

Inverted workflow: find conditional edges in BTC data first, build strategies second.
55 experiments

BOCPD Filter — BB_EXTREME_3 robustness (N=30 seeds)

Promoted
2026-05-19 validationshufflerobustnessbb_extreme_3
Hypothesis
With 30 shuffled-source backtests instead of 5, the BB_EXTREME_3 z-score either firms up above 1.5 (deploy) or collapses below (skip).
Verdict
**FIRMS UP — DEPLOY** — with N=30 the z-score = +1.52 >= 1.5. BB_EXTREME_3 has a real BOCPD signal. Deploy bocpd_filter@0.3 too.
z_score
+1.5207
real_pnl
+12.2107
shuf_p5_pnl
+9.8599
baseline_pnl
+11.4243
shuf_p95_pnl
+11.6485
shuf_std_pnl
+0.5835
real_lift_pct
+6.8836
shuf_mean_pnl
+11.3234
n_shuffle_seeds
30
shuf_mean_lift_pct
-0.8832

BOCPD Filter — BB_EXTREME_3 robustness (N=30 seeds)

2026-05-19 · status: promoted · 184.6s

Hypothesis: With 30 shuffled-source backtests instead of 5, the BB_EXTREME_3 z-score either firms up above 1.5 (deploy) or collapses below (skip).

Verdict: FIRMS UP — DEPLOY — with N=30 the z-score = +1.52 >= 1.5. BB_EXTREME_3 has a real BOCPD signal. Deploy bocpd_filter@0.3 too.

Key metrics

metric value
n_shuffle_seeds 30
baseline_pnl +11.4243
real_pnl +12.2107
shuf_mean_pnl +11.3234
shuf_std_pnl +0.5835
shuf_p5_pnl +9.8599
shuf_p95_pnl +11.6485
real_lift_pct +6.8836
shuf_mean_lift_pct -0.8832
z_score +1.5207

Approach

30 shuffled-source backtests on BB_EXTREME_3 with bocpd_filter at θ=0.3. Same setup as bocpd_filter_shuffle_test but more seeds to firm up the z-score (was +1.06 with N=5).

metric value
baseline_pnl (no filter) $+11.42
real-source filter pnl $+12.21
real lift vs baseline +6.88%
shuffled mean pnl $+11.32
shuffled std pnl $0.583
shuffled 5/95 percentile [$+9.86, $+11.65]
shuffled mean lift vs baseline -0.88%
real - shuf_mean $+0.89
z-score (real vs shuffled distrib.) +1.521

histogram

Verdict

FIRMS UP — DEPLOY — with N=30 the z-score = +1.52 >= 1.5. BB_EXTREME_3 has a real BOCPD signal. Deploy bocpd_filter@0.3 too.