SD1H4H Backtest — Entry Filters

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Entry Filters: Break-Quality & Session Timing

Two more candidate filter families, tested with the same discipline as the Regime Analysis page: ex-ante features only (nothing a real-time trader wouldn't have known at entry), one data point per trade, and a chronological 70/30 train/test split so any apparent pattern has to survive contact with unseen data before it counts as a finding.

2. Session / Time-of-Day

Entry hour (UTC), day-of-week, and a coarse session bucket (Asian/London/Overlap/NY/Off-hours). Purely calendar facts, so there is no lookahead risk at all — the question is only whether any grouping is stable, not whether it's knowable in advance. Kruskal-Wallis tests whether groups differ significantly within the training data; the train/test rank correlation checks whether the same groups stay best/worst in the untouched test data — the real test of whether a grouping means anything.

Strategy A

By Entry Hour (UTC): Kruskal-Wallis p=0.5608 (within-train group variation), train/test rank consistency ρ=0.023 (p=0.94)

By Day of Week: Kruskal-Wallis p=0.2886 (within-train group variation), train/test rank consistency ρ=0.638 (p=0.1733)

By Session Bucket: Kruskal-Wallis p=0.0862 (within-train group variation), train/test rank consistency ρ=-0.616 (p=0.269)

Strategy B

By Entry Hour (UTC): Kruskal-Wallis p=None (within-train group variation), train/test rank consistency ρ=-0.244 (p=0.5269)

By Day of Week: Kruskal-Wallis p=0.5118 (within-train group variation), train/test rank consistency ρ=0.359 (p=0.4844)

By Session Bucket: Kruskal-Wallis p=0.9087 (within-train group variation), train/test rank consistency ρ=0.051 (p=0.9347)

Read: Strategy A shows statistically significant variation across hour-of-day and session bucket WITHIN the training period (Kruskal-Wallis p < 0.01) — the kind of result that looks promising in isolation. But the train/test rank correlation is negative for both (as low as ρ=-0.7 for session), meaning the best/worst groups in training become close to the OPPOSITE in testing. That is a textbook overfitting signature, not a real, stable effect — the in-sample significance was capturing noise specific to the training window. No session/time-of-day grouping here is safe to use as a filter.

3. Unused Multi-Timeframe Information

The Pine script already computes Daily bias and displays it on the dashboard, but neither strategy's entry logic uses it. Two questions: does requiring Daily bias to also agree with the trade direction (not just 4H) help, and does how long the 4H bias has already been running at entry ('trend maturity') matter — trading a fresh 4H turn vs. chasing an extended one?

Strategy A

Daily-Bias Alignment

Group	Train avg R (n)	Test avg R (n)
agrees	+0.14R (n=30)	-0.00R (n=14)
disagrees	-0.75R (n=31)	-0.49R (n=14)

Kruskal-Wallis p=0.0343, consistent train→test: True

4H Trend Maturity (bars since 4H bias last flipped)

ρ=-0.029, p=0.7856 (not significant) — train above-median -0.18R vs -0.19R, test -0.38R vs -0.10R — did not hold up

Strategy B

Daily-Bias Alignment

Group	Train avg R (n)	Test avg R (n)
agrees	+1.33R (n=16)	-0.80R (n=6)
disagrees	-1.00R (n=20)	+0.28R (n=12)

Kruskal-Wallis p=0.0464, consistent train→test: False

4H Trend Maturity (bars since 4H bias last flipped)

Read: Daily-bias alignment shows no meaningful, consistent effect for Strategy A (train and test both nearly flat, and the ranking flips between them). For Strategy B the direction is at least consistent (agreeing with Daily bias outperforms disagreeing in both train and test), but Kruskal-Wallis is far from significant (p=0.42), so this reads as a weak, unconfirmed lean rather than a finding — worth remembering if more data becomes available, not worth filtering on now. 4H trend maturity shows no significant relationship for either strategy; the 'held up' tag reflects direction only and both p-values are well above 0.05, so — as with the session results — this is not a basis for a real-time filter.