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AnimateDread e9c562b39f Add Feature Scaling and Regime Math Classes; Implement Mind Trading Logic

- Introduced `FeatureScale.mqh` with `FeatSquash` function for stateless feature scaling.
- Added `RegimeMath.mqh` class for regime arithmetic, including efficiency and variance calculations.
- Documented the Mind trading logic in `MIND.md`, detailing the trading process and modes.
- Created `VOLNORM_PLAN.md` and `VOLNORM_RESULTS.md` for tick-volume normalization testing.
- Implemented `read_book.py` for analyzing trade book data and correlations.
- Developed `volnorm.py` for testing tick-volume normalization with new and old methods.

2026-09-30 18:36:33 -04:00

3,1 КиБ

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Tick-volume normalization - results (2026-09-30)

Plan: VOLNORM_PLAN.md. Script: python research/volnorm.py H4|H1. Broker M1 .hcc, 4 indices, 2022-01..2026-08.

Verdict

Test 1 confirmed. Tests 2, 3 and 4 fail as written. The repair removes the clock from the feature; it does not make the feature more stable across years and it does not separate paying dips from failing ones. No EA change is justified by this run beyond the DRY clean-up.

H4 (the book's timeframe)

idx	R^2 old	R^2 new	yr-mean sd old	yr-mean sd new	rho old	rho new
SP500	0.567	0.001	0.080	0.120	0.190	0.055
NAS100	0.583	0.005	0.064	0.172	0.159	0.037
US30	0.645	0.003	0.080	0.093	0.176	0.065
DAX40	0.705	0.014	0.080	0.228	0.045	0.057

Dip-z events (n 213-226 per index), top minus bottom tercile of the feature, bp: new = SP500 -19.9, NAS100 -11.9, US30 -2.5, DAX40 -38.5; pooled -18.0 [-38.3, +4.5]. old pooled +0.3 [-18.3, +22.1]. H1 pooled: old -1.6 [-7.3, 3.9], new -3.6 [-10.1, 2.8].

Reading

The defect is real and large. 57-77% of the variance of today's relative-volume inputs is which hour of the day the bar is. After same-slot normalization it is 0.1-1.4%. The three inputs in SignalNeural, WarriorVote::MgmtFeatures and FracDiff::VolumeAt have been feeding the clock to the models as if it were participation.
Test 2 fails. The slot profile is a 60-session median, so it lags any level shift: DAX40's 2022->2023 tick collapse leaves the yearly mean of the new feature at sd 0.23 against 0.08 for the old one. Fix candidate (needs its own pre-registration): normalize by the slot's share of a short trailing daily total instead of a long slot history, so the scale adapts within days.
Test 3 fails literally, and the test was badly framed. The old feature's correlation with next-bar activity (0.16-0.41) is mostly the clock: open bars have both high volume and big moves. What is left after removing the clock is 0.03-0.07. That is small but it is the honest number. A within-hour comparison would have been the right control.
Test 4 fails; a lead, not a finding. The clock-free feature has the same sign on all four indices at H4 (high-volume dips pay LESS, -18 bp pooled), but the CI includes 0 (p about 0.1) and H1 does not repeat it convincingly. Unmodelled: the vol-regime gate is not applied to these events, and it is itself a volume-adjacent variable.

Consequences

Keep the pre-registered outcome: volume normalization does not improve the dip-buy.
Do fix the three duplicated computations into one shared normalizer (DRY) so that any future volume-dependent model (Wyckoff effort-vs-result, NN inputs) is not confounded by the clock.
Any re-test of the Wyckoff/NN volume inputs must use the repaired feature; the earlier null results on those inputs were measured with the clock-contaminated one.
The negative sign at H4 deserves ONE follow-up, pre-registered before it is run: gated events, and the short-window normalizer from point 2.

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