Completes the 2026-08-09 training audit. FORCES A RETRAIN of every
Wyckoff-enabled config (N1 re-keys the fingerprint), and BOTH DLLs must be
redeployed alongside the .ex5 - they carry new exports.
F4 - mini-batch accumulation, TRAIN_BATCH_SIZE=32. Training was pure online
SGD (one weight update per bar), which is the mechanical source of the
era-to-era whipsaw every downstream guard was built to cope with. The O(n^2)
outer product is native - AccumulateWeightGrad / AccumulateWeightGradConv /
AccumulateBufferInto in Network.cl, WarriorCPU and WarriorDML - while the
optimizer step is host-side MQL5 shared by all tiers (ApplyAccumToBlock), so
there is one Adam/SGD implementation instead of four that can drift.
- the LSTM needs no outer-product kernel (WeightsGradient already holds the
sample's full dW) but could NOT simply be left un-zeroed between samples:
CPU_LSTMSeqBackward/DML_LSTMSeqBackward memset it on entry. Hence a
separate accumulator plus an elementwise add.
- batch-norm gamma/beta accumulate in host arrays, not new BatchOptions
slots - BN_OPT_STRIDE is baked into every persisted .nnw.
- scoped to pass 2; online learning keeps immediate updates. Every save /
checkpoint / scoring boundary flushes, scaling by the real sample count.
- degrades to per-sample updates (one log line) on a tier that cannot
accumulate, so old devices and DLL-free builds are unaffected.
- verified offline: DirectML/batch_accum_check.cpp drives the real exports
against an independent reference; at B=1 the accumulator matches the
shipped unbatched kernel's own gradient to 1.1e-16. Math only - the
in-situ check remains the per-layer dW/W report on a real era.
F6 - ComputeFirstLayerWidth budgeted against the RAW input width even where a
conv/LSTM front end had already reduced it, so an LSTM's dense stack was
charged for 1,280 inputs when it receives 64. Confirmed from the deployed
.cfg files: CONV, LSTM and HYBRID were all pinned at the 16-unit floor. Now
budgeted against the front-end output and capped at it (never fan out), with
the derivation reordered so both stages settle first.
N1 - EventCode/EventPhase/StructuralPhase are signed categoricals packing
direction and Wyckoff stage into one scalar across a sign discontinuity. Split
into direction + [0,1] magnitude, the same convention the base OHLC block uses.
Information-preserving; 13 readings now occupy 16 inputs.
Compiled clean (0 errors, 0 warnings); both DLLs rebuilt.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Four of the six findings from research/training_pipeline_audit_2026-08-09.md
(F4 mini-batching and F6 feature re-encode deliberately deferred - see the
report's implementation-status section for why):
- F1: pass-2 Fisher-Yates (and AutoTune's MI block shuffle) used MathRand()%,
which is 15-bit - provably non-uniform on every full-history era over 32,768
queued samples. New 30-bit ShuffleRandomIndex().
- F2: plateau warm restarts were a no-op whenever eta already sat at its
ceiling (the normal state of a non-regressing plateau) - the ladder was just
a 24-era countdown. Restarts now overshoot to 5x the ceiling
(PLATEAU_RESTART_BOOST) and anneal geometrically back over the patience
window, SGDR-style; ETA_MIN widened 1e-4 -> 1e-5 so the decay schedule has
real range.
- F3: checkpoint restores put weights back but kept the rejected trajectory's
Adam moments, so the optimizer immediately pushed back toward the rolled-back
state (the restore->regress->restore oscillation). CNet::ResetOptimizerState()
zeroes moments/momentum/step counters (weights, BN statistics, gamma/beta
untouched) on every mid-run restore, every boosted restart, and the
deploy-time restore that online learning continues from.
- F5: batch-norm running statistics now freeze for the pass-3 OOS scoring walk,
so the selection metric the checkpoint ranking and deploy gate read is a pure
function of the checkpoint instead of partly measuring BN drift. Defensive
unfreeze in FinalizeTrainRun covers stop-mid-pass; live/online adaptation and
the OOS continual-learning simulation stay adaptive by design.
Compiled clean (0 errors, 0 warnings) via the staged-tree recipe.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The MI sample builder used `MathAbs(labelBarOffset)` as a padding, causing rows from offset and non-offset builds to be paired with a double shift. This broke the positive control, failed the 5× gate, and voided all reported mutual‑information figures. Replace with the fixed `MiShiftPad` constant to ensure builds enumerate the same set of bars and row-k alignment is preserved.
Add `BatchOptionsTotal()` to `CNeuronBatchNormOCL` and split the packed BN weight array in the learning report into separate norms for the outgoing dense matrix, gamma, beta, running statistics, and Adam moment buffers. This turns an ambiguous single‑norm reading into precise diagnostics that distinguish weight divergence from scaling issues.
- Moves CLayer neuron construction to AI/Impl/Layer.mqh to keep Network.mqh clean
- Unifies four previously duplicated architecture initialisation blocks (MLP/CONV/LSTM/HYBRID) into a single shared function
- Eliminates risk of behavioural drift where one architecture missed a setter, causing mismatched feature sets or targets
