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Author	SHA1	Message	Date
AnimateDread	371f8aaecd	fix: the Adam second moment was never Adam - all four tiers Root cause of the B=32 regression, and it predates F4 entirely. Every Adam kernel stored v already square-rooted and then fed that stored value back in as if it were the variance: v_new = sqrt(b2 * v_old + (1 - b2) * g^2) That recursion has a fixed point at v ~= b2 = 0.999 for ANY gradient below unit scale, so the denominator stops tracking the gradient and Adam degrades into plain SGD with lr = lt. Measured against the shipped WarriorCPU.dll (batch_accum_check.cpp, TestOptimizerScaleInvariance), 4000 steps of a constant gradient: 3285x less displacement at \|g\|=1e-5 than at \|g\|=1, where a scale-invariant optimizer gives the same distance for both. After the fix all six magnitudes read 1.199 and v tracks \|g\| exactly. It hit conv/LSTM specifically because they sit behind a batch-norm with running variance ~2.6e+05, so their gradients arrive divided by ~500 - deep in the degraded regime - while the dense stack near the loss stayed in the working one. In situ on SP500 H1: lstm1 dW/W 2.62/10.0/7.14% -> 0.024/0.022/ 0.003%, conv1 decaying to 0.000% by era 30. NeuronBatchNorm.mqh already squared v back for gamma/beta and its comment named the kernels as wrong, which is exactly why gamma/beta kept training while the stages behind froze. Persisted .nnw needs no migration - v keeps its std-dev meaning. Also, the two ways F4 exposed it, both mine: - No LR compensation for B fewer steps per era. sqrt(B) for adaptive methods (Krizhevsky 2014; Granziol et al. 2022), applied once in InitialEtaForOptimizer(). Linear scaling (Goyal et al. 2017) is for SGD. - Plateau patience denominated in eras, so raising B made the ladder 32x more impatient in its only unit. PAI converged at era 41 on ~49k updates where the same config had been finding new bests at era 1028. TrainPlateauPatienceEras() stretches it by the same sqrt(B). TRAIN_BATCH_SIZE 32 -> 8 so the patience stretch stays affordable (8 -> 23 eras per stage, not 8 -> 45). Both helpers are identities at B=1. Deploy gate: DEPLOY_MIN_SIDE_RECALL_PCT (10%) folded into tradeableOK. The perceptron reported Sell:0% recall in all 41 eras, cleared the floor on Buy alone at 36.6% vs 34% chance, deployed, and sprayed buy arrows. Folded into the ranking key rather than checked at deploy time so a one-sided era cannot become best-so-far in the first place. Deinit: the arrow purge now runs BEFORE ExtPanel.Destroy(), an unbounded CAppDialog teardown that sat ahead of it - the same ordering inversion the rule there exists to prevent. CONV was force-terminated 4.8 s into OnDeinit (vs ~1.1 s for the three that finished) having reached none of its cleanup, so its arrows stayed on the chart. Steps are now timed in the log. PurgeChart's verification rescan filtered on OBJ_ARROW, the same blind spot as the bulk delete, so "persisted 10 ... cleared 0" passed silently. It now walks every object type and reports the object counts when both are zero. Both build variants compile 0 errors / 0 warnings; both DLLs rebuilt. FORCES A RETRAIN (already forced by N1) and both DLLs must ship with the .ex5. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>	2026-08-09 14:02:35 -04:00
AnimateDread	0c01dc279b	feat: mini-batch gradient accumulation (F4), front-end-aware capacity budget (F6), split Wyckoff categoricals (N1) 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>	2026-08-09 11:48:03 -04:00
AnimateDread	274630f802	fix: training-stability audit fixes F1/F2/F3/F5 - unbiased shuffle, real plateau escapes, fresh optimizer state on restore, pure OOS metric 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>	2026-08-09 10:54:09 -04:00

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AnimateDread

371f8aaecd

fix: the Adam second moment was never Adam - all four tiers

Root cause of the B=32 regression, and it predates F4 entirely. Every Adam
kernel stored v already square-rooted and then fed that stored value back in
as if it were the variance:

    v_new = sqrt(b2 * v_old + (1 - b2) * g^2)

That recursion has a fixed point at v ~= b2 = 0.999 for ANY gradient below
unit scale, so the denominator stops tracking the gradient and Adam degrades
into plain SGD with lr = lt. Measured against the shipped WarriorCPU.dll
(batch_accum_check.cpp, TestOptimizerScaleInvariance), 4000 steps of a
constant gradient: 3285x less displacement at |g|=1e-5 than at |g|=1, where
a scale-invariant optimizer gives the same distance for both. After the fix
all six magnitudes read 1.199 and v tracks |g| exactly.

It hit conv/LSTM specifically because they sit behind a batch-norm with
running variance ~2.6e+05, so their gradients arrive divided by ~500 - deep
in the degraded regime - while the dense stack near the loss stayed in the
working one. In situ on SP500 H1: lstm1 dW/W 2.62/10.0/7.14% -> 0.024/0.022/
0.003%, conv1 decaying to 0.000% by era 30. NeuronBatchNorm.mqh already
squared v back for gamma/beta and its comment named the kernels as wrong,
which is exactly why gamma/beta kept training while the stages behind froze.

Persisted .nnw needs no migration - v keeps its std-dev meaning.

Also, the two ways F4 exposed it, both mine:

- No LR compensation for B fewer steps per era. sqrt(B) for adaptive methods
  (Krizhevsky 2014; Granziol et al. 2022), applied once in
  InitialEtaForOptimizer(). Linear scaling (Goyal et al. 2017) is for SGD.
- Plateau patience denominated in eras, so raising B made the ladder 32x more
  impatient in its only unit. PAI converged at era 41 on ~49k updates where
  the same config had been finding new bests at era 1028.
  TrainPlateauPatienceEras() stretches it by the same sqrt(B).

TRAIN_BATCH_SIZE 32 -> 8 so the patience stretch stays affordable (8 -> 23
eras per stage, not 8 -> 45). Both helpers are identities at B=1.

Deploy gate: DEPLOY_MIN_SIDE_RECALL_PCT (10%) folded into tradeableOK. The
perceptron reported Sell:0% recall in all 41 eras, cleared the floor on Buy
alone at 36.6% vs 34% chance, deployed, and sprayed buy arrows. Folded into
the ranking key rather than checked at deploy time so a one-sided era cannot
become best-so-far in the first place.

Deinit: the arrow purge now runs BEFORE ExtPanel.Destroy(), an unbounded
CAppDialog teardown that sat ahead of it - the same ordering inversion the
rule there exists to prevent. CONV was force-terminated 4.8 s into OnDeinit
(vs ~1.1 s for the three that finished) having reached none of its cleanup,
so its arrows stayed on the chart. Steps are now timed in the log.

PurgeChart's verification rescan filtered on OBJ_ARROW, the same blind spot
as the bulk delete, so "persisted 10 ... cleared 0" passed silently. It now
walks every object type and reports the object counts when both are zero.

Both build variants compile 0 errors / 0 warnings; both DLLs rebuilt.
FORCES A RETRAIN (already forced by N1) and both DLLs must ship with the .ex5.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

2026-08-09 14:02:35 -04:00

AnimateDread

0c01dc279b

feat: mini-batch gradient accumulation (F4), front-end-aware capacity budget (F6), split Wyckoff categoricals (N1)

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>

2026-08-09 11:48:03 -04:00

AnimateDread

274630f802

fix: training-stability audit fixes F1/F2/F3/F5 - unbiased shuffle, real plateau escapes, fresh optimizer state on restore, pure OOS metric

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>

2026-08-09 10:54:09 -04:00