Three backends left, as the operator specified: OpenCL, the CPU DLL,
and pure MQL5. CDirectMLMy was a two-tier wrapper (GPU via
WarriorDML.dll, CPU via WarriorCPU.dll) whose name only ever named the
tier being removed here; the CPU DLL tier - the one actually used on
the training machine (no OpenCL, no DirectML) - is untouched.
AI/NeuronDirectML.mqh -> AI/ComputeDll.mqh: dropped the DML_* #import
block and COMPUTE_TIER_GPU (checked first that nothing persists the
enum value and only one external site reads .Tier() - safe), collapsed
every tier==CPU?CPU_x():DML_x() ternary to a straight CPU_x() call.
Renamed CDirectMLMy->CComputeDll, InitDirectML()->InitComputeDll(),
member directml/DirectML->computeDll/ComputeDll across every AI/ file
that touched a neuron/net backend plus Topology.mqh/OnlineLearning.mqh.
NetBuild.mqh's InitComputeDll also lost the dead D3D12 error-code
switch and the now-impossible GPU-tier log branch.
Verified via per-file brace-balance diff against HEAD and a whole-repo
grep for every removed symbol (CDirectMLMy/InitDirectML/
COMPUTE_TIER_GPU/DML_*) - the only surviving hit is an intentional
historical-note comment in the new file's header.
DirectML\WarriorDML.cpp/.h and its build scripts are now orphaned C++
source, left in place pending an operator decision. Architecture docs
(AI_NETWORK.md, Warrior_EA_System_Overview.md, etc.) still describe the
4-backend/GPU-tier shape and are not updated in this pass.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
The //| box blocks were excluded from 0b06f8e and 5efdb48 and were what
remained: 160 of them ran to 10+ lines, the longest to 88. Compressed to their
leading topic sentences - 5 lines for a function header, 8 for a file header -
keeping the box format and the standard MQL5 name/author lines verbatim.
Verified at the BYTE level this time, across every in-scope file: the list of
non-comment lines is byte-identical to HEAD and braces balance. The first check
compared a locale-decoded 'git show' against a UTF-8 read and flagged 25 files
that had not changed at all - every BOM and every non-ASCII line mismatched.
47,696 -> 40,665 lines in scope; comment share 38% -> 26%.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
MetaEditor: "declaration of 'eta' hides global variable" (Math.mqh:792
vs Network.mqh:80). The standard library's Math\Stat\Math.mqh declares a
local `double eta` in its incomplete-gamma branch, and our bare global
of the same name is in scope there.
Same fault as the b1/b2/lr/momentum macros retired in ea2552e: a
single-token global name living in a header that library code gets
compiled beside. The library cannot move, so ours does - 112 references
across 13 files, whole-word only.
Named g_eta rather than g_learningRate to stay inside the vocabulary
already around it (ETA_DECAY_FACTOR, ETA_MIN, m_etaCeiling, etaBefore),
all of which are untouched and none of which shadow anything.
One log line said "continuing to explore without decaying eta", where
the word was prose rather than a symbol reference; that reads "the
learning rate" now instead of naming a variable at the trader.
Scanned for the next occurrence rather than waiting for it: the only
other bare lowercase globals in the tree are eaName and tableschema,
both distinctive enough not to collide with a library local.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
MQL5's MathRand() is the 15-bit MSVC LCG - 32768 distinct values and
the lattice structure that shape of generator has. Two places here
actually lean on randomness and both were hurt by it:
WEIGHT INIT. Six He/LeCun-uniform sites drew
((MathRand()+1)/32768.0 - 0.5) * 2 * scale, so a first dense layer of
~250k weights had only 32768 possible values and thousands of
connections started byte-identical. Breaking that symmetry is the whole
job of random init.
SHUFFLING. ShuffleRandomIndex() already had to splice TWO MathRand()
draws to reach 30 bits, and its own comment documented the residual
modulo bias it still carried. HQRndUniformI() is rejection-sampled and
exactly uniform, so the splice and the bias note both go.
CHighQualityRand is L'Ecuyer's combined multiplicative congruential
generator - two differenced streams, 31-bit output, period ~2.3e18 -
and it ships with the terminal.
AND A BUG THE MIGRATION EXPOSED. The three MathSrand(GetTickCount())
calls sit immediately before "build a fresh topology", once per model.
GetTickCount() steps in ~15.6 ms on Windows and an ensemble builds every
member inside one OnInit, so members could be handed the SAME seed and
draw the SAME weights wherever their shapes coincide - and members that
start identical are not an ensemble. WarriorRandSeed() takes a salt (the
model id) plus a never-reset call counter, so a collision is impossible
rather than merely unlikely, while the tick keeps the run itself
genuinely unrepeatable the way those call sites asked for.
Seeds are masked positive rather than trusted: HQRndSeed computes
s % (M-1) + 1 and MQL5's % keeps the sign, so a negative seed leaves the
generator in a state its own assertions reject. GetTickCount() is a uint
and goes negative as an int after ~24 days of uptime - a fault that
would surface as "training is broken" on a long-running terminal and
nowhere else.
The indicator tuner's 52 draws move across too: its random search is
where sample quality earns its keep.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The gate's NormalUpperTail was a hand-rolled Abramowitz & Stegun 26.2.17
approximation. Its own comment gave the reason - "drags a chain of headers
behind it" - and that turned out to be one file: Math\Stat\Normal.mqh
includes only Math.mqh, which includes nothing. Swapped for Cody's rational
approximation in the library (~18 significant digits vs |error| < 7.5e-8).
No past verdict changes: at the z the gate operates on, the difference is
orders of magnitude below DEPLOY_FAMILY_WISE_ALPHA.
Adopting it needed the four bare macros in AI\Network.mqh gone first.
"#define b1 AdamBeta1" collides with an identifier in Math.mqh, so the
include would have macro-expanded the library's own local and failed to
compile - the same landmine that made the original author rename the
approximation's coefficients to ntB1..ntB5 rather than use the reference's
b1..b5. lr, b2 and momentum are the same class of hazard: single-token
global macros in a 52k-line codebase. All four now resolve to the input
names they always aliased, which is a pure textual identity - verified zero
bare occurrences remain.
Also:
- SelectionSort over the buffered signals was O(n^2) with an O(n^2) count of
StructToTime calls, because the comparison rebuilt both datetimes from the
six int date fields every time. Now materialises the keys once and does an
insertion sort; ArraySort cannot permute a struct array. IsEarlier goes
with it, MakeDateTime becomes SignalTime.
- Seven FileOpen sites lacked FILE_SHARE_READ|FILE_SHARE_WRITE, including
AtomicWriteBegin, which stages every model save. All 43 sites now carry
them - an exclusive open fails outright when another process holds the
path, which here has meant a silently skipped save.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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>
- BufferDouble: replace hardcoded "DirectML/CPU-DLL" with dynamic backend name
and add buffer index/element count to all error prints for easier debugging.
- NetPersistence: distinguish missing file from transient lock by probing
FileIsExist before logging, eliminating false "sharing violation" warnings
when no saved model exists on first run.
- 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
