BuildFeatureWindow() replaces eight hand-rolled copies of the same loop
and feeds the window OLDEST BAR FIRST. Every copy fed it newest-first,
because MQL5 timeseries indices run backwards and `r + b` with b ascending
walks into the past.
Harmless for PAI and CONV - a dense layer learns a weight per position
either way, a conv learns time-mirrored kernels. Not harmless for the
recurrent stacks:
- LSTM_SeqStepForward reads `inputs + t*Iw`, so step t is block t.
- It writes output[] only when t == steps-1: the visible output IS the
last hidden state.
- c_t = f*c_{t-1} + i*g decays toward the start of the sequence.
lstm_seq_flowcheck.cpp measured block 0's influence on the output at
1.2e-2 of block T-1's, at the shipped forget bias of 1.0.
So the bar being PREDICTED sat at the far end of the decay and the output
was handed to the OLDEST bar in the window - the exact inverse of what the
window is for. ~80x backwards on LSTM and HYBRID, on all three tiers
(OpenCL kernel, CPU DLL, pure-MQL5 inference), which is why it never
surfaced as a backend discrepancy.
This does not create edge - the MI diagnostics read at the noise floor
(p=0.4975) with a working positive control. It makes the one hypothesis
those diagnostics explicitly do NOT cover testable: they are marginal and
per-bar, and state they "cannot rule out one that only exists in
combination or across time". The sequence model is the instrument for
across-time structure and it has been crippled, so that hypothesis has
never been honestly tested.
Fingerprint gets an unconditional |WIN:2 - the vector keeps its shape and
its features, so a stale .nnw would load cleanly and run a model fitted to
one ordering against the other, silently. Re-keying every config is the
point, not collateral damage. FORCES A FULL RETRAIN.
Also: the now-relative bar caches are re-keyed on the two live paths.
EnsureBarCachesCapacity() was only ever called from training paths, but
once m_trainingComplete is set ScheduleTrainingIfNeeded() routes every bar
to RefreshConvergedSignal() and Train() is never re-entered - so nothing
cleared the feature cache again for the life of the process. A chart that
trained to convergence kept replaying the rows computed for the last
training era's bar grid: the live signal froze at its convergence-time
value, and OnlineLearnStep() backpropped those stale features against
freshly resolved labels. Backtests were never affected (an inference-only
process never allocates the arrays, so every read recomputes).
Compiles clean: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Reported: on deinit the panel and status label go, the signal arrows stay.
Two independent causes, both fixed here.
1. It was partly deliberate. ShutdownChartCleanup carried a second
behaviour selected by a `preserveChartArrows` flag derived from the
deinit reason: on RECOMPILE / PARAMETERS / CHARTCHANGE / TEMPLATE the
arrows were left on the chart on purpose, to avoid a reload flicker.
That branch IS the reported symptom, an operator cannot tell it apart
from a cleanup that failed, and it was outright wrong whenever the
reload changed the config - REASON_PARAMETERS means exactly that, and
the preserved arrows then belonged to a model the chart no longer
runs, with nothing marking them stale. It is gone, along with the flag
and m_purgeChartOnDestruct. One path now: persist, clear, restore on
the next attach.
2. Whatever remains was unfalsifiable. PurgeChart was a single
ObjectsDeleteAll(prefix) whose return value was discarded, with no
caller ever looking at the chart again - so "the arrows are still
there" and "the arrows were never there" produced identical evidence,
which is why the report survived three sessions. It now verifies:
after the bulk delete it walks the OBJ_ARROW-typed list (a handful of
objects, not the whole chart), deletes any surviving WarSig_ by name,
and says so. Costs one typed scan when the bulk delete works, which is
the normal case; names the root cause when it does not.
Every failure mode of SaveChartSignals was also silent - it returned void
and had three bare early returns. It returns bool now, logs the open
error with the filename, and the shutdown purge is CONDITIONAL on it: for
a converged model the chart objects are the only copy of its signal
history (nothing redraws them - the renderer runs per training era and a
deployed model has none left), so a chart left littered because the disk
write failed beats a clean chart bought by destroying the history. Either
way the log now says which happened.
Also states the user's rule once, where arrows come back rather than
across InitNeuralNetwork's several exits: no weights loaded for this
config => clear the sidecar and start visually clean. A fresh run must
not inherit calls it never made, and the first save would otherwise adopt
them (the sidecar is rebuilt by scanning the chart).
Compiles 0 errors / 0 warnings, standard and Market. Needs redeploy.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Reported as "the perceptron correctly cleaned its chart on deinit, the
other 3 did not, abnormal termination". Measured from the 2026-08-01 log,
time from "OnDeinit: shutting down" to MetaTrader force-terminating:
PAI 3.75 s -> survived, chart cleaned
CONV 4.71 s -> Abnormal termination
LSTM 4.28 s -> Abnormal termination
HYBRID 4.16 s -> Abnormal termination
In all four the last line printed is the inference census, which is the
end of StopTraining() - so the overrun is inside ShutdownChartCleanup(),
i.e. between saving the arrows and purging them.
The cost is the prune loop at the end of SaveChartSignals():
for(int i = 0; i < prunedCount; i++)
ObjectDelete(0, SIG_ARROW_PREFIX + TimeToString(pruned[i]));
ObjectDelete is O(objects) on a crowded chart, so this is O(n^2). It was
harmless while the model called a direction on ~6% of bars. After the
triple-barrier relabel the models call on 83-94% of bars, the chart
carries many thousands of arrows, and the loop overran MetaTrader's
OnDeinit budget - so PurgeChart() never ran and the arrows stayed on
screen. The slow tidy-up starved the fast one.
The work was pure waste at that moment: ShutdownChartCleanup purges every
arrow with a single bulk ObjectsDeleteAll immediately afterwards.
Deleting them one at a time first has no effect except to prevent the
bulk delete from happening at all.
SaveChartSignals takes a pruneChartObjects flag, and the two shutdown
call sites pass false:
- ShutdownChartCleanup passes `preserveChartArrows`, which is exactly
right: prune when the arrows are STAYING (chart and sidecar must
agree), skip when they are about to be purged wholesale.
- FinalizeTrainRun passes !m_trainingStopRequested. Removing a chart
MID-ERA reaches StopTraining -> FinalizeTrainRun, which took the
expensive path a second time, even earlier, before anything had been
cleared. Same defect one call site up; it only escaped notice because
the observed removals happened to land between eras.
Normal convergence and the live per-era path are unchanged - they still
prune, which is what keeps the chart object count bounded.
This also restores the invariant the 2026-07 fix intended ("chart cleanup
runs BEFORE the heavy weight save so a stall cannot leave the chart
littered"). That fix moved cleanup ahead of the WEIGHT save, but cleanup
had since grown its own slow step ahead of its own fast one.
Both builds compile 0 errors / 0 warnings.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
MEASURED COST OF THE GA, which is what retired it. Per generation:
rung 0: 8 cand x 3 seeds x 3 eras = 72 eras
rung 1: 4 cand x 3 seeds x 8 eras = 96
rung 2: 2 cand x 3 seeds x 20 eras = 120
= 288 eras/generation x 4 generations = 1152 eras BEFORE the winner's
real training began. Against the observed era times on SP500 H1:
PAI 29.1 s/era -> 9.3 h (matches the observed 00:37 -> 09:22)
CONV 41.3 s/era -> 13.2 h
LSTM 150.4 s/era -> 48.1 h
HYBRID 154.6 s/era -> 49.5 h
Two days to tune is not a first-run experience, and it is the phase in
which the panel goes quiet, which is what made it look like a hang.
It also bought nothing. The space is 90 points (10 MA periods x 9 MA
types), so 1152 evaluations revisited each point ~13 times; and rungs of
3 and 8 eras cannot separate two MA periods at all. The 2026-08-01 run
proves it: every finalist scored 25.0-25.9% balanced accuracy - below the
33.3% one-class floor, i.e. indistinguishable noise - and the search then
"deployed the winner" of that.
THE ERROR WAS THE SCORING FUNCTION, not its constants. Using a full
training run to choose a feature's period is a wrapper method paying
wrapper prices for a decision that does not need one. The reference book
does not do this: ch. 3.3 selects inputs by measuring each candidate
indicator's CORRELATION with the target and dropping the ones with none,
with no network involved.
So: rank candidates by the MUTUAL INFORMATION between the resulting
feature vector and the triple-barrier label. MI rather than correlation
because the label is 3-class categorical and the features are not
monotonically related to it. Equal-FREQUENCY binning (rank-based),
because these features are ATR-normalised and heavy-tailed - fixed-width
bins put nearly everything in one bucket and report ~0 information for a
genuinely useful feature.
Scoring is arithmetic over the feature cache, so it costs seconds and its
cost is independent of topology: LSTM now tunes as fast as the MLP.
Coordinate sweep, not product sweep - cost is the SUM of per-parameter
candidate counts, so enabling every indicator stays affordable - with a
second pass that breaks early once nothing moves.
Sampling is IS-ONLY. Letting the OOS window influence which indicator
settings ship would mean the holdout had been used for selection and had
stopped being a holdout.
HONEST LIMIT, recorded because it is the price: MI is marginal, so a
parameter that only pays off in combination with another can be missed
(Guyon & Elisseeff 2003, filter vs wrapper). Given the wrapper it
replaces was ranking pure noise at 48 h a run, this is strictly better.
Deleted with it: GaRungEras/GaExtract/GaStore/GaMutate/GaRandomCandidate/
GaBlockCrossover/GaSortAliveByScoreDesc/GaBreedNextGeneration, 14 m_ga*
members, the GA_*/TUNE_POP_* constants, and ComputeTuneTrialBudget.
AND m_evalMode/m_evalEraBudget, because nothing set them any more - 28
read sites all permanently inert. That is not a tidy-up: the `if
(!m_evalMode)` guard on UpdateClassPriors is exactly what silently
disabled the imbalance correction for entire runs two commits ago. Dead
machinery that still reads like live machinery is this codebase's most
expensive recurring bug, and leaving 28 more instances of it would have
been indefensible.
The panel's tuning-progress state goes too - tuning no longer takes long
enough to need one.
Both builds compile 0 errors / 0 warnings.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Reported as "they seem to be signaling but the label stays stuck at no
directional call yet". The model was right and the panel was wrong.
m_cumIsTotal/m_cumOosTotal are LIFETIME, persisted counters - they are
what the panel presents as the product's accuracy - so they deliberately
skip m_evalMode bars: a throwaway auto-tune candidate must not pollute
the deployed model's reported win rate. That gating is correct and
stays.
The consequence was not handled. While an auto-tune search runs, EVERY
era is an eval-mode candidate, so both counters stay at zero for the
entire search while the model trains, signals, and draws arrows
normally. The panel therefore reported "no directional calls yet" -
directly contradicting the chart the user was looking at - for what is
the longest phase of a first run.
Three states now get three messages:
- search running -> "tuning (round N of M) - measured after"
- final winner retrain-> "training final model..."
- genuinely no calls -> "no directional calls yet" (era > 0), or
"measuring..." before the first era
Round-level progress rather than a bare "tuning" because each candidate
is a full training run repeated across seeds and generations, so this
phase runs for hours; a progress-free wait is indistinguishable from a
hang, which is how it was read.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Neutral collapse on all four topologies by era 5 with a 2:6 barrier
(recall Buy 0% / Sell 0% / Neutral 100%), and the panel stuck on
"measuring...". One root cause, and it was not the barrier.
The labels were fine: Buy 25.4% / Sell 22.0% / Neutral 52.5%, which is
exactly gambler's ruin for m=2,k=6 (2/8 = 25% per side), with only 0.1%
of Neutral coming from the vertical barrier - so the new m*k horizon
scaling is right, arguably generous.
What was broken: Train()'s era-start block wrapped UpdateClassPriors() in
`if(!m_evalMode)`. The auto-tune GA scores every candidate in eval mode,
and AutoTuneIndicators ships ON, so on a default configuration EVERY era
of the search ran with unmeasured priors. ApplyLogitAdjustment() requires
measured priors; without them it calls ClearLogitAdjustment() and returns.
So the entire search trained under PLAIN cross-entropy. With a 52.5%
majority class the optimum of plain CE is "always predict Neutral", and
that is precisely what all four models found. The panel followed: its
counters only advance on bars the model CALLED Buy or Sell, so a
collapsed model leaves them at zero and the line reads "measuring..."
forever.
This was latent, not new. It has been true for every auto-tuned run, but
it was invisible while the labels were near-balanced - last night's
accidental 1:1 barrier gave 43/40/17, where plain CE has no majority to
collapse into. Widening the stop to 2*ATR (correctly - 1*ATR is too tight
to survive noise) moved Neutral to the majority and exposed it.
The guard's stated fear cannot happen. These priors are measured from the
LABEL distribution, and the tuner only perturbs indicator periods
(MA/RSI/MACD/Ichimoku/AD). The barrier label depends on ATR, SL_Mode and
TP_Mode - none of which the search touches - so every candidate sees
byte-identical labels and identical priors. There is nothing to
contaminate. What the guard actually protected was the .stats write, and
that is gated separately: eval candidates never checkpoint and never
persist.
Also, because this is the THIRD quiet no-op to cost a run in this
codebase (after the fictional oversampling log line and the shadow-blend
skip):
- ApplyLogitAdjustment() now WARNS when it declines to install, instead
of silently clearing. A mechanism that cannot announce it is not
running is indistinguishable from one that is.
- The panel distinguishes "measuring..." (before era 1, nothing scored
yet - an honest warm-up) from "no directional calls yet" (eras trained,
zero calls - a finding, not a wait).
Both builds compile 0 errors / 0 warnings. No retrain forced by this
commit itself, but the collapsed models must be discarded.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
"Hybrid 3L [HYB-9369] - learning (era 4, 12%)" leads with a fingerprint hash
that means nothing to an owner. The tag earns its place in the journal and
the State\ folders, where telling one chart's model files from another's is
the whole point - but the default panel is the commercial surface and should
not open with a debug token.
New DisplayName() strips the bracketed suffix; the two plain-language panels
(training and live/idle) use it. Logs, the VerboseMode panels and the
auto-tune line keep the full ID, so nothing needed for diagnosis is lost.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Three separate reports from one deploy.
1. CONV, LSTM and HYBRID all came back tagged [4109]. The weights
fingerprint omits the topology type on purpose - the file path already
separates it (State\CONV\ vs State\LSTM\ vs State\HYB\) and hashing a
value that is constant within a folder buys nothing while re-keying
every trained model into a forced retrain. So the files were never at
risk, but the tag could not do its one job. Prefixing the short id
makes it unique on the display side only; the hex half still greps
straight to the .nnw inside the folder the prefix names.
2. The default panel read like a training console. Six lines down to
three, each answering a question an owner actually has. The deploy
internals (best score, eras-since-best, ladder stage) were developer
diagnostics describing a recall floor that no longer decides anything,
and were already in the era-end journal line. In-sample accuracy left
the panel too: it grades the model on bars it trained on, so it always
flatters, and showing it beside the honest number invites reading the
wrong one. New compile-time DebuggingMode constant - deliberately not
an input - carries the IS/OOS pair and the resolved model path into
the journal instead. No extra Inputs row, no extra Market description
line, no user-reachable firehose.
3. Panel drag and buttons stuttered under training load, exactly as the
2026-07-26 note raising the chunk budget to 200ms warned they might.
Backed off to the documented 120ms - worst-case click latency is that
budget - and the derived topology (~292k weights to ~29k) makes the
throughput this costs far cheaper than when that note was written.
Also halved the panel redraw rate to 2.5 Hz: ChartRedraw repaints the
whole chart, so its cost scales with accumulated arrows, and 5 Hz was
the larger half of the stutter. Era-end still force-refreshes.
Both builds compile 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Arrow cleanup existed on two paths - the panel's reset-weights, and the
topology-mismatch discard - but both are gated on there being a saved .nnw to
delete. The third case had no cleanup at all: a fresh topology at era 0 with no
weights behind it, which is what a changed config produces. A new fingerprint
makes a new m_fileName, so the previous model's files are not "discarded", they
are simply not this model's files, and nothing ever cleared the chart.
That is not cosmetic. Arrows outlive the model that drew them twice over:
1. The chart objects live in the CHART, not the sidecar, so they survive a
remove/re-add, a recompile, a restart and a fresh deploy no matter what
happens to any file on disk.
2. SaveChartSignals() rebuilds the sidecar by SCANNING the chart for
SIG_ARROW_PREFIX objects. So the first save of the fresh run adopts the
dead model's calls and writes them out under the NEW model's filename -
laundering them into the new model's history where nothing can separate
them afterwards.
Extracted the duplicated cleanup into ClearPersistedChartSignals(reason) - it
cancels the deferred restore queue, deletes m_fileName + ".arrows", clears the
namespaced chart objects and logs why - and called it from all three paths.
The call sits at the BuildFreshTopology() call site, not inside it: the genetic
tuner rebuilds a throwaway topology per candidate (AutoTune.mqh) and must never
touch the chart. All three sites run after m_fileName has its config fingerprint
appended, so they target the right sidecar.
Compiles 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
The simple panel showed "Buy/Sell accuracy: IS x% OOS y%" from m_cumIs*/
m_cumOos*, which are monotonic lifetime counters: never reset per era (only on
reset-weights) and restored from .stats across restarts. So the number is the
average over EVERY era ever trained. At era 217 one more era moves it by well
under a percent - it reads flat whether training is healthy or dead, and a model
that started badly and has since recovered still shows low.
That is the only number the non-verbose panel offered, so there was no way to
tell "still improving" from "stuck" while watching four charts.
Added the actual gate. The plateau ladder only auto-deploys a checkpoint that
cleared m_minDirectionalRecallPct on EVERY class (Buy AND Sell AND Neutral,
default MinRecall=60%). If nothing ever clears it, stage 3 deliberately refuses
to deploy, resets the ladder and keeps training to the era cap - correct
anti-collapse behaviour, but externally indistinguishable from being stuck.
Panel now shows:
- era against the cap, not just the era number
- the accuracy line explicitly labelled "(lifetime avg)"
- best balanced accuracy vs the per-class floor it must clear
- eras since best + ladder stage, so plateau escapes are visible
Display only - no training, selection or convergence logic touched.
Compiles 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
ExpertSignalAIBase.mqh was 8216 lines: the class declaration followed by 87
method bodies covering training, labelling, feature extraction, persistence,
chart drawing, online learning, the GA auto-tuner and inference, all in one
file. Train() alone is 1492 lines; a change to arrow drawing meant scrolling
past the era loop.
Moved the bodies into Expert\AIBase\, included at the bottom of the original
after the class declaration:
Training.mqh 1607 era loop, plateau ladder, checkpoint select, deploy
Features.mqh 1093 indicator creation + per-bar input feature vector
ChartUI.mqh 634 arrows, arrow persistence, status panel, cleanup
Persistence.mqh 492 .stats/.cfg sidecars, CPU-inference validation, copy
OnlineLearning.mqh 461 live continual learning, EMA shadow, OOS simulator
Labels.mqh 309 ZigZag pivot labels, async label-cache prebuild
AutoTune.mqh 275 genetic tuner (population, crossover, halving)
Inference.mqh 235 softmax, prior calibration, class priors
ExpertSignalAIBase.mqh 8216 -> 3131 (declaration + topology build only)
This is a pure relocation - verified mechanically, not by eye: HEAD's file
reconstructed from the eight partials plus the surviving remainder is
byte-identical to HEAD, span for span (scratchpad verify_split.py). No
declaration moved, no signature changed, no code rewritten, so behaviour is
unchanged by construction.
Compiles 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
AnimateDread