Leftover chart objects on long-history charts. Two causes, one of them
introduced by 07aa017.
THE ONE I ADDED. The filtered view's overlay draws up to
SIGNAL_RESCAN_LOOKBACK_BARS vote arrows. OnDeinit's EARLY VISIBLE-UI SWEEP
runs with skipArrows=true, which skips any prefix equal to SIG_ARROW_PREFIX -
and "WarSig_VOTE_..." starts with "WarSig_", so every one of them was skipped
by the one sweep that is cheap enough to always complete. They then sat in the
object list while the two expensive scans that follow walked it: a per-member
SaveChartSignals O(total) scan, then the by-name rescan. On a chart with years
of history that is thousands of extra objects walked twice, inside a teardown
budget measured from the stop REQUEST rather than from OnDeinit's first line.
skipArrows exists because the per-model arrows' sidecar is rebuilt by SCANNING
them off the chart, so they cannot be deleted before that write. Vote arrows
have no sidecar - they are a reconstruction, rebuilt on the next attach - so
nothing is preserving them and they now get their own prefix slot, deleted by
one native call in the first few milliseconds.
THE FIVE LOOPS. A time budget bounds THROUGHPUT, not latency to an unload, and
OnDeinit cannot begin until whatever is in flight returns. These all scaled
with history and none of them checked:
* Training passes 2, 2.5 and 3 yielded only on TRAIN_TIME_BUDGET_MS. Pass 1
has checked IsStopped() all along; the other three never have, and they
are the ones that grow with the bar count. Free to fix - the resume state
is written either way, so a stopped chunk simply is not re-entered.
* PruneDirectionalClusters: the one UNCHUNKED sweep left, once per era over
every bar, with its own header noting that raising the training budget
cannot help its cost. Now bails outright.
* AdvanceChartSignalRestore / AdvanceChartSignalRescan: chunked, but the
rescan runs a full feedForward per bar over up to 5000 bars and the
restore can hold MAX_RESTORED_ARROWS entries. Checked on the same
64-object stride as the clock read, since the check is not free either.
* AdvanceFilteredOverlay (mine, 07aa017) replays Direction() on every
classic filter per bar and had no check at all. Now per bar.
ChartUI.mqh had ZERO shutdown checks across six loops before this.
Nothing was added to the purge path itself: that is the work that must
complete, and an IsStopped() check inside it would abort unconditionally -
IsStopped() is already true by the time OnDeinit runs.
NOT COMPILED - user compiles in MetaEditor.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Adds DrawUnfilteredSignals (default OFF) and, with it off, replaces the
per-model arrow layer with the decision the EA would really have made.
THE FILTERED ARROW IS DRAWN AT THE ORDER, NOT AT THE THRESHOLD. Clearing
Min_Vote_Open is not the same as trading: a setup can pass the vote and still
never reach the broker (invalid SL/TP, stops-level, ATR warm-up, unsynced
swing history), and every one of those lands in OpenParams' failure branch.
So DrawVoteArrow() fires only after the order parameters validate, and the
failure branch withdraws any arrow already standing on that bar. One arrow is
one entry the EA would have placed - carrying the vote, the threshold it
cleared, and the SL/TP the order would have had.
Classic signals now draw too, under their own name and weight, so a chart
running MA/RSI/MACD/Ichimoku alongside the nets reads the same way an
ensemble chart does. They can only be drawn from the aggregate's once-per-bar
pass, because unlike the AI members they have no cached per-bar scan.
Two subtleties that would each have produced a quietly wrong chart:
- The raw classic draw sits AFTER filter.Direction(), not beside the
journaling block. GetActivePattern*() are CONSUMING reads holding the
PREVIOUS evaluation - "one tick later", which at Expert_EveryTick=false is
one BAR later. Keyed off those and placed at StartIndex(), every classic
arrow would have been drawn one bar early, which on a chart is
indistinguishable from a model that genuinely leads. Peek*() accessors
(non-consuming) let pattern, weight and bar come from one evaluation.
- CExpertSignalAIBase::DrawObject() early-returns instead of gating its five
call sites, so the switch cannot be honoured in three passes and missed in
the fourth. Its delete counterparts stay ungated so flipping the input off
and rescanning clears the raw layer rather than stranding it.
SIG_ARROW_PREFIX and g_signalsVisible move from ExpertSignalAIBase.mqh down
to ExpertSignalCustom.mqh - the nearest common ancestor - because the classic
signals cannot see the AI header (it is included later in Warrior_EA.mq5).
The vote layer gets SIG_VOTE_PREFIX under the same bare prefix, so
WarriorChartPrefixes()' purge still reaches every arrow without knowing they
exist.
NOT YET BUILT: the reconstructed history behind attach. Filtered arrows
currently start where the EA starts. See the next commit.
NOT COMPILED - user compiles in MetaEditor.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Four user-reported/requested items, one root cause chain:
1) DEAD CONTROL PANEL in AI_HYBRID mode. All members posted custom event
id 1 and handled id 1001, and CExpertCustom broadcasts every chart
event to every filter - so each posted event ran a train chunk in ALL
N members (N*N chunks per round) and the chart thread never idled
long enough to deliver clicks/drags. profiling.csv: 99.45% of time in
OnChartEventHandler. Fix: per-instance study-event ids
(STUDY_EVENT_ID_BASE + construction order, offset above the Controls
library's ON_* codes - id 1 was also ON_DBL_CLICK, so panel
double-clicks fired training chunks). ArmStudyEvent() is the single
post site; lost-event watchdog replaces the accidental
sibling-clears-my-flag rescue.
2) WARM-UP DUPLICATION. The auto-tune sweep is deterministic over
identical features/labels, and it ends in the full MI diagnostic
suite, which the MI-share gate never intercepted on the sweep path -
four members ran four identical ~36s sweep+report blocks. First
member publishes outcome (g_ensembleChartTuneDone/Installed/Settings);
the rest apply it and skip both.
3) DEINIT STRANDED PANEL+ARROWS (user repro 18:52). Root cause from the
log: the 4,500ms budget runs from MetaTrader's stop REQUEST - a heavy
autosave in flight ate it, OnDeinit got ~430ms and died in the first
member's arrow persist ("Abnormal termination" 432ms in). Fix: early
visible-UI sweep (native prefix deletes for status/panel/dialog)
right after ClearStatusLabel, and a fast path for still-training
models - their arrows are re-rendered every era, so they get one bulk
purge instead of scan+atomic-write in the death window.
4) ENSEMBLE FEATURES (user requests): era BARRIER - members advance era
by era together; a member ahead of the slowest still-training member
declines Train() calls and its chunk budget is donated
(TRAIN_TIME_BUDGET_MS = 120/activeTrainers, UI headroom constant).
COMBINED-VOTE OOS SCORE - each member's pass-3 scan contributes its
adjusted per-bar decision (0.0 on abstain) to a shared row buffer;
the last member to finish the era scores the averaged vote vs the
mirrored Min_Vote_Open against the same target-before-stop outcomes
members grade themselves on, publishing an "Ensemble vote" line on
the aggregated panel. Member headlines now carry their lifetime win
rate with break-even.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The ensemble chart UI had a shared-namespace defect that answered the user
question "what do the arrows represent?" with "a bug": all four members drew
arrows under the same WarSig_<bartime> object names, so the chart showed
whichever member rendered LAST, one member Neutral deleted another member Buy
at the same bar, each member init sweep wiped the arrows the previous member
had just restored, and SaveChartSignals - which rebuilds the sidecar by
SCANNING the chart - persisted every other member arrows into its own history
(the exact cross-model laundering its own header warns about, now happening
BETWEEN ensemble members).
Arrows are now namespaced per member (WarSig_PAI_, WarSig_CONV_, WarSig_LSTM_,
WarSig_HYB_): draw, delete, restore, prune, member init sweep, destructor
purge and the sidecar scan are all member-scoped, and the tooltip names the
model. Global purges keep matching the bare WarSig_ prefix, which covers all
member namespaces plus old-format leftovers from earlier builds.
Labels: the ensemble panel header no longer says "HYBRID ensemble" (HYBRID is
one member; the header is the ensemble) and the CONVLSTM member displays as
ConvLSTM instead of Hybrid. Its SHORT id stays HYB deliberately - it names the
model folder and changing it would orphan every model trained under that path.
Deinit: the alt-data mapping dialog namespace (WarriorAltMap_) joins
WarriorChartPrefixes, so both the OnInit purge and the deinit final sweep now
cover it - it was in neither list, so a dialog starved of its own Destroy()
left its controls on the chart permanently.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
All four ensemble members previously wrote their full multi-line panels
to the SAME global label objects - an ensemble chart would flicker
between four stacked panels covering the chart side (user request:
aggregate). Every AI-side SetStatusLabel call site now routes through
CExpertSignalAIBase::PublishStatus - solo charts draw the full panel
exactly as before; an ensemble member claims a slot and contributes
only its HEADLINE to one combined block ("HYBRID ensemble - N models",
then one line per model; the live line leads with the model current
signal). The combined render skips unchanged text and enforces its own
minimum redraw interval so four publishers cannot multiply
ChartRedraw() cost.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Leftover objects survived deinit because the cleanup list had drifted.
PurgeChart()'s own comment said it removed "our namespaced signal arrows
plus the status-label objects" while the code removed arrows ONLY, and
the panel prefix was swept at OnInit and nowhere else - so an ordinary
deinit left the status line, and any panel straggler, on the chart.
Three scattered call sites and a comment cannot be kept in step. There is
now ONE list - WarriorChartPrefixes() - covering arrows, status label and
panel, and one sweep, WarriorPurgeChartObjects(), used by every path.
Add a prefix there when a new object family appears and every cleanup
picks it up.
Two call sites added:
OnInit, before ANYTHING is drawn (including the status label it would
otherwise delete). Chart objects live in the chart PROFILE, not in the
EA, so they outlive the process: a deinit force-terminated at
MetaTrader's ~4,500 ms budget, a crash, a terminal kill, or an .ex5
replaced while attached all strand objects no later deinit will ever
own - and deleting the EA's files does not remove them, which is why
they read as corruption. Arrows are included: LoadChartSignals restores
them from their sidecar moments later and already opens with its own
arrow sweep, so this only removes orphans the sidecar does not account
for - the ones SaveChartSignals would otherwise ADOPT, since it rebuilds
that sidecar by scanning the chart.
OnDeinit, after ExtPanel.Destroy. Destroy walks an unbounded control
tree and ClearStatusLabel clears text rather than guaranteeing object
removal; either can leave a straggler and nothing looked afterwards.
Bounded work - three prefix deletes and one object-list scan - so it
respects the ordering rule that keeps the cheap visible cleanup ahead
of the heavy save. Arrows excluded: ShutdownChartCleanup already
persisted and removed them and re-deleting would race that write.
The two are complementary: the deinit sweep closes the ordinary case, the
OnInit purge closes the case where MetaTrader never let us finish. Only
the second can help after a starved shutdown.
Both sweeps rescan by name across EVERY object type and delete what the
bulk call missed. ObjectsDeleteAll's return has already been observed
disagreeing with a by-name scan of the same chart microseconds apart, and
object commands are queued on the chart rather than applied inline, so a
returned count is not evidence the objects are gone.
Panel create site now uses WARRIOR_PANEL_PREFIX instead of a literal, so
the name cannot drift away from the list that cleans it up.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
SignalClusterWindow 3 -> 10 for all topologies. On H1 a 3-bar window
collapsed only the tightest runs and left visible clusters at every
turn; 10 bars is closer to the spacing of genuinely distinct setups.
ALTERNATION. Rule 1 only collapses a same-direction run INSIDE the
window; past it a second Buy is emitted with no Sell between, giving
Buy/Buy/Buy/Sell. With both directions tradeable that sequence is the
model re-entering a move it is already in rather than finding a new
one. The kept sequence must now alternate: the first signal passes,
and after that a direction passes only if the last KEPT signal was the
opposite one.
Added to ALL THREE consumers, with identical logic, because they must
agree:
- NmsLiveAccept -> the live trade
- pass 3's OOS replay -> the tally the deploy gate grades
- PruneDirectionalClusters -> the drawn history
A rule applied to only some of these certifies one strategy and trades
another - the same defect class as the geometry the gate certified
while OpenParams placed something else (9a7c37f) - and would draw the
user arrows the EA would never have taken.
Deliberately NOT applied to the LABEL. The barrier target has no "must
flip" invariant: consecutive Buy labels are routinely correct, and an
earlier alternation gate was removed with the triple-barrier relabel
for exactly that reason. This filters what is ACTED ON, which is what
"applies to training" can honestly mean here - pass 3's declustered
tally is the training-side number that decides deployment.
BothDirectionsTradeable() is the stated precondition (with one side
disabled there is no opposite to wait for, so alternation would
suppress everything after the first call). This build has no
long-only/short-only input, so it is constant true - kept as a named
predicate so a future direction restriction has one place to change
rather than three call sites silently assuming both sides.
Build tag -> nms-alternate-v4.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The risk/reward explanation was a second, wrapped line and cost more
vertical space than it earned - that detail belongs in the journal,
where the geometry is already logged in full.
The comparison itself stays, in two words: "64% (unseen data, need
67%)". Without it the win rate reads as skill when it is the barrier
geometry's own base rate, which is exactly how four models sitting at
chance came to look like four models at 65%.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Chart objects live in the MT5 chart PROFILE, not in this EA's files.
They survive a terminal restart, a recompile, and deleting every
.nnw/.cfg/.stats/.arrows on disk. Only a deinit that RUNS TO COMPLETION
removes them - and MetaTrader force-terminates OnDeinit at roughly
4,500 ms, so a run killed mid-cleanup orphans them permanently with no
owner left to clean up after. That is the "deleted every file,
recompiled, restarted, old arrows and a stale panel still there"
report: nothing was wrong with the files and deleting them could not
have helped.
Both halves are fixed.
STOP OVERRUNNING THE BUDGET. OnDeinit used to finalise the in-flight
run (StopTraining -> FinalizeTrainRun: checkpoint restore, live-state
re-seed) and then write two full nets per chart. On four charts that is
the bulk of the budget, spent to preserve a PARTIAL era that was never
scored, never checkpointed and never deployable. FlushTrainRun()
discards it instead - drop the resumable bookkeeping, leave the net
neutral (unfreeze BN, flush the batch, batch size 1), skip the save -
and training resumes from the last completed era, which the era-end
save and the periodic autosave have already put on disk. What is
discarded is bounded by one era.
A CONVERGED model keeps the old finalise-and-save path: its weights can
carry online-learning updates made since the last era boundary, and for
a deployed model no further era boundary is coming to persist them.
MAKE CLEANUP SELF-HEALING. Every purge sat behind a branch - no model
loaded, sidecar missing - so the common paths returned leaving whatever
the previous instance stranded. LoadChartSignals now sweeps the arrow
namespace unconditionally before restoring, so the post-init chart
holds exactly what the sidecar holds whichever branch runs, and the
panel gets the same treatment before Create() (CAppDialog namespaces
its controls, so a killed Destroy strands the lot and the next attach
draws a second panel on the corpse).
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
"Buy/Sell calls correct: 65% (unseen data, lifetime)" is a WIN RATE -
m_cumOosCorrect advances on oTradeWon, did the implied trade reach its
target before its stop - not label agreement. A win rate means nothing
without the barrier that produced it.
With the measured geometry a trade risks slMult*ATR to make tpMult*ATR,
so under a driftless walk ANY directional call wins
slMult/(slMult+tpMult) of the time for free. On the shipped 3.33/1.62
pair that is 67.3%, and the empirical long-win base rate on this window
is ~65.7%. All four topologies read 65%: at chance, and below
break-even, while the panel announced "65% correct".
Four models with completely different trade counts agreeing on one
number was the tell - a win rate fixed by the geometry rather than
produced by the network. The deploy gate already benchmarks against
this null (chancePrecPct); the panel did not, and the panel is what a
buyer reads.
The line now carries its own break-even and the risk/reward that sets
it, so the number can never again be read as edge on its own.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The simple panel derived its percentage from pass 2's counters:
(m_isTrainCursor+1) / max(m_isTrainQueueCount,1). During pass 1 those are 0
and 0, so the expression is (0+1)/max(0,1) = 100%. An era spends its first
pass scanning ~38k bars - minutes of work - and the panel reported that phase
as finished the entire time. Observed by the user as "started learning at 100%
of their era and are stuck there", and it actively misled the diagnosis: the
one number on screen said the opposite of what was happening.
The UI cannot fix this on its own - it can see pass 2's counters but has no
way to know which pass owns them. So each pass now PUBLISHES its own progress
and a short phase name through TrainHeartbeat (which every pass already calls
per item), and the panel just displays them: "learning (era 45, scan 34%)".
Published before the heartbeat's 4096-item journal gate, so the panel updates
continuously while the journal stays quiet.
Both build variants compile 0 errors, 0 warnings.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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>
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>
