The previous commit persisted the tier ladder, which fixes this going
forward but did nothing for models whose .stats predates WST7 - they
still had to retrain to mint one. They never did. Every number a
converged model needs in order to vote is a pure function of weights
already on disk plus labels derivable from the chart, so replay them:
stage 1 build the label cache (existing chunked prebuild)
stage 2 rescan history (existing chunked rescan, deployed net)
stage 3 score + rank + persist (one walk over two arrays)
ScoreReplayFromCache() walks m_arrowSignalCache against
m_labelCacheBuy/Sell, fills the same m_oosTierFired/Hits and per-class
totals pass 3 fills, and hands them to RankTiersFromOos() - deliberately
feeding the existing ranker rather than reimplementing it. The shrinkage,
the chance reference and the module trust weight are subtle enough that a
second copy would drift, and a ladder measured by a slightly different
rule would be silently incomparable with every ladder training produced.
AdvanceDeployedRebuild() sequences the three stages off the timer. It has
to be a sequence: stages 1 and 2 are each minutes of work draining in
time-boxed slices, and stage 2's output is meaningless until stage 1 has
labels to score against. The previous version ran the rescan with no
labels at all, which is why it could only ever rebuild arrows and never
the ladder - the thing actually blocking the vote.
The result is written to .stats immediately. The failure being repaired
is state that lived in memory and was never written down; recomputing it
and not saving it would repeat that exactly.
Also routes every rescan completion through one hook, so there is a
single place that knows what a finished rescan means - republish for a
manual one, score and rank for a rebuild.
Compile-verified in _claude_stage: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
THIS IS NOT A DISPLAY BUG. A deployed model could not vote, or trade, at
any point after a terminal restart, and never would have.
LiveVoteContribution() returns 0 for every call until m_tiersSelfRanked
is set - deliberately, and correctly: before RankTiersFromOos() runs,
m_pattern_0..3 hold the constructor's stock 25/50/75/100, which since the
2026-08-18 currency change is the WRONG UNIT rather than a weak opinion,
and one unranked member would drag the whole ensemble over any threshold.
But that ladder is produced ONLY by a completed pass 3, and it was never
persisted - the code comment at LiveVoteContribution says so outright.
A converged model runs no further passes. So on every restart it lost its
entire vote permanently:
LiveVoteContribution -> 0 => no live vote ("0 vote/4 flat")
ReconstructionWeight -> 0 => overlay divisor 0 ("0 had a snapshot")
=> no arrows
=> no fired bars, so g_ensCumOosTotal stays 0
=> "measuring..." forever
Every symptom reported over the last three exchanges is that one cause.
The log is unambiguous: six H4 charts resumed at era 70/71, all 24
rescans completed with ~2700 Buy / ~2200 Sell per model, and the overlay
then swept 4999 bars finding "0 had a snapshot". The calls were there;
nothing was permitted to count them.
WST7 now stores the four tier weights, the module trust weight and the
self-ranked flag beside the model. Restored only when the stored flag
says the ladder was MEASURED - a .stats written before a model's first
pass 3 holds the stock ladder, and adopting that as if measured is the
exact error the flag exists to prevent.
A .stats predating WST7 has no ladder, so existing converged models stay
silent until their next scoring pass mints one. That case now prints a
warning naming all three of its symptoms, because each one independently
looks like a different bug.
Compile-verified in _claude_stage: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The sidecar added in 484a9d8 restores the vote arrows from the previous
session - but there was no previous session to restore from, and a
deployed ensemble could never produce one.
The overlay that draws the vote layer replays each member's
m_overlaySigSnap, published in exactly one place: RankTiersFromOos, at
pass-3 completion. A converged model runs no further eras. So after a
restart every member's snapshot was empty, would never fill, the sweep
had nothing to replay and the chart stayed blank permanently - no route
back by any path.
The chart rescan is the route: it runs the DEPLOYED net forward over
history and rebuilds the per-bar cache, which is the same quantity pass 3
produces, obtained without training. It already existed for the panel's
Show-Signals button; it just never handed its result to the overlay, so
on the default filtered view a rescan rebuilt only the RAW per-member
layer - the one that is hidden - and appeared to do nothing.
- PublishOverlaySnapshotFromCache() extracted from RankTiersFromOos, so
the era end and a completed rescan publish through one implementation.
- A completed rescan now calls it, which also arms the sweep.
- PollTraining auto-arms one rescan for a model that is converged, has no
snapshot, and is on the filtered view. One-shot: a model that
legitimately calls Neutral everywhere must not rescan forever chasing a
snapshot that is correctly empty. On the timer, not in OnInit - it is a
full feedForward per bar over up to 5000 bars and drains in the same
time-boxed slices as a manual rescan.
Together with the sidecar this closes both halves: the rescan covers the
first session and any chart whose file was lost or invalidated by a
threshold change; the sidecar covers every session after one is saved.
Compile-verified in _claude_stage: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Four reported symptoms, three of them one root cause: the ensemble's
certified record was session-scoped and written ONLY at pass-3
completion. A deployed ensemble runs no further eras, so every restart
lost the aggregate win rate, the aggregate panel line and the overlay
snapshots - and could never regenerate them, because regeneration only
happens at an era end that will never come.
THE SELF-CONTRADICTION. Member rows read "Live - learning from new bars"
(from m_trainingComplete) while the line under them read "training, not
tradable yet" (from `prospective`, which means "this number came from
ProspectiveVote() rather than a real Direction() call" - what happens on
any bar where every member abstains, and which says nothing whatever
about training state). Both now resolve through one predicate:
WarriorChartModelsDeployed(), fed by members publishing their own state
on the same slot and cadence as their vote. Adds a third verdict word,
"armed (bar still open)", for a deployed model on a prospective
recompute - the case that used to claim it was training.
DEPLOYED PANEL. Once every published model is converged the per-member
rows are dropped: what ships is the aggregate vote win rate, the live
vote, and the verdict. While training the rows stay - they are the only
way a collapsed or lagging member is visible, since a collapsed member
abstains and so is invisible in the aggregate by construction.
ACCURACY NOW RESPECTS THE ENTRY THRESHOLD. The panel's "precision 65%"
came from m_cumOosCorrect/m_cumOosTotal, which counts every bar a model
called Buy or Sell - threshold-blind, and per-model rather than
per-vote. The correct number already existed (votePrecPct: bars where
|vote| >= threshold and the direction policy allows) and is now what the
panel shows, with the threshold named in the text because the number is
meaningless without it.
VOTE ARROWS PERSIST. With DrawUnfilteredSignals off - the default - the
chart shows SIG_VOTE_PREFIX arrows, and nothing saved them:
CChartUI's .arrows sidecar is member-scoped and never saw that layer.
New CVoteArrowStore mirrors them to a chart-keyed sidecar and restores
them progressively at init, on the same budgeted non-blocking path.
The header stores the open/close thresholds; a mismatch on load DISCARDS
the arrows rather than redrawing a picture of a strategy no longer
configured - stale arrows are worse than none, because none is visibly
empty and stale is confidently wrong.
Also: .stats bumped to WST7 carrying the ensemble record (guarded on
threshold match, most-complete-copy-wins), and the loader's version
tests collapsed from an or-chain to ">=" - the magics are ASCII 'WST1'..
'WST7' so they are already ordered, and a missed arm in that chain reads
the NEXT field's bytes into this one, which fails as plausible numbers
rather than as an error.
Compile-verified in _claude_stage: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Refresh() returning false skipped the whole of Processing(), and
Processing() is where CheckClose() and CheckTrailingStop() live. So on
any tick with unusable quote history, a failed RefreshRates(), or a
period-flag mismatch, an already-open position got no exit check at all -
it rode. Invisible by construction: nothing logged, no order sent, and
next tick the position looks exactly as it should. The only trace is a
stop that should have moved and didn't.
ProtectOpenPosition() now runs the CLOSING half of Processing() on those
ticks. Only the closing half, on purpose: CheckReverse() and the
pending-order block both OPEN exposure, and opening on data just declared
unfit to trade on is the opposite of the point. Closing on an imperfect
quote reduces risk even when the quote is wrong; opening on it does not.
When it acts, it says so in the journal - a degraded-path exit should
never be silent.
Also pins the invariant at the Expert_EveryTick gate: that input
throttles how often the EA forms an OPINION, never how often it can act
on a position it already holds. Exits stay above the gate, and the
comment now says so to the next person editing it.
Pre-existing hole, not introduced by the EveryTick work in 6d48fdb -
that change is what made it worth reading the tick path closely.
Compile-verified in _claude_stage: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Five modes went, all of them staking real risk on the model's confidence:
Intelligent entry (ENTRY_INTELLIGENT), stop (SL_INTELLIGENT), target
(TP_INTELLIGENT), trailing (CTrailingIntelligent) and lot size
(CMoneyIntelligent's quarter-Kelly). With them, the Confidence_Source
input and the CONFIDENCE_SOURCE enum, whose only job was choosing which
number those five read.
The reason is calibration, not correctness: the confidence magnitude is
known to be miscalibrated against the label prior, so every one of these
modes multiplied money by a quantity whose units were never established.
The DB arm had a second, independent defect - since the tester DB guard
(SignalDatabaseActive) it reads 0 in tester and optimizer but non-zero
live, so any backtest of CONF_DB/CONF_BLENDED could not reproduce live
trading. And what the DB produces is a filter-RANKING win rate, not a
per-trade win probability.
Both confidence numbers are still recorded per trade (aiConfidence /
dbConfidence) and still bucketed against outcome in TradeJournalReport.
Recording is what keeps the question answerable; acting on it was the
part with no evidence behind it. ConfidenceBridge.mqh now carries an
explicit telemetry-only rule at the top.
ENUM ORDINALS PINNED. Removing a member vacated a value in four enums at
once and MT5 does not validate an enum input replayed from a saved .set
or a stored optimization pass. TRAILING_STRATEGY and
MONEY_MANAGEMENT_STRATEGY now carry explicit values so the survivors keep
the numbers they were saved as, and ValidateBarrierInputs is widened into
ValidateTradeManagementInputs covering SL_Mode, TP_Mode,
Entry_Multiplier, TrailingStrategy and MM_STRATEGY. Without that gate a
chart saved with the Intelligent stop would feed SL_Mode = -1 into a
multiplier now used verbatim, placing the stop on the wrong side of entry.
RETRAIN-NEUTRAL: neither SL_Mode nor TP_Mode appears in
BuildModelFingerprint() or ComputeDbConfigFingerprint() since the
swing-pivot target replaced the barrier labels. No .nnw, .cfg or .db
re-keys. Also drops the now-dead g_TradeRewardRiskRatio bridge, the
CMoneyRiskBase::AdjustRiskAmount hook and the unsigned AIConfidence().
Compile-verified in _claude_stage: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Three related changes, all aimed at work being repeated at a frequency
nobody chose.
1. OnDeinit gets a tester/optimizer fast path.
Everything in the live teardown exists to leave a CHART clean and a live
model's state on disk. An optimization agent has neither. It was still
running, on EVERY pass: a per-signal arrow-sidecar WRITE
(ShutdownChartCleanup -> PersistAndClearChartSignals) plus two full
chart-object scans plus a ChartRedraw. At optimization scale that is
hundreds of thousands of pointless file writes per agent, against a
~4,500 ms budget MetaTrader force-terminates on - the shape of thing
that stalls an agent rather than failing it.
The fast path keeps MarkShutdown() and FlushTrainRun() (so a killed pass
never leaves a half-written era) and still calls dbm.Deinit() and
Expert.Deinit() - leaking the signal tree or a handle across passes is
its own way to accumulate into a stall. The two now-unreachable
!isTesterRun guards further down are folded away.
2. All four tester handlers are present and documented by WHERE THEY RUN.
OnTesterInit/OnTesterPass/OnTesterDeinit run in the CONTROLLING TERMINAL
once per session; only OnTester runs on the agent, per pass. OnTesterPass
was missing entirely - added empty and deliberately so: it only fires for
passes that shipped FrameAdd() data, which this EA never sends, and
reading frames there would put per-pass work on the terminal's critical
path. Declared so that adding frame-sending later fails loudly instead of
silently dropping every frame.
3. Expert_EveryTick is now actually enforced.
It was passed to Expert.Init() and only ever reached StartIndex() - which
bar a signal READS. The whole pipeline still ran on every quote. It now
gates m_signal.SetDirection() in CExpertCustom::Processing(): that call
drives Direction(), which is a TRANSACTION (NN forward passes, DB rows,
chart arrows, one-shot vote state), and re-running it on every tick of a
4-hour bar repeats all of it.
Scoped deliberately. Everything after that line still runs per tick -
CheckReverse/CheckClose/CheckTrailingStop and pending-order maintenance
are risk management, and a stop that only trails at bar boundaries is a
different strategy, not a faster one. The scheduled close-all in OnTick()
matches a +-1 MINUTE window, so bar-gating it on H4 would step straight
over the thing 100% of label timeouts already resolve against.
g_riskBudget.Update() also stays at quote frequency, by design.
System/NewBar.mqh becomes CNewBar, a class. The free function it replaced
had zero callers and kept its watermark in a `static`: ONE watermark
shared by every caller, so the first caller each tick consumed the
transition and every other caller was told "no new bar" for a bar that
had just opened. Per-instance state fixes that; first observation counts
as new, so a fresh attach acts immediately instead of idling up to a full
bar.
Compile-verified in _claude_stage: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
~2,300 lines. META had real, repeatedly measured ranking skill and ZERO
operating points that ever cleared break-even (0/350 H1 eras, 1/999 H4
pre-2-sigma, 0/8 pooled fitted points). The clinching arithmetic was edge x
width = 0.095 ATR/trade against spread 0.099 ATR/trade, and the
dose-response showed the high-conviction tail is temporally unstable -
the precision-vs-threshold slope flips sign between calib and test on 3 of
4 symbols, so no ex-ante threshold rule exists. It shipped default-off and
never gated a live entry. The self-measured tier weights are what actually
rank the vote, and all six H4 instruments converged on them alone.
RETRAIN-NEUTRAL, and that is the property that made this safe:
- The weights fingerprint emitted "|TGT:META2" or "|TGT:SWG1" from an
if/else. Every direction model already took the SWG1 arm, so
collapsing it to an unconditional append is byte-identical. No .nnw or
.cfg is orphaned or re-keyed.
- NetInputWidth() lost its "+ MetaDescWidth()" term. MetaDescWidth()
returned 0 for every direction model, so the input layer is unchanged.
- DbLegacyAiSlot()'s slot 5 was reachable only with all four Use_* NNs
off AND meta on - a config that never shipped. Every existing .db keeps
its filename.
Deleted outright: Signals/SignalMETA.mqh, Expert/Trading/MetaGate.mqh (the
directory is now empty), Expert/Training/{MetaCorpus,MetaCandidateStore,
MetaFamilies}.mqh, Tests/Test_MetaFamilies.mq5, Meta_Labeling_Design.md.
Unwound in place, the delicate part: Training.mqh carried four
IsMetaTarget() branches whose else-arm WRAPPED the direction body (pass 1
queueing, pass 2 backprop, pass 2.5 calibration, pass 3 OOS scoring). Each
wrapper is removed and the direction body promoted back to its original
nesting - the bodies were never re-indented when the wrappers were added,
so the promoted code is byte-identical to what ran before META existed.
Also gone: the ensemble verdict's meta-veto replay and its
approved/vetoed/unscored counters, the per-family/per-side OOS
decomposition arrays, the m_isTrainQueueCand parallel queue and its
lockstep shuffle, and the S2 era report.
Also removed: the CMetaGate abstraction and the live CheckOpenPosition
veto; m_gates plus AddFilter's non-voter routing and IsVotingSignal()
(META was the only non-voting child, so m_gates was always empty);
m_parentSignal/SetParentSignal (existed only to reach the root's gate);
SweepPrepare/SweepPrepareIndicator (only caller was the corpus sweep);
IsMetaTarget() from all four view interfaces and their adapters;
Use_MetaLabeling, EnableMETA, Meta_ExportDataset, m_trainTarget.
EvalShift is KEPT - HistoricalNetVote() uses it for the filtered overlay,
not just the corpus sweep; only its comment changed. The 2-output softmax
arm in NetForward.mqh is kept too: it costs nothing and is the reusable
binary-head path, now commented as unclaimed rather than as META's.
Compile-verified in _claude_stage: 0 errors, 0 warnings, matching the
pre-edit baseline.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Two removals of work that a backtest was paying for and never using.
1. SignalDatabaseActive() gates the signal DB off in tester/optimizer.
A backtest opened the fingerprinted SQLite DB under FILE_COMMON - and so
did every parallel optimization agent, against the same file, with the
per-tick journal Update() behind them. Measured 2026-08-25 on a 12-agent
SP500 H4 run: zero passes completed in 75 minutes.
It bought nothing, for a reason specific to this EA's current shape: the
DB's only effect on a trading decision is ApplyPatternWeight overriding a
filter's module weight, and that is declined for any self-ranking filter
(CExpertSignalCustom's !filter.SelfRanked() guard). The AI members
self-rank once their tiers are measured, and the classic votes that DID
consume the ranking are gone - so a tester run's DB was written and never
read. Skipping it changes no decision.
One predicate, not two inline guards: OnInit asks the question twice
(InitDatabaseAndJournal, then VerifyDatabaseTransactionCycle) and a run
where those disagreed would try to open a database it never initialised.
The tester now takes journal.InitTrackingOnly(), so close detection,
MAE/MFE and the expectancy-stop feed still run - only the SQLite half is
dropped, and Update() already skipped its INSERT when there is no DB.
Caveat recorded at the predicate: if a future filter consumes DB ranking
WITHOUT self-ranking, this needs revisiting - a backtest would then stop
reproducing live.
2. ExportFeaturesOnly and its two exporters are gone.
Research-only CSV dumps (feature matrix + a hardcoded 8-symbol x 5-TF raw
rates grid), superseded by the research/ python path that reads its own
data. Removed the input, m_exportFeaturesOnly, the setter, both method
declarations, ExportFeatureMatrix()/ExportRawRates() (111 lines in
AutoTune.mqh), the OnTick early-return, and the ctor initialiser.
The config-lock bypass it owned collapses to the plain tester test:
`if(!inTesterOrOpt && !AcquireConfigLock())`. Shared helpers it called -
ServableBars, EnsureBarCachesCapacity, ResizeBuffers, RefreshData - all
have other callers and are untouched.
Compile-verified in _claude_stage: 0 errors, 0 warnings, identical to the
baseline taken before either edit.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Two chart-display fixes reported after watching a converged 4-model
ensemble: the ensemble panel's trailing "(era 69, 4 models,
DEPLOYING)" was frozen at whatever era the ensemble happened to
deploy on, and the separate top-right HUD (one line per model, raw
B/S/N + weight + era + error) was clutter once the vote itself is
what matters.
Root cause of the freeze: g_ensembleVoteLine is written once per era,
at pass-3 completion. A deployed/converged ensemble runs no further
eras (ScheduleTrainingIfNeeded's trainingComplete branch skips
Train() entirely), so that line could never update again - the era
count and "DEPLOYING" marker were permanent set-dressing from the
deploying era, not a live reading.
- EnsembleScoreCombinedVote() drops the era/DEPLOYING tail once
g_ensDeployApproved - nothing left there worth freezing.
- UpdateVoteReadout() (the aggregate "VOTE ..." line, previously its
own top-right chart object) now writes g_liveVoteLine instead of
drawing anything. Both status-label builders - PublishEnsembleStatus
for the ensemble panel, PublishStatus's choke point for the solo
panel - append it as one line, refreshed every tick/timer exactly
as the old HUD was, so the live vote replaces the frozen era tail
in the same visual slot.
- RefreshVoteReadout()'s per-member loop (DisplayHudLine, one
ObjectLabel per model) is deleted outright rather than folded in -
the operator asked for the aggregate only, "without telling me each
individual network".
Follow-on dead-code removal, since DisplayHudLine was the only
caller: the DispProb/DispSignal/MetaGateArmedNow/MetaHasScore/
MetaLastP/MetaLastBe/MetaApproved/MetaVetoed leg of IChartView (and
its AIBaseChartView/AIBaseChartViewImpl/ExpertSignalAIBase forwards)
had no other reader. The underlying data survives untouched -
m_metaTelemetry is still populated live by SignalMETA.mqh,
m_dispSignal still feeds ProspectiveVote - only the chart-view
forwarding that existed solely to reach the deleted HUD is gone.
Compile: 0 errors, 0 warnings (stage).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
DIRECTION_INTELLIGENT and the drift verdict it fed were removed in
the step-3 demolition (8f21646); WarriorDirectionAllows() now
resolves purely from tradingdirection (LONG_ONLY/SHORT_ONLY/BOTH).
Two comments in the OOS-verdict certification path and the filtered-
overlay reconstruction still described the deleted mechanism -
found while auditing both paths for correctness. No behavior change.
Compile: 0 errors, 0 warnings (stage).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The capacity budget is stated in weights per INDEPENDENT observation
and divides by the mean label lifespan to get there. It never once
did: EstimatedInSampleBars() deflates via m_labelOverlap, but it is
only ever called from InitNeuralNetwork, where the label cache does
not exist yet (that same function sets m_labelCachePrebuilt = false
a few lines below), so MeanLifespan() returned its "nothing measured"
default of 1.0 at every call. Every fresh model was sized as though
its labels did not overlap - over-budgeting the first dense layer by
a factor of L, which is several rungs of a power-of-two ladder. The
"expect overfitting, reduce the feature set or pool instruments"
warning is the branch that should fire on H1 and structurally could
not.
Fixed at the source rather than by reordering the boot sequence (the
prebuild is chunked across Train() calls and cannot complete inside
init): MeasureSwingGeometry() walks the ZigZag ONCE at init and
answers both questions from it - the median leg gives the window,
and the leg series gives the mean label lifespan analytically.
SwingPivotDirectionLabel resolves bar i when the SECOND pivot after
it commits, so a bar d bars before pivot P waits d + (the leg
leaving P); summed over every bar of every leg that is exactly the
mean the label walk accumulates.
That also closes the coherence gap the swing target opened: the
window was measured with a private +/-12-bar fractal while the label
aimed at ZigZag(12,5,3) pivots, so it was sized against a leg
distribution the label never used. One pivot source now, the
label's.
Also:
- ResetWeights() re-derives the shape. It rebuilt from the members a
history-starved init had pinned and re-saved them - so the "let
history download, then reset from the panel" advice in both
fallback warnings did nothing at all.
- The CAPACITY line prints the measured lifespan beside the one the
topology was sized for, and warns when they differ by more than a
ladder rung. That is the check that makes the estimator falsifiable.
- Topology reads the view's symbol, not _Symbol (latent for pooling).
- Unmeasured geometry defaults to HISTORY_BARS_FALLBACK, never 1.0:
under-sizing is recoverable, over-sizing silently is not.
Compile: 0 errors, 0 warnings (stage).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The MI suite kept its one irreplaceable job - the label-alignment
lookahead scan, whose margin is priced by the headline permutation
null and whose validity is proven by the positive control. Everything
that judged or vetoed on top of that measurement is gone:
- m_dirEvidence deploy veto deleted from all four deploy sites. The
policy is that screens are priors, not gates; the family-wise
selection test on held-out precision is the deploy protection, and
a marginal per-bar MI test cannot veto a model that reads the
window jointly (the report itself said so on every print).
- Per-column CFeatureSelector deleted; BlockPermuteLabels (the null
engine ScoreMiSample depends on, ragged-tail fix intact) moves to
AutoTune.mqh as a free function.
- Feature-lag profile deleted, with its MI_LAG_* constants and
BuildMiSample's featureBarOffset; MiShiftPad no longer pads by
m_historyBars.
Compile: 0 errors, 0 warnings (stage).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Step 3 of the swing-pivot plan, whole-hog. The swing label is now the ONE
target and the era verdict is precision + recall per class against the
label's own base rate - no win rate, no break-even, no expectancy, no
geometry anywhere in training.
DELETED
- Expert/Excursion/ (4), Expert/BarrierHorizon/ (4), GeometrySweep,
FirstPassageLadder, Labeling/TripleBarrier.mqh (CLabelOverlap survives
in Labeling/LabelOverlap.mqh), 3 test EAs.
- TripleBarrierLabel + walk, fractal label, geometry derivation/scan/
adoption, exit-policy replay, excursion MI targets, the drift verdict
(DIRECTION_INTELLIGENT), the recall floor, balanced-accuracy telemetry,
the barrier defines, the .cfg geometry adopt (slots kept as zeros for
the positional layout), the derived-geometry live-order override.
- TRAINING_TARGET input/enum: direction models are always swing; META2
re-keys the meta head onto label agreement (descriptor loses its two
geometry slots).
REWORKED
- Labels.mqh (1795 -> ~370 lines): AdvanceSwingLabelState with
FINALITY-GATED CACHING - an unresolved bar (pivot pair uncommitted) is
never cached, so it can never freeze as a false Neutral; training,
calibration, OOS scoring and online learning all skip unresolved bars.
- SDeployVerdict: significance-only; SOosTally chance = larger
directional class share; pooled gate poolability = timeframe (record v2).
- Purge/embargo/declustering gaps: the measured mean label resolution
lag (LabelResolutionBars), not a barrier horizon.
- Pool purge key + backfill DB rows: marked at the bar the label
resolved on (m_labelResolveAge), not a fabricated barrier touch.
- Online learning frontier: finality, not a horizon delay.
- m_bestBalancedOos -> m_bestSelectionScore, m_erasSinceBestBalanced ->
m_erasSinceBest, ensemble vote outcome arrays -> label arrays.
STEP 4 folded in: Entry_Multiplier / SL_Mode / TP_Mode / tradingdirection
are inputs again - trade management is the tester GA's search space.
Fingerprints: every direction model re-keys (TGT:SWG1 now unconditional,
CUT token gone); META1 -> META2. Full retrain, as planned.
Compile-verified in _claude_stage: Warrior_EA + both surviving test EAs,
0 errors, 0 warnings each.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
- TrainingTarget defaults to TARGET_SWING.
- LogitAdjustTau input, preset enum and all plumbing deleted: tau is fixed
at 1.0 (the full log-prior, Menon et al.'s consistent value); the
delivered strength is capped to the head's usable logit range from the
priors the prebuild measures. The CAPPED journal line is the step-1
measurement. |LA💯BS becomes a frozen legacy fingerprint slot, so no
existing model re-keys.
- The swing label measures its own resolution lag (idx - P2, the earliest
bar P1 can be final on) into the overlap/SE machinery, capped at
SWING_SCAN_CAP_BARS instead of a barrier horizon it does not have.
- The prebuild line is target-aware: both-won, timeout and horizon-lifespan
fragments are barrier-walk facts and no longer decorate swing counts.
Compile-verified in _claude_stage: 0 errors, 0 warnings.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Operator's observation, verified against Examples/ZigZag.mq5's selection loop:
the only erasures it performs are ZigZagBuffer[last_high_pos] while hunting a
bottom and ZigZagBuffer[last_low_pos] while hunting a peak. A pivot therefore
leaves the erasable slot permanently the moment the OPPOSITE pivot is committed,
and can never move again - the opposite pivot does not itself need to be final.
SwingPivotDirectionLabel now waits for that event instead of for
m_swingConfirmationBars. The bar aims at P1, so it becomes trainable once P2
exists; pivots alternate by construction, so P2 is the next non-zero bar and
needs no type test. Until then the label is not knowable and the bar is Neutral.
Exact rather than a guess, and it removes the need to measure a repaint-lag
distribution at all. SwingConfirmationBars keeps its other uses; it is no longer
this target's lookahead control.
Compile-verified in the staging copy: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Series indices are relative to now, so one new bar moves every cached bar's
index by one. EnsureBarCachesCapacity answered that by wiping the label cache,
the excursion caches, the ladder and the feature cache and rebuilding the whole
prebuild from scratch - on any timeframe where a bar closes before a run
finishes, the labels were being recomputed continuously and the training set
never held still.
The labels do not change when a candle closes. ShiftBarCaches moves every
per-bar cache up by the number of new bars, marks only those newest bars as
unfilled, and leaves the rest exactly as computed. CFirstPassageLadder gets a
matching Shift (resizing directly rather than through Allocate, which zeroes the
ages this is preserving).
Refuses, falling back to the full rebuild, when a prebuild is mid-flight: its
cursor is an index into the array being moved.
Compile-verified in the staging copy: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
TARGET_SWING: the direction models learn which way the next CONFIRMED SWING
PIVOT lies from the current close. Geometry-free - the label owes nothing to a
stop, target or horizon - which is what lets trade management be tuned
separately instead of being baked into what the net learns.
SwingPivotDirectionLabel reuses the ZigZag pivot the horizon and leg-size
measurement already walk, so there is ONE notion of "pivot" in the codebase. It
walks forward in time and stops at m_swingConfirmationBars: a pivot nearer than
that is still repainting, so its label is not knowable yet and the bar stays
Neutral. That boundary is the whole lookahead control for this target.
TrainingTarget input is back (TARGET_BARRIER default, unchanged behaviour) with
TARGET_FRACTAL and TARGET_SWING beside it; |TGT:SWG1 joins the fingerprint so
switching trains a separate model rather than relabelling an existing one.
ADZigZag was renamed to ZigZag throughout (30 identifiers). It has loaded
MetaTrader's stock Examples\ZigZag at its stock defaults for some time - the
migration was done, only the name was left behind, and a name that says "AD"
about a stock indicator is exactly the legacy pointer this codebase should not
carry. No behaviour change: same #resource, same params.
Compile-verified in the staging copy: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
First 35 eras across both charts, this run:
shipped 1.21/2.43 (SP500) and 1.26/2.52 (USDJPY): mean -0.0525R,
positive in 6 of 35 eras
best plateau after the neighbourhood guard: mean +0.0292R,
positive in only 17 of 35
most-recommended pair: 20.00/0.50, seven times - a ~40:1 lottery that is
simply the least negative cell in an all-negative grid
The recommendation jumps between opposite corners of the ladder between
consecutive eras, which is a grid fitting noise rather than a geometry worth
adopting. Two changes so the line cannot be misread:
- GEOSWEEP_MAX_TIMEOUT_SHARE (0.70): a cell where most trades never touch
EITHER barrier is not a geometry being tested, it is the horizon close being
measured. 20.00/20.00 timed out on 100% of trades and was still selected.
Excluded from SELECTION only; the cell stays filled and readable.
- When the winning plateau is <= 0 the line now says so in those words:
"NOTHING ON THE LADDER PAYS ... the pair below is the LEAST NEGATIVE cell,
not an edge."
Still measurement only - nothing reads the recommendation and no geometry moves.
Compile-verified in the staging copy: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Step 1 of decoupling SL/TP from training. The geometry is currently chosen
BEFORE the model exists - excursions -> stop at a quantile -> target at the
policy minimum ratio -> labels -> the net learns those labels - so it has never
been asked which pair maximises expectancy GIVEN WHAT THE MODEL CAN PREDICT.
The scan meant to answer that reports "0 ELIGIBLE candidates" on this config
(every rung disqualified by the close-all clamp), so nothing has ever compared
the shipped pair to an alternative.
This needs no retrain and no backtest. CFirstPassageLadder already stores the
first-touch AGE of every rung on both sides and OutcomeR() resolves ANY pair
exactly with the spread charged the way the fill charges it - so 14x14 pairs
over one era's OOS calls is a few thousand array reads.
- Expert/Training/GeometrySweep.mqh: CGeometrySweep accumulates (n, sumR,
sumR^2, timeouts) per rung pair from the model's own directional OOS calls.
Reads no chart, holds no net, opens no file - exercisable against a
hand-built ladder, same doctrine as SDeployVerdict.
- Best() ranks on the 3x3 NEIGHBOURHOOD mean, not the cell itself. A 14x14 grid
read at its single highest cell is a best-of-196 maximum, biased upward by
construction - the same selection problem the deploy gate corrects across
eras. A pair whose neighbours also pay is a plateau; a lone spike is a lucky
run of trades and does not survive the next window. GEOSWEEP_MIN_TRADES (30)
keeps thin cells out of the selection entirely.
- Wired into pass 3 where the call and the bar index are both in hand, reset per
era, reported at pass-3 completion beside ReportCandidateGeometry. ONE line,
and only when the recommendation CHANGES - it prints the shipped pair's
expectancy and the best pair's on the SAME trades, so "better" is a difference
rather than two numbers from two populations.
Measurement only: nothing reads the recommendation yet and no geometry moves.
Compile-verified in the staging copy: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
selectionScore used to be a win rate in percentage points and printed at one
decimal everywhere. Under DeployOnExpectancy it is expected value in R, so
"%.1f" rendered every real score as "0.0" - era 2's +0.05R and a genuine zero
looked identical, which makes the journal useless for watching the ranking the
plateau ladder is doing.
One formatter, DeployScoreText(), next to the score it formats: "%.3fR" under
expectancy, "%.1f%%" under significance. Routed all nine print sites through it
(ensemble era line, best-so-far, panel, regression, new-best, era-cap prompts,
the convergence line, the deploy dialog) and dropped the "%" suffixes they had
hardcoded. No new prints, no new log lines.
Compile-verified in the staging copy: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
TWO CHANGES, both of which turn a permanent "nothing happens" into a decision.
1. THE DEPLOY GATE ASKS THE WRONG QUESTION. tradeable required the win rate to
clear chance by EDGE_MIN_SIGMAS - "can I PROVE an edge exists" from one OOS
window. On H4 that asks ~66% against a market supplying ~53%, so it is
unreachable by construction and no run has ever deployed through it.
SDeployVerdict now also carries the economics of the geometry actually being
traded - cost-adjusted break-even and reward:risk, both from the new
CostAdjustedGeometry() so a spread convention cannot be applied to one and
missed on the other - and derives
E[R] = (p - p*) * (1 + RR)
which is exactly zero at break-even by construction, so "profitable" and
"beats break-even" can never disagree. Under DeployOnExpectancy (new input,
default ON) tradeable becomes E[R] > 0 and selectionScore ranks eras by
expectancy instead of precision. Coverage and both-sides-live still gate
both: an expectancy over a handful of one-sided calls is not tradeable.
The struct also publishes scoreSE - the SE of selectionScore IN THE SCORE'S
OWN UNITS - because the score changes units with the objective (win-rate
points vs R). Both plateau bands now read it instead of precSE, which was
right for one objective and dimensionally wrong for the other.
Setting DeployOnExpectancy=false restores the previous behaviour exactly.
2. THE FILTERED VIEW COULD NOT DRAW WHILE ANY MODEL WAS TRAINING.
HistoricalNetVote built its divisor from VoteCapableWeight(), which answers
"may this member move real money" and returns 0.0 for an AI member until the
whole run converges. So the reconstruction's divisor was zero on EVERY bar,
every bar was skipped as "nobody looked", and the chart drew nothing at all -
for the entire training run, which before the plateau noise band was forever.
Reported as "no signals drawn since the refactor".
New ReconstructionWeight(): the same weight WITHOUT the converged-run
requirement, overridden on the AI member to ModuleWeight() gated on
SelfRanked() only. The overlay is a picture of what the vote WOULD have
shown, which a mid-training model can answer - the chart HUD already says so
with its "(trn)" marker. Live Direction() still uses VoteCapableWeight(), so
no untrained model gains a say in an order.
Compile-verified in the staging copy: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
TWO INDEPENDENT BLOCKERS, both of which make the EA look like it is working.
1. THE LADDER NEVER ADVANCES. isBetter/isBetterEra compared selectionScore with
a bare `>`. selectionScore is a win rate over a few hundred independent
calls, so it moves several points era to era on noise alone - measured on
SP500 H4 today: 32.8 / 32.2 / 31.6 / 29.6 / 31.4 across consecutive eras, a
~3-point spread with no trend. Any upward blip was recorded as a new best,
which reset BOTH the plateau counter and the stage, which re-armed a x5
learning-rate warm restart, which injected fresh noise and produced the next
blip. The search sustained itself on its own variance and never reached
PLATEAU_STAGE_DEPLOY - the reported "thousands of eras without converging".
A new best now has to clear the incumbent by PLATEAU_NEW_BEST_SIGMAS (2.0)
times precSE, which the deploy gate already computes. 2.0 rather than 1.0
because incumbent and challenger are both noisy, so the SE of the difference
is ~sqrt(2) x SE, and a 1-SE band was already measured too narrow in a
noise-dominated search. Applied at BOTH ranking sites - the ensemble's and
the solo member's - which are documented as the same ordering. The first
scoring era still checkpoints unconditionally.
2. THE BLANK-CHART CENSUS WAS LYING. It printed "No member has a completed era
yet (snapshots fill at each member's first pass-3 completion)" while the
members were on era 23, because it inferred the cause from m_overlayVotedBars
alone - and that counter requires BOTH a non-zero divisor AND a non-zero net.
Three different states collapsed into one sentence. Split out
m_overlayHadDataBars (divisor non-zero) so the line names which it is:
hadData == 0 -> nobody published a snapshot: publication/index
hadData > 0, voted == 0 -> members looked and abstained: calibration
voted > 0, drawn == 0 -> the vote never cleared the threshold
Diagnostic only. It does not fix the missing arrows - it identifies which of
the three is happening, which the current line actively obscures.
Compile-verified in the staging copy: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Every era was a ~1,200-bar chunk of a 16,264-bar window, and the oldest 90% of
the history was never reached.
All four passes yield mid-chunk on the 120ms budget: each one calls
StashEraResume (the single writer of m_eraResumePending) and returns. Those
used to be returns from Train() itself. When the passes were extracted into
their own methods (08c2cec) they became returns from a void helper, and Train()
carried straight on - reporting pass 1 "done" after one budget, running pass 2
over the sliver pass 1 had queued so far, scoring an OOS slice of it, and
letting AdvanceEra count an era. The extraction moved one side of the binding
and left the reader behind.
Measured on SP500 H4 (VerboseMode, 2026-08-24 15:05-15:14):
era 0 TRAINING WINDOW = 16264 bars ... Bars(series) = 16264 <- window fine
era 1277 pass 1 done in 0s - 1144 of 1193 bars usable <- sweep is not
era 1296 pass 1 done in 0s - 3117 of 3166 bars usable
era 1318 pass 1 done in 0s - 1391 of 1440 bars usable
~1,400 eras in ten minutes, the count varying with how many bars a 120ms budget
happened to buy. Downstream: each member held a different tiny OOS slice, so
the combined vote's shared-bar intersection collapsed ("0 shared OOS bars" on
nearly every era, score 0.0), and the plateau ladder counted 46 ungraded eras
as a plateau and fired a boosted warm restart on all four models.
Train() now returns whenever m_eraResumePending is set - after pass 1 (before
ReportPass1Outcome, which has no verdict to give on a yielded sweep), pass 2,
the calibration walk and pass 3. m_modelEta is already saved inside
StashEraResume, so the early returns keep the learning-rate trajectory.
The resume machinery itself was correct and is unchanged: BeginEra's resume arm
restores the cursor, m_passWindowOk/m_passWindowFail accumulate across chunks,
and the m_isPass2Active/m_isPass2Done guard already routes a resumed call to
the right pass.
Expect era numbers to advance slowly now. That is the fix, not a new stall.
Compile-verified in the staging copy: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
With VerboseMode on, pass 1 reported eras of 422 / 949 / 1358 / 2562 bars on
SP500 H4 - four models, same chart, same second - against a series holding
~16,264 bars, and the number moved every era (CONV: 2562, 3671, 3405, 3532,
2830). Nothing in the journal said so. ReportDetectability and the CAPACITY
line both quote EstimatedInSampleBars, which is derived from the configuration
and not from the era, so they kept reporting "11385 in-sample rows / OOS window
4874 bars" for a window that was a tenth of that.
era.bars is MathMin(Bars(symbol, PERIOD_CURRENT, dtStudied, now) + historyBars,
Bars(symbol, PERIOD_CURRENT)). A short era is therefore either a dtStudied that
is too recent or a short price series, and those need opposite fixes - so the
new line carries all three quantities plus the resolved dtStudied and
SERIES_FIRSTDATE, not just the result.
Reported on change only: an era over a warm feature cache runs in a fraction of
a second here, and a per-era line would bury the journal.
Diagnostic only - no training behaviour is changed by this commit.
Compile-verified in the staging copy: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Two charts (SP500 H4 + USDJPY H4) ran with the pool enabled and produced no
TrainPool directory, no adopted rows and not one journal line. The pool was
inert and there was no way to tell that from "the feature is off".
It could never have fired: the fingerprint is not symbol-invariant. It hashes
NeuronsCount, which counts the alt-data columns - and those are per-symbol
(SP500 carries cot_spec_net, the FX majors cot_idx_1y/3y/chg_4w) - and the
cross-asset block appends ":IDX2" when base currency == profit currency, true
of an index and false of a pair. SP500 came out 50 features wide under
XA:6:IDX2, USDJPY 52 wide under XA:6. Compatible() gates on both, so adoption
was zero by construction.
- STrainPoolHeader::MismatchReason() replaces the bare Compatible() predicate
and names the mismatch; Compatible() now delegates to it, so "may I adopt"
and "why not" can never drift apart.
- CTrainPoolReader::Adopt() reports its own verdict - adopted, alone, or every
peer rejected with the reason per file - and reports it on CHANGE only. An
era over a warm feature cache runs in a fraction of a second here, so a
per-era line would bury the journal. The duplicate Print in RunPass2 is gone;
pool state is now reported from exactly one place.
- CTrainPoolWriter::Publish() rate-limits to TRAINPOOL_MIN_PUBLISH_SEC (300s).
Every era re-derives the same rows from the same in-sample span, so per-era
publishing rewrote a multi-megabyte file continuously for no new information.
The first publish is never delayed.
Compile-verified in the staging copy: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Peer rows join m_isTrainQueue as NEGATIVE sentinels before the shuffle, so they interleave with
this chart's samples instead of training in a block at one end. A block would be a curriculum:
whatever the optimizer saw last would decide where it landed.
TrainPoolStep is a separate path on purpose. Everything in pass 2's local branch after the
forward pass reaches for something indexed by a LOCAL bar - m_labelCache, m_winLongCache, the
excursion target, the arrow cache, m_Time - and a peer row has none of those. Sharing the path
would mean inventing values for all of them, which is how another instrument's outcomes end up
inside m_cumIsCorrect and the operating point gets fitted to them. The IS-vs-OOS gap is read as
THE overfitting signal, so polluting the IS side would not crash anything; it would just quietly
stop meaning what it says.
The purge key reuses the label walk's own two bounds - the horizon and NextScheduledCloseAll -
rather than approximating with a bar offset. A second horizon model here would drift from the
real one, and this project already measured that the close-all, not the nominal horizon, is what
actually terminates labels. Cutoff is the OLDEST OOS BAR'S TIME, in wall clock, because bar
indices cannot be compared across instruments that each have their own calendar.
Contribution happens while the window is still in TempData and before the forward pass
overwrites it, and is gated to direction models: the meta head trains a different target on a
wider input, which the fingerprint gate alone would NOT catch, since a meta model's fingerprint
matches its own peers perfectly well.
Use_Training_Pool ships false and does nothing until a second chart runs a matching fingerprint.
Compile-verified against a BASELINE of the same tree without the wiring: both produce 12
errors, all error 313 invalid-resource-path from #resource directives that cannot resolve in a
headless staged build (stock Controls res\*.bmp, plus the pre-existing Network.cl). Code errors
0, warnings 0, identical to baseline. Staging copy and junctions removed; the live .ex5 was
never touched.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
PooledGate pools the DECISION; this pools the DATA. Measured in research/edge.py with both arms
sharing calendar folds, exit-time purge, benchmark and scoring so training breadth is the only
variable: H4 k=2 gap +2.02pp at t_mkt 3.97, which CLEARS the Sidak bar of 3.69 over five feature
sets at df=6, replicated independently at D1 k=1 (+2.03pp, t_mkt 2.79). The per-instrument arm
was NEGATIVE on every feature set at both timeframes - it loses to "always take the drift side".
This EA trains one net per chart, which is that arm.
Rows, not symbols. Pointing the feature stack at another symbol needs per-symbol indicator
handles and this project has been bitten there twice - the handle leak that never released the
old handle, and the twelve "dead" handles that were one shared refcounted iMA. Each chart
instead computes its own features with its own handles and shares the NUMBERS. Sound only
because FeatureBuilder already ATR-normalises every price-unit feature, for exactly this reason
("instead of feeding e.g. 0.0005 on EURUSD").
Not a fingerprint participant: pooling changes what the model is trained ON, not what it IS, so
adding it would re-key every .nnw to record something outside the model's identity. The
fingerprint instead GATES adoption - it is the assertion that column k means the same thing in
both files - alongside a width check (a fingerprint match with a width mismatch means one side
pinned an older layout) and an exit-TIME purge, since a bar index cannot be compared across
instruments that each have their own calendar.
Writer and reader are separate classes: different reasons to change, different lifecycles, and
one class would carry the export buffers through every read. The file layout lives in one
STrainPoolHeader used by both sides so a layout change cannot be applied to the writer and
missed in the reader. Staging goes through System\AtomicFile rather than a second hand-rolled
temp-and-rename.
Compile-verified in isolation: 0 errors, 0 warnings. Staging junctions and harness removed; the
deployed .ex5 was never touched.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
RSI, MACD, Ichimoku and the five AD/Wyckoff indicators (CumulativeDelta,
ShorteningOfThrust, WyckoffEventStream, WyckoffFailedStructure,
WyckoffSignificantBarInversion). All eight inputs shipped false and each carries a
closed verdict: the three oscillators are the same patterns that measured at chance
as entries, and the Wyckoff family returned zero out-of-sample on five independent
instruments - which is what closed the context score.
RETRAIN-NEUTRAL, and this one is worth stating precisely because the change looks
larger than it is. Every removed group contributed `flag ? N : 0` to the input
width, and every flag was false, so the width was ALREADY zero for all eight: no
.nnw's input layer changes. On the fingerprints, UseRSI and the five AD flags were
hashed unconditionally and become literal 0 legacy slots (the convention the
m_focalGamma slot above them already uses); UseMACD/UseIchimoku were appended only
when enabled, so their segments simply never appear - byte-identical to every
fingerprint ever produced, since neither ever shipped on.
CADIndicatorTuner IS DELIBERATELY NOT SHRUNK. Its flat parameter array is persisted
inside every .nnw, and Unflatten() rejects a size mismatch by falling back to
constructor defaults - so dropping the dead fields would silently revert the tuned
MA period of every model on disk while keeping its trained weights. That is the
feature/weight mismatch this project has already paid for twice, and it is not
worth 200 lines. AD_TUNE_PARAM_COUNT stays 42, the dead slots are still written and
read, and AutoTune's ParamOwner gate now matches only owner 5 (MA) so nothing
searches them. The class comment says all of this at the declaration.
Also renamed ReInitADIndicators -> ReInitTunableIndicators: it rebuilds exactly one
indicator now, and a name saying "AD" for the MA handle is the kind of stale label
that gets believed later. Its release-AFTER-recreate ordering is untouched - that
is a documented fix, not bookkeeping.
Compile-verified in the stage copy: 0 errors, 0 warnings, against the same 0/0
baseline.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
research/classic.py transcribed all 26 shipped vote patterns (MA 4, RSI 4, MACD 6,
Ichimoku 12) with their constructor weights and tested them as entries on 178k-bar
histories, four instruments x three barrier geometries. Nothing separated from
chance - not one pattern, not the averaged vote at any threshold 10-70, not a
2/3/4-module quorum, not event-plus-confirmation. Residual E[R] everywhere was
-0.01 to -0.08 R, which is approximately the spread. The +4 sigma reading that had
once justified the set was two bars of lookahead: closing it took MACD_p4 on EURUSD
from +5.05pp to -0.02pp.
All four inputs have shipped false ever since, so this deletes dormant code rather
than changing behaviour.
RETRAIN-NEUTRAL, deliberately. EnableMA and EnableRSI were hashed UNCONDITIONALLY
into the DB config fingerprint, so they become literal 0 legacy slots - the same
treatment the ind_Periods slot two lines above already uses, and every existing
database keeps its key. EnableMACD/EnableIchimoku were appended only when enabled,
so with both gone the segment simply never appears, which is byte-identical to
today. No .nnw or .db is orphaned.
WHAT THIS COSTS, STATED PLAINLY: these four were CSignalMETA's only wired candidate
sources, so the on-chart ladder sweep (BuildCorpusBySweep) now has nothing to sweep
and a META chart is no longer self-contained. That is survivable rather than fatal
because MetaPrepareEra already falls back to CMetaCorpus::LoadLargestOnDisk, and its
own comment names this exact case - "charts whose classic filters are disabled".
Use_MetaLabeling ships false regardless. SignalMETA.mqh is otherwise UNTOUCHED, and
its 26-slot one-hot stays at 26: a tester-built corpus on disk still encodes those
pattern ids, and narrowing the descriptor would invalidate every stored corpus.
Signals/SignalMA.mqh SignalRSI.mqh SignalMACD.mqh SignalIchimoku.mqh deleted
Signals/OscillatorDivergence.mqh deleted - RSI and MACD were its only users
Classic_Shift deleted - the four votes were its only readers
Compile-verified in the stage copy: 0 errors, 0 warnings, against a 0/0 baseline
taken before any edit.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
AcquireConfigLock/ReleaseConfigLock moved off CExpertSignalAIBase into
Expert/ConfigLock/CConfigLock, same view+adapter shape as BarrierHorizon/ExcursionHead.
Stateful: m_configLockName is exclusive (grep-verified, nothing outside Lifecycle.mqh's
old body touched it). Pure relocation - same FNV-1a hash, same owner-liveness check,
same log wording. Left uncommitted mid-campaign; independently compile-verified in
isolation now (0 errors/0 warnings) before this commit.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
FeatureScreen.mqh's MI/permutation-null diagnostics (mean/best-col
report, excursion report, lag-profile family-wise test, barrier-
geometry scan) and AutoTune.mqh's TuneIndicatorsByFilter install gate
each spelled out the add-one-smoothed Monte-Carlo p-value
(1+atLeast)/(draws+1) independently. Added PermutationPValue(atLeast,
draws) to System/BinomialStats.mqh (returns 1.0 for draws<=0, matching
every existing call site's own guard) and replaced all six inline
expressions with a call to it. Pure arithmetic substitution, no
control-flow change.
ApplyClassificationSoftmax/AdjustedSignalFromSoftmax/DirectionalMargin each
re-derived `pBuy > pSell && pBuy > pNeutral` (and the Sell mirror)
independently, one of them documenting the duplication by comment rather
than eliminating it. Added Argmax3() as the single derivation (ties to
Neutral); all three now branch on its ENUM_SIGNAL result instead of
re-testing the comparison. Pure relocation, statement-by-statement
equivalent - verified by diff.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
LoadLargestOnDisk() and LoadFromConfigDb() each built an MqlDateTime
struct field-by-field then called StructToTime() to get an Add()
timestamp. Added a shared static BuildStamp(y,mo,d,h,mi); both loops
now call it. Pure relocation, no arithmetic/ordering change.
CloseAndDeleteAllForSymbol() had two structurally identical ~15-line
loops back to back (select-by-ticket, filter by symbol+magic, freeze
guard, act) differing only in the Position*/Order* API calls. Added
private CloseAllLoop(target, symbol, caller) dispatched by a small
CLOSEALL_POSITION/CLOSEALL_ORDER enum, matching the RetryFileSystemOp
precedent (b8f936f). caller is threaded through as __FUNCTION__ from
the two call sites so TCLog text is unchanged. Pure relocation - every
filter/freeze-check/act statement and log message verified unchanged.
CopyFileWithRetry (System/SharedFileCopy.mqh) and CModelPersistence::
LoadNetWithRetry independently implemented the identical 5-attempt
Sleep-doubled-and-capped retry shape around a different single
operation, with a comment on the latter pointing at the former as the
"same reasoning" instead of sharing code. Added System/RetryWithBackoff.mqh:
an IRetryableOp interface (one bool TryOnce(bool quiet) method, MQL5 has
no closures/function pointers that bind per-call-site arguments) plus the
RetryWithBackoff(op, attempts, initialDelayMs, delayCapMs) loop. Each call
site now defines a tiny local operand class (CCopySharedFileOp,
CLoadNetOnceOp) and keeps its own tuning constants (150ms/1000ms cap vs
200ms/2000ms cap) unchanged - pure mechanical relocation, no behavior
change. CModelPersistence stays stateless (grep-verified in the prior
Persistence extraction): CLoadNetOnceOp is a separate local class, not a
new member on CModelPersistence itself.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
CExpertCustom's three Long/Short pairs (OpenLong/OpenShort,
TrailingStopLong/Short, TrailingOrderLong/Short) each duplicated the
same pre-send gate logic, differing only in the order-type constant
and which base CExpert::Xxx method to delegate to - the same shape
CExpertSignalCustom already fixed for CheckOpenPosition/CheckClosePosition.
Added OpenPosition/TrailingStopCommon/TrailingOrderCommon, each
isLong-parameterized with a caller string threaded through (passed as
__FUNCTION__ from each 1-line wrapper) so every TCLog message keeps
its original per-direction function name and topic tag. Pure
relocation - every log string, arithmetic expression and branch order
verified unchanged against the pre-edit file.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
ParamCandidates() repeated an identical resize+copy+return-count block
9x, once per discrete MA/RSI/MACD/Ichimoku preset list. Added a private
FillCandidatesFromPresets(src[], out[]) doing that once; all 9 sites
now a one-line call. Pure mechanical dedup, no arithmetic/ordering
change.
CExpertCustom and CExpertSignalCustom each defined their own
stops-level order-type decision against the same TCStopsLevel()
helper, identical except CExpertSignalCustom's guard skipped the
EMPTY_VALUE check (a huge finite double would fall through into the
ask/bid comparison and misclassify as a pending order instead of a
market order). Moved the logic into TCResolveOrderType() in
System/TradeChecks.mqh, keeping the more defensive guard; both
classes now delegate. ask/bid are still passed in from each class's
own m_symbol so routing keeps using whatever RefreshRates() snapshot
that class already had - only the duplicated arithmetic moved.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Expert/AIBase/Labels.mqh (1807 lines) exclusivity-grepped almost entirely
SHARED: the label/win/excursion/ladder caches and the geometry-derivation/
prebuild state are touched with real per-bar array logic by Training.mqh's
hot era loop (m_labelCacheBuy/Sell/HasValue at 22+ sites), by FeatureScreen.mqh's
geometry scan (direct writes to m_barrierScanSlMult/TpMult, m_geometryAdopted,
m_geometryCfgSaved), by AutoTune.mqh and by SignalMETA.mqh - moving that state
into a collaborator would mean wrapping dense hot-loop array indexing behind
method calls across 5 files for no coupling reduction (same judgment already
recorded for AutoTune.mqh's remainder / Inference.mqh).
One genuinely closed sub-cluster survived the grep: the scheduled close-all
budget and the horizon ladder snap (NextScheduledCloseAll, MeasureCloseAllBudget,
EffectiveHorizonMax, RequiredHorizonBars, SnapHorizonToLadder, GrantedHorizonBars).
Only 2 fields are exclusive (m_closeAllCycleBars/m_closeAllMeanBudget - grep-
verified, Lifecycle.mqh's touch was constructor-init-list only) and NONE of the
6 methods has any external caller outside Labels.mqh (grep-verified whole-repo),
so nothing needed rewiring. New Expert/BarrierHorizon/: IBarrierHorizonView.mqh
(abstract, 4 accessors, 3 reused from the signal's existing Chart* getters, 1
new HorizonSwingMedianBars() wrapper) + AIBaseBarrierHorizonView.mqh/
AIBaseBarrierHorizonViewImpl.mqh (the adapter) + BarrierHorizon.mqh (CBarrierHorizon,
STATEFUL - owns the 2 exclusive fields as real members). Every method body is a
verbatim relocation (diffed programmatically against git HEAD modulo the field->
view substitutions - identical except one comment-wording update). The original
6 declarations on CExpertSignalAIBase became one-line forwards at their existing
position; Labels.mqh's own callers of these six needed zero changes since they
call them unqualified, which now resolves through the forwards.
Labels.mqh: 1807 -> 1643 lines. The rest of the file (label-cache population,
TripleBarrierLabel, DeriveBarrierGeometry, StartLabelCachePrebuild/
AdvanceLabelCachePrebuild, exit-policy simulation) is deliberately left as a
raw-include partial - not separable without relocating Training.mqh's era-loop
coupling, not reducing it.
Self-compiled 0 errors, 0 warnings (_claude_stage, ~94s).
Expert/AIBase/Features.mqh (2017 lines, 38 methods) split by exclusivity grep
(whole-repo, not just Expert/): 30 methods -> Expert/Features/FeatureBuilder.mqh
(CFeatureBuilder + CFeaturesView/CAIBaseFeaturesView), 8 stay behind as a much
smaller raw partial.
CFeatureBuilder is STATEFUL, same shape as Excursion/OnlineLearning: owns the
10 feature-only indicator handles (m_Volumes/m_MA/m_RSI/m_MACDFeature/
m_Ichimoku/5 AD* CiCustom indicators - grep-verified touched nowhere else in
the repo, only their bare declarations) plus the depth-probe/handle-repair/
spread-series/detectability-latch scalars (exclusive, Lifecycle.mqh ctor-init
only elsewhere). m_Open/m_Close/m_High/m_Low/m_Time/m_ATR/m_ADZigZag stay
signal-owned - Labels.mqh/AutoTune.mqh/Training.mqh read them directly - and
are reached read-only through the view (FeatureOpenAt/FeatureHighAt/
FeatureLowAt/ChartBarClose/ChartBarTime/OnlineAtrMain, all reused where a
forward already existed).
Deliberately did NOT move InitOpen/InitClose/InitHigh/InitLow/InitTime/
InitADZigZag/ResizeBuffers/RefreshData: they manage the 7 shared indicators'
Create/BufferResize/Refresh lifecycle, which would need a pure-relay wrapper
per operation per indicator for zero coupling benefit - same judgment as
Topology's boot sequence. They stay in Expert/AIBase/Features.mqh and reach
CFeatureBuilder's 10 owned indicators through 20 new Feature*BufferResize()/
Feature*Refresh() forwards (signal calling into its own owned collaborator
directly, no view needed in that direction).
Whole-repo grep (not just Expert/) caught a real external miss the campaign's
own doctrine warns about: Signals/SignalMETA.mqh read m_spreadSeries/
m_spreadSeriesBars directly as an inherited protected field (a subclass, not
an AIBase/*.mqh partial) - fixed with two new FeatureSpreadSeriesBars()/
FeatureSpreadSeriesAt() forwards.
Verified: if(/for(/while( counts identical between the original file and the
new split (269/20/1); return-count delta (+12) fully accounted for by the 12
new trivial one-line forwards added (10 indicator BufferResize + 2 spread-
series getters); quoted-string-literal diff empty except two doc-comment
paraphrases. Self-compiled 0 errors, 0 warnings.
Expert/AIBase/Topology.mqh (1191 lines) held two genuinely different jobs: the
fingerprint/derived-shape/BuildFreshTopology math, and InitNeuralNetwork/
InitFeatureIndicators - the network boot sequence (config-lock, tester-cache
seeding, load/save the .cfg, net-load backend fallback, chart/persistence/
online-learning orchestration).
Extracted the first job to Expert/Topology/ as CTopology + CTopologyView/
CAIBaseTopologyView (20 methods: BuildModelFingerprint, the Estimated*/Compute*
budget math, the Conv*/Lstm* shape helpers, Add*Stage, BuildFreshTopology).
STATELESS, like ModelPersistence - grep-verified zero exclusive fields, every
member these methods touch is shared elsewhere in the signal. Reused ~15
existing Data*/Chart*/Persist*/Exc* getters per the established convention;
added ~20 new getter overloads next to their existing setters (UseVolumes(),
MinDirectionalRecall(), etc. - same pattern as SignalClusterWindow) and ~16
new Topology*() wrappers for fields with no prior accessor. The Net-pointer
swap in BuildFreshTopology is one consolidated view call
(TopologyReplaceNetFromTopology), same doctrine as Persistence's
RunCpuInferenceSelfCheck - irreducible pointer work, not signal state.
Deliberately did NOT extract InitNeuralNetwork/InitFeatureIndicators: they
orchestrate nearly every other collaborator (chart, persistence, online-
learning, cross-asset, config-lock) rather than deriving a shape, so moving
them would just relocate a hub, not reduce coupling - same judgment call as
Inference.mqh (assessed, not extracted). They stay in the AIBase/Topology.mqh
partial, byte-identical to before (diffed against git HEAD to confirm), and
now call the extracted math through the same public forwards every other
caller already used.
Verified: string- and numeric-literal diff of the old file's 20 method bodies
against the new CTopology methods (0 differences), InitNeuralNetwork/
InitFeatureIndicators byte-identical, self-compiled 0 errors/0 warnings.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Expert\AIBase\OnlineLearning.mqh (595 lines) -> Expert\OnlineLearning\:
IOnlineLearningView.mqh (abstract, ~50 accessors) + AIBaseOnlineLearningView.mqh/
AIBaseOnlineLearningViewImpl.mqh (the adapter) + OnlineLearning.mqh (COnlineLearning).
STATEFUL, unlike CModelPersistence: grep-verified the shadow net, the OOS
continual-learning simulation state and the pattern-database backfill state are
genuinely exclusive to this file's own methods - Training.mqh/Topology.mqh/
Lifecycle.mqh/the signal's own header only ever CHECKED or RESET this state at
era/lifecycle boundaries, never owned it, so it moved onto the collaborator as
real members (same doctrine as Excursion). Those external touch points became
consolidated view/forward calls instead of raw field pokes - AbortSimIfActive()
replaces THREE separate copies of the same delete/null/false triple (Training.mqh's
stop path, FlushTrainRun, ResetWeights), matching the geometry-scan duplicate-reset
precedent in project memory. ResetForFreshTopology() replaces Topology.mqh's five-
field reset block, DeployNet() replaces the shadow-preferred net selection duplicated
in Inference.mqh and ChartScoreBarForRescan, and BlendTowardNet() replaces the era-end
blend Training.mqh used to poke m_shadowNet for directly.
Reused the signal's existing Data*()/Chart*()/Persist*() getters wherever one already
answered the question (labels/outcome/history/horizon/priors/servable-bars/etc.);
added ~30 new Online*() wrappers only for what nothing else exposed yet. The three
PersistOnline*() get/set pairs (WST3 .stats fields) now forward through the owning
member instead of touching the field directly - CModelPersistence is unaffected.
Every method body is a pure relocation of the original's statements in original
order; verified against `git show HEAD~1:Expert/AIBase/Excursion.mqh`-style diff
against the pre-extraction file kept in the working tree until this commit.
Compiled 0 errors, 0 warnings (stage mirror + MetaEditor64 /compile, ~91s).
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Expert/AIBase/Excursion.mqh was 13 method bodies of CExpertSignalAIBase,
#include'd after its declaration - same "not a module" problem already
fixed for ChartUI (S2) and Persistence (S3). Extracted to
Expert/Excursion/CExcursionHead behind CExcursionHeadView/
CAIBaseExcursionHeadView, same view+adapter shape.
STATEFUL, unlike Persistence (0 exclusive fields): grep-verified 26
fields (m_excNet and every accumulator/trailing-ring field) touched
nowhere else in Expert\ except Lifecycle.mqh's old ctor-init-list
defaults and destructor deletes (now moved onto CExcursionHead's own
ctor/dtor). m_geo (SGeometryScan) and m_ladder (CFirstPassageLadder)
stay on the signal - both are genuinely shared with Labels.mqh/
Training.mqh at era boundaries - and are reached only through 15 new
Exc*() view wrappers, including one consolidated
ExcGeometryScanAccumulate() call (same doctrine as Persistence's
RunCpuInferenceSelfCheck) rather than field-by-field pokes.
All 13 original public methods stay at their same declaration point as
one-line forwards to m_excursionHead. Training.mqh's 2 raw m_excUs
reads now go through the new ExcursionMicroseconds() forward. Every
method body is a pure relocation, verified statement-by-statement
against the original (git show HEAD~1:Expert/AIBase/Excursion.mqh).
Compile-verified: 0 errors, 0 warnings.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Expert/AIBase/Persistence.mqh (598 lines, 8 methods) -> Expert/Persistence/:
IPersistenceView.mqh (abstract, 68 read+write accessors) + AIBasePersistenceView.mqh/
AIBasePersistenceViewImpl.mqh (the adapter) + ModelPersistence.mqh (CModelPersistence,
the real collaborator - signal owns m_modelPersistence and binds it to m_persistenceView,
same shape as ChartUI's S2).
Grep-verified before starting: every field these 8 methods touch is ALSO touched
elsewhere in the class (Training/Lifecycle/OnlineLearning/Topology/FeatureScreen/
Labels.mqh) or already exposed via ChartView. Zero exclusive state, unlike ChartUI's
arrow-restore/rescan queues - CModelPersistence is stateless, holding only the
borrowed view pointer, operating entirely through 68 Persist*/PersistSet*() accessors
on the signal.
ValidateCpuInference's Net-pointer/throwaway-clone core is ONE consolidated view call
(PersistRunCpuInferenceSelfCheck) rather than field-by-field - irreducible pointer/
object work, not signal state, same doctrine as ChartScoreBarForRescan.
LoadNetWithRetry keeps its original CheckPointer(Net)-free Net.Load() call unchanged
(no guard added - would change failure behaviour on what must be a pure relocation).
This code writes the actual on-disk .cfg/.stats binary layouts every deployed model
depends on (explicit "DO NOT REORDER" comment in the original), so beyond compiling
clean (0 errors, 0 warnings) this was verified with a positional field-order diff:
every FileWrite*/FileRead* call's target field, extracted and normalized from both
the original and the new file, matches 1:1 in the same order (43/43 on the write
side covering SaveModelStats+SaveTopologyConfiguration, 17/17 on LoadModelStats'
read side; LoadAndCompareTopologyConfiguration's local-variable read block was
copied verbatim, untouched, so nothing to diff there). The magic-version
conditionals (WST2-6, haveDerivedStages/haveBarrierGeometry/etc.) moved unchanged.
All 8 methods keep their exact original signatures as one-line forwards - zero
external call sites changed.
Neither ever touched a CExpertSignalAIBase member - both take everything as
parameters. Moved to System\SharedFileCopy.mqh (free functions, same doctrine as
TradeChecks.mqh's TC*), matching what they actually are instead of carrying them as
methods on a class they don't depend on. Topology.mqh's call sites are unchanged -
unqualified calls from within a class method resolve to the free function exactly
the same way. Compiled clean.
Correction to project_oop_module_pattern's Persistence(696) note: LoadNetWithRetry
is NOT a third pure utility alongside these two - it touches Net/dError/dUndefine/
dForecast/dtStudied/m_activeFileName/m_activeFileCommon/m_eraCount/m_trainingComplete.
The rest of Persistence.mqh (EnforceTopologyContract, Save/LoadModelStats,
ValidateCpuInference, Save/LoadAndCompareTopologyConfiguration, ReadAltDataPinFromCfg)
is heavily coupled to signal state - a real view+adapter extraction on the scale of
ChartUI's, not attempted here.
CSignalMA/RSI/MACD/Ichimoku each re-overrode SweepPrepare() with an identical body -
call the base, resize/refresh one indicator buffer, return - differing only by the
buffer's field name. Base class now does the shared price-series prep once and calls
a new SweepPrepareIndicator() hook; each signal overrides only the hook. Compiled clean.
Expert/AIBase/ChartUI.mqh was 869 lines of method bodies of
CExpertSignalAIBase, #included after the class declaration - free to touch
any of its ~500 members. First of the eleven AIBase/*.mqh partials to come
out (fewest inbound edges - see the SOLID campaign session order), using
the same view+adapter shape already proven for CTrainingDataView.
CChartView (Expert/Chart/IChartView.mqh) is the abstract read/behaviour
surface a chart-rendering collaborator needs - identity, bar/model access,
the prediction cache, and the training/vote/meta scalars the panel and HUD
line summarise. CAIBaseChartView is the adapter the signal owns and binds
to itself (MQL5 gives a class exactly one base, so CExpertSignalAIBase
cannot implement the view directly). CChartUI is the real collaborator: it
owns the arrow-restore queue, the rescan queue/tally, the last-arrows-saved
count and the purge-mismatch latch as its own fields (verified via grep to
be touched nowhere else in Expert/), and reaches everything else - including
StartChartSignalRescan, moved in from its old inline home in the header
since it drives the exact same rescan state machine AdvanceChartSignalRescan
drains - through the view.
m_arrowSignalCache and m_signalClusterWindow stay on the signal: Training.mqh
writes the cache directly every era and the training-data view already reads
it, so moving it would mean rewriting Training.mqh's write sites too - out of
scope here. CChartUI reaches it through four bounds-checked accessors instead
of a raw member poke. All 10 public methods keep their exact signatures and
become one-line forwards on the signal, so no other file's call sites change
except Training.mqh's one era-end status refresh, which now reads
RefreshStatusLabel() rather than reaching into CChartUI's now-private
last-displayed-neuron cache directly.
Verified structurally, not compiled (never compile - the operator does, in
MetaEditor): brace balance checked on every touched/new file against HEAD,
and the view/adapter/impl method lists cross-diffed to confirm all 59
accessors match 1:1 across the interface, the adapter declaration and the
adapter body.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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>
