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>
Evidence (MQL5\Logs, SP500 H1, 2026-07-29):
Perceptron era 61 Buy 32% Sell 27% Neut 94% bal 51%
LSTM era 160 Buy 16% Sell 11% Neut 98% bal 42% (peaked 49% @ era 44)
Hybrid era 179 Buy 5% Sell 2% Neut 99% bal 35% (peaked 41%)
CONV era 228 Buy 2% Sell 4% Neut 99% bal 35% (peaked 40% @ era 122)
Every model peaks early then decays monotonically toward Neutral, and nothing
stops it: the restore-best-weights + decay-eta handler is gated on
m_bestPassedRecall, which stays false forever when no checkpoint ever clears the
per-class floor. CONV ran 228 eras with eta pinned at its 0.000300 start. The
plateau ladder cannot end such a run either (stage 3 refuses to deploy without a
recall pass, so it resets ~27 times), making it a 1000-era one-way trip.
The gate's own justification had expired. It was written when the pre-pass
tiebreak was blended-accuracy-only, where "best" really did mean "called Neutral
most confidently". The balanced-selection change replaced that with
`balancedOosEra > m_bestBalancedOos` plus an isFullyCollapsedEra exclusion, so a
Neutral-only era now scores ~33% - the FLOOR of the balanced metric - and cannot
anchor the checkpoint at all. Pre-pass "best" now means "most class-balanced so
far", which is worth defending; and isWorseEra is itself a balanced-accuracy
regression, so it cannot fire merely for trading Neutral calls for Buy/Sell.
The original concern still holds while the best-so-far IS near-collapse, so the
escape is margin-guarded: defend the checkpoint only once balanced accuracy sits
more than BALANCED_WORTH_DEFENDING_MARGIN_PCT (5pp) above the one-class floor of
100/3. Against the run above that engages for all three stuck topologies
(42.3/41.3/50.0 vs a 38.3 threshold) while a genuinely collapsed run still
explores freely.
Two inputs restored to the regime that actually produced a deploy:
- MinRecall 60 -> 40. The one successful auto-deploy in the logs (Hybrid, 28th
00:50, best balanced 66.0%) ran against a 40% floor. 60 has never been shown
reachable here - a floor above what the config can reach is the same "target
set too high" failure the surrounding comment already warns about.
- OversampleParity 60 -> 90. 60 overcorrected. Runs now START Neutral-dominant
(Buy 0-11% recall at era 1) and call Buy/Sell on 0-4% of bars against a ~6%
true base rate - under-calling, with no headroom to converge down from. The
deploying run began at Buy 90% / Sell 36%, 24% of bars called, and settled into
the floor from above. Raw over-calling is the intended starting condition; live
calls are base-rate-calibrated by AILogitPriorStrength, which is why the input's
own note says to judge over-calling by live-fired precision, not raw counts.
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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.
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Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
REFACTOR_NOTES.md records what was found, what was changed, what was
deliberately left alone, and the one investigation that is still open (the
MLP CPU-DLL slowdown, with the parameter counts that rule out my earlier
"largest weight matrix" explanation).
Also restores the missing opening rule on ReInitADIndicators' comment banner.
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>
DRY - topology construction
---------------------------
CSignalCONV and CSignalHYBRID each built the Conv+Pool front-end from scratch;
CSignalLSTM and CSignalHYBRID each built the LSTM stage from scratch. The
duplicates had already drifted: HYBRID guarded the LSTM step with
MathMax(1, historyBars/2), CSignalLSTM divided unguarded, so a historyBars of 1
gave two different steps for what is documented as the same layer.
Extracted AddConvPoolStage() and AddLstmStage() onto CExpertSignalAIBase. The
three overrides are now compositions:
CONV = AddConvPoolStage
LSTM = AddLstmStage
HYBRID = AddConvPoolStage && AddLstmStage
HYBRID's "matches the standalone CONV front-end exactly, then adds LSTM" is
enforced by construction instead of by comment. Took the guarded step for both.
Also fixed a descriptor leak the duplicates shared: on a failed topology.Add()
the CLayerDescription was neither owned by the array nor deleted.
Dead code
---------
- CNet::SaveCheckpoint / CNet::LoadCheckpoint (123 lines). Superseded by the
in-memory CaptureWeights/RestoreWeights pair; Network.mqh:1312 already said so
("This replaces the file-based SaveCheckpoint/LoadCheckpoint"). Zero call
sites - every remaining mention was a comment. The five comments that
referenced them have been reworded rather than left dangling.
- CExpertSignalCustom::CheckForDuplicateTrade / FindLastTradeIndex /
UpdateTradeStatusAndExit: declared, never defined anywhere, never called.
They only made it look as though duplicate-trade detection existed.
Compiles 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
FileOpen(FILE_WRITE) truncates its target on open. CNet::Save already staged
the .nnw through a temp file + rename for that reason, but the three sidecars
written beside it did not:
.stats ExpertSignalAIBase.mqh:5918
.arrows ExpertSignalAIBase.mqh:6224
.cfg ExpertSignalAIBase.mqh:7329
Two defects followed.
1. An interrupted write published a truncated sidecar. For .cfg that is the
worst case: LoadAndCompareTopologyConfiguration() reads a short file as a
mismatch, which discards the trained model and restarts from era 0.
2. Windows file sharing is a mutual contract - a writer opened with no
FILE_SHARE_* blocks every concurrent open regardless of the reader's flags.
All three read paths carry FILE_SHARE_READ|FILE_SHARE_WRITE specifically so
a tester agent can read them while a live chart runs; an exclusive writer on
the same path defeated that.
Extracted CNet::Save's proven pattern into System\AtomicFile.mqh
(AtomicWriteBegin/AtomicWriteEnd) and routed all four writers through it. This
also encodes the FileMove gotcha once instead of per call site: the destination
location comes from FILE_COMMON inside the 4th arg, NOT inherited from the
source, and getting it wrong moves the file to the wrong sandbox silently.
Also fixed while in these functions:
- SaveTopologyConfiguration had 13 copy-pasted 6-line error blocks that each
returned WITHOUT FileClose(handle), leaking the handle on every write
failure. Collapsed to one ok-chain that closes exactly once. The on-disk
field order and types are unchanged (asserted during the rewrite) so existing
.cfg files still load.
- SaveChartSignals documented that pruning runs only after a successful write
("a failed write above leaves both the file AND the chart untouched") but
never checked any write result, so a partial write still deleted the chart
objects. Results are checked now, making the existing comment true.
Compiles 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
The DFA (Direct Feedback Alignment) option was never a correct implementation:
it deterministically flipped the sign of half of all gradients based on
connection index parity, causing permanent gradient ascent for those weights
and guaranteed divergence. The backward pass was also incompatible with the
OpenCL/DirectML neuron model (layer.Total() == 1). This change removes all DFA
logic, including the enum value and `DfaFeedbackSignal` method, and replaces it
with plain gradient descent in all momentum update kernels. The `optimizer`
kernel argument is retained for binary compatibility but is no longer used.
Changed updateInputWeights signature from accepting `CObject*` to `CObject*&` to allow the method to modify the caller's pointer, preventing potential object copying or pointer invalidation. A large block of outdated commentary in ExpertSignalAIBase.mqh was also removed, cleaning up documentation no longer applicable after previous refactoring.
Introduce Direct Feedback Alignment (DFA) backward pass with gradient clipping, feedback matrix initialization, and a dedicated backPropDfa method. Add optimizer snapshot/restore hooks (CaptureOptimizerSnapshot, RestoreOptimizerSnapshot, SetOptimizerForAllNeurons) to temporarily switch the entire network's optimizer for replay-only updates during pass 2, preserving the original optimizer state. Support all neuron types including dropout, deconv, LSTM, and softmax in the snapshot logic.
In AI/Network.mqh, return early from InitDirectML during
tester/optimization/forward runs to prevent agent-side file-lock
failures caused by rapid stop/restart cycles accessing DLL imports.
In Expert/ExpertSignalAIBase.mqh, add MathIsValidNumber checks in
CalibratedConfidenceMagnitude and SignaledConfidence to safely handle
NaN values, and refactor ShutdownChartCleanup to accept a preserve
flag, avoiding unnecessary chart purges during tester runs for faster
shutdowns. Also add m_purgeChartOnDestruct member.
In AI/NeuronDirectML.mqh, clean up a minor comment formatting issue.
In inference-only backtests, dtStudied could be ahead of the test range, causing new-bar detection to freeze. Replaced with m_lastBarTime to keep detection aligned with runtime history. Added diagnostic logging when a non-neutral softmax output is neutralized by prior correction. Also added validation for order_price, sl, and tp in stop-checking functions to catch non-finite or negative values.
Added a new `m_modelLoadedFromDisk` flag to distinguish models loaded from a saved `.nnw` file from fresh random topologies. Modified the readiness gates in `LongCondition` and `ShortCondition` so that an inference-only tester run can execute trades using a model loaded from disk even if its persisted `trainingComplete` flag is `false`. This enables replay of a partially trained exported model without requiring full convergence, while still blocking fresh random-weight topologies from trading.
Introduce a global boolean `g_signalsVisible` to control whether signal
objects are displayed across all timeframes or hidden entirely. When
enabled, chart arrows and restore objects are set to `OBJ_ALL_PERIODS`;
otherwise they use `OBJ_NO_PERIODS`, allowing signals to be shown or
hidden at runtime without losing saved state.
Add `ShouldTraceTradeRejections()` helper that returns true only when
running in the Strategy Tester, optimization, or forward testing modes.
Use it to print diagnostic messages when trades are rejected due to a
prohibition signal or when `OpenLongParams`/`OpenShortParams` fail to
produce valid stop/take-profit levels. This provides targeted debugging
output without cluttering live trading logs.
Wrap the `TCWarnIfSlow` calls in `CExpertCustom::OnTick` and the
`TCWarnIfMemoryAbove` call in `CExpertCustom::OnTimer` with `VerboseMode`
guards. This silences routine diagnostic noise during normal operation
while still allowing detailed performance tracing when `VerboseMode`
is enabled.
Raise MIN_ACTIVATION_DERIVATIVE from 1e-4 to 1e-3 in both Network.cl and Network.mqh
to strengthen the escape signal through saturated hidden neurons. This provides a 10x
stronger safety net against fp32 OpenCL-specific saturation, complementing the earlier
output-layer fix (logit activation instead of sigmoid) that resolved the primary neutral
collapse bug.
Add MathSrand(GetTickCount()) before BuildFreshTopology() in ExpertSignalAIBase.mqh
to guarantee genuinely random weight initialization after genetic tuner evaluations,
matching the final-retrain path and Warrior_EA.mq5's OnInit. This prevents the previous
deterministic RNG state from dominating weight init.
Also remove UTF‑8 BOM from ExpertSignalAIBase.mqh and Network.mqh for cleaner encoding.
Add freeze-level checks, no-change modification skipping, entry price routing, and per-tick/memory budget monitoring. Override trade actions (Open, Close, Reverse, TrailingStop, TrailingOrder) to validate at the final gate before sending orders.
Add MACD_FAST, MACD_SLOW, MACD_SIGNAL presets and Ichimoku Tenkan, Kijun, Senkou presets to InputEnums.mqh. All combinations are designed to satisfy the respective indicator's validation rules (fast < slow for MACD, Tenkan < Kijun < Senkou B for Ichimoku), eliminating init errors and allowing the auto-tuner to perturb settings independently.
Introduce VOTE_CLOSE_PRESETS enum with a Disabled option (value 101) that bypasses vote-driven position closing via arithmetic thresholding, removing the need for a separate boolean flag. This ensures positions exit only via stop-loss, take-profit, or trailing when disabled.
Removes standalone AI confidence parameters (MinAIConfidence, MinAIExitConfidence) and replaces them with unified Min_Vote_Open and Min_Vote_Close thresholds that apply to both AI and classic engines. Updates all code comments, report suggestions, and market descriptions accordingly, simplifying configuration and ensuring consistent vote requirements across entry and exit logic.
Add BeginVote/RevokeVote lifecycle hooks to ExpertSignalCustom and ExpertSignalAIBase.
Snapshot m_lastNonNeutralSignal before condition evaluation in Direction(), and restore
the snapshot if the vote is later discarded (e.g., Hybrid quorum shortfall).
Previously, a discarded vote still consumed the alternation gate, which could
permanently gate out valid signals until the opposite direction appeared.
:106 — window key changed from the 0-59 sec field to a full datetime. Fixing #1 alone would have replaced "always 0" with "cumulative average of every bar since startup", since the window still never rolled over.
:746 — restored result /= number. number was being counted and then never used, leaving a raw sum where the base class averages. MA's 60 + RSI's 100 = 160 tripped the ±100 range check and got zeroed — it discarded precisely the strongest agreed setups.
- Arrow deletion now scoped to rescan window (barsNow) instead of all arrows, preventing loss of multi-year arrow history on Show Signals click.
- Removed temporary throughput diagnostic counters and logging added to investigate performance regression; issue resolved.
The commit adds three counters (`m_rescanRawBuy`, `m_rescanRawSell`, `m_rescanRawNeutral`) that accumulate the raw argmax signal before the prior-correction step in `AdvanceChartSignalRescan`. This distinguishes a model that outputs mostly Neutral from one where adjusted signals suppress non-Neutral arrows. The rescan completion log now reports both the pre-decluttering tallies and the time. Additionally, `TRAIN_TIME_BUDGET_MS` is increased from 120 to 200 ms to improve throughput (as noted in the comment), balancing UI responsiveness and training speed.
The old RescanChartSignals ran the entire per-bar inference loop synchronously,
blocking the button-click handler for a potentially long duration on large lookbacks.
Replaced with StartChartSignalRescan (cheap setup) and AdvanceChartSignalRescan
(time-boxed slices) so the heavy work is drained from PollTraining's timer without
freezing the UI.
Introduce `RescanChartSignals()` method and `SIGNAL_RESCAN_LOOKBACK_BARS` define to allow operators to replace stale historical arrows (e.g., from years-old training runs) with fresh inferences from the currently deployed weights on recent bars. This prevents outdated signals from lingering on the chart and ensures the displayed set matches what a live re-render would produce.
PersistWeightsOnShutdown now returns early when m_inferenceOnly is true,
preventing the tester cache from being overwritten with untrained state.
InitNeuralNetwork compares modification timestamps of the production model
(FILE_COMMON) and the agent-local cache, re-seeding when the production
copy is newer. This ensures backtests always run the latest deployed
weights even when the cache file persists across runs. Also moved
LoadChartSignals() call to after the config fingerprint is appended,
so that persisted arrows are correctly keyed per configuration.
Add FILE_SHARE_READ and FILE_SHARE_WRITE to all FileOpen calls in CNet::Load,
CNet::LoadCheckpoint, and in ExpertSignalAIBase (LoadModelStats, LoadChartSignals)
so that reading model files does not fail with error 5004 when another process
(e.g., a live chart) holds the file. Replace FileCopy with the new CopySharedFile
function for sidecar files (.cfg, .stats, _shadow.nnw) to perform share-aware
copying, preventing the same failure during tester seeding. This ensures the EA
can read and copy model artifacts even while a deployed instance is running.
Replace fixed short-delay retries in CopyFileWithRetry and LoadNetWithRetry with exponential backoff (8 attempts, cap at 2s) to reliably handle transient file locks from AV/EDR or concurrent saves.
Change autosave trigger from a fixed 300-second wall-clock timer to firing on new bar close, reducing unnecessary overwrites and narrowing the collision window with backtest FileCopy operations.
Add retry logic (CopyFileWithRetry, LoadNetWithRetry) to the tester's seeding path in ExpertSignalAIBase.mqh, and print diagnostic error messages in CNet::Load (Network.mqh) when FileOpen fails. This addresses silent failures caused by transient Windows sharing violations that occur when a live chart's atomic Save() overlaps with a Strategy Tester agent's FileCopy or FileOpen on the same production file. The new helpers retry the operation a few times with a short pause instead of silently falling back to an untrained model.
The previous TP calculation used ATR from entry, decoupled from the swing-anchored SL distance. This caused the Min_Risk_Reward_Ratio rejection filter to always fail because reward < 2*risk with default settings, preventing any trades. Now TP is a multiple of the actual trade risk (entry-to-stop distance), restoring coupling and ensuring the default RR filter is satisfiable. Also enforce minimum SL distance before TP calculation to maintain correct risk-ratio.
The queue duplicates minority bars up to repCount (~21x at 30.7:1 imbalance), causing IS accuracy to be scored over an ~58%-directional set while OOS counts the real ~6% distribution. This made the IS/OOS gap misleading (e.g. 77% vs 12%), appearing as catastrophic overfitting when OOS was actually stronger (1.97x vs 1.33x lift). By marking only the first occurrence per bar with `m_isTrainQueuePrimary` and using that flag in the counter, both metrics now measure the same natural class distribution, making the gap directly interpretable as generalization. Backprop and training remain unaffected—every occurrence still trains as before.
Extract shared persistence logic into PersistDeployedModel() to guarantee consistency across all deploy paths (ladder, era-cap, stop, and manual). Add DeployNow() to let the panel button finalize the current best checkpoint as the live model, and RetrainDeployed() to revert deployment and resume training from the deployed weights without starting from scratch. This enables operator-controlled deployment while preserving online continual learning.
Implement a plateau detection mechanism that escalates through warm restart, gamma annealing, and eventual deployment when balanced accuracy stagnates for `PLATEAU_PATIENCE_ERAS`. Also add a compatibility shim for the removed `MinWR` input to preserve model filenames and `.cfg` layout.
A single EA run instantiates several CNet objects (main net, EMA shadow, OOS clones, etc.). Each previously ran its own OpenCL/DirectML probe, printing redundant error banners (e.g., "OpenCL not found") and compute tier messages.
Added process-wide static flags `s_openclUnavailable` and `s_computeTierLogged` to skip subsequent probes and log once per process. This eliminates log clutter and avoids unnecessary probe overhead on hosts without OpenCL.
Rename the original `CreateElement` with a defaulted `weighScale` parameter to `CreateElementScaled` and provide a proper virtual override that matches the base `CArrayObj::CreateElement` signature exactly. This ensures `CArrayObj::Load()` dispatches correctly, fixing a bug where every saved model load failed at the first layer. Update all call sites in `CNeuronBase::Init` and `CNeuronPool::Init`. Additionally, enhance error diagnostics in `CNet::Load` to distinguish between file truncation and code faults (such as the signature mismatch).
The `Load` method now accepts an optional `quiet` flag that suppresses diagnostic
`Print` statements when set to `true`. This is used by callers that anticipate
a load failure and handle it gracefully — for example, the EMA shadow-net
bootstrap on a CPU-DLL box, which cannot hold a second full network. The main
model load keeps `quiet=false` so actual failures remain visible.
- In Network.mqh: Add upfront file size check to distinguish truncated files from backend allocation failures during model load, improving error diagnosis.
- In ExpertSignalAIBase.mqh: Remove redundant shadow net save on shutdown to avoid doubling shutdown cost and exceeding MT5's deinit budget, preventing abnormal termination and subsequent retrain.
- In Warrior_EA.mq5: Reduce training timer interval from 5s to 250ms to allow more frequent training cycles instead of sitting idle ~98% of the time.
The previous save directly wrote to the target file, which could leave a truncated/partial file if the process was force-killed (e.g., MT5 deinit timeout). Now the save writes to a temporary file (.savetmp) and only renames it over the real file after a successful write. This ensures that an interrupted save never corrupts the last good model. Additionally, improved diagnostics in CNet::Load to distinguish corrupt/incompatible files from compute errors.
Updated comments across ExpertSignalAIBase.mqh to explicitly state that the
compounded accuracy metric is a directional win-rate (Buy/Sell predictions only)
and that neutral/no-trade calls are excluded to avoid inflating the rate (since
neutral is the ~94% majority). The cumulative counters now only increment when
the prediction is directional, ensuring the panel reflects genuine trade quality
rather than overall label accuracy.
Replace the display-only MIN_FIRED_FOR_HITRATE with persistent cumulative accuracy counters (m_cumIsCorrect, m_cumIsTotal, m_cumOosCorrect, m_cumOosTotal) that survive era-to-era and restart via .stats. New ComputeCompoundedAccuracyLine() builds the panel line from these counts, providing a stable "accuracy over all validated signals so far" metric.
Split `PersistOnShutdown` into `PersistWeightsOnShutdown` (heavy weights save) and `ShutdownChartCleanup` (save arrows + purge chart). In `OnDeinit`, run panel destruction and per‑signal chart cleanup **before** the weight persistence, preventing leftover chart objects when the weight save stalls or faults past MT5's deinit budget.
Add MIN_FIRED_FOR_HITRATE constant (10) to the simple panel and training status hit-rate display. Update ScheduleTrainingIfNeeded and UpdateTrainingStatusLabel to only show a percentage when a side has at least MIN_FIRED_FOR_HITRATE live-fired calls; otherwise show "n/a" or "measuring". This prevents misleading early "0%" or "measuring" output from a single sample, while keeping full detail in logs and verbose panel.
- ResetWeights now passes `false` (not `m_isInitialized`) to SaveTopologyConfiguration so the config fingerprint matches the fresh-init write (always false). Prevents spurious "Configuration mismatch" that would discard the just-reset weights.
- UpdateTrainingStatusLabel: collapse hit-rate display to "measuring..." when neither side has fired yet; once at least one side fired, show both with "n/a" for missing side.
