Warrior_EA/Expert/AIBase/Lifecycle.mqh

//+------------------------------------------------------------------+
//+------------------------------------------------------------------+
//| Lifecycle.mqh                                                    |
//|                                                                  |
//| Construction/destruction, the CExpertSignal vote API             |
//| (LongCondition/ShortCondition/ConfidenceTier/pattern weights),   |
//| tick + chart-event dispatch, and the per-config chart lock.      |
//+------------------------------------------------------------------+
#ifndef WARRIOR_AIBASE_LIFECYCLE_MQH
#define WARRIOR_AIBASE_LIFECYCLE_MQH
//+------------------------------------------------------------------+
//| Constructor                                                      |
//+------------------------------------------------------------------+
//--- These are only fallback defaults for a fresh object before the EA's OnInit() applies the
//--- active input values via the public setters in Warrior_EA.mq5. The input-driven values are the
//--- source of truth for the actual run configuration.
CExpertSignalAIBase::CExpertSignalAIBase(void) :
   ID("NULL"),
   m_neuronsCount(0),
   m_minTrainYear(1970),
   m_optimizationAlgo(TrainingOptimizer), // see the member declaration comment
   //--- Placeholder only; InitNeuralNetwork() replaces it with ComputeFirstLayerWidth() before anything
   //--- reads it. Deliberately the floor rather than 0, so a hypothetical path that built a topology
   //--- without going through init would produce a small usable net instead of a zero-width layer.
   m_initialNeuronsCount(FIRST_LAYER_MIN_WIDTH),
   m_outputNeuronsCount(OUTPUT_CLASSIFICATION),
   //--- Frozen. Nothing reads these to build a topology any more - the taper derives its own
   //--- endpoints (BuildFreshTopology) - but they still occupy positional slots in the .cfg sidecar
   //--- and the weights fingerprint.
   m_minNeuronsCount(MIN_NEURONS_20),
   m_neuronsReduction(RF_70),
   m_hiddenLayersCount(3),
   m_lstmHiddenSize(32),
   m_convFilterCount(16),
   m_historyBars(14),
   m_fractalPeriods(5),
   m_pattern_0(25),
   m_pattern_1(50),
   m_pattern_2(75),
   m_pattern_3(100),
   m_trainTarget(0),
   m_ensembleMember(false),
   m_ensemblePanelSlot(-1),
   m_fracLegCount(0),
   m_metaCandCount(0),
   m_useVolumes(true),
   m_useTime(true),
   m_useATR(true),
   m_useMA(false),
   m_useRSI(false),
   m_useMACD(false),
   m_useIchimoku(false),
   m_useSwingContext(false),
   m_useNews(false),
   m_useCrossAsset(false),
   m_useSpreadFeature(false),
   m_spreadSeriesBars(0),
   m_spreadSeriesAnchor(0),
   m_crossAssetAnchor(0),
   m_crossAssetPairsPinned(""),
   m_crossAssetCfgSaved(false),
   m_useAltData(false),
   m_altDataEnabled(true),
   m_altDataLateWarned(false),
   m_altDataNamesPinned(""),
   m_newsFeatureWindowMinutes(60),
   m_useADCumulativeDelta(false),
   m_useADShorteningOfThrust(false),
   m_useADWyckoffEventStream(false),
   m_useADWyckoffFailedStructure(false),
   m_useADWyckoffSignificantBarInversion(false),
   m_autoTuneIndicators(false),
   m_indicatorsPtr(NULL),
   Net(NULL),
   m_shadowNet(NULL),
   TempData(NULL),
   dError(-1),
   dUndefine(0),
   dForecast(0),
   dPrevSignal(0),
   m_refreshOk(0),
   m_refreshFailFeatures(0),
   m_refreshFailShort(0),
   m_refreshBuy(0),
   m_refreshSell(0),
   m_refreshNeutral(0),
   m_voteGateBlocked(0),
   m_voteGatePassed(0),
   m_voteGateCompleteAtFirst(-1),
   m_voteGateLoadedAtFirst(-1),
   m_signalClusterWindow(6),
   m_nmsLiveBuyTime(0),
   m_nmsLiveSellTime(0),
   m_nmsLiveBuyAccept(false),
   m_nmsLiveSellAccept(false),
   m_nmsLiveKeptTime(0),
   m_nmsLiveKeptDir(Neutral),
   m_nmsLiveKeptConf(0),
   dtStudied(0),
   m_eraCount(0),
   m_trainingComplete(false),
   m_inferenceOnly(false),
   m_modelLoadedFromDisk(false),
   m_topologySuperseded(false),
   m_mqlInferenceValidated(false),
   m_shadowBootstrapAttempted(false),
   m_enableOnlineLearning(true),
   m_freezePriorCalibration(false),
   m_onlineLearnedUpToTime(0),
   m_onlineRollingAcc(-1.0),
   m_onlineSamples(0),
   m_onlineBarsSincePersist(0),
   m_onlineBlendFrozen(false),
   bEventStudy(false),
   m_oosSplitPct(30),
   dOosError(-1),
   dOosForecast(0),
   m_oosSamples(0),
   m_cumIsCorrect(0),
   m_cumIsTotal(0),
   m_cumOosCorrect(0),
   m_cumOosTotal(0),
   m_oosOutSpreadSum(0),
   m_oosOutCount(0),
   m_spreadAtr(0.0),
   m_oosNeutralStrict(0),
   m_oosNeutralTie(0),
   m_oosTieBuySell(0),
   m_oosRailBars(0),
   m_countBuySignals(0),
   m_countSellSignals(0),
   m_countNeutralSignals(0),
   m_trueBuyCount(0),
   m_trueSellCount(0),
   m_trueNeutralCount(0),
   m_logitAdjustTau(1.0),
   m_logitAdjustLogged(false),
   m_logitAdjustSkipWarned(false),
   m_prevEraTrueBuyCount(0),
   m_prevEraTrueSellCount(0),
   m_prevEraTrueNeutralCount(0),
   m_oosBuyHits(0),
   m_oosBuyTotal(0),
   m_oosSellHits(0),
   m_oosSellTotal(0),
   m_oosNeutralHits(0),
   m_oosNeutralTotal(0),
   m_oosBuyPredicted(0),
   m_oosBuyPredictedHits(0),
   m_oosSellPredicted(0),
   m_oosSellPredictedHits(0),
   m_oosNeutralPredicted(0),
   m_oosNeutralPredictedHits(0),
   m_oosBuyPredictedWins(0),
   m_oosSellPredictedWins(0),
   m_oosWinLongTotal(0),
   m_oosWinShortTotal(0),
   m_oosConfidenceSum(0),
   m_confidenceCalScale(1.0),
   m_minDirectionalRecallPct(40),
   m_lastProgressLogTick(0),
   //--- No vote-driven exit until the inputs say otherwise - matches Signal_ThresholdClose's shipped Disabled.
   m_exitVoteThreshold(0.0),
   m_exitHoldToBarrier(false),
   m_simRSum(0.0),
   m_simRSumSq(0.0),
   m_simTrades(0),
   m_simVoteExits(0),
   m_simBarrierWins(0),
   m_simTpHits(0),
   m_closeAllCycleBars(0),
   m_closeAllMeanBudget(0),
   m_geoDiffSum(0.0),
   m_geoDiffSumSq(0.0),
   m_geoIncSum(0.0),
   m_geoCandSum(0.0),
   m_geoTrades(0),
   m_geoIncOpen(0),
   m_geoCandOpen(0),
   m_geoCandSl(0.0),
   m_geoCandTp(0.0),
   m_geoStartTick(0),
   m_simTimeouts(0),
   m_simTimeoutRSum(0.0),
   m_lastTimeoutShare(-1.0),
   m_lastTimeoutMeanR(0.0),
   m_exitReplayReported(false),
   m_lastRecallFloorPct(0.0),
   m_detectabilityReported(false),
   m_priorBuy(0.0),
   m_priorSell(0.0),
   m_priorNeutral(0.0),
   m_oosBuyFired(0),
   m_oosBuyFiredHits(0),
   m_oosSellFired(0),
   m_oosSellFiredHits(0),
   m_oosNmsFired(0),
   m_oosNmsHits(0),
   m_oosNmsLastBuyIdx(-1),
   m_oosNmsLastSellIdx(-1),
   m_oosNmsKeptIdx(-1),
   m_oosNmsKeptConf(0.0),
   m_oosNmsKeptDir(Neutral),
   m_lastBuyFiredPrecPct(-1),
   m_lastSellFiredPrecPct(-1),
   m_lastBuyFired(0),
   m_lastSellFired(0),
   m_swingConfirmationBars(100),
   m_barrierHorizonBars(BARRIER_HORIZON_FALLBACK),
   m_barrierHorizonResolved(false),
   m_barrierFallbackWarned(false),
   m_lastBarrierTimedOut(false),
   m_lastLabelWeekendCut(false),
   m_swingMedianLegAtr(0.0),
   m_metaGateArmed(false),
   m_metaGateLastP(-1.0),
   m_metaGateLastBe(-1.0),
   m_metaGateApproved(0),
   m_metaGateVetoed(0),
   m_barrierHorizonLegStarved(false),
   m_horizonStarvedWarned(false),
   m_geometryCfgSaved(false),
   m_geometryAdopted(false),
   m_dirEvidence(false),
   m_dirEvidenceWhy("not measured yet"),
   m_lastBarrierBothWon(false),
   m_lastBarrierBothWonTied(false),
   m_labelPrebuildTimeoutCount(0),
   m_labelPrebuildWeekendCutCount(0),
   m_labelPrebuildBothWonCount(0),
   m_labelPrebuildBothWonTieCount(0),
   m_maxErasPerRun(300),
   m_arrowRestoreIndex(0),
   m_arrowRestorePending(false),
   m_arrowRestoreStartMs(0),
   m_rescanIndex(0),
   m_rescanHi(0),
   m_rescanBarsNow(0),
   m_rescanPending(false),
   m_rescanStartMs(0),
   m_rescanRawBuy(0),
   m_rescanRawSell(0),
   m_rescanRawNeutral(0),
   m_trainRunActive(false),
   m_eraResumePending(false),
   m_resumeBars(0),
   m_resumeTotalIter(0),
   m_resumeOosCutoff(0),
   m_resumeBarIndex(0),
   m_resumeAddLoop(false),
   m_isTrainQueueCount(0),
   m_isTrainCursor(0),
   m_isPass2Active(false),
   m_isPass2Done(false),
   m_isPass3Active(false),
   m_oosScoreIndex(0),
   m_oosScoreStartIndex(0),
   m_isCalibActive(false),
   m_isCalibDone(false),
   m_calibIndex(0),
   m_calibStartIndex(0),
   m_excNet(NULL),
   m_excTgt(NULL),
   m_excOut(NULL),
   m_excHeadFailed(false),
   m_excBaseTotal(0),
   m_excScored(0),
   m_excScoredD(0),
   m_excDiffSum(0.0),
   m_excDiffSumSq(0.0),
   m_excTrailDiffSum(0.0),
   m_excTrailDiffSumSq(0.0),
   m_excMonoViol(0),
   m_excTrainTick(0),
   m_excUs(0),
   m_excTrailHead(0),
   m_excTrailCount(0),
   m_excTrailN(0),
   m_excTrailScored(0),
   m_lastStatusLabelUpdateTick(0),
   m_lastBuyRecallPct(-1),
   m_lastSellRecallPct(-1),
   m_lastDisplayNeuron0(0),
   m_lastDisplayNeuron1(0),
   m_lastDisplayNeuron2(0),
   m_lastDisplaySignal(0),
   m_lastBarTime(0),
   m_modelEta(InitialEtaForOptimizer()),
   m_etaCeiling(InitialEtaForOptimizer()),
   m_erasSinceCooldown(0),
   m_bestOosForecast(-1),
   m_bestBalancedOos(-1),
   m_bestPassedRecall(false),
   m_bestBothSidesLive(false),
   m_eraStartTick(0),
   m_passFeatUs(0),
   m_passNetUs(0),
   m_passHeartbeatPrints(0),
   m_passWindowOk(0),
   m_passWindowFail(0),
   m_consecutiveRegressions(0),
   m_featureFailTransient(false),
   m_featureFailBlock(""),
   m_featureFailIdx(-1),
   m_windowFailSlot(-2),
   m_windowFailTotal(0),
   m_featureWidthWarned(false),
   m_featureHealthReported(false),
   m_lastHeartbeatTick(0),
   m_passProgressPct(0),
   m_passLabel("starting"),
   m_lastEraCompleteTick(0),
   m_lastStallReportTick(0),
   m_haveOosCheckpoint(false),
   m_bestDirPrecPct(-1.0),
   m_bestChancePrecPct(-1.0),
   m_bestDirCalls(0),
   m_deployCandidateEras(0),
   m_oosStable(false),
   m_objectiveMet(false),
   m_erasSinceBestBalanced(0),
   m_plateauStage(0),
   m_bestIsError(-1.0),
   m_erasSinceBestIsError(0),
   m_isErrorPlateaued(false),
   m_restartBoostErasLeft(0),
   m_syncWaitStartTick(0),
   m_warmupPassesRemaining(0),
   m_coldSweepTick(0),
   m_indicatorDepthCapBars(0),
   m_indicatorDepthDeadWarned(false),
   m_handleRepairTick(0),
   m_barrierEraSeen(-1),
   m_barrierEraTick(0),
   m_barrierExcluded(false),
   m_barrierPhaseProgress(false),
   m_barrierHoldReportTick(0),
   m_inferenceDepthRefusalWarned(false),
   m_prebuildBlockWarned(false),
   m_depthSettleStart(0),
   m_depthProbeTick(0),
   m_depthProbeLast(0),
   m_depthProbeStable(0),
   m_labelCacheBars(0),
   m_labelCacheAnchorTime(0),
   m_labelCachePrebuilt(false),
   m_lastExcUp(0.0),
   m_lastExcDown(0.0),
   //--- 0 samples => MeanLabelLifespan() returns 1.0 => EffectiveSampleSize() is the identity, so an
   //--- un-prebuilt model behaves exactly as it did before the overlap correction existed rather than
   //--- shrinking its own samples on a guess. See m_lastLabelLifespan.
   m_lastLabelLifespan(0),
   m_labelLifespanSum(0.0),
   m_labelLifespanCount(0),
   //--- -1 = the deploy gate has not run its arithmetic yet this era; the era line then omits the bar
   //--- rather than printing a stale one from a previous era.
   m_lastEdgeFloorPct(-1.0),
   m_lastPrecSE(-1.0),
   m_lastEffN(-1.0),
   m_lastRungLifespan(0.0),
   m_poolWriteWarned(false),
   m_lastPoolPasses(false),
   m_lastPoolReport(""),
   m_derivedSlMult(0.0),
   m_derivedTpMult(0.0),
   //--- 0.0 = unthresholded until the first pass 2 fits it (see DIR_CONF_THRESHOLD_BINS). Deliberately
   //--- the permissive value: a model that has not measured its own operating point must not silently
   //--- abstain on everything.
   m_dirConfThreshold(0.0),
   m_bestDirConfThreshold(0.0),
   m_dirConfPrimaryBars(0),
   m_dirConfSparseWarned(false),
   m_geometryDerived(false),
   m_geometryDerivePasses(0),
   m_swingMedianBars(0),
   m_labelPrebuildActive(false),
   m_prebuildSeedPending(false),
   m_labelPrebuildBars(0),
   m_labelPrebuildOosCutoff(0),
   m_labelPrebuildIndex(-1),
   m_labelPrebuildBuyCount(0),
   m_labelPrebuildSellCount(0),
   m_labelPrebuildNeutralCount(0),
   m_simOosNet(NULL),
   m_simOosRunActive(false),
   m_simOosCutoff(0),
   m_simOosBarIndex(-1),
   m_simOosForecast(0),
   m_simOosSamples(0),
   m_dbBackfillActive(false),
   m_dbBackfillDone(false),
   m_dbBackfillIndex(0),
   m_dbBackfillStartIndex(0),
   m_dbBackfillStopIndex(2),
   m_dbBackfillBars(0),
   m_dbBackfillFired(0),
   m_dbBackfillEra(-1),
   m_tuneTrialIndex(-1),
   m_tuneBestOosForecast(-1),
   m_tuneLastTrialWasWin(true),
   m_tuneHaveBestCheckpoint(false),
   m_tuneStartTrainBar(0),
   m_tuneFilterDone(false),
   m_trainingPaused(false),
   m_trainingStopRequested(false),
    m_activeFileCommon(true),
    m_configLockName(""),
    m_isInitialized(false),
    m_shutdownInProgress(false),
    m_lastArrowsSaved(0),
    m_purgeMismatchWarned(false),
    m_miBestColumn(0.0),
    m_miLabelEntropy(0.0),
    m_miStrideBars(0),
    m_miNullBlocks(0),
    m_miReportDone(false),
   m_tiersSelfRanked(false),
   m_baselineDone(false),
   m_overlaySnapBars(0),
   m_prospectiveSigSnap(-2.0),
   m_dispSignal(0.0),
   m_dispValid(false),
   m_dispStamp(0),
   m_dispEra(-1),
   m_lastEnsRefusalKey(0),
    m_miReportDeferrals(0),
    m_barrierScanSlMult(0.0),
    m_barrierScanTpMult(0.0),
    m_barrierScanLiveLabels(false),
    m_barrierScanTimeouts(0),
    m_barrierHorizonClamped(false)
  {
//--- Claim this instance's study-event id - see STUDY_EVENT_ID_BASE (ExpertSignalAIBase.mqh) for why
//--- these are per instance and offset above the Controls library's event codes.
   m_studyEventId = (ushort)(STUDY_EVENT_ID_BASE + g_warriorStudyEventSeq++);
   m_studyArmedTick = 0;
   m_ensembleIndex = -1; // not an ensemble member until EnsembleMember(true) registers one (the flag itself is in the init list)
//--- per-era stash the ensemble verdict reads (see EnsembleStashEraStats); -1/false = "no era yet"
   m_eraStatPrecPct = -1.0;
   m_eraStatChancePct = -1.0;
   m_eraStatCalls = 0;
   m_eraStatTradeable = false;
   m_eraStatTwoSided = false;
   m_eraStatScore = 0.0;
   m_eraStatBlended = 0.0;
   m_eraStatThreshold = 0.0;
   m_checkpointEra = -1;
//--- indicator tuning defaults live in CADIndicatorTuner's own constructor (Expert\ADIndicatorTuner.mqh),
//--- which runs automatically for the m_indicatorTuner member above.
  }
//+------------------------------------------------------------------+
//| Destructor                                                       |
//+------------------------------------------------------------------+
CExpertSignalAIBase::~CExpertSignalAIBase(void)
  {
   //--- deliberately NOT calling PersistOnShutdown() here: OnDeinit() (Warrior_EA.mq5) already
   //--- calls it explicitly for every signal, one call stack frame shallower, BEFORE
   //--- Expert.Deinit() tears these objects down.
   if(CheckPointer(Net) != POINTER_INVALID)
      delete Net;
   if(CheckPointer(m_shadowNet) != POINTER_INVALID)
      delete m_shadowNet;
   if(CheckPointer(TempData) != POINTER_INVALID)
      delete TempData;
   if(CheckPointer(m_simOosNet) != POINTER_INVALID)
      delete m_simOosNet;
   //--- Excursion head: never persisted (Stage 1 is a measurement), so teardown is the whole lifecycle.
   if(CheckPointer(m_excNet) != POINTER_INVALID)
      delete m_excNet;
   if(CheckPointer(m_excTgt) != POINTER_INVALID)
      delete m_excTgt;
   if(CheckPointer(m_excOut) != POINTER_INVALID)
      delete m_excOut;
   //--- Unconditional now that the warm-reload "leave the arrows up" branch is gone (see
   //--- ShutdownChartCleanup). Cheap and idempotent: OnDeinit already purged, so this normally deletes
   //--- nothing - it exists for the teardown paths that never reach OnDeinit (a failed OnInit).
   PurgeChart();
   //--- Last, and cheap by design (one global-variable delete): the claim must outlive every save above
   //--- it, or a chart re-attaching during this teardown could start writing the same files mid-save.
   ReleaseConfigLock();
  }
//+------------------------------------------------------------------+
//| Sets the file/id identity a subclass constructor would otherwise  |
//| repeat verbatim (ID, m_id, m_folderPath, m_fileName, pattern count)|
//+------------------------------------------------------------------+
void CExpertSignalAIBase::SetIdentity(string id, string shortId, int patternCount = 4)
  {
   ID = id;
   m_id = shortId;
   m_folderPath = eaName + "\\" + "Neural Networks" + "\\" + "State" + "\\" + m_id + "\\";
   m_fileName = m_folderPath + _Symbol + "_" + IntegerToString(_Period);
   m_pattern_count = patternCount;
  }
//+------------------------------------------------------------------+
//| CONTROL-PANEL COMMAND. One switch, so a button can never reach    |
//| some models and miss others: the panel resolves the direction     |
//| once and every model in the tree is told the same thing.          |
//+------------------------------------------------------------------+
bool CExpertSignalAIBase::OnSignalCommand(const ENUM_SIGNAL_COMMAND cmd)
  {
   switch(cmd)
     {
      case SIGCMD_PAUSE_TRAINING:
         PauseTraining();
         return true;
      case SIGCMD_RESUME_TRAINING:
         ResumeTraining();
         return true;
      case SIGCMD_STOP_TRAINING:
         StopTraining();
         return true;
      case SIGCMD_START_TRAINING:
         StartTraining();
         return true;
      //--- These three report SUCCESS rather than "I understood the command", because the panel
      //--- counts them back to the operator and a failed deploy or a failed rebuild is exactly what
      //--- they need told about.
      case SIGCMD_DEPLOY:
         return DeployNow();
      case SIGCMD_RESET_WEIGHTS:
         return ResetWeights();
      case SIGCMD_SAVE_WEIGHTS:
         return SaveWeightsNow();
      case SIGCMD_LOAD_WEIGHTS:
         return LoadWeightsNow();
      case SIGCMD_RETRAIN_DEPLOYED:
         RetrainDeployed();
         return true;
      //--- Queues a rescan; returns whether one was actually queued, which is what tells the EA to
      //--- defer the "arrows shown" alert until every queued scan has drained.
      case SIGCMD_RESCAN_SIGNALS:
         return StartChartSignalRescan();
      case SIGCMD_REPORT_IDENTITY:
         Print("  " + RegistryLine());
         return true;
     }
   return false;
  }
//+------------------------------------------------------------------+
//| Panel button labels ask these; see ENUM_SIGNAL_TRAIT.             |
//+------------------------------------------------------------------+
bool CExpertSignalAIBase::HasSignalTrait(const ENUM_SIGNAL_TRAIT trait)
  {
   switch(trait)
     {
      case SIGTRAIT_TRAINABLE:
         return true;
      case SIGTRAIT_TRAINING_PAUSED:
         return m_trainingPaused;
      case SIGTRAIT_TRAINING_STOPPED:
         return m_trainingStopRequested;
      case SIGTRAIT_TRAINING_COMPLETE:
         return m_trainingComplete;
      //--- "still trainable AND has never checkpointed an era that cleared the per-class recall
      //--- floor" - the same bar the plateau ladder refuses to cross on its own.
      case SIGTRAIT_DEPLOY_SKIPS_RECALL:
         return (!m_trainingComplete && !m_bestPassedRecall);
      case SIGTRAIT_RESCAN_PENDING:
         return m_rescanPending;
     }
   return false;
  }
//+------------------------------------------------------------------+
//| "Voting" that price will grow.                                   |
//+------------------------------------------------------------------+
int CExpertSignalAIBase::LongCondition(void)
  {
   int result = 0;
   //--- Readiness gate: live trading still requires a converged model, but an inference-only tester
   //--- run may replay a model that was ACTUALLY loaded from disk even if its persisted
   //--- trainingComplete flag is false.
   NoteVoteGate(DoubleToSignal(dPrevSignal) == Buy);
   if(!m_trainingComplete && !(m_inferenceOnly && m_modelLoadedFromDisk))
      return 0;
   //--- No alternation gate any more - see the removal note at m_voteGateBlocked's declaration.
   //--- Under triple-barrier labels consecutive same-direction setups are ordinary and correct.
   if(dPrevSignal == -2)
      return 0;
   //--- NO confidence floor here, by design - see m_minSignalConfidence's former declaration site. A
   //--- weak call is not blocked at the AI's own boundary; it votes at its tier weight (as low as
   //--- m_pattern_0) and is then filtered by Min vote to open, exactly like a weak classic vote.
   if(DoubleToSignal(dPrevSignal) == Buy)
     {
      int tier = ConfidenceTier();
      result = PatternWeightForTier(tier);
      m_active_pattern = "Pattern_" + IntegerToString(tier);
      m_active_direction = "Buy";
     }
   return(result);
  }
//+------------------------------------------------------------------+
//| "Voting" that price will fall.                                   |
//+------------------------------------------------------------------+
int CExpertSignalAIBase::ShortCondition(void)
  {
   int result = 0;
   //--- Readiness gate - see LongCondition's matching comment.
   NoteVoteGate(DoubleToSignal(dPrevSignal) == Sell);
   if(!m_trainingComplete && !(m_inferenceOnly && m_modelLoadedFromDisk))
      return 0;
   //--- "not yet studied" sentinel, and no confidence floor - see LongCondition's matching comments.
   if(dPrevSignal == -2)
      return 0;
   if(DoubleToSignal(dPrevSignal) == Sell)
     {
      int tier = ConfidenceTier();
      result = PatternWeightForTier(tier);
      m_active_pattern = "Pattern_" + IntegerToString(tier);
      m_active_direction = "Sell";
     }
   return result;
  }
//+------------------------------------------------------------------+
//| Buckets a confidence magnitude into one of 4 equal bands between  |
//| the head's own structural floor and 1.0 - see m_pattern_0's       |
//| declaration comment for the resulting tier/weight table. Neither  |
//| head has a configurable floor: the boundary is 1/3 for the 3-class|
//| softmax and 0.5 for regression (DoubleToSignal's own threshold),  |
//| both arithmetic properties of the head rather than settings.      |
//+------------------------------------------------------------------+
int CExpertSignalAIBase::ConfidenceTierFor(const double signal)
  {
   //--- The head's own STRUCTURAL decision boundary - the lowest confidence magnitude that head can
   //--- possibly report for a directional call - not a user setting: - 3-class classification: the
   //--- winning class of a 3-way softmax is arithmetically >= 1/3, since three probabilities
   //--- summing to 1 cannot all be below it.
   double floorConf = (m_outputNeuronsCount == 3) ? (1.0 / 3.0) : 0.5;
   double span = MathMax(1.0 - floorConf, 0.0001);
   //--- RAW magnitude, not the calibrated one (fixed 2026-08-16).
   double rawMag = MathAbs(signal);
   if(!MathIsValidNumber(rawMag))
      return 0;
   double t = (MathMin(1.0, rawMag) - floorConf) / span;
   int tier = (int)MathFloor(t * 4.0);
   return MathMax(0, MathMin(tier, 3));
  }
//+------------------------------------------------------------------+
//| The live bar's tier - the only caller shape that existed before   |
//| ConfidenceTierFor() was split out, kept so LongCondition()/       |
//| ShortCondition() read exactly as they did.                        |
//+------------------------------------------------------------------+
int CExpertSignalAIBase::ConfidenceTier(void)
  {
   return ConfidenceTierFor(dPrevSignal);
  }
//+------------------------------------------------------------------+
//| The signed vote this member would cast for a given decision, in  |
//| the units CExpertSignalCustom::Direction() sums - see the        |
//| declaration comment for why this is a shared function and not    |
//| two parallel expressions.                                        |
//+------------------------------------------------------------------+
double CExpertSignalAIBase::LiveVoteContribution(const double signal)
  {
   //--- A MEMBER THAT HAS NEVER RANKED ITSELF DOES NOT VOTE. Until RankTiersFromOos() runs once,
   //--- m_pattern_0..3 hold the constructor's stock 25/50/75/100 ladder - and since 4858507 the vote
   //--- currency is a WIN RATE, so an unranked tier-3 call enters the mean claiming a 100% win rate
   //--- against ranked members contributing ~25. That is not a strong opinion, it is the wrong unit:
   //--- one unranked member drags the whole ensemble over any threshold. It bites on every fresh
   //--- deploy AND every resume, because the tier weights are not persisted in the .nnw - they exist
   //--- only as the output of a completed pass 3. Found 2026-08-23 on USDJPY, whose measured ceiling
   //--- is ~19 and which was firing anyway.
   if(!m_tiersSelfRanked)
      return 0.0;
   ENUM_SIGNAL s = DoubleToSignal(signal);
   if(s != Buy && s != Sell)
      return 0.0;   // abstention - live drops it from the sum AND the divisor
   double w = (double)PatternWeightForTier(ConfidenceTierFor(signal));
   if(!MathIsValidNumber(w) || w <= 0.0)
      return 0.0;   // a pattern ranked to weight 0 contributes nothing and is not a voter
   double contribution = m_weight * w;
   if(!MathIsValidNumber(contribution))
      return 0.0;
   return (s == Buy) ? contribution : -contribution;
  }
//+------------------------------------------------------------------+
//| Returns the given tier's current pattern weight (0-100)          |
//+------------------------------------------------------------------+
int CExpertSignalAIBase::PatternWeightForTier(int tier)
  {
   switch(tier)
     {
      case 0:
         return m_pattern_0;
      case 1:
         return m_pattern_1;
      case 2:
         return m_pattern_2;
      default:
         return m_pattern_3;
     }
  }
//+------------------------------------------------------------------+
//| TURN THIS ERA'S HELD-OUT OUTCOMES INTO THE VOTE WEIGHTS.         |
//+------------------------------------------------------------------+
void CExpertSignalAIBase::RankTiersFromOos(void)
  {
   //--- SNAPSHOT FIRST, unconditionally - this runs at pass-3 completion, the single moment the
   //--- arrow cache is complete for the era, and everything display-side (the overlay sweep, the
   //--- prospective readout) reads the snapshot instead of the cache precisely because the cache is
   //--- about to be wiped when the next era starts.
   int snapN = MathMin(ArraySize(m_arrowSignalCache), SIGNAL_RESCAN_LOOKBACK_BARS + 16);
   if(snapN > 0)
     {
      ArrayResize(m_overlaySigSnap, snapN);
      ArrayCopy(m_overlaySigSnap, m_arrowSignalCache, 0, 0, snapN);
     }
   m_overlaySnapBars = snapN;
   m_prospectiveSigSnap = -2.0;
   for(int pi = 1; pi <= 16 && pi < snapN; pi++)
      if(m_overlaySigSnap[pi] != -2.0 && MathIsValidNumber(m_overlaySigSnap[pi]))
        {
         m_prospectiveSigSnap = m_overlaySigSnap[pi];
         break;
        }
   //--- ...and tell the EA THIS member's snapshot is fresh. The sweep waits for every enrolled
   //--- member's bit - see g_warriorOverlayReadyMask for why a rate limit was not enough.
   if(m_ensembleIndex >= 0 && m_ensembleIndex < ENS_MAX_MEMBERS)
      g_warriorOverlayReadyMask |= (((uint)1) << m_ensembleIndex);
   g_warriorOverlayArmRequest = true;
   int pooledFired = 0, pooledHits = 0;
   for(int t = 0; t < 4; t++)
     {
      pooledFired += m_oosTierFired[t];
      pooledHits  += m_oosTierHits[t];
     }
   //--- Nothing fired this era (all-Neutral, or a stopped/cap-hit era): leave the previous era's
   //--- weights standing rather than collapsing every tier to a prior built on no evidence at all.
   if(pooledFired <= 0)
      return;
   double pooledPct = 100.0 * pooledHits / pooledFired;
   //--- WHAT A MEMBER WITH NO EVIDENCE IS WORTH: the coin-flip rate on this era's OOS bars, which is
   //--- what the gate calls zero skill. Shrinking toward it means "few fires -> speaks at chance",
   //--- where shrinking toward the member's own pooled rate would mean "few fires -> speaks at
   //--- whatever those few fires said", which is no shrinkage at all.
   int zsBars = m_oosBuyTotal + m_oosSellTotal + m_oosNeutralTotal;
   double chancePct = (zsBars > 0)
                      ? 50.0 * ((double)m_oosWinLongTotal + (double)m_oosWinShortTotal) / zsBars
                      : pooledPct;
   double pooledEffN = MathMax(0.0, EffectiveSampleSize((double)pooledFired));
   double trustPct = ShrunkRatePct(pooledEffN * ((double)pooledHits / pooledFired), pooledEffN,
                                   chancePct, MODULE_PRIOR_EFF_N);
   //--- WHY NOT WinRateFromCounts(), which is the estimator the classic ladders use: it returns
   //--- NO_DATA_WIN_RATE for anything under MIN_TRADES_FOR_WIN_RATE raw trades, BEFORE it shrinks.
   string line = "";
   for(int t = 0; t < 4; t++)
     {
      int fired = m_oosTierFired[t];
      int hits  = m_oosTierHits[t];
      double w = trustPct;
      if(fired > 0)
        {
         double effN = MathMax(0.0, EffectiveSampleSize((double)fired));
         double effHits = effN * ((double)hits / fired);
         //--- Same estimator the classic ladders use (ShrunkRatePct), with the prior deliberately
         //--- far smaller: it is counted in the same EFFECTIVE units as the evidence, and a tier
         //--- holds ~8-15 of those, so the classic path's 100 would drown every tier in the pool.
         //--- Toward the SHRUNK pooled rate, not the raw one: a member whose pooled evidence is
         //--- thin must not hand its tiers a confident prior it does not have itself.
         w = ShrunkRatePct(effHits, effN, trustPct, TIER_PRIOR_EFF_N);
        }
      //--- Rounded to the nearest INTEGER, not to the nearest 10 as NormalizeWinRate() does. After
      //--- shrinkage the tiers legitimately sit within a few points of each other, and decade rounding
      //--- would collapse them back into one number - undoing the separation this exists to produce.
      int wi = (int)MathMax(0, MathMin(100, MathRound(w)));
      ApplyTierWeight(t, wi);
      line += StringFormat(" T%d=%d(%d fires, %.1f eff)", t, wi, fired,
                           (fired > 0 ? EffectiveSampleSize((double)fired) : 0.0));
     }
   //--- MODULE WEIGHT = how much this model's opinion COUNTS in the weighted mean, which since the
   //--- 2026-08-18 currency change is a trust weight and no longer a discount on the estimate. The
   //--- pooled holdout win rate is the honest measure of that trust.
   Weight(MathMax(0.0, MathMin(1.0, trustPct / 100.0)));
   m_tiersSelfRanked = true;
   //--- THROTTLED (2026-08-19): the re-rank happens (and must happen) every era, but saying so
   //--- every era was ~950 near-identical lines/member/day once the system was confirmed working.
   //--- The weights it prints are visible live on the member HUD lines anyway.
   if(TrainLogDue())
      Print(ID + StringFormat(": tier weights re-ranked from %d held-out fires (%.1f effective,"
                           " pooled %.1f%% raw -> %.1f%% shrunk toward the %.1f%% coin-flip rate on"
                           " %.0f prior-equivalent calls) ->%s | module weight %.2f. These are the"
                           " weights the NEXT era votes with.",
                           pooledFired, pooledEffN, pooledPct, trustPct, chancePct,
                           MODULE_PRIOR_EFF_N, line, ModuleWeight()));
  }
//+------------------------------------------------------------------+
//| Set the specified pattern's weight to the specified value        |
//+------------------------------------------------------------------+
void CExpertSignalAIBase::ApplyPatternWeight(int patternNumber, int weight)
  {
   //--- THE SIGNAL DB DOES NOT OUTRANK THE HOLDOUT. UpdateSignalsWeights() calls this hourly for
   //--- every filter it can find rows for, and for an AI filter those rows are LIVE-journaled fires
   //--- accumulated across eras - i.e. across models. Without this the ranking pass would silently
   //--- undo every era's self-ranking within the hour.
   if(m_tiersSelfRanked)
      return;
   switch(patternNumber)
     {
      case 0:
         Pattern_0(weight);
         break;
      case 1:
         Pattern_1(weight);
         break;
      case 2:
         Pattern_2(weight);
         break;
      case 3:
         Pattern_3(weight);
         break;
      default:
         break;
     }
  }
//+------------------------------------------------------------------+
//| OnTick function                                                  |
//+------------------------------------------------------------------+
void CExpertSignalAIBase::OnTickHandler(void)
  {
   ScheduleTrainingIfNeeded();
  }
//+------------------------------------------------------------------+
//| Schedules the next training pass (if one is due) and refreshes    |
//| the per-tick status label. Factored out of OnTickHandler() so    |
//| Warrior_EA.mq5's always-on timer (see PollTraining()) can drive   |
//| this on a fixed wall-clock schedule too - training must not stall |
//| just because the market is closed and no ticks are arriving.      |
//+------------------------------------------------------------------+
void CExpertSignalAIBase::ScheduleTrainingIfNeeded(void)
  {
   //--- stopped: no new training passes get scheduled at all (StartTraining() re-arms this).
   //--- paused: still schedule so bEventStudy/dtStudied bookkeeping stays current, but Train() itself
   //--- blocks at the next era boundary until resumed - keeps in-memory state coherent either way.
   //--- complete: training already converged - a plain new bar must NOT re-enter Train()'s full era
   //--- loop, which would otherwise reset the best-checkpoint/g_eta-decay tracking and run real
   //--- Net.backProp() passes again, forever, once per bar, on an already-converged model (see
   //--- RefreshConvergedSignal()'s declaration comment). Just keep the live signal current instead.
   //--- publish this signal's current signed confidence for the intelligent trailing (and any other
   //--- live-confidence consumer) - see g_LiveAISignedConfidence in Variables\ConfidenceBridge.mqh.
   //--- Cheap: SignedAIConfidence() just reads the already-computed dPrevSignal.
   //--- ENSEMBLE: the AVERAGE across members, not this member's own reading. Every member ran this line
   //--- unconditionally, every tick, so the global was simply whichever member's OnTick happened to run
   //--- last. So on a four-model chart an LSTM entry could have its stop moved on the Perceptron's
   //--- opinion alone, purely by scheduling order. Not the vote, not a weighted blend - an arbitrary
   //--- member. (User-identified 2026-08-17.) The only consumer left is the intelligent trailing stop;
   //--- the AI early-exit route that was the other one is gone with the vote-layer simplification.
   //--- Averaging matches how the ensemble actually trades: the open decision is the weighted-average
   //--- vote, and a member that abstains contributes 0 and dilutes, exactly as it does there. Members
   //--- still training read 0 from SignedAIConfidence(), so a half-trained ensemble reads WEAKER rather
   //--- than louder, which is the safe direction for an exit trigger.
   //--- Currently latent, and worth keeping that way deliberately: Signal_ThresholdClose ships Disabled (101,
   //--- unreachable on both scales it drives) and TrailingStrategy is off, so neither consumer fires
   //--- today. This is fixed now precisely because the plan is to enable vote exits once the models are
   //--- accurate - at which point a scheduling-order exit would be actively harmful and very hard to see.
   //--- PUBLISH ONLY THIS SIGNAL'S OWN VOTE. Combining is the orchestrator's job, never a member's - see
   //--- the vote board in Variables\ConfidenceBridge.mqh for the scheduling bug this replaces and for why
   //--- "have a member average its siblings" was the wrong shape of fix in a codebase whose whole point is
   //--- that signals do not reach into each other. A solo AI signal owns slot 0 and the aggregate is then
   //--- just its own value, so the non-ensemble path is unchanged.
   PublishAIVote(m_ensembleMember ? m_ensembleIndex : 0, SignedAIConfidence());
   datetime lastBarDate = (datetime)SeriesInfoInteger(m_symbol.Name(), m_period, SERIES_LASTBAR_DATE);
   //--- A failed lookup (0) must not silently read as "dtStudied is already caught up, nothing
   //--- pending" - that would freeze this function into never re-triggering training/signal refresh
   //--- again until some other path happens to bump dtStudied.
   bool newBarPending = (dPrevSignal == -2 || lastBarDate <= 0 || ((m_inferenceOnly ? m_lastBarTime : dtStudied) < lastBarDate));
   //--- A MODEL THAT HAS NOT FINISHED TRAINING IS ALWAYS PENDING. Gating them on the watermark
   //--- meant training could only advance when a new BAR closed. On H1 that is one 120ms chunk per
   //--- hour. A POST-TRAINING WALK IS ALSO PENDING.
   bool postTrainWalkPending = (m_simOosRunActive || m_dbBackfillActive);
   bool trainingPending = !(m_trainingComplete || m_inferenceOnly) || postTrainWalkPending;
   //--- m_inferenceOnly (single backtest) takes the converged/inference branch even if the seeded model
   //--- wasn't flagged complete, so a backtest never drops into Train()'s era loop - see m_inferenceOnly.
   if((m_trainingComplete || m_inferenceOnly) && !m_trainingStopRequested && !m_trainRunActive &&
      !postTrainWalkPending)
     {
      if(newBarPending)
         RefreshConvergedSignal();
     }
   else
     {
      //--- Lost-event watchdog: an armed event that never arrived (chart-event queue overflow) would
      //--- otherwise leave bEventStudy true forever and silently stall training - the accidental rescue
      //--- (any sibling's event clearing this flag) went away with the per-instance ids. See
      //--- STUDY_EVENT_LOST_MS for why a false trip is not a realistic concern.
      if(bEventStudy && GetTickCount() - m_studyArmedTick > STUDY_EVENT_LOST_MS)
        {
         Print(ID + ": study event armed " + IntegerToString(STUDY_EVENT_LOST_MS / 1000) +
               "s ago never arrived (chart-event queue overflow?) - re-arming.");
         bEventStudy = false;
        }
      if(!m_trainingStopRequested && !bEventStudy && (newBarPending || trainingPending))
         ArmStudyEvent((long)MathMax(0, MathMin(iTime(m_symbol.Name(), PERIOD_CURRENT, (int)(100 * Net.recentAverageSmoothingFactor * (m_trainingComplete ? 1 : 10))), dtStudied)), "New Bar");
     }
   //--- Train() (see its declaration comment) now yields every ~TRAIN_TIME_BUDGET_MS instead of
   //--- blocking for a whole era, so while a run is active this per-tick line would otherwise
   //--- overwrite Train()'s own full-detail status label on every single tick between chunks -
   //--- flickering between the two instead of showing one steady picture.
   if(m_ensembleMember && EnsembleEraBarrierHolds())
      return;
   if(!m_trainRunActive)
     {
      //--- Compact, accurate end-state text.
      bool onlineActive = m_enableOnlineLearning && !m_inferenceOnly
                          && !MQLInfoInteger(MQL_TESTER) && !MQLInfoInteger(MQL_OPTIMIZATION) && !MQLInfoInteger(MQL_FORWARD)
                          && CheckPointer(Net) != POINTER_INVALID && !Net.CpuInference();
      //--- Simple end-state panel (default, VerboseMode off): plain-language status + the model's
      //--- compounded/persistent Buy/Sell win-rate (directional accuracy, Neutral excluded - persisted in
      //--- .stats WST5, so it survives a fresh chart reload and is meaningful the moment a drop-and-go user
      //--- attaches the EA) + the current call. The verbose era/forecast dump below stays for power users.
      if(!VerboseMode)
        {
         string statusPlain;
         if(m_trainingComplete)
            statusPlain = onlineActive ? "Live - learning from new bars" : "Ready for live trading";
         else if(m_trainingStopRequested)
            statusPlain = "Paused - progress saved";
         else if(m_trainingPaused)
            statusPlain = "Paused";
         else
            statusPlain = "Getting ready...";
         ENUM_SIGNAL liveSig = DoubleToSignal(dPrevSignal);
         string liveSigPlain = (liveSig == Buy) ? "Buy" : (liveSig == Sell) ? "Sell" : "Neutral (no trade)";
         string simpleLive = DisplayName() + "  -  " + statusPlain + "\n";
         //--- Only show the accuracy line once at least one signal has been validated (compounded counts
         //--- persist across restarts, so a deployed model shows real numbers immediately, not "measuring").
         if(m_cumIsTotal > 0 || m_cumOosTotal > 0)
            simpleLive += ComputeCompoundedAccuracyLine() + "\n";
         //--- The ensemble headline is the FIRST line, so lead with the signal - on the combined
         //--- panel each member's one line must answer "what is this model saying right now".
         if(m_ensembleMember)
           {
            simpleLive = statusPlain + "  ->  " + liveSigPlain;
            //--- one-line accuracy on the member headline (user request 2026-08-16: the solo panel
            //--- shows accuracy, the ensemble panel did not).
            if(m_cumOosTotal > 0)
              {
               double slBeH = 0.0, tpBeH = 0.0;
               BarrierMultiples(slBeH, tpBeH);
               simpleLive += StringFormat("  |  win %d%%", (int)MathRound(m_cumOosCorrect * 100.0 / m_cumOosTotal));
               if(slBeH > 0.0 && tpBeH > 0.0)
                  simpleLive += StringFormat(" (need %d%%)", (int)MathRound(100.0 * slBeH / (slBeH + tpBeH)));
              }
           }
         PublishStatus(simpleLive);
         return;
        }
      string completeText = onlineActive ? "Complete - live (adapting to new bars)" : "Complete - ready for live (inference)";
      string trainingState = m_trainingStopRequested
                             ? (m_trainingComplete ? completeText : "Stopped - resumable (weights kept)")
                             : (m_trainingPaused ? "Paused" : (m_trainingComplete ? completeText : "In progress"));
      //--- same "Forecast: <signal> -> <value>" line the active training loop's status label ends on
      //--- (see the classLine-terminated StringFormat below), instead of a raw bEventStudy/dPrevSignal/
      //--- dtStudied debug dump - this is what stays on screen once training stops/pauses/completes.
      PublishStatus(StringFormat(
                       ID + " : Era %d -> Training %s\n" +
                       "Forecast: %s -> %.2f",
                       m_eraCount, trainingState,
                       EnumToString(DoubleToSignal(dPrevSignal)), dPrevSignal));
     }
  }
//+------------------------------------------------------------------+
//| Timer-driven equivalent of OnTickHandler()'s scheduling, called   |
//| from Warrior_EA.mq5's always-on OnTimer() so training keeps        |
//| progressing purely on wall-clock time - no dependency on ticks,   |
//| which simply don't arrive while the market is closed.             |
//+------------------------------------------------------------------+
void CExpertSignalAIBase::PollTraining(void)
  {
   //--- Drain a slice of the queued chart-arrow restore FIRST, and skip training work on any tick
   //--- where restoring is still in flight.
//--- STOP BEFORE ANY OF IT. Drawing arrows for a chart that is unloading is the worst case of the
//--- three: it lengthens the very sweep that has to finish.
   if(ShutdownRequested())
      return;
   if(m_arrowRestorePending)
     {
      AdvanceChartSignalRestore();
      return;
     }
   //--- Same one-thread reasoning as the arrow restore above: a manual rescan (Show Signals) also competes
   //--- for the single MQL5 thread, and its per-bar feedForward is real compute rather than a cheap object
   //--- write, so it must finish its own slices before training resumes rather than interleaving with it.
   if(m_rescanPending)
     {
      AdvanceChartSignalRescan();
      return;
     }
   if(m_isInitialized)
      ScheduleTrainingIfNeeded();
  }
//+------------------------------------------------------------------+
//|                                                                  |
//+------------------------------------------------------------------+
void CExpertSignalAIBase::OnChartEventHandler(const int id,
                                              const long &lparam,
                                              const double &dparam,
                                              const string &sparam)
  {
   //--- Match THIS instance's id only. See STUDY_EVENT_ID_BASE (ExpertSignalAIBase.mqh) for the
   //--- full failure shape.
   if(id == CHARTEVENT_CUSTOM + m_studyEventId)
     {
      //--- The study event IS the training driver, so a queued one landing after the stop request would
      //--- open a whole era inside the teardown window. Checked here as well as in Warrior_EA.mq5's
      //--- OnChartEvent because CExpertCustom re-posts these between members.
      if(ShutdownRequested())
         return;
      TuneIndicatorsAndTrain(lparam);
      bEventStudy = false;
      OnTickHandler();
     }
  }
//--- Set when a start is refused for a reason no retry can change. OnInit's retry loop reads it so
//--- the operator's LAST log line names the cause instead of "Failed to initialize Indicators".
string g_initFatalReason = "";

//+------------------------------------------------------------------+
//| Claim m_activeFileName for this chart, terminal-wide.            |
//+------------------------------------------------------------------+
bool CExpertSignalAIBase::AcquireConfigLock(void)
  {
   //--- FNV-1a over the resolved filename: every retrain-affecting input is already folded into that
   //--- name, so equal names mean genuinely equal configs and nothing else has to be compared. Hashed
   //--- because MQL5 caps global-variable names at 63 characters and the path alone exceeds that.
   uint h = 2166136261;
   int len = StringLen(m_activeFileName);
   for(int i = 0; i < len; i++)
     {
      h ^= (uint)StringGetCharacter(m_activeFileName, i);
      h *= 16777619;
     }
   string name = "WarriorAI_" + m_id + "_" + StringFormat("%08x", h);
   long self = ChartID();
   //--- Atomic: true means it did not exist and is now ours.
   if(GlobalVariableTemp(name))
     {
      GlobalVariableSet(name, (double)self);
      m_configLockName = name;
      return true;
     }
   long owner = (long)GlobalVariableGet(name);
   //--- Our own entry: this chart is re-initializing after a parameter change or a recompile whose
   //--- OnDeinit never reached ReleaseConfigLock(). Reclaim it instead of refusing to start.
   if(owner == self)
     {
      m_configLockName = name;
      return true;
     }
   //--- Owner recorded but its chart no longer runs an expert - take the claim over. owner == 0 is
   //--- deliberately NOT treated as stale: it means another instance created the variable microseconds
   //--- ago and has not stamped its id yet, which is a live claim, not a dead one.
   bool ownerAlive = false;
   if(owner != 0)
     {
      long id = ChartFirst();
      while(id >= 0)
        {
         if(id == owner)
           {
            ownerAlive = (StringLen(ChartGetString(id, CHART_EXPERT_NAME)) > 0);
            break;
           }
         id = ChartNext(id);
        }
     }
   if(owner != 0 && !ownerAlive)
     {
      GlobalVariableSet(name, (double)self);
      m_configLockName = name;
      return true;
     }
   Print(ID + ": REFUSED to start - another chart is already training this exact configuration. Both" +
         " would save into the same files (" + m_activeFileName + ".nnw plus its .cfg/.stats/checkpoints)" +
         " and overwrite each other's progress with no error reported anywhere. Owner: " +
         (owner != 0 ? "chart " + IntegerToString(owner) + " (" + ChartSymbol(owner) + " " +
          EnumToString((ENUM_TIMEFRAMES)ChartPeriod(owner)) + ")" : "another chart, still initializing") +
         ". Change the enabled NN set (Use_MLP/Use_CONV/Use_LSTM/Use_CONVLSTM) or any retrain-affecting" +
         " input on THIS chart so it trains its own model, or remove one of the two charts. Note the" +
         " private build defaults ALL FOUR direction NNs on - two same-symbol charts left at their" +
         " defaults land here.");
   g_initFatalReason = "another chart already owns this configuration (" + m_activeFileName + ")";
   return false;
  }
//+------------------------------------------------------------------+
//| Drop this instance's claim (see AcquireConfigLock).                |
//+------------------------------------------------------------------+
void CExpertSignalAIBase::ReleaseConfigLock(void)
  {
   if(StringLen(m_configLockName) == 0)
      return;
   //--- Only delete a claim we still hold: if a later instance took this entry over via the stale-owner
   //--- path above, deleting it here would silently hand the config to a third chart.
   if((long)GlobalVariableGet(m_configLockName) == ChartID())
      GlobalVariableDel(m_configLockName);
   m_configLockName = "";
  }
#endif