//+------------------------------------------------------------------+ //| Lifecycle.mqh | //| | //| Construction/destruction, the CExpertSignal vote API | //| (LongCondition/ShortCondition/ConfidenceTier/pattern weights), | //| tick + chart-event dispatch, and the per-config chart lock. | //| | //| PARTIAL IMPLEMENTATION FILE - not standalone. | //| CExpertSignalAIBase method BODIES only. The class declaration | //| lives in Expert\ExpertSignalAIBase.mqh, which includes this file | //| at the bottom, after the declaration. Do not include it | //| anywhere else and do not compile it on its own. | //+------------------------------------------------------------------+ #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. Held at their historical defaults so both stay byte-stable; changing either value //--- would re-key every model on disk for no behavioural reason whatsoever. 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_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_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_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_oosConfidenceSum(0), m_confidenceCalScale(1.0), m_minDirectionalRecallPct(40), 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_lastBuyFiredPrecPct(-1), m_lastSellFiredPrecPct(-1), m_lastBuyFired(0), m_lastSellFired(0), m_maxClassSampleWeight(1.5), m_swingConfirmationBars(100), m_barrierHorizonBars(BARRIER_HORIZON_FALLBACK), m_barrierHorizonResolved(false), m_barrierFallbackWarned(false), m_lastBarrierTimedOut(false), m_labelPrebuildTimeoutCount(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_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_haveOosCheckpoint(false), m_oosStable(false), m_objectiveMet(false), m_erasSinceBestBalanced(0), m_plateauStage(0), m_syncWaitStartTick(0), m_warmupPassesRemaining(0), m_labelCacheBars(0), m_labelCacheAnchorTime(0), m_labelCachePrebuilt(false), m_lastExcUp(0.0), m_lastExcDown(0.0), m_derivedSlMult(0.0), m_derivedTpMult(0.0), 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_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_miBestColumn(0.0), m_miLabelEntropy(0.0), m_miStrideBars(0), m_miNullBlocks(0), m_miReportDone(false), m_miReportDeferrals(0), m_barrierScanSlMult(0.0), m_barrierScanTpMult(0.0), m_barrierScanLiveLabels(false), m_barrierScanTimeouts(0), m_barrierHorizonClamped(false) { //--- 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. Doing it again here nested inside that same teardown cascade doubled the //--- stack depth of an already-deep recursive save (layers -> neurons -> connections) right at the //--- point in MT5's lifecycle (EA recompile while attached) that has the least stack headroom, and //--- reliably crashed the terminal with a stack overflow. Keep this destructor cheap. 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; //--- 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; } //+------------------------------------------------------------------+ //| "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. Without that exception the tester could seed/refresh dPrevSignal from the deployed model //--- and draw chart arrows from those weights, yet this gate would still hard-zero the trading vote. //--- A fresh random topology still cannot trade in the tester because m_modelLoadedFromDisk stays false. //--- Census: this gate is invisible to the refresh-path counters and is a live candidate for the //--- all-bars-zero-direction backtest - see m_voteGateBlocked. 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. //--- "not yet studied" sentinel - dPrevSignal == -2 is not a real Sell. Its MAGNITUDE is 2, so it //--- passed straight through the confidence floor that used to sit here (|-2| exceeds any 0..1 //--- threshold); only the m_trainingComplete gate above was keeping it out. Checked explicitly now //--- that the floor is gone, rather than left resting on that. 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 the live 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::ConfidenceTier(void) { //--- 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. Nothing can ever be read below this. //--- - single-neuron regression: 0.5, DoubleToSignal()'s own decision boundary. //--- Quartiling from HERE (rather than from an input, as the classification branch used to) is what //--- makes the tier boundaries a fixed property of the model instead of something that silently moves //--- whenever the trader adjusts an unrelated vote threshold - and it is what lets the same tier //--- weights mean the same thing on both heads. See m_pattern_0's declaration comment for the weights. double floorConf = (m_outputNeuronsCount == 3) ? (1.0 / 3.0) : 0.5; double span = MathMax(1.0 - floorConf, 0.0001); double t = (CalibratedConfidenceMagnitude() - floorConf) / span; int tier = (int)MathFloor(t * 4.0); return MathMax(0, MathMin(tier, 3)); } //+------------------------------------------------------------------+ //| 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; } } //+------------------------------------------------------------------+ //| Set the specified pattern's weight to the specified value | //+------------------------------------------------------------------+ void CExpertSignalAIBase::ApplyPatternWeight(int patternNumber, int weight) { 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/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. g_LiveAISignedConfidence = 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. Treat a failed lookup as pending instead (same >0-guard // philosophy as the SERIES_FIRSTDATE lookup elsewhere in this class) so a transient history-sync // hiccup costs one extra harmless check, not a silent stall. bool newBarPending = (dPrevSignal == -2 || lastBarDate <= 0 || ((m_inferenceOnly ? m_lastBarTime : dtStudied) < lastBarDate)); //--- 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) { if(newBarPending) RefreshConvergedSignal(); } else if(!m_trainingStopRequested && !bEventStudy && newBarPending) bEventStudy = EventChartCustom(ChartID(), 1, (long)MathMax(0, MathMin(iTime(m_symbol.Name(), PERIOD_CURRENT, (int)(100 * Net.recentAverageSmoothingFactor * (m_trainingComplete ? 1 : 10))), dtStudied)), 0, "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. Only write this terse //--- summary when nothing else is actively updating the status label (idle/stopped/paused/cooldown). if(!m_trainRunActive) { //--- Compact, accurate end-state text. The completed state distinguishes a model that is genuinely //--- adapting live (online learning active - a live chart with EnableOnlineLearning, not the tester) //--- from one running pure inference (the Strategy Tester, or online learning off), so the label is //--- literally true either way and never over-promises "keeps learning" when it doesn't - see //--- OnlineLearnStep()'s gate for exactly when adaptation runs. 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"; simpleLive += "Current signal: " + liveSigPlain; SetStatusLabel(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: -> " 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. SetStatusLabel(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. Both compete for the one MQL5 thread; letting the arrows finish //--- quickly (a few hundred ms of slices) means the user sees a complete chart almost immediately, //--- whereas interleaving them with 80ms training chunks would stretch the restore over minutes. 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) { if(id == 1001) { TuneIndicatorsAndTrain(lparam); bEventStudy = false; OnTickHandler(); } } //+------------------------------------------------------------------+ //| Claim m_activeFileName for this chart, terminal-wide. | //| | //| Two charts running the same AIType with the same retrain-affecting| //| inputs resolve to the SAME .nnw/.cfg/.stats/checkpoint set. Both | //| then train independently and save over each other, so whichever | //| writes last wins and the other's eras are discarded - silently, | //| because every individual file operation succeeds. A five-chart | //| comparison run on 2026-07-29 lost both its HYBRID models this way | //| (one chart left at the AIType default), and the only evidence | //| anywhere was that model path appearing twice as often in the log. | //| | //| A terminal-wide global variable is the right lock rather than a | //| lock FILE: GlobalVariableTemp() is an atomic create-if-absent, | //| and a TEMPORARY variable dies with the terminal, so a crash can | //| never leave a stale lock that blocks the next start. Within one | //| session a stale entry is still possible (an EA removed without a | //| clean deinit), so the owner's chart id is stored and revalidated. | //+------------------------------------------------------------------+ 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 AIType or any retrain-affecting input on THIS chart so it trains its own model, or" + " remove one of the two charts. Note AIType defaults to " + EnumToString(AI_HYBRID) + " - a chart whose AIType was never actually changed lands here."); 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