forked from animatedread/Warrior_EA
2de93539d4
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>
479 lines
27 KiB
MQL5
479 lines
27 KiB
MQL5
//+------------------------------------------------------------------+
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//| Warrior_EA |
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//| AnimateDread |
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//| |
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//| Live continual learning, the EMA shadow net, and the OOS continu|
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//| |
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//| PARTIAL IMPLEMENTATION FILE - not standalone. |
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//| This holds CExpertSignalAIBase method BODIES only. The class |
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//| declaration lives in Expert\ExpertSignalAIBase.mqh, which |
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//| #includes this file at the bottom, after the declaration. Do not |
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//| include it anywhere else and do not compile it on its own. |
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//| |
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//| Split out purely to make the 8216-line original navigable; the |
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//| code inside was moved verbatim, not rewritten. |
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//+------------------------------------------------------------------+
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#ifndef WARRIOR_AIBASE_ONLINELEARNING_MQH
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#define WARRIOR_AIBASE_ONLINELEARNING_MQH
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//+------------------------------------------------------------------+
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//| Clones the just-converged Net into a separate CNet (m_simOosNet) |
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//| and arms a chunked bar-by-bar walk through the OOS window - see |
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//| AdvanceOosSimulationChunk(). Evaluation-only: the clone's learned |
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//| weights are never written back to Net or any persisted file. |
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//+------------------------------------------------------------------+
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void CExpertSignalAIBase::StartOosContinualSimulation(int bars, int oosCutoff)
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{
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if(m_simOosRunActive)
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{
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delete m_simOosNet;
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m_simOosNet = NULL;
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m_simOosRunActive = false;
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}
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if(oosCutoff <= 0)
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return; // nothing to walk this run
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//--- Clone via the full Save()/Load() pair. Load() calls InitOpenCL()/InitDirectML() before
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//--- reconstructing layers, so a bare "new CNet(NULL)" ends up with a GPU/DirectML backend matching
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//--- production. Any lighter-weight restore that reused the CALLER's opencl/directml pointers would
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//--- be wrong here: on a fresh CNet(NULL) (whose constructor no-ops for a NULL description) those are
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//--- unset, and the clone would come out degenerate. (A file-based checkpoint pair used to sit beside
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//--- Save/Load and had exactly that flaw; it has been removed - the in-run snapshot is now the
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//--- in-memory CNet::CaptureWeights/RestoreWeights.)
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//--- Co-locate this ephemeral clone temp with the active model (COMMON on a live chart, LOCAL in the
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//--- tester sandbox) instead of always LOCAL. Uses m_activeFileName for the same reason (the active
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//--- model's base name, whichever context we're in).
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string simFile = m_activeFileName + "_simoos.tmp";
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int simFlags = m_activeFileCommon ? FILE_COMMON : 0;
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double ip[];
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if(!Net.Save(simFile, 0.0, 0.0, 0.0, dtStudied, m_activeFileCommon, m_eraCount, m_trainingComplete, ip))
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return;
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m_simOosNet = new CNet(NULL);
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double loadE, loadU, loadF;
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datetime loadTime;
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long loadEra;
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bool loadComplete;
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double loadIp[];
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bool loaded = m_simOosNet.Load(simFile, loadE, loadU, loadF, loadTime, m_activeFileCommon, loadEra, loadComplete, loadIp, true /*quiet: this evaluation-only sim is optional - on a miss it simply doesn't run*/);
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FileDelete(simFile, simFlags);
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if(!loaded)
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{
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delete m_simOosNet;
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m_simOosNet = NULL;
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return;
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}
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m_simOosCutoff = oosCutoff;
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m_simOosBarIndex = oosCutoff - 1;
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m_simOosForecast = 0;
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m_simOosSamples = 0;
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m_simOosRunActive = true;
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}
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//+------------------------------------------------------------------+
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//| Advances the evaluation-only continual-learning OOS walk by up |
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//| to TRAIN_TIME_BUDGET_MS of work, then yields (same chunking |
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//| pattern as the real era loop's m_eraResumePending). For each bar, |
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//| oldest-OOS to newest: predict with the clone's CURRENT weights, |
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//| score against the cached true label, THEN let the clone learn |
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//| from it (single pass, no oversampling replay) - simulating how |
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//| the model would adapt bar-by-bar in real forward trading. Never |
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//| touches Net, never Saves the clone - purely an evaluation metric.|
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//+------------------------------------------------------------------+
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void CExpertSignalAIBase::AdvanceOosSimulationChunk(void)
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{
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const uint SIM_TIME_BUDGET_MS = 80;
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uint chunkStartTick = GetTickCount();
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//--- Mirror OnlineLearnStep()'s pinned rate for the duration of this chunk, and hand the shared
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//--- global back on BOTH exit paths - this simulation is only a valid forecast of live continual
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//--- learning if it steps at the same size, and `eta` is shared by every signal instance.
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double savedEta = eta;
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eta = m_modelEta * ONLINE_LEARN_ETA_SCALE;
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int i;
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for(i = m_simOosBarIndex; i >= 0; i--)
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{
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if(GetTickCount() - chunkStartTick >= SIM_TIME_BUDGET_MS)
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{
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m_simOosBarIndex = i;
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eta = savedEta;
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return;
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}
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if(i >= ArraySize(m_labelCacheHasValue) || !m_labelCacheHasValue[i])
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continue; // no cached label for this bar (e.g. right at a window edge) - nothing to learn from
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TempData.Clear();
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TempData.Reserve((int)m_historyBars * m_neuronsCount);
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//--- Window ends AT (includes) bar i - see Train()'s matching r declaration comment for why.
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int r = i;
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bool bufferOk = true;
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for(int b = 0; b < (int)m_historyBars; b++)
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{
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if(!BufferTempData(r + b))
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{
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bufferOk = false;
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break;
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}
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}
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if(!bufferOk || TempData.Total() < (int)m_historyBars * m_neuronsCount)
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continue;
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m_simOosNet.feedForward(TempData);
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m_simOosNet.getResults(TempData);
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double simSignal = (m_outputNeuronsCount == 3) ? ApplyClassificationSoftmax() : TempData[0];
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//--- Pre-update softmax probabilities, read before TempData is rebuilt as the target vector -
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//--- feeds the same alpha-balanced focal weight the live path applies (OnlineSampleWeight).
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double sBuy = (TempData.Total() > 0) ? TempData.At(0) : 0.0;
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double sSell = (TempData.Total() > 1) ? TempData.At(1) : 0.0;
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double sNeutral = (TempData.Total() > 2) ? TempData.At(2) : 0.0;
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bool buy = m_labelCacheBuy[i];
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bool sell = m_labelCacheSell[i];
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ENUM_SIGNAL trueSignal = buy ? Buy : (sell ? Sell : Neutral);
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bool hit = (DoubleToSignal(simSignal) == trueSignal);
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m_simOosSamples++;
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if(hit)
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m_simOosForecast += (100 - m_simOosForecast) / Net.recentAverageSmoothingFactor;
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else
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m_simOosForecast -= m_simOosForecast / Net.recentAverageSmoothingFactor;
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TempData.Clear();
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if(m_outputNeuronsCount == 1)
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TempData.Add(buy && !sell ? 1 : !buy && sell ? -1 : 0);
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else
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if(m_outputNeuronsCount == 3)
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{
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TempData.Add(buy ? LABEL_SMOOTH_HIGH : LABEL_SMOOTH_LOW);
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TempData.Add(sell ? LABEL_SMOOTH_HIGH : LABEL_SMOOTH_LOW);
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TempData.Add((!buy && !sell) ? LABEL_SMOOTH_HIGH : LABEL_SMOOTH_LOW);
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}
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m_simOosNet.backProp(TempData, OnlineSampleWeight(trueSignal, sBuy, sSell, sNeutral));
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}
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eta = savedEta;
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delete m_simOosNet;
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m_simOosNet = NULL;
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m_simOosRunActive = false;
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Print(ID + ": continual-learning OOS simulation complete - " + IntegerToString(m_simOosSamples) + " samples, accuracy " + DoubleToString(m_simOosForecast, 1) + "%");
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}
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//+------------------------------------------------------------------+
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//| Alpha-balanced focal weight for one streamed bar - the cost-level |
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//| imbalance correction used by BOTH the live continual-learning path |
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//| and its OOS simulation. See the declaration comment and the |
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//| ONLINE_LEARN_* block's CLASS IMBALANCE note for the derivation. |
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//+------------------------------------------------------------------+
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double CExpertSignalAIBase::OnlineSampleWeight(ENUM_SIGNAL trueSignal, double pBuy, double pSell, double pNeutral)
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{
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//--- Regression head has no class structure to balance.
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if(m_outputNeuronsCount != 3)
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return 1.0;
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double weight = 1.0;
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//--- alpha_c: inverse class frequency from the measured, persisted priors, normalised so the
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//--- MAJORITY class is exactly 1.0 (a majority bar is never down-weighted below parity) and only
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//--- minority bars are ever up-weighted. Unmeasured priors - a model deployed before any prior was
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//--- recorded - skip the alpha term rather than divide by zero; focal's (1-p_t)^gamma still applies.
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double priorMax = MathMax(m_priorNeutral, MathMax(m_priorBuy, m_priorSell));
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bool priorsUsable = (priorMax > 0.0 && m_priorBuy > 0.0 && m_priorSell > 0.0 && m_priorNeutral > 0.0);
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if(priorsUsable && trueSignal != Neutral)
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{
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double truePrior = (trueSignal == Buy) ? m_priorBuy : m_priorSell;
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//--- Same shape as Train()'s repCount: measured ratio dialled by the OversampleParity input, so
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//--- one knob governs both engines. Capped so a single rare bar can never deliver an outsized
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//--- kick to an already-validated deployed model; ConstrainReplay tightens the cap to 3.0, the
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//--- effective correction Train() applies under that same input.
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double alphaCap = m_constrainReplay ? 3.0 : ONLINE_LEARN_MAX_CLASS_WEIGHT;
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weight = MathMin(MathMax(1.0, (priorMax / truePrior) * m_oversampleParity), alphaCap);
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}
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//--- gamma: the CONFIGURED m_focalGamma, never m_focalGammaRuntime - the runtime value is a training
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//--- schedule artifact the plateau ladder anneals toward 0 and it has no meaning post-deploy.
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//--- Down-weights bars the model already gets right (the overwhelming Neutral majority), so the
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//--- update concentrates on genuinely informative confirmations.
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if(m_focalGamma > 0.0)
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{
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double pt = (trueSignal == Buy) ? pBuy : (trueSignal == Sell) ? pSell : pNeutral;
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pt = MathMax(0.0, MathMin(1.0, pt));
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weight *= MathPow(1.0 - pt, m_focalGamma);
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}
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return weight;
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}
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//+------------------------------------------------------------------+
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//| Online continual-learning step - LIVE CHART ONLY. Once a model is |
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//| deployed (m_trainingComplete) it keeps learning from real market |
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//| structure the same supervised way it was trained: predicting the |
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//| ZigZag reversal label for each bar. The critical rule the user |
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//| asked for is the confirmation delay - a ZigZag pivot on a recent |
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//| bar REPAINTS until m_swingConfirmationBars more bars have closed |
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//| after it (see m_swingConfirmationBars / AdvanceZigZagLabelState), |
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//| so the model must NEVER backprop the newest bars against a still- |
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//| provisional label, even though it happily EMITS a live signal on |
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//| them. This method therefore only ever learns from the "confirmable |
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//| frontier" and older: the newest bar whose now-relative index is |
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//| >= m_swingConfirmationBars. Everything newer than that is inference-|
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//| only until it, too, matures - identical to how training holds its |
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//| recent bars in the OOS holdout and embargoes the boundary band. |
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//| |
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//| Mechanism per newly-matured bar (oldest->newest, exactly |
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//| AdvanceOosSimulationChunk()'s predict-score-then-learn step, but |
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//| on the REAL deployed Net): build the same feature window training |
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//| used, feedForward, score the prediction against the confirmed |
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//| label (guardrail EMA), then backProp that label. The deployed |
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//| SHADOW is nudged toward Net by SHADOW_WEIGHT_TAU only while the |
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//| rolling accuracy holds up; if it decays the blend FREEZES (live |
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//| keeps trading the last-good shadow, Net keeps adapting so it can |
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//| recover) - drift can never reach the account. State persists in the |
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//| .stats sidecar so a restart neither re-learns old bars nor skips. |
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//+------------------------------------------------------------------+
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void CExpertSignalAIBase::OnlineLearnStep(void)
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{
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//--- Hard gates. m_inferenceOnly covers BOTH the single backtest and every optimization pass (see
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//--- its declaration comment): in the tester the model is held FIXED, so continual learning is a
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//--- live-chart-only behaviour (forward-test it on a demo account, not the Strategy Tester).
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if(!m_enableOnlineLearning || m_inferenceOnly || m_evalMode || m_trainRunActive)
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return;
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if(!m_trainingComplete || m_trainingStopRequested || m_trainingPaused)
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return;
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if(MQLInfoInteger(MQL_TESTER) || MQLInfoInteger(MQL_OPTIMIZATION) || MQLInfoInteger(MQL_FORWARD))
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return; // belt-and-braces: never adapt weights inside any tester context
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if(CheckPointer(Net) == POINTER_INVALID || Net.CpuInference())
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return; // DLL-free inference build has no backend to backprop through
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if(CheckPointer(m_shadowNet) == POINTER_INVALID)
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return; // nothing deployed to blend into yet (RefreshLatestSignal bootstraps it first)
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if(m_outputNeuronsCount != 1 && m_outputNeuronsCount != 3)
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return;
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int conf = MathMax(m_swingConfirmationBars, 1);
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int barsAvail = Bars(m_symbol.Name(), PERIOD_CURRENT);
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//--- Need the frontier bar (now-relative index conf) plus a full feature window BEHIND it, plus a
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//--- little slack so a short catch-up walk stays in-bounds.
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int need = conf + (int)m_historyBars + 2;
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if(barsAvail < need)
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return;
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//--- Load enough history for the frontier window and a bounded catch-up; RefreshConvergedSignal()
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//--- only sized buffers relative to dtStudied (newest bars), which is too shallow to reach the
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//--- confirmation frontier.
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int wantBars = MathMin(need + ONLINE_LEARN_MAX_CATCHUP, barsAvail);
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if(!ResizeBuffers(wantBars) || !RefreshData())
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return;
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datetime frontierTime = m_Time.GetData(conf);
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if(frontierTime <= 0)
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return;
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//--- First step of this deployment (or a model that never online-learned): DON'T retroactively
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//--- backfill the whole history through backprop in one shot - that could shift the just-validated
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//--- deployed model materially before any live confirmation. Anchor the watermark at the current
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//--- frontier and begin learning from genuinely new confirmations forward.
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if(m_onlineLearnedUpToTime <= 0)
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{
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m_onlineLearnedUpToTime = frontierTime;
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return;
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}
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if(frontierTime <= m_onlineLearnedUpToTime)
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return; // no bar has matured past the watermark since last time
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//--- Seed the guardrail EMA from the model's deploy-time OOS accuracy the first time we actually
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//--- learn, so the floor is meaningful from the very first update (not a cold 0 that would trip it).
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if(m_onlineRollingAcc < 0.0)
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m_onlineRollingAcc = (dForecast > 0.0 && dForecast <= 100.0) ? dForecast : 100.0;
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//--- Guardrail floor: deploy baseline minus a margin, never below the absolute minimum.
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double baseline = (dForecast > 0.0 && dForecast <= 100.0) ? dForecast : 100.0;
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double accFloor = MathMax(ONLINE_LEARN_MIN_ACC, baseline - ONLINE_LEARN_ACC_MARGIN);
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//--- Find the oldest not-yet-learned confirmed bar: walk from the frontier (index conf) toward older
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//--- bars (increasing index) until we pass the watermark or hit the catch-up cap, then learn newest-
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//--- ward from there so bars are consumed in strict chronological (oldest->newest) order.
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int oldestIdx = conf;
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while(oldestIdx < barsAvail - 1
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&& oldestIdx < conf + ONLINE_LEARN_MAX_CATCHUP
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&& m_Time.GetData(oldestIdx) > m_onlineLearnedUpToTime)
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oldestIdx++;
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//--- oldestIdx now points at the first bar whose time is <= watermark (already learned) or the cap;
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//--- the newest UNLEARNED bar is one step newer (idx-1). Learn from idx = oldestIdx-1 down to conf.
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//--- Pin the learning rate for the duration of this walk and restore it after: `eta` is a GLOBAL
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//--- shared by every signal instance, so leaving it modified would corrupt another model's training
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//--- chunk - see ONLINE_LEARN_ETA_SCALE's comment.
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double savedEta = eta;
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eta = m_modelEta * ONLINE_LEARN_ETA_SCALE;
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int learned = 0;
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for(int idx = oldestIdx - 1; idx >= conf; idx--)
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{
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datetime bt = m_Time.GetData(idx);
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if(bt <= m_onlineLearnedUpToTime)
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continue; // already learned (defensive; the walk above should exclude it)
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//--- Build this bar's feature window - IDENTICAL to Train()/RefreshLatestSignal(): ends AT bar idx
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//--- and extends m_historyBars into the past. No lookahead (all bars are older than idx).
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TempData.Clear();
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TempData.Reserve((int)m_historyBars * m_neuronsCount);
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bool bufferOk = true;
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for(int b = 0; b < (int)m_historyBars; b++)
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if(!BufferTempData(idx + b))
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{
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bufferOk = false;
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break;
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}
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if(!bufferOk || TempData.Total() < (int)m_historyBars * m_neuronsCount)
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{
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//--- window not buildable this bar (e.g. an indicator hole) - advance the watermark past it so
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//--- we don't wedge re-trying the same bar forever, but learn nothing from it.
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m_onlineLearnedUpToTime = bt;
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continue;
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}
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//--- Predict with the CURRENT (pre-update) weights, then score against the confirmed label for the
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//--- rolling guardrail - exactly AdvanceOosSimulationChunk()'s predict-before-learn measurement.
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Net.feedForward(TempData);
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Net.getResults(TempData);
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double predSignal = (m_outputNeuronsCount == 3) ? ApplyClassificationSoftmax() : TempData[0];
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ENUM_SIGNAL trueSignal = ConfirmedZigZagLabel(idx);
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bool hit = (DoubleToSignal(predSignal) == trueSignal);
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m_onlineRollingAcc += (100.0 * (hit ? 1.0 : 0.0) - m_onlineRollingAcc) / ONLINE_ACC_SMOOTH;
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//--- Per-class softmax probabilities as of THIS bar's pre-update prediction. Must be read here,
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//--- before TempData is rebuilt as the target vector below - ApplyClassificationSoftmax() has
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//--- already normalised TempData[0..2] in place into a genuine distribution (same contract pass 2
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//--- relies on for its own focal term).
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double pBuy = (TempData.Total() > 0) ? TempData.At(0) : 0.0;
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double pSell = (TempData.Total() > 1) ? TempData.At(1) : 0.0;
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double pNeutral = (TempData.Total() > 2) ? TempData.At(2) : 0.0;
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//--- Build the target vector - identical encoding to Train()/AdvanceOosSimulationChunk().
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bool buy = (trueSignal == Buy);
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bool sell = (trueSignal == Sell);
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TempData.Clear();
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if(m_outputNeuronsCount == 1)
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TempData.Add(buy && !sell ? 1 : (!buy && sell ? -1 : 0));
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else
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{
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TempData.Add(buy ? LABEL_SMOOTH_HIGH : LABEL_SMOOTH_LOW);
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TempData.Add(sell ? LABEL_SMOOTH_HIGH : LABEL_SMOOTH_LOW);
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TempData.Add((!buy && !sell) ? LABEL_SMOOTH_HIGH : LABEL_SMOOTH_LOW);
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}
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Net.backProp(TempData, OnlineSampleWeight(trueSignal, pBuy, pSell, pNeutral));
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m_onlineSamples++;
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//--- Deploy the improvement ONLY while accuracy holds. Warmup: allow the first few blends (the
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//--- model was just validated at deploy, steps are tiny) until the EMA has enough samples to judge.
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bool blendOk = (m_onlineSamples <= ONLINE_LEARN_WARMUP) || (m_onlineRollingAcc >= accFloor);
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if(blendOk)
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{
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m_shadowNet.BlendWeightsFrom(Net, SHADOW_WEIGHT_TAU);
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if(m_onlineBlendFrozen)
|
|
{
|
|
m_onlineBlendFrozen = false;
|
|
Print(ID + ": online-learning deployment RESUMED - rolling accuracy recovered to "
|
|
+ DoubleToString(m_onlineRollingAcc, 1) + "% (floor " + DoubleToString(accFloor, 1) + "%)");
|
|
}
|
|
}
|
|
else if(!m_onlineBlendFrozen)
|
|
{
|
|
m_onlineBlendFrozen = true;
|
|
Print(ID + ": online-learning deployment FROZEN - rolling accuracy " + DoubleToString(m_onlineRollingAcc, 1)
|
|
+ "% fell below floor " + DoubleToString(accFloor, 1) + "%; live keeps trading the last-good model while it adapts");
|
|
}
|
|
m_onlineLearnedUpToTime = bt;
|
|
learned++;
|
|
m_onlineBarsSincePersist++;
|
|
}
|
|
//--- Hand the shared global back exactly as found, on BOTH exit paths below - see the matching
|
|
//--- savedEta assignment above for why this must not leak out of this function.
|
|
eta = savedEta;
|
|
if(learned <= 0)
|
|
return;
|
|
//--- Periodic durable persistence so a crash loses at most ONLINE_LEARN_PERSIST_EVERY bars of
|
|
//--- adaptation (shutdown also persists via PersistOnShutdown()).
|
|
if(m_onlineBarsSincePersist >= ONLINE_LEARN_PERSIST_EVERY)
|
|
{
|
|
double ip[];
|
|
m_indicatorTuner.Flatten(ip);
|
|
bool saveOk = Net.Save(m_activeFileName + ".nnw", dError, dUndefine, dForecast, dtStudied, m_activeFileCommon, m_eraCount, m_trainingComplete, ip);
|
|
if(!saveOk)
|
|
Print(ID + ": ERROR - online-learning Net.Save failed for " + m_activeFileName + ".nnw. Retrying next persist interval instead of resetting the bars-since-persist counter.");
|
|
SaveShadowNet(ip);
|
|
if(!SaveModelStats(m_activeFileName, m_activeFileCommon))
|
|
Print(ID + ": ERROR - online-learning SaveModelStats failed for " + m_activeFileName + ".");
|
|
// Only reset the counter on a successful weight save - resetting unconditionally on a
|
|
// transient failure would silently double the effective data-loss window on the NEXT failure too.
|
|
if(saveOk)
|
|
{
|
|
m_onlineBarsSincePersist = 0;
|
|
PrintVerbose(ID + ": online-learning checkpoint saved (" + IntegerToString((int)m_onlineSamples)
|
|
+ " total updates, rolling acc " + DoubleToString(m_onlineRollingAcc, 1) + "%)");
|
|
}
|
|
}
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
void CExpertSignalAIBase::SaveShadowNet(const double &indicatorParams[])
|
|
{
|
|
if(CheckPointer(m_shadowNet) == POINTER_INVALID)
|
|
return;
|
|
m_shadowNet.Save(m_activeFileName + "_shadow.nnw", dError, dUndefine, dForecast, dtStudied, m_activeFileCommon, m_eraCount, m_trainingComplete, indicatorParams);
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
void CExpertSignalAIBase::EnsureShadowNet(void)
|
|
{
|
|
if(CheckPointer(m_shadowNet) != POINTER_INVALID)
|
|
return;
|
|
//--- Pure-MQL5 inference (DLL-free backtest): no era blending happens, so the shadow would just be a
|
|
//--- copy of Net - and bootstrapping one via Save/Load would spin a compute backend up on the clone,
|
|
//--- defeating the DLL-free goal. Skip it; RefreshLatestSignal() falls back to Net directly.
|
|
if(CheckPointer(Net) != POINTER_INVALID && Net.CpuInference())
|
|
return;
|
|
string shadowFile = m_activeFileName + "_shadow.nnw";
|
|
if(FileIsExist(shadowFile, m_activeFileCommon ? FILE_COMMON : 0))
|
|
{
|
|
CNet *loaded = new CNet(NULL);
|
|
if(CheckPointer(loaded) != POINTER_INVALID)
|
|
{
|
|
double loadE, loadU, loadF;
|
|
datetime loadTime;
|
|
long loadEra;
|
|
bool loadComplete;
|
|
double loadIp[];
|
|
if(loaded.Load(shadowFile, loadE, loadU, loadF, loadTime, m_activeFileCommon, loadEra, loadComplete, loadIp, true /*quiet: a miss just falls through to the clone bootstrap below*/))
|
|
{
|
|
m_shadowNet = loaded;
|
|
//--- This second CNet spins up its OWN compute backend, so on a fresh attach the log shows a
|
|
//--- second backend-init block right after the main model's. Name it here (verbose) so it
|
|
//--- reads as "the shadow net came up" rather than "the EA started twice".
|
|
PrintVerbose(ID + ": EMA shadow net restored from " + shadowFile + " (its own network instance - hence a second compute-backend init)");
|
|
return;
|
|
}
|
|
delete loaded;
|
|
}
|
|
}
|
|
//--- No compatible persisted shadow - bootstrap from Net's current weights. Clone via the full
|
|
//--- Save()/Load() pair - see StartOosContinualSimulation()'s matching comment for why a lighter
|
|
//--- restore is not safe here (it would need opencl/directml already initialized on the target
|
|
//--- CNet, which a bare "new CNet(NULL)" does not have).
|
|
if(CheckPointer(Net) == POINTER_INVALID)
|
|
return;
|
|
//--- Attempt the clone bootstrap at most once per topology (see m_shadowBootstrapAttempted). On the
|
|
//--- tester's CPU-DLL fallback a second full-net clone can fail to load; retrying every bar would
|
|
//--- rebuild the compute backend each tick and crawl. Falling back to Net is correct and lossless here.
|
|
if(m_shadowBootstrapAttempted)
|
|
return;
|
|
m_shadowBootstrapAttempted = true;
|
|
//--- Co-locate the ephemeral clone temp with the active model (COMMON on a live chart, LOCAL in the
|
|
//--- tester sandbox) instead of always LOCAL.
|
|
string cloneFile = m_activeFileName + "_shadowclone.tmp";
|
|
int cloneFlags = m_activeFileCommon ? FILE_COMMON : 0;
|
|
double ip[];
|
|
if(!Net.Save(cloneFile, 0.0, 0.0, 0.0, dtStudied, m_activeFileCommon, m_eraCount, m_trainingComplete, ip))
|
|
return;
|
|
CNet *clone = new CNet(NULL);
|
|
if(CheckPointer(clone) == POINTER_INVALID)
|
|
{
|
|
FileDelete(cloneFile, cloneFlags);
|
|
return;
|
|
}
|
|
double loadE, loadU, loadF;
|
|
datetime loadTime;
|
|
long loadEra;
|
|
bool loadComplete;
|
|
double loadIp[];
|
|
bool loaded = clone.Load(cloneFile, loadE, loadU, loadF, loadTime, m_activeFileCommon, loadEra, loadComplete, loadIp, true /*quiet: best-effort clone, the miss is handled gracefully below*/);
|
|
FileDelete(cloneFile, cloneFlags);
|
|
if(!loaded)
|
|
{
|
|
delete clone;
|
|
//--- Best-effort: without a shadow, live signals read the main Net directly (RefreshLatestSignal's
|
|
//--- deployNet fallback), which is correct and lossless - so one calm line, not an error.
|
|
//--- This used to be described as EXPECTED on a CPU-DLL box ("can't allocate a 2nd net"). It is not:
|
|
//--- the clone load was failing for the same reason the MAIN model load was - CLayer::CreateElement
|
|
//--- had stopped overriding CArrayObj::CreateElement, so every CNet::Load failed at layer 0
|
|
//--- regardless of backend (see AI\Network.mqh). With that fixed this path should be rare; if it
|
|
//--- shows up repeatedly, investigate rather than assume a hardware limit.
|
|
PrintVerbose(ID + ": EMA shadow net could not be bootstrapped - live signals use the main model directly (lossless fallback).");
|
|
return;
|
|
}
|
|
m_shadowNet = clone;
|
|
//--- See the matching note on the restore path above: a second CNet means a second compute-backend init
|
|
//--- in the log, which is expected, not a duplicated EA.
|
|
PrintVerbose(ID + ": EMA shadow net bootstrapped from the main model's current weights (its own network instance - hence a second compute-backend init)");
|
|
}
|
|
#endif // WARRIOR_AIBASE_ONLINELEARNING_MQH
|