Warrior_EA/Expert/AIBase/Topology.mqh

//+------------------------------------------------------------------+
//+------------------------------------------------------------------+
//| Topology.mqh                                                     |
//|                                                                  |
//| The network BOOT SEQUENCE: InitNeuralNetwork() (indicator init,  |
//| config-lock, tester-cache seeding, load/save the .cfg, net load  |
//| with backend fallback, chart-signal restore, arm the first study |
//| event) and InitFeatureIndicators() (the ~15 per-feature indicator|
//| Init* calls that size m_neuronsCount). This is orchestration     |
//| across nearly every other collaborator - chart, persistence,     |
//| online-learning, cross-asset - not shape derivation, so it stays |
//| a raw-include partial of CExpertSignalAIBase rather than moving  |
//| into a collaborator of its own; see project_oop_module_pattern   |
//| memory for the "diagnose before applying the pattern" doctrine   |
//| this follows. The FINGERPRINT, the derived shape (width/taper/   |
//| depth/conv filters/LSTM hidden), the conv/LSTM/batch-norm stages |
//| and BuildFreshTopology are a genuinely separable job and now     |
//| live in Expert\Topology\Topology.mqh as CTopology, reached       |
//| through the one-line forwards on CExpertSignalAIBase.            |
//+------------------------------------------------------------------+
#ifndef WARRIOR_AIBASE_TOPOLOGY_MQH
#define WARRIOR_AIBASE_TOPOLOGY_MQH
//+------------------------------------------------------------------+
//| Common network bootstrap shared by every AI signal: sets up      |
//| indicators, then loads a saved network or builds a fresh one     |
//| whose only per-signal-type difference is AddCustomLayers().      |
//+------------------------------------------------------------------+
bool CExpertSignalAIBase::InitNeuralNetwork(CIndicators *indicators)
  {
   if(m_isInitialized)
      return true;
   if(indicators == NULL)
      return false;
   m_indicatorsPtr = indicators;
   if(!CExpertSignalCustom::InitIndicators(indicators))
      return false;
   if(!InitFeatureIndicators(indicators))
      return false;
   //--- Kick the terminal's async history sync for every cross-asset reference symbol NOW, at init,
   //--- so the ~minute of cross-symbol download runs while the model loads and the label cache
   //--- prebuilds - instead of starting only when the first Build() call finds the symbols unselected
   //--- and the first era (and the one-shot MI report) runs with the panel absent. Non-blocking.
   if(m_useCrossAsset)
      m_crossAsset.Warm((ENUM_TIMEFRAMES)m_period);
   Net = new CNet(NULL);
   if(CheckPointer(Net) == POINTER_INVALID)
      return false;
   //--- Size the first dense layer to the data. InitIndicators() above is what finalises
   //--- m_neuronsCount, so this is the earliest point the input width is actually known. ORDER
   //--- MATTERS, and it changed on 2026-08-09.
   m_historyBars = DeriveHistoryBars();
   m_convFilterCount = ComputeConvFilterCount();
   m_lstmHiddenSize  = ComputeLstmHiddenSize();
   m_initialNeuronsCount = ComputeFirstLayerWidth();
   //--- Depth LAST of the four: it is derived from the first-layer width above, so it cannot be settled
   //--- before that one is. All four are overwritten from the .cfg further below if this configuration
   //--- already has a trained model - see the adopt-don't-compare block there.
   m_hiddenLayersCount = ComputeHiddenLayerCount();
   //--- The name used to carry a dense-depth tag ("Perceptron 3L"), from when AIType let a user
   //--- pick MLP_3L vs MLP_4L and the depth was the only thing separating two charts of the same
   //--- family.
   string fp = BuildModelFingerprint();
   //--- FNV-1a 32-bit -> 8 hex chars: compact, deterministic, order-stable, collision-safe enough for
   //--- the small optimizer grids in play (a collision would merely fail the .cfg guard and retrain).
   uint fpHash = 2166136261;
   int fpLen = StringLen(fp);
   for(int fpi = 0; fpi < fpLen; fpi++)
     {
      fpHash ^= (uint)StringGetCharacter(fp, fpi);
      fpHash *= 16777619;
     }
   m_fileName += "_" + DoubleToString(MathRound(m_outputNeuronsCount)) + "_" + DoubleToString(MathRound(m_optimizationAlgo)) + "_" + StringFormat("%08x", fpHash);
   //--- Finish the display name with the model's short id and the leading 4 hex digits of that same
   //--- fingerprint, so every log line and panel names the model file it belongs to. Their files
   //--- were never at risk; the TAG was simply unable to do its one job.
   string cfgTag = " [" + m_id + "-" + StringSubstr(StringFormat("%08x", fpHash), 0, 4) + "]";
   if(StringFind(ID, cfgTag) < 0)
      ID += cfgTag;
   //--- One self-verifying config line per chart, deliberately NOT gated on VerboseMode. A multi-
   //--- chart comparison is only valid if every chart is identical except the axis under test, and
   //--- until now a drifted setting was invisible: the filename carries a HASH, so two charts that
   //--- should match and do not look merely "different" with no indication of WHICH field moved.
   Print(ID + ": config - " + IntegerToString(m_hiddenLayersCount) + " dense from " +
         IntegerToString(m_initialNeuronsCount) + " units | batchnorm " +
         ((EnableBatchNorm && BatchNormWindow > 1) ? "ON(" + IntegerToString(BatchNormWindow) + ")" : "OFF") +
         //--- "requested", not effective: the delivered tau is capped against the head's usable
         //--- logit range and cannot be known until the class priors are measured -
         //--- ApplyLogitAdjustment logs the value in force.
         " | class-imbalance logit-adjust(tau 1.00 requested)" +
         " | input " + IntegerToString((int)m_historyBars * m_neuronsCount) +
         " (" + IntegerToString((int)m_historyBars) + " bars x " + IntegerToString(m_neuronsCount) + ")" +
         //--- The front-end stages are DERIVED (see ComputeConvFilterCount/ComputeLstmHiddenSize),
         //--- so without them this "self-verifying" line verified only half the topology - it
         //--- printed the dense taper while the conv/recurrent stages that actually dominate
         //--- CONV/LSTM/HYBRID were invisible.
         FrontEndConfigSummary());
   //--- Kept as its own line and deliberately free of any per-chart prefix INSIDE the string, so
   //--- the six startup lines diff textually against each other.
   Print(ID + ": fingerprint - " + fp);
   //--- The resolved path is DebuggingMode-only: the tag above already names the folder (its m_id half)
   //--- and the file's hash suffix (its hex half), so this line is derivable rather than new information,
   //--- and a third startup line per chart is not worth spending on a user who will never open the file.
   if(DebuggingMode)
      Print(ID + ": model file - " + m_fileName + ".nnw");
   //--- Strategy Tester / optimizer: target a LOCAL (agent-sandboxed, non-FILE_COMMON) cache file
   //--- instead of the shared production weights, so genetic/complete optimization passes on this
   //--- same agent can reuse an already-trained model whenever the topology-relevant inputs
   //--- (neuron counts, layers, history bars, output count, opt algo, study period, ...) are
   //--- unchanged from a previous pass, instead of re-running every training era from scratch each
   //--- pass. The live/manual-chart production .nnw/.cfg under FILE_COMMON are never touched by
   //--- this path, so a backtest can never corrupt or overwrite the deployed live model.
   bool inTesterOrOpt = MQLInfoInteger(MQL_TESTER) || MQLInfoInteger(MQL_OPTIMIZATION) || MQLInfoInteger(MQL_FORWARD);
   m_activeFileName = inTesterOrOpt ? (m_fileName + "_optcache") : m_fileName;
   m_activeFileCommon = !inTesterOrOpt;
   //--- Claim these files before anything reads or writes them, and refuse to start if another
   //--- chart in this terminal already holds them (see AcquireConfigLock).
   if(!inTesterOrOpt && !AcquireConfigLock())
      return false;
   //--- Any Strategy-Tester run - a single backtest OR an optimization pass - runs pure inference
   //--- on the deployed model, never trains.
   m_inferenceOnly = MQLInfoInteger(MQL_TESTER);
   //--- Seed the agent-local optcache from the deployed production model on the first tester/opt
   //--- pass. Re-seeds when the cache is MISSING *or* STALE. Copies FROM FILE_COMMON (the
   //--- live/manual-chart model) INTO the agent-local sandbox only; the production files are read,
   //--- never written, so a backtest still can't corrupt the deployed model.
   bool cacheMissing = !FileIsExist(m_activeFileName + ".nnw");
   bool cacheStale = false;
   if(inTesterOrOpt && !cacheMissing && FileIsExist(m_fileName + ".nnw", FILE_COMMON))
     {
      datetime prodModified  = (datetime)FileGetInteger(m_fileName + ".nnw", FILE_MODIFY_DATE, true);
      datetime cacheModified = (datetime)FileGetInteger(m_activeFileName + ".nnw", FILE_MODIFY_DATE, false);
      //--- both timestamps must be readable before trusting the comparison; a 0 means "couldn't tell",
      //--- and re-seeding on an unreadable timestamp every single pass would be worse than not checking.
      cacheStale = (prodModified > 0 && cacheModified > 0 && prodModified > cacheModified);
      if(cacheStale)
         Print(__FUNCTION__ + ": the deployed model is newer than this agent's cached copy - re-seeding so the backtest runs the CURRENT model, not the previously cached one.");
     }
   if(inTesterOrOpt && (cacheMissing || cacheStale))
     {
      if(FileIsExist(m_fileName + ".nnw", FILE_COMMON))
        {
         //--- The .nnw is the only copy that MUST succeed - retried (see CopyFileWithRetry's
         //--- declaration comment) because a live chart's own atomic Save() can be mid-rename on
         //--- this exact file.
         if(CopyFileWithRetry(m_fileName + ".nnw", m_activeFileName + ".nnw"))
           {
            //--- Best-effort sidecars: not retried - losing one just means a cold
            //--- calibration/shadow-blend start rather than a wrong/untrained model, which the .nnw
            //--- copy above already guards against.
            if(FileIsExist(m_fileName + ".cfg", FILE_COMMON))
               CopySharedFile(m_fileName + ".cfg", m_activeFileName + ".cfg", false);
            if(FileIsExist(m_fileName + "_shadow.nnw", FILE_COMMON))
               CopySharedFile(m_fileName + "_shadow.nnw", m_activeFileName + "_shadow.nnw", false);
            //--- carry the calibration sidecar into the agent sandbox too, so a seeded backtest calibrates its
            //--- live decisions with the deployed model's priors instead of the un-adjusted cold defaults.
            if(FileIsExist(m_fileName + ".stats", FILE_COMMON))
               CopySharedFile(m_fileName + ".stats", m_activeFileName + ".stats", false);
            Print(__FUNCTION__ + ": seeded tester cache from the deployed production model (" + m_fileName + ") - this run reuses the deployed weights instead of retraining");
           }
         //--- else: CopyFileWithRetry already logged why. Fall through - the Net.Load() below will
         //--- correctly report "no file" and BuildFreshTopology() takes over, same as a genuine first pass.
        }
      else if(m_inferenceOnly)
         //--- Name the exact file (symbol + timeframe + config fingerprint) it looked for: the
         //--- model is keyed on the CHART TIMEFRAME, so the #1 cause of this is running the tester
         //--- on a different timeframe than the model was trained on (e.g. an H4 model, tester set
         //--- to H1) - which reads as "no model" when one exists under a different timeframe.
         Print(__FUNCTION__ + ": WARNING - no deployed production model found at '" + m_fileName +
               ".nnw' (shared folder) for " + _Symbol + " " + EnumToString((ENUM_TIMEFRAMES)_Period) +
               ". A single backtest runs inference only and will NOT train. Most common cause: the tester" +
               " timeframe differs from the one the model was trained on (the filename is keyed on timeframe)." +
               " Otherwise, train this configuration on a chart first, then re-run the backtest.");
     }
   //--- ADOPT-DON'T-COMPARE PROTECTS TRAINED WEIGHTS. WITH NO WEIGHTS THERE IS NOTHING TO PROTECT.
   //---
   //--- The .cfg pins four DERIVED sizes (first-layer width, depth, conv filters, LSTM hidden), and
   //--- adopting them is right for a model that has weights shaped by them. It was also being done
   //--- for a model with NO .nnw at all, and that turned one unlucky moment into a permanent
   //--- property of the fleet:
   //---
   //--- ComputeFirstLayerWidth budgets against EstimatedInSampleBars, which counts this chart's own
   //--- bars PLUS the training pool. On a COLD fleet start every chart derives its topology before
   //--- any chart has published a pool file - measured 2026-08-26, model creation 18:13:21 against a
   //--- first publish at 18:13:48 - so all six sized as if training alone, wrote that into .cfg, and
   //--- then adopted it back on every subsequent start even though the pool had been full for hours.
   //--- SP500 sat at a first layer floored to 16 while adopting 30229 peer rows.
   //---
   //--- Re-deriving when there are no weights is free (nothing to discard), cannot loop (once weights
   //--- exist the .cfg is authoritative again), and cannot fragment the pool: the derived width is
   //--- NOT part of BuildModelFingerprint, which keys only on the FEATURE layout.
   bool haveWeights = FileIsExist(m_activeFileName + ".nnw", m_activeFileCommon ? FILE_COMMON : 0);
   bool cfgAdopted = haveWeights &&
                     LoadAndCompareTopologyConfiguration(m_activeFileName, m_initialNeuronsCount, m_hiddenLayersCount, m_neuronsReduction, m_minNeuronsCount, m_optimizationAlgo, m_historyBars, m_outputNeuronsCount, m_neuronsCount, m_minTrainYear, m_isInitialized, LEGACY_CONVERGE_WR_SLOT, m_fractalPeriods, m_convFilterCount, m_lstmHiddenSize, m_activeFileCommon);
   if(!haveWeights && FileIsExist(m_activeFileName + ".cfg", m_activeFileCommon ? FILE_COMMON : 0))
      Print(__FUNCTION__ + ": " + ID + " - a .cfg exists but no weights do, so its DERIVED sizes were"
            " re-measured rather than adopted (first layer " + IntegerToString(m_initialNeuronsCount) +
            ", depth " + IntegerToString(m_hiddenLayersCount) + "). A .cfg written before the training"
            " pool had any peer files would otherwise pin a training-alone topology forever.");
   if(!cfgAdopted)
     {
      //--- Topology/input params diverged from what produced the saved .nnw (or no .cfg exists yet;
      //--- for inTesterOrOpt this is also the normal "first pass on this agent" case).
      if(FileIsExist(m_activeFileName + ".nnw", m_activeFileCommon ? FILE_COMMON : 0))
        {
         Print(__FUNCTION__ + ": " + m_activeFileName + " - topology/input params changed since last save; discarding incompatible saved weights and starting fresh");
         FileDelete(m_activeFileName + ".nnw", m_activeFileCommon ? FILE_COMMON : 0);
         //--- Reaching here means a TRAINED model was just thrown away, so its drawn signals are
         //--- stale for exactly the same reason ResetWeights() clears them: they would otherwise be
         //--- restored moments later (LoadChartSignals runs at the end of this function) and shown
         //--- as if they belonged to the model about to be trained.
         ClearPersistedChartSignals("saved weights discarded - topology/input params changed");
        }
      if(FileIsExist(m_activeFileName + "_ckpt.tmp", m_activeFileCommon ? FILE_COMMON : 0))
         FileDelete(m_activeFileName + "_ckpt.tmp", m_activeFileCommon ? FILE_COMMON : 0);
      // Same reasoning applies to the EMA shadow-weight file (see m_shadowNet's declaration comment) -
      // it's shaped for the OLD topology too, and EnsureShadowNet() has no independent way to detect
      // that mismatch on Load() (CNet::Load() doesn't cross-validate against an expected shape). Drop
      // it so EnsureShadowNet() cleanly misses and re-bootstraps from the fresh Net instead.
      if(FileIsExist(m_activeFileName + "_shadow.nnw", m_activeFileCommon ? FILE_COMMON : 0))
         FileDelete(m_activeFileName + "_shadow.nnw", m_activeFileCommon ? FILE_COMMON : 0);
      //--- the calibration sidecar is tied to the discarded weights - drop it too so a fresh run
      //--- re-measures priors from scratch instead of adjusting with a stale model's base rates.
      if(FileIsExist(m_activeFileName + ".stats", m_activeFileCommon ? FILE_COMMON : 0))
         FileDelete(m_activeFileName + ".stats", m_activeFileCommon ? FILE_COMMON : 0);
      //--- and the pattern-database backfill marker (see StartPatternDatabaseBackfill): it records
      //--- the era of the model whose OOS calls were written into the ranking tables.
      if(FileIsExist(m_activeFileName + ".dbfill", m_activeFileCommon ? FILE_COMMON : 0))
         FileDelete(m_activeFileName + ".dbfill", m_activeFileCommon ? FILE_COMMON : 0);
      SaveTopologyConfiguration(m_activeFileName, m_initialNeuronsCount, m_hiddenLayersCount, m_neuronsReduction, m_minNeuronsCount, m_optimizationAlgo, m_historyBars, m_outputNeuronsCount, m_neuronsCount, LEGACY_STUDY_PERIOD_SLOT, m_minTrainYear, m_isInitialized, LEGACY_CONVERGE_WR_SLOT, m_fractalPeriods, m_convFilterCount, m_lstmHiddenSize, m_activeFileCommon);
     }
   double loadedIndicatorParams[];
   //--- Inference-only backtest: if this deployed model was validated MQL5-inference-safe at deploy
   //--- (marker in its .stats), load it host-only and run the pure-MQL5 forward path so the backtest
   //--- never loads WarriorDML/WarriorCPU.dll - no DLL file-lock class of failure, and the exact math
   //--- the Market build ships. Falls back to a compute backend just below if that load fails.
   if(m_inferenceOnly && CheckPointer(Net) != POINTER_INVALID)
     {
      LoadModelStats(m_activeFileName, m_activeFileCommon); // reads m_mqlInferenceValidated (and priors)
      if(m_mqlInferenceValidated)
        {
         Net.SetCpuInference(true);
         PrintVerbose(__FUNCTION__ + ": " + ID + " - inference-only backtest running pure-MQL5 (DLL-free): the deployed model is validated MQL5-inference-safe");
        }
     }
   bool netLoaded = LoadNetWithRetry(loadedIndicatorParams);
   //--- Pure-MQL5 load failed unexpectedly (should not happen for a validated model) - drop back to a
   //--- compute backend and retry once so the backtest still runs via the DLL rather than on a fresh net.
   if(!netLoaded && CheckPointer(Net) != POINTER_INVALID && Net.CpuInference())
     {
      Print(__FUNCTION__ + ": " + ID + " - pure-MQL5 load failed; retrying with a compute backend (DLL)");
      Net.SetCpuInference(false);
      netLoaded = LoadNetWithRetry(loadedIndicatorParams);
     }
   //--- the file may carry a superseded architecture - correct it before anything reads the net
   if(netLoaded)
      EnforceTopologyContract();
//--- A superseded conv receptive field cannot be repaired in place (different weight-tensor shape),
//--- so the loaded net is discarded and the fresh-topology path below rebuilds and retrains.
   if(netLoaded && m_topologySuperseded)
      netLoaded = false;
   //--- restore the calibration sidecar (priors + confidence scale) that pairs with these weights, so a
   //--- restart - including a buyer's inference-only backtest - calibrates live decisions exactly as the
   //--- saved model did instead of running with cold defaults (priors 0 => no adjustment). See LoadModelStats().
   if(netLoaded)
     {
      LoadModelStats(m_activeFileName, m_activeFileCommon);
      //--- ...and rebuild the ensemble headline from whatever record that just restored. Without this
      //--- a reloaded DEPLOYED chart had no aggregate line at all: the only other caller runs at
      //--- pass-3 completion, and a deployed ensemble runs no further eras to reach it - so the panel
      //--- fell back to one row per member, which is exactly the readout the operator asked to be rid
      //--- of. No "this era" figure is passed: there has not been one this session, and showing the
      //--- stored era's number here would read as live.
      if(m_ensembleMember)
         PublishEnsembleAccuracyLine(-1.0, 0);
      //--- CONVERGED BUT MUTE. Say so, loudly and once, because every downstream symptom of it looks
      //--- like something else: no arrows reads as a drawing fault, "0 vote/4 flat" reads as models
      //--- that disagree, and "measuring..." reads as a panel that has not caught up. All three are
      //--- the same thing - a model with no measured tier ladder returns 0 from LiveVoteContribution
      //--- by design, so it cannot vote, cannot be counted in the reconstruction divisor, and cannot
      //--- contribute to the aggregate win rate. A .stats written before WST7 has no ladder in it.
      if(m_trainingComplete && !m_tiersSelfRanked)
         Print(ID + ": WARNING - resumed CONVERGED but with NO MEASURED TIER LADDER, so this model"
               " CANNOT VOTE and will place no trades. The tier weights are produced only by a"
               " completed scoring pass and were not stored by the build that trained this model"
               " (.stats predates WST7). It will mint and store them at the end of its next scoring"
               " pass, after which restarts keep them. Until then this member is silent - that is the"
               " cause of an empty chart, a '0 vote' readout and a 'measuring...' win rate, all three.");
     }
   m_modelLoadedFromDisk = netLoaded;
   //--- Make a successful resume visible (the counterpart to the fresh-start / mismatch messages below):
   //--- on a live chart this confirms the saved model was found and loaded rather than silently retrained.
   if(netLoaded && !inTesterOrOpt)
      Print(ID + ": resumed saved model from era " + IntegerToString(m_eraCount) + " (trainingComplete=" + (string)m_trainingComplete + ") - continuing, not retraining from era 0.");
   //--- RESUMED MODELS GET THE SAME WARM-UP AS FRESH ONES (2026-08-13; was `netLoaded ? 0 : 3`).
   //--- Three no-op passes cost seconds. The label cache itself, however, is NEVER restored from
   //--- the .nnw checkpoint - it lives only in the in-memory m_labelCacheBuy/Sell/HasValue arrays,
   //--- which start empty every process start regardless of netLoaded.
   m_warmupPassesRemaining = 3;
   m_labelCachePrebuilt = false;
   if(inTesterOrOpt && netLoaded)
      Print(__FUNCTION__ + ": " + ID + " - reused cached weights from a previous optimization/tester pass on this agent (era " + IntegerToString(m_eraCount) + ", trainingComplete=" + (string)m_trainingComplete + ") - skipping redundant training for this unchanged config");
   if(netLoaded && ArraySize(loadedIndicatorParams) == AD_TUNE_PARAM_COUNT)
     {
      //--- Restart deploying previously AutoTune-d indicator params even with
      //--- AutoTuneIndicators=false now.
      AdoptIndicatorParams(loadedIndicatorParams, indicators);
     }
   if(!netLoaded)
     {
      int error_code = GetLastError();
      //--- Do NOT present error_code as the cause: on a no-GPU/CPU-DLL box it is the harmless 5100
      //--- (OpenCL-not-found) left by the compute probe inside CNet::Load, NOT the reason the file
      //--- was rejected.
      if(error_code != 5004) // not "file not found"
         ResetLastError();
      //--- CRITICAL: a failed load may have ALREADY overwritten the training-state out-params from
      //--- the bad file's header before it was rejected - notably a corrupt/empty 0-layer stub
      //--- whose header still says trainingComplete=1 (see CNet::Load's 0-layer guard).
      m_trainingComplete = false;
      m_eraCount = 0;
      dtStudied = 0;
      dForecast = 0;
      //--- Cold the in-memory calibration so the freshly-rebuilt (untrained) topology below runs
      //--- with no stale prior-correction until a retrain re-measures it (priors 0 =>
      //--- AdjustedSignalFromSoftmax is a no-op; scale 1.0 = the constructor default).
      m_priorBuy = 0.0;
      m_priorSell = 0.0;
      m_priorNeutral = 0.0;
      m_confidenceCalScale = 1.0;
         //--- Accurate diagnostic (do NOT cite GetLastError() - inside CNet::Load the OpenCL probe leaves 5100
         //--- there on a no-GPU/CPU-DLL box, which has nothing to do with the file). Distinguish an ordinary
         //--- fresh start (no file yet) from a real read failure of an existing file by testing existence.
         if(!inTesterOrOpt)
           {
            int loadFlags = m_activeFileCommon ? FILE_COMMON : 0;
            if(FileIsExist(m_activeFileName + ".nnw", loadFlags))
               Print(ID + ": could not read the existing model file " + m_activeFileName + ".nnw - rebuilding a fresh topology to retrain from era 0. Existing .stats/_shadow.nnw are KEPT (they refresh as training runs). If this recurs, that .nnw is likely corrupt - back it up, then use the panel's reset-weights to start clean.");
            else
               Print(ID + ": no saved model for this config yet - starting a fresh training run from era 0.");
           }
         //--- Re-seed before building a fresh topology so weight init is genuinely random. See
         //--- System\Random.mqh. Matches ResetWeights() and OnInit.
         WarriorRandSeed(ID);
         //--- Era 0 with no weights behind it, so any arrow currently on this chart was drawn by a
         //--- DIFFERENT model - the previous fingerprint's, or a corrupt .nnw's. Deliberately at
         //--- this call site rather than inside BuildFreshTopology(): the genetic tuner calls that
         //--- for every throwaway candidate (AutoTune.mqh) and must not touch the chart.
         ClearPersistedChartSignals("fresh topology at era 0 - arrows belong to a previous model");
         if(!BuildFreshTopology())
            return false;
     }
   TempData = new CArrayDouble();
   if(CheckPointer(TempData) == POINTER_INVALID)
      return false;
   if(netLoaded)
      // Populate dPrevSignal from the just-loaded weights immediately, rather than leaving it at
      // its blank constructor default until the next (asynchronous, queued) training pass happens
      // to run - matters most for the tester cache-reuse path above, where training may be skipped
      // entirely for this run because dtStudied already covers the whole backtest window.
      RefreshLatestSignal();
   //--- Status line must match what the gate below (if(!m_trainingComplete && !m_inferenceOnly)) will
   //--- actually do - otherwise an inference-only single backtest logs "resuming full training now" right
   //--- under the "runs inference only and will NOT train" warning, which reads as a contradiction.
   string trainState = m_trainingComplete
                       ? "already complete - staying converged, no full retrain on this restart"
                       : (m_inferenceOnly
                          ? "NOT complete, but this is an inference-only backtest - NOT training (see warning above); deploy a trained model for meaningful results"
                          : "NOT complete (interrupted or never converged) - resuming full training now");
   Print(__FUNCTION__ + ": " + m_activeFileName + " - training " + trainState);
   //--- Only kick off a full Train() run here if the loaded model genuinely isn't converged yet - an
   //--- already-complete model used to get one full era-loop retrain (real Net.backProp() over the
   //--- whole IS window) on every single EA restart/reattach for no reason, since this "Init" event
   //--- bypassed ScheduleTrainingIfNeeded()'s m_trainingComplete gate entirely. dPrevSignal is already
   //--- fresh from RefreshLatestSignal() above; ScheduleTrainingIfNeeded()'s normal per-tick check
   //--- will call RefreshConvergedSignal() itself once a genuinely new bar closes.
   if(!m_trainingComplete && !m_inferenceOnly)
      ArmStudyEvent((long)MathMax(0, MathMin(iTime(_Symbol, PERIOD_CURRENT, (int)(100 * Net.recentAverageSmoothingFactor * (m_trainingComplete ? 1 : 10))), dtStudied)), "Init");
   //--- Restore arrows persisted from a previous session (see SaveChartSignals). MUST run here, not in
   //--- InitIndicators(): the arrows file is keyed on the FULL m_fileName including the per-config
   //--- fingerprint, which is only appended above - see the note left at InitIndicators()'s old call site.
   LoadChartSignals();
   //--- bootstrap (or restore) the EMA shadow net now rather than waiting for the first
   //--- RefreshLatestSignal()/era-blend call to lazily trigger it - see m_shadowNet's declaration
   //--- comment.
   EnsureShadowNet();
   m_isInitialized = true;
   return true;
  }
//+------------------------------------------------------------------+
//| Creates the OHLC + ZigZag indicators the feature builder reads.   |
//| Called by InitNeuralNetwork(), not by the framework - the public  |
//| InitIndicators() override is the framework entry point.           |
//+------------------------------------------------------------------+
bool CExpertSignalAIBase::InitFeatureIndicators(CIndicators *indicators)
  {
//--- Reset only the status label on (re-)init; deliberately do NOT PurgeChart() here so previously drawn
//--- signal arrows survive an EA re-init (recompile / param change / timeframe switch) instead of
//--- vanishing every time - see SIG_ARROW_PREFIX. Full cleanup still happens in the destructor.
   ClearStatusLabel();
//--- NOTE: LoadChartSignals() is deliberately NOT called here any more. The mismatch made the
//--- restore silently no-op on every restart from the moment the fingerprint was introduced. Same
//--- family as the fingerprint trap documented at BuildConfigFingerprint: anything keyed on
//--- m_fileName must run AFTER it is fully built.
   if(!InitOpen(indicators))
      return false;
   if(!InitClose(indicators))
      return false;
   if(!InitLow(indicators))
      return false;
   if(!InitHigh(indicators))
      return false;
   //--- label source, always created unconditionally, same as the OHLC indicators above - see
   //--- m_zigZag's declaration comment. Optionally ALSO read as an input feature (m_useSwingContext,
   //--- below) using the same already-running indicator instance - no separate init needed for that.
   if(!InitZigZag(indicators))
      return false;
   m_neuronsCount = 4; // (close-open)/atr, (high-open)/atr, (low-open)/atr, bullish/bearish flag
   if(m_useVolumes)
     {
      // change ratio, level vs 50-bar baseline, absorption (range per unit volume), volume x range -
      // see BufferTempDataCompute()'s matching block, and research/test_volume.py for the measurement
      // that justified widening this from 1. m_neuronsCount is already in the config fingerprint, so
      // this re-keys existing caches on its own: correct, the input vector genuinely changed shape.
      m_neuronsCount += 4;
      if(!InitVolumes(indicators))
         return false;
     }
   // Unconditional, same reasoning as m_ATR/m_zigZag below: m_Time.GetData() is read
   // unconditionally elsewhere (label-eligibility gate, cache anchor, online-learning watermark,
   // arrow timestamps) regardless of whether the cyclical time-of-day/day-of-week values are also
   // opted into as an explicit feature via m_useTime - so the indicator itself must always exist.
   if(!InitTime(indicators))
      return false;
   if(m_useTime)
     {
      m_neuronsCount += 6;
     }
   if(m_useATR)
     {
      //already init in the base class
      m_neuronsCount++;
     }
   if(m_useMA)
     {
      if(!InitMA(indicators))
         return false;
      m_neuronsCount += 5; // (open-MA)/atr, (high-MA)/atr, (low-MA)/atr, (close-MA)/atr, (MA-MA[1])/atr
     }
   if(m_useSwingContext)
      m_neuronsCount += 9; // 5 confirmed-pivot features (direction, distance-since-pivot, prior-leg magnitude, retracement ratio, bars-since-pivot) + 4 recent-context features (Donchian pos 20/50, 20-bar return, 20-bar SMA extension) - see BufferTempDataCompute()'s matching block
   if(m_useNews)
      m_neuronsCount += 2; // NewsRecency, NewsProximity - see BufferTempDataCompute()'s matching block
   if(m_useSpreadFeature)
      m_neuronsCount += 2;                   // spread/ATR (volatility-regime reading), spread change ratio
                                             // - see BufferTempDataCompute()'s matching block
   if(m_useCrossAsset)
      m_neuronsCount += CROSSASSET_FEATURES; // FX: base/quote strength + divergence; index: denom/risk-proxy strength
   //--- ALT DATA (2026-08-16). Externally collected, publication-stamped features (COT positioning,
   //--- VIX complex, macro) exported by research/altdata/export.py into
   //--- Common\Files\Warrior_EA\AltData\{SYMBOL}_{TF}.csv - see System\AltData.mqh for the
   //--- lookahead/degradation contracts.
   if(m_altDataEnabled)
     {
      //--- A MODEL'S OWN .cfg STILL WINS. Adopt-don't-compare: an existing model keeps the column set
      //--- its weights were trained against, exactly as it keeps its topology. Only a FRESH model
      //--- takes the fleet set - which is what makes this change retrain-forcing rather than
      //--- silently re-keying a trained model's inputs.
      string altPin = ReadAltDataPinFromCfg();
      bool fleetPin = false;
      if(altPin == "")
        {
         //--- FRESH MODEL: pin the FLEET set, not this symbol's file header. Letting the file decide
         //--- is what split the fleet into three incompatible training pools and orphaned SP500 -
         //--- see ALTDATA_FLEET_COLUMNS for the full reasoning and the cost.
         altPin = ALTDATA_FLEET_COLUMNS;
         fleetPin = true;
        }
      m_altData.SetPinnedNames(altPin);
      m_altData.Load(m_symbol.Name(), (ENUM_TIMEFRAMES)m_period);   // logs its own outcome; absence is normal
      m_altDataNamesPinned = altPin;                                // stamped into the .cfg on the first save
      if(fleetPin)
         Print(ID + ": alt-data pinned to the FLEET column set (" +
               IntegerToString(m_altData.FeatureCount()) + " columns) rather than this symbol's file"
               " header. Every chart therefore publishes the same feature layout and can pool with"
               " every other; a per-symbol set is what left SP500 training alone.");
      m_useAltData = (m_altData.FeatureCount() > 0);
      if(m_useAltData)
         m_neuronsCount += m_altData.FeatureCount();
     }
   else
     {
      //--- Operator opt-out (EnableAltData=false): zero width, nothing pinned. On a model trained
      //--- WITH alt features this shrinks neuronsCount, mismatches the .cfg compare and correctly
      //--- starts fresh - stated in the input's comment rather than silently absorbed.
      m_useAltData = false;
      m_altDataNamesPinned = "";
     }
   if(!FolderCreate(m_folderPath, FILE_COMMON))
     {
      if(GetLastError() != 5010) // If the error is not because the folder already exists
        {
         Print("Failed to create folder: " + m_folderPath);
        }
      else
        {
         ResetLastError(); // Reset the error code
        }
     }
   return true;
  }
#endif