Warrior_EA/Expert/AIBase/AutoTune.mqh

//+------------------------------------------------------------------+
//|                                                       Warrior_EA |
//|                                                     AnimateDread |
//|                                                                  |
//| Filter-based indicator auto-tuner (mutual information scoring).  |
//+------------------------------------------------------------------+
#ifndef WARRIOR_AIBASE_AUTOTUNE_MQH
#define WARRIOR_AIBASE_AUTOTUNE_MQH
//--- ONCE-PER-CHART gate for the MI diagnostic suite on a multi-member ensemble. The first member
//--- to reach it runs it; the rest log one line and skip. Solo charts are untouched.
bool g_ensembleChartMiReportDone = false;
//--- ONCE-PER-CHART share of the indicator auto-tune SWEEP on a multi-member ensemble, same doctrine as
//--- the MI gate above: the sweep scores candidate indicator settings by feature/label MI, and every
//--- ensemble member holds identical indicators, identical cached features and identical labels, so all
//--- N sweeps are the same deterministic calculation (verified 2026-08-16 on SP500 H4: four members,
//--- byte-identical scores, spans and selection p). Worse, the sweep ends in the full MI diagnostic
//--- suite (ReportFeatureLabelInformation at its tail), which the MI gate above never intercepts on the
//--- sweep path - so each duplicate sweep also duplicated the ~200-draw permutation nulls, the slowest
//--- single block of "getting ready". The first member runs the sweep and publishes its outcome here;
//--- the rest apply the outcome (install the winner, or keep the configured settings the sweep restored)
//--- and skip both the sweep and the report. Same caveat as the MI gate: any winner ADOPTION is made by
//--- the donor and applied to every member via the flattened settings below, which is the consistent
//--- choice - members training on divergent feature vectors would not be an ensemble. Solo charts are
//--- untouched.
bool   g_ensembleChartTuneDone = false;
bool   g_ensembleChartTuneInstalled = false;   // did the donor's sweep clear the family-wise gate and install?
double g_ensembleChartTuneSettings[];          // CADIndicatorTuner::Flatten() of the donor's final settings
//--- The genetic + successive-halving helpers that used to live here (GaRungEras, GaExtract,
//--- GaStore, GaMutate, GaRandomCandidate, GaBlockCrossover, GaSortAliveByScoreDesc,
//--- GaBreedNextGeneration) were deleted on 2026-08-01 together with the search they served.
//+------------------------------------------------------------------+
//| MUTUAL INFORMATION between one cached feature column and the     |
//| swing label, in nats, over a sample of in-sample bars.           |
//+------------------------------------------------------------------+
double CExpertSignalAIBase::FeatureColumnMI(const double &vals[], const int &labels[], int n)
  {
   if(n < MI_MIN_SAMPLES)
      return 0.0;
   double sorted[];
   ArrayResize(sorted, n);
   ArrayCopy(sorted, vals, 0, 0, n);
   ArraySort(sorted);
   //--- A column that never varies carries no information; short-circuit so the log below is never
   //--- reached with a degenerate single-bin histogram.
   if(sorted[0] == sorted[n - 1])
      return 0.0;
   int joint[]; ArrayResize(joint, MI_BINS * 3); ArrayInitialize(joint, 0);
   int px[];    ArrayResize(px, MI_BINS);        ArrayInitialize(px, 0);
   int py[];    ArrayResize(py, 3);              ArrayInitialize(py, 0);
   for(int i = 0; i < n; i++)
     {
      //--- rank via binary search on the sorted copy; ties land in the same bin, which is correct
      int lo = 0, hi = n - 1, rank = 0;
      while(lo <= hi)
        {
         int mid = (lo + hi) / 2;
         if(sorted[mid] < vals[i])
           {
            rank = mid + 1;
            lo = mid + 1;
           }
         else
            hi = mid - 1;
        }
      int bx = (int)((double)rank * MI_BINS / n);
      if(bx >= MI_BINS)
         bx = MI_BINS - 1;
      int by = labels[i];
      if(by < 0 || by > 2)
         continue;
      joint[bx * 3 + by]++;
      px[bx]++;
      py[by]++;
     }
   double mi = 0.0;
   for(int b = 0; b < MI_BINS; b++)
     {
      if(px[b] <= 0)
         continue;
      for(int c = 0; c < 3; c++)
        {
         int j = joint[b * 3 + c];
         if(j <= 0 || py[c] <= 0)
            continue;
         double pxy = (double)j / n;
         mi += pxy * MathLog(pxy / (((double)px[b] / n) * ((double)py[c] / n)));
        }
     }
   return (mi > 0.0) ? mi : 0.0;
  }
//+------------------------------------------------------------------+
//| Scores the CURRENT indicator parameters by how much the          |
//| resulting feature vector tells us about the label - the mean     |
//| per-column mutual information over a stratified sample of in-    |
//| sample bars.                                                     |
//+------------------------------------------------------------------+
int CExpertSignalAIBase::BuildMiSample(double &cols[], int &labels[], int labelBarOffset = 0)
  {
   int bars = m_labelCacheBars;
   if(bars <= 0 || m_neuronsCount <= 0)
     {
      //--- INSTRUMENTED 2026-08-26. This function has five distinct -1 exits and the caller can only
      //--- see that the MI report collapsed to "-1.00000 nats over 0 permutations". On a COLD start
      //--- the auto-tuner scored MI fine (0.00843) and the report seconds later returned -1 on the
      //--- same chart, and three plausible explanations each failed to survive the log. Naming the
      //--- exit costs one throttled line and ends the guessing.
      TCLog("mi-sample-bars:" + ID,
            StringFormat("%s: BuildMiSample abandoned - label cache holds %d bars and the feature"
                         " vector is %d wide; both must be positive.", ID, bars, m_neuronsCount));
      return -1;
     }
   //--- Sample the IS region only. The OOS window must not influence which indicator settings ship, or
   //--- the holdout has been used for selection and stops being a holdout at all.
   int oosCutoff = (int)(MathMax(0, MathMin(100, m_oosSplitPct)) / 100.0
                         * MathMax(bars - MathMax(m_historyBars, 0), 0));
   int lo = MathMax(oosCutoff, 2);
   int hi = bars - MathMax(m_historyBars, 0) - 1;
//--- Keep the OFFSET label lookup inside the same bounds as the features, so a shifted scan
//--- measures a shift and not an edge effect. THE PAD IS FIXED, NOT |labelBarOffset|.
   int shiftPad = MiShiftPad();
   if(MathAbs(labelBarOffset) > shiftPad)
     {
      TCLog("mi-sample-shift:" + ID,
            StringFormat("%s: BuildMiSample abandoned - caller asked for a %d-bar label shift but the"
                         " pad only covers %d.", ID, labelBarOffset, shiftPad));
      return -1;                                  // caller asked for a shift the pad does not cover
     }
   lo += shiftPad;
   hi -= shiftPad;
   if(hi - lo < MI_MIN_SAMPLES)
     {
      //--- THE LIKELY ONE, and the numbers say which term collapsed the window: the OOS cutoff, the
      //--- history window, or a shift pad that scales with the measured label resolution.
      TCLog("mi-sample-window:" + ID,
            StringFormat("%s: BuildMiSample abandoned - usable IS window is %d rows (lo %d, hi %d) but"
                         " %d are required. bars=%d, oosSplit=%d%%, historyBars=%d, shiftPad=%d"
                         " (label resolution %d bars).",
                         ID, hi - lo, lo, hi, MI_MIN_SAMPLES, bars, m_oosSplitPct, m_historyBars,
                         shiftPad, LabelResolutionBars()));
      return -1;
     }
   int stride = (int)MathMax(1, (hi - lo) / MI_SAMPLE_BARS);
   //--- Published so the positive control can say how many BARS apart two sample rows are without
   //--- recomputing this arithmetic at the call site, where it would silently drift out of agreement.
   m_miStrideBars = stride;
   int cap = (hi - lo) / stride + 1;
   ArrayResize(cols, cap * m_neuronsCount);
   ArrayResize(labels, cap);
   int n = 0;
   for(int i = lo; i < hi && n < cap; i += stride)
     {
      //--- Features come from bar i; the LABEL may be taken from a neighbouring bar (labelBarOffset != 0)
      //--- so the caller can scan for a feature/label misalignment - see the alignment scan in
      //--- ReportFeatureLabelInformation(). Both bars must carry a valid label for the row to count.
      int li = i + labelBarOffset;
      if(i >= ArraySize(m_labelCacheHasValue) || !m_labelCacheHasValue[i])
         continue;
      if(li < 0 || li >= ArraySize(m_labelCacheHasValue) || !m_labelCacheHasValue[li])
         continue;
      //--- BufferTempData(), NOT BufferTempDataCompute(). The Compute variant APPENDS the bar's
      //--- features to TempData and never touches m_featureCache - only the caching wrapper writes
      //--- that array.
      TempData.Clear();
      if(!BufferTempData(i) || TempData.Total() < m_neuronsCount)
         continue;
      for(int f = 0; f < m_neuronsCount; f++)
         cols[n * m_neuronsCount + f] = TempData.At(f);
      labels[n] = m_labelCacheBuy[li] ? 0 : (m_labelCacheSell[li] ? 1 : 2);
      n++;
     }
   TempData.Clear();
   return n;
  }
//+------------------------------------------------------------------+
//| BLOCK PERMUTATION of a label column, in place. Fisher-Yates over  |
//| BLOCK ORDER, within-block order untouched - that is what          |
//| preserves the local dependence overlapping labels carry. A free   |
//| shuffle would destroy it and report a null far too tight.         |
//|                                                                  |
//| THE RAGGED TAIL: when blockRows does not divide n the LAST block  |
//| is short, and a version that wrote fixed-length blocks clamped    |
//| its overrun to labels[n-1], duplicating one label and skewing     |
//| every p-value toward significance. Each block now contributes     |
//| exactly its own length, and the class-count invariance is CHECKED |
//| rather than asserted - a failed draw returns false so the caller  |
//| skips it instead of poisoning the null.                           |
//+------------------------------------------------------------------+
bool BlockPermuteLabels(int &labels[], const int n, const int blockRows, int &blocksOut)
  {
   blocksOut = 0;
   if(n <= 0 || blockRows <= 0 || ArraySize(labels) < n)
      return false;
   int rows = (blockRows > n) ? n : blockRows;
   int blocks = (n + rows - 1) / rows;
   blocksOut = blocks;
   if(blocks <= 1)
      return true;                      // one block: any permutation of it is itself
   int before[3] = {0, 0, 0};
   for(int i = 0; i < n; i++)
      if(labels[i] >= 0 && labels[i] < 3)
         before[labels[i]]++;
   int order[];
   ArrayResize(order, blocks);
   for(int b = 0; b < blocks; b++)
      order[b] = b;
   for(int b = blocks - 1; b > 0; b--)
     {
      //--- ShuffleRandomIndex, not MathRand() modulo: with blockRows == 1 the block count equals
      //--- the row count, which can exceed MathRand()'s 15-bit range - the same bias that was
      //--- fixed in the pass-2 training queue shuffle.
      int j = ShuffleRandomIndex(b + 1);
      int t = order[b];
      order[b] = order[j];
      order[j] = t;
     }
   int shuffled[];
   ArrayResize(shuffled, n);
   int lastLen = n - (blocks - 1) * rows;  // > 0 by construction of blocks
   int w = 0;
   for(int b = 0; b < blocks; b++)
     {
      int src = order[b] * rows;
      int len = (order[b] == blocks - 1) ? lastLen : rows;
      for(int q = 0; q < len; q++)
         shuffled[w++] = labels[src + q];
     }
   //--- w == n unless the length arithmetic above is wrong, and a partial copy would leave the tail
   //--- of labels[] holding the PREVIOUS draw - a null quietly correlated with the one before it.
   if(w != n)
      return false;
   for(int i = 0; i < n; i++)
      labels[i] = shuffled[i];
   int after[3] = {0, 0, 0};
   for(int i = 0; i < n; i++)
      if(labels[i] >= 0 && labels[i] < 3)
         after[labels[i]]++;
   return (before[0] == after[0] && before[1] == after[1] && before[2] == after[2]);
  }
//+------------------------------------------------------------------+
//| Score an already-extracted sample. Split out from the extraction |
//| above so the permutation test can reuse ONE sample across every  |
//| draw: feature extraction dominates the cost, and re-running it   |
//| per shuffle is what would have made a few hundred permutations   |
//| unaffordable.                                                    |
//+------------------------------------------------------------------+
double CExpertSignalAIBase::ScoreMiSample(const double &cols[], int &labels[], int n, bool shuffleLabels)
  {
   if(n < MI_MIN_SAMPLES)
      return -1.0;
   //--- PERMUTATION BASELINE. So a raw MI figure is uninterpretable on its own: 0.004 nats could be
   //--- a genuine weak signal or could be pure noise.
//--- BLOCK permutation, not a free one, and the difference is the whole validity of the test. That
//--- was label autocorrelation leaking through an independence assumption, not an edge. It is Lopez
//--- de Prado ch.
   if(shuffleLabels)
     {
      int blockRows = (m_miStrideBars > 0)
                      ? (int)MathCeil(MathMax(MeanLabelLifespan(), 1.0) / m_miStrideBars) : 1;
      if(blockRows < 1)
         blockRows = 1;
      if(blockRows > n)
         blockRows = n;
      if(!BlockPermuteLabels(labels, n, blockRows, m_miNullBlocks))
         return -1.0;
     }
   //--- H(Y) over the sampled labels, so the caller can express MI as a fraction of the information the
   //--- label actually contains. Computed AFTER any shuffle, which leaves it unchanged by construction:
   //--- a permutation preserves the class counts. That invariance is now genuinely CHECKED, inside
   //--- BlockPermuteLabels, which returns false if it fails - this comment used to claim the invariance was
   //--- "itself a check on the shuffle" while nothing anywhere compared the counts, and the shuffle it
   //--- was vouching for had in fact been breaking it whenever blockRows did not divide n.
   int classCount[3] = {0, 0, 0};
   for(int k = 0; k < n; k++)
      classCount[labels[k]]++;
   m_miLabelEntropy = 0.0;
   for(int c = 0; c < 3; c++)
     {
      if(classCount[c] <= 0)
         continue;
      double pc = (double)classCount[c] / n;
      m_miLabelEntropy -= pc * MathLog(pc);
     }
   double colVals[];
   ArrayResize(colVals, n);
   //--- Retained per column, not just summed - see m_miColumn's declaration. Costs one array write
   //--- per column per call and nothing else; the MI itself was always computed here.
   if(ArraySize(m_miColumn) != m_neuronsCount)
      ArrayResize(m_miColumn, m_neuronsCount);
   double total = 0.0;
   m_miBestColumn = 0.0;
   for(int f = 0; f < m_neuronsCount; f++)
     {
      for(int k = 0; k < n; k++)
         colVals[k] = cols[k * m_neuronsCount + f];
      double mi = FeatureColumnMI(colVals, labels, n);
      m_miColumn[f] = mi;
      total += mi;
      if(mi > m_miBestColumn)
         m_miBestColumn = mi;
     }
   return total / m_neuronsCount;
  }
//+------------------------------------------------------------------+
//| Extract + score in one call - the form the coordinate sweep uses, |
//| where each candidate genuinely needs a fresh extraction because   |
//| the indicator settings (and therefore the features) just changed. |
//+------------------------------------------------------------------+
double CExpertSignalAIBase::ScoreCurrentParamsByMI(bool shuffleLabels = false)
  {
   double cols[];
   int labels[];
   int n = BuildMiSample(cols, labels);
   if(n < MI_MIN_SAMPLES)
      return -1.0;
   return ScoreMiSample(cols, labels, n, shuffleLabels);
  }
//+------------------------------------------------------------------+
//| FILTER-BASED indicator tuning. Replaced the genetic +            |
//| successive- halving search on 2026-08-01.                        |
//+------------------------------------------------------------------+
void CExpertSignalAIBase::TuneIndicatorsByFilter(void)
  {
   double best[];
   m_indicatorTuner.Flatten(best);
   double bestScore = ScoreCurrentParamsByMI();
   if(bestScore < 0.0)
     {
      Print(ID + ": auto-tune skipped - not enough labelled in-sample bars to score indicator settings");
      return;
     }
   double startScore = bestScore;
   int evaluated = 0;
   uint t0 = GetTickCount();
   //--- SPREAD OF THE CANDIDATE SCORES. Without it "no improvement" is ambiguous between two
   //--- readings that want opposite responses: INERT (trial scores identical to the incumbent
   //--- because the parameter change never reaches the scored features, so `sc > bestScore` can
   //--- never fire) versus LIVE and genuinely finding nothing.
   double candMin = DBL_MAX, candMax = -DBL_MAX;
   int readyMin = INT_MAX;
   //--- The configured settings, kept so a winner that fails the gate below can be handed back. best[] is
   //--- mutated in place by the descent, so it cannot serve as the restore point.
   double configured[];
   ArrayCopy(configured, best);
   for(int pass = 0; pass < MI_TUNE_PASSES; pass++)
     {
      bool improvedThisPass = false;
      for(int p = 0; p < AD_TUNE_PARAM_COUNT; p++)
        {
         //--- skip parameters whose indicator is switched off - they cannot affect the feature vector
         int owner = m_indicatorTuner.ParamOwner(p);
         //--- OWNER 5 IS THE MA FEATURE and is the only one left: owners 0-4 (the AD/Wyckoff family)
         //--- and 6-8 (RSI, MACD, Ichimoku) lost their feature groups on 2026-08-24. The owner
         //--- NUMBERING is deliberately unchanged - CADIndicatorTuner's flat parameter array is
         //--- persisted inside every .nnw, so renumbering it would silently discard the tuned MA
         //--- period of every model already on disk (Unflatten refuses a size mismatch and falls
         //--- back to constructor defaults). Dead owners simply never match now.
         bool on = (owner == 5 && m_useMA);
         if(!on)
            continue;
         double cands[];
         int nc = m_indicatorTuner.ParamCandidates(p, cands);
         double keep = best[p];
         for(int c = 0; c < nc; c++)
           {
            //--- The longest uninterruptible stretch in the EA: every candidate re-creates handles,
            //--- refreshes, and scores a full MI sample. Asked per candidate so a stop request costs at
            //--- most one candidate rather than the rest of the descent - see ShutdownRequested().
            if(ShutdownRequested())
              {
               //--- Hand the OPERATOR's settings back before leaving. best[] is mutated in place by
               //--- the descent and the tuner object currently carries the LAST TRIAL's parameters,
               //--- which nothing chose and which the .cfg would otherwise persist as if it had
               //--- been selected.
               m_indicatorTuner.Unflatten(configured);
               PrintFormat("%s: auto-tune ABANDONED after %d candidates - stop requested. Configured"
                           " indicator settings restored; nothing installed.", ID, evaluated);
               return;
              }
            if(cands[c] == keep)
               continue; // already scored as the incumbent
            double trial[];
            ArrayCopy(trial, best);
            trial[p] = cands[c];
            m_indicatorTuner.Unflatten(trial);
            ReInitTunableIndicators(m_indicatorsPtr); // also invalidates the feature cache (params changed)
            //--- REFRESH, or the re-init changes nothing that the scorer can see. Without this the
            //--- buffers still hold values copied from the PREVIOUS handle, so every candidate is
            //--- scored on identical features.
            RefreshData();
            int ready = TunableBarsCalculated();
            if(ready >= 0)
               readyMin = (int)MathMin(readyMin, ready);
            double sc = ScoreCurrentParamsByMI();
            evaluated++;
            if(sc >= 0.0)
              {
               candMin = MathMin(candMin, sc);
               candMax = MathMax(candMax, sc);
              }
            if(sc > bestScore)
              {
               bestScore = sc;
               keep = cands[c];
               improvedThisPass = true;
              }
           }
         best[p] = keep;
        }
      if(!improvedThisPass)
         break; // coordinate descent has converged - further passes cannot move anything
     }
   //--- SELECTION GATE. bestScore is a MAXIMUM over every candidate scored, so it carries the same
   //--- defect the barrier-geometry winner test and the lag profile were fixed for: the maximum of
   //--- N draws from a null sits well above any single draw, and installing on "it beat the
   //--- incumbent" alone crowns noise.
   bool install = (bestScore > startScore);
   double pFamily = 1.0;
   int distinct = (int)MathMax(evaluated + 1, 1); // candidates scored, plus the incumbent
   if(install)
     {
      double wc[];
      int wl[];
      int wn = BuildMiSample(wc, wl);
      if(wn >= MI_MIN_SAMPLES)
        {
         double obs = ScoreMiSample(wc, wl, wn, false);
         int atLeast = 0, draws = 0;
         for(int s = 0; s < MI_NOISE_PERMUTATIONS; s++)
           {
            //--- A truncated null is not a smaller null, it is a WRONG one - fewer draws shifts p toward
            //--- significance. So a stop here abandons the test entirely (draws stays 0, pFamily stays
            //--- 1.0, install becomes false) rather than installing on a partial null.
            if(ShutdownRequested())
              {
               draws = 0;
               break;
              }
            double d = ScoreMiSample(wc, wl, wn, true);
            if(d < 0.0)
               continue;
            if(d >= obs)
               atLeast++;
            draws++;
           }
         if(draws > 0)
           {
            double pSingle = PermutationPValue(atLeast, draws);
            pFamily = 1.0 - MathPow(1.0 - pSingle, (double)distinct);
           }
        }
      install = (pFamily <= MI_TUNE_ALPHA);
     }
   if(!install)
     {
      ArrayCopy(best, configured);
      bestScore = startScore;
     }
   //--- install the winner and leave the indicators/feature cache consistent with it
   m_indicatorTuner.Unflatten(best);
   ReInitTunableIndicators(m_indicatorsPtr);
   RefreshData();
   //--- A gated INSTALL is chart-level news, not just this model's: persist the winning periods so
   //--- the classic votes, the signal-DB key and every later tuner seed adopt them on the next
   //--- attach (restart-grained - see Variables\TunedPeriods.mqh for why not mid-run).
   if(install)
      SaveTunedPeriods(m_indicatorTuner.maPeriod, m_indicatorTuner.maType, m_indicatorTuner.rsiPeriod,
                       m_indicatorTuner.macdFast, m_indicatorTuner.macdSlow, m_indicatorTuner.macdSignal,
                       m_indicatorTuner.ichiTenkan, m_indicatorTuner.ichiKijun, m_indicatorTuner.ichiSenkou);
   double candSpread = (evaluated > 0 && candMax >= candMin) ? (candMax - candMin) : 0.0;
   Print(ID + StringFormat(": auto-tune complete - %d candidate settings scored in %.1fs, "
                           "feature/label mutual information %.5f -> %.5f nats%s | candidate scores span "
                           "%.5f (%.5f..%.5f)%s",
                           evaluated, (GetTickCount() - t0) / 1000.0, startScore, bestScore,
                           (bestScore <= startScore ? " (no improvement - keeping the configured settings)" : ""),
                           candSpread, (evaluated > 0 ? candMin : 0.0), (evaluated > 0 ? candMax : 0.0),
                           (evaluated > 0 && candSpread <= 0.0
                            ? StringFormat(" <-- ZERO SPREAD: every candidate scored identically, so the "
                                           "parameter change is STILL not reaching the scored features even "
                                           "with the post-re-init RefreshData(). Least-ready tunable handle "
                                           "had %d bars calculated - if that is 0 or far below the study "
                                           "window, the handles are simply not done calculating yet and the "
                                           "tuner needs to yield between candidates rather than score them "
                                           "back to back.", (readyMin == INT_MAX ? -1 : readyMin))
                            : StringFormat(" | winner %s (selection p=%.4f after correcting for %d "
                                           "candidates, need <=%.2f)",
                                           (install ? "INSTALLED" : "REJECTED - keeping the configured "
                                            "settings, since the best of N noise draws beats its incumbent "
                                            "almost every time"),
                                           pFamily, distinct, MI_TUNE_ALPHA))));
   //--- An EXACTLY zero score is not a weak feature set, it is a broken measurement. Landing on
   //--- 0.0000 means every column read back constant, which is what a feature-extraction fault
   //--- looks like.
   if(bestScore <= 0.0)
      Print(ID + ": WARNING - every candidate scored 0.0000 nats. Finite-sample bias alone should put "
            "noise above zero, so this indicates the feature values are not being read, not that the "
            "features are uninformative. Indicator settings left at their configured values.");
   ReportFeatureLabelInformation();
  }
//+------------------------------------------------------------------+
//| The MI evidence screen (ReportFeatureLabelInformation and its     |
//| lag-profile sub-report) moved to FeatureScreen.mqh on 2026-08-23  |
//| - SEARCH (this file) vs MEASUREMENT (that one) are two            |
//| responsibilities. FeatureColumnMI/BuildMiSample/ScoreMiSample     |
//| above stay here: both files call them, and a shared dependency   |
//| used by two consumers is not itself a reason to split further.   |
//+------------------------------------------------------------------+
void CExpertSignalAIBase::TuneIndicatorsAndTrain(datetime StartTrainBar = 0)
  {
//--- FIRST STATEMENT IN THE WHOLE TRAINING ENTRY POINT, ahead of every latch below it (m_tuneFilterDone,
//--- g_ensembleChartTuneDone) so a stop cannot mark a sweep as "already run" without running it. The
//--- individual scans yield on ShutdownRequested() as well; this simply refuses to start the chain.
   if(ShutdownRequested())
      return;
//--- Publish the caller's window anchor so StartLabelCachePrebuild() sizes its window with the SAME
//--- expression Train() uses.
   m_tuneStartTrainBar = StartTrainBar;
   bool anyTunable = m_useMA;   //--- see ParamOwner's note: the MA feature is the last tunable one
   //--- Tune once per fresh model, before any weight has been trained. Gated on m_labelCachePrebuilt
   //--- because the score needs labels, and on era 0 because re-tuning a partly-trained network would
   //--- change its inputs out from under weights already fitted to the old ones.
   if(m_autoTuneIndicators && anyTunable && !m_tuneFilterDone && m_labelCachePrebuilt && m_eraCount == 0)
     {
      m_tuneFilterDone = true;
      if(m_ensembleMember && g_ensembleChartTuneDone)
        {
         //--- Another member on this chart already ran the identical sweep - apply its outcome
         //--- instead of recomputing it (see g_ensembleChartTuneDone at the top of this file). SAY
         //--- IT ON THE PANEL, not only in the journal.
         PublishStatus(ID + " : adopting the chart's tuned indicators...");
         //--- THE PARAMETERS are adopted only when a winner was installed...
         if(g_ensembleChartTuneInstalled)
            m_indicatorTuner.Unflatten(g_ensembleChartTuneSettings);
         //--- ...but the HANDLES must be rebuilt EITHER WAY, and that is not a tidiness point - it
         //--- is the cause of the "silent block failure" that cost six sessions. That is the whole
         //--- finding: it was never four handles, it was ONE.
         ReInitTunableIndicators(m_indicatorsPtr);
         RefreshData();
         Print(ID + ": indicator auto-tune already ran on this chart - same indicators, same features, "
               "same labels, same answer. " +
               (g_ensembleChartTuneInstalled
                ? "Adopting the installed winner so every member trains on the same feature vector."
                : "Keeping the configured settings (the sweep's winner was rejected by the selection gate).") +
               " The first member's auto-tune report above is this model's too.");
        }
      else
        {
         //--- Names the SCOPE, because the scope is what the other rows' silence means.
         PublishStatus(ID + (m_ensembleMember
                             ? " : scoring indicator settings for the whole chart..."
                             : " : scoring indicator settings..."));
         //--- Snapshot the configured settings first: "did the sweep install?" is answered by comparing
         //--- against the final settings, since a rejected winner is restored to exactly these values.
         double tuneCfgBefore[];
         m_indicatorTuner.Flatten(tuneCfgBefore);
         TuneIndicatorsByFilter();
         if(m_ensembleMember)
           {
            m_indicatorTuner.Flatten(g_ensembleChartTuneSettings);
            g_ensembleChartTuneInstalled = false;
            for(int tp = 0; tp < ArraySize(tuneCfgBefore); tp++)
               if(g_ensembleChartTuneSettings[tp] != tuneCfgBefore[tp])
                 {
                  g_ensembleChartTuneInstalled = true;
                  break;
                 }
            g_ensembleChartTuneDone = true;
            //--- The sweep ends in ReportFeatureLabelInformation(), so the chart-level MI report is
            //--- done too - mark it, or every other member would rerun the ~200-draw nulls the MI
            //--- gate below exists to save.
            if(m_miReportDone)
               g_ensembleChartMiReportDone = true;
           }
        }
      //--- the winning parameters change the input vector, so the network must start from scratch on it
      BuildFreshTopology();
     }
   //--- The DIAGNOSTIC half runs even when the sweep does not: on a resumed model, on one whose
   //--- tuner is switched off, and on one with nothing tunable.
   else if(!m_miReportDone && !m_labelCachePrebuilt && !m_labelPrebuildActive)
     {
      //--- Announce only on a start that actually took. StartLabelCachePrebuild() returns without arming
      //--- if the buffers/history are not ready yet and is simply retried on the next call, so printing
      //--- unconditionally would repeat the line once per bar event until it succeeds.
      StartLabelCachePrebuild();
      //--- Says WHICH case this is rather than asserting the resumed one. A diagnostic that
      //--- misreports its own trigger is worse than one that says nothing, because it gets quoted
      //--- back as evidence.
      if(m_labelPrebuildActive)
         Print(ID + (m_modelLoadedFromDisk
                     ? ": MI diagnostics need a complete label cache and this model resumed from disk "
                       "(labels are filled lazily, so the cache covers only the bars training has "
                       "visited) - running the one-time pre-scan now, then the report. Training resumes "
                       "where it left off."
                     : ": MI diagnostics need a complete label cache and this model has not built one yet "
                       "- running the pre-scan now, then the report."));
     }
   else if(!m_miReportDone && m_labelCachePrebuilt)
     {
      //--- WAIT FOR THE CROSS-ASSET PANEL. It is part of the feature vector but it is built inside
      //--- Train(), so on a fresh run this diagnostic would otherwise describe a NARROWER vector
      //--- than the one training goes on to use.
      if(m_ensembleMember && g_ensembleChartMiReportDone)
        {
         //--- see g_ensembleChartMiReportDone at the top of this file
         m_miReportDone = true;
         //--- Same reasoning as the tuner's adopt branch above: published, not just printed, so the row
         //--- says why it is not repeating the measurement.
         PublishStatus(ID + " : reusing the chart's information report...");
         Print(ID + ": MI diagnostics already measured by another ensemble member on this chart - "
               "same features, same labels, same answer. Skipped (saves the slowest part of the "
               "ensemble's warm-up; the first member's report above is this model's too).");
        }
      else
         if(m_crossAsset.IsReady() || m_miReportDeferrals >= MI_REPORT_MAX_DEFERRALS)
           {
            //--- THE LONGEST SINGLE STRETCH OF THE WARM-UP - the MI suite and the lag profile, each
            //--- with its own few-hundred-draw permutation null - and until now it published NOTHING.
            PublishStatus(ID + (m_ensembleMember
                                ? " : measuring feature/label information for the whole chart..."
                                : " : measuring feature/label information..."));
            ReportFeatureLabelInformation();
            if(m_ensembleMember && m_miReportDone)
               g_ensembleChartMiReportDone = true;
           }
         else
            m_miReportDeferrals++;
     }
   Train(StartTrainBar);
  }
#endif // WARRIOR_AIBASE_AUTOTUNE_MQH