//+------------------------------------------------------------------+ //| 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