style: normalize indentation in Orchestrator.mqh via clang-format

Whitespace-only: consistent 3-space indentation throughout (was a mix of
3/4/5/6.. spaces). Repo conventions preserved: for(/if( spacing, access
labels, attached braces. Adds .clang-format so the style is reproducible
(run: clang-format -i --style=file <file>).

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

MQL5/Experts/MultiAgentTest/.clang-format

@@ -0,0 +1,15 @@
+BasedOnStyle: LLVM
+IndentWidth: 3
+TabWidth: 3
+UseTab: Never
+ColumnLimit: 0
+BreakBeforeBraces: Attach
+AllowShortFunctionsOnASingleLine: All
+AllowShortBlocksOnASingleLine: Always
+AllowShortIfStatementsOnASingleLine: true
+AllowShortLoopsOnASingleLine: true
+SortIncludes: false
+SpaceBeforeParens: Never
+IndentCaseLabels: true
+AccessModifierOffset: -3
+PointerAlignment: Right

MQL5/Experts/MultiAgentTest/Core/Orchestrator.mqh

@@ -233,21 +233,25 @@ public:
 
    int OpenTradeCount() const {
       int c = 0;
-      for(int i=0; i<maxOpenTrades; i++) if(openTrades[i].active) c++;
+      for(int i = 0; i < maxOpenTrades; i++)
+         if(openTrades[i].active) c++;
       return c;
    }
    int OpenTradeCount(bool isBuy) const {
       int c = 0;
-      for(int i=0; i<maxOpenTrades; i++) if(openTrades[i].active && openTrades[i].isBuy == isBuy) c++;
+      for(int i = 0; i < maxOpenTrades; i++)
+         if(openTrades[i].active && openTrades[i].isBuy == isBuy) c++;
       return c;
    }
    bool HasOpenTrade() const { return OpenTradeCount() > 0; }
    int OpenTicket() const {
-      for(int i=0; i<maxOpenTrades; i++) if(openTrades[i].active) return openTrades[i].ticket;
+      for(int i = 0; i < maxOpenTrades; i++)
+         if(openTrades[i].active) return openTrades[i].ticket;
       return -1;
    }
    double EntryPrice() const {
-      for(int i=0; i<maxOpenTrades; i++) if(openTrades[i].active) return openTrades[i].entryPrice;
+      for(int i = 0; i < maxOpenTrades; i++)
+         if(openTrades[i].active) return openTrades[i].entryPrice;
       return 0;
    }
    void ResetTradeState() {
@@ -373,13 +377,21 @@ public:
    int TotalAgents() const { return agentCount; }
 
    double Analyze(const MarketData &data) {
-      if(agentCount == 0) { combinedZ = 0; return 0; }
+      if(agentCount == 0) {
+         combinedZ = 0;
+         return 0;
+      }
 
       double zs[MAX_AGENTS], ws[MAX_AGENTS], corrSign[MAX_AGENTS];
 
       // Fase 1: Analisi individuale
       for(int i = 0; i < agentCount; i++) {
-         if(!agents[i].enabled) { zs[i] = 0; ws[i] = 0; corrSign[i] = 1.0; continue; }
+         if(!agents[i].enabled) {
+            zs[i] = 0;
+            ws[i] = 0;
+            corrSign[i] = 1.0;
+            continue;
+         }
          zs[i] = agents[i].Analyze(data);
          double rho = GetCorrelation(i);
          // Competenza = |correlazione| segnale/ritorni (con floor weightMin, così
@@ -401,21 +413,25 @@ public:
 
       // Fase 3: Neural o Softmax Gating
       // Se la rete neurale è attiva e addestrata, usa forward pass
-        bool neuralReady = m_useNeural && m_neuralNet != NULL
+      bool neuralReady = m_useNeural && m_neuralNet != NULL && m_neuralNet.IsInitialized() && m_neuralNet.EpochsTrained() > 0;
-                           && m_neuralNet.IsInitialized()
-                           && m_neuralNet.EpochsTrained() > 0;
 
       if(neuralReady) {
          // Estrai feature vector: [z_Hurst, z_ADX, z_MA, z_Mom, z_Consensus, z_Pattern, agreement, trendStrength]
          vector nnInput(NN_FEATURES);
          for(int f = 0; f < NN_FEATURES; f++) nnInput[f] = 0.0;
          for(int i = 0; i < agentCount; i++) {
-                    if(agents[i].name == "Hurst")     nnInput[0] = agents[i].lastZScore;
+            if(agents[i].name == "Hurst")
-               else if(agents[i].name == "ADX")       nnInput[1] = agents[i].lastZScore;
+               nnInput[0] = agents[i].lastZScore;
-               else if(agents[i].name == "MA")        nnInput[2] = agents[i].lastZScore;
+            else if(agents[i].name == "ADX")
-               else if(agents[i].name == "Momentum")  nnInput[3] = agents[i].lastZScore;
+               nnInput[1] = agents[i].lastZScore;
-               else if(agents[i].name == "Consensus") nnInput[4] = agents[i].lastZScore;
+            else if(agents[i].name == "MA")
-               else if(agents[i].name == "Hunter")    nnInput[5] = agents[i].lastZScore;
+               nnInput[2] = agents[i].lastZScore;
+            else if(agents[i].name == "Momentum")
+               nnInput[3] = agents[i].lastZScore;
+            else if(agents[i].name == "Consensus")
+               nnInput[4] = agents[i].lastZScore;
+            else if(agents[i].name == "Hunter")
+               nnInput[5] = agents[i].lastZScore;
          }
          nnInput[6] = SHARED_regimeAgreement;
          nnInput[7] = SHARED_trendStrength;
@@ -505,7 +521,10 @@ public:
       // Trova slot libero
       int idx = -1;
       for(int i = 0; i < maxOpenTrades; i++) {
-             if(!openTrades[i].active) { idx = i; break; }
+         if(!openTrades[i].active) {
+            idx = i;
+            break;
+         }
       }
       if(idx < 0) { // non dovrebbe mai succedere dopo EnsureTradeCapacity
          Print("ERROR: slot non disponibile nonostante capacity expansion");
@@ -531,12 +550,18 @@ public:
       // Feature vector per NN
       for(int f = 0; f < NN_FEATURES; f++) openTrades[idx].entryFeatures[f] = 0.0;
       for(int i = 0; i < agentCount; i++) {
-                  if(agents[i].name == "Hurst")     openTrades[idx].entryFeatures[0] = agents[i].lastZScore;
+         if(agents[i].name == "Hurst")
-             else if(agents[i].name == "ADX")       openTrades[idx].entryFeatures[1] = agents[i].lastZScore;
+            openTrades[idx].entryFeatures[0] = agents[i].lastZScore;
-             else if(agents[i].name == "MA")        openTrades[idx].entryFeatures[2] = agents[i].lastZScore;
+         else if(agents[i].name == "ADX")
-             else if(agents[i].name == "Momentum")  openTrades[idx].entryFeatures[3] = agents[i].lastZScore;
+            openTrades[idx].entryFeatures[1] = agents[i].lastZScore;
-             else if(agents[i].name == "Consensus") openTrades[idx].entryFeatures[4] = agents[i].lastZScore;
+         else if(agents[i].name == "MA")
-             else if(agents[i].name == "Hunter")    openTrades[idx].entryFeatures[5] = agents[i].lastZScore;
+            openTrades[idx].entryFeatures[2] = agents[i].lastZScore;
+         else if(agents[i].name == "Momentum")
+            openTrades[idx].entryFeatures[3] = agents[i].lastZScore;
+         else if(agents[i].name == "Consensus")
+            openTrades[idx].entryFeatures[4] = agents[i].lastZScore;
+         else if(agents[i].name == "Hunter")
+            openTrades[idx].entryFeatures[5] = agents[i].lastZScore;
       }
       openTrades[idx].entryFeatures[6] = SHARED_regimeAgreement;
       openTrades[idx].entryFeatures[7] = SHARED_trendStrength;
@@ -600,7 +625,10 @@ void OnTradeClose(int ticket, double closePrice, string exitReason = "MANUAL") {
       // Trova il trade nell'array
       int idx = -1;
       for(int i = 0; i < maxOpenTrades; i++) {
-          if(openTrades[i].active && openTrades[i].ticket == ticket) { idx = i; break; }
+         if(openTrades[i].active && openTrades[i].ticket == ticket) {
+            idx = i;
+            break;
+         }
       }
       if(idx < 0) return;
 
@@ -673,15 +701,21 @@ UpdateCorrelation(i, openTrades[idx].entryZScores[i], actualReturnZ);
          double target[NN_TARGETS];
          for(int f = 0; f < NN_FEATURES; f++) features[f] = openTrades[idx].entryFeatures[f];
 
-           target[0] = 0; target[1] = 0; target[2] = 0;
+         target[0] = 0;
+         target[1] = 0;
+         target[2] = 0;
          int tradeDir = openTrades[idx].isBuy ? 1 : -1;
 
          if(actualReturn > 0) {
-              if(tradeDir == 1)     target[0] = 1;
+            if(tradeDir == 1)
-              else                  target[2] = 1;
+               target[0] = 1;
+            else
+               target[2] = 1;
          } else {
-              if(tradeDir == 1)     target[2] = 1;
+            if(tradeDir == 1)
-              else                  target[0] = 1;
+               target[2] = 1;
+            else
+               target[0] = 1;
          }
 
          double sampleWeight = MathAbs(actualReturn) + 1.0; // trade profittevoli/perdenti pesano di più
@@ -970,7 +1004,10 @@ UpdateHealth(actualReturn);
       bool allHaveSamples = true;
       maxDeltaRho = 0;
       for(int i = 0; i < agentCount; i++) {
-         if(corrCount[i] < corrMinSamples) { allHaveSamples = false; break; }
+         if(corrCount[i] < corrMinSamples) {
+            allHaveSamples = false;
+            break;
+         }
          double rho = GetCorrelation(i);
          double delta = MathAbs(rho - prevCorr[i]);
          if(delta > maxDeltaRho) maxDeltaRho = delta;
@@ -1007,8 +1044,7 @@ UpdateHealth(actualReturn);
             // z_0.95 ≈ invNormalCDF(0.95) tramite approssimazione di Abramowitz & Stegun 26.2.23
             double p05 = 0.95;
             double t = MathSqrt(-2.0 * MathLog(1.0 - p05));
-              double z095 = t - (2.515517 + 0.802853*t + 0.010328*t*t)
+            double z095 = t - (2.515517 + 0.802853 * t + 0.010328 * t * t) / (1.0 + 1.432788 * t + 0.189269 * t * t + 0.001308 * t * t * t);
-                              / (1.0 + 1.432788*t + 0.189269*t*t + 0.001308*t*t*t);
             if(sharpeZ > z095)
                Print("  ✓ Sharpe significativamente positivo (z=", StringFormat("%.2f", sharpeZ), ")");
          }
@@ -1078,7 +1114,10 @@ UpdateHealth(actualReturn);
       double maxTotalRisk = bal * m_riskTotal;
       double usedRisk = TotalRiskUsed();
       double remainingRisk = maxTotalRisk - usedRisk;
-         if(remainingRisk <= 0) { Print("  Risk budget esaurito (", StringFormat("%.2f", usedRisk), "/", StringFormat("%.2f", maxTotalRisk), ")"); return 0; }
+      if(remainingRisk <= 0) {
+         Print("  Risk budget esaurito (", StringFormat("%.2f", usedRisk), "/", StringFormat("%.2f", maxTotalRisk), ")");
+         return 0;
+      }
       riskAmount = MathMin(riskAmount, remainingRisk);
 
       // Costo in valuta conto per 1 lotto a questa distanza SL
@@ -1115,8 +1154,10 @@ UpdateHealth(actualReturn);
          int jj = (ii - lag + MAX_HISTORY) % MAX_HISTORY;
          double x = history[ii].combinedZ;
          double y = history[jj].combinedZ;
-         sum += x; sumSq += x*x;
+         sum += x;
-         sumShift += y; sumSqShift += y*y;
+         sumSq += x * x;
+         sumShift += y;
+         sumSqShift += y * y;
          sumCov += x * y;
       }
       double mean = sum / cnt, meanS = sumShift / cnt;
@@ -1188,8 +1229,10 @@ UpdateHealth(actualReturn);
       string fn = (m_modelFilename != "") ? m_modelFilename
                                           : "TR_Agent_NN_" + Symbol() + "_" + EnumToString(Period()) + "_" + m_runTimestamp + ".dat";
       bool ok = m_neuralNet.Save(fn);
-       if(ok) Print("NN model saved: ", fn);
+      if(ok)
-       else   Print("NN model save FAILED: ", fn);
+         Print("NN model saved: ", fn);
+      else
+         Print("NN model save FAILED: ", fn);
       return ok;
    }
 
@@ -1209,9 +1252,7 @@ UpdateHealth(actualReturn);
    }
 
    bool IsNeuralReady() const {
-       return m_useNeural && m_neuralNet != NULL 
+      return m_useNeural && m_neuralNet != NULL && m_neuralNet.IsInitialized() && m_neuralNet.EpochsTrained() > 0;
-              && m_neuralNet.IsInitialized() 
-              && m_neuralNet.EpochsTrained() > 0;
    }
 
    string NeuralInfo() const {
@@ -1230,10 +1271,7 @@ UpdateHealth(actualReturn);
          int biasN = agents[i].predictionError.Count();
          double rho = agents[i].predCorr.Ready() ? agents[i].predCorr.Correlation() : 0;
          int rhoN = agents[i].predCorr.Count();
-       FileWriteString(fh, agents[i].name + ","
+         FileWriteString(fh, agents[i].name + "," + StringFormat("%+.6f", bias) + "," + (string)biasN + "," + StringFormat("%+.6f", rho) + "," + (string)rhoN + "," + StringFormat("%.6f", agents[i].weight) + "\r\n");
-            + StringFormat("%+.6f", bias) + "," + (string)biasN + ","
-            + StringFormat("%+.6f", rho) + "," + (string)rhoN + ","
-            + StringFormat("%.6f", agents[i].weight) + "\r\n");
       }
       FileClose(fh);
       Print("Agent learning CSV saved: ", fn);
@@ -1390,7 +1428,10 @@ UpdateHealth(actualReturn);
    void SaveAnalysisCSV(string symbol, ENUM_TIMEFRAMES tf) const {
       string fn = "TR_Agent_Analysis_" + symbol + "_" + EnumToString(tf) + "_" + m_runTimestamp + ".csv";
       int fh = FileOpen(fn, FILE_TXT | FILE_WRITE | FILE_COMMON);
-        if(fh == INVALID_HANDLE) { Print("SaveAnalysis: errore apertura ", fn); return; }
+      if(fh == INVALID_HANDLE) {
+         Print("SaveAnalysis: errore apertura ", fn);
+         return;
+      }
 
       // ===================== INTESTAZIONE =====================
       FileWriteString(fh, "=== TR_Agent Analysis Report ===\r\n");
@@ -1516,7 +1557,10 @@ UpdateHealth(actualReturn);
    void SaveBarHistoryCSV(string symbol, ENUM_TIMEFRAMES tf) const {
       string fn = "BarHistory_" + symbol + "_" + EnumToString(tf) + "_" + m_runTimestamp + ".csv";
       int fh = FileOpen(fn, FILE_TXT | FILE_WRITE | FILE_COMMON);
-        if(fh == INVALID_HANDLE) { Print("BarHistoryCSV: errore apertura ", fn); return; }
+      if(fh == INVALID_HANDLE) {
+         Print("BarHistoryCSV: errore apertura ", fn);
+         return;
+      }
 
       string header = "bar,time,combinedZ";
       string agentNames[MAX_AGENTS];
@@ -1550,7 +1594,10 @@ UpdateHealth(actualReturn);
    void SaveTradeHistoryCSV(string symbol, ENUM_TIMEFRAMES tf) const {
       string fn = "TradeHistory_" + symbol + "_" + EnumToString(tf) + "_" + m_runTimestamp + ".csv";
       int fh = FileOpen(fn, FILE_TXT | FILE_WRITE | FILE_COMMON);
-        if(fh == INVALID_HANDLE) { Print("TradeHistoryCSV: errore apertura ", fn); return; }
+      if(fh == INVALID_HANDLE) {
+         Print("TradeHistoryCSV: errore apertura ", fn);
+         return;
+      }
 
       string header = "ticket,isBuy,entryTime,closeTime,barsHeld,entryPrice,closePrice,entryATR,entryZ,slPrice,highestPrice,lowestPrice,maeATR,mfeATR,actualReturn,exitReason";
       string agentNames[MAX_AGENTS];
@@ -1618,7 +1665,10 @@ UpdateHealth(actualReturn);
    void SaveAgentInteractionCSV(string symbol, ENUM_TIMEFRAMES tf) const {
       string fn = "AgentInteraction_" + symbol + "_" + EnumToString(tf) + "_" + m_runTimestamp + ".csv";
       int fh = FileOpen(fn, FILE_TXT | FILE_WRITE | FILE_COMMON);
-        if(fh == INVALID_HANDLE) { Print("AgentInteractionCSV: errore apertura ", fn); return; }
+      if(fh == INVALID_HANDLE) {
+         Print("AgentInteractionCSV: errore apertura ", fn);
+         return;
+      }
 
       string header = "bar,time,combinedZ";
       for(int i = 0; i < agentCount; i++) {
@@ -1651,7 +1701,10 @@ UpdateHealth(actualReturn);
    void SaveDecisionLogCSV(string symbol, ENUM_TIMEFRAMES tf) const {
       string fn = "DecisionLog_" + symbol + "_" + EnumToString(tf) + "_" + m_runTimestamp + ".csv";
       int fh = FileOpen(fn, FILE_TXT | FILE_WRITE | FILE_COMMON);
-        if(fh == INVALID_HANDLE) { Print("DecisionLogCSV: errore apertura ", fn); return; }
+      if(fh == INVALID_HANDLE) {
+         Print("DecisionLogCSV: errore apertura ", fn);
+         return;
+      }
 
       FileWriteString(fh, "time,action,direction,zScore,combinedZ,price,ticket,agreeingCount,totalAgents,confidence,minZ\r\n");
       for(int d = 0; d < decisionCount; d++) {