MultiAgentTest v7: zero magic constants, neural orchestrator, multi-position, agent interaction fixes

2026-06-13 13:58:38 +02:00
commit 5c00f17121
19 changed files with 5522 additions and 0 deletions
@@ -0,0 +1,112 @@
+#ifndef AGENT_BASE_MQH
+#define AGENT_BASE_MQH
+#include "../Core/MarketData.mqh"
+#include "../Core/Statistics.mqh"
+
+double SHARED_regimeZ = 0.0;
+double SHARED_regimeH = 0.5;
+double SHARED_trendStrength = 0.0;
+double SHARED_adxZ = 0.0;
+double SHARED_adxRaw = 0.0;
+double SHARED_regimeConsensus = 0.0;
+double SHARED_regimeAgreement = 0.0;
+double SHARED_patternCode = 0.0;
+string SHARED_patternName = "";
+
+class IAgent {
+public:
+   string         name;
+   double         weight;
+   bool           enabled;
+   string         symbol;
+   ENUM_TIMEFRAMES timeframe;
+
+   double         lastZScore;
+   double         lastRawSignal;
+   KalmanNormalizer signalStats;
+
+   // --- Learning infrastructure ---
+   RunningStats   predictionError;    // predictedZ - actualReturnZ (bias)
+   RunningCorrelation predCorr;        // correlation predictedZ vs actualReturnZ
+   int            learnMinSamples;    // minimo campioni prima di applicare learning
+
+   IAgent(string n, double w=1.0)
+      : name(n), weight(w), enabled(true), symbol(""), timeframe(PERIOD_CURRENT),
+        lastZScore(0), lastRawSignal(0), signalStats(0.001, 1.0, 30),
+        predictionError(0.1, 5, 500), predCorr(0.1, 5), learnMinSamples(5) {}
+
+   virtual ~IAgent() {}
+
+   virtual void Init(string sym, ENUM_TIMEFRAMES tf) {
+      symbol = sym;
+      timeframe = tf;
+   }
+
+   virtual void Release() {}
+
+   virtual double Analyze(const MarketData &data) = 0;
+
+   virtual void Interact(IAgent *&allAgents[], int count) {}
+
+   // --- Apprendimento da trade outcome ---
+   // predictedZ = z-score dell'agente al momento dell'entrata
+   // actualReturnZ = ritorno normalizzato del trade chiuso
+   virtual void Learn(double predictedZ, double actualReturnZ) {
+      predictionError.Update(predictedZ - actualReturnZ);
+      predCorr.Update(predictedZ, actualReturnZ);
+   }
+
+   // Applica bias correction + confidence scaling a un raw z-score
+   double CalibrateZ(double rawZ) {
+      if(predictionError.Count() < learnMinSamples) return rawZ;
+
+      double corrected = rawZ;
+
+      // Bias correction: solo se statisticamente significativo (> 2 SE)
+      double bias = predictionError.Mean();
+      double biasSE = predictionError.Std() / MathSqrt((double)predictionError.Count());
+      if(MathAbs(bias) > 2.0 * biasSE) {
+         corrected -= bias * 0.3;
+      }
+
+      // Confidence scaling: se correlazione bassa o negativa, riduci magnitudine
+      if(predCorr.Ready()) {
+         double rho = predCorr.Correlation();
+         if(rho < 0.2) {
+            corrected *= MathMax(0.05, MathMax(0.0, rho) / 0.2);
+         }
+      }
+
+      return corrected;
+   }
+
+   virtual void Save(int fh) const {
+      FileWriteInteger(fh, 3);
+      signalStats.Save(fh);
+      predictionError.Save(fh);
+      predCorr.Save(fh);
+   }
+   virtual void Load(int fh) {
+      int ver = FileReadInteger(fh);
+      if(ver == 3) {
+         signalStats.Load(fh);
+         predictionError.Load(fh);
+         predCorr.Load(fh);
+      }
+      else if(ver == 2) {
+         signalStats.Load(fh);
+      }
+   }
+
+   virtual void Reset() {
+      signalStats.Reset();
+      predictionError.Reset();
+      lastZScore = 0;
+      lastRawSignal = 0;
+   }
+
+   virtual string SignalInfo() const {
+      return name + " z=" + StringFormat("%+.3f", lastZScore);
+   }
+};
+#endif