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