pietro_giacobazzi
472 lines
19 KiB
Plaintext
472 lines
19 KiB
Plaintext
#property copyright "MultiAgent Test v7"
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#property version "7.00"
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#property description "6 agenti: Hurst, ADX, Consensus, MA, Momentum, Hunter"
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#property description "Neural orchestrator (CNeuralNet) + Pattern hunter cross-agente"
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#include <Trade\Trade.mqh>
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#include <Trade\PositionInfo.mqh>
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#include "Core\Orchestrator.mqh"
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#include "Agents\RegimeDetector.mqh"
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#include "Agents\RegimeADX.mqh"
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#include "Agents\RegimeConsensus.mqh"
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#include "Agents\PatternHunter.mqh"
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#include "Agents\MAAgent.mqh"
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#include "Agents\MomentumAgent.mqh"
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input string Inp_Symbol = ""; // Simbolo da testare (vuoto = simbolo corrente)
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input ENUM_TIMEFRAMES Inp_TF = PERIOD_CURRENT; // Timeframe del test
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input double Inp_MinZ = 0.0; // Min |z| per aprire trade (0 = soglia adattiva automatica)
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input double Inp_WMin = 0.0; // Peso minimo per agente (0 = 1/(N*0.1) automatico)
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input int Inp_BufferBars = 0; // Barre buffer storico (0 = max(HurstPeriod*10, 300))
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input double Inp_SLRiskATR = 0.0; // SL in ATR (0 = SL adattivo basato su MAE storico)
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input double Inp_TPRiskATR = 0.0; // TP in ATR (0 = stesso fattore dello SL)
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input int Inp_HurstPeriod = 0; // Periodo Hurst (0 = 200)
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input int Inp_ADXPeriod = 0; // Periodo ADX (0 = 14)
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input bool Inp_UseNeural = false; // Usa Neural Network invece di softmax pesato
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input string Inp_NNModelFile = ""; // File modello NN da caricare (vuoto = auto)
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input bool Inp_TrainMode = false; // Addestra NN durante il test (colleziona campioni)
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input int Inp_TrainEpochs = 100; // Epoche di training NN per sessione
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input double Inp_TrainLR = 0.001; // Learning rate NN
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input int Inp_NNHidden = 6; // Neuroni hidden layer NN
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input double Inp_RiskPerTrade = 0.01; // % capitale da rischiare per trade (0.01 = 1%)
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input double Inp_RiskTotal = 0.05; // % capitale massima in rischio su TUTTE le posizioni
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input bool Inp_UseReversalClose = true; // Chiudi posizione se segnale opposto (false = long+short coesistono)
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Orchestrator *orchestrator;
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CTrade *trade;
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CPositionInfo posInfo;
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string sym;
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int dig;
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datetime lastBarTime = 0;
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int lastBarTotal = 0;
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int bufferBars = 0;
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int magicNumber;
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// Hash semplice per magic number
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int StringHash(string s) {
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int h = 0;
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int len = StringLen(s);
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for(int i=0; i<len; i++) {
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h = (h * 31 + (int)StringGetCharacter(s, i)) % 999999;
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}
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return h;
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}
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int OnInit() {
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int nAgents = 6; // Hurst, ADX, MA, Momentum, Consensus, Hunter
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// Buffer bars auto: HurstMaxPeriod * 10
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int hurstMaxP = (Inp_HurstPeriod > 0) ? Inp_HurstPeriod : 200;
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bufferBars = (Inp_BufferBars > 0) ? Inp_BufferBars : MathMax(300, hurstMaxP * 10);
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// Peso minimo auto: 1/agentCount * 0.1
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double wMinAuto = 1.0 / nAgents * 0.1;
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double wMin = (Inp_WMin > 0.0) ? Inp_WMin : wMinAuto;
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sym = (Inp_Symbol == "") ? _Symbol : Inp_Symbol;
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magicNumber = StringHash(sym + (string)Inp_TF + "MAT") % 999999;
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if(magicNumber < 10000) magicNumber += 10000;
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// weightAlpha: 1/(N+10) per N agenti
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orchestrator = new Orchestrator(Inp_MinZ, wMin, 1.0 / (nAgents + 10),
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Inp_UseNeural, Inp_NNModelFile,
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Inp_TrainMode, Inp_TrainEpochs,
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Inp_TrainLR, Inp_NNHidden,
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Inp_RiskPerTrade, Inp_RiskTotal);
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trade = new CTrade();
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dig = (int)SymbolInfoInteger(sym, SYMBOL_DIGITS);
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trade.SetExpertMagicNumber(magicNumber);
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orchestrator.AddAgent(new RegimeDetector("Hurst", 1.0, Inp_HurstPeriod));
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orchestrator.AddAgent(new RegimeADX("ADX", 1.0, Inp_ADXPeriod));
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orchestrator.AddAgent(new RegimeConsensus("Consensus", 1.0));
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orchestrator.AddAgent(new MAAgent("MA", 1.0, 8, 40));
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orchestrator.AddAgent(new MomentumAgent("Momentum", 1.0, 6, 40));
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orchestrator.AddAgent(new PatternHunter("Hunter", 1.0));
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orchestrator.InitAgents(sym, Inp_TF);
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orchestrator.LoadState(sym, Inp_TF);
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// Carica o inizializza modello NN
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if(Inp_UseNeural || Inp_TrainMode) {
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if(!orchestrator.LoadNNModel()) {
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if(Inp_UseNeural) {
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Print(" NN model not found, initializing fresh network...");
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}
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orchestrator.InitNeuralNet(8, Inp_NNHidden, 3, Inp_UseNeural);
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}
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}
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string modeStr = Inp_UseNeural ? "NEURAL" : "SOFTMAX";
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Print("MultiAgentTest v7 avviato: ", sym, " ", EnumToString(Inp_TF), " [", modeStr, "]");
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Print("Agenti: ", orchestrator.TotalAgents(), " (Hurst, ADX, MA, Momentum, Consensus, Hunter)");
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Print("Min |z|: ", Inp_MinZ, " | Buffer: ", bufferBars, " barre | Neural: ", modeStr);
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// Warm-up statistiche: passa i dati storici per inizializzare le EWMA
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MarketData warmup(sym, Inp_TF, bufferBars);
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if(warmup.Fetch()) {
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Print("Warm-up statistiche... (", warmup.count, " barre)");
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// Processa ogni barra storica una volta, dalla più vecchia alla più recente
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for(int bar = warmup.count - 1; bar >= 1; bar--) {
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MarketData single(sym, Inp_TF, 1);
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single.open[0] = warmup.open[bar];
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single.high[0] = warmup.high[bar];
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single.low[0] = warmup.low[bar];
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single.close[0] = warmup.close[bar];
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single.volume[0] = warmup.volume[bar];
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single.time[0] = warmup.time[bar];
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single.count = 1;
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orchestrator.Analyze(single);
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}
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// Ultima barra (current) processata una volta
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orchestrator.Analyze(warmup);
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Print("Hurst: H=", StringFormat("%.3f", SHARED_regimeH),
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" ADX: ", StringFormat("%.1f", SHARED_adxRaw),
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" Consensus: ", StringFormat("%+.2f", SHARED_regimeConsensus),
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" pattern: ", SHARED_patternName);
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}
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if(Inp_UseNeural) {
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if(orchestrator.IsNeuralReady()) {
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Print(" Neural: READY — ", orchestrator.NeuralInfo());
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} else {
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Print(" Neural: INITIALIZED (untrained — will fallback to softmax)");
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}
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}
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if(Inp_TrainMode) Print(" TrainMode: ON (collecting samples)");
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Print(orchestrator.TotalAgents(), " agenti pronti.");
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return INIT_SUCCEEDED;
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}
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void OnDeinit(const int reason) {
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CloseAllPositions();
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Print("=== MultiAgentTest: Final Report ===");
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Print(" Reason: ", reasonToStr(reason));
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Print(" Symbol: ", sym, " | TF: ", EnumToString(Inp_TF));
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Print(" Mode: ", Inp_UseNeural ? "NEURAL" : "SOFTMAX",
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" | TrainMode: ", Inp_TrainMode ? "ON" : "OFF");
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Print(" Trades: ", orchestrator.TradeCount(),
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" | Win rate: ", orchestrator.TradeCount() > 0
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? StringFormat("%.1f%%", 100.0 * orchestrator.WinCount() / orchestrator.TradeCount())
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: "N/A");
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PrintAgentStats();
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// Neural: training e salvataggio
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bool nnTrained = false;
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if(Inp_TrainMode && orchestrator.TradeCount() >= 3) {
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Print("=== Neural Network Training (fine backtest) ===");
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Print(" Trade count: ", orchestrator.TradeCount());
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double loss = orchestrator.TrainNN();
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if(loss >= 0) {
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nnTrained = true;
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Print(" Training complete: loss=", StringFormat("%.6f", loss));
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PrintNeuralStats();
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} else {
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Print(" Training skipped: insufficient samples (need >=3)");
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}
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}
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// Agent learning summary
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orchestrator.PrintAgentLearningSummary();
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orchestrator.SaveAgentLearningCsv();
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// Per-bar agent z-scores, combinedZ, SHARED variables
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orchestrator.SaveBarHistoryCSV(sym, Inp_TF);
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// Per-bar agent weights, biases, correlations, raw signals
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orchestrator.SaveAgentInteractionCSV(sym, Inp_TF);
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// Per-trade details with PnL, exitReason, full features
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orchestrator.SaveTradeHistoryCSV(sym, Inp_TF);
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// Entry/exit decision log
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orchestrator.SaveDecisionLogCSV(sym, Inp_TF);
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// Analisi completa: tutti i parametri derivati, soglie, periodi, stats
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orchestrator.SaveAnalysisCSV(sym, Inp_TF);
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// Save state file (include NN weights inline se addestrata)
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orchestrator.SaveState(sym, Inp_TF);
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// Salva anche NN standalone (utile per debug/backup)
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if(nnTrained || orchestrator.IsNeuralReady())
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orchestrator.SaveNNModel();
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orchestrator.ReleaseAgents();
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delete orchestrator;
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delete trade;
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Print("=== MultiAgentTest terminato ===");
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}
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string reasonToStr(int r) {
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switch(r) {
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case REASON_PROGRAM: return "Program";
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case REASON_REMOVE: return "Remove";
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case REASON_CHARTCLOSE: return "Chart Close";
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case REASON_PARAMETERS: return "Parameters Changed";
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case REASON_RECOMPILE: return "Recompiled";
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case REASON_ACCOUNT: return "Account Changed";
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case REASON_TEMPLATE: return "Template";
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case REASON_INITFAILED: return "Init Failed";
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case REASON_CLOSE: return "Terminal Close";
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default: return "Unknown (" + (string)r + ")";
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}
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}
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void PrintAgentStats() {
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Print(" Hurst H: ", StringFormat("%.3f", SHARED_regimeH),
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" | ADX: ", StringFormat("%.1f", SHARED_adxRaw),
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" | Consensus: ", StringFormat("%+.2f", SHARED_regimeConsensus),
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" | Agreement: ", StringFormat("%.2f", SHARED_regimeAgreement),
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" | Pattern: ", SHARED_patternName);
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}
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void PrintNeuralStats() {
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Print(" " + orchestrator.NeuralInfo());
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// Feature importanza approssimata: media |W1| per input
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if(orchestrator.IsNeuralReady()) {
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// Non possiamo accedere direttamente ai pesi
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Print(" (vedi CSV per loss history e training samples)");
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}
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}
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int CountPositions() {
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int count = 0;
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for(int i=PositionsTotal()-1; i>=0; i--) {
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if(posInfo.SelectByIndex(i)) {
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if(posInfo.Symbol() == sym && posInfo.Magic() == magicNumber)
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count++;
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}
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}
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return count;
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}
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void CloseAllPositions() {
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for(int i=PositionsTotal()-1; i>=0; i--) {
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if(posInfo.SelectByIndex(i)) {
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if(posInfo.Symbol() == sym && posInfo.Magic() == magicNumber)
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trade.PositionClose(posInfo.Ticket());
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}
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}
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}
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void OnTick() {
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if(!IsNewBar()) return;
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MarketData data(sym, Inp_TF, bufferBars);
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if(!data.Fetch()) return;
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double z = orchestrator.Analyze(data);
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FinalSignal fs = orchestrator.GetFinalSignal(Inp_MinZ);
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double minZ = (Inp_MinZ > 0) ? Inp_MinZ : orchestrator.AdaptiveMinZ();
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if(fs.IsActionable(minZ)) {
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ManagePositions(data, fs);
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}
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LogSignal(data, fs);
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if(CountPositions() == 0 && orchestrator.HasOpenTrade()) {
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orchestrator.ResetTradeState();
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}
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}
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bool IsNewBar() {
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int barsTotal = Bars(sym, Inp_TF);
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datetime timeArr[];
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CopyTime(sym, Inp_TF, 0, 1, timeArr);
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if(ArraySize(timeArr) < 1) return false;
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datetime barTime = timeArr[0];
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if(lastBarTime == 0) {
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lastBarTime = barTime;
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lastBarTotal = barsTotal;
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return false;
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}
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if(barTime != lastBarTime || barsTotal != lastBarTotal) {
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lastBarTime = barTime;
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lastBarTotal = barsTotal;
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return true;
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}
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return false;
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}
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void ManagePositions(const MarketData &data, const FinalSignal &fs) {
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double minZ = (Inp_MinZ > 0) ? Inp_MinZ : orchestrator.AdaptiveMinZ();
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// 1. Update MAE/MFE per tutti gli aperti (traccia high/low intra-barra)
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orchestrator.UpdateOpenTrades(data.High(0), data.Low(0));
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orchestrator.TrailStops();
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// 2. Reversal close (opzionale): chiudi posizioni con segnale opposto
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// Se disabilitato, long e short coesistono — massimizza profitto multi-direzionale
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if(Inp_UseReversalClose) {
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int closeTickets[];
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orchestrator.GetTradesToClose(closeTickets, fs.zScore, minZ);
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for(int c = 0; c < ArraySize(closeTickets); c++) {
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int tkt = closeTickets[c];
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if(PositionSelectByTicket(tkt)) {
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bool isBuy = orchestrator.IsBuyTrade(tkt);
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double closePrice = isBuy ? SymbolInfoDouble(sym, SYMBOL_BID) : SymbolInfoDouble(sym, SYMBOL_ASK);
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orchestrator.OnTradeClose(tkt, closePrice, "REVERSAL");
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trade.PositionClose(tkt);
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}
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}
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}
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// 3. Aggiorna trailing stop sul terminale (tester supporta PositionModify)
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for(int i=PositionsTotal()-1; i>=0; i--) {
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if(posInfo.SelectByIndex(i)) {
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if(posInfo.Symbol() != sym || posInfo.Magic() != magicNumber) continue;
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int tkt = (int)posInfo.Ticket();
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double newSL = orchestrator.GetTradeSL(tkt);
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if(newSL <= 0) continue;
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double curSL = posInfo.StopLoss();
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bool isBuy = posInfo.PositionType() == POSITION_TYPE_BUY;
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bool shouldUpdate = isBuy ? (newSL > curSL + _Point) : (newSL < curSL - _Point);
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if(shouldUpdate) {
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double curTP = posInfo.TakeProfit();
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trade.PositionModify(tkt, newSL, curTP);
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}
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}
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}
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// 4. Apri nuova posizione OGNI BARRA con segnale actionable (aggressivo)
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if(MathAbs(fs.zScore) > minZ) {
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double ask = SymbolInfoDouble(sym, SYMBOL_ASK);
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double bid = SymbolInfoDouble(sym, SYMBOL_BID);
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double price = (fs.direction == 1) ? ask : bid;
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// ATR period: scala con N agenti e buffer disponibile
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int atrPeriod = MathMax(orchestrator.TotalAgents() + 1, MathMin(bufferBars / 15, 50));
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double atr = data.ATR(atrPeriod);
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if(atr <= 0) return;
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// SL adattivo via MAE (trailing stop gestisce l'uscita, no TP fisso)
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double slWidth = (Inp_SLRiskATR > 0) ? Inp_SLRiskATR : orchestrator.AdaptiveSLWidth();
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double sl = (fs.direction == 1) ? price - atr * slWidth
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: price + atr * slWidth;
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sl = NormalizeDouble(sl, dig);
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double slPoints = price - sl;
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if(fs.direction == -1) slPoints = sl - price;
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if(slPoints <= 0) { Print("SL troppo stretto — skip"); return; }
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// Posizione sizing risk-based tramite Orchestrator
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double lot = orchestrator.CalcRiskLot(fs.zScore, slPoints);
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if(lot <= 0) { Print("Lot calcolato = 0 — skip"); return; }
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// Solo il margine libero limita i trade
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double freeMargin = AccountInfoDouble(ACCOUNT_MARGIN_FREE);
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double marginReq = lot * SymbolInfoDouble(sym, SYMBOL_MARGIN_INITIAL);
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if(marginReq >= freeMargin && freeMargin > 0) {
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Print("Margine insufficiente: lot=", lot, " free=", freeMargin, " req=", marginReq);
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return; // Skip — solo il margine blocca i trade
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}
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int ticket = 0;
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// Calcolo TakeProfit opzionale (usiamo lo stesso fattoreATR per TP)
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double tp = 0;
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// TakeProfit: se impostato usa Inp_TPRiskATR, altrimenti usa lo stesso fattore di SL (dynamic)
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double tpWidth = (Inp_TPRiskATR > 0) ? Inp_TPRiskATR : ((Inp_SLRiskATR > 0) ? Inp_SLRiskATR : orchestrator.AdaptiveSLWidth());
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if(tpWidth > 0) {
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tp = (fs.direction == 1) ? price + atr * tpWidth : price - atr * tpWidth;
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tp = NormalizeDouble(tp, dig);
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}
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if(fs.direction == 1) {
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// BUY – validate SL and TP
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if(sl <= 0 || sl >= ask) {
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Print("Invalid BUY SL (", sl, ") – order skipped");
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} else if(tp > 0 && tp <= sl) {
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Print("Invalid BUY TP (", tp, ") – must be > SL – order skipped");
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} else {
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ticket = trade.Buy(lot, sym, ask, sl, tp); // TP may be 0 (no TP)
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}
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} else {
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// SELL – validate SL and TP
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if(sl <= 0 || sl <= bid) {
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Print("Invalid SELL SL (", sl, ") – order skipped");
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} else if(tp > 0 && tp >= sl) {
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Print("Invalid SELL TP (", tp, ") – must be < SL – order skipped");
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} else {
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ticket = trade.Sell(lot, sym, bid, sl, tp);
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}
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}
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if(ticket > 0) {
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orchestrator.OnTradeOpen(ticket, price, atr, lot);
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}
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}
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}
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void LogSignal(const MarketData &data, const FinalSignal &fs) {
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string timeStr = TimeToString(TimeCurrent(), TIME_DATE|TIME_MINUTES);
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string dir = "NONE";
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if(fs.direction == 1) dir = "BUY";
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if(fs.direction == -1) dir = "SELL";
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string mode = Inp_UseNeural && orchestrator.IsNeuralReady() ? "NN" : "SM";
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string log = timeStr + " [" + mode + "] " + dir;
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log += " | z=" + StringFormat("%+.3f", fs.zScore);
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log += " | conf=" + StringFormat("%.0f%%", fs.confidence*100);
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log += " | agree=" + (string)fs.agreeingCount + "/" + (string)fs.totalAgents;
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static int printCounter = 0;
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if(printCounter % 5 == 0) {
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orchestrator.PrintAgentStatus();
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if(Inp_UseNeural)
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Print(" ", orchestrator.NeuralInfo());
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}
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printCounter++;
|
||
|
||
// Log decision even for non-actionable signals
|
||
double minZ = (Inp_MinZ > 0) ? Inp_MinZ : orchestrator.AdaptiveMinZ();
|
||
orchestrator.LogDecision("SIGNAL", fs.direction, minZ, data.Close(0));
|
||
|
||
if(MathAbs(fs.zScore) > Inp_MinZ) {
|
||
Print(log);
|
||
Print(" Agents: ", fs.contributingAgents);
|
||
}
|
||
}
|
||
|
||
// Rileva chiusure (SL/TP) fatte dal broker — multi-trade
|
||
void OnTrade() {
|
||
static int onTradeCloses = 0;
|
||
static int saveInterval = 0;
|
||
if(saveInterval == 0) {
|
||
// Minimum 5 trades, scaled with number of agents
|
||
saveInterval = MathMax(5, orchestrator.TotalAgents() * 5);
|
||
}
|
||
if(!orchestrator.HasOpenTrade()) return;
|
||
int maxSlots = orchestrator.MaxTradeSlots();
|
||
for(int t = 0; t < maxSlots; t++) {
|
||
int tkt = orchestrator.GetTrackedTicket(t);
|
||
if(tkt < 0) break; // no more active trades
|
||
bool found = false;
|
||
for(int p = PositionsTotal() - 1; p >= 0; p--) {
|
||
if(posInfo.SelectByIndex(p)) {
|
||
if((int)posInfo.Ticket() == tkt) { found = true; break; }
|
||
}
|
||
}
|
||
if(!found) {
|
||
// Trade closed by SL/TP (not by ManagePositions)
|
||
bool isBuy = orchestrator.IsBuyTrade(tkt);
|
||
double closePrice = isBuy ? SymbolInfoDouble(sym, SYMBOL_BID) : SymbolInfoDouble(sym, SYMBOL_ASK);
|
||
orchestrator.OnTradeClose(tkt, closePrice, "SLTP");
|
||
onTradeCloses++;
|
||
Print("SL/TP chiuso trade #", tkt, " (totale SL/TP: ", onTradeCloses, ")");
|
||
if(onTradeCloses % saveInterval == 0)
|
||
orchestrator.SaveState(sym, Inp_TF);
|
||
}
|
||
}
|
||
}
|
||
|
||
double OnTester() {
|
||
double sharpe = orchestrator.TradeSharpe();
|
||
int trades = orchestrator.TradeCount();
|
||
Print("OnTester: ", trades, " trade, Sharpe=", StringFormat("%.3f", sharpe));
|
||
return sharpe;
|
||
}
|