mql5/Experts/NeuralNetEA.mq5

//+------------------------------------------------------------------+
//|                                                  NeuralNetEA.mq5 |
//|                                    Copyright 2025, Google Gemini |
//|                                             https://www.mql5.com |
//+------------------------------------------------------------------+
#property copyright "Copyright 2025, Google Gemini"
#property link      "https://www.google.com"
#property version   "3.00"

#include <Trade\Trade.mqh>
#include <NeuralNet.mqh>

input string   InpModelFile   = "PriceActionNN_v3.bin"; 
input double   InpConfidence  = 0.8; 
input double   InpLotSize     = 0.1; 
input int      InpMagic       = 123456;     

// --- Model Parameters (Loaded from File) ---
int    gLookback;
int    gAsianStart, gAsianEnd;
int    gLondonStart, gLondonEnd;
int    gNYStart, gNYEnd;
double gTargetTP_Mult, gTargetSL_Mult;

CTrade Trade;
CNeuralNet *Net;
int GlobalTopology[];
datetime lastBarTime = 0;
int handleEMA50, handleEMA200, handleATR_D1;

// Forward Decl
bool LoadModelWithHeader(string filename);

int OnInit()
{
   Trade.SetExpertMagicNumber(InpMagic);
   handleEMA50  = iMA(_Symbol, _Period, 50, 0, MODE_EMA, PRICE_CLOSE);
   handleEMA200 = iMA(_Symbol, _Period, 200, 0, MODE_EMA, PRICE_CLOSE);
   handleATR_D1 = iATR(_Symbol, PERIOD_D1, 14);
   if(handleEMA50 == INVALID_HANDLE || handleEMA200 == INVALID_HANDLE || handleATR_D1 == INVALID_HANDLE) return(INIT_FAILED);
   
   if(!LoadModelWithHeader(InpModelFile)) {
       Print("Fatal Error: Could not load model and metadata.");
       return(INIT_FAILED);
   }
   
   Print("Model Loaded Successfully.");
   PrintFormat("Config: Lookback=%d, Risk=%.1f/%.1f ATR", gLookback, gTargetSL_Mult, gTargetTP_Mult);
   
   return(INIT_SUCCEEDED);
}

void OnDeinit(const int reason)
{
   if(CheckPointer(Net) != POINTER_INVALID) delete Net;
   IndicatorRelease(handleEMA50);
   IndicatorRelease(handleEMA200);
   IndicatorRelease(handleATR_D1);
}

// Optimized GetSession: Only scans necessary period
void GetSession(MqlRates &rates[], int currentIdx, int startHour, int endHour, double &H, double &L)
{
    // Need Past Finished Session.
    // currentIdx is index in 'rates'. rates[0] is newest.
    // If current Hour >= endHour, we scan today (until index reaches endHour).
    // If current Hour < endHour, we scan Yesterday.
    
    // Simplification for EA: 
    // Just scan 24h back from currentIdx, and verify Hour check.
    
    H = -1; L = 999999;
    bool found = false;
    
    datetime t = rates[currentIdx].time;
    MqlDateTime dt; TimeToStruct(t, dt);
    int curH = dt.hour;
    
    int scanStart = 0; // bars back from currentIdx
    
    // Logic: 
    // If currently In Session or Before Session -> We need YESTERDAY's session.
    // If currently After Session -> We need TODAY's session.
    
    bool lookForYesterday = (curH < endHour);
    
    // We scan back up to 2880 bars (2 days) to be safe
    for(int k=1; k<2000; k++) {
       int idx = currentIdx + k;
       if(idx >= ArraySize(rates)) break;
       
       datetime subT = rates[idx].time;
       MqlDateTime subDt; TimeToStruct(subT, subDt);
       int h = subDt.hour;
       
       // Check if this bar belongs to the Target Session
       if(h >= startHour && h < endHour) {
          // Found a bar in session.
          // Is it the RIGHT day?
          // If we lookForYesterday, day_of_year must be != dt.day_of_year
          // (Edge case: New Year)
          
          bool isSameDay = (subDt.day_of_year == dt.day_of_year && subDt.year == dt.year);
          
          if(lookForYesterday && isSameDay) continue; // Skip today's bars
          
          // If we are here, it's valid.
          if(rates[idx].high > H || H == -1) H = rates[idx].high;
          if(rates[idx].low < L) L = rates[idx].low;
          found = true;
       } else {
          // If we were inside the session loop and now we are OUT (h < startHour), we are done?
          // Careful with day boundaries.
          if(found) {
             // We found session bars, now we exited the session window. It's fully scanned.
             break; 
          }
       }
    }
    
    if(H == -1) { H=rates[currentIdx].high; L=rates[currentIdx].low; } // Fallback
}


void OnTick()
{
   datetime time = iTime(_Symbol, _Period, 0);
   if(time == lastBarTime) return;
   lastBarTime = time;
   
   int count = gLookback + 2000; 
   MqlRates rates[];
   ArraySetAsSeries(rates, true);
   
   double ema50[], ema200[];
   ArraySetAsSeries(ema50, true);
   ArraySetAsSeries(ema200, true);
   
   if(CopyRates(_Symbol, _Period, 0, count, rates) < count ||
      CopyBuffer(handleEMA50, 0, 0, gLookback+1, ema50) < gLookback ||
      CopyBuffer(handleEMA200, 0, 0, gLookback+1, ema200) < gLookback) return;
   
   // Pre-calc sessions for Current Time (Index 0)
   
   int featuresPerBar = 17;
   int inputSize = gLookback * featuresPerBar;
   double inputs[]; 
   ArrayResize(inputs, inputSize);
   
   // Cache to avoid rescanning if we are in same context?
   double prevAH=-1.0;
   double prevAL=0.0, prevLH=0.0, prevLL=0.0, prevNH=0.0, prevNL=0.0;
   int prevHour = -1;
   
   for(int k=0; k<gLookback; k++) {
       int off = k * featuresPerBar;
       
       double o = rates[k].open;
       double c = rates[k].close;
       double h = rates[k].high;
       double l = rates[k].low;
       double pH = rates[k+1].high;
       double pL = rates[k+1].low;
       
       inputs[off+0] = (c - o)/o * 1000.0;
       inputs[off+1] = (h - o)/o * 1000.0;
       inputs[off+2] = (l - o)/o * 1000.0;
       inputs[off+3] = ((h - pH)/pH) * 1000.0;
       inputs[off+4] = ((l - pL)/pL) * 1000.0;
       inputs[off+5] = (rates[k+1].tick_volume - rates[k+2].tick_volume) / (rates[k+2].tick_volume + 1.0) * 10.0;  // Completed Bar Volume difference normalized
       inputs[off+6] = ((c - ema50[k])/ema50[k]) * 1000.0;
       inputs[off+7] = ((c - ema200[k])/ema200[k]) * 1000.0;
       
       // Session Logic
       datetime t = rates[k].time;
       MqlDateTime dt; TimeToStruct(t, dt);
       
       double aH, aL, lH, lL, nH, nL;
       
       // Optimization: If hour is same as prev k (very likely for M1), levels are same.
       if(dt.hour == prevHour && prevAH != -1) {
           aH = prevAH; aL = prevAL;
           lH = prevLH; lL = prevLL;
           nH = prevNH; nL = prevNL;
       } else {
           GetSession(rates, k, gAsianStart, gAsianEnd, aH, aL);
           GetSession(rates, k, gLondonStart, gLondonEnd, lH, lL);
           GetSession(rates, k, gNYStart, gNYEnd, nH, nL);
           prevHour = dt.hour;
           prevAH=aH; prevAL=aL; prevLH=lH; prevLL=lL; prevNH=nH; prevNL=nL;
       }

       inputs[off+8] = ((c - aH)/aH) * 1000.0;
       inputs[off+9] = ((c - aL)/aH) * 1000.0; 
       inputs[off+10] = ((c - lH)/lH) * 1000.0;
       inputs[off+11] = ((c - lL)/lH) * 1000.0;
       inputs[off+12] = ((c - nH)/nH) * 1000.0;
       inputs[off+13] = ((c - nL)/nH) * 1000.0;
       
       inputs[off+14] = (double)dt.day_of_week / 7.0;
       inputs[off+15] = (double)dt.hour / 24.0;
       inputs[off+16] = (double)dt.min / 60.0;
   }
   
   Net.FeedForward(inputs);
   double results[]; Net.GetResults(results);
   double signal = results[0];
   
   // Get current and previous D1 ATR
   double atrArray[2];
   if(CopyBuffer(handleATR_D1, 0, 0, 2, atrArray) < 2) return;
   
   double currentDayATR = atrArray[0];
   double prevDayATR = atrArray[1];
   
   // Use larger of current or previous day's ATR
   // Use larger of current or previous day's ATR
   
   CopyBuffer(handleATR_D1, 0, 0, 2, atrArray);
   double atrToUse = (atrArray[0] > atrArray[1]) ? atrArray[0] : atrArray[1];
   
   double distTP = atrToUse * gTargetTP_Mult; 
   double distSL = atrToUse * gTargetSL_Mult;
   
   double ask = SymbolInfoDouble(_Symbol, SYMBOL_ASK);
   double bid = SymbolInfoDouble(_Symbol, SYMBOL_BID);
   
   Print("Signal: ", signal, " | ATR: ", DoubleToString(atrToUse, _Digits), " | TP: ", DoubleToString(distTP, _Digits), " SL: ", DoubleToString(distSL, _Digits));
   
   if(signal > InpConfidence && !PositionSelect(_Symbol)) {
      Trade.Buy(InpLotSize, _Symbol, 0, ask - distSL, ask + distTP, "NN Buy");
   }
   else if(signal < -InpConfidence && !PositionSelect(_Symbol)) {
      Trade.Sell(InpLotSize, _Symbol, 0, bid + distSL, bid - distTP, "NN Sell");
   }
}

// Load Model with Header
bool LoadModelWithHeader(string filename)
{
   int handle = FileOpen(filename, FILE_READ|FILE_BIN|FILE_COMMON);
   if(handle == INVALID_HANDLE) { Print("Error opening model file."); return false; }
   
   int version = FileReadInteger(handle);
   if(version != 2) { Print("Error: Model version match (Expected 2)"); FileClose(handle); return false; }
   
   gLookback = FileReadInteger(handle);
   int topologyCnt = FileReadInteger(handle);
   ArrayResize(GlobalTopology, topologyCnt);
   for(int i=0; i<topologyCnt; i++) GlobalTopology[i] = FileReadInteger(handle);
   
   gTargetTP_Mult = FileReadDouble(handle);
   gTargetSL_Mult = FileReadDouble(handle);
   
   gAsianStart = FileReadInteger(handle);
   gAsianEnd = FileReadInteger(handle);
   gLondonStart = FileReadInteger(handle);
   gLondonEnd = FileReadInteger(handle);
   gNYStart = FileReadInteger(handle);
   gNYEnd = FileReadInteger(handle);
   
   // Create Net with loaded Topology
   if(CheckPointer(Net) != POINTER_INVALID) delete Net;
   Net = new CNeuralNet(GlobalTopology);
   
   // Load Weights
   if(!Net.Load(handle, GlobalTopology)) { FileClose(handle); return false; }
   
   FileClose(handle);
   return true;
}