RL_ONNX_EA.mq5

//+------------------------------------------------------------------+
//|                                             RL_ONNX_EA.mq5       |
//|                                   Copyright 2026, Algorembrant   |
//|                                   Rembrant Oyangoren Albeos      |
//+------------------------------------------------------------------+
#property copyright "Algorembrant, Rembrant Oyangoren Albeos"
#property link      "https://github.com/Algorembrant"
#property version   "1.00"

#include <Trade\Trade.mqh>
CTrade trade;

// Inputs compatible with the RL training setup
input string ONNX_Filename = "RL_Agent_XAUUSD.onnx";
input double RiskPercent = 2.0;

long model_handle = INVALID_HANDLE;
double max_lot_size = 20.0;

int OnInit() {
    Print("Initializing RL XAUUSDc ONNX EA...");
    
    // Load the ONNX model trained in Google Colab
    model_handle = OnnxCreate(ONNX_Filename, ONNX_DEFAULT);
    if(model_handle == INVALID_HANDLE) {
        Print("Error loading ONNX model ", ONNX_Filename, " : ", GetLastError());
        return INIT_FAILED;
    }
    
    trade.SetExpertMagicNumber(2026101); // 2026 methodologies
    
    return INIT_SUCCEEDED;
}

void OnDeinit(const int reason) {
    if(model_handle != INVALID_HANDLE)
        OnnxRelease(model_handle);
    Print("EA Deinitialized.");
}

void OnTick() {
    // Strictly execute on close-price only (Wait for new bar generation)
    static datetime last_time = 0;
    datetime current_time = iTime(_Symbol, _Period, 0);
    
    // If the bar hasn't closed / new bar hasn't opened, do nothing
    if(current_time == last_time) return;
    last_time = current_time;
    
    // --- 1. Fetch data & Indicators
    // The ONNX model requires the exact 100+ vectorized attributes as built by pandas_ta in python.
    // In this production script, we construct the input float array shape based on observation_space.
    // Ensure length matches `features.shape[1]` exactly.
    float features[];
    int num_features = 100; // MUST MATCH exactly the CSV features
    ArrayResize(features, num_features); 
    
    for(int i=0; i<ArraySize(features); i++) {
        // [In a complete system, map MT5 runtime M3 moving averages, FDI, oscillators to these indices here]
        features[i] = 0.0f; 
    }
    
    // --- 2. Predict with ONNX Model
    long action_output[] = {3}; // Default 3 = Do Nothing
    
    // onnx model inference
    if(!OnnxRun(model_handle, ONNX_NO_CONVERSION, features, action_output)) {
        Print("ONNX Run Error: ", GetLastError());
        return;
    }
    
    long action = action_output[0];
    
    // --- 3. Execute Actions & Handle Fragment/Sizing
    double point = SymbolInfoDouble(_Symbol, SYMBOL_POINT);
    double spread = SymbolInfoInteger(_Symbol, SYMBOL_SPREAD) * point;
    double closePrice = iClose(_Symbol, _Period, 1);
    double ask = SymbolInfoDouble(_Symbol, SYMBOL_ASK);
    double bid = SymbolInfoDouble(_Symbol, SYMBOL_BID);
    
    // System rule: After opened, wait until tookprofit or stopout. No averaging.
    if(PositionsTotal() > 0) return; 

    // StopLoss distance constraints: SL distance never less than spread * 10
    double sl_dist = MathMax(closePrice * 0.005, spread * 10.0);
    
    // Calculate Sizing (2% Risk)
    double balance = AccountInfoDouble(ACCOUNT_BALANCE);
    double risk_amount = balance * (RiskPercent / 100.0);
    
    // MT5 Standard contract size computation for Gold (usually $100 per lot per $1)
    double sl_dollar_risk_per_lot = sl_dist * 100.0;
    double lots = 0.01;
    
    if(sl_dollar_risk_per_lot > 0)
        lots = risk_amount / sl_dollar_risk_per_lot;
        
    // Fragmentize execution cap
    lots = MathRound(lots * 100.0) / 100.0;
    if(lots < 0.01) lots = 0.01;
    
    // Fragmenting Logic: Open multiple positions if lot size exceeds cap
    int fragments = 1;
    double current_fragment_lot = lots;
    
    if(lots > max_lot_size) {
        fragments = (int)MathCeil(lots / max_lot_size);
        current_fragment_lot = max_lot_size;
        Print("Notice: Position fragmentization triggered. Total Lot = ", lots, " -> Fragmented into ", fragments, " orders.");
    }

    if(action == 0) { // BUY
        double stoploss = ask - sl_dist;
        double takeprofit = ask + (sl_dist * 1.5); // TP >= 1R
        
        for(int f=0; f<fragments; f++) {
            if(f == fragments - 1 && lots > max_lot_size) {
                // Remaining fraction stringency
                current_fragment_lot = lots - (max_lot_size * (fragments - 1));
            }
            trade.Buy(current_fragment_lot, _Symbol, ask, stoploss, takeprofit, "RL_BUY");
        }
    }
    else if(action == 1) { // SELL
        double stoploss = bid + sl_dist;
        double takeprofit = bid - (sl_dist * 1.5); // TP >= 1R
        
        for(int f=0; f<fragments; f++) {
            if(f == fragments - 1 && lots > max_lot_size) {
                current_fragment_lot = lots - (max_lot_size * (fragments - 1));
            }
            trade.Sell(current_fragment_lot, _Symbol, bid, stoploss, takeprofit, "RL_SELL");
        }
    }
}