import torch
from torch import nn, optim

# Example DRL Model (Adjust according to your architecture)
class TradingModel(nn.Module):
    def __init__(self):
        super(TradingModel, self).__init__()
        self.fc1 = nn.Linear(NumSignals, Neurons)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(Neurons, 1)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        x = self.relu(self.fc1(x))
        x = self.sigmoid(self.fc2(x))
        return x

# Training function
def train_model(data_path, model_save_path):
    data = np.loadtxt(data_path, delimiter=',')  # Load data from CSV
    X, y = data[:, :-1], data[:, -1]
    X = torch.tensor(X, dtype=torch.float32)
    y = torch.tensor(y, dtype=torch.float32).view(-1, 1)

    model = TradingModel()
    criterion = nn.BCELoss()
    optimizer = optim.Adam(model.parameters(), lr=Rate)

    for epoch in range(Epochs):
        optimizer.zero_grad()
        predictions = model(X)
        loss = criterion(predictions, y)
        loss.backward()
        optimizer.step()

    # Save model for use in Zorro
    torch.save(model.state_dict(), model_save_path)

train_model("Data/Signals.csv", "model.pth")

import torch
import numpy as np
from TradingModel import TradingModel  # Import your model class

# Initialize model
model = TradingModel()
model.load_state_dict(torch.load("model.pth"))  # Load saved model
model.eval()  # Set model to evaluation mode

def predict(input_data):
    input_tensor = torch.tensor(input_data, dtype=torch.float32)
    with torch.no_grad():
        prediction = model(input_tensor)
    return prediction.item()

# Example usage for testing
# Replace [0.5, 1.2, -0.7, ...] with real data passed from Zorro
print(predict([0.5, 1.2, -0.7, ...]))  # Output a prediction for testing

function run()
{
    vars InputData = series(priceClose());
    double Data[NumSignals];
    for(int i = 0; i < NumSignals; i++)
        Data[i] = InputData[i];  // Gather recent data for model input

    // Save input data to CSV format expected by predict.py
    file_write("Data\\input_data.csv", Data, 0);

    // Call Python predict script and retrieve prediction
    Rx("Y <- predict('Data/input_data.csv')");
    var prediction = Rd("Y[1]"); // Assuming Y[1] holds the prediction result

    // Based on prediction, send buy or sell order
    if(prediction > 0.5)
        enterLong();  // Buy if prediction is above 0.5 threshold
    else
        enterShort(); // Sell if prediction is 0.5 or below
}