Tuesday, 31 December 2024
Sunday, 29 December 2024
Friday, 27 December 2024
MYM-A MURCIELAGO
import MetaTrader5 as mt5
import torch
import torch.nn as nn
import torch.optim as optim
import random
# Initialize MetaTrader 5
def connect_mt5():
if not mt5.initialize():
print("Failed to initialize MT5")
else:
print("Successfully connected to MT5")
# Shut down MT5 connection
def shutdown_mt5():
mt5.shutdown()
# Create a simple neural network model using PyTorch
class ExplorationModel(nn.Module):
def __init__(self):
super(ExplorationModel, self).__init__()
self.fc1 = nn.Linear(50, 100) # Input layer
self.fc2 = nn.Linear(100, 50) # Hidden layer
self.fc3 = nn.Linear(50, 1) # Output layer
def forward(self, x):
x = torch.relu(self.fc1(x))
x = torch.relu(self.fc2(x))
x = self.fc3(x)
return x
# Function to simulate exploration and real trades logic
def run_exploration(num_trades=50):
model = ExplorationModel()
optimizer = optim.Adam(model.parameters(), lr=0.01)
criterion = nn.MSELoss()
# Dummy input for exploration
exploration_data = torch.randn(num_trades, 50) # Random input data for exploration
rewards = torch.zeros(num_trades) # Initialize rewards
profits = torch.zeros(num_trades) # Track profits for reward calculations
real_trades_placed = 0
for trade_idx in range(num_trades):
# Simulating real trade and exploration
real_trade_result = random.uniform(0.01, 0.1) # Random trade result (between 1% to 10% gain)
exploration_result = model(exploration_data[trade_idx])
# Reward assignment (based on profit gain)
profit = real_trade_result # Assuming real profit is directly proportional
rewards[trade_idx] = profit
# Update model on real trade results (Exploration + Real trade clustering)
total_outcome = exploration_result + profit
# Calculate reward based on the outcome
if trade_idx < 3: # Top 3 profitable trades
reward = total_outcome * 2 # Give more weight to top 3 profitable trades
else:
reward = total_outcome
profits[trade_idx] = reward
# Backpropagate and update the model
optimizer.zero_grad()
loss = criterion(exploration_result, reward)
loss.backward()
optimizer.step()
# Track placed trades
real_trades_placed += 1
if real_trades_placed >= 50: # Set limit of 50 real trades only
break
return profits
# Function to control the reinforcement learning loop
def reinforcement_learning():
# Setup exploration parameters
num_trades = 50
# Run exploration and real trade logic
profits = run_exploration(num_trades)
# Print out final profit for each trade (For example purposes)
for idx, profit in enumerate(profits):
print(f"Trade {idx+1} Profit: {profit.item():.2f}")
# Main logic
connect_mt5()
reinforcement_learning()
shutdown_mt5()
