python c:/Users/ds1020254/Desktop/dante.py import tkinter as tk from tkinter import messagebox import MetaTrader5 as mt5 import pandas as pd import numpy as np import matplotlib.pyplot as plt from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg from scipy.signal import find_peaks from sklearn.preprocessing import MinMaxScaler from tensorflow.keras.models import Sequential from tensorflow.keras.layers import LSTM, Dense import threading import time def main(): root = tk.Tk() root.title("MT5MYM-A") root.geometry("1200x800") login_frame = tk.Frame(root, width=400, height=600, bg="lightgrey") login_frame.pack(side=tk.LEFT, fill=tk.BOTH, expand=True) login_title = tk.Label(login_frame, text="🏧MYM-A", font=("Helvetica", 20), bg="lightgrey") login_title.pack(pady=20) login_label = tk.Label(login_frame, text="Login:", font=("Helvetica", 14), bg="lightgrey") login_label.pack(pady=5) login_entry = tk.Entry(login_frame, font=("Helvetica", 14)) login_entry.pack(pady=5) login_entry.insert(0, "312128713") password_label = tk.Label(login_frame, text="Password:", font=("Helvetica", 14), bg="lightgrey") password_label.pack(pady=5) password_entry = tk.Entry(login_frame, show="*", font=("Helvetica", 14)) password_entry.pack(pady=5) password_entry.insert(0, "Sexo247420@") server_label = tk.Label(login_frame, text="Server:", font=("Helvetica", 14), bg="lightgrey") server_label.pack(pady=5) server_entry = tk.Entry(login_frame, font=("Helvetica", 14)) server_entry.pack(pady=5) server_entry.insert(0, "XMGlobal-MT5 7") connect_button = tk.Button(login_frame, text="Connect", font=("Helvetica", 14), command=lambda: connect_to_mt5(login_entry.get(), password_entry.get(), server_entry.get(), root)) connect_button.pack(pady=20) main_frame = tk.Frame(root, width=800, height=600) main_frame.pack(side=tk.RIGHT, fill=tk.BOTH, expand=True) label = tk.Label(main_frame, text="USD/MXN", font=("Helvetica", 24)) label.pack(expand=True) loading_label = tk.Label(main_frame, text="Loading...", font=("Helvetica", 14)) loading_label.pack(side=tk.BOTTOM, pady=10) root.mainloop() def connect_to_mt5(login, password, server, root): if not mt5.initialize(): messagebox.showerror("Error", "initialize() failed") mt5.shutdown() return authorized = mt5.login(login=int(login), password=password, server=server) if authorized: print("Connected to MetaTrader 5") display_account_info(root) fetch_and_display_chart(root) start_auto_trading() else: messagebox.showerror("Error", "Failed to connect to MetaTrader 5") def display_account_info(root): account_info = mt5.account_info() if account_info is None: messagebox.showerror("Error", "Failed to get account info") return info_window = tk.Toplevel(root) info_window.title("Account Information") info_window.geometry("400x300") info_labels = [ f"Account ID: {account_info.login}", f"Balance: {account_info.balance}", f"Equity: {account_info.equity}", f"Margin: {account_info.margin}", f"Free Margin: {account_info.margin_free}", f"Leverage: {account_info.leverage}" ] for info in info_labels: label = tk.Label(info_window, text=info, font=("Helvetica", 14)) label.pack(pady=5) def fetch_and_display_chart(root): symbol = "USDMXN" timeframe = mt5.TIMEFRAME_D1 days = 1200 data = fetch_historical_data(symbol, timeframe, days) scaled_data, scaler = preprocess_data(data) model = train_lstm_model(scaled_data) future_days = 60 predicted_prices = predict_future(model, scaled_data, future_days) predicted_prices = scaler.inverse_transform(np.array(predicted_prices).reshape(-1, 1)) predicted_dates = pd.date_range(start=data['time'].iloc[-1], periods=future_days + 1, closed='right') predicted_df = pd.DataFrame({'time': predicted_dates, 'close': predicted_prices.flatten()}) plot_predictions(data, predicted_df, root) def fetch_historical_data(symbol, timeframe, days): rates = mt5.copy_rates_from_pos(symbol, timeframe, 0, days) if rates is None or len(rates) == 0: raise Exception("Failed to retrieve rates") df = pd.DataFrame(rates) df['time'] = pd.to_datetime(df['time'], unit='s') return df[['time', 'close']] def preprocess_data(data): scaler = MinMaxScaler(feature_range=(0, 1)) scaled_data = scaler.fit_transform(data['close'].values.reshape(-1, 1)) return scaled_data, scaler def train_lstm_model(scaled_data): time_step = 60 X_train, y_train = create_train_data(scaled_data, time_step) model = Sequential() model.add(LSTM(100, return_sequences=True, input_shape=(X_train.shape[1], 1))) model.add(LSTM(100, return_sequences=False)) model.add(Dense(50)) model.add(Dense(1)) model.compile(optimizer='adam', loss='mean_squared_error') model.fit(X_train, y_train, batch_size=32, epochs=3) return model def create_train_data(scaled_data, time_step): X_train, y_train = [], [] for i in range(time_step, len(scaled_data)): X_train.append(scaled_data[i-time_step:i, 0]) y_train.append(scaled_data[i, 0]) X_train, y_train = np.array(X_train), np.array(y_train) X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1)) return X_train, y_train def predict_future(model, data, future_days): predictions = [] time_step = 60 input_seq = data[-time_step:] for _ in range(future_days): input_seq = input_seq.reshape((1, input_seq.shape[0], 1)) predicted_price = model.predict(input_seq)[0] predictions.append(predicted_price) input_seq = np.append(input_seq[:, 1:], predicted_price) return predictions def plot_predictions(data, predicted_df, root): combined_df = pd.concat([data, predicted_df]) fig, ax = plt.subplots() ax.plot(combined_df['time'], combined_df['close'], label="USD/MXN") ax.plot(predicted_df['time'], predicted_df['close'], 'r--', label="Predicted USD/MXN") peaks, _ = find_peaks(data['close']) ax.plot(data['time'].iloc[peaks], data['close'].iloc[peaks], "ro", markersize=5) ax.set_xlabel("Date") ax.set_ylabel("Price") ax.set_title("USD/MXN Daily Chart with Predictions") ax.legend() chart_frame = tk.Frame(root) chart_frame.pack(side=tk.RIGHT, fill=tk.BOTH, expand=True) canvas = FigureCanvasTkAgg(fig, master=chart_frame) canvas.draw() canvas.get_tk_widget().pack(side=tk.TOP, fill=tk.BOTH, expand=True) def start_auto_trading(): threading.Thread(target=auto_trade_loop).start() def auto_trade_loop(): symbol = "USDMXN" while True: open_trade(symbol, mt5.ORDER_TYPE_BUY) time.sleep(240) close_trade(symbol) open_trade(symbol, mt5.ORDER_TYPE_SELL) time.sleep(240) close_trade(symbol) def open_trade(symbol, trade_type): lot_size = 0.1 price = mt5.symbol_info_tick(symbol).ask if trade_type == mt5.ORDER_TYPE_BUY else mt5.symbol_info_tick(symbol).bid request = { "action": mt5.TRADE_ACTION_DEAL, "symbol": symbol, "volume": lot_size, "type": trade_type, "price": price, "deviation": 10, "magic": 234000, "comment": "Auto-trading", "type_time": mt5.ORDER_TIME_GTC, "type_filling": mt5.ORDER_FILLING_IOC, } result = mt5.order_send(request) if result.retcode != mt5.TRADE_RETCODE_DONE: print(f"Failed to send order: {result.retcode}") def close_trade(symbol): positions = mt5.positions_get(symbol=symbol) for position in positions: request = { "action": mt5.TRADE_ACTION_DEAL, "symbol": symbol, "volume": position.volume, "type": mt5.ORDER_TYPE_SELL if position.type == mt5.ORDER_TYPE_BUY else mt5.ORDER_TYPE_BUY, "position": position.ticket, "price": mt5.symbol_info_tick(symbol).bid if position.type == mt5.ORDER_TYPE_BUY else mt5.symbol_info_tick(symbol).ask, "deviation": 10, "magic": 234000, "comment": "Auto-trading", "type_time": mt5.ORDER_TIME_GTC, "type_filling": mt5.ORDER_FILLING_IOC, } result = mt5.order_send(request) if result.retcode != mt5.TRADE_RETCODE_DONE: print(f"Failed to close order: {result.retcode}") if __name__ == "__main__": main()