changes made to bot execution of mt5

app/bot.py

@@ -1,59 +1,78 @@
-import time
-import pandas as pd
-import numpy as np
-from sklearn.preprocessing import MinMaxScaler
-import talib
-import matplotlib.pyplot as plt
-import pymt5 as mt5
-from keras.models import Sequential
-from keras.layers import Dense, Dropout
 from keras.optimizers import Adam
-import socket
+from keras.layers import Dense, Dropout
+from keras.models import Sequential
+import pymt5
+import matplotlib.pyplot as plt
+import talib
+from sklearn.preprocessing import MinMaxScaler
+import numpy as np
+import pandas as pd
+import time
+import os
+os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
 
 
-def start_mt5(username, password, server, path):
+def connect_to_mt5_container():
-    # Ensure that all variables are the correct type
+    server = "localhost"  # Change to the appropriate IP or hostname if necessary
-    uname = int(username)  # Username must be an int
+    port = 15555  # Change to the appropriate port if necessary
-    pword = str(password)  # Password must be a string
+    login = 123456  # Change to your MetaTrader login number if necessary
-    trading_server = str(server)  # Server must be a string
+    password = "your_password"  # Change to your MetaTrader password if necessary
-    filepath = str(path)  # Filepath must be a string
+
     # Connect to MetaTrader 5
-    if mt5.initialize(login=uname, password=pword, server=trading_server, path=filepath):
+    mt5 = pymt5.PyMT5()
-        # Login to MT5
+    mt5.onConnected = onConnected
-        if mt5.login(login=uname, password=pword, server=trading_server):
+    mt5.onDisconnected = onDisconnected
-            return True
+    mt5.onData = onData
-        else:
-            print("Login Fail")
-            quit()
-            return PermissionError
-    else:
-        print("MT5 Initialization Failed")
-        quit()
-        return ConnectionAbortedError
 
+    # Wait for the connection to be established
+    while not onConnected:
+        time.sleep(0.1)
 
-def connect_to_mt5(username, password, server, path):
+    # Send login request
-    # Start the MetaTrader 5 instance
+    login_request = {
-    if start_mt5(username, password, server, path):
+        'ver': '3',
-        print("Connected to MetaTrader 5")
+        'type': '1',
+        'login': str(login),
+        'password': password,
+        'res': '0'
+    }
+    mt5.broadcast(login_request)
+
+    # Wait for the login response
+    while not onConnected:
+        time.sleep(0.1)
+
+    # Check if login was successful
+    if onConnected:
+        print(f"Connected to MetaTrader 5: {onConnected}")
     else:
         print("Failed to connect to MetaTrader 5")
-        return
 
 
-# Define the symbols and timeframes
+def onConnected(client_info):
-symbol = 'EURUSD'
+    print(f"Connected: {client_info}")
-timeframe = mt5.TIMEFRAME_H1
-
-# Set up initial variables
-lot_size = 0.01
-stop_loss = 100
-take_profit = 150
-
-# Define TensorFlow neural network model
 
 
-def create_neural_network_model(input_shape):
+def onDisconnected(client_info):
+    print(f"Disconnected: {client_info}")
+
+
+def onData(data):
+    print(f"Received data: {data}")
+
+
+def start_mt5_bot():
+    # Define the symbols and timeframes
+    symbol = 'EURUSD'
+    timeframe = 60  # H1 timeframe (1 hour)
+
+    # Set up initial variables
+    lot_size = 0.01
+    stop_loss = 100
+    take_profit = 150
+
+    # Define TensorFlow neural network model
+    def create_neural_network_model(input_shape):
         model = Sequential()
         model.add(Dense(64, activation='relu', input_shape=input_shape))
         model.add(Dropout(0.2))
@@ -62,28 +81,26 @@ def create_neural_network_model(input_shape):
         model.add(Dense(1, activation='sigmoid'))
         return model
 
+    # Define input shape for the neural network
+    # Adjust the input shape based on your features and data
+    input_shape = (10,)
 
-# Define input shape for the neural network
+    # Create the neural network model
-input_shape = (10,)  # Adjust the input shape based on your features and data
+    neural_network_model = create_neural_network_model(input_shape)
 
-# Create the neural network model
+    # Compile the model
-neural_network_model = create_neural_network_model(input_shape)
+    neural_network_model.compile(optimizer=Adam(
-
-# Compile the model
-neural_network_model.compile(optimizer=Adam(
         learning_rate=0.001), loss='binary_crossentropy')
 
-
+    def get_historical_data():
-def get_historical_data():
         # Retrieve historical data
-    rates = mt5.copy_rates_from_pos(symbol, timeframe, 0, 1000)
+        rates = pymt5.copy_rates_from_pos(symbol, timeframe, 0, 1000)
         df = pd.DataFrame(rates)
         df['time'] = pd.to_datetime(df['time'], unit='s')
         df.set_index('time', inplace=True)
         return df
 
-
+    def calculate_indicators_and_detect_patterns(df):
-def calculate_indicators_and_detect_patterns(df):
         # Calculate RSI
         rsi_period = 14
         df['rsi'] = talib.RSI(df['close'], rsi_period)
@@ -123,12 +140,12 @@ def calculate_indicators_and_detect_patterns(df):
             (df['low'].shift(2) < df['low']) & (
                 df['low'].shift(-2) < df['low']), 'Double Bottom', 'None'
         )
-    df.loc[df['bottom_pattern'] != 'None', 'pattern'] = df['bottom_pattern']
+        df.loc[df['bottom_pattern'] != 'None',
+               'pattern'] = df['bottom_pattern']
 
         return df
 
-
+    def generate_signals(df):
-def generate_signals(df):
         # Determine trade signals based on divergences, patterns, and trend direction
         df['signal'] = 'None'
         df['divergence_signal'] = np.where((df['rsi_divergence'] == True) & (df['pattern'] != 'None'), 'Both',
@@ -158,8 +175,7 @@ def generate_signals(df):
 
         return df
 
-
+    def execute_trade(signal, df):
-def execute_trade(signal, df):
         # Implement risk management and trade execution logic based on the signals generated
         # Update TensorFlow neural network model with trade outcome (loss or win)
 
@@ -176,43 +192,15 @@ def execute_trade(signal, df):
         try:
             if signal == 'Buy':
                 # Place a buy trade
-            result = mt5.ORDER_RESULT_FAIL
+                result = pymt5.order_send(symbol, pymt5.OP_BUY, lot_size, 0, stop_loss, take_profit,
-            request = {
+                                          "Buy trade", 123456, pymt5.ORDER_TIME_GTC, 0)
-                "action": mt5.TRADE_ACTION_DEAL,
+                outcome = 'Win' if result.retcode == pymt5.TRADE_RETCODE_DONE else 'Loss'
-                "symbol": symbol,
-                "volume": lot_size,
-                "type": mt5.ORDER_TYPE_BUY,
-                "price": mt5.symbol_info_tick(symbol).ask,
-                "sl": mt5.symbol_info_tick(symbol).ask - stop_loss * mt5.symbol_info(symbol).point,
-                "tp": mt5.symbol_info_tick(symbol).ask + take_profit * mt5.symbol_info(symbol).point,
-                "deviation": 20,
-                "magic": 123456,
-                "comment": "Buy trade",
-                "type_time": mt5.ORDER_TIME_GTC,
-                "type_filling": mt5.ORDER_FILLING_RETURN,
-            }
-            result = mt5.order_send(request)
-            outcome = 'Win' if result.retcode == mt5.TRADE_RETCODE_DONE else 'Loss'
 
             elif signal == 'Sell':
                 # Place a sell trade
-            result = mt5.ORDER_RESULT_FAIL
+                result = pymt5.order_send(symbol, pymt5.OP_SELL, lot_size, 0, stop_loss, take_profit,
-            request = {
+                                          "Sell trade", 123456, pymt5.ORDER_TIME_GTC, 0)
-                "action": mt5.TRADE_ACTION_DEAL,
+                outcome = 'Win' if result.retcode == pymt5.TRADE_RETCODE_DONE else 'Loss'
-                "symbol": symbol,
-                "volume": lot_size,
-                "type": mt5.ORDER_TYPE_SELL,
-                "price": mt5.symbol_info_tick(symbol).bid,
-                "sl": mt5.symbol_info_tick(symbol).bid + stop_loss * mt5.symbol_info(symbol).point,
-                "tp": mt5.symbol_info_tick(symbol).bid - take_profit * mt5.symbol_info(symbol).point,
-                "deviation": 20,
-                "magic": 123456,
-                "comment": "Sell trade",
-                "type_time": mt5.ORDER_TIME_GTC,
-                "type_filling": mt5.ORDER_FILLING_RETURN,
-            }
-            result = mt5.order_send(request)
-            outcome = 'Win' if result.retcode == mt5.TRADE_RETCODE_DONE else 'Loss'
 
             # Example trade outcome information
             trade_outcome = {
@@ -228,15 +216,15 @@ def execute_trade(signal, df):
             update_neural_network_model(trade_outcome)
 
             # Example print statements for debugging
-        print(f"Executed {signal} trade with position size: {position_size}")
+            print(
+                f"Executed {signal} trade with position size: {position_size}")
             print(f"Trade outcome: {trade_outcome}")
 
             # Additional logic for trade management, monitoring, etc.
         except Exception as e:
             print(f"Error executing trade: {str(e)}")
 
-
+    def update_neural_network_model(trade_outcome):
-def update_neural_network_model(trade_outcome):
         # Implement code to update the neural network model based on trade outcome
         pattern = trade_outcome['pattern']
         divergence_strength = trade_outcome['divergence_strength']
@@ -267,10 +255,9 @@ def update_neural_network_model(trade_outcome):
         neural_network_model.fit(X_train, y_train, epochs=10, batch_size=32)
 
         # Save the updated model weights
-    neural_network_model.save_weights('model_weights.h5')
+        neural_network_model.save_weights('weights/model_weights.h5')
 
-
+    def preprocess_data(dataset):
-def preprocess_data(dataset):
         # Define the numerical features (if any)
         numerical_features = []  # Update with the actual numerical feature column names
 
@@ -296,8 +283,7 @@ def preprocess_data(dataset):
         # Return the preprocessed input features and target labels
         return input_features, target_labels
 
-
+    def visualize_data(df):
-def visualize_data(df):
         plt.figure(figsize=(10, 6))
         plt.plot(df.index, df['close'], label='Close')
         # Add visualizations for other indicators, levels, and patterns
@@ -308,14 +294,9 @@ def visualize_data(df):
         plt.legend()
         plt.show()
 
-
+    def run_trading_bot():
-def run_trading_bot():
+        # Connect to MetaTrader 5 container
-    # Connect to MetaTrader 5
+        connect_to_mt5_container()
-    connect_to_mt5(username='your_username', password='your_password',
-                   server='your_server', path='your_mt5_installation_path')
-
-    # Load TensorFlow neural network model weights
-    neural_network_model.load_weights('model_weights.h5')
 
         while True:
             try:
@@ -343,9 +324,15 @@ def run_trading_bot():
             # Wait for the next iteration
             time.sleep(60)  # Adjust the time interval as needed
 
+    # Run the trading bot
+    run_trading_bot()
 
-# Run the trading bot
+    # Load TensorFlow neural network model weights
-run_trading_bot()
+    neural_network_model.load_weights('weights/model_weights.h5')
 
-# Disconnect from MetaTrader 5
+    # Disconnect from MetaTrader 5
-mt5.shutdown()
+    pymt5.shutdown()
+
+
+# Start the MetaTrader 5 bot
+start_mt5_bot()

docker-compose.yml

@@ -12,6 +12,10 @@ services:
       - "15558:15558"
     volumes:
       - ejtraderMT:/data
+    environment:
+      - DISPLAY=host.docker.internal:0
+      - MT5_PASSWORD=your_password
+      - MT5_SERVER=your_server
 
   trading_bot:
     container_name: trading_bot
@@ -28,7 +32,8 @@ services:
     build:
       context: .
       dockerfile: docker/DockerFile.mt5_bridge
-    volumes: -./bridge:/bridge
+    volumes:
+      - ./bridge:/bridge
     depends_on:
       - metatrader_service
       - trading_bot

docker/DockerFile.mt5_bridge

@@ -7,6 +7,6 @@ WORKDIR /app
 COPY bridge/mt5_bridge.py .
 
 # Install any dependencies required by the bridge script
-RUN pip install python-socketio python-engineio
+RUN pip install python-socketio python-engineio requests
 
 CMD ["python", "mt5_bridge.py"]