Merge pull request #1 from CodeDestroyer19/dev-windows

Dev windows

app/MQHScripts/ZeroMQServer.mql5

@@ -1,59 +0,0 @@
-#import <ZmqMql4Connector.mqh>
-
-// Define the ZeroMQ server endpoint
-#define ZMQ_SERVER_ENDPOINT "tcp://*:5900"
-// Replace 'metatrader-port' with the port number you want to use for communication
-
-// Define the ZeroMQ socket and context
-CZmqMql4Server g_server;
-CZmqContext g_context;
-
-// Define a function to handle incoming messages
-void OnMessageReceived(string message)
-{
-    // Process the received message and perform necessary actions
-    // ...
-
-    // Send a response message (if needed)
-    string responseMessage = "Response from MetaTrader";
-    g_server.Send(responseMessage);
-}
-
-// The start function that is called when the EA/script is initialized
-int OnInit()
-{
-    // Initialize the ZeroMQ server
-    if (!g_server.Initialize(ZMQ_SERVER_ENDPOINT, g_context, OnMessageReceived))
-    {
-        Print("Failed to initialize ZeroMQ server");
-        return INIT_FAILED;
-    }
-
-    // Start the ZeroMQ server
-    if (!g_server.Start())
-    {
-        Print("Failed to start ZeroMQ server");
-        return INIT_FAILED;
-    }
-
-    // ...
-
-    return INIT_SUCCEEDED;
-}
-
-// The main function that is called on each tick
-void OnTick()
-{
-    // ...
-}
-
-// The deinitialization function that is called when the EA/script is stopped
-void OnDeinit(const int reason)
-{
-    // Stop the ZeroMQ server
-    g_server.Stop();
-
-    // Deinitialize the ZeroMQ server and context
-    g_server.Deinitialize();
-    g_context.Terminalize();
-}

app/MQHScripts/ZmqMql4Connector.mql5

@@ -1,105 +0,0 @@
-// ZmqMql4Connector.mqh
-
-// Define the ZMQ message callback function type
-typedef void OnZmqMessageReceived(string message);
-
-class CZmqMql4Server
-{
-private:
-    string m_endpoint;                  // ZeroMQ server endpoint
-    int m_socket;                       // ZeroMQ socket
-    int m_context;                      // ZeroMQ context
-    OnZmqMessageReceived @m_callback;   // Message callback function
-
-public:
-    // Constructor
-    CZmqMql4Server()
-    {
-        m_socket = -1;
-        m_context = -1;
-    }
-
-    // Destructor
-    ~CZmqMql4Server()
-    {
-        Deinitialize();
-    }
-
-    // Initialize the ZeroMQ server
-    bool Initialize(string endpoint, OnZmqMessageReceived @callback)
-    {
-        m_endpoint = endpoint;
-        m_callback = @callback;
-
-        m_context = zmq_init(1);
-        if (m_context == -1)
-            return false;
-
-        m_socket = zmq_socket(m_context, ZMQ_REP);
-        if (m_socket == -1)
-            return false;
-
-        int bindResult = zmq_bind(m_socket, m_endpoint);
-        if (bindResult == -1)
-            return false;
-
-        return true;
-    }
-
-    // Start the ZeroMQ server
-    bool Start()
-    {
-        if (m_socket == -1 || m_context == -1)
-            return false;
-
-        while (true)
-        {
-            string message = Receive();
-            if (message != "")
-                m_callback(message);
-        }
-
-        return true;
-    }
-
-    // Stop the ZeroMQ server
-    void Stop()
-    {
-        if (m_socket != -1)
-            zmq_close(m_socket);
-        m_socket = -1;
-    }
-
-    // Send a message from the server
-    void Send(string message)
-    {
-        if (m_socket != -1)
-            zmq_send(m_socket, message, StringLen(message), 0);
-    }
-
-    // Receive a message in the server
-    string Receive()
-    {
-        if (m_socket != -1)
-        {
-            string message;
-            int receivedBytes = zmq_recv(m_socket, message, 4096, 0);
-            if (receivedBytes > 0)
-                return message;
-        }
-
-        return "";
-    }
-
-    // Deinitialize the ZeroMQ server
-    void Deinitialize()
-    {
-        if (m_socket != -1)
-            zmq_close(m_socket);
-        if (m_context != -1)
-            zmq_term(m_context);
-
-        m_socket = -1;
-        m_context = -1;
-    }
-};

app/bot.py

@@ -1,6 +1,7 @@
 from keras.optimizers import Adam
 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
@@ -8,15 +9,62 @@ import numpy as np
 import pandas as pd
 import time
 import os
-import zmq
-
 os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
 
 
+def connect_to_mt5_container():
+    server = "localhost"  # Change to the appropriate IP or hostname if necessary
+    port = 15555  # Change to the appropriate port if necessary
+    login = 123456  # Change to your MetaTrader login number if necessary
+    password = "your_password"  # Change to your MetaTrader password if necessary
+
+    # Connect to MetaTrader 5
+    mt5 = pymt5.PyMT5()
+    mt5.onConnected = onConnected
+    mt5.onDisconnected = onDisconnected
+    mt5.onData = onData
+
+    # Wait for the connection to be established
+    while not onConnected:
+        time.sleep(0.1)
+
+    # Send login request
+    login_request = {
+        'ver': '3',
+        '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")
+
+
+def onConnected(client_info):
+    print(f"Connected: {client_info}")
+
+
+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 = 'H1'  # H1 timeframe (1 hour)
+    timeframe = 60  # H1 timeframe (1 hour)
 
     # Set up initial variables
     lot_size = 0.01
@@ -44,15 +92,12 @@ def start_mt5_bot():
     neural_network_model.compile(optimizer=Adam(
         learning_rate=0.001), loss='binary_crossentropy')
 
-    def get_historical_data(socket):
+    def get_historical_data():
-        # Request historical data from MetaTrader app
+        # Retrieve historical data
-        socket.send_string(
+        rates = pymt5.copy_rates_from_pos(symbol, timeframe, 0, 1000)
-            f"GET_HISTORICAL_DATA {symbol} {timeframe} 01/01/2022 31/12/2022")
+        df = pd.DataFrame(rates)
-
+        df['time'] = pd.to_datetime(df['time'], unit='s')
-        # Receive historical data from MetaTrader app
+        df.set_index('time', inplace=True)
-        response = socket.recv_string()
-        data = pd.read_json(response)
-        df = data[['open', 'high', 'low', 'close', 'tick_volume']]
         return df
 
     def calculate_indicators_and_detect_patterns(df):
@@ -64,8 +109,8 @@ def start_mt5_bot():
         macd_fast_period = 12
         macd_slow_period = 26
         macd_signal_period = 9
-        _, _, df['macd'] = talib.MACD(df['close'], fastperiod=macd_fast_period,
+        df['macd'], _, df['macd_signal'] = talib.MACD(df['close'], fastperiod=macd_fast_period,
-                                      slowperiod=macd_slow_period, signalperiod=macd_signal_period)
+                                                      slowperiod=macd_slow_period, signalperiod=macd_signal_period)
 
         # Detect divergence based on RSI and MACD
         df['rsi_divergence'] = np.where(
@@ -84,15 +129,17 @@ def start_mt5_bot():
 
         # Detect double tops and bottoms
         df['pattern'] = 'None'
-        df['top_pattern'] = np.where((df['high'].shift(1) < df['high']) & (df['high'].shift(-1) < df['high']) &
+        df['top_pattern'] = np.where(
-                                     (df['high'].shift(2) > df['high']) & (
+            (df['high'].shift(1) < df['high']) & (df['high'].shift(-1) < df['high']) &
-                                         df['high'].shift(-2) > df['high']),
+            (df['high'].shift(2) > df['high']) & (
-                                     'Double Top', 'None')
+                df['high'].shift(-2) > df['high']), 'Double Top', 'None'
+        )
         df.loc[df['top_pattern'] != 'None', 'pattern'] = df['top_pattern']
-        df['bottom_pattern'] = np.where((df['low'].shift(1) > df['low']) & (df['low'].shift(-1) > df['low']) &
+        df['bottom_pattern'] = np.where(
-                                        (df['low'].shift(2) < df['low']) & (
+            (df['low'].shift(1) > df['low']) & (df['low'].shift(-1) > df['low']) &
-                                            df['low'].shift(-2) < df['low']),
+            (df['low'].shift(2) < df['low']) & (
-                                        'Double Bottom', 'None')
+                df['low'].shift(-2) < df['low']), 'Double Bottom', 'None'
+        )
         df.loc[df['bottom_pattern'] != 'None',
                'pattern'] = df['bottom_pattern']
 
@@ -128,9 +175,9 @@ def start_mt5_bot():
 
         return df
 
-    def execute_trade(signal, df, socket):
+    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
+        # Update TensorFlow neural network model with trade outcome (loss or win)
 
         # Calculate risk and position size based on lot size, stop loss, and take profit
         risk = lot_size * stop_loss
@@ -145,17 +192,15 @@ def start_mt5_bot():
         try:
             if signal == 'Buy':
                 # Place a buy trade
-                socket.send_string(
+                result = pymt5.order_send(symbol, pymt5.OP_BUY, lot_size, 0, stop_loss, take_profit,
-                    f"PLACE_TRADE {symbol} BUY {lot_size} {stop_loss} {take_profit}")
+                                          "Buy trade", 123456, pymt5.ORDER_TIME_GTC, 0)
-                response = socket.recv_string()
+                outcome = 'Win' if result.retcode == pymt5.TRADE_RETCODE_DONE else 'Loss'
-                outcome = 'Win' if response == 'TRADE_EXECUTED' else 'Loss'
 
             elif signal == 'Sell':
                 # Place a sell trade
-                socket.send_string(
+                result = pymt5.order_send(symbol, pymt5.OP_SELL, lot_size, 0, stop_loss, take_profit,
-                    f"PLACE_TRADE {symbol} SELL {lot_size} {stop_loss} {take_profit}")
+                                          "Sell trade", 123456, pymt5.ORDER_TIME_GTC, 0)
-                response = socket.recv_string()
+                outcome = 'Win' if result.retcode == pymt5.TRADE_RETCODE_DONE else 'Loss'
-                outcome = 'Win' if response == 'TRADE_EXECUTED' else 'Loss'
 
             # Example trade outcome information
             trade_outcome = {
@@ -249,41 +294,45 @@ def start_mt5_bot():
         plt.legend()
         plt.show()
 
-    # Connect to MetaTrader app using ZeroMQ
+    def run_trading_bot():
-    context = zmq.Context()
+        # Connect to MetaTrader 5 container
-    socket = context.socket(zmq.REQ)
+        connect_to_mt5_container()
-    socket.connect("tcp://metatrader_service:5900")
-    # Replace 'metatrader-container-ip' and 'metatrader-port' with the IP address and port of the MetaTrader container
 
-    while True:
+        while True:
-        try:
+            try:
-            # Get historical data
+                # Get historical data
-            df = get_historical_data(socket)
+                df = get_historical_data()
 
-            # Calculate indicators and detect patterns
+                # Calculate indicators and detect patterns
-            df = calculate_indicators_and_detect_patterns(df)
+                df = calculate_indicators_and_detect_patterns(df)
 
-            # Generate trade signals
+                # Generate trade signals
-            df = generate_signals(df)
+                df = generate_signals(df)
 
-            # Execute trades
+                # Execute trades
-            for i in range(1, len(df)):
+                for i in range(1, len(df)):
-                signal = df['signal'].iloc[i]
+                    signal = df['signal'].iloc[i]
-                if signal != 'None':
+                    if signal != 'None':
-                    execute_trade(signal, df, socket)
+                        execute_trade(signal, df)
 
-            # Visualize data
+                # Visualize data
-            visualize_data(df)
+                visualize_data(df)
 
-        except Exception as e:
+            except Exception as e:
-            print(f"Error running trading bot: {str(e)}")
+                print(f"Error running trading bot: {str(e)}")
 
-        # Wait for the next iteration
+            # Wait for the next iteration
-        time.sleep(60)  # Adjust the time interval as needed
+            time.sleep(60)  # Adjust the time interval as needed
 
-    # Disconnect from ZeroMQ socket
+    # Run the trading bot
-    socket.close()
+    run_trading_bot()
+
+    # Load TensorFlow neural network model weights
+    neural_network_model.load_weights('weights/model_weights.h5')
+
+    # Disconnect from MetaTrader 5
+    pymt5.shutdown()
 
 
-# Start the MetaTrader bot
+# Start the MetaTrader 5 bot
 start_mt5_bot()

app/requirements.txt

@@ -4,4 +4,4 @@ TA-Lib
 matplotlib
 scikit-learn
 tensorflow
-pyzmq
+pymt5

bridge/mt5_bridge.py

@@ -4,7 +4,7 @@ import time
 # Connect to the trading bot container
 sio = socketio.Client()
 # Replace with the appropriate URL and port of your trading bot container
-sio.connect('tcp://trading_bot:3000')
+sio.connect('http://trading_bot:3000')
 
 # Handle events from the trading bot container
 

docker-compose.yml

@@ -1,14 +1,19 @@
 version: "3"
 services:
   metatrader_service:
-    image: ejtrader/metatrader:5
+    build:
+      context: .
+      dockerfile: docker/DockerFile.xorg
     container_name: metatrader
     restart: unless-stopped
     environment:
       - DISPLAY=$DISPLAY
     privileged: true
     volumes:
-      - ejtraderMT:/data
+      - /tmp/.X11-unix:/tmp/.X11-unix
+      - ./mt5:/mt5
+    devices:
+      - /dev/dri:/dev/dri
     ports:
       - "5900:5900"
       - "15555:15555"
@@ -20,7 +25,6 @@ services:
 
   trading_bot:
     container_name: trading_bot
-    restart: unless-stopped
     build:
       context: .
       dockerfile: docker/DockerFile
@@ -49,6 +53,3 @@ services:
 networks:
   trading_network:
     driver: bridge
-
-volumes:
-  ejtraderMT: {}

docker/DockerFile

@@ -5,7 +5,7 @@ FROM python:3.10
 WORKDIR /app
 
 # Copy the requirements file to the working directory
-COPY app/requirements.txt . 
+COPY app/requirements.txt .
 
 # Copy the Tab-Lib dependencies to the working directory
 COPY app/Tab-Lib-deps/ta-lib-0.4.0-src.tar.gz .
@@ -21,10 +21,9 @@ RUN tar -xzf ta-lib-0.4.0-src.tar.gz && \
 
 # Install the Python dependencies
 RUN pip install --no-cache-dir -r requirements.txt
-RUN pip install ejtraderMT -U
 
 # Copy the application code to the container
 COPY app/ .
 
-# Run the bot script using xvfb-run
+# Run the bot script when the container launches
 CMD [ "python", "bot.py" ]

docker/DockerFile.xorg

@@ -1,6 +1,39 @@
-FROM ejtrader/metatrader:5
+# Base docker image.
+FROM ubuntu:focal
 
-# Add your custom configuration and scripts here, if needed
+# Install Wine and necessary dependencies
+RUN dpkg --add-architecture i386 && \
+    apt-get update && \
+    apt-get install -y --no-install-recommends \
+    ca-certificates \
+    gnupg \
+    software-properties-common \
+    wget \
+    winbind \
+    xauth \
+    xvfb \
+    cabextract
 
-# Start MetaTrader 5
+# Download and install Wine from WineHQ repository
-CMD ["/root/.wine/drive_c/Program Files/MetaTrader 5/terminal64.exe"]
+RUN wget -qO- https://dl.winehq.org/wine-builds/winehq.key | gpg --dearmor -o /etc/apt/trusted.gpg.d/winehq.gpg && \
+    add-apt-repository 'deb https://dl.winehq.org/wine-builds/ubuntu/ focal main' && \
+    apt-get update && \
+    apt-get install -y --install-recommends winehq-stable winetricks
+
+# Create a non-root user
+RUN useradd -m -s /bin/bash trader
+
+# Set the working directory
+WORKDIR /home/trader
+
+# Install X server utilities
+RUN apt-get install -y x11-xserver-utils x11vnc xvfb
+
+# Configure X server
+RUN mkdir /tmp/.X11-unix && \
+    chown trader:trader /tmp/.X11-unix
+
+# Set up entrypoint script
+COPY mt5/entrypoint.sh /entrypoint.sh
+RUN chmod +x /entrypoint.sh
+ENTRYPOINT ["/entrypoint.sh"]

mt5/entrypoint.sh

@@ -8,12 +8,5 @@ export DISPLAY=:0
 # Install necessary dependencies using winetricks
 winetricks -q corefonts
 
-# Add a small delay for X server initialization
-sleep 2
-
 # Run MetaTrader 5
-exec su - trader -c 'wine "/home/trader/.wine/drive_c/Program Files/MetaTrader 5/terminal64.exe"'
+su - trader -c 'wine "/home/trader/.wine/drive_c/Program Files/MetaTrader 5/terminal64.exe"'
-
-# Clean up X server resources
-killall Xvfb
-rm -rf /tmp/.X11-unix