gavindiaz/Neural-Network-MT5-Trading-Bot
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Mike
2023-07-17 17:15:08 +02:00
parent 58c682bcfd
commit 528422aa24
7 changed files with 82 additions and 157 deletions
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app/bot.py
+59 -108
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@@ -1,7 +1,6 @@
from keras.optimizers import Adam from keras.optimizers import Adam
from keras.layers import Dense, Dropout from keras.layers import Dense, Dropout
from keras.models import Sequential from keras.models import Sequential
import pymt5
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import talib import talib
from sklearn.preprocessing import MinMaxScaler from sklearn.preprocessing import MinMaxScaler
@@ -9,62 +8,15 @@ import numpy as np
import pandas as pd import pandas as pd
import time import time
import os import os
import zmq
os.environ["CUDA_VISIBLE_DEVICES"] = "-1" 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(): def start_mt5_bot():
# Define the symbols and timeframes # Define the symbols and timeframes
symbol = 'EURUSD' symbol = 'EURUSD'
timeframe = 60 # H1 timeframe (1 hour) timeframe = 'H1' # H1 timeframe (1 hour)
# Set up initial variables # Set up initial variables
lot_size = 0.01 lot_size = 0.01
@@ -92,12 +44,15 @@ def start_mt5_bot():
neural_network_model.compile(optimizer=Adam( neural_network_model.compile(optimizer=Adam(
learning_rate=0.001), loss='binary_crossentropy') learning_rate=0.001), loss='binary_crossentropy')
def get_historical_data(): def get_historical_data(socket):
# Retrieve historical data # Request historical data from MetaTrader app
rates = pymt5.copy_rates_from_pos(symbol, timeframe, 0, 1000) socket.send_string(
df = pd.DataFrame(rates) f"GET_HISTORICAL_DATA {symbol} {timeframe} 01/01/2022 31/12/2022")
df['time'] = pd.to_datetime(df['time'], unit='s')
df.set_index('time', inplace=True) # Receive historical data from MetaTrader app
response = socket.recv_string()
data = pd.read_json(response)
df = data[['open', 'high', 'low', 'close', 'tick_volume']]
return df return df
def calculate_indicators_and_detect_patterns(df): def calculate_indicators_and_detect_patterns(df):
@@ -109,8 +64,8 @@ def start_mt5_bot():
macd_fast_period = 12 macd_fast_period = 12
macd_slow_period = 26 macd_slow_period = 26
macd_signal_period = 9 macd_signal_period = 9
df['macd'], _, df['macd_signal'] = talib.MACD(df['close'], fastperiod=macd_fast_period, _, _, df['macd'] = 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 # Detect divergence based on RSI and MACD
df['rsi_divergence'] = np.where( df['rsi_divergence'] = np.where(
@@ -129,17 +84,15 @@ def start_mt5_bot():
# Detect double tops and bottoms # Detect double tops and bottoms
df['pattern'] = 'None' df['pattern'] = 'None'
df['top_pattern'] = np.where( df['top_pattern'] = np.where((df['high'].shift(1) < df['high']) & (df['high'].shift(-1) < 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']) & ( df['high'].shift(-2) > df['high']),
df['high'].shift(-2) > df['high']), 'Double Top', 'None' 'Double Top', 'None')
)
df.loc[df['top_pattern'] != 'None', 'pattern'] = df['top_pattern'] df.loc[df['top_pattern'] != 'None', 'pattern'] = df['top_pattern']
df['bottom_pattern'] = np.where( df['bottom_pattern'] = np.where((df['low'].shift(1) > df['low']) & (df['low'].shift(-1) > 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']) & ( df['low'].shift(-2) < df['low']),
df['low'].shift(-2) < df['low']), 'Double Bottom', 'None' 'Double Bottom', 'None')
)
df.loc[df['bottom_pattern'] != 'None', df.loc[df['bottom_pattern'] != 'None',
'pattern'] = df['bottom_pattern'] 'pattern'] = df['bottom_pattern']
@@ -175,9 +128,9 @@ def start_mt5_bot():
return df return df
def execute_trade(signal, df): def execute_trade(signal, df, socket):
# Implement risk management and trade execution logic based on the signals generated # Implement risk management and trade execution logic based on the signals generated
# Update TensorFlow neural network model with trade outcome (loss or win) # Update TensorFlow neural network model with trade outcome
# Calculate risk and position size based on lot size, stop loss, and take profit # Calculate risk and position size based on lot size, stop loss, and take profit
risk = lot_size * stop_loss risk = lot_size * stop_loss
@@ -192,15 +145,17 @@ def start_mt5_bot():
try: try:
if signal == 'Buy': if signal == 'Buy':
# Place a buy trade # Place a buy trade
result = pymt5.order_send(symbol, pymt5.OP_BUY, lot_size, 0, stop_loss, take_profit, socket.send_string(
"Buy trade", 123456, pymt5.ORDER_TIME_GTC, 0) f"PLACE_TRADE {symbol} BUY {lot_size} {stop_loss} {take_profit}")
outcome = 'Win' if result.retcode == pymt5.TRADE_RETCODE_DONE else 'Loss' response = socket.recv_string()
outcome = 'Win' if response == 'TRADE_EXECUTED' else 'Loss'
elif signal == 'Sell': elif signal == 'Sell':
# Place a sell trade # Place a sell trade
result = pymt5.order_send(symbol, pymt5.OP_SELL, lot_size, 0, stop_loss, take_profit, socket.send_string(
"Sell trade", 123456, pymt5.ORDER_TIME_GTC, 0) f"PLACE_TRADE {symbol} SELL {lot_size} {stop_loss} {take_profit}")
outcome = 'Win' if result.retcode == pymt5.TRADE_RETCODE_DONE else 'Loss' response = socket.recv_string()
outcome = 'Win' if response == 'TRADE_EXECUTED' else 'Loss'
# Example trade outcome information # Example trade outcome information
trade_outcome = { trade_outcome = {
@@ -294,45 +249,41 @@ def start_mt5_bot():
plt.legend() plt.legend()
plt.show() plt.show()
def run_trading_bot(): # Connect to MetaTrader app using ZeroMQ
# Connect to MetaTrader 5 container context = zmq.Context()
connect_to_mt5_container() socket = context.socket(zmq.REQ)
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() df = get_historical_data(socket)
# 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) execute_trade(signal, df, socket)
# 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
# Run the trading bot # Disconnect from ZeroMQ socket
run_trading_bot() socket.close()
# Load TensorFlow neural network model weights
neural_network_model.load_weights('weights/model_weights.h5')
# Disconnect from MetaTrader 5
pymt5.shutdown()
# Start the MetaTrader 5 bot # Start the MetaTrader bot
start_mt5_bot() start_mt5_bot()
app/requirements.txt
+1 -1
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@@ -4,4 +4,4 @@ TA-Lib
matplotlib matplotlib
scikit-learn scikit-learn
tensorflow tensorflow
pymt5 pyzmq
bridge/mt5_bridge.py
+1 -1
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@@ -4,7 +4,7 @@ import time
# Connect to the trading bot container # Connect to the trading bot container
sio = socketio.Client() sio = socketio.Client()
# Replace with the appropriate URL and port of your trading bot container # Replace with the appropriate URL and port of your trading bot container
sio.connect('http://trading_bot:3000') sio.connect('tcp://trading_bot:3000')
# Handle events from the trading bot container # Handle events from the trading bot container
docker-compose.yml
+6 -7
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@@ -1,19 +1,14 @@
version: "3" version: "3"
services: services:
metatrader_service: metatrader_service:
build: image: ejtrader/metatrader:5
context: .
dockerfile: docker/DockerFile.xorg
container_name: metatrader container_name: metatrader
restart: unless-stopped restart: unless-stopped
environment: environment:
- DISPLAY=$DISPLAY - DISPLAY=$DISPLAY
privileged: true privileged: true
volumes: volumes:
- /tmp/.X11-unix:/tmp/.X11-unix - ejtraderMT:/data
- ./mt5:/mt5
devices:
- /dev/dri:/dev/dri
ports: ports:
- "5900:5900" - "5900:5900"
- "15555:15555" - "15555:15555"
@@ -25,6 +20,7 @@ services:
trading_bot: trading_bot:
container_name: trading_bot container_name: trading_bot
restart: unless-stopped
build: build:
context: . context: .
dockerfile: docker/DockerFile dockerfile: docker/DockerFile
@@ -53,3 +49,6 @@ services:
networks: networks:
trading_network: trading_network:
driver: bridge driver: bridge
volumes:
ejtraderMT: {}
docker/DockerFile
+2 -1
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@@ -21,9 +21,10 @@ RUN tar -xzf ta-lib-0.4.0-src.tar.gz && \
# Install the Python dependencies # Install the Python dependencies
RUN pip install --no-cache-dir -r requirements.txt RUN pip install --no-cache-dir -r requirements.txt
RUN pip install ejtraderMT -U
# Copy the application code to the container # Copy the application code to the container
COPY app/ . COPY app/ .
# Run the bot script when the container launches # Run the bot script using xvfb-run
CMD [ "python", "bot.py" ] CMD [ "python", "bot.py" ]
docker/DockerFile.xorg
+4 -37
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@@ -1,39 +1,6 @@
# Base docker image. FROM ejtrader/metatrader:5
FROM ubuntu:focal
# Install Wine and necessary dependencies # Add your custom configuration and scripts here, if needed
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
# Download and install Wine from WineHQ repository # Start MetaTrader 5
RUN wget -qO- https://dl.winehq.org/wine-builds/winehq.key | gpg --dearmor -o /etc/apt/trusted.gpg.d/winehq.gpg && \ CMD ["/root/.wine/drive_c/Program Files/MetaTrader 5/terminal64.exe"]
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 -1
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@@ -8,5 +8,12 @@ export DISPLAY=:0
# Install necessary dependencies using winetricks # Install necessary dependencies using winetricks
winetricks -q corefonts winetricks -q corefonts
# Add a small delay for X server initialization
sleep 2
# Run MetaTrader 5 # Run MetaTrader 5
su - trader -c 'wine "/home/trader/.wine/drive_c/Program Files/MetaTrader 5/terminal64.exe"' exec 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