gavindiaz/Neural-Network-MT5-Trading-Bot
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This commit is contained in:
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.layers import Dense, Dropout
from keras.models import Sequential
import pymt5
import matplotlib.pyplot as plt
import talib
from sklearn.preprocessing import MinMaxScaler
@@ -9,62 +8,15 @@ 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 = 60 # H1 timeframe (1 hour)
timeframe = 'H1' # H1 timeframe (1 hour)
# Set up initial variables
lot_size = 0.01
@@ -92,12 +44,15 @@ def start_mt5_bot():
neural_network_model.compile(optimizer=Adam(
learning_rate=0.001), loss='binary_crossentropy')
def get_historical_data():
# Retrieve historical data
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)
def get_historical_data(socket):
# Request historical data from MetaTrader app
socket.send_string(
f"GET_HISTORICAL_DATA {symbol} {timeframe} 01/01/2022 31/12/2022")
# 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
def calculate_indicators_and_detect_patterns(df):
@@ -109,8 +64,8 @@ def start_mt5_bot():
macd_fast_period = 12
macd_slow_period = 26
macd_signal_period = 9
df['macd'], _, df['macd_signal'] = talib.MACD(df['close'], fastperiod=macd_fast_period,
slowperiod=macd_slow_period, signalperiod=macd_signal_period)
_, _, df['macd'] = talib.MACD(df['close'], fastperiod=macd_fast_period,
slowperiod=macd_slow_period, signalperiod=macd_signal_period)
# Detect divergence based on RSI and MACD
df['rsi_divergence'] = np.where(
@@ -129,17 +84,15 @@ 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['high'].shift(2) > df['high']) & (
df['high'].shift(-2) > df['high']), 'Double Top', 'None'
)
df['top_pattern'] = np.where((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.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['low'].shift(2) < df['low']) & (
df['low'].shift(-2) < df['low']), 'Double Bottom', 'None'
)
df['bottom_pattern'] = np.where((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.loc[df['bottom_pattern'] != 'None',
'pattern'] = df['bottom_pattern']
@@ -175,9 +128,9 @@ def start_mt5_bot():
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
# 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
risk = lot_size * stop_loss
@@ -192,15 +145,17 @@ def start_mt5_bot():
try:
if signal == 'Buy':
# Place a buy trade
result = pymt5.order_send(symbol, pymt5.OP_BUY, lot_size, 0, stop_loss, take_profit,
"Buy trade", 123456, pymt5.ORDER_TIME_GTC, 0)
outcome = 'Win' if result.retcode == pymt5.TRADE_RETCODE_DONE else 'Loss'
socket.send_string(
f"PLACE_TRADE {symbol} BUY {lot_size} {stop_loss} {take_profit}")
response = socket.recv_string()
outcome = 'Win' if response == 'TRADE_EXECUTED' else 'Loss'
elif signal == 'Sell':
# Place a sell trade
result = pymt5.order_send(symbol, pymt5.OP_SELL, lot_size, 0, stop_loss, take_profit,
"Sell trade", 123456, pymt5.ORDER_TIME_GTC, 0)
outcome = 'Win' if result.retcode == pymt5.TRADE_RETCODE_DONE else 'Loss'
socket.send_string(
f"PLACE_TRADE {symbol} SELL {lot_size} {stop_loss} {take_profit}")
response = socket.recv_string()
outcome = 'Win' if response == 'TRADE_EXECUTED' else 'Loss'
# Example trade outcome information
trade_outcome = {
@@ -294,45 +249,41 @@ def start_mt5_bot():
plt.legend()
plt.show()
def run_trading_bot():
# Connect to MetaTrader 5 container
connect_to_mt5_container()
# Connect to MetaTrader app using ZeroMQ
context = zmq.Context()
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:
try:
# Get historical data
df = get_historical_data()
while True:
try:
# Get historical data
df = get_historical_data(socket)
# Calculate indicators and detect patterns
df = calculate_indicators_and_detect_patterns(df)
# Calculate indicators and detect patterns
df = calculate_indicators_and_detect_patterns(df)
# Generate trade signals
df = generate_signals(df)
# Generate trade signals
df = generate_signals(df)
# Execute trades
for i in range(1, len(df)):
signal = df['signal'].iloc[i]
if signal != 'None':
execute_trade(signal, df)
# Execute trades
for i in range(1, len(df)):
signal = df['signal'].iloc[i]
if signal != 'None':
execute_trade(signal, df, socket)
# Visualize data
visualize_data(df)
# Visualize data
visualize_data(df)
except Exception as e:
print(f"Error running trading bot: {str(e)}")
except Exception as e:
print(f"Error running trading bot: {str(e)}")
# Wait for the next iteration
time.sleep(60) # Adjust the time interval as needed
# Wait for the next iteration
time.sleep(60) # Adjust the time interval as needed
# Run the trading bot
run_trading_bot()
# Load TensorFlow neural network model weights
neural_network_model.load_weights('weights/model_weights.h5')
# Disconnect from MetaTrader 5
pymt5.shutdown()
# Disconnect from ZeroMQ socket
socket.close()
# Start the MetaTrader 5 bot
# Start the MetaTrader bot
start_mt5_bot()
app/requirements.txt
+1 -1
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@@ -4,4 +4,4 @@ TA-Lib
matplotlib
scikit-learn
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
sio = socketio.Client()
# 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
docker-compose.yml
+6 -7
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@@ -1,19 +1,14 @@
version: "3"
services:
metatrader_service:
build:
context: .
dockerfile: docker/DockerFile.xorg
image: ejtrader/metatrader:5
container_name: metatrader
restart: unless-stopped
environment:
- DISPLAY=$DISPLAY
privileged: true
volumes:
- /tmp/.X11-unix:/tmp/.X11-unix
- ./mt5:/mt5
devices:
- /dev/dri:/dev/dri
- ejtraderMT:/data
ports:
- "5900:5900"
- "15555:15555"
@@ -25,6 +20,7 @@ services:
trading_bot:
container_name: trading_bot
restart: unless-stopped
build:
context: .
dockerfile: docker/DockerFile
@@ -53,3 +49,6 @@ services:
networks:
trading_network:
driver: bridge
volumes:
ejtraderMT: {}
docker/DockerFile
+3 -2
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@@ -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 .
@@ -21,9 +21,10 @@ 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 when the container launches
# Run the bot script using xvfb-run
CMD [ "python", "bot.py" ]
docker/DockerFile.xorg
+4 -37
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@@ -1,39 +1,6 @@
# Base docker image.
FROM ubuntu:focal
FROM ejtrader/metatrader:5
# 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
# Add your custom configuration and scripts here, if needed
# Download and install Wine from WineHQ repository
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"]
# Start MetaTrader 5
CMD ["/root/.wine/drive_c/Program Files/MetaTrader 5/terminal64.exe"]
mt5/entrypoint.sh
+8 -1
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@@ -8,5 +8,12 @@ export DISPLAY=:0
# Install necessary dependencies using winetricks
winetricks -q corefonts
# Add a small delay for X server initialization
sleep 2
# 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