Changes
This commit is contained in:
Mike
+59
-108
@@ -1,7 +1,6 @@
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from keras.optimizers import Adam
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from keras.layers import Dense, Dropout
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from keras.models import Sequential
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import pymt5
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import matplotlib.pyplot as plt
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import talib
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from sklearn.preprocessing import MinMaxScaler
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@@ -9,62 +8,15 @@ import numpy as np
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import pandas as pd
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import time
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import os
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import zmq
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os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
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def connect_to_mt5_container():
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server = "localhost" # Change to the appropriate IP or hostname if necessary
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port = 15555 # Change to the appropriate port if necessary
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login = 123456 # Change to your MetaTrader login number if necessary
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password = "your_password" # Change to your MetaTrader password if necessary
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# Connect to MetaTrader 5
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mt5 = pymt5.PyMT5()
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mt5.onConnected = onConnected
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mt5.onDisconnected = onDisconnected
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mt5.onData = onData
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# Wait for the connection to be established
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while not onConnected:
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time.sleep(0.1)
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# Send login request
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login_request = {
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'ver': '3',
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'type': '1',
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'login': str(login),
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'password': password,
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'res': '0'
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}
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mt5.broadcast(login_request)
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# Wait for the login response
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while not onConnected:
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time.sleep(0.1)
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# Check if login was successful
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if onConnected:
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print(f"Connected to MetaTrader 5: {onConnected}")
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else:
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print("Failed to connect to MetaTrader 5")
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def onConnected(client_info):
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print(f"Connected: {client_info}")
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def onDisconnected(client_info):
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print(f"Disconnected: {client_info}")
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def onData(data):
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print(f"Received data: {data}")
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def start_mt5_bot():
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# Define the symbols and timeframes
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symbol = 'EURUSD'
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timeframe = 60 # H1 timeframe (1 hour)
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timeframe = 'H1' # H1 timeframe (1 hour)
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# Set up initial variables
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lot_size = 0.01
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@@ -92,12 +44,15 @@ def start_mt5_bot():
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neural_network_model.compile(optimizer=Adam(
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learning_rate=0.001), loss='binary_crossentropy')
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def get_historical_data():
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# Retrieve historical data
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rates = pymt5.copy_rates_from_pos(symbol, timeframe, 0, 1000)
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df = pd.DataFrame(rates)
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df['time'] = pd.to_datetime(df['time'], unit='s')
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df.set_index('time', inplace=True)
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def get_historical_data(socket):
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# Request historical data from MetaTrader app
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socket.send_string(
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f"GET_HISTORICAL_DATA {symbol} {timeframe} 01/01/2022 31/12/2022")
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# Receive historical data from MetaTrader app
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response = socket.recv_string()
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data = pd.read_json(response)
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df = data[['open', 'high', 'low', 'close', 'tick_volume']]
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return df
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def calculate_indicators_and_detect_patterns(df):
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@@ -109,8 +64,8 @@ def start_mt5_bot():
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macd_fast_period = 12
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macd_slow_period = 26
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macd_signal_period = 9
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df['macd'], _, df['macd_signal'] = talib.MACD(df['close'], fastperiod=macd_fast_period,
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slowperiod=macd_slow_period, signalperiod=macd_signal_period)
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_, _, df['macd'] = talib.MACD(df['close'], fastperiod=macd_fast_period,
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slowperiod=macd_slow_period, signalperiod=macd_signal_period)
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# Detect divergence based on RSI and MACD
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df['rsi_divergence'] = np.where(
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@@ -129,17 +84,15 @@ def start_mt5_bot():
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# Detect double tops and bottoms
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df['pattern'] = 'None'
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df['top_pattern'] = np.where(
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(df['high'].shift(1) < df['high']) & (df['high'].shift(-1) < df['high']) &
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(df['high'].shift(2) > df['high']) & (
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df['high'].shift(-2) > df['high']), 'Double Top', 'None'
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)
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df['top_pattern'] = np.where((df['high'].shift(1) < df['high']) & (df['high'].shift(-1) < df['high']) &
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(df['high'].shift(2) > df['high']) & (
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df['high'].shift(-2) > df['high']),
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'Double Top', 'None')
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df.loc[df['top_pattern'] != 'None', 'pattern'] = df['top_pattern']
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df['bottom_pattern'] = np.where(
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(df['low'].shift(1) > df['low']) & (df['low'].shift(-1) > df['low']) &
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(df['low'].shift(2) < df['low']) & (
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df['low'].shift(-2) < df['low']), 'Double Bottom', 'None'
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)
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df['bottom_pattern'] = np.where((df['low'].shift(1) > df['low']) & (df['low'].shift(-1) > df['low']) &
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(df['low'].shift(2) < df['low']) & (
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df['low'].shift(-2) < df['low']),
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'Double Bottom', 'None')
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df.loc[df['bottom_pattern'] != 'None',
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'pattern'] = df['bottom_pattern']
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@@ -175,9 +128,9 @@ def start_mt5_bot():
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return df
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def execute_trade(signal, df):
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def execute_trade(signal, df, socket):
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# Implement risk management and trade execution logic based on the signals generated
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# Update TensorFlow neural network model with trade outcome (loss or win)
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# Update TensorFlow neural network model with trade outcome
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# Calculate risk and position size based on lot size, stop loss, and take profit
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risk = lot_size * stop_loss
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@@ -192,15 +145,17 @@ def start_mt5_bot():
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try:
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if signal == 'Buy':
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# Place a buy trade
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result = pymt5.order_send(symbol, pymt5.OP_BUY, lot_size, 0, stop_loss, take_profit,
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"Buy trade", 123456, pymt5.ORDER_TIME_GTC, 0)
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outcome = 'Win' if result.retcode == pymt5.TRADE_RETCODE_DONE else 'Loss'
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socket.send_string(
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f"PLACE_TRADE {symbol} BUY {lot_size} {stop_loss} {take_profit}")
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response = socket.recv_string()
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outcome = 'Win' if response == 'TRADE_EXECUTED' else 'Loss'
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elif signal == 'Sell':
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# Place a sell trade
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result = pymt5.order_send(symbol, pymt5.OP_SELL, lot_size, 0, stop_loss, take_profit,
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"Sell trade", 123456, pymt5.ORDER_TIME_GTC, 0)
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outcome = 'Win' if result.retcode == pymt5.TRADE_RETCODE_DONE else 'Loss'
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socket.send_string(
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f"PLACE_TRADE {symbol} SELL {lot_size} {stop_loss} {take_profit}")
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response = socket.recv_string()
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outcome = 'Win' if response == 'TRADE_EXECUTED' else 'Loss'
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# Example trade outcome information
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trade_outcome = {
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@@ -294,45 +249,41 @@ def start_mt5_bot():
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plt.legend()
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plt.show()
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def run_trading_bot():
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# Connect to MetaTrader 5 container
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connect_to_mt5_container()
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# Connect to MetaTrader app using ZeroMQ
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context = zmq.Context()
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socket = context.socket(zmq.REQ)
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socket.connect("tcp://metatrader_service:5900")
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# Replace 'metatrader-container-ip' and 'metatrader-port' with the IP address and port of the MetaTrader container
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while True:
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try:
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# Get historical data
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df = get_historical_data()
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while True:
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try:
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# Get historical data
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df = get_historical_data(socket)
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# Calculate indicators and detect patterns
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df = calculate_indicators_and_detect_patterns(df)
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# Calculate indicators and detect patterns
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df = calculate_indicators_and_detect_patterns(df)
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# Generate trade signals
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df = generate_signals(df)
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# Generate trade signals
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df = generate_signals(df)
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# Execute trades
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for i in range(1, len(df)):
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signal = df['signal'].iloc[i]
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if signal != 'None':
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execute_trade(signal, df)
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# Execute trades
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for i in range(1, len(df)):
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signal = df['signal'].iloc[i]
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if signal != 'None':
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execute_trade(signal, df, socket)
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# Visualize data
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visualize_data(df)
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# Visualize data
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visualize_data(df)
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except Exception as e:
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print(f"Error running trading bot: {str(e)}")
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except Exception as e:
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print(f"Error running trading bot: {str(e)}")
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# Wait for the next iteration
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time.sleep(60) # Adjust the time interval as needed
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# Wait for the next iteration
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time.sleep(60) # Adjust the time interval as needed
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# Run the trading bot
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run_trading_bot()
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# Load TensorFlow neural network model weights
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neural_network_model.load_weights('weights/model_weights.h5')
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# Disconnect from MetaTrader 5
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pymt5.shutdown()
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# Disconnect from ZeroMQ socket
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socket.close()
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# Start the MetaTrader 5 bot
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# Start the MetaTrader bot
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start_mt5_bot()
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@@ -4,4 +4,4 @@ TA-Lib
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matplotlib
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scikit-learn
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tensorflow
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pymt5
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pyzmq
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