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@@ -1,351 +1,338 @@
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import time
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import pandas as pd
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import numpy as np
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from sklearn.preprocessing import MinMaxScaler
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import talib
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import matplotlib.pyplot as plt
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import MetaTrader5 as mt5
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from keras.models import Sequential
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from keras.layers import Dense, Dropout
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from keras.optimizers import Adam
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import socket
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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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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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os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
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def start_mt5(username, password, server, path):
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# Ensure that all variables are the correct type
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uname = int(username) # Username must be an int
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pword = str(password) # Password must be a string
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trading_server = str(server) # Server must be a string
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filepath = str(path) # Filepath must be a string
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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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if mt5.initialize(login=uname, password=pword, server=trading_server, path=filepath):
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# Login to MT5
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if mt5.login(login=uname, password=pword, server=trading_server):
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return True
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else:
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print("Login Fail")
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quit()
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return PermissionError
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else:
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print("MT5 Initialization Failed")
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quit()
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return ConnectionAbortedError
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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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def connect_to_mt5(username, password, server, path):
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# Start the MetaTrader 5 instance
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if start_mt5(username, password, server, path):
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print("Connected to MetaTrader 5")
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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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return
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# Define the symbols and timeframes
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symbol = 'EURUSD'
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timeframe = mt5.TIMEFRAME_H1
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# Set up initial variables
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lot_size = 0.01
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stop_loss = 100
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take_profit = 150
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# Define TensorFlow neural network model
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def onConnected(client_info):
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print(f"Connected: {client_info}")
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def create_neural_network_model(input_shape):
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model = Sequential()
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model.add(Dense(64, activation='relu', input_shape=input_shape))
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model.add(Dropout(0.2))
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model.add(Dense(64, activation='relu'))
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model.add(Dropout(0.2))
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model.add(Dense(1, activation='sigmoid'))
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return model
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def onDisconnected(client_info):
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print(f"Disconnected: {client_info}")
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# Define input shape for the neural network
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input_shape = (10,) # Adjust the input shape based on your features and data
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# Create the neural network model
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neural_network_model = create_neural_network_model(input_shape)
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# Compile the model
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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 onData(data):
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print(f"Received data: {data}")
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def get_historical_data():
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# Retrieve historical data
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rates = mt5.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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return df
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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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# Set up initial variables
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lot_size = 0.01
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stop_loss = 100
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take_profit = 150
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def calculate_indicators_and_detect_patterns(df):
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# Calculate RSI
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rsi_period = 14
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df['rsi'] = talib.RSI(df['close'], rsi_period)
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# Define TensorFlow neural network model
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def create_neural_network_model(input_shape):
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model = Sequential()
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model.add(Dense(64, activation='relu', input_shape=input_shape))
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model.add(Dropout(0.2))
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model.add(Dense(64, activation='relu'))
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model.add(Dropout(0.2))
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model.add(Dense(1, activation='sigmoid'))
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return model
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# Calculate MACD
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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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# Define input shape for the neural network
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# Adjust the input shape based on your features and data
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input_shape = (10,)
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# Detect divergence based on RSI and MACD
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df['rsi_divergence'] = np.where(
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df['rsi'].diff().shift(-1) * df['macd'].diff().shift(-1) < 0, True, False)
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df['macd_divergence'] = np.where(
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df['macd'].diff().shift(-1) * df['rsi'].diff().shift(-1) < 0, True, False)
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# Create the neural network model
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neural_network_model = create_neural_network_model(input_shape)
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# Detect support and resistance levels
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window = 10
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df['support'] = df['low'].rolling(window).min()
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df['resistance'] = df['high'].rolling(window).max()
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# Compile the model
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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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# Determine trend direction
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df['trend_200'] = df['close'].rolling(window=200).mean()
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df['trend_50'] = df['close'].rolling(window=50).mean()
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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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return df
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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.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.loc[df['bottom_pattern'] != 'None', 'pattern'] = df['bottom_pattern']
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def calculate_indicators_and_detect_patterns(df):
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# Calculate RSI
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rsi_period = 14
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df['rsi'] = talib.RSI(df['close'], rsi_period)
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return df
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# Calculate MACD
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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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# Detect divergence based on RSI and MACD
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df['rsi_divergence'] = np.where(
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df['rsi'].diff().shift(-1) * df['macd'].diff().shift(-1) < 0, True, False)
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df['macd_divergence'] = np.where(
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df['macd'].diff().shift(-1) * df['rsi'].diff().shift(-1) < 0, True, False)
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def generate_signals(df):
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# Determine trade signals based on divergences, patterns, and trend direction
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df['signal'] = 'None'
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df['divergence_signal'] = np.where((df['rsi_divergence'] == True) & (df['pattern'] != 'None'), 'Both',
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np.where(df['rsi_divergence'] == True, 'RSI', 'Pattern'))
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df['strongest_divergence_signal'] = df[[
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'divergence_signal', 'macd_divergence']].max(axis=1)
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df['support_resistance_signal'] = np.where(df['close'] > df['resistance'], 'Resistance',
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np.where(df['close'] < df['support'], 'Support', 'None'))
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df['trend_signal'] = np.where(df['close'] > df['trend_200'], 'Uptrend',
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np.where(df['close'] < df['trend_200'], 'Downtrend', 'None'))
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for i in range(1, len(df)):
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prev_divergence_signal = df['divergence_signal'].iloc[i - 1]
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curr_divergence_signal = df['divergence_signal'].iloc[i]
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strongest_divergence_signal = df['strongest_divergence_signal'].iloc[i]
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support_resistance_signal = df['support_resistance_signal'].iloc[i]
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trend_signal = df['trend_signal'].iloc[i]
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# Detect support and resistance levels
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window = 10
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df['support'] = df['low'].rolling(window).min()
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df['resistance'] = df['high'].rolling(window).max()
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if strongest_divergence_signal != 'None':
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df['signal'].iloc[i] = strongest_divergence_signal
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elif prev_divergence_signal == curr_divergence_signal and curr_divergence_signal != 'None':
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df['signal'].iloc[i] = curr_divergence_signal
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else:
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df['signal'].iloc[i] = support_resistance_signal
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# Determine trend direction
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df['trend_200'] = df['close'].rolling(window=200).mean()
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df['trend_50'] = df['close'].rolling(window=50).mean()
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if trend_signal != 'None' and df['signal'].iloc[i] != 'None':
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df['signal'].iloc[i] = trend_signal
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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.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.loc[df['bottom_pattern'] != 'None',
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'pattern'] = df['bottom_pattern']
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return df
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return df
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def generate_signals(df):
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# Determine trade signals based on divergences, patterns, and trend direction
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df['signal'] = 'None'
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df['divergence_signal'] = np.where((df['rsi_divergence'] == True) & (df['pattern'] != 'None'), 'Both',
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np.where(df['rsi_divergence'] == True, 'RSI', 'Pattern'))
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df['strongest_divergence_signal'] = df[[
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'divergence_signal', 'macd_divergence']].max(axis=1)
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df['support_resistance_signal'] = np.where(df['close'] > df['resistance'], 'Resistance',
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np.where(df['close'] < df['support'], 'Support', 'None'))
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df['trend_signal'] = np.where(df['close'] > df['trend_200'], 'Uptrend',
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np.where(df['close'] < df['trend_200'], 'Downtrend', 'None'))
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for i in range(1, len(df)):
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prev_divergence_signal = df['divergence_signal'].iloc[i - 1]
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curr_divergence_signal = df['divergence_signal'].iloc[i]
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strongest_divergence_signal = df['strongest_divergence_signal'].iloc[i]
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support_resistance_signal = df['support_resistance_signal'].iloc[i]
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trend_signal = df['trend_signal'].iloc[i]
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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)
|
|
|
|
|
if strongest_divergence_signal != 'None':
|
|
|
|
|
df['signal'].iloc[i] = strongest_divergence_signal
|
|
|
|
|
elif prev_divergence_signal == curr_divergence_signal and curr_divergence_signal != 'None':
|
|
|
|
|
df['signal'].iloc[i] = curr_divergence_signal
|
|
|
|
|
else:
|
|
|
|
|
df['signal'].iloc[i] = support_resistance_signal
|
|
|
|
|
|
|
|
|
|
# Calculate risk and position size based on lot size, stop loss, and take profit
|
|
|
|
|
risk = lot_size * stop_loss
|
|
|
|
|
strongest_divergence_signal = df['strongest_divergence_signal'].iloc[-1]
|
|
|
|
|
if strongest_divergence_signal == 'RSI':
|
|
|
|
|
risk *= 1.2 # Increase risk by 20% if RSI divergence is the strongest
|
|
|
|
|
elif strongest_divergence_signal == 'Pattern':
|
|
|
|
|
risk *= 1.5 # Increase risk by 50% if pattern divergence is the strongest
|
|
|
|
|
if trend_signal != 'None' and df['signal'].iloc[i] != 'None':
|
|
|
|
|
df['signal'].iloc[i] = trend_signal
|
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|
|
|
|
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|
|
position_size = risk / (take_profit - stop_loss)
|
|
|
|
|
return df
|
|
|
|
|
|
|
|
|
|
try:
|
|
|
|
|
if signal == 'Buy':
|
|
|
|
|
# Place a buy trade
|
|
|
|
|
result = mt5.ORDER_RESULT_FAIL
|
|
|
|
|
request = {
|
|
|
|
|
"action": mt5.TRADE_ACTION_DEAL,
|
|
|
|
|
"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,
|
|
|
|
|
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)
|
|
|
|
|
|
|
|
|
|
# Calculate risk and position size based on lot size, stop loss, and take profit
|
|
|
|
|
risk = lot_size * stop_loss
|
|
|
|
|
strongest_divergence_signal = df['strongest_divergence_signal'].iloc[-1]
|
|
|
|
|
if strongest_divergence_signal == 'RSI':
|
|
|
|
|
risk *= 1.2 # Increase risk by 20% if RSI divergence is the strongest
|
|
|
|
|
elif strongest_divergence_signal == 'Pattern':
|
|
|
|
|
risk *= 1.5 # Increase risk by 50% if pattern divergence is the strongest
|
|
|
|
|
|
|
|
|
|
position_size = risk / (take_profit - stop_loss)
|
|
|
|
|
|
|
|
|
|
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'
|
|
|
|
|
|
|
|
|
|
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'
|
|
|
|
|
|
|
|
|
|
# Example trade outcome information
|
|
|
|
|
trade_outcome = {
|
|
|
|
|
'pattern': df['pattern'].iloc[-1],
|
|
|
|
|
'divergence_strength': strongest_divergence_signal,
|
|
|
|
|
'time': df.index[-1],
|
|
|
|
|
'trend_direction': df['trend_signal'].iloc[-1],
|
|
|
|
|
'indicator_used': strongest_divergence_signal,
|
|
|
|
|
'outcome': outcome
|
|
|
|
|
}
|
|
|
|
|
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
|
|
|
|
|
request = {
|
|
|
|
|
"action": mt5.TRADE_ACTION_DEAL,
|
|
|
|
|
"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'
|
|
|
|
|
# Update TensorFlow neural network model with trade outcome
|
|
|
|
|
update_neural_network_model(trade_outcome)
|
|
|
|
|
|
|
|
|
|
# Example trade outcome information
|
|
|
|
|
trade_outcome = {
|
|
|
|
|
'pattern': df['pattern'].iloc[-1],
|
|
|
|
|
'divergence_strength': strongest_divergence_signal,
|
|
|
|
|
'time': df.index[-1],
|
|
|
|
|
'trend_direction': df['trend_signal'].iloc[-1],
|
|
|
|
|
'indicator_used': strongest_divergence_signal,
|
|
|
|
|
'outcome': outcome
|
|
|
|
|
}
|
|
|
|
|
# Example print statements for debugging
|
|
|
|
|
print(
|
|
|
|
|
f"Executed {signal} trade with position size: {position_size}")
|
|
|
|
|
print(f"Trade outcome: {trade_outcome}")
|
|
|
|
|
|
|
|
|
|
# Update TensorFlow neural network model with trade outcome
|
|
|
|
|
update_neural_network_model(trade_outcome)
|
|
|
|
|
# Additional logic for trade management, monitoring, etc.
|
|
|
|
|
except Exception as e:
|
|
|
|
|
print(f"Error executing trade: {str(e)}")
|
|
|
|
|
|
|
|
|
|
# Example print statements for debugging
|
|
|
|
|
print(f"Executed {signal} trade with position size: {position_size}")
|
|
|
|
|
print(f"Trade outcome: {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']
|
|
|
|
|
time = trade_outcome['time']
|
|
|
|
|
trend_direction = trade_outcome['trend_direction']
|
|
|
|
|
indicator_used = trade_outcome['indicator_used']
|
|
|
|
|
outcome = trade_outcome['outcome']
|
|
|
|
|
|
|
|
|
|
# Additional logic for trade management, monitoring, etc.
|
|
|
|
|
except Exception as e:
|
|
|
|
|
print(f"Error executing trade: {str(e)}")
|
|
|
|
|
# Example update code: Append trade outcome information to a dataset for future training
|
|
|
|
|
trade_data = pd.DataFrame({
|
|
|
|
|
'pattern': [pattern],
|
|
|
|
|
'divergence_strength': [divergence_strength],
|
|
|
|
|
'time': [time],
|
|
|
|
|
'trend_direction': [trend_direction],
|
|
|
|
|
'indicator_used': [indicator_used],
|
|
|
|
|
'outcome': [outcome]
|
|
|
|
|
})
|
|
|
|
|
# Append the trade data to the dataset for future training
|
|
|
|
|
dataset = pd.read_csv('trade_dataset.csv') # Load existing dataset
|
|
|
|
|
updated_dataset = pd.concat([dataset, trade_data], ignore_index=True)
|
|
|
|
|
# Save updated dataset
|
|
|
|
|
updated_dataset.to_csv('trade_dataset.csv', index=False)
|
|
|
|
|
|
|
|
|
|
# Example retraining code: Retrain the neural network model with the updated dataset
|
|
|
|
|
# Preprocess data as per your requirements
|
|
|
|
|
X_train, y_train = preprocess_data(updated_dataset)
|
|
|
|
|
# Example retraining step
|
|
|
|
|
neural_network_model.fit(X_train, y_train, epochs=10, batch_size=32)
|
|
|
|
|
|
|
|
|
|
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']
|
|
|
|
|
time = trade_outcome['time']
|
|
|
|
|
trend_direction = trade_outcome['trend_direction']
|
|
|
|
|
indicator_used = trade_outcome['indicator_used']
|
|
|
|
|
outcome = trade_outcome['outcome']
|
|
|
|
|
# Save the updated model weights
|
|
|
|
|
neural_network_model.save_weights('weights/model_weights.h5')
|
|
|
|
|
|
|
|
|
|
# Example update code: Append trade outcome information to a dataset for future training
|
|
|
|
|
trade_data = pd.DataFrame({
|
|
|
|
|
'pattern': [pattern],
|
|
|
|
|
'divergence_strength': [divergence_strength],
|
|
|
|
|
'time': [time],
|
|
|
|
|
'trend_direction': [trend_direction],
|
|
|
|
|
'indicator_used': [indicator_used],
|
|
|
|
|
'outcome': [outcome]
|
|
|
|
|
})
|
|
|
|
|
# Append the trade data to the dataset for future training
|
|
|
|
|
dataset = pd.read_csv('trade_dataset.csv') # Load existing dataset
|
|
|
|
|
updated_dataset = pd.concat([dataset, trade_data], ignore_index=True)
|
|
|
|
|
# Save updated dataset
|
|
|
|
|
updated_dataset.to_csv('trade_dataset.csv', index=False)
|
|
|
|
|
def preprocess_data(dataset):
|
|
|
|
|
# Define the numerical features (if any)
|
|
|
|
|
numerical_features = [] # Update with the actual numerical feature column names
|
|
|
|
|
|
|
|
|
|
# Example retraining code: Retrain the neural network model with the updated dataset
|
|
|
|
|
# Preprocess data as per your requirements
|
|
|
|
|
X_train, y_train = preprocess_data(updated_dataset)
|
|
|
|
|
# Example retraining step
|
|
|
|
|
neural_network_model.fit(X_train, y_train, epochs=10, batch_size=32)
|
|
|
|
|
# Define the input features
|
|
|
|
|
input_features = dataset[[
|
|
|
|
|
'pattern', 'divergence_strength', 'trend_direction', 'indicator_used']]
|
|
|
|
|
|
|
|
|
|
# Save the updated model weights
|
|
|
|
|
neural_network_model.save_weights('model_weights.h5')
|
|
|
|
|
# Convert categorical features to one-hot encoding
|
|
|
|
|
input_features = pd.get_dummies(input_features)
|
|
|
|
|
|
|
|
|
|
# Normalize numerical features (if any)
|
|
|
|
|
if numerical_features:
|
|
|
|
|
scaler = MinMaxScaler()
|
|
|
|
|
input_features[numerical_features] = scaler.fit_transform(
|
|
|
|
|
input_features[numerical_features])
|
|
|
|
|
|
|
|
|
|
def preprocess_data(dataset):
|
|
|
|
|
# Define the numerical features (if any)
|
|
|
|
|
numerical_features = [] # Update with the actual numerical feature column names
|
|
|
|
|
# Extract target labels from the dataset
|
|
|
|
|
target_labels = dataset['outcome']
|
|
|
|
|
|
|
|
|
|
# Define the input features
|
|
|
|
|
input_features = dataset[[
|
|
|
|
|
'pattern', 'divergence_strength', 'trend_direction', 'indicator_used']]
|
|
|
|
|
# Convert target labels to numerical representation (0s and 1s)
|
|
|
|
|
target_labels = target_labels.map({'Loss': 0, 'Win': 1})
|
|
|
|
|
|
|
|
|
|
# Convert categorical features to one-hot encoding
|
|
|
|
|
input_features = pd.get_dummies(input_features)
|
|
|
|
|
# Return the preprocessed input features and target labels
|
|
|
|
|
return input_features, target_labels
|
|
|
|
|
|
|
|
|
|
# Normalize numerical features (if any)
|
|
|
|
|
if numerical_features:
|
|
|
|
|
scaler = MinMaxScaler()
|
|
|
|
|
input_features[numerical_features] = scaler.fit_transform(
|
|
|
|
|
input_features[numerical_features])
|
|
|
|
|
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
|
|
|
|
|
plt.scatter(df[df['pattern'] == 'Double Top'].index, df[df['pattern'] == 'Double Top']['high'],
|
|
|
|
|
color='red', marker='v', label='Double Top')
|
|
|
|
|
plt.scatter(df[df['pattern'] == 'Double Bottom'].index, df[df['pattern'] == 'Double Bottom']['low'],
|
|
|
|
|
color='green', marker='^', label='Double Bottom')
|
|
|
|
|
plt.legend()
|
|
|
|
|
plt.show()
|
|
|
|
|
|
|
|
|
|
# Extract target labels from the dataset
|
|
|
|
|
target_labels = dataset['outcome']
|
|
|
|
|
def run_trading_bot():
|
|
|
|
|
# Connect to MetaTrader 5 container
|
|
|
|
|
connect_to_mt5_container()
|
|
|
|
|
|
|
|
|
|
# Convert target labels to numerical representation (0s and 1s)
|
|
|
|
|
target_labels = target_labels.map({'Loss': 0, 'Win': 1})
|
|
|
|
|
while True:
|
|
|
|
|
try:
|
|
|
|
|
# Get historical data
|
|
|
|
|
df = get_historical_data()
|
|
|
|
|
|
|
|
|
|
# Return the preprocessed input features and target labels
|
|
|
|
|
return input_features, target_labels
|
|
|
|
|
# Calculate indicators and detect patterns
|
|
|
|
|
df = calculate_indicators_and_detect_patterns(df)
|
|
|
|
|
|
|
|
|
|
# Generate trade signals
|
|
|
|
|
df = generate_signals(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
|
|
|
|
|
plt.scatter(df[df['pattern'] == 'Double Top'].index, df[df['pattern'] == 'Double Top']['high'],
|
|
|
|
|
color='red', marker='v', label='Double Top')
|
|
|
|
|
plt.scatter(df[df['pattern'] == 'Double Bottom'].index, df[df['pattern'] == 'Double Bottom']['low'],
|
|
|
|
|
color='green', marker='^', label='Double Bottom')
|
|
|
|
|
plt.legend()
|
|
|
|
|
plt.show()
|
|
|
|
|
# Execute trades
|
|
|
|
|
for i in range(1, len(df)):
|
|
|
|
|
signal = df['signal'].iloc[i]
|
|
|
|
|
if signal != 'None':
|
|
|
|
|
execute_trade(signal, df)
|
|
|
|
|
|
|
|
|
|
# Visualize data
|
|
|
|
|
visualize_data(df)
|
|
|
|
|
|
|
|
|
|
def run_trading_bot():
|
|
|
|
|
# Connect to MetaTrader 5
|
|
|
|
|
connect_to_mt5(username='your_username', password='your_password',
|
|
|
|
|
server='your_server', path='your_mt5_installation_path')
|
|
|
|
|
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
|
|
|
|
|
|
|
|
|
|
# Run the trading bot
|
|
|
|
|
run_trading_bot()
|
|
|
|
|
|
|
|
|
|
# Load TensorFlow neural network model weights
|
|
|
|
|
neural_network_model.load_weights('model_weights.h5')
|
|
|
|
|
neural_network_model.load_weights('weights/model_weights.h5')
|
|
|
|
|
|
|
|
|
|
while True:
|
|
|
|
|
try:
|
|
|
|
|
# Get historical data
|
|
|
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df = get_historical_data()
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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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# 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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# 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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# Wait for the next iteration
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time.sleep(60) # Adjust the time interval as needed
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# Disconnect from MetaTrader 5
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pymt5.shutdown()
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# Run the trading bot
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run_trading_bot()
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# Disconnect from MetaTrader 5
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mt5.shutdown()
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# Start the MetaTrader 5 bot
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start_mt5_bot()
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Mike