from keras.optimizers import Adam from keras.layers import Dense, Dropout from keras.models import Sequential import matplotlib.pyplot as plt import talib from sklearn.preprocessing import MinMaxScaler import numpy as np import pandas as pd import time import os import zmq os.environ["CUDA_VISIBLE_DEVICES"] = "-1" def start_mt5_bot(): # Define the symbols and timeframes symbol = 'EURUSD' timeframe = 'H1' # H1 timeframe (1 hour) # Set up initial variables lot_size = 0.01 stop_loss = 100 take_profit = 150 # Define TensorFlow neural network model def create_neural_network_model(input_shape): model = Sequential() model.add(Dense(64, activation='relu', input_shape=input_shape)) model.add(Dropout(0.2)) model.add(Dense(64, activation='relu')) model.add(Dropout(0.2)) model.add(Dense(1, activation='sigmoid')) return model # Define input shape for the neural network # Adjust the input shape based on your features and data input_shape = (10,) # Create the neural network model neural_network_model = create_neural_network_model(input_shape) # Compile the model neural_network_model.compile(optimizer=Adam( learning_rate=0.001), loss='binary_crossentropy') 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): # Calculate RSI rsi_period = 14 df['rsi'] = talib.RSI(df['close'], rsi_period) # Calculate MACD macd_fast_period = 12 macd_slow_period = 26 macd_signal_period = 9 _, _, 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( df['rsi'].diff().shift(-1) * df['macd'].diff().shift(-1) < 0, True, False) df['macd_divergence'] = np.where( df['macd'].diff().shift(-1) * df['rsi'].diff().shift(-1) < 0, True, False) # Detect support and resistance levels window = 10 df['support'] = df['low'].rolling(window).min() df['resistance'] = df['high'].rolling(window).max() # Determine trend direction df['trend_200'] = df['close'].rolling(window=200).mean() df['trend_50'] = df['close'].rolling(window=50).mean() # 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.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.loc[df['bottom_pattern'] != 'None', 'pattern'] = df['bottom_pattern'] return df def generate_signals(df): # Determine trade signals based on divergences, patterns, and trend direction df['signal'] = 'None' df['divergence_signal'] = np.where((df['rsi_divergence'] == True) & (df['pattern'] != 'None'), 'Both', np.where(df['rsi_divergence'] == True, 'RSI', 'Pattern')) df['strongest_divergence_signal'] = df[[ 'divergence_signal', 'macd_divergence']].max(axis=1) df['support_resistance_signal'] = np.where(df['close'] > df['resistance'], 'Resistance', np.where(df['close'] < df['support'], 'Support', 'None')) df['trend_signal'] = np.where(df['close'] > df['trend_200'], 'Uptrend', np.where(df['close'] < df['trend_200'], 'Downtrend', 'None')) for i in range(1, len(df)): prev_divergence_signal = df['divergence_signal'].iloc[i - 1] curr_divergence_signal = df['divergence_signal'].iloc[i] strongest_divergence_signal = df['strongest_divergence_signal'].iloc[i] support_resistance_signal = df['support_resistance_signal'].iloc[i] trend_signal = df['trend_signal'].iloc[i] 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 if trend_signal != 'None' and df['signal'].iloc[i] != 'None': df['signal'].iloc[i] = trend_signal return 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 # 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 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 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 = { '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 } # Update TensorFlow neural network model with trade outcome update_neural_network_model(trade_outcome) # Example print statements for debugging print( f"Executed {signal} trade with position size: {position_size}") print(f"Trade outcome: {trade_outcome}") # Additional logic for trade management, monitoring, etc. except Exception as e: print(f"Error executing trade: {str(e)}") 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'] # 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) # Save the updated model weights neural_network_model.save_weights('weights/model_weights.h5') def preprocess_data(dataset): # Define the numerical features (if any) numerical_features = [] # Update with the actual numerical feature column names # Define the input features input_features = dataset[[ 'pattern', 'divergence_strength', 'trend_direction', 'indicator_used']] # 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]) # Extract target labels from the dataset target_labels = dataset['outcome'] # Convert target labels to numerical representation (0s and 1s) target_labels = target_labels.map({'Loss': 0, 'Win': 1}) # Return the preprocessed input features and target labels return input_features, target_labels 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() # 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(socket) # Calculate indicators and detect patterns df = calculate_indicators_and_detect_patterns(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, socket) # Visualize data visualize_data(df) 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 # Disconnect from ZeroMQ socket socket.close() # Start the MetaTrader bot start_mt5_bot()