Upload untitled2.py

untitled2.py

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+# -*- coding: utf-8 -*-
+"""Untitled2.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1AD0k2uVsW9mIm-3IOBCrDQ8iDI4PCKCf
+"""
+
+pip install alpaca-trade-api
+
+pip install ccxt
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.preprocessing import MinMaxScaler
+from tensorflow.keras.models import Sequential, Model
+from tensorflow.keras.layers import LSTM, Dense, Dropout, Conv1D, MaxPooling1D, Flatten, Input, concatenate
+import ccxt
+from sklearn.metrics import mean_absolute_error
+import alpaca_trade_api as tradeapi
+import time
+
+# Initialize Alpaca API for cryptocurrency trading
+api_key_crypto = 'PKBPQOK8HUFC98FPHFYA'
+api_secret_crypto = 'hDesl4V2vc72c7zceUcyrE5Y5gyundwzPrEJZghu'
+base_url_crypto = 'https://paper-api.alpaca.markets'  # Use the paper trading environment for testing
+alpaca_api_crypto = tradeapi.REST(api_key_crypto, api_secret_crypto, base_url_crypto, api_version='v2')
+
+# Initialize the CCXT exchange object for cryptocurrency data
+exchange = ccxt.coinbasepro()
+
+# Configuration for cryptocurrency data
+symbol_crypto = 'BTC/USD'  # Replace with the cryptocurrency pair you want to analyze
+timeframe_crypto = '1m'  # 1-hour timeframe for cryptocurrency data
+sequence_length_crypto = 30
+refresh_interval_seconds_crypto = 60  # 1-hour refresh interval
+max_iterations_crypto = 1000  # Set a limit to the number of iterations
+
+# Configuration for cryptocurrency trading
+initial_cash_crypto = 10  # Initial cash balance for cryptocurrency trading
+crypto_buy_threshold = 0.02  # Buy threshold for cryptocurrency trading (2% increase)
+crypto_sell_threshold = -0.02  # Sell threshold for cryptocurrency trading (2% decrease)
+
+# Initialize portfolio for cryptocurrency trading
+portfolio_crypto = {'cash': initial_cash_crypto, 'crypto_balance': 0}
+position_crypto = None
+
+def get_crypto_data(symbol, timeframe, sequence_length, max_iterations):
+    iterations = 0
+    while iterations < max_iterations:
+        try:
+            # Fetch historical cryptocurrency price data using CCXT
+            ohlcv = exchange.fetch_ohlcv(symbol, timeframe)
+            data = pd.DataFrame(ohlcv, columns=['Timestamp', 'Open', 'High', 'Low', 'Close', 'Volume'])
+            data['Timestamp'] = pd.to_datetime(data['Timestamp'], unit='ms')
+            data.set_index('Timestamp', inplace=True)
+
+            # Prepare data for prediction
+            prices = data['Close'].values.reshape(-1, 1)
+            scaler = MinMaxScaler()
+            prices_scaled = scaler.fit_transform(prices)
+
+            # Split data into training and testing sets
+            train_size = int(len(prices_scaled) * 0.8)
+            train_data = prices_scaled[:train_size]
+            test_data = prices_scaled[train_size:]
+
+            # Create sequences for training
+            X_train, y_train = [], []
+            for i in range(len(train_data) - sequence_length):
+                X_train.append(train_data[i:i+sequence_length])
+                y_train.append(train_data[i+sequence_length])
+
+            X_train, y_train = np.array(X_train), np.array(y_train)
+
+            # Build and train the model (as previously shown)
+            lstm_model = Sequential()
+            lstm_model.add(LSTM(50, activation='relu', input_shape=(sequence_length, 1)))
+            lstm_model.add(Dense(1))
+
+            cnn_input = Input(shape=(sequence_length, 1))
+            cnn_layer = Conv1D(filters=64, kernel_size=3, activation='relu')(cnn_input)
+            cnn_layer = MaxPooling1D(pool_size=2)(cnn_layer)
+            cnn_layer = Flatten()(cnn_layer)
+            cnn_output = Dense(1)(cnn_layer)
+
+            combined_model = concatenate([lstm_model.output, cnn_output])
+            output_layer = Dense(1)(combined_model)
+
+            model = Model(inputs=[lstm_model.input, cnn_input], outputs=output_layer)
+            model.compile(optimizer='adam', loss='mean_squared_error')
+            model.fit([X_train, X_train], y_train, epochs=10, batch_size=64)
+
+            # Prepare test data for evaluation
+            X_test, y_test = [], []
+            for i in range(len(test_data) - sequence_length):
+                X_test.append(test_data[i:i+sequence_length])
+                y_test.append(test_data[i+sequence_length])
+
+            X_test, y_test = np.array(X_test), np.array(y_test)
+
+            # Make predictions on the test data
+            predictions = model.predict([X_test, X_test])
+
+            # Inverse transform the predictions to the original scale
+            predictions = scaler.inverse_transform(predictions)
+
+            # Get the most recent data point for prediction
+            last_data_point = prices_scaled[-sequence_length:]
+            last_data_point = np.array([last_data_point])
+
+            # Print the current time period's data
+            print(f'Crypto Close Price: {data["Close"].iloc[-1]:.2f} USD')
+
+            # Print the forecasted cryptocurrency price for the next period and its time
+            print(f'Forecasted Crypto Price for the Next Period: ${predictions[-1][0]:.2f}')
+
+            # Calculate the Mean Absolute Error (MAE) between predictions and actual close prices
+            mae = mean_absolute_error(y_test, predictions)
+            print(f"Mean Absolute Error (MAE): {mae:.2f}")
+
+            # Handle cryptocurrency trading
+            handle_crypto_trading(data['Close'].iloc[-1], predictions[-1][0])
+
+            # Print portfolio status (combined portfolio)
+            portfolio_value_crypto = portfolio_crypto['cash'] + (portfolio_crypto['crypto_balance'] * data['Close'].iloc[-1])
+            print(f"Portfolio Value: Crypto: ${portfolio_value_crypto:.2f}")
+
+            # Sleep for the specified interval
+            time.sleep(refresh_interval_seconds_crypto)
+
+            # Increment the iterations counter
+            iterations += 1
+
+        except Exception as e:
+            print(f"An error occurred in cryptocurrency prediction: {str(e)}")
+            # Handle errors gracefully, e.g., retry or log the error
+            time.sleep(refresh_interval_seconds_crypto)  # Sleep before retrying
+
+def handle_crypto_trading(current_price, forecasted_price):
+    global portfolio_crypto
+    global position_crypto
+    print(forecasted_price - current_price)
+    # Buy signal (if the forecasted price increase exceeds the threshold)
+    if (forecasted_price - current_price) / current_price >= crypto_buy_threshold:
+
+        # Calculate the quantity to buy based on available cash
+        buy_quantity = portfolio_crypto['cash'] / current_price
+        if buy_quantity > 0:
+            # Execute the buy order
+            # Adjust portfolio and position variables accordingly
+            print(f"Buy signal - Bought {buy_quantity:.6f} BTC at ${current_price:.2f}")
+            position_crypto = alpaca_api_crypto.submit_order(
+            symbol=symbol_crypto.split('/')[0]+symbol_crypto.split('/')[1],  # Extract the base asset from the symbol
+            qty=buy_quantity,
+            side='buy',
+            type='limit',
+            time_in_force='gtc',
+            limit_price=current_price
+            )
+            portfolio_crypto['cash'] -= buy_quantity * current_price
+            portfolio_crypto['crypto_balance'] += buy_quantity
+
+    # Sell signal (if the forecasted price decrease exceeds the threshold)
+    elif (forecasted_price - current_price) / current_price <= crypto_sell_threshold:
+        # Ensure there's a position to sell
+        if position_crypto is not None:
+            # Calculate the quantity to sell based on the crypto balance
+            sell_quantity = portfolio_crypto['crypto_balance']
+            if sell_quantity > 0:
+                # Execute the sell order
+                # Adjust portfolio and position variables accordingly
+                print(f"Sell signal - Sold {sell_quantity:.6f} BTC at ${current_price:.2f}")
+                alpaca_api_crypto.cancel_order(position_crypto.id)
+                alpaca_api_crypto.close_position(symbol=symbol_crypto.split('/')[0]+symbol_crypto.split('/')[1])  # Close the position
+                position_crypto = None
+                portfolio_crypto['cash'] += sell_quantity * current_price
+                portfolio_crypto['crypto_balance'] -= sell_quantity
+
+# Define the main function to start cryptocurrency trading
+def main():
+    while True:
+        try:
+            get_crypto_data(symbol_crypto, timeframe_crypto, sequence_length_crypto, max_iterations_crypto)
+        except Exception as e:
+            print(f"An error occurred in cryptocurrency trading: {str(e)}")
+            time.sleep(refresh_interval_seconds_crypto)  # Sleep before retrying
+
+if __name__ == "__main__":
+    main()