Automated trading using deep learning and machine learning, integration of real-time trading system with the trained model. The trained model executes trades in real-time by linking with the actual exchange API.

With the rapid changes in cryptocurrency, investors are seeking more efficient and faster trading strategies. Deep learning and machine learning can automate this process and serve as useful tools to support investment decisions. In this article, we will take a closer look at how to build a Bitcoin automated trading system using deep learning and machine learning, and how to integrate the trained model with exchange APIs to execute trades in real-time.

1. Overview of Automated Trading Systems

An automated trading system is software that automatically executes trades according to a specific algorithm. This system generates buy and sell signals through the analysis of historical data and predictive models, helping investors to react to the market in real time.

2. Technology Stack to Use

  • Programming Language: Python
  • Deep Learning Libraries: TensorFlow, Keras
  • Data Collection: CCXT (Cryptocurrency Exchange API Library)
  • Deployment Platforms: AWS, Google Cloud, or Local Machine

3. Data Collection

First, we need to collect price data for Bitcoin. For this, we can use the CCXT library to access the exchange API and retrieve the data.

3.1. Installing CCXT

pip install ccxt

3.2. Example of Data Collection


import ccxt
import pandas as pd
import time

# Create exchange object for Binance
exchange = ccxt.binance()

def fetch_data(symbol, timeframe, limit):
    # Fetch latest exchange data
    candles = exchange.fetch_ohlcv(symbol, timeframe, limit=limit)
    df = pd.DataFrame(candles, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
    df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
    return df

# Example: fetch Bitcoin data
btc_data = fetch_data('BTC/USDT', '1h', 100)
print(btc_data.head())

4. Data Preprocessing

The collected data must be transformed into a format suitable for model training. Common preprocessing methods include normalization, dimensionality reduction, and constructing time series data.

4.1. Data Normalization


from sklearn.preprocessing import MinMaxScaler

def normalize_data(df):
    scaler = MinMaxScaler(feature_range=(0, 1))
    df['close'] = scaler.fit_transform(df['close'].values.reshape(-1, 1))
    return df, scaler

btc_data, scaler = normalize_data(btc_data)

5. Model Configuration

Now, we will configure the deep learning model to train. The LSTM (Long Short-Term Memory) network is suitable for time series data analysis and is used for predicting Bitcoin prices.

5.1. Building the LSTM Model


from keras.models import Sequential
from keras.layers import LSTM, Dense, Dropout

def create_model(input_shape):
    model = Sequential()
    model.add(LSTM(50, return_sequences=True, input_shape=input_shape))
    model.add(Dropout(0.2))
    model.add(LSTM(50, return_sequences=False))
    model.add(Dropout(0.2))
    model.add(Dense(1))  # Price prediction
    model.compile(optimizer='adam', loss='mean_squared_error')
    return model

# Input data shape
X_train, y_train = # (Preparing selected data reset)
model = create_model((X_train.shape[1], 1))

6. Model Training

Train the configured model using the training data. To evaluate the model’s performance, monitor the loss function during the training process.

6.1. Example of Model Training Code


model.fit(X_train, y_train, epochs=100, batch_size=32)

7. Model Prediction

Use the trained model to predict Bitcoin prices. The predicted values will later be used for trading decisions.

7.1. Example Prediction Code


predicted_prices = model.predict(X_test)
predicted_prices = scaler.inverse_transform(predicted_prices)  # Convert back to original price

8. Implementing Trading Strategies

Implement a simple trading strategy that makes buy and sell decisions based on predicted prices. For example, you can set a rule to buy when the price rises and sell when it falls.

8.1. Example of Trading Strategy Code


def trading_strategy(predicted_prices, threshold=0.01):
    buy_signal = []
    sell_signal = []
    
    for i in range(1, len(predicted_prices)):
        if predicted_prices[i] > predicted_prices[i - 1] * (1 + threshold):
            buy_signal.append(i)
        elif predicted_prices[i] < predicted_prices[i - 1] * (1 - threshold):
            sell_signal.append(i)
    
    return buy_signal, sell_signal

buy_signal, sell_signal = trading_strategy(predicted_prices)

9. Integrating with the Exchange

Finally, integrate the trained model with the exchange API to execute real trades. Consider recording transaction histories and managing the portfolio for automated trading.

9.1. Integrating with Exchange API


import time

def execute_trade(symbol, amount, action):
    if action == 'buy':
        exchange.create_market_buy_order(symbol, amount)
    elif action == 'sell':
        exchange.create_market_sell_order(symbol, amount)

amount = 0.001  # Amount of Bitcoin
for i in buy_signal:
    execute_trade('BTC/USDT', amount, 'buy')
    time.sleep(1)  # Provide interval between API calls

for i in sell_signal:
    execute_trade('BTC/USDT', amount, 'sell')
    time.sleep(1)  # Provide interval between API calls

10. Conclusion

An automated Bitcoin trading system using deep learning and machine learning enhances investment efficiency by automating complex data analysis and decision-making. However, such systems do not guarantee 100% profits, and it is essential to establish appropriate risk management strategies considering market volatility.