Automatic trading using deep learning and machine learning, Time series prediction using LSTM LSTM (Long Short-Term Memory) is a method to predict the time series data of Bitcoin.

In recent years, Bitcoin has emerged as the most notable asset in the cryptocurrency market, with many investors leveraging it to seek profits. However, predicting the price of Bitcoin is quite challenging due to its high volatility. This article will discuss how to predict Bitcoin’s time series data using a deep learning method known as LSTM (Long Short-Term Memory) network.

1. What is Time Series Data?

Time series data is a dataset that records the values of each variable at specific times, generally collected over time. In other words, data such as Bitcoin’s price and trading volume change over time, allowing for predictions and analysis based on this information. Examples of time series data include stock prices, weather information, and sales data.

2. What is an LSTM Network?

The LSTM (Long Short-Term Memory) network is a type of RNN (Recurrent Neural Network) developed to address the long-term dependency problem inherent in recurrent neural networks. LSTM has memory cells that allow it to store information for extended periods and uses three main gates to regulate information.

  • Input Gate: Decides what information to add to the cell state based on the current input and previous output information.
  • Forget Gate: Determines what information to discard from the previous cell state.
  • Output Gate: Decides what information to output from the cell state.

3. Building a Bitcoin Prediction Model Using LSTM

This section will explain how to predict Bitcoin’s future prices using LSTM. Below are the steps necessary to carry out this process.

3.1 Data Collection

There are several APIs available for collecting Bitcoin price data. Generally, CryptoCompare, Binance, and CoinGecko can be used. In this example, we will demonstrate how to collect and process data using Pandas and NumPy.

Example Code: Data Collection


import pandas as pd
import numpy as np

# Example of data collection using Binance API
def fetch_data(symbol='BTCUSDT', interval='1d', limit=1000):
    url = f'https://api.binance.com/api/v3/klines?symbol={symbol}&interval={interval}&limit={limit}'
    df = pd.read_json(url)
    df = df[[0, 4]].rename(columns={0: 'timestamp', 4: 'close_price'})
    df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
    return df

# Download data
df = fetch_data()
print(df.head())

3.2 Data Preprocessing

The collected data needs to be processed to be suitable for model training. Generally, what we need is ‘normalization’. The LSTM model performs better when input values are within a small range, so we will use the Min-Max normalization method.

Example Code: Data Preprocessing


from sklearn.preprocessing import MinMaxScaler

# Data normalization
scaler = MinMaxScaler(feature_range=(0, 1))
df['scaled_close'] = scaler.fit_transform(df['close_price'].values.reshape(-1, 1))

# Data splitting
train_size = int(len(df) * 0.8)
train_data = df['scaled_close'][:train_size]
test_data = df['scaled_close'][train_size:]

# Sequence generation
def create_dataset(data, time_step=1):
    X, Y = [], []
    for i in range(len(data) - time_step - 1):
        X.append(data[i:(i + time_step)])
        Y.append(data[i + time_step])
    return np.array(X), np.array(Y)

time_step = 10
X_train, y_train = create_dataset(train_data.values, time_step)
X_test, y_test = create_dataset(test_data.values, time_step)

# Reshape input data dimensions
X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)
X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)

3.3 Building and Training the LSTM Model

Now, we build and train the LSTM model. You can configure the LSTM model using the Keras library.

Example Code: Building and Training the LSTM Model


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

# Building the LSTM model
model = Sequential()
model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1)))
model.add(Dropout(0.2))
model.add(LSTM(units=50, return_sequences=False))
model.add(Dropout(0.2))
model.add(Dense(units=1))  # Output layer

# Compile the model
model.compile(optimizer='adam', loss='mean_squared_error')

# Train the model
model.fit(X_train, y_train, epochs=50, batch_size=32)

3.4 Prediction and Result Visualization

Once the model is trained, predictions can be made using the test data, and the results can be visualized.

Example Code: Prediction and Visualization


import matplotlib.pyplot as plt

# Perform predictions
train_predict = model.predict(X_train)
test_predict = model.predict(X_test)

# Reverse data scaling
train_predict = scaler.inverse_transform(train_predict)
test_predict = scaler.inverse_transform(test_predict)

# Visualization
plt.figure(figsize=(14, 5))
plt.plot(df['timestamp'][:train_size], scaler.inverse_transform(train_data.values[time_step:-1]), label='Train Data', color='blue')
plt.plot(df['timestamp'][train_size + time_step:-1], scaler.inverse_transform(test_data.values[time_step:-1]), label='Test Data', color='orange')
plt.plot(df['timestamp'][time_step:train_size], train_predict, label='Train Predict', color='red')
plt.plot(df['timestamp'][train_size + time_step:], test_predict, label='Test Predict', color='green')
plt.legend()
plt.show()

4. Model Evaluation and Improvement

Evaluating the model is essential for improving prediction accuracy and making necessary improvements. The RMSE (Root Mean Squared Error) can be used to calculate the differences between predicted data and actual data from the model.

Example Code: Calculating RMSE


from sklearn.metrics import mean_squared_error

# Calculate RMSE
train_rmse = np.sqrt(mean_squared_error(scaler.inverse_transform(train_predict), scaler.inverse_transform(train_data.values[time_step:-1])))
test_rmse = np.sqrt(mean_squared_error(scaler.inverse_transform(test_predict), scaler.inverse_transform(test_data.values[time_step:-1])))
print(f'Train RMSE: {train_rmse}, Test RMSE: {test_rmse}')

5. Additional Considerations

After building the model, further considerations are necessary. Performance can vary based on various hyperparameter adjustments, model complexity management, and data collection methods according to the nature of the data. Here are some tips.

  • Data Augmentation: It is advisable to collect more data and provide more features to the model by using various cycles.
  • Hyperparameter Tuning: Adjusting hyperparameters such as the number of units in LSTM and learning rate is important to find the optimal combination.
  • Batch Normalization: Adding batch normalization before LSTM layers can increase the learning speed.
  • Ensemble Learning: Combining multiple models can enhance the reliability of predictions.

6. Conclusion

This article discussed how to predict Bitcoin’s time series data using LSTM. LSTM is a powerful tool that can improve the accuracy of time series data prediction by addressing long-term dependency issues. However, it is crucial to design the model well and improve it appropriately. Further research and experimentation can yield even better performance.

More advanced strategies for automated Bitcoin trading involve combining various algorithms beyond LSTM. For instance, you can consider using CNN (Convolutional Neural Network) to recognize price patterns or reinforcement learning (RL) to find the optimal trading timing. Given the complexity of time series data, these various approaches can provide even more advantages.

References