Predicting Crypto-Currency Price Using RNN lSTM & GRU

Introduction

In this tutorial, we will explore how to predict cryptocurrency prices using Recurrent Neural Networks (RNNs), specifically focusing on Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models. This guide will take you through the steps necessary to implement these models in Python, allowing you to evaluate their effectiveness in predicting whether the price of a cryptocurrency will increase or decrease.

Step 1: Set Up Your Environment

Before you start coding, ensure you have the necessary tools and libraries installed.

• Install Python: Make sure you have Python installed on your machine. You can download it from python.org.
• Install Required Libraries: Use pip to install the libraries you'll need:

  pip install numpy pandas matplotlib scikit-learn keras tensorflow
  

Step 2: Gather and Preprocess Data

For any machine learning project, data collection and preprocessing are crucial.

• Data Source: Obtain historical cryptocurrency price data. You can find datasets on platforms like Kaggle or use APIs from cryptocurrency exchanges.
• Load the Data: Use pandas to load your data into a DataFrame.

  import pandas as pd
  
  data = pd.read_csv('crypto_prices.csv')
  

• Data Cleaning: Check for missing values and remove or fill them as necessary.

  data.dropna(inplace=True)
  

• Feature Selection: Choose the relevant features for your model, such as 'Open', 'Close', 'High', 'Low', and 'Volume'.

Step 3: Normalize the Data

Normalization helps improve model performance and convergence speed.

• Min-Max Scaling: Scale your features to a range of [0, 1].

  from sklearn.preprocessing import MinMaxScaler
  
  scaler = MinMaxScaler(feature_range=(0, 1))
  scaled_data = scaler.fit_transform(data[['Close']])
  

Step 4: Create Training and Testing Datasets

Split your data into training and testing sets to evaluate the model's performance.

• Define Training Size: Choose a percentage for training (e.g., 80%).
• Split the Data:

  train_size = int(len(scaled_data) * 0.8)
  train_data = scaled_data[:train_size]
  test_data = scaled_data[train_size:]
  

Step 5: Prepare Data for RNN

Convert the data into a suitable format for the RNN.

• Create Sequences: Define a function to create sequences of data.

  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), 0])
          y.append(data[i + time_step, 0])
      return np.array(X), np.array(y)
  
  time_step = 10
  X_train, y_train = create_dataset(train_data, time_step)
  X_test, y_test = create_dataset(test_data, time_step)
  

Step 6: Reshape Data for LSTM and GRU

Reshape the input data to be compatible with LSTM and GRU layers.

• Reshape Input:

  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)
  

Step 7: Build and Train the LSTM Model

Create and train the LSTM model.

• Define the Model:

  from keras.models import Sequential
  from keras.layers import LSTM, Dense, Dropout
  
  model = Sequential()
  model.add(LSTM(50, return_sequences=True, input_shape=(X_train.shape[1], 1)))
  model.add(Dropout(0.2))
  model.add(LSTM(50, return_sequences=False))
  model.add(Dropout(0.2))
  model.add(Dense(1))
  

• Compile and Train:

  model.compile(optimizer='adam', loss='mean_squared_error')
  model.fit(X_train, y_train, epochs=50, batch_size=32)
  

Step 8: Evaluate the Model

Assess the model's performance on the testing data.

• Make Predictions:

  predictions = model.predict(X_test)
  

• Inverse Transform: Convert the scaled predictions back to original values.

  predictions = scaler.inverse_transform(predictions)
  

Step 9: Build and Train the GRU Model

Repeat the process for the GRU model.

• Define the GRU Model:

  from keras.layers import GRU
  
  gru_model = Sequential()
  gru_model.add(GRU(50, return_sequences=True, input_shape=(X_train.shape[1], 1)))
  gru_model.add(Dropout(0.2))
  gru_model.add(GRU(50, return_sequences=False))
  gru_model.add(Dropout(0.2))
  gru_model.add(Dense(1))
  

• Compile and Train:

  gru_model.compile(optimizer='adam', loss='mean_squared_error')
  gru_model.fit(X_train, y_train, epochs=50, batch_size=32)
  

Conclusion

In this tutorial, you learned how to predict cryptocurrency prices using LSTM and GRU models. You covered data collection, preprocessing, model building, training, and evaluation.

Next steps include experimenting with different hyperparameters, exploring additional features, and deploying your model for real-time predictions. Dive deeper into the world of RNNs, and enhance your understanding of time series forecasting in cryptocurrency and other domains.