Automated trading using deep learning and machine learning, predicting price volatility using neural networks Predicting the price volatility of Bitcoin using Multi-Layer Perceptron (MLP).

1. Introduction

Bitcoin is an attractive investment asset in itself, but it has a very high price volatility. To predict and utilize this volatility, many investors are using deep learning and machine learning techniques. This article introduces how to predict Bitcoin’s price volatility using a Multi-Layer Perceptron (MLP). This can lay the groundwork for building an automated trading system.

2. Introduction to Deep Learning and Machine Learning

Deep learning is a branch of machine learning that uses artificial neural networks to analyze and predict data. The basic idea of machine learning is to learn patterns based on data and make predictions or decisions based on those patterns. In deep learning, more complex patterns can be learned through multiple layers of neural networks.

3. What is a Multi-Layer Perceptron (MLP)?

A Multi-Layer Perceptron (MLP) is an artificial neural network composed of multiple layers, consisting of an input layer, hidden layers, and an output layer. Nodes in each layer are connected to nodes in the next layer, and they calculate output values through an activation function. MLPs are particularly useful for learning complex non-linear functions.

4. Predicting Using Bitcoin Price Data

To predict Bitcoin’s price data, we first need to collect and preprocess the data. Various data sources can be utilized for this, such as using exchange APIs to retrieve data.

4.1 Data Collection

import pandas as pd

# Load Bitcoin price data from a CSV file.
df = pd.read_csv('bitcoin_price.csv')
print(df.head())

4.2 Data Preprocessing

The collected data usually contains missing values or noise, so it needs to be processed appropriately to convert it into a format suitable for the model. Common methods include calculating price differences, log transformations, and more to cleanse the data. 

# Remove missing values
df.dropna(inplace=True)

# Calculate price volatility (log returns)
df['returns'] = df['Close'].pct_change()
df.dropna(inplace=True)

4.3 Splitting into Training and Testing Data

To train the model, we need to split the data into training and testing sets. Generally, 70-80% of the data is used for training, and the remaining 20-30% is used for testing. 

from sklearn.model_selection import train_test_split

X = df[['Open', 'High', 'Low', 'Volume']]
y = df['returns']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

5. Building the MLP Model

It is now time to build the Multi-Layer Perceptron (MLP) model. The Keras library makes it easy to construct the model. 

from keras.models import Sequential
from keras.layers import Dense

# Create MLP model
model = Sequential()
model.add(Dense(64, input_dim=X_train.shape[1], activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(1, activation='linear'))

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

6. Training the Model

To train the model, we call the fit() method. 

model.fit(X_train, y_train, epochs=100, batch_size=10)

7. Evaluating the Model

We evaluate the trained model using the test data. Predictions can be performed on the test data and compared to the actual values. 

y_pred = model.predict(X_test)

from sklearn.metrics import mean_squared_error
mse = mean_squared_error(y_test, y_pred)
print(f'Mean Squared Error: {mse}')

8. Visualizing Prediction Results

To easily verify prediction performance, visualizations can be created. The Matplotlib library can be used to draw graphs. 

import matplotlib.pyplot as plt

plt.figure(figsize=(12, 6))
plt.plot(y_test.values, label='Actual')
plt.plot(y_pred, label='Predicted', alpha=0.7)
plt.title('Bitcoin Return Prediction')
plt.xlabel('Time Step')
plt.ylabel('Returns')
plt.legend()
plt.show()

9. Conclusion

In this lesson, we learned how to predict Bitcoin’s price volatility using a Multi-Layer Perceptron (MLP). To build an automated trading system, one must consider how to apply the prediction results to actual trading strategies. Additionally, improved performance can be expected through further research involving hyperparameter tuning, various neural network architectures, and the use of different data sources.

10. References