Automated trading using deep learning and machine learning, anomaly detection using Autoencoder Detecting abnormal movements in price data for risk management.

The cryptocurrency market, like Bitcoin, poses significant risks to investors due to high volatility and uncertainty. To manage these risks, automated trading systems utilizing deep learning and machine learning techniques are gaining attention. In particular, Autoencoder has established itself as a useful tool for risk management by detecting anomalous movements in data. This article will explain the concept of Autoencoder, its theoretical background, an application example of outlier detection in Bitcoin price data, and how to integrate this into an automated trading system.

1. What is an Autoencoder?

An Autoencoder is an unsupervised learning model that compresses and reconstructs input data. The input and output share the same structure, with a low-dimensional representation known as latent space in between. An Autoencoder is divided into two main components:

  • Encoder: Converts input data into the latent space.
  • Decoder: Restores the original input data from the latent space.

The goal of an Autoencoder is to make the input data and output data as similar as possible. Typically, the Mean Squared Error is used as the loss function.

2. Structure of an Autoencoder

The basic structure of an Autoencoder is as follows:


class Autoencoder(nn.Module):
    def __init__(self):
        super(Autoencoder, self).__init__()
        self.encoder = nn.Sequential(
            nn.Linear(3, 2),
            nn.ReLU(True)
        )
        self.decoder = nn.Sequential(
            nn.Linear(2, 3),
            nn.Sigmoid()
        )

    def forward(self, x):
        x = self.encoder(x)
        x = self.decoder(x)
        return x

3. Bitcoin Price Data and Outlier Detection

The price data of Bitcoin is influenced by various factors, which can lead to abnormal price fluctuations. The process of detecting outliers using an Autoencoder can be broadly divided into three stages:

  1. Price Data Preprocessing
  2. Training the Autoencoder Model
  3. Outlier Detection

3.1 Price Data Preprocessing

The process of loading and preprocessing Bitcoin price data is as follows.


import pandas as pd

# Load data
data = pd.read_csv('bitcoin_price.csv')
data['Date'] = pd.to_datetime(data['Date'])
data.set_index('Date', inplace=True)

# Select necessary columns
price_data = data['Close'].values.reshape(-1, 1)

# Normalization
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
normalized_data = scaler.fit_transform(price_data)

3.2 Training the Autoencoder Model

After preparing the data, we create and train the Autoencoder model.


import torch
import torch.nn as nn
import torch.optim as optim

# Hyperparameters
num_epochs = 100
learning_rate = 0.001

# Prepare dataset
tensor_data = torch.FloatTensor(normalized_data)

# Initialize model
model = Autoencoder()
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# Training
for epoch in range(num_epochs):
    model.train()
    optimizer.zero_grad()
    output = model(tensor_data)
    loss = criterion(output, tensor_data)
    loss.backward()
    optimizer.step()

    if epoch % 10 == 0:
        print(f'Epoch [{epoch}/{num_epochs}], Loss: {loss.item():.4f}')

3.3 Outlier Detection

Using the trained model, we calculate the reconstruction error of the input data and detect data as outliers if they exceed a certain threshold.


# Model evaluation
model.eval()
with torch.no_grad():
    reconstructed = model(tensor_data)
    reconstruction_loss = criterion(reconstructed, tensor_data)

# Outlier detection
reconstruction_loss_values = torch.sum((tensor_data - reconstructed) ** 2, axis=1).numpy()
threshold = 0.1  # Example threshold
anomalies = reconstruction_loss_values > threshold

# Outlier indices
anomaly_indices = [i for i, x in enumerate(anomalies) if x]
print(f'Outlier indices: {anomaly_indices}')

4. Integration into Automated Trading System

If anomalous movements are detected at specific points in time through outlier detection, the automated trading system can generate buy or sell signals. To do this, it is necessary to define trading strategies based on the detected outliers.

4.1 Example Trading Strategy

Let’s consider a simple strategy to take a sell position when an outlier is detected:


# Trading strategy
for index in anomaly_indices:
    price = price_data[index][0]
    # Sell about abnormal price fluctuation
    print(f'Outlier detected - Sell: Price {price} at index {index}')

5. Conclusion

Outlier detection using deep learning and machine learning techniques, particularly Autoencoders, is an effective tool for risk management of highly volatile assets such as Bitcoin. In this article, we explained how to implement an Autoencoder in Python to detect outliers and integrate it into an automated trading system. This system allows investors to make more data-driven decisions and contributes to reducing uncertainty.

Future areas for improvement include experimenting with various algorithms, adding more input variables, and optimizing trading strategies to enhance performance. This will lead to the development of smarter and more effective automated trading systems.