Machine Learning and Deep Learning Algorithm Trading, Single-Layer Feedforward Autoencoder

1. Introduction

Financial markets are complex and dynamic environments, where investors continuously seek profits. As a result,
machine learning and deep learning technologies have gained attention. This course explains how to build trading systems
using one of these technologies, the single-layer feedforward autoencoder.

2. Overview of Machine Learning and Deep Learning

Machine learning is an algorithm that learns models from data and makes predictions or decisions based on that.
In contrast, deep learning is a subfield of machine learning based on neural networks, which uses deep neural networks
to recognize complex patterns in data. These two technologies demonstrate excellent performance, particularly in the
analysis of high-dimensional data and the processing of unstructured data.

3. What is an Autoencoder?

An autoencoder is an unsupervised learning model that compresses and reconstructs input data. It typically consists of
an encoder and decoder, and is used to learn the important features of the data. The single-layer feedforward
autoencoder is the most basic form, consisting of a single hidden layer.

3.1. Structure of an Autoencoder

An autoencoder includes the following components:

  • Input Layer: Receives the original data.
  • Hidden Layer: Learns the latent representation of the data.
  • Output Layer: Reconstructs the input data.

4. Implementing a Single-Layer Feedforward Autoencoder

Let’s look at the basic implementation of a single-layer feedforward autoencoder. This process will use Python and
the popular deep learning framework TensorFlow.

4.1. Preparing the Data

Prepare financial data such as stock data. This data may include historical prices, trading volumes, etc. 

import pandas as pd

# Load stock data
data = pd.read_csv('stock_data.csv')
features = data[['Open', 'High', 'Low', 'Close', 'Volume']].values

4.2. Defining the Autoencoder Model

Define a simple single-layer feedforward autoencoder model. 

import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Dense

# Configure the model
input_layer = Input(shape=(features.shape[1],))
encoded = Dense(32, activation='relu')(input_layer)
decoded = Dense(features.shape[1], activation='sigmoid')(encoded)

autoencoder = Model(input_layer, decoded)
autoencoder.compile(optimizer='adam', loss='mean_squared_error')

4.3. Training the Model

Train the model using the prepared data. 

# Train the model
autoencoder.fit(features, features, epochs=50, batch_size=256, shuffle=True)

5. Interpreting Results and Performance Evaluation

After training, evaluate the performance of the autoencoder. Compare the original data with the reconstructed data to
determine the model’s accuracy.

5.1. Visualizing Prediction Results

Visualize the prediction results to intuitively understand the model’s performance. 

import matplotlib.pyplot as plt

# Prediction
predicted = autoencoder.predict(features)

# Visualize results
plt.figure(figsize=(12, 6))
plt.plot(features[0], label='Original')
plt.plot(predicted[0], label='Reconstructed')
plt.legend()
plt.show()

6. Conclusion

This course covered the basics of algorithmic trading using a single-layer feedforward autoencoder.
Machine learning and deep learning techniques can help discover important patterns and make predictions on
financial data. Advanced courses utilizing more sophisticated models like multi-layer autoencoders and LSTM models
are also planned for the future.

7. References

  • Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
  • Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. O’Reilly Media.
  • Max Value Stock Investment Website