Machine Learning and Deep Learning Algorithm Trading, Generating Synthetic Data with GAN

Quant trading is a method of making trading decisions in the financial market based on data. By utilizing various machine learning and deep learning techniques, it is possible to find patterns in data and build automated trading systems based on them. This article will explain algorithm trading using machine learning and deep learning and introduce how to generate synthetic data using Generative Adversarial Networks (GAN).

1. Overview of Machine Learning and Deep Learning

Machine learning is a field of artificial intelligence that employs algorithms and statistical models to enable computers to perform specific tasks. Deep learning is a subset of machine learning that uses neural networks to learn high-level representations from data. In the case of financial data, machine learning and deep learning algorithms can analyze past data and predict future price fluctuations.

1.1 Machine Learning Algorithms

Machine learning algorithms can be broadly divided into three types:

  • Supervised Learning: Trains a model using labeled data.
  • Unsupervised Learning: Finds the structure and patterns of data using unlabeled data.
  • Reinforcement Learning: The agent learns to maximize rewards by interacting with the environment.

1.2 Deep Learning Models

In deep learning, neural networks composed of multiple layers are used to analyze data. The commonly used deep learning models include:

  • Neural Networks: The basic deep learning structure consisting of input, hidden, and output layers.
  • Convolutional Neural Networks (CNN): A structure optimized for image data, efficient in recognizing patterns within images.
  • Recurrent Neural Networks (RNN): A structure suitable for time series prediction, remembering and processing information from previous data.

2. Concept of Algorithm Trading

Algorithm trading refers to a system that automatically executes trades based on specific algorithms. This system analyzes various market data in real-time and makes buy or sell decisions when certain conditions are met.

2.1 Advantages of Algorithm Trading

  • Elimination of Emotion: Mechanical trading removes emotions, allowing for more objective decision-making.
  • Speed: Enables rapid trading by processing data in real-time.
  • Implementation of Various Strategies: Allows for the management of diverse strategies by executing multiple algorithms simultaneously.

2.2 Algorithm Design Process

The process of designing an algorithm trading system includes the following steps:

  1. Strategy Development: Develop a strategy to gain a competitive edge through market research and data analysis.
  2. Model Selection: Choose an appropriate machine learning or deep learning model.
  3. Data Collection: Collect necessary historical and real-time data.
  4. Training and Validation: Train the selected model with the data and validate its performance.
  5. Live Trading: Apply the system to the actual market to execute trades.

3. Overview of GAN (Generative Adversarial Networks)

GAN is a generative model proposed by Ian Goodfellow in 2014, consisting of two neural networks. The generator tries to create new data, while the discriminator tries to determine whether the provided data is real or fake. The two networks learn by competing against each other.

3.1 Structure of GAN

GAN consists of the following structure:

  • Generator: Takes random noise as input and generates fake data.
  • Discriminator: Takes actual data and fake data created by the generator as input, distinguishing between the two.

3.2 Learning Process of GAN

The learning process of GAN is as follows:

  1. The generator creates data from random noise.
  2. The generated data and actual data are input to the discriminator.
  3. The discriminator determines the authenticity of the two data.
  4. Based on the discriminator’s decisions, the generator is updated to create better fake data.
  5. This process is repeated, improving the generator’s performance.

4. Generating Synthetic Data using GAN

Synthetic data is artificially generated data that can substitute real-world data. The advantages of generating synthetic data using GAN include:

  • Data Augmentation: Can be beneficial in situations where real data cannot be used.
  • Privacy Protection: Allows for the use of synthetic data with removed personally identifiable information from real data.
  • Realistic Data Generation: Due to GAN’s superior generation capability, it can create data that closely resembles real data.

4.1 Implementation of Generating Synthetic Data using GAN

The basic code for implementing GAN to generate synthetic data is as follows:


import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras import layers

# GAN model creation function
def create_gan():
    # Define generator model
    generator = tf.keras.Sequential([
        layers.Dense(128, activation='relu', input_shape=(100,)),
        layers.Dense(784, activation='sigmoid')
    ])

    # Define discriminator model
    discriminator = tf.keras.Sequential([
        layers.Dense(128, activation='relu', input_shape=(784,)),
        layers.Dense(1, activation='sigmoid')
    ])

    # Define GAN model
    discriminator.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
    discriminator.trainable = False
    gan_input = layers.Input(shape=(100,))
    fake_image = generator(gan_input)
    gan_output = discriminator(fake_image)
    gan = tf.keras.models.Model(gan_input, gan_output)
    gan.compile(loss='binary_crossentropy', optimizer='adam')

    return generator, discriminator, gan

# Generate data and train model
generator, discriminator, gan = create_gan()

for epoch in range(10000):
    # Generate real samples from existing data
    real_samples = np.random.rand(32, 784)
    
    # Generate fake data
    noise = np.random.normal(0, 1, size=[32, 100])
    fake_samples = generator.predict(noise)
    
    # Train discriminator
    discriminator.train_on_batch(real_samples, np.ones((32, 1)))
    discriminator.train_on_batch(fake_samples, np.zeros((32, 1)))
    
    # Train GAN
    noise = np.random.normal(0, 1, size=[32, 100])
    gan.train_on_batch(noise, np.ones((32, 1)))
    
# Visualization after data generation
generated_images = generator.predict(np.random.normal(0, 1, size=[10, 100]))
plt.figure(figsize=(10, 10))
for i in range(10):
    plt.subplot(5, 5, i + 1)
    plt.imshow(generated_images[i].reshape(28, 28), cmap='gray')
    plt.axis('off')
plt.show()

5. Conclusion

In this article, we explored the concept of algorithm trading utilizing machine learning and deep learning techniques, and the technology of generating synthetic data using GAN. The ability to extract patterns from data and generate synthetic data will be a powerful tool for improving quant trading systems. To successfully apply machine learning and deep learning techniques in the future financial market, systematic data analysis and algorithm development will be necessary.