Comparison of Generative Models and Discriminative Models
1. Introduction
In recent years, the popularity of automated trading and algorithmic trading in financial markets has rapidly increased. Along with this trend, many traders are seeking profits through the application of machine learning and deep learning technologies in trading strategies. This course will compare the two main approaches in algorithmic trading—generative models and discriminative models—and discuss how they can be applied.
2. Overview of Machine Learning and Deep Learning
Machine learning is a technology that enables computers to learn from experience and make predictions or decisions based on that learning. Deep learning, a subset of machine learning, utilizes neural networks to learn patterns from data. These technologies can be applied to various areas in finance, such as stock price prediction, risk management, and portfolio optimization.
3. Basic Structure of Automated Trading Systems
Automated trading systems consist of the following basic components.
- Data Collection: Collects data such as stock prices and trading volumes.
- Data Preprocessing: Cleans and transforms the collected data as necessary.
- Feature Extraction: Extracts features to be input into the machine learning model.
- Model Training: Trains the model using the selected algorithm.
- Trading Signal Generation: Generates trading signals using the trained model.
- Execution: Automatically executes trades.
4. Generative Models and Discriminative Models
In machine learning, a Generative Model learns the distribution of a given dataset to generate new data. In contrast, a Discriminative Model learns the boundaries between two classes to predict which class new data belongs to. Each of these approaches has its own unique advantages and disadvantages.
4.1 Generative Models
Representative examples of generative models include GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders). GANs consist of two neural networks (a generator and a discriminator) that compete with each other during training. The generator creates new data by mimicking real data, while the discriminator evaluates whether the generated data is real. This method allows for the generation of data that closely resembles reality.
4.2 Discriminative Models
Representative examples of discriminative models include SVMs (Support Vector Machines), Logistic Regression, and deep learning-based CNNs (Convolutional Neural Networks). These models utilize classification techniques based on input data to process information. Unlike generative models that find the distribution of the given data, discriminative models learn decision boundaries for the classes of input data. Discriminative models are often used in practice because they tend to have high accuracy for given data.
5. Application of Generative Models in Financial Markets
Generative models can be applied in various financial areas such as stock price prediction, options pricing, and trading strategy simulations. For example, GANs can be used to simulate stock data to better understand non-linear patterns and improve trading strategies based on this understanding.
6. Application of Discriminative Models in Financial Markets
Discriminative models are widely used for generating trading signals, portfolio rebalancing, and predicting market volatility. For instance, deep learning-based CNNs can be utilized to predict price fluctuations of specific stocks, enabling the construction of a system that makes trading decisions. Generally, discriminative models tend to provide more accurate predictions than generative models.
7. Comparison of Generative Models and Discriminative Models
| Feature | Generative Model | Discriminative Model |
|---|---|---|
| Goal | Generate new data | Classify and predict data |
| Examples | GAN, VAE | SVM, CNN |
| Pattern Recognition | Learning overall distribution of data | Learning decision boundaries for input values |
| Application Cases | Market simulation, price prediction | Generating trading signals, risk management |
8. Practice: Applying Generative Models and Discriminative Models
This section will cover a simple implementation of generative models and discriminative models using Python. You can train both models using deep learning frameworks such as TensorFlow or PyTorch.
8.1 Data Preparation
Prepare the necessary datasets to get started. Stock market data can be collected via the Yahoo Finance API. This data can be used to generate training and testing datasets.
8.2 Implementing a Generative Model
An example of implementing a generative model using GANs is as follows:
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers
# Generator model
def build_generator(latent_dim):
model = tf.keras.Sequential()
model.add(layers.Dense(128, activation='relu', input_dim=latent_dim))
model.add(layers.Dense(256, activation='relu'))
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(1, activation='tanh')) # Stock prices are continuous values
return model
# Discriminator model
def build_discriminator():
model = tf.keras.Sequential()
model.add(layers.Dense(512, activation='relu', input_shape=(1,)))
model.add(layers.Dense(256, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid')) # Binary classification
return model
# Building the GAN model
latent_dim = 100
generator = build_generator(latent_dim)
discriminator = build_discriminator()