In the world of modern trading, quantitative trading is becoming increasingly complex and sophisticated. In particular, advancements in data and algorithms are contributing to the improvement of commercial trading strategies. This article will take an in-depth look at the history of algorithmic trading based on machine learning and deep learning, as well as the key innovations of the CNN (Convolutional Neural Network) architecture.
1. Basic Concepts of Algorithmic Trading
Algorithmic trading refers to the method of automating trading strategies to consistently approach the market. An algorithm refers to a computer program that executes trades according to specific rules. This algorithm analyzes data such as stock prices, trading volumes, and technical indicators to generate trading signals.
1.1 The Role of Machine Learning and Deep Learning
Machine learning is a technique that learns patterns from data to make predictions. Deep learning, a subset of machine learning, enables deeper and more complex representations of data through neural networks. These technologies are utilized in algorithmic trading in the following ways:
- Market prediction
- Risk management
- Determining optimized trading timing
2. Development of CNN Architecture
Convolutional Neural Networks (CNNs) are representative deep learning structures for image and video analysis. However, CNNs are also suitable for analyzing financial data, especially time series data. The development of CNNs includes several innovative architectures and techniques.
2.1 Early CNN Architectures
LeNet-5, presented by Yann LeCun in 1998, is a typical early CNN architecture. This model was used for digit recognition and consists of the following main components:
- Convolutional layers
- Pooling layers
- Fully connected layers
The structure of LeNet-5 was simple yet effective, and it laid the foundation for various CNN architectures that followed.
2.2 AlexNet and the ReLU Activation Function
In 2012, Alex Krizhevsky developed AlexNet, which led to the evolution of CNN architectures. AlexNet is famous for winning a deep learning competition and has the following features:
- Introduction of the ReLU activation function: This added non-linearity and significantly improved the learning speed.
- Dropout technique: This prevented overfitting and enhanced the model’s generalization ability.
2.3 VGGNet and Model Depth
VGGNet, published in 2014, is characterized by its very deep network structure. VGGNet consists of 16-19 layers, aiming to use small filters to construct a deeper network. This allows for more effective recognition of various patterns in time series data.
2.4 ResNet: Introduction of Residual Learning
In 2015, ResNet, presented by Microsoft Research, introduced ‘residual learning’ to address the difficulties of training deep learning models. Residual learning helps to learn deeper networks by essentially adding the output of previous layers to the current layer. This assists in effectively reflecting market volatility in algorithmic trading.
3. Applications of CNN in Algorithmic Trading
Due to their ability to effectively process time series data, CNNs have garnered attention in algorithmic trading. There are several studies employing CNNs, and we will explore their methodologies and results.
3.1 Price Prediction
CNNs are used to predict the next day’s stock prices by learning patterns from stock price data. By taking past price data as input, CNNs recognize specific patterns and derive prediction results. Studies show that CNNs outperform traditional machine learning techniques in prediction capabilities.
3.2 Event-Based Trading Strategies
By analyzing unstructured data such as news articles and social media data, CNNs can generate event-based trading strategies. CNNs, combined with natural language processing (NLP), enable prediction of market reactions.
3.3 Portfolio Optimization
Research on portfolio optimization using CNNs employs past asset return data as input to learn correlations among assets, thereby suggesting optimal portfolio configurations. The excellent feature extraction ability of CNNs aids in understanding complex asset relationships.
4. Future of CNN Architectures
CNN architectures are evolving every day and contributing significantly to algorithmic trading. The prospects and research directions for the future are as follows:
4.1 Development of Hybrid Models
In the future, the development of hybrid models that combine CNNs with time series analysis techniques like LSTM (Long Short-Term Memory) is anticipated. These models are expected to improve prediction accuracy of price fluctuations by considering temporal dependencies.
4.2 Application of Reinforcement Learning
Reinforcement learning is a technique where an agent learns optimal behaviors in varied environments. It is highly likely that this will be combined with CNNs and applied in algorithmic trading. Research is underway to adjust initial trading decisions automatically while functioning alongside a reward system.
4.3 Importance of Interpretability
Even if the results of deep learning models are provided, their internal operations are often opaque. Therefore, research is necessary to enhance interpretability. Investors and traders must be able to understand the model’s decisions to trust the algorithmic trading system.
5. Conclusion
Machine learning and deep learning in algorithmic trading are continuously evolving, with the evolution of CNN architectures becoming an essential component. The future of algorithmic trading will become clearer with the development of new models that enable complex data processing and predictions. If continuous research and development are conducted, machine learning-based algorithmic trading will provide sustainable and efficient investment strategies.