The modern financial market is rapidly changing into a data-driven environment. This shift
further emphasizes the importance of automated trading strategies utilizing advanced analytical
techniques such as machine learning and deep learning. In this article, we will detail the principles
of algorithmic trading using machine learning and deep learning, and how their hierarchical structure
contributes to solving high-dimensional data problems.
1. Basics of Algorithmic Trading
Algorithmic trading is a trading method that automatically executes orders based on a pre-set
algorithm. This method generally involves three main steps: data analysis, trading signal generation,
and order execution. Each step can be performed more sophisticatedly through machine learning and
deep learning technologies.
1.1 Data Collection
The first step in algorithmic trading is data collection. Data is gathered from various sources,
including stock prices, trading volumes, news, and economic indicators. To handle high-dimensional
data, it is essential to effectively collect and refine it.
1.2 Trading Signal Generation
In the step of generating trading signals based on the collected data, various machine learning
algorithms can be utilized. For example, regression analysis, decision trees, and neural networks
can be used to create stock price prediction models. Here, the hierarchical structure plays an
important role.
1.3 Order Execution
The true purpose of algorithmic trading is to automatically execute orders based on the generated
trading signals. In this process, machine learning techniques can also be employed to optimize
order execution.
2. Differences Between Machine Learning and Deep Learning
Machine learning and deep learning are closely related but distinctly different concepts.
Machine learning refers to a set of algorithms that learn patterns from data to make predictions,
while deep learning is a subfield of machine learning that processes data using multilayer neural networks.
The interaction between the two is essential for maximizing effectiveness in algorithmic trading.
2.1 Key Techniques in Machine Learning
- Regression Analysis: Useful for predicting continuous values.
- Classification Algorithms: Used for classifying data into one of several classes.
- Clustering: Groups data points with similar characteristics.
2.2 Key Techniques in Deep Learning
- Artificial Neural Networks (ANN): Strong in learning nonlinear relationships.
- Convolutional Neural Networks (CNN): Primarily used for image data.
- Recurrent Neural Networks (RNN): Effective for time series data analysis.
3. Issues with High-Dimensional Data
High-dimensional data refers to situations where the number of variables exceeds the number of observations.
In such cases, computational costs increase, and one may face the issue of overfitting. This problem
becomes particularly critical when dealing with datasets rich in features, like financial data.
3.1 Curse of Dimensionality
The curse of dimensionality refers to the phenomenon where the density of data becomes sparse as the
number of dimensions increases, thus complicating analysis. Many machine learning algorithms can
experience this issue, which diminishes their performance in high-dimensional data.
3.2 Overfitting Problem
When the dataset is small, learning all high-dimensional features can lead to a decrease in performance,
known as the overfitting problem. This particularly negatively impacts the performance of algorithmic trading.
4. Introduction of Hierarchical Structures
At this point, hierarchical structures play a crucial role in alleviating high-dimensional problems
of data. Hierarchical techniques help reduce the complexity of data while preserving its essence.
4.1 Concept of Hierarchical Models
A hierarchical model is an algorithm that learns features at multiple levels. It allows for data
analysis at different levels, making it much easier to discover relationships among various variables.
4.2 Hierarchical Clustering
Hierarchical clustering is a technique that forms groups based on the similarities between data points.
This can reduce the high dimensionality of data and contribute to discovering useful patterns.
4.3 Hierarchical Structures in Deep Learning
Each layer of a deep learning network learns different characteristics of the data. For example,
the initial layers extract basic features (edges, patterns, etc.), while subsequent layers learn
more complex characteristics (objects, shapes, etc.), allowing for the capture of various data properties.
5. Characteristics of Financial Data and Application of Hierarchical Structures
Financial data changes over time and often exhibits frequent box-like patterns, as well as
univariate or multivariate characteristics. To effectively understand and learn these complex data
characteristics, the application of hierarchical structures is essential.
5.1 Time Series Analysis
Financial data often changes in a time series format. This data has a flow over time, which may
not be well captured by traditional machine learning techniques. Hierarchical structures such as
RNN and LSTM in deep learning are very effective for modeling this time series data.
5.2 Image-Based Trading Signals
Recently, the generation of trading signals based on image data has also gained attention.
For example, methods for visually analyzing chart patterns and price fluctuations exist.
In this process, hierarchical analysis through CNN also plays an important role, providing
users with more intuitive insights.
6. Performance Improvement Theory Through Hierarchical Structures
Applying hierarchical structures to improve the precision of problem-solving is very effective in
reducing the complexity of data. This allows for the removal of unnecessary dimensions and
enables a more focused learning of necessary features.
6.1 Feature Selection
Important features can be selected through a hierarchical approach. Many machine learning
algorithms tend to reduce their performance as the number of features increases, and this
hierarchical structure helps to extract the fundamental features and maximize the focus of analysis.
6.2 Performance Evaluation and Validation
In financial data, backtesting and validation are particularly important. Hierarchical structures
allow for easy analysis and comparison of the learned content at each sub-layer, making it
advantageous for selecting the optimal model.
7. Conclusion and Future Directions
With advancements in machine learning and deep learning, the potential of algorithmic trading
is expanding infinitely. The introduction of hierarchical structures to solve high-dimensional
problems in data will significantly contribute to this advancement.
With the continued development of more sophisticated models and algorithms, next-generation
algorithmic trading strategies are expected to emerge.
This course covered the basics and importance of algorithmic trading utilizing machine learning
and deep learning, and explained how hierarchical structures can contribute to alleviating
the problems of high-dimensional data. Based on this content, further advanced algorithmic
trading strategies and in-depth research into stock market analysis will continue.