The current financial market is rapidly changing due to the development of data analysis and algorithmic trading. Investors are utilizing artificial intelligence (AI), machine learning (ML), and deep learning (DL) technologies to build market analysis and automated trading systems. In particular, trading using machine learning and deep learning algorithms enables data-driven investment decisions, and the proper utilization of these technologies can contribute to maximizing profitability. However, with the advancement of these automated systems, risks such as hostile attacks and fraud are also increasing.
1. Overview of Machine Learning and Deep Learning Based Trading
Machine learning (ML) is a technology that develops algorithms capable of learning from data to make predictions and decisions. Deep learning (DL) is a sub-field of machine learning that uses artificial neural networks to learn complex patterns in data. These technologies are widely utilized in financial data analysis, market prediction, and strategy development.
1.1 Data Collection
The first step of algorithmic trading is data collection. Data is collected from various markets such as stocks, bonds, foreign exchange, and cryptocurrencies. For example, in the stock market, the following data can be collected:
- Price data: open price, high price, low price, closing price, volume
- Financial data: earnings, assets, liabilities of companies
- Market indicators: technical indicators (moving averages, relative strength index, etc.)
- News data: company news, economic indicator announcements, and various events
1.2 Data Preprocessing
The collected data may contain noise and can be highly volatile. Therefore, data preprocessing is essential. The tasks that can be performed during preprocessing include:
- Removing and interpolating missing values
- Data scaling: standardization or normalization
- Feature selection: removing irrelevant variables
- Time series data transformation: converting to time series data suitable for modeling
1.3 Model Development
Based on the preprocessed data, machine learning or deep learning models can be developed. Representative machine learning algorithms include:
- Linear Regression
- Decision Tree
- Support Vector Machine
- Random Forest
In deep learning, artificial neural networks (ANN), recurrent neural networks (RNN), and long short-term memory networks (LSTM) can be used. These models can learn various financial data patterns, enhancing prediction accuracy.
2. Adversarial Training and Fraud Tool Development
Adversarial Training is a process designed to increase the robustness of machine learning and deep learning models. It aims for the model to learn to become more resilient to adversarial attacks. In the financial market, adversarial attacks primarily arise from attempts to twist the strategies of other investors or exploit vulnerabilities in the system for profit.
2.1 Adversarial Environment and Strategies
The concept of a zero-sum game describes situations where one party’s loss is equal to another party’s gain. Financial markets exhibit the characteristics of a zero-sum game. In other words, one investor’s profit represents another investor’s loss, so adversarial training is designed based on this principle.
2.2 Necessity of Adversarial Training
Examples of adversarial attacks include:
- When an algorithm makes trades based on incorrect predictions
- Manipulating market prices by exploiting an opponent’s trading strategies
- When rumors spread based on inaccurate information that affects the market
2.3 Methods for Implementing Adversarial Training
Adversarial training can be applied using the following methods:
- Using data with intentionally added noise during model training
- Adjusting the model’s parameters to make it more robust
- Generating adversarial examples and repeatedly retraining the model
3. Examples of Adversarial Attacks
Let’s look at some examples of adversarial attacks.
3.1 Market Manipulation
Market manipulation is the act of artificially altering the price of a specific stock or asset. For instance, submitting large buy or sell orders in advance can distort the market. In this case, machine learning models may learn incorrect patterns and make unfavorable trading decisions.
3.2 Distortion of Information
Spreading false information or writing inaccurate news articles can effectively distort prices in the market. In such cases, algorithms are more likely to find incorrect insights from basic data.
3.3 Disabling Algorithms
This attack method involves understanding and disrupting an opponent’s algorithm to maximize one’s own profit. By artificially lowering stock prices, algorithmic traders may incur losses, allowing a strategy of buying low to be utilized.
4. Defense Strategies Against Adversarial Attacks
To protect models, a variety of defense strategies are necessary. This is very important, as incorrect decisions can lead to significant losses.
4.1 Model Diversity
Using multiple differently trained models can be beneficial. Since each model operates independently, overall system damage can be minimized even if a specific model is attacked.
4.2 Continuous Performance Evaluation
It is essential to continuously monitor and evaluate the model’s performance. This allows for early detection of signs of attack and appropriate measures to be taken.
4.3 Validation of Data Integrity
Establishing procedures to verify the source and validity of data is crucial for using reliable data. Accurate predictions and decision-making can be made based on trust in the data.
5. Conclusion
The world of machine learning and deep learning algorithm trading is a complex yet fascinating field. Although there are risk factors such as adversarial attacks, these risks can be minimized through proper training and defense strategies. Investors can enhance their competitiveness in the market by well-understanding and utilizing such technologies, and in the long run, they can expect better investment outcomes. Therefore, it is necessary to combine adversarial training and strategic approaches to build a safe and effective trading system.