1. Introduction
In modern financial markets, machine learning and deep learning are becoming important technologies driving changes in trading systems. As the amount and complexity of data increase, the use of machine learning-based models is rising over traditional algorithms. This course aims to cover the implementation methods of trading systems using machine learning and deep learning, along with considerations to keep in mind.
2. Basic Concepts of Machine Learning and Deep Learning
2.1 Machine Learning
Machine learning is a set of algorithms that learn from data to perform specific tasks. Unlike classical statistical methods, machine learning processes large amounts of data to find patterns and make predictions based on them.
2.2 Deep Learning
Deep learning is a branch of machine learning that uses algorithms based on artificial neural networks. It is very effective at recognizing patterns in complex data and is widely used in the fields of image, speech, and text recognition. In the financial market, deep learning shows strengths in processing data with complex characteristics.
3. Basic Principles of Algorithmic Trading
Algorithmic trading refers to computer programs that automatically execute trades. Investors set trading rules, and the algorithm executes transactions automatically based on these rules. Algorithmic trading can involve various factors such as technical analysis, financial indicators, and market psychology.
4. Design and Implementation of Machine Learning and Deep Learning Trading Models
4.1 Data Collection
The success of a trading model depends on the quality and quantity of data. It is necessary to collect various data from diverse sources, such as price data, trading volume, news, and social media, to build a database.
4.2 Data Preprocessing
The collected data often contains missing values, outliers, or noise. These issues need to be addressed during the preprocessing stage, which includes processes like feature engineering, normalization, and scaling.
4.3 Model Selection
Depending on the type of problem, an appropriate machine learning or deep learning model should be selected. For regression problems, linear regression or decision tree regression can be used, while logistic regression, SVM, and deep learning models can be considered for classification problems.
5. Training Machine Learning and Deep Learning Models
5.1 Splitting Training and Testing Data
It is essential to separate training and testing data to evaluate the generalization performance of the model. Typically, 70-80% of the data is used for training, with the remaining 20-30% for testing.
5.2 Model Training
The model is trained using the selected algorithm. This stage includes processes to optimize model performance, such as hyperparameter tuning and cross-validation.
6. Performance Evaluation
Evaluating the performance of the model is a crucial step. Typically, metrics such as Accuracy, Precision, Recall, F1 Score, and AUC-ROC are used for evaluation. In finance, financial metrics like Sharpe Ratio and Max Drawdown should also be considered.
7. Implementation of a Real Trading System
7.1 Developing Trading Strategies
Based on the trained model, a real trading strategy should be developed. During strategy development, careful decisions regarding risk management, position sizing, and entry timing should be made.
7.2 Building an Automated Trading System
A system to automatically execute the developed trading strategy is constructed. This connects with the exchange via APIs and must include real-time data processing and order execution logic.
8. Conclusion
Algorithmic trading using machine learning and deep learning holds great potential and remains an actively researched area. However, alongside this, risk management and regulatory compliance are also important. Based on the understanding gained from this course, I hope you apply it to actual trading.