In recent years, algorithmic trading in financial markets has surged, driven by machine learning (ML) and deep learning (DL) technologies. Instead of the classical mean-variance optimization (Mean-Variance Optimization, MVO) approach, trading strategies that utilize these new technologies are being widely adopted. This course will teach you algorithmic trading techniques based on machine learning and deep learning, and explain how to utilize them as an alternative to mean-variance optimization.
1. What is Algorithmic Trading?
Algorithmic trading refers to a method of executing trades based on predefined rules by a program. It executes trades automatically according to the algorithm, minimizing errors caused by psychological factors or emotional decisions.
1.1 Advantages of Algorithmic Trading
- Rapid Trade Execution: Automated systems can execute trades much faster than humans.
- Accurate Data Processing: Data analysis allows for the implementation of more sophisticated strategies.
- Emotion Exclusion: Since trades are made by algorithms, emotional decisions are avoided.
2. Mean-Variance Optimization (MVO)
The MVO proposed by Harry Markowitz is a method for finding the optimal asset allocation by considering the expected return and risk of a portfolio. MVO uses the expected returns, variances, and covariances of assets to find the optimal portfolio.
2.1 Limitations of MVO
Traditional mean-variance optimization has several key limitations:
- Normality Assumption: It assumes that asset returns follow a normal distribution, but real financial data often does not meet this assumption.
- Stability Issues: Small changes in the data can lead to drastic changes in portfolio composition.
- Neglect of Non-linearity: MVO uses linear regression analysis, which may overlook non-linear relationships.
3. Alternatives through Machine Learning Techniques
Machine learning is a technique that learns patterns from data to build predictive models, and many financial professionals are using it to develop better trading strategies. Machine learning models excel in handling non-linearity and complexity, making them advantageous in overcoming the limitations of MVO.
3.1 Key Machine Learning Algorithms
- Linear Regression: A fundamental machine learning technique for building predictive models that can be used to predict asset returns.
- Decision Trees: Useful for modeling non-linearity and interactions, making interpretation easy.
- Random Forest: Combines multiple decision trees to maximize predictive performance.
- Neural Networks: Excellent at recognizing complex patterns, while deep learning models enable more sophisticated predictions.
4. The Role of Deep Learning
Deep learning is a powerful technique that automatically extracts features from data to learn patterns. Given the complexity and volatility of financial data, deep learning models can provide high predictive power.
4.1 Deep Learning Architectures
- Multi-Layer Perceptron (MLP): A basic form of a deep learning model, consisting of neural networks with multiple layers of nodes.
- Recurrent Neural Networks (RNN): Suitable for processing time series data, advantageous for recognizing patterns over time.
- Convolutional Neural Networks (CNN): Mainly used in image recognition, but can also be effectively applied to pattern detection in financial data.
5. Building Trading Strategies Using Machine Learning and Deep Learning
The process of building trading strategies utilizing machine learning and deep learning involves the following steps:
5.1 Data Collection
Gather the necessary data from reliable sources. This can involve considering various financial data, alpha factors, and economic indicators.
5.2 Data Preprocessing
Raw data must be processed to be suitable for analysis. This process includes handling missing values, normalization, and feature selection.
5.3 Model Selection and Training
Select a suitable machine learning or deep learning model and proceed with training using the training data. Evaluate the model’s performance through cross-validation.
5.4 Portfolio Optimization
Optimize asset allocation based on the trained model. In this process, use the predictions from machine learning models to maximize the profitability of the portfolio instead of traditional MVO.
5.5 Performance Evaluation and Rebalancing
Continuous monitoring and performance evaluation of how the model performs in real markets are necessary. Periodically rebalance the portfolio to optimize it.
6. Conclusion and Outlook
Machine learning and deep learning are opening new possibilities in algorithmic trading. Various methodologies are being developed to overcome the limitations of mean-variance optimization and achieve better investment performance. Ongoing research and experimentation will continue to evolve this field.
Through this course, I hope you gain the foundational knowledge and tools to provide better insights into business and investment strategies. Continue to learn and handle financial data in the language of machine learning and deep learning, enabling you to create more innovative trading strategies.