In recent years, the use of machine learning (ML) and deep learning (DL) in financial markets has increased dramatically. In the past, algorithmic trading was based on mathematical models or rule-based systems, but it has now evolved into a data-driven approach. This course will cover the basics of automated trading using machine learning and deep learning algorithms, along with a detailed examination of deterministic and probabilistic approximate inference.
1. Overview of Machine Learning and Deep Learning
Machine learning is a technology that enables computers to learn from experience and improve performance. It is a research area that combines statistics and computer science, focusing on recognizing patterns and making predictions from data. Deep learning is a subfield of machine learning that learns complex data patterns based on artificial neural networks.
1.1 The Necessity of Machine Learning
Traditional trading is conducted based on fixed rules, but financial markets have a very non-linear and complex structure. With the explosive increase in data volume, extracting useful information from it has become crucial. The necessity for machine learning arises for the following reasons:
- Processing Complex Data: Automatically processing large volumes of market data to enable pattern recognition.
- Non-linearity Handling: Learning non-linear relationships in data without relying on the linearity assumptions made by traditional models.
- Real-time Analysis: Performing data analysis and predictions in real-time to quickly respond to market volatility.
1.2 The Evolution of Deep Learning
Deep learning, as a subfield of machine learning, is highly effective in recognizing more complex patterns in data through multi-layer neural network structures. It shows excellent performance, especially in image and natural language processing (NLP), enabling numerous applications in financial markets. For example, sentiment analysis through news articles or pattern detection through chart image recognition.
2. Components of Algorithmic Trading
Algorithmic trading refers to systems that automatically execute trades based on given conditions. The main components of algorithmic trading include:
- Data Collection: Collecting external data such as market data, news, and economic indicators.
- Data Preprocessing: Transforming data into usable forms through processes such as handling missing values, normalization, and cleansing.
- Feature Selection: Selecting features that provide important information for model training.
- Model Selection: Choosing the machine learning or deep learning algorithm to be used.
- Training and Testing: Evaluating performance using separate test data after training the model.
- Execution and Monitoring: Executing actual trades and monitoring performance in real-time.
2.1 Data Collection
In the data collection stage, the source and form of the data are important. Quantitative data such as stock prices and trading volumes are used in strategies like arbitrage, while qualitative data such as news and social media data are useful for analyzing market sentiment.
2.2 Data Preprocessing
Data preprocessing is a critical process in algorithmic trading. Preprocessing steps such as handling missing values, removing outliers, and normalization can maximize the model’s performance. The techniques used in this process include:
- Scaling: Ensuring consistency in data ranges using methods like Min-Max Scaling and Standardization.
- One-Hot Encoding: Converting categorical data into numerical data.
- Time Series Processing: Generating features considering the temporal incrementality in stock price data.
3. Machine Learning and Deep Learning Algorithms
Let’s take a closer look at the algorithms primarily used in machine learning and deep learning.
3.1 Machine Learning Algorithms
Machine learning algorithms can generally be classified into supervised learning, unsupervised learning, and reinforcement learning.
- Linear Regression: Used for predicting continuous values such as stock price forecasts.
- Logistic Regression: Frequently utilized for binary classification problems.
- Decision Trees: A tree-structured model that splits data based on features.
- Random Forest: Combines multiple decision trees to improve prediction performance.
3.2 Deep Learning Algorithms
Deep learning algorithms are primarily based on neural networks. Aside from basic neural networks, various structures exist.
- Multi-Layer Perceptron (MLP): A basic neural network that includes multiple hidden layers.
- Convolutional Neural Network (CNN): An effective structure for processing image data, suitable for chart image analysis.
- Recurrent Neural Network (RNN): Strong in processing sequence data, frequently utilized for stock price prediction.
- Long Short-Term Memory (LSTM): An advanced form of RNN, advantageous for retaining memories of older data.
4. Deterministic and Probabilistic Approximate Inference
Understanding both deterministic and probabilistic approaches, in addition to model selection, is critical in algorithmic trading. These two approaches have their own strengths and weaknesses, and suitable choices are needed depending on the situation.
4.1 Deterministic Approximate Inference
Deterministic Approximation involves finding exact patterns from data and forecasting based on established rules. It predicts the future using past data and specific algorithms. The advantages of this approach include:
- Clear Interpretation: Results can be easily interpreted and understood.
- Reliability: Having clear rules based on data allows for stable performance expectations.
However, deterministic approaches can always fail, and as they constantly rely on past data, they may not adequately respond to new market situations.
4.2 Probabilistic Approximate Inference
Probabilistic Approximation involves forecasting the future based on probability models from data. It is useful for understanding and modeling market uncertainties.
- Capturing Volatility: Directly modeling the volatility of financial markets enhances prediction accuracy.
- Adaptability: The model can be continuously updated and improved based on new data.
However, probabilistic approaches can be difficult to interpret, and if the model becomes too complex, issues such as overfitting may arise.
5. Conclusion
Algorithmic trading utilizing machine learning and deep learning has many advantages over traditional trading methods but requires a high level of technical knowledge and experience. Understanding and appropriately utilizing deterministic and probabilistic approximate inference is critical in establishing successful trading strategies. Based on the content explained in this course, we encourage you to challenge algorithmic trading based on machine learning and deep learning.
6. References
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
- Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
- Tsay, R. S. (2010). Analysis of Financial Time Series. Wiley.
- Karatzas, I., & Shreve, S. E. (1998). Brownian Motion and Stochastic Calculus. Springer.