Recently, automated trading algorithms utilizing machine learning and deep learning techniques have been attracting attention in financial markets. These algorithms help enhance the accuracy of data analysis and eliminate emotional judgment from human decision-making, enabling profitable trades. In this course, we will explore trading methods using machine learning, focusing on decision tree algorithms.
What is a Decision Tree?
A decision tree is a non-parametric machine learning algorithm used to classify data or perform regression. It constructs a tree structure based on decision rules that correspond to the characteristics of the data. Nodes represent features, branches represent split conditions, and leaf nodes signify final outcomes (decisions).
Advantages of Decision Trees
- Ease of Interpretation: Decision trees are geometrically clear, making it easy to understand the conditions under which specific decisions are made.
- Ability to Model Non-linear Relationships: They can effectively model non-linear relationships between variables.
- Minimized Preprocessing: Data preprocessing requirements are relatively low. For example, scaling or creating dummy variables is not necessary.
Disadvantages of Decision Trees
- Overfitting: They may become too tailored to the data, reducing their generalization ability.
- Instability: A small change in data can significantly alter the tree structure.
Basic Structure of Trading Using Machine Learning
Algorithmic trading typically proceeds through the following steps:
- Data Collection: Collect various data such as stock prices, trading volumes, and economic indicators.
- Data Preprocessing: Transform the data into a format suitable for modeling through processes like handling missing values and normalization.
- Feature Selection: Select important variables from the data to enhance model performance.
- Model Training: Train using machine learning models like decision trees.
- Prediction: Use the trained model to predict future price movements.
- Trade Strategy Development: Determine buy and sell strategies based on the prediction results.
- Performance Evaluation: Evaluate the actual trading results to improve model performance.
Utilizing Decision Trees in Trading
The process of generating trading signals using decision trees can be described as follows:
1. Data Collection and Preparation
Collect stock price data along with technical indicators and other relevant financial data (e.g., moving averages, RSI, etc.). Using Python’s Pandas library, one can easily handle the data.