1. Introduction
The importance of data analysis in the trading field is increasing day by day, and machine learning (ML) and deep learning (DL) techniques are being utilized more and more in investment strategies. This course will explore how to build automated trading systems using ML and DL and the tools associated with them. These technologies complement traditional analysis methods and provide solutions for pursuing higher returns.
2. Difference Between Machine Learning and Deep Learning
Machine learning and deep learning are both subfields of AI but have different characteristics and applications. Machine learning includes algorithms that learn patterns from data to make predictions, while deep learning uses neural networks to understand and learn from more complex data structures.
- Machine Learning: Utilizes various algorithms (e.g., regression, decision trees, SVMs, etc.) to learn from data.
- Deep Learning: Processes high-dimensional data using multi-layered neural networks, including structures like CNN and RNN.
3. Basics of Algorithmic Trading
Algorithmic trading refers to the method of generating trading signals and executing trades automatically through computer programs. This system is designed to analyze various data to make optimal trading decisions. The main advantages of algorithmic trading include:
- Elimination of emotional factors
- Fast execution and high accuracy
- Ability to trade 24/7
- Ease of implementing complex strategies
4. Machine Learning Tools and Libraries
Various tools and libraries can be used to build ML and DL models. Here are some of the open-source and commercial tools:
- Pandas: A library for data manipulation and analysis.
- Numpy: A library supporting high-performance numerical calculations.
- Scikit-learn: A library containing various algorithms for machine learning.
- TensorFlow: A deep learning framework developed by Google, suitable for building large-scale models.
- PyTorch: Another deep learning framework developed by Facebook, known for its flexibility and ease of use.
5. Data Collection and Preprocessing
The performance of machine learning and deep learning heavily relies on the quality and quantity of data. Therefore, appropriate data collection and preprocessing are essential. Methods for data collection include:
- Real-time data collection using APIs
- Importing existing data from CSV, Excel files, etc.
- Data collection through web scraping
In the preprocessing phase, tasks such as data cleaning, handling missing values, and normalization are required. This phase significantly impacts the accuracy of model training and should be performed carefully.
6. Model Selection and Training
The choice of machine learning model varies depending on the problem domain and the characteristics of the data. Commonly used models for time series forecasting problems, such as stock price prediction, include:
- Linear Regression
- Random Forest
- XGBoost
- Recurrent Neural Networks (RNN)
- Long Short-Term Memory (LSTM)
After selecting a model, it must be trained with data, and hyperparameter tuning plays an important role during the training process.
7. Model Evaluation
There are various methods to evaluate the performance of a model. Common evaluation metrics include:
- MSE (Mean Squared Error): The average of the squared differences between predicted and actual values
- RMSE (Root Mean Squared Error): The square root of MSE
- R-squared: A metric indicating the explanatory power of the model
Additionally, various trading strategies can utilize regression analysis or machine learning techniques to evaluate performance.
8. Building an Actual Trading System
In the phase of building a real trading system using the model, it is necessary to define the trading strategy and write code to execute trades based on it. Factors to consider in practice include slippage, trading costs, and risk management.
9. Conclusion
Trading using machine learning and deep learning is still an evolving field that requires continuous research and development. This course aimed to provide a variety of information from basic knowledge to practical application. I hope you can further enhance your trading system through more experiments and improvements in the future.
10. References
Here are a few recommended materials or books for further study:
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow – Aurélien Géron
- Deep Learning for Time Series Forecasting – Jason Brownlee
- Algorithmic Trading: Winning Strategies and Their Rationale – Ernie Chan