Introduction
This course will cover the concepts of algorithmic trading based on machine learning and deep learning, as well as an understanding of Convolutional Neural Networks (CNN) in the processing of satellite images, and how to develop practical trading strategies using these technologies. Due to the complexity of modern financial markets and the vast amount of data, machine learning and deep learning techniques have become essential, and particularly, object recognition technology using satellite images is very helpful in creating new investment opportunities.
Basics of Machine Learning
Machine learning is an algorithm that learns patterns from data and makes predictions based on this learning. It is generally divided into classification and regression problems, and different methods are used to learn data depending on the characteristics of the algorithm. When applying machine learning in the stock market, the goal is to support investors’ decision-making and maximize profits through stock price predictions.
Introduction to Key Algorithms
- Linear Regression: Modeling the linear relationship between input and output variables.
- Logistic Regression: Used to solve binary classification problems.
- Decision Tree: Classifies data through a tree structure.
- Support Vector Machine: Finds the optimal boundary that separates the data.
- Random Forest: Combines multiple decision trees to improve prediction performance.
Basics of Deep Learning
Deep learning is a field of machine learning that utilizes artificial neural networks to process data through multiple layers of neurons. The main advantage of deep learning is its ability to perform non-linear transformations effectively. In the stock market, deep learning is effective in learning complex patterns from high-dimensional data.
Convolutional Neural Networks (CNN)
Convolutional Neural Networks (CNN) are deep learning models primarily used for image processing, optimized for capturing spatial hierarchical structures. CNNs excel at automatically extracting features from images, demonstrating strong performance in areas like satellite image processing.
Satellite Images and Object Recognition
Satellite images refer to images captured by satellites used to photograph the Earth’s surface. Such images are utilized in various fields, including agriculture, forestry, and urban planning. Object recognition refers to the process of identifying and classifying specific objects within an image. CNNs can enhance the performance of this object recognition.
Structure of CNN
CNNs consist of the following key layers:
- Convolutional Layer: Applies filters to the input image to create feature maps.
- Pooling Layer: Reduces the size of the feature maps to decrease computational load and emphasize important features.
- Fully Connected Layer: The final layer for classifying the classes, with a softmax function used at the end.
Application of Machine Learning and Deep Learning in Trading
To apply machine learning and deep learning algorithms in trading, the process of data collection and processing is crucial. It is necessary to combine stock price data, satellite images, and other features to train the model.
Data Collection
Data necessary for trading can be collected from various sources. Stock price data is freely available from sources like Yahoo Finance API, Alpha Vantage API, and satellite images can be accessed from platforms like Google Earth Engine and Sentinel Hub.
Data Processing
Once the data is prepared, preprocessing it is critical. Handling missing values, normalizing, and standardizing the data can improve the model’s performance. Additionally, feature selection techniques can be employed to select only the important features, reducing the model’s complexity.
Model Training and Evaluation
After splitting the data into training and evaluation sets and training the model, the performance is measured using the evaluation data. This helps to prevent overfitting and improves the model’s generalization performance.
Model Performance Metrics
- Accuracy: Overall accuracy of predictions.
- Precision: The ratio of actual positives among the positive predictions.
- Recall: The ratio of positive predictions among actual positives.
Case Studies
Now, let’s examine successful cases of algorithmic trading using machine learning and deep learning. Many hedge funds and financial institutions have adopted AI-based trading systems and reported positive results. For instance, Bill Gross’s PIMCO has used machine learning to predict interest rate fluctuations and improve portfolio performance.
Agricultural Investment Based on Satellite Images
By analyzing agricultural data using satellite images, it is possible to predict climate change and yield variations, helping to make investment decisions concerning agricultural stocks. CNNs can be used to identify crop types and assess the production potential of specific areas.
Conclusion
Machine learning and deep learning have brought innovative changes to algorithmic trading. In particular, object recognition technology based on satellite images offers new investment opportunities and opens possibilities for the integration of data science and finance. Based on the knowledge gained in this course, we hope you develop real trading strategies and achieve success.
References
- Russell, A. (2020). Machine Learning for Asset Managers. Springer.
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
- 衛星データが変える未来の投資 (Satellite Data Changes the Future of Investment). (2021). Translated by Author. Kyoto University Press.