As artificial intelligence (AI), machine learning (ML), and deep learning (DL) technologies advance, these technologies are increasingly being applied in the field of algorithmic trading. In particular, deep learning models such as CNN (Convolutional Neural Network) are strong in processing image data and are used in market data and price pattern analysis. This article will take a closer look at the basics of machine learning and deep learning, focusing on the historical model of LeNet5.
1. Basic Understanding of Machine Learning and Deep Learning
1.1 What is Machine Learning?
Machine learning is a technology that enables computers to analyze data and make predictions without explicit programming. It utilizes various algorithms and models to understand data and discover specific patterns or rules.
1.2 What is Deep Learning?
Deep learning is a subset of machine learning based on artificial neural networks. It can generally learn complex patterns through multiple layers from input data. Deep learning is particularly effective in processing unstructured data, such as image and speech recognition.
2. Development of Deep Learning Algorithmic Trading
In recent years, algorithmic trading has advanced significantly, with various machine learning and deep learning models developed for data analysis and prediction. These methods have helped discover more complex patterns than traditional statistical modeling.
2.1 Market Trends and Demands
The necessity of deep learning-based algorithmic trading has become more evident as the volume and complexity of data increase. It is becoming increasingly difficult to capture the more complex market using only traditional trading techniques.
2.2 Advantages of Deep Learning
- Large-scale data processing: Deep learning models can efficiently handle vast amounts of data.
- Pattern recognition: Effectively recognizes random patterns or non-linearities.
- Automation: The process of learning the field is automated, allowing for continuous learning.
3. LeNet5: The Historical Foundation of CNN
LeNet5 is one of the first CNN architectures developed by Yann LeCun in 1998, primarily designed for handwritten digit recognition. This model presented a fundamental perspective on image recognition tasks and inspired many subsequent CNN models.
3.1 Structure of LeNet5
LeNet5 consists of the following key layers:
- Layer 1: Input Layer – Receives images of size 32×32.
- Layer 2: Convolutional Layer – Uses 6 filters of size 5×5 to generate feature maps.
- Layer 3: Subsampling Layer – Reduces the size of the feature map through pooling, increasing information abstraction.
- Layer 4: Another Convolutional Layer – Extracts features more deeply using 16 filters of size 5×5.
- Layer 5: Another Subsampling Layer – Deepens higher-level abstraction through the feature maps from the previous layer.
- Layer 6: Fully Connected Layer – Combines to derive classification results with 120 neurons.
- Layer 7: Output Layer – Outputs the classification results.
3.2 How It Works
LeNet5 operates as follows:
- The input image passes through the convolutional layer, where features are extracted.
- The subsampling layer reduces the dimensions of the feature maps, summarizing each feature further.
- This process is repeated, learning features in high-dimensional space.
- In the final stage, all features are classified into categories through the fully connected layer.
4. Application in Algorithmic Trading
Structures like CNN, such as LeNet5, are useful for interpreting financial data and price charts. For example, stock price graphs over time can be converted into images and input into LeNet5 to detect ideal trading signals.
4.1 Data Preprocessing
Data preprocessing is crucial in algorithmic trading. It is necessary to convert stock price charts into images or visually represent specific indicators to make them suitable for CNN models.
4.2 Model Training
LeNet5 can be trained with various financial data, just as it was used for handwritten digit recognition. This allows for training the model and learning to generate optimal trading signals.
5. Conclusion
Machine learning and deep learning possess rich possibilities for algorithmic trading, and CNN models like LeNet5 can further expand these possibilities. With technological advancements, improved trading systems will be developed, and the future of algorithmic trading is expected to be brighter.
5.1 References
- Yann LeCun et al. (1998). Gradient-Based Learning Applied to Document Recognition.
- Ian Goodfellow, Yoshua Bengio, Aaron Courville (2016). Deep Learning.
- J.P. Morgan (2019). The Future of Trading: How Technology is Driving Change.
- Nicolas Papernot et al. (2016). The Limitations of Deep Learning in Adversarial Settings.