Asset trading in the financial markets has become more complex than ever, necessitating more sophisticated algorithms and data analysis techniques.
Today’s algorithmic trading achieves higher performance with the help of machine learning and deep learning.
This article aims to introduce the basic concepts of algorithmic trading using machine learning and deep learning, and to discuss how to update assumptions based on empirical evidence.
1. Basic Concepts of Algorithmic Trading
Algorithmic trading refers to the automatic execution of trades based on specific rules or formulas.
By combining machine learning and deep learning technologies, we can build models that analyze and predict market data.
These models learn from historical data to forecast future price movements and generate buy and sell signals.
1.1 Application of Machine Learning
Machine learning is the technology that learns from data to perform predictions. In algorithmic trading, machine learning can be utilized in the following ways:
- Market data analysis: Collecting large amounts of financial data and detecting patterns for algorithmic trading use.
- Trade signal generation: Automatically generating buy and sell signals through classification algorithms.
- Risk management: Developing risk management strategies with machine learning to minimize losses.
1.2 Application of Deep Learning
Deep learning is a machine learning technology based on artificial neural networks, capable of processing large amounts of data.
In algorithmic trading, deep learning primarily serves the following roles:
- Complex data processing: Analyzing unstructured data such as text and images for predictions.
- Time series forecasting: Predicting future changes based on past time series data.
- Generative modeling: Creating new trading strategies or complementing other models.
2. How to Update Assumptions Based on Empirical Evidence
A crucial part of algorithmic trading is validating and updating assumptions based on empirical evidence.
By examining whether existing strategies have statistical significance and retraining models with new data, performance can be improved.
2.1 Hypothesis Setting and Validation
In the early stages of algorithmic trading, it is important to set clear hypotheses.
For example, one might set the hypothesis, “A buy signal occurs based on historical data when stock prices drop by X%.”
The following procedures are necessary at this stage:
- Data collection: Gathering various financial data and indicators.
- Stock price pattern analysis: Analyzing past stock price patterns that need to be revisited.
- Statistical validation: Applying statistical techniques to verify whether the hypothesis is significant regarding historical data.
2.2 Data Updating
If the hypothesis has been validated, the next step is to update the model based on actual data and empirical evidence.
In this stage, the following methods can be used:
- Building a feedback loop: Continuously reflecting newly collected data into the model.
- Model retraining: Retraining the model whenever new data accumulates to keep it up-to-date.
- Performance evaluation: Periodically evaluating the model’s performance and revisiting assumptions if necessary using historical data.
3. Case Studies
3.1 Application of Specific Techniques
Many companies and researchers have successfully applied machine learning and deep learning techniques to algorithmic trading.
For example, there are cases where a stock price prediction model was created to maximize returns for a specific asset.
The following techniques were used in this process:
- Feature selection and prediction using random forests.
- Time series data forecasting using recurrent neural networks (RNNs).
- Portfolio optimization through reinforcement learning.
3.2 Lessons from Failures
Not all strategies guarantee success. Rather, one can learn more from failures.
Analyzing lessons from failed models or strategies is very important.
The insights gained from these failure cases can lead to the development of better strategies or improvements to current models.
4. Conclusion
Machine learning and deep learning algorithmic trading are becoming increasingly important in modern financial markets.
The technology for updating assumptions based on empirical evidence is a key element in developing successful trading strategies.
Based on the basic principles and techniques presented in this article, I hope you can also advance your own automated trading systems.
5. References
– “Deep Learning for Financial Market Predictions” – Research Paper
– “Machine Learning: A Probabilistic Perspective” – Kevin P. Murphy
– “Advances in Financial Machine Learning” – Marcos López de Prado