Discussing the article: "Data Science and ML (Part 38): AI Transfer Learning in Forex Markets"
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Check out the new article: Data Science and ML (Part 38): AI Transfer Learning in Forex Markets.
The AI breakthroughs dominating headlines, from ChatGPT to self-driving cars, aren’t built from isolated models but through cumulative knowledge transferred from various models or common fields. Now, this same "learn once, apply everywhere" approach can be applied to help us transform our AI models in algorithmic trading. In this article, we are going to learn how we can leverage the information gained across various instruments to help in improving predictions on others using transfer learning.
Here is a real-world example of where AI experts use transfer learning;
Let’s say you’re building a cat vs. dog image classifier but, you only have 1,000 images. Training a deep CNN from scratch would be tough, instead, you can take a model like ResNet50 or VGG16 that’s already trained on ImageNet (which has millions of images across 1000 classes), then use its convolutional layers as feature extractors, then add your custom classification layer(s), and fine-tune it on your smaller cat/dog dataset.
This process enables the sharing of model information, which makes our life easier as developers as we don't want to reinvent the wheel every time, instead of training a model from scratch you can scale based on available models purposed for a very similar task.
It is said that most people who know how to skate or go skating on a regular seem to also perform well in skiing or the ski sport and vice versa despite not undergoing intensive training on each. This is simply because these two sports have some similarities.
This is also true for financial markets, where despite having different instruments (symbols) which represent different economic assets or financial markets all the markets behave similarly most of the time as they are all driven and affected by supply and demand.
If you take a closer look at the market from a technical aspect, all markets tend to go up and down, similar candlestick patterns across all markets are displayed, indicators exhibit similar patterns on different instruments, and much more. This is the main reason why we often learn a technical analysis trading strategy on one instrument and apply the knowledge learned across all markets regardless of the differences in price magnitudes each instrument offers.
In machine learning, models don't often understand that these markets are comparable. In this article, we are going to discuss how we can leverage transfer learning to help models understand patterns in various financial instruments for effective model training, what are the merits and demerits of this technique, and the number of things to consider for effective transfer learning.
Author: Omega J Msigwa