Traders are constantly seeking new ways to gain an edge over their competitors, and in recent years, deep learning has emerged as a powerful tool for achieving this goal. Deep learning, a subfield of machine learning, involves training artificial neural networks to learn from vast amounts of data and make predictions or decisions based on that learning. In the context of trading, deep learning algorithms can be used to analyze market data and identify patterns that are invisible to the human eye. This article explores the potential benefits and challenges of using deep learning in trading, as well as real-world examples of its application.
What is deep learning?
Deep learning is a type of machine learning and artificial intelligence (AI) that imitates the way humans gain certain types of knowledge. Deep learning is an important element of data science, which includes statistics and predictive modeling. It is extremely beneficial to data scientists who are tasked with collecting, analyzing and interpreting large amounts of data; deep learning makes this process faster and easier.
At its simplest, deep learning can be thought of as a way to automate predictive analytics. While traditional machine learning algorithms are linear, deep learning algorithms are stacked in a hierarchy of increasing complexity and abstraction.
To understand deep learning, imagine a toddler whose first word is dog. The toddler learns what a dog is — and is not — by pointing to objects and saying the word dog. The parent says, “Yes, that is a dog,” or, “No, that is not a dog.” As the toddler continues to point to objects, he becomes more aware of the features that all dogs possess. What the toddler does, without knowing it, is clarify a complex abstraction — the concept of dog — by building a hierarchy in which each level of abstraction is created with knowledge that was gained from the preceding layer of the hierarchy.
How deep learning works
Computer programs that use deep learning go through much the same process as the toddler learning to identify the dog. Each algorithm in the hierarchy applies a nonlinear transformation to its input and uses what it learns to create a statistical model as output. Iterations continue until the output has reached an acceptable level of accuracy. The number of processing layers through which data must pass is what inspired the label deep.
In traditional machine learning, the learning process is supervised, and the programmer has to be extremely specific when telling the computer what types of things it should be looking for to decide if an image contains a dog or does not contain a dog. This is a laborious process called feature extraction, and the computer’s success rate depends entirely upon the programmer’s ability to accurately define a feature set for dog. The advantage of deep learning is the program builds the feature set by itself without supervision. Unsupervised learning is not only faster, but it is usually more accurate.
Initially, the computer program might be provided with training data — a set of images for which a human has labeled each image dog or not dog with metatags. The program uses the information it receives from the training data to create a feature set for dog and build a predictive model. In this case, the model the computer first creates might predict that anything in an image that has four legs and a tail should be labeled dog. Of course, the program is not aware of the labels four legs or tail. It will simply look for patterns of pixels in the digital data. With each iteration, the predictive model becomes more complex and more accurate.
Unlike the toddler, who will take weeks or even months to understand the concept of dog, a computer program that uses deep learning algorithms can be shown a training set and sort through millions of images, accurately identifying which images have dogs in them within a few minutes.
To achieve an acceptable level of accuracy, deep learning programs require access to immense amounts of training data and processing power, neither of which were easily available to programmers until the era of big data and cloud computing. Because deep learning programming can create complex statistical models directly from its own iterative output, it is able to create accurate predictive models from large quantities of unlabeled, unstructured data. This is important as the internet of things (IoT) continues to become more pervasive because most of the data humans and machines create is unstructured and is not labeled.
Advantages of Deep Learning in Trading
Source: Medium
One of the key advantages of using deep learning in trading is the ability to process vast amounts of data in real-time. This enables traders to make more informed decisions based on a more comprehensive analysis of market conditions. Deep learning algorithms are also highly adaptable, able to adjust to changing market conditions and evolving trading strategies.
Another advantage of deep learning in trading is the ability to identify patterns and correlations that may not be visible to the human eye. By analyzing large amounts of historical market data, deep learning algorithms can detect subtle patterns and relationships that may be indicative of future market movements.
Challenges and Limitations of Deep Learning in Trading
Source: Towards Data Science
Despite its many advantages, there are also challenges and limitations to using deep learning in trading. One of the biggest challenges is the need for large amounts of high-quality data to train the algorithms. In addition, deep learning algorithms can be highly complex and difficult to interpret, which can make it challenging for traders to fully understand the reasoning behind the algorithms’ predictions.
An additional challenge of deep learning in trading is the potential for overfitting. Overfitting occurs when an algorithm becomes too closely tailored to the training data, which can lead to poor performance when applied to new data sets. To address this challenge, traders must carefully balance the need for complexity and accuracy with the risk of overfitting.
Real-World Examples of Deep Learning in Trading
Source: Maven
There are numerous successful real-world applications of deep learning in trading. For instance, some hedge funds are using deep learning algorithms to analyze vast amounts of financial data in real-time, allowing them to make more informed investment decisions. Other companies are using deep learning to identify market anomalies and predict market trends.
Ethical Considerations and Future Implications of Deep Learning in Trading
As with any new technology, there are also ethical considerations and future implications to be considered when it comes to deep learning in trading. One concern is the potential for deep learning algorithms to perpetuate existing biases in the financial industry. Another concern is the potential for deep learning to further automate trading, potentially reducing the need for human traders.
