Machine Learning: how big is its potential in trading?

The world of trading is an immensely complex place, with investors deploying a range of different strategies to try and maximise their profits. In this digital age, technology has only furthered the intricacies of trading – for instance, the plethora of online platforms available makes it easier than ever for individuals to start trading. Given the ever-increasing adaptation of artificial intelligence (AI), it was only a matter of time before the technology entered the trading world. One of the best examples of this is machine learning (ML).

ML is the name given to a branch of AI that is focused on allowing computers and machines to imitate the way that humans learn. It not only performs tasks autonomously, but also uses vast datasets to improve performance and accuracy. This kind of automated ML is revolutionising the financial markets, providing traders with a number of tools that work to enhance their strategy.

The main way to implement automation is through algorithmic trading, which uses computer programs to automate activity. This use of automation has evolved from a traditional rule-based system into this new form of ML. Nowadays, advanced algorithms don’t just automate trading, but they also have the ability to utilise huge data sets to self-learn and improve their performance over time.

A good example of this is ML models using technical indicators and historical price data, as well as incorporating fundamental and sentiment analysis from media sources to predict movements in the market and, ultimately, make trading decisions. Another popular application is the use of reinforcement learning to develop trading strategies. These algorithms learn by their interactions with the trading environment, receiving rewards or penalties based on the success of their actions.

The benefits of machine learning

Increased speed

ML models can process and analyse data much faster than a human could, which allows for quicker decision making and trade execution. In high-frequency trading, the element of speed is particularly important as milliseconds can make a significant difference. ML automates the analysis and execution process, ensuring that trades are executed at an optimal time to maximise the profits.

Improved accuracy

Where ML models are precise, they also enhance the accuracy of trading strategies. As the models learn from datasets and improve over time, they can produce much more reliable and accurate predictions. A great example of this are ensemble learning techniques, which utilise multiple models to improve the accuracy of predictions, and are widely used in trading. This capability aids in developing strategies that are closely aligned with market conditions.

Reduced emotional bias 

Often, the issue of being influenced by emotions such as fear and greed affects human traders, which leads to irrational decisions. ML algorithms make decisions purely based on data and their predefined rules, meaning there is no emotional bias rooted in what they choose to do. This objectivity allows for the implementation of a trading plan at an optimal level.

The challenges of machine learning

The quality and quantity of data

How effective an ML model is depends heavily on the quality and quantity of the data available. It is crucial for an accurate training model to have high-quality and comprehensive datasets. Obtaining this level of data can be challenging and time consuming. What’s more, if the quality of an investor's data is poor, it can lead to inaccurate predictions and suboptimal trading strategies.

Overfitting

Overfitting is what happens when an ML model is too closely tailored to historical data, meaning it performs well on past data but struggles with new and unseen data. The issue with this is that it often leads to incorrect predictions and poor trading decisions. Techniques such as cross-validation, out-of-sample testing, regularisation and dropout are used to identify and help mitigate against overfitting – but it remains a significant issue in developing ML models.

Complexity and cost

To build and maintain an effective ML model requires substantial investment, while the process is also very complex. The cost of technology and the expertise level required act as a barrier for smaller traders and firms. Added to that, the complexity of these models can make it difficult for investors to trust and interpret them.

Best practices for machine learning

Adaptation and continuous learning

To ensure ML models are effective, it is important they’re regularly updated with new data. Continuous learning allows models to adapt to changing market conditions, which improves the accuracy of their predictions over time. One way to improve on this is to implement automated model retraining processes to help maintain the performance and relevance of the model.

Risk management

When implementing ML in trading, it is crucial to incorporate robust risk management strategies. ML models offer a more accurate understanding of the dynamics of the market, thus allowing for a more informed assessment of the risks in the market. This risk management can adjust to a portfolio in response to market conditions, as well as provide optimal asset allocation to create stable portfolios. Tools such as value-at-risk (VaR) and stress testing should be integrated into ML models to increase and enhance the risk management. Furthermore, the addition of natural language processing (NLP) can help detect potential risks that may arise from sentimental factors.

It is vital that when deploying ML models, all regulatory standards and ethical trading practices are followed to the letter of the law. These regulations are designed to prioritise transparency, accountability and fairness in algorithmic trading practices. This is key in ensuring a fair treatment for all individuals participating in trading, as well as to prevent the rise of any discriminatory practices.

Successfully using ML in trading

Ultimately the potential of ML in trading is huge. It has the ability to transform the industry entirely, with speed, accuracy and efficiency. However, the only way traders can achieve this is by understanding and applying the advanced techniques correctly. There are also a number of challenges such as the quality of data, overfitting and complexity which can easily derail an ML model's potential.

As technology evolves, ongoing education and adherence to ethical standards will remain fundamental in utilising ML for successful trading.