Impact of Machine Learning in Systematic Trading

The use of AI in trading has been hailed for increasing efficiency, accuracy, and speed in the development and execution of trading strategies, with a prediction and data processing capability that is impossible to replicate using traditional pattern analysis techniques. This has been reflected in the job market, with Quantitative researchers specialised in ML-driven algorithms being some of the hottest commodities in the market currently. Here are a few areas that machine learning has its greatest impact on the trade lifecycle:

Pattern detecting and predictive modelling

One of the most widespread uses of machine learning in trading is in the development of predictive models. These models rely on data from various sources that are analysed to identify patterns and trends to produce trading strategies.

Traditionally, supervised learning has been a common approach for market movement prediction, with labelled data used to train models and unsupervised learning for identifying patterns in unlabelled data. These core classifications have given way to fields of machine learning with common applications in trading:

Reinforcement Learning produces an algorithm that learns from experience and repeated trial and error with rewards for positive outcomes. This is used to optimize trading strategies based on past performance and to develop more adaptive models that will react to changing market conditions and volatility.

Deep Learning uses artificial neural networks to model complex relationships between input and output data. Neural networks are used to analyze large amounts of data to identify patterns.

Natural Language Processing involves giving computers the ability to analyse and comprehend human language. It can be used to generate trading strategies in tasks like sentiment analysis, which involves categorizing the sentiments of people about the stock value of a company, through data sources like social media, websites, and forums.

Data processing

Another area of impact from the increased utilisation of AI in trading is in data processing. With the use of machine learning algorithms and models, real-time analytics can be implemented, allowing data to be analysed as it is generated. This allows traders to identify patterns in real-time, allowing more informed trading decisions.

Increasing efficiency

Speed provides the edge to the top tier of trading firms and is especially important in high-frequency trading. The implementation of more machine learning techniques and algorithms has helped with efficiency and speed through automation in several areas within trading:

• Execution
• Risk management
• Backtesting
• Weighting signals
• Coding tasks

In summary, the use and development of machine learning techniques will continue to have a large impact on the way traders generate and execute trading strategies. With trading firms pioneering new techniques and applications generating the largest returns due to their edge over the rest of the market. However, similarly to most of the uses of ML, there will undoubtedly always be a need for human intelligence and discretion in trading. The human touch is still undoubtedly needed, whether it is providing insight into unexpected market volatility, or discretion about the quality of data and model bias. It seems that these predictions will continue to be used as tools for traders to inform their decisions, rather than replacing them.