Over the past couple of decades, the stock market has fast developed due to the integration of artificial intelligence (AI) in trading. Tools supported by AI technology have changed the entire playing field for traders in terms of the quality and speed of data analysis, trend detection, and transaction processing to approximately zero for the human today. But it is interesting to see who benefits more from the role of AI–retail or institutional algorithmic traders.

In this blog, we will delve into the application of AI in the context of algorithmic trading, the distinctions between retail and institutional traders, and who the victory from these developments is most intended for.

Understanding Algorithmic Trading AI

Algorithmic trading AI is the incorporation of intelligent machines into trading using algorithms. This type of trading is performed by AI-driven tools and includes gathering and monitoring market data and placing orders. AI algorithmic trading systems analyse past prices and current trends to assist with forecasting and deciding the future price rate or changes on a particular asset.

It goes without saying that one of the major benefits of exploiting an AI algo trading system is the fact that it is devoid of the emotional aspect, which tends to cloud judgment and often leads traders to make bad trading decisions. In a similar vein, stock trading AI algorithms for stock trading are able to collect and process information far quicker than human traders, hence any individual whose aim to enhance his or her trading tactics will definitely view AI hard technology as a useful tool.

However, the impact of AI in trading varies depending on who is using it–a retail trader or an institutional investor.

Role of Retail Traders in AI Algorithmic Trading

A retail trader is a person who buys or sells securities for their account but does not manage funds for third party investors and usually possesses less capital than an institutional trader. AI-enabling algorithmic trading systems are used to a great extent by retail scalpers as most retail traders have turned to use systems like the uTrade Algos platform that has customisable AI-driven tools for the target market.

AI algo trading is advantageous since it bridges the competitive gap between individual retail traders and institutional investors by availing high-level data analytics and automation to the former. On the other hand, retail investors do not have the necessary time and capabilities to go through a lot of trade data in the market and evaluate the information. Stock trading AI algorithms perform this task for them.

When it comes to retail trading, AI trading has many advantages, which include:

  • Analysis of Data and Recognition of Patterns: AI has the ability to examine the available historical and current data, including video display screens and determine how such data is transmitted to detect patterns that even a human being cannot see. Such helps in making informed decisions for retail traders without having to deeply analyse the market for long hours.
  • Trading that is Automated: In AI algorithmic trading, retail traders are able to predefine what aspects would entail a trade. Deals are executed by the AI based on such predefined aspects, hence allowing retail traders to keep out of the market, but remain benefiting from the various trading opportunities.
  • Availability of Advanced Tools: Platforms like the uTrade Algos platform provide retail traders with access to advanced tools that were previously only available to institutional investors. They include market analysis and backtesting, procedures for monitoring and examining multiple stocks, and other tools enabling small players to compete fairly.

AI comes with a lot of advantages for retail traders, however, one important fact is that retail trader

Role of Institutional Investors in AI Quantitative Trading

Hedge funds, mutual funds, and pension funds are examples of institutional investors who deploy finances entrusted to them by clients. These investors have led the way in AI quantitative trading and have been employing algorithms to facilitate investment decisions. The sheer scale and resources available to institutional investors enable them to create and execute AI applications that are more complex than those employed by retail traders.

AI-driven investment strategies have multiple advantages for institutional investors when compared to traditional approaches:

  1. Scalability: Addressing and managing a great deal of trades manually becomes very complicated for institutional investors. AI algorithms can analyse and trade data on a massive scale, which is impossible for traders without the use of robots. Such scalability is very important to institutional investors who are seeking to enhance their trading activities.
  2. Proactive Risk Management: Trading parameters are instant and the trader's decision can be altered within the current market circumstances. So, the risk is controlled at all times without compromising on the effectiveness of the investment. This means institutional traders are able to manage risk effectively, even while executing aggressive and efficient trades.
  3. Flexible Implementation: In most cases, institutional investors are offered stock trading AI, which can be customised to fit their specific investment goals. This adaptability enables them to modify their trading perspectives more rapidly, in order to counter changing market situations.
  4. High-Frequency Trading (HFT): Institutional Investors are synchronously characterised by high-frequency trading which is the execution of thousands if not millions of trades in a short period. Such trading involves the use of trading algorithms to be positioned in or out of trades almost instantaneously after analysing information. As a result, the ability to trade via algorithms pertaining to the AI is a must for HFT trading as it requires the processing of vast amounts of data in real time and the ability to execute trades within fractions of a second.

However, despite these advantages, institutional investors also face challenges when using AI in trading. There have been technologies that have gone live, and incorporated into trading strategies, only for them to fail during execution and incur huge losses. Consequently, institutional investors should not only embrace the implementation of trading strategies driven by artificial intelligence but also ensure that it is constantly reviewed and updated.

Retail Traders Vs Institutional Investors: Who Benefits the Most?

There is a common understanding that both retail as well as institutional investors can make use of AI in modern trading, yet how these groups will benefit is determined by their needs and the resources available.

In the case of retail traders, the use of AI has one obvious limitation‐the only advantage is the provision of advanced tools that allow them to be active participants in the share market. 

On the opposite side, institutional investors take advantage of the use of AI quantitative trading due to its expansion capability, sophisticated risk management, and settlement customisation. They are able to carry out more complicated AI implementation that includes high-frequency trading hence their upper hand in the market.

In the end, both retail and institutional investors are able to gain from the utilisation of AI technologies and algorithmic trading but to what extent they are able to do that depends on how they use these technologies in pursuing their ends. Institutional investors employ AI to enhance maximum efficiency and scaling out, whereas retail investors use it to make the competition fair.

To sum up, AI is changing the face of algorithmic trading in a way that brings different advantages to retail and institutional investors. As AI continues to develop, its role in financial markets will become even more important, providing new opportunities for traders of all sizes. While the tools may differ in complexity, platforms like the uTrade Algos platform ensure that both retail and institutional investors can harness the power of AI for improved decision-making in the ever-changing stock market.