Over the last few decades, the implementation of algo trading has become more common thanks to its effectiveness and reduced need for human involvement. At first, institutional traders were the main users of the technology, but now it has expanded to everyday investors. The rise of AI is contributing further to this shift. The ability of AI to trawl through enormous volumes of data, spot data patterns, and take decisive actions almost in real time is changing the whole concept of trading. AI-powered algo trading has, indeed, started to open new opportunities to individual traders by making sophisticated strategies more accessible and adaptable to changing market conditions.

Challenges and Considerations

With the onset of the era of AI-based trading, opportunities have come along with challenges. To the average investor, hence, it would be important to know where the balance between the advantages of AI and its risks lies.

The first challenge is their ‘black box’ nature, i.e., the inability to understand the decision-making rationale behind their trades. This causes issues for regulators who need to assess the risks of using such a system such that there is trading accountability.

Another is that such algorithms can increase volatility within the market, especially if multiple AI algorithms react to market events similarly, leading to flash crashes. One needs to find a balance between such aspects, hence reaping the benefits of AI and maintaining systemic stability.

Yet another cause of concern is related to data bias. The performance of AI-driven algorithms is dependent on the quality of the data it processes. If the data that is being used is, itself, biased or incomplete, the predictions and decisions given by the algos will be flawed, thus leading to wrong decisions and maybe even losses.

AI Solutions for Algo Trading Challenges

Despite the above hindrances, the use of AI-driven algorithmic trading is on the rise due to the various benefits it comes with.

Decision-Making Ability

With AI, one can create ‘intelligent’ systems that can search the Internet for historical information, and current news, assess social media on market attitudes, and even check out economic factors. Using machine learning (ML) and deep learning, AI can comprehend patterns and connections that are beyond human comprehension. For example, AI can link unrelated factors like geopolitical events and social sentiment to make more accurate forecasts. This enhances trade execution accuracy and brings down costs.

Speed and Efficiency

With AI incorporated into the algorithmic structure, data can be analysed much faster. This helps to adjust trading positions quickly based on real-time market conditions, thus taking advantage of the short-lived opportunities presented; for example, if the market sentiment suddenly changes, AI will detect it, after which an automated trade takes place–and all this before the human trader even realises what is happening!

Also, if circuit breakers and volatility controls are introduced then traders can reduce adverse impacts. This, in fact, will give traders time to reassess during extreme market movements, hence, bringing down the risk of flash crashes.

Eliminating Human Bias and Emotion

AI can regulate irrational behavioural aspects that may lead to uncalculated risks in trading. AI-based trades are executed based on predefined strategies which rely solely on data. Hence, trading decisions are more objective and devoid of any emotion. Also, such solutions do not require rest and can operate 24/7. This helps more in seamless trading, especially in global markets that operate in different time zones.

Additionally, by implementing explainable AI frameworks transparency can be improved, which, in turn, will help traders to understand the rationale behind AI-driven decisions. This will address the concern regarding the ‘black box’ nature of AI. Together with this if clear guidelines for human oversight in AI trading are set then that will ensure that human judgment remains integral to the trading process. This will provide an additional layer of accountability.

Risk Management and Portfolio Diversification

AI plays an important role in risk management. AI can analyse historical data, market trends, and possible threats, thus helping an investor form a less risky portfolio with assets that are not vulnerable to volatility in the market. Since real-time risks can be assessed, traders can manage their strategies better, thus minimising loss chances. Such AI-driven risk management tools are useful, especially to everyday investors, because they can now automatically adjust their strategies and enhance performance even if they are inexperienced.

Sometimes, the reliability of AI predictions can be questionable. To alleviate this issue, conducting regular audits of the data sets used for training AI systems can help identify and correct errors.

Lowering Barriers for Retail Investors

AI has helped retail investors in overcoming barriers to entry. With the help of cloud technology and available AI applications, anyone can use sophisticated trading systems. Complex activities such as strategy formulation, backtesting, and execution, which required a lot of computing power and investment expertise, have now been transformed into AI systems for regular traders to the extent that they no longer need individual investors. This low-cost access to technological resources is democratising algo trading–it is no longer reserved for institutions.

The future of AI-driven algo trading, indeed, looks bright, more so with advancements in explainable AI and natural language processing (NLP), as these simplify decision-making and forecasting. However, while AI is improving trading, it cannot replace human involvement since ethics plays an important role in ensuring accountability and transparency. The final decision should always rest with the trader, thus making AI a tool rather than an independent entity. After all, even advanced AI systems can make errors or unjust decisions without human support and the opacity of some AI systems highlights, even more, the importance of human intervention to mitigate unintended consequences.

(This article is authored by Kunal Nandwani, Co-founder and CEO, uTrade)