Artificial intelligence (AI) is rapidly transforming the global financial services industry, playing a key role in everything from fraud detection and compliance to banking chatbots and robo-advisory services. It’s also changing the ever-evolving world of algorithmic trading helping to eliminate human error and streamlining decision-making processes. But, how exactly is AI utilised in this sector and what are the overall benefits? Let’s take a closer look.
What exactly is AI?
Before you can really get to grips with how AI is used in the algorithmic trading sector, you must first understand what it is. Coined in 1955 by John McCarthy, AI is a term which describes the intelligence displayed by machines, in contrast to the natural intelligence displayed by humans. AI systems will typically demonstrate at least some of the following behaviours including planning, learning, reasoning, problem-solving, knowledge representation and perception.
Important AI applications
While AI is rather broad by definition, there are specific branches that play a prominent role within the algorithmic trading sector including ‘machine learning’ (ML). Named by Arthur Samuel of IBM in 1959, ML is an AI application that focusses on the idea that machines can learn for themselves by accessing Big Data. Such systems can automatically improve based on experience, without being explicitly programmed.
‘Deep learning’ (DL) is another AI concept and a branch of ML which revolved around problem-solving. Such networks do not necessarily need structure or labels to make sense of data. You may have also across ‘neural networks.’ These have AI roots and are inspired by the way humans think. They’re becoming increasingly integrated into today’s AI-related trading world.
Algorithmic trading uses powerful computers, running complex mathematical formulas, to generate returns. This is very different from days gone by where humans used to crowd busy exchanges or pick out the best assets to buy and sell from an office.
Sophisticated algorithms now play a significant role in market transactions and while algorithmic trading isn’t necessarily new, artificial intelligence is giving algorithmic traders extra tools to enhance their performance. Indeed, feeding AI predictions into algorithms can give you a more solid overview of the market including when to enter and exit positions and the best assets to long and short.
So, how exactly does AI tie in with today’s algorithmic trading sector?
Well, algorithmic trading is all about executing orders using automated and pre-programmed trading instructions, accounting for numerous variables such as volume, price and time. Algorithmic trading nowadays involves the use of complex AI systems with computers generating 50-70% of equity market trades, 60% of futures trades and 50% of treasuries.
The benefits of AI in algorithmic trading.
Fast trading speeds and improved accuracy
When it comes to algorithmic trading, large numbers of orders are executed within seconds adding liquidity to the market. High-Frequency Trading (HFT) of this kind happens in a fraction of a second and simply can’t be done by humans alone – that’s why algorithms are needed to execute and place bids before the market changes.
Automation streamlines the entire process with AI and machine learning adding an extra clever twist. Essentially ML computer systems are trained to recognise market movements with impressive accuracy, helping algorithms to bid accordingly. By accessing and understanding large data sets, ML systems can predict future outcomes, enhance trading strategies and tweak portfolios accordingly.
AI-enhanced algorithmic trading therefore helps to improve the performance and meet the demands of target clientele including hedge funds, propriety trading houses, corporates, bank propriety trading desks and next-generation marketing makers.
8TOPUZ ARE THE PIONEERS OF AI-BASED AUTOMATED INVESTING
Elimination of human error
Algorithmic trading also helps to reduce errors based on emotional and psychological factors. Often, traders let past trades, FOMO or market pressures affect their judgement and this can lead to poor decision making.
But with algorithmic trading, algorithms are used to ensure trader order placement is instance and accurate – based on pre-defined sets of instructions.
With the help of AI, it’s also possible for computer systems to check multiple market conditions and adjust trades instantly depending on the market environment. Of course, if this were to be done manually, it would take hours and hours of physical labour, research and fact-checking. And even then, errors might occur. Opportunities are likely to be missed too which is why AI is rapidly being integrated into financial institutions and shaping the sector significantly.
Case Study: Sentient Technologies – an AI company based in the US that operates a hedge fund – has developed an algorithm that processes millions of data points to find trading patterns and forecast trends. Based on trillions of simulated trading scenarios, Sentient’s algorithms use those scenarios to identify and blend successful trading patterns and devise new strategies. Not only does this reduce human labour, but it allows for optimum accuracy. Indeed, most empirical research indicates that evidence-based algorithms more accurately predict the future than do human forecasters.
AI and algorithmic trading in the real world
AI is not just something that’s being talked about. It’s already here and changing the financial world significantly, especially when it comes to trading practices. Top financial institutions including UBS and JP Morgan have already introduced AI into their trading tools with the former using AI techniques to trade volatility (which is notoriously difficult to navigate) and the latter using AI algorithms to execute equity trades. Algorithms enhanced by AI are also being used to guide venture capitalist investments.
So, as you can see, AI is being increasingly utilised in the algorithmic trading sector and offers many benefits. As 80% of all data is completely unstructured, AI and its complexed applications including ML and DL aims to deliver a more structured, organised and data-fuelled approach to the trading world, helping to make the whole process efficient, while providing split-second insights.