From Algorithms to Assets: AI’s Role in Revolutionizing Trading

Nearly every aspect of life feels the relentless march of artificial intelligence (AI), but the financial sector experiences it primarily through the market. Risk capital funds invested approximately $56 billion in AI-focused startups just last year, and the stock market is dominated by technology firms that regularly unveil more advanced AI versions. Some funds recognized AI’s unparalleled advantage in finance years ago, and it appears that those unable to adopt this technology, as in other sectors, may struggle to survive in this competitive arena.

The immense advantages of AI in the financial sector are striking. An experienced trader might achieve 5,000 trades in five years, gaining experience over time with numerous ups and downs. A good trader remembers many of the trades they make, and undoubtedly, one of the key advantages of a skilled trader is the experience they accumulate. Additionally, each team can choose a different trading strategy, whether based on technical analysis, fundamental analysis, arbitrage, etc., and can trade in various assets such as stocks, commodities, forex, options, and more. AI, on the other hand, can learn and remember over a million trades in a day. In a week, the machine can perform 7 million trades, far exceeding what a team of experienced traders could accomplish even if they worked every day for decades.

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the financial sector still lags behind other industries in adopting artificial intelligence. This is partly due to a deep lack of understanding of the potential

Furthermore, it’s relatively easy to isolate variables and teach the machine to focus solely on them, such as observing stock price behaviors over the years, analyzing stock charts, and then allowing the machine to execute buy and sell actions. We can show the machine only the company’s reports from its IPO date and stock price, then combine the models. The AI model can also capture real-time information, learn automatically, and continuously improve.

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These models can serve as vast information repositories, which, with the help of generative AI, allow company traders to query and derive answers easily through analyses performed by the model. For instance, determining the correlation between employment rate increases and the automotive sector rise or analyzing annual real estate yields per state in the USA. AI can answer all these questions with precision and speed.

AI can compare pension plans and provide the most accurate assessment of the most profitable plan, considering management fees and portfolio mix. A hedge fund can develop tools to protect itself when executing shorts by using a model that learns the correlation between high short volume on a stock and retail investors’ sentiment on social media regarding short squeezes, combined with a language learning model that analyzes sentiment.
These examples merely scratch the surface of AI’s potential in the competitive and profitable realm of funds.

Already five years ago, A.I. Capital Management provided an AI agent with 27 years of price movement data on currency pairs. Subsequently, they allowed it to trade on various currency pairs, and the worst return it achieved was on the EUR/USD pair, where it generated a return of 382% per year. For comparison, the return on the GBP/USD pair was 982%. These phenomenal
returns were achieved five years ago and based on just one variable

And despite all these advantages, the financial sector still lags behind other industries in adopting artificial intelligence. This is partly due to a deep lack of understanding of the potential inherent in AI technology among many in the finance sector and partly due to the heavy regulations and oversight imposed on the field. However, there is an emerging realization that those who fail to adopt this technology swiftly will find themselves out of the competitive market, similar to other sectors where artificial intelligence has already made significant inroads.

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