AI Use in Trading
Quant Trading
AI Use in Trading
Quant Trading
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AI Use in Trading

Artificial Intelligence has altered numerous sectors, and the world of trading is no exception. However, it is still more of a helper to traders.

Artificial Intelligence has altered numerous sectors, and the world of trading is no exception. However, it is still more of a helper to traders rather than a tool that would be superior to human trading. Also, it is worth noting that AI trading algorithms are not always synonymous with algorithmic trading. Traditional algorithms are based on a set of instructions. They operate on an “if-then” logic, which prevents them from improving with time through new data. Fortunately, AI and the wide use of Machine Learning have ushered in a new generation of algorithms that are moving away from traditional "rule-based" algorithms.

Machine learning is a subset of AI that learns from large amounts of data and uses this knowledge to make predictions and analyses. Furthermore, these algorithms learn and improve automatically as time goes on. This has great potential in the trading industry and already has several applications.

Sentiment analysis

This is one of the most popular applications for AI in the trading sector. It aids traders in determining what sentiment is on social media and news. Traders rely on news and, particularly, Twitter for information. However, even the best traders cannot read every news release or tweet and make judgments about overall sentiment. As a result, sentiment algorithms based on Natural Language Processing (a branch of machine learning that examines human language) interpret text posted about stocks and conclude the overall sentiment. Simply put, it informs you how traders feel about a certain company.

Finding patterns

AI may find patterns that would otherwise go unnoticed by analyzing a large data set. Then the findings can be reviewed and assessed by traders to determine whether this pattern is worth exploring and potentially trading.

Boosting High-Frequency Trading algorithms

HFT algorithms execute thousands to millions of transactions each day. However, because they are rule-based, humans who wrote them must regularly modify their code manually as market conditions change. That's when AI comes in handy and can automatically adjust algorithm parameters whenever the market shifts, saving traders a lot of time and work.

Estimating returns

Estimating returns is one of the most crucial aspects of trading that traders must consider when deciding whether or not to enter a trade. ML can also be used here, utilizing neural networks and other learning approaches to identify and analyze elements that impact stock prices. These variables are referred to as predictors, and ML uses them to make stock price predictions. Traders may use these estimates to back up their argument and feel more comfortable executing a particular trade.

Ranking stocks

ML can rank stocks in a variety of ways. Algorithms may be created that evaluate companies based on fundamental analysis or their price and technical indicators. Stocks might be ranked, for example, according to their momentum, value (whether the stock is underpriced or overpriced), or growth (how much companies have grown over a specific period). In other words, a trader may use ML to build a ranking algorithm with criteria essential to him/herself.


In conclusion, AI and ML have a lot of potential in the trading industry and are already being utilized in a variety of ways. Sentiment analysis, pattern recognition, boosting high-frequency trading algorithms, calculating returns, and ranking scores are just a few of the AI applications that assist traders to make better judgments. As AI and ML continue to develop, it is likely that their impact on the trading world will only grow. With this in mind, however, AI is still more of an assistant to traders rather than a direct replacement for them.

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