How AI can improve trading decisions: The 2023 guide

AI-assisted trading is a strategy that has gained a lot of traction in the last couple of years, particularly as a way to improve trading decisions. Technological advancements are making it easier than ever before to make serious money through trading. That said, trading markets have never been so competitive, and AI-assisted trading is one of the latest trends that’s here to stay.

This article will give you an overview of what AI-assisted trading is, how it works, the advantages it can offer traders such as how it can improve trading decisions, and what types of AI algorithms are used for this purpose.

What is AI-assisted trading?

AI-assisted trading is the use of artificial intelligence to help make trading decisions in financial markets. AI algorithms are computer programs that enable the prediction of outcomes. This means that they can identify patterns and make predictions based on past events. The future value of a given asset is a prediction. The algorithm learns and can make predictions based on past data. Traders use artificial intelligence to use data that’s already been collected to make predictions. In other words, AI is the application of algorithms to automated data collection to create predictions.

Why use AI in trading?

AI-assisted trading has been making headlines for more than a couple of years now. This technology is being developed to make trading more efficient, improve trading decisions and less prone to human error, and more effective. AI can also improve your returns by identifying trading opportunities that you might not be able to see but that other traders are already exploiting.

How does AI-assisted trading work?

AI-assisted trading starts with the collection of data. AI is programmed to analyze the data to identify patterns and predict future behaviors. Therefore, when a trader wants to make a specific trading decision, they only need to feed that decision into the AI software. For example, you have a certain trading algorithm and you want to know if it is profitable. You feed in the trading algorithm, and the AI software returns the profit or loss. You can also feed in the trading algorithm and see if it is profitable on a certain day, week, month, or year. Because AI is programmed to learn, it can also feed in past data and see if that trading strategy has been successful.

Advantages of AI-assisted trading

Increased efficiency

AI can be used to increase efficiency by identifying the best times and places to trade. It can also be used to optimize your portfolio, ensuring that you don’t over- or under-trade. 


AI can be used to cut costs by automating repetitive tasks. Therefore, traders are not required to monitor the markets 24/7/365.

Increased market penetration 

AI can be used to expand the trader’s reach to new markets. For instance, you might have a high-volume trading algorithm that is capable of making millions of trades per day and expanding the trader’s reach to international markets.

Disadvantages of AI-assisted Trading

Overreliance on algorithms

AI-assisted trading can lead to a reliance on algorithms, which can be prone to errors or biases.

Lack of human oversight

AI systems can make decisions without human input, which can lead to unintended consequences or mistakes.

Difficulty in explaining decisions

AI systems can be opaque, making it difficult for humans to understand how they arrived at a particular decision.

Risk of system failures

AI systems are complex and can be vulnerable to system failures, which can have significant financial consequences.

Limited understanding of market dynamics

Depending on how advanced the AI is as well as the technical ability of the human who has trained it, an AI system may not have the ability to fully understand market dynamics and may be less able to react to unexpected events.

High cost of maintenance and development

AI systems can be expensive to develop and maintain, and they may require specialized expertise, such as that offered by Permutable.

Legal and regulatory challenges

AI systems may raise legal and regulatory challenges, such as issues related to data privacy, intellectual property, and liability.

Examples of AI-assisted trading

Algorithmic trading

This involves using algorithms to automatically execute trades based on pre-determined rules and market conditions.

Predictive modeling

This involves using machine learning models to predict future market movements, which can inform trading decisions.

Sentiment analysis

This involves using natural language processing techniques to analyze social media, news articles, and other sources of information to gauge market sentiment and inform trading decisions.

Portfolio optimization

This involves using AI algorithms to optimize a portfolio’s composition, in order to maximize returns or minimize risk.

High-frequency trading

This involves using AI systems to execute trades at a high speed, in order to take advantage of small price movements in the market.

Risk management

This involves using AI systems to analyze market data and assess the risk associated with different trades, in order to manage risk more effectively.


This involves using AI systems to provide investment advice to individuals or institutional investors, which can inform investment decisions.

How Permutable uses AI to improve trading decisions

Some ways in which Permutable has previously used AI to improve trading decisions include:
Signal Generation (R2 API)
Identifying chart patterns across 1000s of assets and using them as entry/exit signals for trading strategies. This below two images below are an example strategy based on up and down channels detected by AIt
using AI to improve trading decisions
Using AI to improve trading decisions
Strategy Optimisation (R2 Backtesting API)
Optimising the parameters of trading strategies. The first image below shows an example optmisation search space with the optimal solution for the parameters of a strategy.
Sentiment Analysis (NLP API)
Training AI models to read market sentiment using natural language processing (NLP) techniques to analyze text data, such as news articles, social media posts, and financial reports, to understand how people feel about a particular stock, sector, or market trend.
Strategy Optimisation (R2 Backtesting API)

AI-assisted trading is one of the latest trends to take off in the trading industry. It involves using artificial intelligence to improve trading decisions. The advantage of using AI for trading is that it can make trading more efficient and lower the costs of trading. However, it can also result in trading decisions that are not in the best interests of the investor. This can be a risk when it comes to investing.

Get in touch to find out how our AI can help you make better trading decisions