What is Algorithmic Trading?

Introduction 

Algorithmic trading is the use of a computer program to place and execute trades based on a set of encoded instructions and rules. This is expected to generate profit more quickly than a human trader. The trades are carried out with regards to price, timing and other mathematical models. The benefits of algorithmic trading are numerous. With it, trades can be executed at what the programmer considers the best possible offer and at the exact time the conditions apply, ensuring high precision and maximum accuracy in trading. In addition, the system can monitor trades better and more efficiently than humans alone and can also employ the use of past information to predict future trends in the market. Algorithms are also expected to better protect the markets from the impact of emotions and psychology of human buyers and sellers. In this regard, specific rules and instructions ensure that the best possible decision is made at all times without human factors. The most important resources in successful algorithmic trading include trend monitoring and taking advantage of differences in trading prices in different markets. Using algorithmic trading, stocks purchased in one market can be resold at a higher price in a different market if proper monitoring is done. 

Globally, algorithmic trading appears to be taking hold in the trading world. In 2021 alone, the market reached a value of $13bn and is expected to reach $24bn by 2027. In developed business ecosystems such as the US, up to 60-75% of trades are carried out using these algorithms and the number is expected to rise as technology becomes more accessible and is integrated more globally. 

To better ensure the precision and accuracy of algorithmic trading, artificial intelligence can be employed in making analyses and deciding what sets of instructions or codes are most beneficial to a participant. Using machine learning, deep learning, predictive analytics and other AI tools, a wide range of advanced systems can be made available to improve business outcomes. In contrast to classic automated trading, artificial intelligence enabled systems are able to predict trends based on previous customer behavior and data and make forecasts rather than focusing on set rules and instructions alone. In addition, they are able to apply what has been observed in certain areas to other markets with less data and make decisions based on that pre existing knowledge. Such systems are also able to make trades and market decisions at even faster rates than the traditional trading systems.

Task Automation: Task automation using AI has great value in improving the frequency of trades. To achieve this, numerous machine learning tools are applied to create several models that can close trades quickly and more efficiently than human traders and traditional algorithms.

Uncovering Patterns: Patterns in trends can also be found quickly and used to the advantage of traders. This improves competition in the market as several participants may discover similar patterns and seek to apply them in trading. 

Sentiment Analysis: Sentiment analysis is also being used to determine possible outcomes of changes in stock prices. For instance, social media commentary and activity can be analysed to determine the attitudes of people to the behavior of a particular company’s stock. This then reflects the strength of that company’s stock and the sustainability of prices. In order to achieve this, natural language processing is used to classify people’s sentiment regarding a company.

Risk Forecasting: Multiple real life factors can be applied in predicting a company’s future performance and this can be used to forecast the stock value of that product. Such analysis is also important in identifying risks and predicting possible pitfalls that may occur. 

Conversational AI: Chatbots can be used to present traders with historical information that may be useful in placing trades. These may also provide updates about current prices and alerts about sudden changes in the market.

AI Advisors: AI advisors which are cheaper and more cost effective than consultants can be useful in directing a buyer to various investment and trading options. These can be used to create and manage portfolios and provide adequate information regarding different possible decisions and how they can benefit the trader. 

Despite these advantages, artificial intelligence systems have shown difficulty in navigating unusual situations. It has been observed that when anomalies for which there is no previous data to refer to crop up, the systems are not likely to suggest workable solutions. This is expected because the systems function almost entirely based on learning from data that has been previously processed and may not yet have the capacity for judgement at the same level as the human mind. In order to prevent disastrous outcomes, it is advised that artificial intelligence systems are not employed in a manner as to replace human traders but should rather be applied in combination with experts who can appropriately develop solutions when required. Such a hybrid approach will no doubt provide the best of the two worlds by seamlessly merging machines with human expertise and assuredly provide the best possible outcomes for traders and customers. 


References 

https://www.investopedia.com/articles/active-trading/101014/basics-algorithmic-trading-concepts-and-examples.asp

https://www.einnews.com/pr_news/578622567/algorithmic-trading-market-2022-2027-statistics-size-share-growth-analysis-strategies-and-report

https://www.entrepreneur.com/en-in/technology/how-ai-algorithms-are-changing-trading-forever/377985

https://www.kdnuggets.com/2022/04/adoption-ai-algorithmic-trading-affected-finance-industry.html

https://www.researchgate.net/publication/342195031_APPLICATION_OF_ARTIFICIAL_INTELLIGENCE_IN_ALGORITHMIC_TRADING

https://logicai.io/blog/applications-machine-learning-trading/



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