Algorithmic Trading I

In this article, we try to explain algorithmic trading in a simple language for beginners. The very base of algorithmic trading is utilizing complex mathematical formulae and human insights tailored for decision making in order to buy or sell financial securities on the exchange. As the function of this method might have already implied, this is not for conducting or managing micro trades but rather evaluating trades on large scale, for example, providing companies with valuable information on which stocks to buy or sell in a matter of seconds. Thus, algorithmic trading is a terminology that very well introduces its function: an algorithm is a set of methods or ways for solving problems and these solutions are used in trading. These problem-solving methods are used for estimating variables such as price, volume, and timing.

One of the popular methods used in algorithmic trading is high-frequency trading. This method utilizes powerful computers to run huge programs on transacting a large number of orders in a matter of a few seconds. Thus, possessing the right infrastructure and facilities play an important part in advancing in this field. This large-scale analysis is then used for analyzing multiple markets simultaneously and analyze their conditions from different aspects as different orders. Evidently, firms executing their operations with higher speeds will make more profit out of these transactions.

High-frequency trading thrived due to the exchanges offering incentives to companies for the sake of adding liquidity to the market. Usually, exchanges of this kind have a group of liquidity providers who add competition and liquidity for the quotes on these specific exchanges. As the incentive, the exchange in question will pay a small fee for providing this liquidity. With so many trades transacting each day on the stock market, even very small values bring in huge profits. Lack of liquidity and its related crisis are not unfamiliar concepts to traders, so providing liquidity and adopting the correct strategy for promoting the exchanges and making them competitive is of great value. So in short, high-frequency trading is considered a successful solution to adding liquidity to the markets.

However, high-frequency trading also has its disadvantages. As we pointed out before, this strategy demands cutting-edge technology and up to date facilities for running the huge algorithms. This can only be provided by large companies and institutions. Thus, they will always have the upper-hand in performing transactions and trading large blocks. The second problem is the temporariness nature of the liquidity added to the market. As fast as it comes, it is gone due to the fast nature of the transactions made this way. Hence, it would be extremely difficult to make actual use of it for traders. Although this method has gained popularity since 1980s, it has also caused stock market troubles due to its nature and fast decision makings on large scale such as immediate lack of liquidity or causing imbalances in stock trading.

All in all, high-frequency trading has been very controversial as there are big profits made by “small people” through institutions that do not share their final profits with their employees and experts. Also, huge deals with no reasons can be closed as sometimes the decision making is only based on the computer algorithms without human logic and insight involved.

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