Algorithmic trading is rooted in transacting and utilizing advanced computational methods for recognizing the trends of the markets and giving investors and traders further insight. This computerized model of trading shot to fame when wall street companies became extremely competitive and directly engaged with developing high-performance computers. These two factors introduced a new structure and model of electronic trading in the USA. Supercomputers actually facilitated the communications with exchanges and enabled the companies to take advantage of their competitors due to their fast transaction and decision-making strategies. Algorithmic trading actually gave these companies the power to analyze the transactions made by average investors and use this information against them by winning over them.
In the last article, we argued that one of the controversial aspects of high-frequency trading is that all the power is held by the company heads and the profit made by this method is not fairly divided between the corporate workers and those who carry out and shoulder the actual job. Some large companies, on the other hand, are giving opportunities to amateur programmers to participate in this trading directly. They actually crowdsource algorithms developed by amateur programmers competing over writing the most profitable code for performing a transaction and thus win commissions on the closed deal. This has become possible very recently due to the availability of high-speed internet all around the globe and relatively high-performance computers for cheap prices (this is relative based on the form of the operation a computer would be capable of carrying out). Thus, if you are a programmer who wants to try their hands at algorithmic trading, there are trading platforms tailored to serve day traders willing to contribute to this field.
Talking about the latest technologies used in wall street these days, machine learning has gained popularity very fast and has proved to be a game-changer from many aspects. The huge advantage of machine learning algorithms is that they can improve themselves through iterative processes (deep learning) and catch up with the changes in the market by learning from then. All they need is to be injected with and have access to the proper information. Recently, developers are trying to improve this technology and taking it to another level by making the algorithms independent of data for learning. In short, deep learning methods develop themselves to become more profitable.
Algorithmic trading is usually used for cutting down the trading costs. As we explained in the previous article, this method is not suitable for performing small scale transactions. Trading costs associated with large scale transactions become important and sometimes can cover up to 10% of the whole volume of the trading.
One of the great advantages of using algorithmic trading is enabling marketers to expedite and facilitate the execution of orders. This is especially very attractive for exchange traders, because it allows for taking advantage and make profits from small changes in prices. Since the transactions made through this method are rather fast, buying and selling are performed in a matter of seconds. One of the most famous methods in this context is scalping which will be the topic of the next article in this series.