Algorithmic Trading Systems

Algorithmic trading essentially involves breaking up of different trading orders, and these are processed by software robots. The processing of the trade orders by the software can vary as per requirement of the user, or according to the complexity of the process. This could be as simple as allocating pre-determined order status to certain trade orders, or even taking a decision on a particular situation, as per preset sequential data. For instance, a trading decision of selling or buying may be recommended by a software robot after processing acquired data regarding rising or falling stock rates. According to the situation, the software can initiate recommended course of action or even act upon it, as is desirable.
Although the presence of algorithmic trading systems is not very new to the industry, its use in various stock exchanges throughout the world is rapidly rising. The reasons for this popularity are obvious. Such software can work without human intervention and error, and decisions can be taken instantaneously, with minimal loss of time. This makes the whole process much more effective and productive.
Popularly known as ‘algo trading’, ‘black box trading’, or ‘robo trading’, it is most commonly employed in institutional trader settings. For example, stock exchanges, foreign currency exchanges, hedge funds, mutual funds, pension funds, etc. In these settings a large number of trading orders need to be processed and algorithmic trading systems make the job quite simple and efficient.
Currently, the New York Exchange has approximately eighty percent of its trading conducted through algorithmic trading systems, and that was last year in 2008. Algorithmic systems will significantly dominate the market in the coming future.
If one looks at the current scenario of the algorithmic trading systems research and development, we can even conclude that these systems are still in their infancy. This statement can be easily substantiated with the way industry leaders are spending millions of dollars on making these systems more reliable and “intuitive”.
Currently, infrastructure and set-up costs are limiting factors that are posing some hurdles for implementing algorithmic trading systems on a much wider scale. For instance, both ‘ends’ (the buyer and the seller) have to have a compatible network and systems so that the exchange of data between the buyer and the seller can be productively translated in to meaningful data. The other flipside is that these systems are still only ‘machines’. They act according to the algorithm they are programmed with. The forex market requires more than this; importantly, human intuition.
To improve versatility in this regard, modern algorithmic systems have the provision of news feedings that help them to understand the sensitivity of a concerned economic region. Irrespective of the currently debated shortcomings of algorithmic systems, we can safely conclude that these systems have positively transformed forex business trends making the whole process more efficient and fruitful.