In the financial markets, there are nearly as many trading strategies as there are investors and traders. The markets are increasingly accessible electronically, opening up even more possibilities for the development of trading systems. One of these is algorithmic trading, a trading system that uses advanced mathematical models called algorithms for making decisions and transactions in the financial markets. A computer, programmed with an algorithm, will enter electronic trading orders when certain technical conditions are met. These conditions can include timing, price, the quantity of the order, and general market trends, among other factors.
Algorithmic trading is most widely used by large institutional investors such as hedge funds, mutual funds, and pension funds. This is the case because the advantages it presents are most relevant to large funds. When a fund buys a large quantity of a given stock, for example, this can have the effect of raising the price of the stock enough to negatively impact the profit margin that the fund hoped to achieve. However, with algorithmic trading, it is a simple matter to divide one large trade into several smaller trades to reduce the market impact.
Institutional investors have the further advantage of the speed with which automated algorithmic trading programs can make decisions. When market information is received electronically, trading decisions are made automatically, often without the necessity of any human intervention at all. Decisions are made, and orders are initiated before human traders are even aware of the information. This constitutes part of the large competitive edge that hedge funds and similar traders can have over individual investors.
Trading algorithms themselves have a much longer history than algorithmic trading. An algorithm simply refers to a sequence of steps to recognize patterns in real-time market data to detect trading opportunities. Historically, investment firms would employ a large number of individual traders to manually carry out the process of building trading algorithms. However, with the advanced technologies available now, it is a much faster process to build trading algorithms and put them to use, and many fewer personnel are necessary. Algorithmic trading has effectively replaced many of the personnel formerly needed by investment firms.
Traders are still necessary, though, for algorithmic trading to be employed. In many cases, a trader will monitor the data of many algorithms at once on a digital dashboard, making the trader much more productive. The work of traders and analysts is also still needed in order to devise new algorithms and to optimize existing ones.
