Algorithmic trading or Algo trading is a methodology for executing stock market transactions. It is based on mathematical models and algorithms which create clear and specific rules. The mathematical models and algorithms of Algo-trading use simple values such as the trading volume of a stock or derivative. They take into consideration complex indicators and oscillators, as well as market news and announcements. Through this article, we will introduce the basics of Algo trading. We will also discuss how the shift from traditional trading actually occurred through the markets’ long trading years.
A historical overview and the gradual shift to Algo-trading
The rapid development of technology has brought about significant changes in the way financial markets function and the way assets are traded. Until the 1990s most transactions took place between large financial institutions, funds, and brokers. The Individual brokers’ clients had a relatively small share due to the large commissions and spreads (the difference between the offered price and the demand price) required, while transactions were made in person or by telephone (Irene Aldridge 2010).
With the development of the internet, investors began to use computers in order to perform automated transactions. Financial companies were able to offer their customers services without informing their stockbrokers. In this way, they were able to execute the transactions themselves through electronic trading platforms. The speed and quality of transactions in this type of market encouraged investors to start using more automated transactions.
Algorithmic trading started in the USA in the 1970s with some simple strategies. It took off in the 1990s with the use of computers and electronic trading platforms. Today, it has been enriched with all the modern means of science such as software/automation and artificial intelligence. The above technological achievements allowed the average trader/investor to have in his hands tools that a few years ago were only possessed by the big banks and hedge funds. Features such as forex trading automation, unimaginable execution speeds, sophisticated market analysis tools, and the use of artificial intelligence are now accessible to the average traders/investors. They can leverage this whole new cutting-edge technology from their home and office computers or mobile phones.
The development of algorithmic trading
Algorithmic trading or otherwise automated trading, black box trading, or algo-trading are often described as High-Frequency Trading (HFT). Investors who carry out high-frequency trading strategies search for ways of processing data and information to execute orders with unimaginable speed and accuracy. This way of trading has brought about a huge change in the market. In this, a computer algorithm automatically determines individual command parameters e.g. the start, the time, the price, the quantity, or the management of the order after its submission. This process required little or no human intervention. Stock markets with algorithmic trading (Irene Aldridge 2010)
The correct use/exploitation of algorithmic trading requires a series of knowledge and skills. Examples include mathematics (mainly probability theory and statistics), programming languages , and econometric models. As a result, many traders and investors choose ready-made solutions that give buy/sell signals, as well as other useful information (eg the required risk) based on algorithmic trading.
Regarding the use of automated trading systems in various stock markets, we observe that the European and American markets hold a higher percentage of algorithmic transactions than the rest of the markets. In the US in 2009 about 60-70% of the total trading volume was executed with high-frequency transactions. These strategies are highly controversial. Many investors argue that profits, in the long run, are quite small in contrast to profits derived from classic trading. The American Securities and Exchange Commission and the Commodity Futures Trading Commission stated in their reports that algorithmic trading triggered a wave in 2010 sales that led to the financial crisis. In 2010, the volume of transactions with automated systems began to decline (Hendershott, Riordan, August 2009).
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