The Birth And Evolution Of Algo Trading Explained In Simple Terms
Technology has been increasing its footprint incessantly, slowly diversifying and spreading to almost all parts of life. From complex tasks like rocket launches and radio-wave transmissions to mundane chores like shopping for groceries and banking, the presence of technology is seen as irreplaceable.
One of the areas where technology has played the role of a catalyst and has completely revolutionized the field is stock market trading. Computer-based trading brought with it an era of quick, efficient, and error-free trades, resulting in higher profits and savings. There are new leaps being made in this sphere, even as we speak.
In the digital era of the stock markets, one thing that has immensely benefited traders has been the phenomenon of algorithmic trading. In simple terms, sophisticated mathematical algorithms are specifically designed to help computers identify profitable avenues by analyzing past and present data in the markets.
These algorithms can be designed in a manner such that they make use of a wide variety of parameters, like trading averages, charts, differentials, and so on, and come up with trades that have the potential to return profits. Such algorithms are first tested on historical data to confirm their veracity.
Automated trading has spread exponentially in markets worldwide because of their edge over manual trading. Other than just providing mechanical precision in placing trades and forecasting profits, many swear by the technique because it eliminates the emotional factor out of the equation, thus taking completely logic-based actions. Algorithms are being continuously improved and loaded with features that enable them to make trades on their own, which has led to the development of trading bots in recent years.
A Short History Of Algorithmic Trading
The foundation of algorithmic trading is laid with technical analysis. Technical analysis involves the use of several mathematical tools and instruments to analyze movements in the market, and draw conclusions regarding further changes or to identify patterns.
The earliest instances of technical analysis go back in time, as far as the 1600s when lottery price trends were plotted. Homma Muneshisa, a Japanese rice trader in the 1700s is attributed with the invention of the much-used candlestick graph for tracking price movements.
Technical analysis in the stock market took a formal shape and better recognition with the publication of the book Technical Analysis of Stock Trends in 1948.
In the 70s, though, both investors and the stock market began making use of algorithms for placing and processing trades. Many new innovative techniques were introduced simultaneously.
There was the Designated Order Turnaround system which was adopted by the New York Stock Exchange in 1976. Called DOT, this electronic system bypassed the need for running orders through a broker and instead, routed them directly to a specialist on the trading floor in the NYSE.
Primarily used for limit orders and basket program trades, the DOT allowed the user, either an investor or a broker, to directly feed the order into a system, which reaches the specialist on the trading floor. Once the order is executed, a real-time transaction receipt is generated for the buyer and seller.
Individuals and companies have been testing, using, and improving software for automated trading since the 1970s. The incorporation of trading software was started by a hospital administrator turned stock market trader, Louis B. Mendelsohn.
Mendelsohn wanted to create software that would give him an edge over other traders. In 1983, his article on using a PC for trading prompted many a trader to take to the practice. He was one of the first people to use the ProfitTaker Futures Trading Software. This software allowed backtesting, optimization and even provided opening and closing signals for the next day.
Robert Pardo is another known name when it comes to trading software. He introduced the charting and price signaling software Chartist in the year 1982. But his most significant contribution to the industry was the Commodity Analysis Toolkit or CAT which is still made use of.
CompuTrac was a pioneer in charting softwares. It used the BASIC coding language and was created by Tim Slater, with the help of Jim Schmit, who wrote the code for it. The system was designed for Apple 2. It analyzed a large volume of data, organized it, and plotted it. TeleTrac charting and analysis was a pioneer in the sense that it was the first one to make use of real-time data when other software still relied on historical data.
At the moment, there’s a plethora of trading software available in the market, with a variety of functions being added every day. Most of these softwares use real-time prices and volumes along with historical data in order to compute areas of profit.
APIs are interfaces connecting market players to market databases. Some of the most widely used databases are Intrinio, Xignite, Polygon, and AlphaVantage. Algo trading APIs are codes that help stock market players use the heap trade data provided by the stock exchange, sieve the relevant information for their use, analyze that data in various scenarios and offer a solution for the trader’s query.
APIs can provide you with exchange specific, sector-specific and at micro-level, company-specific information in an organized and presentable state. Algorithms are written not only to conduct trade but also to devise new financial products.
APIs come in a variety of types, and you can choose one suited best to your needs. REST APIs are the most commonly used APIs. They offer real-time data in standard time. TCP APIs provide data at high speed, and FTP APIs provide end-of-the-day data and are relatively pocket friendly.
There has been a shift in the trading realm as technology has slowly been integrated into the everyday working of the market, even at the grass-root level. Almost 70% of all trades in the United States are automated, and the percentage is only growing.
Other nations are quick in catching up. Algo trading makes it very easy for traders and brokers to parse salient data from raw data in complex language and helps make accurate and prudent trades. Moreover, algorithms can also be used to create ingenious financial instruments.