Algorithmic trading: history and modernization
The history of algorithmic trading is quite fascinating. Even though it may be commonplace now, the sector has to overcome several challenges to advance and become widely available. When the New York Stock Exchange (NYSE) introduced its first electronic order system, the “designated order turnaround (DOT)” system, in the 1970s, algo trading was born. It enabled remote investors to place orders for fulfillment by specialists operating on the exchange floor.
The NYSE introduced an improved “SuperDOT” electronic order system later in the 1980s. Similar computerized order systems were introduced by other exchanges, which helped to establish the algorithmic trading industry as we know it today. Its enormous success is the result of extensive industrial developments over time.
Let’s explore algorithmic trading’s origin and how it became the cutting-edge industry it is today.
Algorithmic trading’s history
The beginnings of the stock market
In the 1600s, Amsterdam (Dutch Republic) hosted the founding of the first contemporary stock market. The first publicly traded firm and the sole publicly traded corporation for a considerable amount of time was the legendary Dutch East India Company.
The public started buying or selling shares from one other after shares that were easily transferable were introduced. They soon stopped buying stock in its unprocessed form. Instead, sophisticated derivative instruments like futures, options, and forwards were established.
The forerunner to the Netherlands, the Dutch Republic, already had a functional stock market by 1680. It contributed to the area being a major financial hub at the time.
Algorithmic trading’s historical context
A functioning stock market had emerged globally by the 1900s. The similar system eventually expanded to other types of assets, including raw materials, money, precious metals, and so forth. At the time, and as of now, America had the largest stock market.
All trading tasks were once handled by specialized brokers employed by the floor exchange. You were required to make a call to your broker and ask them to carry out the transaction for you if you wished to purchase or sell a stock. Despite not being the most practical system, it performed admirably considering the state of technology at the time.
In the 1970s, things did, however, alter. In 1976, the New York Stock Exchange (NYSE) created the electronic order system known as “designated order turnaround (DOT).” It allowed traders to electronically communicate orders to professionals working on the exchange floor for execution. Compared to calling brokers, this technique was faster and more practical. Even though it allowed investors to avoid stockbrokers, the fact that the professionals had to complete them physically meant that it wasn’t “electronic” trading.
More advancements were made throughout the 1980s. The NYSE introduced the more sophisticated SuperDOT trading system in 1984. Users were able to input orders through the system, which were quickly sent to the professionals for implementation. Therefore, it expanded the number of shares that may be traded at once from 100 to up to 100,000 shares.
In 1984, the NASDAQ exchange unveiled the “Small Order Execution System (SOES),” a proprietary computerized trading platform. Orders for up to 1000 shares were eligible for automatic execution.
In the 1990s, online trading started to replace electronic trading. Established brokers like Charles Schwab and Fidelity tried to profit from the emerging internet phenomenon by developing online trading platforms for its clients. Aside from that, several brokerage companies, like ETrade and Ameritrade, that specialize in online trading also came into existence.
Algorithmic trading appeared with the development of electronic and online trading. Traders created computer programmes to execute specific trades based on predefined circumstances, as they could now order shares and have them automatically fulfilled. Electronic communication networks (ECNs) assisted the practice by automatically matching buy orders with comparable sell orders. Further, trading was now permitted after regular market hours.
By the year 2000, algorithmic trading had become commonplace. Algo trading now accounts for a large portion of all trading activities worldwide. According to Mordor Intelligence, it represents 60–73% of all equity trading in US markets. It is 60% in Europe and 45% in the Asia Pacific.
Modern algorithmic trading
In the present world, algorithmic trading has advanced while becoming more practical. With the help of modern platforms, dealers may now purchase assets worth millions of dollars from the comfort of their computers. You can exchange assets listed on another continent while residing on the first. The industry has grown really quickly, which is truly amazing.
Today’s easy algorithmic trading is made possible by a number of platforms: the industry standard, in this case, is MetaTrader 5. These are platforms that make it simple and quick for users to create and implement automated trading systems. To be an effective algorithmic trader, you need one such platform.
Modern algorithmic trading requirements
Being a modern algorithmic trader has specific needs. They comprise:
Knowledge of computer programming
In order for a computer to execute trades, predetermined trading instructions must be created for algorithmic trading. To write those instructions, you must therefore be familiar with computer language. C++, Java, and Python are the most common programming languages appropriate for algorithmic trading. Still now other languages, like Julia and Rust are quickly emerging.
If you believe that computer programming is not for you, don’t give up. For the coding of your theoretical trading algorithms, you can hire qualified programmers. There are online marketplaces, where you may purchase and copy trading techniques created by professionals.
Examination of infrastructure
Without first conducting any testing, you cannot simply build automated trading instructions and release them to the market. Your algorithms must be thoroughly tested in order to determine how well they will function in practice.
Back-testing and forward-testing are the two primary testing methods. The terms “back-testing” and “forward-testing” refer to testing with past market data and real-time market data, respectively.
Availability of a network
Every millisecond counts in algorithmic trading. Therefore, having a network that connects to the world markets as quickly as feasible would be beneficial.
For algo-trading, choosing the right trading platform is essential. You should stay away from certain platforms because they could not have the elements necessary for algorithmic trading to be successful.
Even though algorithmic trading is widespread, it is still expanding globally. Allied Market Research predicts that the worldwide algorithmic trading market will increase from $12 billion in 2020 to $31 billion by 2028.