The Rise of Algorithmic Trading

Interest in algorithmic trading is growing rapidly. It is faster, cheaper, and analyses all past and present data. Now accounting for the majority of trading volume all across the planet, It can execute intricate maths and remove obsolete information. Read ahead to understand why "algo-trading" has become such an integral part of the financial industry and shows a promising future. 

What is trading?

Trading, an ancient economic concept, simply consists of buying and selling of goods and services. Compensation is usually paid by the buyer to the seller, which is often done through the exchange of goods or services. The most common medium of exchange is money, the sovereign currency that we all use every day. Trading, in general, can refer to the exchange of footy cards between a group of friends to multinational corporations establishing deals to import and export between countries.

Trading in the financial world builds upon the basic notion of trading. It is the buying & selling of securities (i.e. any tradeable financial asset) such as stock. Stock traders usually take advantage of short-term price volatility rather than its long-term success. Trades can last anywhere between a few microseconds to weeks on-end. Traders study price patterns, supply and demand, market emotion, and trade support. Trading in the financial industry can be split into two overarching areas of Fundamental Trading and Technical Trading.

Overview of Fundamental Trading.

Fundamental trading is what most of the public interprets trading to be about; such traders attempt to look at the company's intrinsic value. The aspects range from macroeconomic factors such as the economy and the industry to microeconomic ones such as financial conditions and the management. In essence, it attempts to research the particular company and its sector. Recently, even fundamental trading elements are being automatically processed to due to the increased computing power and capabilities.

Overview of Technical Trading.

Technical trading, as opposed to fundamental trading, primarily uses stock charts to make decisions. There is far greater emphasis on the trading data as opposed to the actual stock or commodity; technical traders often believe that all information is already included in the price movement. The condition of being mathematically backed and having access to thousands of technical indicators makes it very suitable for the task to be automated.

Humans have a fundamental limit to converting their visual feedback into logical thought and respond; this happens to be about 150ms. Currently, a trading algorithm can execute a trade up to 100,000 times faster than human can even make a decision. Hence naturally, parties have been able to take advantage of this and established algorithmic trading.


Algorithmic Trading

The stock market with people screaming over one another has now been replaced by the sound of computers crunching numbers. Algorithmic trading consists of using computers and algorithms to make trades which can be accurately executed at an instant. They ensure that the best possible prices are selected and are far less prone to human or manual error.

The algorithm simultaneously follows multiple markets based on predefined set of instructions to place orders. Strategies can be based on timing, price, quantity, or mathematical models. Furthermore, algorithms can process real-time events, news, and social media to generate improved trading decisions. This allows nullifying of human emotion, and bias; hence only implement financial models and data.

Applications of Algorithmic Trading

Medium to long term investors, with large portions of securities for money-management purposes, use algorithms to split up their order as they do not want to influence the stock prices with large-volume investments. While short-term traders and firms that deal with stocks, bonds, and foreign exchange use them to automate trade execution due to its speed and accuracy. This further aids the creation of liquidity for sellers in the market.

High-Frequency Trading (HFT), which accounts for the largest portion of algorithmic trading, capitalises on a large number of orders as they use its very high speed and the ability to analyse multiple markets along with other decision-making parameters. These algorithms account for about half of the equity trades in the US markets while relying on ultra-fast processing speeds and data-access to vital information.

Strategies of Algorithmic Trading

Algorithmic traders implement a various range of strategies into methods to succeed in the industry. This allows them to diversify and take advantage of different techniques. There are some industry standards which are vastly known and frequently implemented, some of these are as follows:

  • Trend Following Strategies

    • Easiest and simplest to apply as they do not make predictions or forecast prices

    • Based on occurrence of desirable trend

  • Arbitrage Opportunities

    • Buying a dual listed stock at a lower price and simultaneously sell it

    • Takes advantage of prices differing in 2 markets

  • Trading Range (Mean Reversion)

    • Strategy based on the idea that stocks and assets revert to their mean periodically

    • Identifying this range allows algorithms to place trades when the price breaks in and out of its predefined range

  • Volume Weighted Average Price

    • This breaks up a large order and releases dynamically determined smaller chunks into the market based on stock profile

    • This calculation relies on the stock's historical volume profiles and aims to execute the order close to this weighted average by volume price

Obviously, it is not as simple and firms spend many years attempting to perfect their methods. Those who are looking to enter the field of technical trading need to have the programming skills and the ability to understand complex systems to create profitable opportunities.


The practice of algorithmic trading can be hard to maintain and execute as many participants in the financial industry use this method, hence the prices can fluctuate rapidly in microseconds.

Exchanges now compete and are reducing their latency of turnaround times to attract algorithmic traders, as they increase volume and liquidity in their market. With such high volatility in the market, it becomes necessary to process and filter the massive complex datasets to ensure mistakes are eliminated.

Ultimately, the use of algorithms in the financial industry clearly has considerable potential in various conditions; they have proved successful since their introduction. One needs to ensure that the system is thoroughly tested and limits are met, as well as consider the impact of the algorithm on the market conditions.