What is the future of Algorithmic Trading?

Peter Sondergaard of Gartner has rightly said, “Information is the oil of the 21st century, and analytics is the combustion engine.” The faster one receives the data the faster one can make a decision. The volume of data available from all markets is continuously increasing hence it is important to analyze the data. For example, major exchanges were not able to handle and the information flow with the rise in transactions carried out by automated systems on the electronic market over the past few years. Automation is everywhere, from booking travel tickets to self-driven vehicles, drones delivering the food and the financial sector is not an exception here. Development of Technology provides an edge people becoming more and more educated, more and more automation & tools will continue to come as better solutions for better pricing not just for large companies, but also for retail investors.

Algorithmic Trading is a method of buying and selling back securities on a predetermined collection of rules. For backtesting, the said rules are subject to historical data. Algo trading is associated with many names such as automated trading, Black box trading. The approach is based on analyzing different market conditions from which it can generate profits. Then applying these particular strategies corresponding to a particular situation, automate, and manage the trade. The overall benefit is that you do not need to keep an eye on the market. Thus creating the profits out of rising or fall in the market while reducing the volatility of the overall portfolio at the same time. The program makes all-important work such as searching, timing, and trading for the user mechanically. It also removes the biasness, as no humans are involved and faster than manual trading.

Human beings are not always balanced while making investment decisions. The innovation in technologies has given an advantage to the traders making fast execution of trades with limits in a changing environment, as computer-programmed software is unbiased. Trading with pre-defined laws minimizes human interference thus removing human biasness. A trader has a trading cycle where he passes through different stages of feelings according to gains and losses in the market this hampers his decision-making capabilities.

A user can design as many programs using different programming languages making into account different strategies. Such trading strategies depend on complicated mathematical formulas and high-speed programs. Before Algorithmic trading the speculators and Arbitrageurs who used to keep trades. They used to recognize price differences between exchange and financial instruments and making profits. A trading algorithm can work 24*7 making a trade on behalf of the client. Even if your trading strategy is not ideal according to the market, the advantage is that self-learned algorithms will adapt according to various trends and update the rules to meet market conditions.

In the Indian market, SEBI allowed algorithmic trading by allowing exchange members to offer Direct Market Access (DMA) facility to institutional clients in 2009. Also in 2009, FIIs started using DMA facility through investment managers later many fintech firms introduced trading platforms in India. Algo trading accounts for more than one-third of total turnover on the exchanges.

The larger part of the market is into North America, Europe, Asia Pacific, Latin America, Middle East, and Africa. Among developed nations, North America contributes the largest largely due to technological advancements and increasing use of algorithm trading among end-users such as banks and financial institutions. Fast, efficient, and successful order execution and cutting in transactions are major factors driving the size of the Algorithmic Trading Market. Cloud-based algorithmic could be the next bet and play a significant role in the development of the financial market. For example automating processes, data maintenance, and cost-friendly thus better management. This method uses remote server networks to store, handle, and process data usually accessed over the internet.

Risk is always associated with finance. The case of Flash Crash “had happened in the US in 2010 due to algorithmic trading. There is a need for better regulation and some risk model should be made by the exchange such as maximum trade value or trade/seconds, or in the terms of quantity. In a normal scenario, Algo trading is used for high-frequency trading. High-frequency traders or flash traders place many orders in different markets and decision variable extending their business scope and increase their chances of making a profit. HFT activities exist, because of change in innovations and because the financial market system has better capabilities now due to advancements.

Algorithmic trading, in short, has changed stock markets used to perform. It brings many benefits at the same time losses too. With the convergence of the market-wide risk model, there is a pressure on retail investors tilting towards algorithmic trading gains in favor of short-term and cheaper researched details.

It is important to note that Algorithmic trading is not the market driver; it is only a resource exchange facilitator providing direction on liquidity and arbitration. The real drivers are mutual funds, hedge funds, pension funds, or banks who play a big role and make long-term goals. There is a common misconception in the market that with the help of this technique, they can make millions but the reality is that it works on a set of rules embedded in the system and eliminate impulsive decisions, unlike humans. Time is an important factor thus even timely booking of your target and loss increases the chances of making profits.