What is the future of Algorithmic Trading?

As I write this answer in NSE about 55% of the Orders are fired by Algo trading. In developed countries, this percentage is even higher, probably around 75%.

Why algo is everywhere?

“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 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 food and the financial sector is not an exception here. Development of Technology provides an edge for 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 in the stock market

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 bias, 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 strategies minimizes human interference thus removing human biases. A trader has a trading cycle where he passes through different stages of feelings like greed and fear in the market which 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 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.

Algorithmic trading in India

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 facilities through investment managers; later many fintech firms introduced trading platforms in India. Algo trading accounts for more than one-third of the 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 models should be made by the exchange such as maximum trade value or trade/seconds, or in 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 variables extending their business scope and increasing 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.

Flash crash of Dow Jones in 2010

There was a famous flash crash that happened in 2010 where Dow Jones almost plummeted around 9% for about a few minutes. There are many documentaries, books that explain this flash crash. But to keep it simple this flash crash was created as a result of automated bots fighting over each other.

There was a really big short order(probably by a mistake) that entered the market(according to references the order was about 4 billion USD). This big order was immediately detected by the high-frequency algorithms across the exchange and immediately withdrew all the Bids in the markets. As a result, momentarily there were no Buyers in the exchanges and Stocks started to fall.

Elon Musk Tweet

1st May 2020 - Elon Musk tweeted that “Tesla stock price is too high ”. Within few minutes Tesla's stock fell about 10%. His own tweet cost Elon Musk 20 Million USD.

The current scenario in algorithmic trading

These days live news feeds from many sources (ET, WSJ, Bloomberg, Reuters & Twitter et al)are being read by Algo software. This software performs Sentiment Analysis on all the news feeds and assigns a positive and negative ranking. Based on these rankings Algo Softwares fires up Buy or Sell orders automatically.

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 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.

Now there is a whole new avenue ruling Stock markets these days i.e; Machine Learning. Using Machine Learning we can train the systems to learn new patterns themselves rather than Programmers feeding the patterns like momentum, Eliot Waves et al. Humans can identify probably 100 patterns by themselves but with Machine Learning algorithms Programs can learn infinite patterns by themselves and make them better each day.

Now to your question on the future of Algorithmic trading: Until about the last 5 years only big institutional investors and Investment banks can afford the infrastructure needed for Algo trading. But now with many brokers offering their own APIs to automate Orders, providing historical and tick data, even Retail investors can adapt to Algo Trading with minimal infrastructure.

In the coming days, the percentage of Algo trades happening in exchanges will keep increasing even more