High Frequency Trading: HFT in Algorithmic Trading | Share India (2024)

We have come a long way in terms of investor participation in financial markets in India. Not only has the number of retail investors increased, but the way they participate has also drastically changed. From phoning brokers to placing orders, the introduction of Demat accounts has helped us place orders with a few clicks. However, advancement does not stop there, as all the major institutions have adopted automation into their trading systems. In other words, institutions trade in the market using algorithms to execute different strategies like high-frequency trading.

What Is High-Frequency Trading?

This article focuses on High-Frequency Trading or HFT, which falls under the umbrella of algorithmic trading. So, to understand HFT, we first need to know what algorithmic trading or algo-trading is. In simple words, algorithmic trading is performed by programming computers to trade based on the instructions provided in the system. Since it is the computer performing the trade, there are no interventions of human emotions that result in a deviation from the planned strategy.

Now that you know what algorithmic trading is, let us look at high-frequency trading. HFT makes use of algorithms to take advantage of opportunities that last for an infinitesimal period. In a time period that may span for a few milliseconds, the algorithm may capitalise on miniscule price movement. So, in simple words, it sends trade orders to the exchange at instantaneous speeds. This type of trading allows traders to make instant trades, which otherwise would not have been possible due to the limitations of our perceptual and motor mechanisms.

For example, institutions like insurance companies and pension funds use HFT to place large orders. With the help of it, they split their large order into a myriad of small orders. That is because the order volume of institutions is large enough that, if placed at once, it can severely impact the price of the asset.

Difference Between HFT and Algo-Trading

The terms algorithmic trading and high-frequency trading may be used interchangeably by traders while colloquially discussing a relevant subject. However, from the above paragraphs, you know that HFT is a branch of algorithmic trading. Hence, every high-frequency trade is an algorithmic trade, but every algorithmic trade is not a high-frequency trade. For instance, an algorithm written to execute a buy order every time the price of a specific asset cuts above its 50-day Moving Average and sell if it cuts under will not qualify as a high-frequency trade. You can refer to the table to know what makes HFT unique as compared to other forms of algo-trading.

To summarise, the HFT algorithm is more concrete and well-defined, and one requires high computing power to execute such trades.

Advantages of High-Frequency Trading

The capability to execute transactions at instantaneous speeds gives high-frequency traders plenty of advantages over those who practise regular trading and even other forms of algo-trading. The advantages of HFT are:

Profits on Small Price Movements

HFT makes it possible for traders to make profits on even the slightest movement of the asset’s price. The human body is incapable of reacting promptly to capitalise on a price movement that lasts for a few milliseconds. Hence, HFT allows institutional investors to attain bid-spread returns.

More Opportunities

An HFT trader, or any practitioner of algo-trading for that matter, can take advantage of more trading opportunities compared to regular online trades. That is because algorithms can scan through multiple charts within a few minutes. On the other hand, manual scanning of that many charts would take more than a day.

Higher Liquidity

HFT gives rise to higher market liquidity due to the huge volumes and transaction speeds. As it creates more liquidity in the markets, HFT even benefits regular retail investors indirectly.

Challenges of High-Frequency Trading

However, despite its advantages, the critiques of high-frequency trading argue that algorithms can be misused to spoof traders. Algorithms can be designed to send numerous fake orders and cancel them immediately. This leads to a false spike in the demand or supply that results in price irregularities. Likewise, they also argue that the liquidity created by HFT is not real since securities are only held for a few seconds.
Moreover, only the institutions that have access to systems can perform HFT. Hence, only the institutions reap fruits while the regular investor struggles to compete with these institutions. For a regular investor to perform HFT, they will require the knowledge to write code and develop compatible high-frequency trading software. Institutions, on the other hand, can easily hire professionals to develop high-frequency trading software and get their systems up and running. Secondly, retail investors also lack the capital to set up the physical infrastructure required to perform that type of trading. At the same time, this space is getting so competitive among institutions in the developed markets that can afford the prerequisites that immense resources go towards improving the algorithm.

Conclusion

In India, algorithmic trading is in the initial stages of its growth phases, as it is mostly the institutions that carry out algo-trading. However, in developed economies like the United States, it is estimated that more than a third of orders are placed by computer algorithms. Both markets are expected to see the number of algo-traders increase, albeit the former is likely to see faster growth than the latter. In India, we have share brokers like Share India trying to propagate algo trading solutions in the retail segment.

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