Introduction
As a trader, you're probably excited to leverage tech and data to maximise your returns in a quickly changing financial market. High-frequency trading (HFT) and quantitative trading are powerful strategies to help you do that. However, they are different and may well suit different trading personalities. This article will explore HFT trading and quantitative trading.
What is High-Frequency Trading
The execution of thousands of trades instantly, profiting from minute price movements. This is high-frequency trading, whereby success is based on speed. HFT trading utilises high-tech algorithms to make trades in milliseconds, often using cutting-edge technology. In India, HFT trading exists on stock exchanges like the NSE and BSE, where firms leverage co-location. Co-location is an approach where firms place their servers close to the exchange's data centre to minimise latency. In this way, your algorithms would pick up fleeting opportunities (for example, price differences in a stock) and act in a flash.
High-Frequency-Trading (HFT) strategies fall into three categories: arbitrage (buying and selling price discrepancies in different markets or instruments as quickly as possible), market making (quoting a buyer and seller price to provide liquidity to the market), and momentum trading (exploiting the short-term directional movement in an instrument's price). For instance, although Tata Steel may be trading at a marginally lower price on the NSE than the BSE, an HFT system would buy Tata Steel on the NSE and sell it on the BSE, producing a small profit several times without spending too much time in the trade.
High-frequency trading requires considerable resources: servers with high processing speeds, low-latency local area networks, and significant capital. SEBI regulations include and are not limited to pre-trade risk controls and circuit breakers, so HFT cannot harm India's financial markets.
SEBI regulations, however, can become cumbersome to HFT firms and traders. If you are a trader, you may be left out of using HFT strategies unless you have the backing of a firm with the resources to fund this type of trading. Although HFT strategies are costly and regulated, they enable faster market discipline by lowering both the bid and asking price for securities and increasing the available liquidity in the market.
Exploring Quantitative Strategies
Now think of a trading approach grounded in data, rather than speed. Quantitative trading refers to using mathematics and statistical models to find expected profitable opportunities over much more extended periods than seconds or milliseconds, which can be hours, days, or weeks.
For example, you could establish a model for mean-reversion, where you are speculating that Reliance Industries will return to its average price after a run-up in prices. Other example strategies of quantitative trading include momentum trading (simply riding a trend of price movement) or statistical arbitrage (finding mispricing in a set of instruments). In contrast to HFT, speed is not essential in quantitative; your concern is doing a solid analysis and using your judgment, procedures, and tools such as Python or R. You may be analysing and developing models based on data from the NSE or any of the global market indices, while also considering events, and your model for the event such as RBI policy changes or earnings announcements from a company/nascent sector.
Most importantly, quantitative trading can be easier because quant systems do not require ultra-fast systems. Many investors can start with a laptop, free data sets, and open-source software. The significant implication, however, will be making models that effectively navigate India's volatile markets, where economic surprises such as announcements of budgets or other news can undermine your prediction model.
How They Compare
So, when comparing HFT trading and quantitative strategies, what do we find? Here's a simplified assessment:
- Speed: HFT thoroughly relies on its speed and ultra-fast execution, which is usually measured in microseconds, while quantitative strategies place importance on analysis, which requires hours/days.
- Time horizon: HFT aims for short-term profit opportunities, where a position lasts less than a second. Quantitative trading looks for longer-term profit opportunities, which facilitates diversification.
- Technology: HFT requires a considerable amount of technology, such as co-located servers with configurations priced in crores, whereas quantitative trading requires access to data and computing power, which can be accommodated with a modest amount of technology.
- Access: HFT is widely accessible only to institutional players in India because of the cost and SEBI's ability to monitor the specific trading activity. Quantitative trading, however, their models utilise very little technology and are much more open to experimentation.
- Risks: HFT can increase volatility and have its risk management practices while monitoring trading positions all the time during market trading, as the disruptive conditions during a global market crash, India in March 2020 in particular, is an example when quantitative strategies can become obsolete if market conditions shift.
Conclusion
If you are getting started on your quantitative strategies, learn some coding languages such as Python and R to analyse your raw data and apply a quantitative approach. Another good option is to use a trading platform and tools to automate and test your models. Develop your models around the specific challenges and considerations typical in India, including the monsoon considerations in agricultural stocks or the RBI's decisions on interest rates. If you are planning solely high-frequency trading, it will likely mean that you will also need to get hired by a proprietary trading firm, as it is challenging to get access as an individual trader. Finally, focus on SEBI rules to ensure compliance.
Both approaches are risky. HFT can result in losses if the systems fail, but quantitative models also have risks in an unpredictable market environment. You must backtest your strategies, start small, and tailor your plan to your financial situation.
Disclaimer: Trading in financial instruments can be risky. Before trading, consult a financial adviser and read the SEBI regulations.
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