What Is High-Frequency Trading (HFT)?

When thousands of trades are completed in the blink of an eye, this is the true speed of High Frequency Trading (HFT). It uses advanced algorithms and superfast computers, which make trading decisions in just a few microseconds. Today, approximately 60% of transactions in the Indian stock market involve HFT trading and algo trading. In this blog, we’ll explore what is HFT, how it works, which HFT companies are leading the way, and what its growing influence in India indicates.

What is High-Frequency Trading? 

• HFT or High-Frequency Trading, is an advanced trading technique that uses high-speed computers and complex algorithms to execute orders extremely quickly, sometimes thousands of trades per second. Human intervention is virtually nonexistent, as the entire process is fully automated.
• Speed ​​and Co-location Advantage: HFT’s greatest strength is its speed. As soon as market data is generated, these systems process it within microseconds and execute trades instantly. Co-location also plays a significant role when an HFT company’s server is located very close to the exchange’s server. This reduces data transmission time and can yield milliseconds of trading gains.

Read Also: Top Algorithmic Trading Strategies

How Does High-Frequency Trading Work?

1. Real-Time Data Feeds: HFT systems read live price quotes, order-book updates, and trade ticks from the exchange in microseconds. The faster and cleaner the data, the faster the algorithms can identify opportunities.
2. Signal Generation: Quant models look for patterns in the incoming data such as minor price mismatches, order-book imbalances, or short-term momentum. Many firms now also use adaptive ML models so that the models can update themselves in response to changing markets.
3. Order Routing & Execution: As soon as a signal is received, the system immediately creates an order and sends it to the exchange’s matching engine. Orders are changed or canceled at the same speed. The goal is to achieve entry/exit speed while minimizing slippage, even with very small price gaps.
4. Co-location & Low-Latency Infra: To reduce latency, servers are co-located within/near the exchange’s data center. Packet processing is further accelerated using high-speed fiber, microwave/millimeter-wave links, smart NICs, and sometimes FPGA-based computing.
5. Risk Controls & Compliance: Strict guardrails operate with speed—maximum position limits, kill switches, order-rate limits, and real-time P&L/variance checks. This allows the system to immediately reduce exposure in the event of an error or malfunction and ensure compliance with regulatory requirements (e.g., OTR, logging, circuit breakers).
6. Monitoring & Post-Trade Analytics: Granular analysis of latency, fill rates, and slippage is performed after a trade. This data is what tunes models next time—which venues are faster, which strategies work best at what time, where to optimize the network/code, etc.

HFT Process Flow Table

Step	Description	Goal
Data Collection	Acquire and process live market data in real time	Making decisions based on the latest information
Signal Generation	Identifying patterns or opportunities through algorithms	Finding potentially profitable trades
Order Execution	Send or cancel trade orders in microseconds	Fastest transaction completion time
Co-location Setup	Keeping the server close to the exchange	Minimizing Latency
Risk Controls	Enforcing trading limits and security checks	Protection against damage and system errors
Post-Trade Analysis	Post-trade performance data analysis	Improving the algorithm for the next trades

Read Also: Best Algo Trading Platform

Key Strategies Used in HFT

High-Frequency Trading (HFT) isn’t just a game of fast computers and algorithms; its true strength lies in its strategies. Each HFT firm develops unique strategies to make profits by making accurate decisions in microseconds.

1. Market Making

In this strategy, HFT firms maintain liquidity in the market by continuously placing orders on both the bid and ask sides. Profits are generated from the small spread between the bid and ask. For example, if a stock is being bought at ₹100 and sold at ₹100.05, the HFT system profits by replicating this small spread multiple times.

2. Statistical Arbitrage

This strategy is based on mathematical models and data patterns. The system searches for temporary price gaps in two or more related stocks or indices.

3. Latency Arbitrage

This strategy relies solely on a speed advantage. HFT firms co-locate their servers to minimize data transfer delays. If a price change is first visible on one exchange, and another exchange shows it a few microseconds later, the system can immediately capitalize on the earlier change.

4. Momentum Ignition

In this strategy, the system identifies an ongoing trend and trades in that direction to capture market momentum. Sometimes, the system attempts to trigger momentum by placing small orders, as if to signal increased buying in the market.

5. Event-Based Arbitrage

Whenever major news breaks, such as RBI policies, company quarterly results, or economic data, the HFT system immediately reads the news and trades within seconds.

For example, if a company’s profits are better than expected, the system can immediately buy its shares, even before humans can react to the news.

6. Liquidity Detection

Some HFT models attempt to predict when and where large institutional investors are likely to place orders. If the system detects a buy order from a large fund, it preemptively positions in that direction. This allows the HFT firm to profit from market movements before they even begin.

HFT in India: Growth, Regulations & Major Players

1. Current Situation : Algorithmic/high-frequency trading is now a significant part of the market in India. According to some reports, approximately 55–60% of total trades on the NSE/BSE are believed to be algo/HFT-based. This figure may vary depending on the segment and source, but the dominance of fast-trading is clear.
2. Major Firms (Who’s Active) : Both international and domestic prop-trading and HFT firms are active in India. Examples include Tower Research, QuadEye Securities, Graviton Research/Graviton Capital, AlphaGrep, and Estee Advisors; these firms focus on low-latency trading and quantitative strategies. (Lists and profiles are available in public sources).
3. Infrastructure and History : Co-location services in India, introduced around 2010, offered the potential to reduce server-based latency, contributing to the growth of HFT. The nature of co-location and data feeds made speed-based strategies viable. (This issue has also generated public scrutiny and controversy, which has been subject to appropriate regulatory scrutiny.)
4. Regulations and Reforms (SEBI’s Approach) : SEBI has tightened the requirements and monitoring protocols for algorithmic/HFT activities, including co-location access, order-to-trade limits, audit trails, and agency/broker-level transparency. Additionally, SEBI has published recommendations/advisories on a framework for algorithmic trading for retail investors to balance risk and transparency.

Read Also: What is Tick Trading? Meaning & How Does it Work?

HFT vs. Algorithmic Trading

Aspect	High-Frequency Trading (HFT)	Algorithmic Trading
Definition	Ultra-fast technology, executing trades in microseconds.	The process of automatically placing trades according to set strategies or rules.
Speed	Extremely fast—trades in microseconds or milliseconds.	Relatively slow trades can take seconds, minutes or hours.
Goal	Making repeated profits from small price differences.	Making decisions based on long-term strategies.
Technical Requirement	High-speed servers, co-location and low-latency networks.	Also possible with common server and brokerage APIs.
Risk level	Very high dependent on speed and technical errors.	Relatively low dependence on the success of the strategy.
User	Large institutional firms or quant trading houses.	Used by both retail and professional traders.
Regulation	Strict monitoring by SEBI and the exchange.	Relatively simple regulatory oversight.

HFT vs. Traditional Trading

Aspect	High-Frequency Trading (HFT)	Traditional Trading
Method of trading	Fully automated done by algorithms and computers.	Manual Humans place orders and make decisions.
Speed	Thousands of trades in microseconds.	Limited trades in minutes or hours.
Decision making process	Based on data and machine learning models.	Based on experience, emotions and market sentiment.
Cost	Very low spreads and minimal fees.	Relatively high due to time, brokerage and manual errors.
Risk	Major losses are possible due to technical glitches and wrong codes.	The potential for harm due to human judgment or emotional error.
Accuracy	Highly accurate, as there is no human intervention.	Limited accuracy, human error possible.
User	Institutional investors and quant trading firms.	Retail investors and traditional traders.
Control and monitoring	Under high-level surveillance systems and regulatory rules.	Less oversight, relying on individual responsibility.

Benefits of High-Frequency Trading

1. Improved Market Liquidity: HFT firms continuously place buy and sell orders, ensuring buyers and sellers are present in the market at all times. This reduces the bid-ask spread (the difference between the buy and sell prices) and allows investors to obtain better deals. Consequently, the presence of HFT makes the market more liquid and active.
2. Faster Price Discovery: When news or economic data is released about a company, HFT systems immediately identify it and trade accordingly. This helps the stock price reach the “right level” faster, meaning the market absorbs the new information more quickly. In the long run, this makes the market more efficient.
3. Lower Transaction Costs: HFT reduces trading spreads and increases execution speed, thereby reducing transaction costs. This benefits both large institutions and ordinary investors, as they are able to complete trades with a shorter timeframe.
4. Improved Competition and Transparency: The emergence of HFT firms has required brokerages and trading platforms to provide better technology and faster services. This not only increases competition but also brings transparency to the market. The record and execution of every trade can now be tracked within seconds.
5. Technological Improvements and Market Stability: Technologies developed for HFT such as low-latency networks, faster servers, and co-location systems are now strengthening the entire market infrastructure. These improvements have made trading more secure, stable, and faster.

Read Also: What is Scalping Trading Strategy?

Criticism, Risks & Controversies

1. Market Manipulation: Some firms use techniques like spoofing, i.e., misleading the market by placing fake orders. This can cause temporary price swings, leaving small investors at a disadvantage.The NSE co-location case demonstrated that unequal data access can impact “fair play.”
2. Risk of a Flash Crash: When thousands of algorithms work together, a technical or emotional movement can trigger a flash crash. This is what happened in the US in 2010, when the market plummeted by billions of dollars in a matter of minutes. Such accidents raise questions about market stability.
3. Unequal Access: HFT firms locate their servers very close to exchanges to gain a microsecond advantage. This makes it difficult for retail investors to compete, as “speed” becomes the driving force.
4. System Failures: Even a minor programming error can lead to losses worth crores. For example, in 2012, Knight Capital suffered massive losses in a matter of minutes due to a software bug. Therefore, firms now use real-time risk control and kill-switch systems.
5. Ethical and Regulatory Challenges: When some players profit solely through technological advantage, questions of fairness arise. If multiple HFT systems trade in the same direction, the market can become volatile. For this reason, regulators like SEBI are continuously increasing surveillance to ensure that the market remains transparent and balanced.

Read Also: Different Types of Trading in the Stock Market

Conclusion

High-frequency trading has made the world of trading faster and more data-driven than ever before. Trades are now completed in the blink of an eye, and markets appear more dynamic than ever. This provides investors with better prices and liquidity, but it has also presented challenges such as technical glitches and unequal access. The way forward is to use technology wisely to keep markets both fast and fair for all.

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