What is HFT, and why do many brokers ban HFT?

Following our EA trading competition, where we added a rule prohibiting the use of HFT EAs, many participants wanted to know what HFT is, how it’s classified, and why it’s banned. Let’s go through this from the beginning. (This content is supported by EA Writing Guide with fxDreema Volume 1 and Volume 2)

Definition of High-Frequency Trading (HFT)

High-Frequency Trading (HFT) refers to a category of algorithmic trading behaviors that focus on extremely high trading speed and a very large volume of orders. It relies on low-latency technological infrastructure—such as server colocation near the matching engine and order transmission via FIX protocol or high-speed market data feeds—to generate profits from tiny, micro-level price movements within the market’s microstructure.

According to a report by the Markets Committee of the Bank for International Settlements (BIS), HFT in the foreign exchange (FX) market is characterized by small trade sizes executed in very large numbers, low profit margins per trade, millisecond-level processing speeds, and extremely short risk-holding periods (BIS Markets Committee, 2011).

From a regulatory perspective, ESMA classifies HFT as a “subset of algorithmic trading,” identifiable through a shared set of characteristics, including very high order submission rates, high order cancellation rates, the use of colocation or proximity services, and extremely short holding periods. ESMA also proposes a quantitative framework for identifying HFT activity within market data (ESMA, 2014).

Simply put, “HFT = high order frequency + low latency + algorithm-driven decision-making + intensive order-submission and cancellation within extremely short timeframes.” It is not the name of a single trading strategy, but rather a “cluster of behaviors” that can be observed across multiple strategies—such as latency arbitrage, high-frequency market making, or statistical arbitrage operating on timeframes of just a few seconds. This view is consistent with contemporary literature in both policy analysis and financial economics (BIS Markets Committee, 2011; ESMA, 2014).

How to Identify What Constitutes HFT

Determining whether a trading strategy or EA qualifies as HFT should be based on the system’s behavioral characteristics, rather than the strategy’s name alone. Academic research and financial regulatory publications suggest the following key criteria for identification:

Holding Period (Position Holding Time)

• HFT characteristic: Positions or orders are held for extremely short durations—ranging from milliseconds to just a few seconds—before being closed.
• Reference: BIS (2011) reports that HFT traders in the FX market typically hold positions for seconds or less, in contrast to conventional trading, where positions may be held for minutes to hours (BIS Markets Committee, 2011).
• Implication for classification: If an EA closes orders almost immediately—with an average holding time of < 10 seconds—it is likely to be classified as HFT

Order Volume (Order Frequency and Message Rates)

• HFT characteristic: A very large number of orders are submitted within a short period of time, with order cancellation rates often exceeding 90% of submitted orders.
• Reference: ESMA (2014) defines HFT as having “high order-to-trade ratios” and “very high message rates,” meaning that the number of orders submitted is many times greater than the number of trades actually executed (European Securities and Markets Authority, 2014).
• Implication for classification: If an EA submits orders dozens of times per minute >or more, it should be considered HFT.

Use of Technological Infrastructure (Latency / Colocation)

• HFT characteristic: Relies on speed advantages (latency advantages), such as colocation near exchange servers or the use of FIX APIs to bypass the latency inherent in standard retail trading platforms
• Reference: Carver (2014) points out that retail traders cannot realistically compete with HFT firms because they lack colocation and low-latency infrastructure. Therefore, EAs designed to exploit latency arbitrage at the broker level are considered to fall within the scope of HFT (Carver, 2014).>
• Implication for classification: If an EA is designed to exploit price discrepancies caused by latency,> it should be classified as HFT.

Strategies Commonly Used in HFT

• Latency Arbitrage: >Uses faster-arriving market data to arbitrage bid/ask prices or price discrepancies across markets.
• High-Frequency Market Making: >Continuously places bid and ask quotes, capturing very small spreads but doing so across a large number of trades.
• High-Frequency Statistical Arbitrage: >Exploits very short-term deviations between correlated assets.
• According to Chan (2013) in Algorithmic Trading,>these strategies cannot be executed at the retail level without ultra-low-latency infrastructure (Chan, 2013).

The Difference Between HFT and Scalping

Although HFT and scalping are sometimes viewed as “the same” because both aim to profit from small price movements in the market, from both academic and practical perspectives, the two are clearly distinct in several key aspects, as outlined below.

Holding Period

• HFT: Extremely short holding periods—ranging from milliseconds to just a few seconds.
  • BIS (2011) finds that in the FX market, HFT traders typically hold positions for only a few seconds or less (BIS Markets Committee, 2011).
• Scalping: Longer holding periods, typically tens of seconds to several minutes (e.g., 1–15 minutes).
  • Chan (2013) notes that retail scalping strategies on MT4/MT5 can be profitable even without a latency advantage, because positions are held for minutes rather than seconds (Chan, 2013).

Order Frequency

• HFT: Exhibits extremely high order frequency—dozens to hundreds of orders per second—along with high order cancellation or rejection rates.
  • ESMA (2014) confirms that HFT exhibits abnormally high order-to-trade ratios and submits orders at extremely high frequencies (European Securities and Markets Authority, 2014).
• Scalping: Operates at a much lower scale, typically tens to hundreds of orders per day, depending on the EA’s strategy.
  • Davey (2014) provides examples of retail scalping systems that typically execute only 10–50 trades per day (Davey, 2014).

Strategy Types

• HFT: Latency arbitrage, high-frequency market making, Statistical arbitrage operating at the millisecond level
• Scalping: Breakout trading, mean reversion, trend-following intraday

In short, the conclusion is as follows: if an EA opens and closes orders within just a few seconds and submits a large number of orders, >it should be classified as HFT. Conversely, if an EA opens and closes orders on a minute-level timeframe and executes only tens to hundreds of trades per day, >it should be considered scalping.

Can HFT EAs actually generate profits in the real Forex market?

The use of HFT EAs in the Forex market has long been a subject of debate. While HFT has proven successful in equity and futures markets, the FX market—particularly the retail Forex segment—faces several structural limitations.

HFT at the Institutional FX Level

• In the interbank FX market, HFT plays a clearly defined role, particularly on EBS and Reuters Matching platforms, which serve as the primary trading venues for financial institutions.
• The BIS (2011) report states that HFT participants are significant players in the FX spot market and contribute to enhanced liquidity and tighter spreads under normal market conditions (BIS Markets Committee, 2011).
• However, during periods of heightened market volatility, HFT participants may withdraw from the market, causing liquidity to disappear rapidly, which poses a form of systemic risk.

HFT in the Retail Forex Market

• In the retail FX market (via MT4/MT5 brokers), the use of HFT EAs is generally unable to generate sustainable profits, due to the following reasons:
  • Latency: Retail traders do not have access to colocation and must route orders through the broker’s servers>, resulting in a significant speed disadvantage compared to institutional players—simply put, they cannot compete on speed with large institutions.
  • Execution policy: Most brokers actively detect and prohibit latency arbitrage or micro-scalping strategies that open and close positions within fractions of a second.
  • Slippage และ Spread widening: Under real market conditions, transaction costs and slippage tend to erase the profits of retail HFT EAs.
• Carver (2014) emphasizes that retail traders cannot compete with institutional-level HFT due to the lack of high-speed infrastructure (Carver, 2014).

Scalping vs HFT in the Real Forex Market

• Scalping EAs (with minute-level holding periods) can still be profitable in the retail Forex market if the strategy is properly designed and the broker offers favorable trading conditions.
• HFT EAs that attempt to profit from millisecond-level arbitrage often encounter the following issues
  • Requotes
  • Slippage
  • Profit cancellation by the broker (considered unfair execution).

Why do many retail Forex brokers ban HFT?

Although HFT is an integral part of institutional trading and the interbank FX market, many retail Forex brokers choose to ban or restrict the use of HFT EAs for the following reasons.

Latency Arbitrage Issues

• Most retail HFT EAs focus on latency arbitrage, seeking to exploit price gaps caused by latency between the broker’s data feed and the true market price.
• Brokers view this as “unfair trading” because the profits are derived not from genuine market analysis, but from temporary inefficiencies or instability in the broker’s price quotation system.
• ESMA (2014) states that HFT commonly relies on latency advantages, such as colocation and ultra-fast connectivity, to profit from price discrepancies (European Securities and Markets Authority, 2014).

Cost and Order Management Risks

• When an EA submits a large number of orders per second, the broker’s systems> must handle a heavy processing load, thereby increasing infrastructure costs.
• A very high order cancellation rate can disrupt the broker’s liquidity balance (BIS Markets Committee, 2011).

Liquidity Risk

• During periods of market volatility, HFT EAs often withdraw their orders immediately>, forcing the broker to absorb the resulting price exposure (market risk).
• This observation aligns with BIS (2011), which notes that HFT can enhance liquidity under normal market conditions, but that liquidity can disappear abruptly during periods of volatility (BIS Markets Committee, 2011).

Incompatibility with the Broker’s Business Model

• Pure market maker brokers are at a significant disadvantage when facing HFT EAs that exploit latency>, because the broker acts as the direct counterparty to those trades.
• Even under ECN/STP models, some brokers still prohibit HFT due to slippage issues, toxic order flow, and complaints or dissatisfaction from liquidity providers.

Summary

High-Frequency Trading (HFT) is a form of algorithmic trading that emphasizes extremely high execution speed and the submission of a massive number of orders, with holding periods of only a few seconds or less. This differs from scalping, which—although also a short-term trading approach—typically holds positions for minutes and can be viable at the retail level.

Research by BIS and ESMA clearly indicates that HFT is identified by extremely short holding periods, high order frequency, high order cancellation rates, and the use of latency advantages.

Although HFT can generate real profits in institutional-level FX markets, it is nearly impossible in the retail market due to structural limitations, speed constraints, and broker-level order management restrictions.

For these reasons, most Forex brokers choose to ban HFT EAs, as they are viewed as system-exploiting strategies rather than genuine trading, and as a measure to protect market liquidity stability.

อ้างอิง

• BIS Markets Committee. (2011). High-frequency trading in the foreign exchange market. Bank for International Settlements. https://www.bis.org/publ/mktc05.pdf
• European Securities and Markets Authority. (2014). High-frequency trading activity in EU equity markets (ESMA Economic Report No. 1). https://www.esma.europa.eu/sites/default/files/library/2015/11/esma20141_-_hft_activity_in_eu_equity_markets.pdf
• Carver, R. (2014). Leveraged Trading: A professional approach to trading FX, stocks on margin, CFDs, spread bets and futures for all traders. Harriman House.
• Chan, E. P. (2013). Algorithmic Trading: Winning Strategies and Their Rationale. John Wiley & Sons.
• Davey, K. J. (2014). Building Winning Algorithmic Trading Systems: A Trader’s Journey from Data Mining to Monte Carlo Simulation to Live Trading. John Wiley & Sons.