Setting Up Protection From HFT Predation
Updated: Aug 15, 2022
While the majority of high frequency trader (HFT) activity in the market is deemed as positive by many participants (such as market making, cross asset/market arbitrage, and statistical arbitrage), there are strategies that exist which are believed to be ‘predatory’ in nature.
To protect themselves, institutional investors should take steps. Here are some ways that they can protect themselves from HFT predation:
Use strict price limits whenever possible and monitor these in real time. This is one way your trades are generally not prey to momentum ignition strategies (see the definition in the chart).
Avoid the frequent use of pure schedule-based algorithms (VWAP, TWAP, POV) that allow for ease of pattern detection, especially for trades > 3% Average Daily Volume.
Require minimum size trades ‘in the dark’ before transacting, minimizing information leakage and preventing small trades from ‘gaming’ you.
Customize the minimum sizes your require ‘in the dark’ in order for the amounts to
stay relevant across different securities.
When sending passive orders, take an active role in specifying marketplaces in order to maximize your fill potential, jumping ahead of HFT participants in many cases.
Strategically choose execution venues recognizing that marketplace fees are typically embedded in the commissions paid to the executing broker.
Route orders like ‘retail’ traders as much as possible to help mask your trading footprint.
Executing brokers should be carefully selected based partly on their technological know-how, customization options, and general market structure savvy.
Closely monitor developments in the Canadian market micro-structure to ensure that you are aware of new marketplaces and order types that can be used to improve/preserve execution quality.
Actively engage with Canadian regulatory bodies to ensure that the best interests of you and your clients are considered in the formulation of market structure regulations.
Detecting and trying to trade in front of what is expected to be large trading interest by using publicly available exchange data (price, volume, bid-ask…)
Dark order signaling
Detecting and trying to trade in front of what is expected to be large trading interest by using ‘dark pinging’ strategies or by using publicly available ‘dark’ execution data
Initiating a series of order and trades in an attempt to ignite a rapid price move either up or down and incite others to trade at artificially high or low prices
Placing multiple, large orders to ‘push’ the book away, which could temporarily create artificially low or high prices that can be acted upon by incoming orders
Anticipating orders via the use of speed through co-location to exploit the latency differentials caused by the geographical separation of marketplaces
Benefiting from ‘stale’ quote data
Generating orders via the use of co-location and direct data feeds to take advantage of the ‘stale’ quote data from marketplaces that use a slower data feed (Ie. the SIP in the U.S.)
Sending an unusual number of orders to trade a security and immediately cancelling them to ‘flood’ trading systems with excessive market data messages (causes latency disruptions)
Sub-penny queue jumping
Using the structure of the marketplace fees (maker/taker) to improve queue priority of passive orders
Investment Industry Regulatory Organization of Canada, ‘Guidance on Certain Manipulative and Deceptive Trading Practices’ (Notice 13-0053, February 14, 2013)
Norges Bank Investment Management, ‘High Frequency Trading – An Asset Manager’s Perspective’ (Discussion Note, September 10, 2013)
Kelly Reynolds is head trader at Hillsdale Investment Management Inc.