Using Quote Cancellations and Timing to Gauge HFT Activity
Summary
The document considers how to estimate high-frequency trading activity for an individual security in near real time when trader identities are unavailable. Suggested proxies use order-book behavior: count quote cancellations to detect elevated order churn, compare cancellation activity with quote rates, or examine how quickly a trade follows a quote update. Shorter quote-to-trade intervals are presented as a sign that HFT may be more likely.
These measures are indirect and cannot identify who submitted an order or prove that activity is high-frequency trading. Cancellation and quote patterns can also change with market conditions; in particular, volatility may cause market makers to withdraw quotes. The answers recommend controlling for volatility and mention that high-frequency flow can be episodic. No validated threshold, estimation procedure, or empirical performance comparison is provided, so the measures are best treated as indicators for monitoring rather than definitive classifications.
Key ideas
- Trader identity is not observable from public trade and quote data, so HFT activity must be estimated indirectly.
- Order-book cancellation activity can serve as a proxy for quote churn.
- The time between a quote update and a trade within that quote may help indicate high-frequency activity.
- Cancellation rates should be interpreted alongside quote rates and market volatility.
- These proxies do not establish which type of trader caused the observed activity.
Tags
Full text
# What is the most effective way of determining & measuring the level of HFT activity in a stock in (close to) real time? # What is the most effective way of determining & measuring the level of HFT activity in a stock in (close to) real time? On a security by security basis, I want to be able to quantify the level of HFT activity (and later institutional & retail activity). Is it higher than it normally is? How much so? What would you say is the best way to measure? Number of quotes? # of quotes relative to volume? Relative to # of trades? Spread volatility? Reversals after block prints? ## Answer by chrisaycock (score 8) https://quant.stackexchange.com/a/607 You won't know who made the trade, so you'll need to look at the quotes. Specifically, you should look to see if there are a lot of cancellations in the full order book. That will tell you if there's higher "churn" for a particular stock since HTFs often have low fill ratios (<1% for some shops). But you'll need to control for volatility since wild market swings in general will cause market makers to pull their quotes. ## Answer by glyphard (score 5) https://quant.stackexchange.com/a/605 A simple way to do this with the TAQ database (Nasdaq trade and quote) is to measure the amount of time between a quote update and a trade inside that quote. The shorter that time, the higher probability HFT is present. ## Answer by Louis Marascio (score 5) https://quant.stackexchange.com/a/1857 You might find the paper "Low-Latency Trading" by Hasbrouck and Saar useful. In it they discuss the episodic nature of some high-frequency flow and construct some useful measures of this flow. Generally, I would think some model that relates the cancel rate with the quote rate is most useful.
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