Categorization and Data

CFTC Technology Advisory Committee

Subcommittee on Automated and 

High Frequency Trading

Working Group #2
WG2 Agenda

• Team Members and Assignment

• WG1 Proposed Definition

• Categorization

• Data
Working Group #2 Team Members

WG2 Team Members

• Keith Fishe, Managing Partner, TradeForecaster Global Markets LLC, KHF@TradeForecaster.com

• Chris Isaacson, SVP, COO, BATS Global Markets, Inc., CIsaacson@BATSTrading.com

• Paul Kepes, Co-Founder, Chicago Trading Company, Paul@ChicagoTrading.com

• Chris Lorenzen, Founder and CEO, Eagle Seven, LLC, CLorenzen@EagleSeven.com

• Jim Northey, Partner and Co-Founder, LaSalle Technology Group, JimN@LasalleTech.com

Commission Staff

• Harold Hild, Economist, CFTC, HHild@CFTC.gov

• Jorge Herrada, Associate Director, CFTC, JHerrada@CFTC.gov

Additional Contributors

• Jeromee Johnson, VP Market Development, BATS Global Markets, Inc., JJohnson@BATSTrading.com

• Christina Sciotto, Executive Director, Chicago Trading Company, Christina.Sciotto@ChicagoTrading.com

Additional Meeting Participants Included: Andrei Kirilenko (CFTC), Ben Van Vliet (IIT), JonMarc Buffa 

(CFTC), Mark Smith (TFGM) and Tim Sargent (Markit)
Assignment

Should there be multiple categories of HFT, high frequency trading, (as 

defined) in the context of automated trading strategies? There is 

currently no consensus as to whether HFTs are primarily liquidity 

providers, aggressive liquidity takers or a combination of the two. 

Some HFTs may be very fast liquidity providers. Other HFTs may 

mostly take liquidity. Yet a third group of HFTs may provide about as 

much liquidity as they take.

Recognizing that the distinctions between trading activities and 

regulatory obligations (including compliance with exchange rules), 

representatives of the Subcommittee on Automated and High 

Frequency Trading will consider assigning additional categories to a 

broad class of ATS traders defined as HFTs and make recommendations 

as to embedding these categories into message tags for identification 

in, among other things, reference databases utilized by exchanges and 

the Commission for oversight and enforcement purposes.
Working Group #1 Proposed 

Definition of HFT

High frequency trading is a form of automated 

trading that employs:

(a) algorithms for decision making, order initiation, 

generation, routing, or execution, for each 

individual transaction without human direction;

(b) low-latency technology that is designed to minimize 

response times, including proximity and co-location 

services;

(c) high speed connections to markets for order entry; 

and

(d) high message rates (orders, quotes or cancellations).

CATEGORIZATION
Category Concepts

• Trading strategies

• Trading methods

• Latency

• Holding periods

• Order durations

• Economic value of speed

• Quantitative measures

• Market quality
Strategies that may utilize HFT Techniques Arbitrage

Cross Asset Arbitrage

Cross Market Arbitrage

Latency Arbitrage

Stat Arb / Relative Value Trading

Directional / Short Term

Momentum Based

Mean Reversion Liquidity Provision

Market Making

Rebate Capture

Spread Capture (Scalping) Event Driven

Liquidity Detection

Pinging / Sniping / Sniffing

Spoofing Quote Matching Ignition Strategies Quote Stuffing

Layering

ETF

Basis Trading Index

Pair Trading (Correlation) Portfolio Volatility Trading Inter-month Spread

Trend following

The Working Group does not and cannot opine on the legality of any of the above strategies. Their inclusion does not constitute or imply endorsement by the Working Group or the CFTC.
Trading Methods

• Hedging

• Execution algorithms

– Participation-based (e.g., VWAP)

– Time-based (e.g., TWAP)

– Multi-leg (e.g., exchange implied spread)

– Single execution - smart routing / transaction cost 

reduction

• Liquidity detection

– Pinging / snipping / sniffing
DATA
Observations

• Not always possible to associate an order with 

a specific strategy

– Intent may change over life of order

– May be part of multiple trading strategies

• Difficult to analyze market participant 

behavior within a specific market

– Necessity to hedge 

– Cross market arbitrage
Available Data

• Exchanges already capture information for 

regulatory and operational analysis

• Data sources

– Fields (tags) in API messages to and from exchange

– Exchange enrichment to API messages

• “Book data” 

– Exchange connection configuration information

• How firms are connected

• Speed of connection, bandwidth, latency

– Information provided to exchanges when firm 

connects to the exchange
API Information

Data Item Description and usage Usual FIX Field (Tag)

Trader identifier This information is captured within the exchange APIs on a 

message level basis.

SenderSubID(50)

Firm identifier Identifies the firm SenderCompID(49)

Trading 

computer 

software and 

version

The name and version of the computer product or application that 

is being used to generate orders.

The CME requires this information on the logon message.

The ICE collects information during conformance testing.

CTI Code The CFTC Customer Type Indicator

1 – Member trading for their own account

2 – Clearing firm trading for its own prop account

3 – Member trading for another member

4 – Any other

http://www.nfa.futures.org/news/newsNotice.asp?ArticleID=1362

CustOrderCapacity(582)

Automatic order 

generation

Exchanges require that firms identify orders as being generated by 

an automated system vs. being entered into the system manually 

by a user.

ManualOrderIndicator

(1028)

Aggressor 

indicator

Exchanges report whether or not the firm was the aggressor in the 

trade.

AggressorIndicator(1057)
Derived Information

Statistic Aggregation Level Unit Description

Order Chain 

Duration

Per Order Microseconds Length of time order remained in book 

before being filled or cancelled

Order Duration Per Order Microseconds Length of time order remained in book 

before being modified, filled, or cancelled

Average Order 

Duration 

Per Trader

Per Firm

Per Instrument

Microseconds Average order duration for all orders for a 

specific instrument.

Order to Fill Ratio Per Trader SenderSubID(tag 50)

Per Firm

Per Instrument

Count Number of orders entered to number of 

orders filled.

Order Efficiency Per Trader

Per Firm

Per Instrument

Count Execution Quantity or Notional Value (rather 

than execution count) to Quantity/Value 

entered/modified

Order 

Aggressiveness

Per Trader

Per Firm

Per Instrument

Count Orders that are on/near the Top of the Book 

are more valuable/subject to more risk than 

those that are away from the market

Reject/Order 

Ratio

Per Trader

Per Firm

Per Instrument

* by reject Reason

Count Can indicate systems issues or risk issues
Challenges

• In general

– Volume of information

• Cross market analysis

– System clock synchronization across markets

– Nonstandard identification of market participants 

across markets
Disclaimer: The statements made herein represent the collective views of the

members of the CFTC Technology Advisory Committee, Subcommittee on

Automated and High Frequency Trading, Working Group #2 (the “Working Group”)

and are not necessarily the views of each member’s firm or any one member

individually. While the Working Group has made every effort to present accurate

and reliable information in this material, this material is currently in draft form and

is subject to change at any time without notice in the sole discretion of the

Working Group. The Working Group does not guarantee the accuracy,

completeness, efficacy, or timeliness of the information contained herein. 

Reference herein to any product, process or service does not constitute or imply

endorsement by the Working Group.