Skip to main content

Main menu

  • Home
  • Past Issues
  • Videos
  • Submit an article
  • More
    • About the JOT
    • Editorial Board
  • IPR Logo
  • About Us
  • Journals
  • Publish
  • Advertise
  • Videos
  • Webinars
  • More
    • Awards
    • Article Licensing
    • Academic Use
  • Follow PMR on LinkedIn
  • Follow PMR on Twitter

User menu

  • Sample our Content
  • Request a Demo
  • Log in

Search

  • ADVANCED SEARCH: Discover more content by journal, author or time frame
The Journal of Trading
  • IPR Logo
  • About Us
  • Journals
  • Publish
  • Advertise
  • Videos
  • Webinars
  • More
    • Awards
    • Article Licensing
    • Academic Use
  • Sample our Content
  • Request a Demo
  • Log in
The Journal of Trading

The Journal of Trading

ADVANCED SEARCH: Discover more content by journal, author or time frame

  • Home
  • Past Issues
  • Videos
  • Submit an article
  • More
    • About the JOT
    • Editorial Board
  • Follow PMR on LinkedIn
  • Follow PMR on Twitter

Bayesian Adaptive Trading with a Daily Cycle

Robert Almgren and Julian Lorenz
The Journal of Trading Fall 2006, 1 (4) 38-46; DOI: https://doi.org/10.3905/jot.2006.654300
Robert Almgren
Head of Quantitative Strategies in Electronic Trading Services at Banc of America Securities in NewYork, NY.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: robert.almgren@bofasecurities.com
Julian Lorenz
A doctoral student in the Institute of Theoretical Computer Science at ETH Zürich in Zürich, Switzerland.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: julian.lorenz@gmail.com
  • Article
  • Info & Metrics
  • PDF (Subscribers Only)
Loading

Abstract

Standard models of algorithmic trading neglect the presence of a daily cycle. We construct a model in which the trader uses information from observations of price evolution during the day to continuously update his estimate of other traders' target sizes and directions. He uses this information to determine an optimal trade schedule to minimize total expected cost of trading, subject to sign constraints (never buy as part of a sell program). We argue that although these strategies are determined using very simple dynamic reasoning—at each moment they assume that current conditions will last until the end of trading—they are in fact the globally optimal strategies as would be determined by dynamic programming.

TOPICS: Equity portfolio management, portfolio construction, exchanges/markets/clearinghouses

  • © 2006 Pageant Media Ltd

Don’t have access? Click here to request a demo

Alternatively, Call a member of the team to discuss membership options

US and Overseas: +1 646-931-9045

UK: 0207 139 1600

Log in using your username and password

Forgot your user name or password?
PreviousNext
Back to top

Explore our content to discover more relevant research

  • By topic
  • Across journals
  • From the experts
  • Monthly highlights
  • Special collections

In this issue

The Journal of Trading
Vol. 1, Issue 4
Fall 2006
  • Table of Contents
  • Index by author
Download PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for your interest in spreading the word on The Journal of Trading.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Bayesian Adaptive Trading with a Daily Cycle
(Your Name) has sent you a message from The Journal of Trading
(Your Name) thought you would like to see the The Journal of Trading web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
Bayesian Adaptive Trading with a Daily Cycle
Robert Almgren, Julian Lorenz
The Journal of Trading Sep 2006, 1 (4) 38-46; DOI: 10.3905/jot.2006.654300

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Save To My Folders
Share
Bayesian Adaptive Trading with a Daily Cycle
Robert Almgren, Julian Lorenz
The Journal of Trading Sep 2006, 1 (4) 38-46; DOI: 10.3905/jot.2006.654300
del.icio.us logo Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Tweet Widget Facebook Like LinkedIn logo

Jump to section

  • Article
  • Info & Metrics
  • PDF (Subscribers Only)
  • PDF (Subscribers Only)

Similar Articles

Cited By...

  • No citing articles found.
  • Google Scholar
LONDON
One London Wall, London, EC2Y 5EA
United Kingdom
+44 207 139 1600
 
NEW YORK
41 Madison Avenue, New York, NY 10010
USA
+1 646 931 9045
pm-research@pageantmedia.com
 

Stay Connected

  • Follow PMR on LinkedIn
  • Follow PMR on Twitter

MORE FROM PMR

  • Home
  • Awards
  • Investment Guides
  • Videos
  • About PMR

INFORMATION FOR

  • Academics
  • Agents
  • Authors
  • Content Usage Terms

GET INVOLVED

  • Advertise
  • Publish
  • Article Licensing
  • Contact Us
  • Subscribe Now
  • Log In
  • Update your profile
  • Give us your feedback

© 2022 Pageant Media Ltd | All Rights Reserved | ISSN: 1559-3967 | E-ISSN: 2168-8427

  • Site Map
  • Terms & Conditions
  • Privacy Policy
  • Cookies