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Bayesian Adaptive Trading with a Daily Cycle
 

Summary: Bayesian Adaptive Trading
with a Daily Cycle
Robert Almgren
and Julian Lorenz
July 28, 2006
Abstract
Standard models of algorithmic trading neglect the presence of a
daily cycle. We construct a model in which the trader uses infor-
mation from observations of price evolution during the day to con-
tinuously 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 al-
though 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 opti-
mal strategies as would be determined by dynamic programming.

Electronic Trading Services, Banc of America Securities LLC, New York;
Robert.Almgren@bofasecurities.com.

  

Source: Almgren, Robert F. - Courant Institute of Mathematical Sciences, New York University

 

Collections: Mathematics