Bayesian Adaptive Trading with a Daily Cycle

  title={Bayesian Adaptive Trading with a Daily Cycle},
  author={Robert Almgren and Julian Lorenz},
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… CONTINUE READING
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Publications referenced by this paper.
Showing 1-7 of 7 references

Algorithmic decision-making framework

R. Kissell, R. Malamut
J. Trading 1(1), 12–21. 2 • 2006
View 1 Excerpt

Predatory trading

M. K. Brunnermeier, L. H. Pedersen
J. Finance 60(4), 1825–1863. 2 • 2005
View 1 Excerpt

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