Algorithmic Trading with Learning

28 Pages Posted: 1 Jan 2014 Last revised: 13 Oct 2015

See all articles by Álvaro Cartea

Álvaro Cartea

University of Oxford; University of Oxford - Oxford-Man Institute of Quantitative Finance

Sebastian Jaimungal

University of Toronto - Department of Statistics

Damir Kinzebulatov

The Fields Institute for Mathematical Sciences

Date Written: October 12, 2015

Abstract

We propose a model where an algorithmic trader takes a view on the distribution of prices at a future date and then decides how to trade in the direction of her predictions using the optimal mix of market and limit orders. As time goes by, the trader learns from changes in prices and updates her predictions to tweak her strategy. Compared to a trader who cannot learn from market dynamics or form a view of the market, the algorithmic trader's profits are higher and more certain. Even though the trader executes a strategy based on a directional view, the sources of profits are both from making the spread as well as capital appreciation of inventories. Higher volatility of prices considerably impairs the trader's ability to learn from price innovations, but this adverse effect can be circumvented by learning from a collection of assets that co-move. Finally, we provide a proof of convergence of the numerical scheme to the viscosity solution of the dynamic programming equations which uses new results for systems of PDEs.

Keywords: Algorithmic Trading, High Frequency Trading, Nonlinear Filtering, Brownian Bridge, Stochastic Optimal Control, Adverse Selection

Suggested Citation

Cartea, Álvaro and Jaimungal, Sebastian and Kinzebulatov, Damir, Algorithmic Trading with Learning (October 12, 2015). Available at SSRN: https://ssrn.com/abstract=2373196 or http://dx.doi.org/10.2139/ssrn.2373196

Álvaro Cartea

University of Oxford ( email )

Mansfield Road
Oxford, Oxfordshire OX1 4AU
United Kingdom

University of Oxford - Oxford-Man Institute of Quantitative Finance ( email )

Eagle House
Walton Well Road
Oxford, Oxfordshire OX2 6ED
United Kingdom

Sebastian Jaimungal (Contact Author)

University of Toronto - Department of Statistics ( email )

100 St. George St.
Toronto, Ontario M5S 3G3
Canada

HOME PAGE: http://http:/sebastian.statistics.utoronto.ca

Damir Kinzebulatov

The Fields Institute for Mathematical Sciences ( email )

222 College Street, Second Floor
Toronto, Ontario M5T 3J1
Canada

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
2,938
Abstract Views
12,814
rank
4,913
PlumX Metrics