Fetching the paper…

Reading the bibliography…

Double Deep Q-Learning for Optimal Execution · Around

2018

Double Deep Q-Learning for Optimal Execution

Ning, Brian, Lin, Franco Ho Ting, Jaimungal, Sebastian

Original
PDF
Post on X

Understand

Optimal trade execution is an important problem faced by essentially all traders.

Much research into optimal execution uses stringent model assumptions and applies continuous time stochastic control to solve them.
Here, we instead take a model free approach and develop a variation of Deep Q-Learning to estimate the optimal actions of a trader.
The model is a fully connected Neural Network trained using Experience Replay and Double DQN with input features given by the current state of the limit order book, other trading signals, and available execution actions, while the output is the Q-value function estimating the future rewards under an arbitrary action.

Built on

UMI Order No. GAX93-22750
L.-J. Lin · 1992
Earlier work this paper cites.
C. J. C. H. Watkins and P. Dayan
1992
Earlier work this paper cites.
R. S. Sutton and A. G. Barto
1998
Earlier work this paper cites.
R. Almgren and N. Chriss
2001
Earlier work this paper cites.
O. E. Barndorff-Nielsen and N. Shephard
2002
Earlier work this paper cites.

Similar

Y. Nevmyvaka, Y. Feng, and M. Kearns
2006
Cited alongside, same era.
V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. A. Riedmiller
Original
2013
Cited alongside, same era.
D. Hendricks and D. Wilcox
2014
Cited alongside, same era.
Á. Cartea, S. Jaimungal, and J. Penalva
2015
Cited alongside, same era.
O. Guéant and C.-A. Lehalle
2015
Cited alongside, same era.

Then

H. van Hasselt, A. Guez, and D. Silver
Original
2015
Later among the works it cites.
Á. Cartea and S. Jaimungal
2016
Later among the works it cites.
P. Casgrain and S. Jaimungal
2018
Closest in time.
J. A. Sirignano
2018
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…