no code implementations • 13 Feb 2024 • Fabian Krause, Jan-Peter Calliess
In Statistical Arbitrage (StatArb), classical mean reversion trading strategies typically hinge on asset-pricing or PCA based models to identify the mean of a synthetic asset.
no code implementations • 20 Jan 2023 • Peer Nagy, Jan-Peter Calliess, Stefan Zohren
We employ deep reinforcement learning (RL) to train an agent to successfully translate a high-frequency trading signal into a trading strategy that places individual limit orders.
1 code implementation • EMNLP 2021 • Maximilian Ahrens, Julian Ashwin, Jan-Peter Calliess, Vu Nguyen
To this end, we combine a supervised Bayesian topic model with a Bayesian regression framework and perform supervised representation learning for the text features jointly with the regression parameter training, respecting the Frisch-Waugh-Lovell theorem.
no code implementations • 18 Aug 2020 • Peter Belcak, Jan-Peter Calliess, Stefan Zohren
As a simple illustration, we employ our toolbox to investigate the role of the order processing delay in normal trading and for the scenario of a significant price change.
no code implementations • 29 Nov 2019 • Kyriakos Polymenakos, Luca Laurenti, Andrea Patane, Jan-Peter Calliess, Luca Cardelli, Marta Kwiatkowska, Alessandro Abate, Stephen Roberts
Gaussian Processes (GPs) are widely employed in control and learning because of their principled treatment of uncertainty.
no code implementations • 28 Feb 2017 • Jan-Peter Calliess
Techniques known as Nonlinear Set Membership prediction, Kinky Inference or Lipschitz Interpolation are fast and numerically robust approaches to nonparametric machine learning that have been proposed to be utilised in the context of system identification and learning-based control.
no code implementations • 31 Dec 2016 • Jan-Peter Calliess
Techniques known as Nonlinear Set Membership prediction, Lipschitz Interpolation or Kinky Inference are approaches to machine learning that utilise presupposed Lipschitz properties to compute inferences over unobserved function values.
no code implementations • 17 Feb 2014 • Jan-Peter Calliess, Michael Osborne, Stephen Roberts
Existing work in multi-agent collision prediction and avoidance typically assumes discrete-time trajectories with Gaussian uncertainty or that are completely deterministic.
no code implementations • 18 Nov 2013 • Jan-Peter Calliess, Antonis Papachristodoulou, Stephen J. Roberts
In contrast to previous work that has used stochastic processes for identification, we leverage the structural knowledge afforded by Lagrangian mechanics and learn the drift and control input matrix functions of the control-affine system separately.