| October 1, 2022
Project overview
As part of our work on automated trading systems, we use model-based reinforcement learning algorithms. These algorithms exploit a model of the prices in time. However, classical regression approaches are limited and the current state of the art, XGBoost, does not achieve reliable results.
We therefore seek to explore solutions from physics, such as [1]. These approaches exploit a time series decomposition to extract noise and non-Markovian components.
The project aims at conducting a comparative study of non-Markovian Dynamics Modeling solutions. Among these solutions, we find the Mori-Zwanzig, Nakajima-Zwanzig or k-PCA formalism, among others. Our main use of the models is the simulation of temporal sequences, coming from the same unknown distribution as the samples of the data set. The metric to be optimized is therefore the Kolmogorov-Smirnov metric [3], which compares the distances between two observed distributions. The final objective is to train a RL agent on a real, augmented or synthetic environment to compare the performance in Portfolio Allocation.
During this project, you will:
- draw a state of the art of existing modeling techniques,
- implement algorithms, with libraries [4],
- benchmark those methods.
References
[1] Spindler et al, “Time Series Analysis of Real-world Complex Systems—Climate, Finance, Proteins, and Physiology”. (2007) Chap. 9.
[2] Hassanibesheli et al, “Reconstructing Complex System Dynamics from Time Series: A Method Comparison.” New Journal of Physics 22, no. 7 (2020) https://iopscience.iop.org/article/10.1088/1367-2630/ab9ce5/pdf
[3] Dimitrova et al, “Computing the Kolmogorov–Smirnov Distribution when the Underlying cdf is Purely Discrete, Mixed or Continuous”. Journal of Statistical Software (2020)
[4] MZProjection, https://github.com/smaeyama/mzprojection