UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Generative adversarial networks for financial trading strategies fine-tuning and combination

Koshiyama, A; Firoozye, N; Treleaven, P; (2020) Generative adversarial networks for financial trading strategies fine-tuning and combination. Quantitative Finance 10.1080/14697688.2020.1790635. (In press).

[img] Text
1901.01751v3.pdf - Accepted version
Access restricted to UCL open access staff until 2 March 2022.

Download (7MB)

Abstract

Systematic trading strategies are algorithmic procedures that allocate assets aiming to optimize a certain performance criterion. To obtain an edge in a highly competitive environment, an analyst needs to appropriately fine-tune their strategy, or discover how to combine weak signals in novel alpha creating manners. Both aspects, namely fine-tuning and combination, have been extensively researched using several methods, but emerging techniques such as Generative Adversarial Networks can have an impact on such aspects. Therefore, our work proposes the use of Conditional Generative Adversarial Networks (cGANs) for trading strategy calibration and aggregation. To this end, we provide a full methodology on: (i) the training and selection of a cGAN for time series data; (ii) how each sample is used for strategy calibration; and (iii) how all generated samples can be used for ensemble modelling. To provide evidence that our approach is well grounded, we have designed an experiment with multiple trading strategies, encompassing 579 assets. We compared cGAN with an ensemble scheme and model validation methods, both suited for time series. Our results suggest that cGANs are a suitable alternative for strategy calibration and combination, providing outperformance when the traditional techniques fail to generate any alpha.

Type: Article
Title: Generative adversarial networks for financial trading strategies fine-tuning and combination
DOI: 10.1080/14697688.2020.1790635
Publisher version: https://doi.org/10.1080/14697688.2020.1790635
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Generative adversarial networks, Trading strategies, Backtesting, Model combination
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10113486
Downloads since deposit
1Download
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item