Generative Adversarial Networks for Financial Trading Strategies Fine-Tuning and Combination

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, the analyst needs to proper fine-tune its 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 into such aspects. Therefore, our work proposes the use of Conditional Generative Adversarial Networks (cGANs) for trading strategies calibration and aggregation. To this purpose, 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 strategies 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 strategies calibration and combination, providing outperformance when the traditional techniques fail to generate any alpha.

References in corpus (10)

• Conditional Generative Adversarial Nets
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• Generative Adversarial Networks: An Overview
• Energy-based Generative Adversarial Network
• Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs
• CMA-ES for Hyperparameter Optimization of Deep Neural Networks
• AdaGAN: Boosting Generative Models
• Ensembles of Generative Adversarial Networks
• Autoregressive Quantile Networks for Generative Modeling
• Evolutionary Generative Adversarial Networks

Cited by in corpus (5)

• CorrGAN: Sampling Realistic Financial Correlation Matrices Using Generative Adversarial Networks
• Augmenting correlation structures in spatial data using deep generative models
• Generating Realistic Stock Market Order Streams
• Decision-Aware Conditional GANs for Time Series Data
• Finding the optimal Nash equilibrium in a discrete Rosenthal congestion game using the Quantum Alternating Operator Ansatz