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

arXiv: https://arxiv.org/abs/1901.01751 · HTML: https://mlsanity.com/paper/1901.01751/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2019.

## Built on by (most cited first, 22 of 22)

- [Sig-Wasserstein GANs for Time Series Generation](https://mlsanity.com/paper/2111.01207/) (2021)
- [Probabilistic Conformal Prediction Using Conditional Random Samples](https://mlsanity.com/paper/2206.06584/) (2022)
- [Designing Universal Causal Deep Learning Models: The Geometric (Hyper)Transformer](https://mlsanity.com/paper/2201.13094/) (2022)
- [Multi-Asset Spot and Option Market Simulation](https://mlsanity.com/paper/2112.06823/) (2021)
- [Distributionally Robust End-to-End Portfolio Construction](https://mlsanity.com/paper/2206.05134/) (2022)
- [Time-Causal VAE: Robust Financial Time Series Generator](https://mlsanity.com/paper/2411.02947/) (2024)
- [A Quantum Generative Adversarial Network for distributions](https://mlsanity.com/paper/2110.02742/) (2021)
- [New Money: A Systematic Review of Synthetic Data Generation for Finance](https://mlsanity.com/paper/2510.26076/) (2025)
- [Time Series (re)sampling using Generative Adversarial Networks](https://mlsanity.com/paper/2102.00208/) (2021)
- [Simulating financial time series using attention](https://mlsanity.com/paper/2207.00493/) (2022)
- [Universal randomised signatures for generative time series modelling](https://mlsanity.com/paper/2406.10214/) (2024)
- [Synthetic Data for Portfolios: A Throw of the Dice Will Never Abolish Chance](https://mlsanity.com/paper/2501.03993/) (2025)
- [cCorrGAN: Conditional Correlation GAN for Learning Empirical Conditional Distributions in the Elliptope](https://mlsanity.com/paper/2107.10606/) (2021)
- [Estimating risks of option books using neural-SDE market models](https://mlsanity.com/paper/2202.07148/) (2022)
- [Implementing Quantum Generative Adversarial Network (qGAN) and QCBM in Finance](https://mlsanity.com/paper/2308.08448/) (2023)
- [INTAGS: Interactive Agent-Guided Simulation](https://mlsanity.com/paper/2309.01784/) (2023)
- [Drift Estimation for Stochastic Differential Equations with Denoising Diffusion Models](https://mlsanity.com/paper/2602.17830/) (2026)
- [Generating Financial Time Series by Matching Random Convolutional Features](https://mlsanity.com/paper/2606.05138/) (2026)
- [Diffusion Models for Adaptive Sequential Data Generation](https://mlsanity.com/paper/2606.06007/) (2026)
- [Discriminating modelling approaches for Point in Time Economic Scenario Generation](https://mlsanity.com/paper/2108.08818/) (2021)
- [Generating drawdown-realistic financial price paths using path signatures](https://mlsanity.com/paper/2309.04507/) (2023)
- [Generative Adversarial Regression (GAR): Learning Conditional Risk Scenarios](https://mlsanity.com/paper/2603.08553/) (2026)

## Abstract (arXiv)

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.