Synthetic data in cryptocurrencies using generative models

TL;DR

This paper introduces a method using Conditional GANs with LSTM and MLP to generate synthetic cryptocurrency price data, preserving market patterns and enabling privacy-safe analysis.

Contribution

It proposes a novel deep learning approach combining LSTM and MLP within CGANs for realistic synthetic cryptocurrency data generation.

Findings

The model reproduces key temporal patterns in crypto prices.

Synthetic data preserves market trends and dynamics.

GAN-based generation is computationally efficient.

Abstract

Data plays a fundamental role in consolidating markets, services, and products in the digital financial ecosystem. However, the use of real data, especially in the financial context, can lead to privacy risks and access restrictions, affecting institutions, research, and modeling processes. Although not all financial datasets present such limitations, this work proposes the use of deep learning techniques for generating synthetic data applied to cryptocurrency price time series. The approach is based on Conditional Generative Adversarial Networks (CGANs), combining an LSTM-type recurrent generator and an MLP discriminator to produce statistically consistent synthetic data. The experiments consider different crypto-assets and demonstrate that the model is capable of reproducing relevant temporal patterns, preserving market trends and dynamics. The generation of synthetic series through GANs is an efficient alternative for simulating financial data, showing potential for applications such as market behavior analysis and anomaly detection, with lower computational cost compared to more complex generative approaches.