Simulating Multi-Asset Classes Prices Using Wasserstein Generative Adversarial Network: A Study of Stocks, Futures and Cryptocurrency

Feng Han,Jiheng Zhang,Xiaojuan Ma

doi:10.3390/jrfm15010026

Abstract

Financial data are expensive and highly sensitive with limited access. We aim to generate abundant datasets given the original prices while preserving the original statistical features. We introduce the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) into the field of the stock market, futures market and cryptocurrency market. We train our model on various datasets, including the Hong Kong stock market, Hang Seng Index Composite stocks, precious metal futures contracts listed on the Chicago Mercantile Exchange and Japan Exchange Group, and cryptocurrency spots and perpetual contracts on Binance at various minute-level intervals. We quantify the difference of generated results (836,280 data points) and original data by MAE, MSE, RMSE and K-S distances. Results show that WGAN-GP can simulate assets prices and show the potential of a market simulator for trading analysis. We might be the first to look into multi-asset classes in a systematic approach with minute intervals across stocks, futures and cryptocurrency markets. We also contribute to quantitative analysis methodology for generated and original price data quality.

Highlights

Various scholars are trying to solve price prediction problems to capture short-term alphas in the markets (Ariyo et al (2014); Foster (2002); Abraham et al (2018))
−αgenerator · RMSprop; end for Algorithm 1 WGAN-GP trains each asset class with customized parameters, and we systematically examine the difference between generated data prices and original prices by mean absolute error (MAE), mean squared error (MSE), root-mean-square deviation (RMSE) and KS test in Section 4 for stocks markets, Section 5 for precious metal futures listed on CME and JPX and Section 6 for cryptos listed on Binance including spots and perpetual futures
We measured the difference by mean absolute error (MAE), mean squared error (MSE), and root-mean-square deviation (RMSE)

Summary

Introduction

Various scholars are trying to solve price prediction problems to capture short-term alphas in the markets (Ariyo et al (2014); Foster (2002); Abraham et al (2018)). A more fundamental contribution can be understanding the underlying price behavior of multiple asset classes with limited data. One of the critical processes is to generate enough data for observations and backtesting, we want to learn the distribution of asset prices based on limited price information. We hope to generate richer varieties of data to simulate the original prices while preserving the original statistical features. Stock prices are considered to be random walks (and crypto as well) by Palamalai et al (2021). To simulate the price behaviors, we adopt WGAN-GP to generate richer data with noise to discover hidden characteristics. To ensure the quality of generated real and fake data discriminated by WGAN-GP, we use MAE, MSE, RMSE and KS distance to calculate the price differences with multiple minute level intervals

Objectives

Results

Discussion

Conclusion