GENERATING CURRENCY EXCHANGE RATE DATA BASED ON QUANT-GAN MODEL

The aim of the research. This paper discusses use machine learning algorithms to generate data that meets demands academia and industry in context exchange rate fluctuations. Research results. builds a Quant-GAN model using temporal convolutional neural networks (CNN) trains it on end-of-day intraday high-frequency rates currency pairs global market. generated is evaluated various statistical methods found effectively simulate real dataset. Experimental results show by fits characteristics typical facts training datasets with good overall fit. provide effective means for FX market participants carry out tasks such as stress tests scenario simulations. Future work includes accumulating increasing computing power, optimizing improving GAN models, establishing evaluation standards generating price data. As power continues grow, model’s ability process ultra-large-scale expected improve.