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Intra-day price variations in financial markets are driven by the sequence oforders, called the order flow, that is submitted at high frequency by traders.This paper introduces a novel application of the Sequence GenerativeAdversarial Networks framework to model the order flow, such that randomsequences of the order flow can then be generated to simulate the intra-dayvariation of prices. As a benchmark, a well-known parametric model from thequantitative finance literature is selected. The models are fitted, and thenmultiple random paths of the order flow sequences are sampled from each model.Model performances are then evaluated by using the generated sequences tosimulate price variations, and we compare the empirical regularities betweenthe price variations produced by the generated and real sequences. Theempirical regularities considered include the distribution of the pricelog-returns, the price volatility, and the heavy-tail of the log-returnsdistributions. The results show that the order sequences from the generativemodel are better able to reproduce the statistical behaviour of real pricevariations than the sequences from the benchmark.

Intra-Day Price Simulation with Generative Adversarial Modelling of the Order Flow