## 1 Introduction  
Employing machine learning tasks to successfully model financial time series (FTS) data (e.g., stock prices, FX rates) is a challenging but valuable exercise [1]. For example, a supervised learning model for predicting future asset returns may be used to make directional investment decisions (i.e., a bet on the future direction of an asset's price, or to prioritise and weight trades). However, there are difficulties in the FTS data domain that make supervised learning tasks more challenging than other domains: (1) a lack of training data versus other data domains, e.g., images, video, text, audio, etc., which results in higher model estimation and generalisation errors, thus degrading performance, and (2) persistently low signal-to-noise ratios, as market efficiency dampens detectable signals [2]. Another valuable machine learning task for aiding investment decision making is to learn the data distribution of the underlying data generation process (DGP) [3]. Generative models could be used to simulate the evolution of FTS data and help investment and risk professionals make better risk-based investment decisions (i.e., approaches to hedging or assessing risk-reward trade-offs) and improve capital efficiency. Generative modelling approaches, such as variational auto-encoders (VAEs) [4; 5] and generative adversarial networks (GANs) [6], have been used for DGP simulation tasks in other domains. In the image domain, GANs are known to be more successful than VAEs in generating'sharper' synthetic data when compared to VAE-based approaches [7; 8]. In the FTS domain, GANs have only recently been explored for simulating FTS data [9; 10; 11; 12; 13; 14; 15; 16; 17]. A key issue when training predictive or generative FTS models in real-world settings is the non-stationary behaviour of the FTS data domain. Non-stationarity in asset return behaviours is a feature of complex market systems, driven by exogenous factors (e.g., government policies, interventions, regulations, industry structural changes) and endogenous factors (e.g., market expectations, participant behaviour, feedback loops, and dynamic algorithms). Market participants often describe a contiguous period of persistent market behaviours (e.g., directions, volatilities, correlations) as a market'regime'. A regime change is a significant DGP feature that contributes to the non-stationarity of observed FTS data. The performance of predictive or generative FTS models significantly depends on whether future data will be drawn from the same DGP as the training period. However, regime class imbalance has often been overlooked in FTS model training. Failure to account for this feature could lead to 'unconditional' models being trained in'mixed-regime' environments and not be reflective of the modeller's explicit expectations for current or future regimes. This paper proposes a novel approach to the generative modelling of regime-specific FTS data by using conditional generative adversarial networks (cGANs) [18]. The application of cGANs to the FTS domain enables the class-conditional simulation of asset price behaviour that may be characteristic of historic market regime environments, such as past financial crises. The key benefits are that (1) users would have the flexibility to simulate market behaviours not dependent on recent regime conditions if conditions have changed or are changing and (2) it gives users the flexibility to explicitly simulate mixture models by generating class-conditional synthetic data and weighting their prevalence with regime class priors. Few papers in the FTS GAN literature have applied a cGAN to modelling FTS data. Each of these studies demonstrated a range of conditioning approaches. [PERSON] et al. [15] used categorical, ordinal, or continuous conditioning representations to learn toy Gaussian mixtures and vector autoregression (VAR) processes. [PERSON] et al.