The creation of high fidelity synthetic data has long been an important goal in machine learning, particularly in fields like finance where the scarcity of available training and test data make it difficult to utilize many of the deep learning techniques which have proven so powerful in other domains. Despite ample research into different types of synthetic generation techniques, which in recent years have largely focused on generative adversarial networks, there remain key holes in many of the architectures and techniques being utilized. In particular, there are currently no techniques available which can generate multiple financial time series of returns concurrently while capturing the specific statistical properties of financial time series and which incorporate extra information that influences the series, such as macroeconomic factors. We propose the Market Conditional Transformer-Encoder Generative Adversarial Network (MC-TE-GAN), a novel generative adversarial neural network architecture that satisfies the aforementioned challenges. When trained on daily data and compared with the benchmark CoMeTS-GAN model, MC-TE-GAN is able to more accurately capture the relevant univariate statistical properties of financial returns such as linear unpredictability and heteroskedasticity. It is also able to capture the multivariate correlations between concurrently generated series. Furthermore, we demonstrate that when incorporating macroeconomic data as conditioning information MC-TE-GAN works as a compelling historical scenario simulator which is able to create realistic synthetic market crashes or bull runs, even in unseen data.