The ability to construct a realistic simulator of financial exchanges, including reproducing the dynamics of the limit order book, can give insight into many counterfactual scenarios, such as a flash crash, a margin call, or changes in macroeconomic outlook. In recent years, agent-based models have been developed that reproduce many features of an exchange, as summarised by a set of stylised facts and statistics. However, the ability to calibrate simulators to a specific period of trading remains an open challenge. In this work, we develop a novel approach to the calibration of market simulators by leveraging recent advances in deep learning, specifically using neural density estimators and embedding networks. We demonstrate that our approach is able to correctly identify high probability parameter sets, both when applied to synthetic and historical data, and without reliance on manually selected or weighted ensembles of stylised facts.

Smart meters are intended to allow suppliers electricity network technologies, collectively called to access detailed energy consumption data and, more the smart grid (US Department Of Energy 2003; Galvin importantly, provide network information, such as real-time and Yeager 2008; UK Department of Energy and Climate pricing (RTP) signals, to consumers in an attempt to better Change 2009). A major component of this future vision is control or reduce demand when electricity is expensive that of energy storage. In particular, there is potential seen or carbon intensive on the grid (Hammerstrom et al. 2008; in the widespread adoption of small scale consumer storage Smith 2010). Accordingly, we envisage that micro-storage devices (i.e., micro-storage), which would allow consumers will be controlled by autonomous software agents that will to store electricity when demand is low, in order for react to RTP signals to minimise their owner's costs (i.e., it to be used during peak loads (Bathurst and Strbac 2003; they are self-interested). In this vein, we note our recent Ramchurn et al. 2011a; Vytelingum et al. 2010). This technology work (Vytelingum et al. 2010) in which we showed that, has the added advantage that it requires no significant when acting purely selfishly, large numbers of micro-storage change in how home appliances are used, and thus allows agents can cause instability in the aggregate demand profile.