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Market simulation is an increasingly important method for evaluating and training trading strategies and testing "what if" scenarios. The extent to which results from these simulations can be trusted depends on how realistic the environment is for the strategies being tested. As a step towards providing benchmarks for realistic simulated markets, we enumerate measurable stylized facts of limit order book (LOB) markets across multiple asset classes from the literature. We apply these metrics to data from real markets and compare the results to data originating from simulated markets. We illustrate their use in five different simulated market configurations: The first (market replay) is frequently used in practice to evaluate trading strategies; the other four are interactive agent based simulation (IABS) configurations which combine zero intelligence agents, and agents with limited strategic behavior. These simulated agents rely on an internal "oracle" that provides a fundamental value for the asset. In traditional IABS methods the fundamental originates from a mean reverting random walk. We show that markets exhibit more realistic behavior when the fundamental arises from historical market data. We further experimentally illustrate the effectiveness of IABS techniques as opposed to market replay.