Enhancing trading strategies with order book signals

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Data-Driven Market-Making via Model-Free Learning (2020, background): “From the extensive literature on market microstructure, such as [Cartea and Jaimungal, 2016; Spooner et al. , 2018; Cartea et al. , 2018], these are some attributes commonly used to describe the condition of the market and the decision-maker: the imbalance of the book size on both the bid and ask…”
Incorporating order-flow into optimal execution (2016, unknown): “In the context of optimal execution the recent work of [6] and [8] employ order-flow information to develop optimal execution models that take into account market impact and informational costs, see also [18] and [9].”
Deep learning for limit order books (2016, unknown): “The order book imbalance referred to as ‘queue imbalance’) has been identified as a key driver of best bid and best ask price dynamics; see Gould and Bonart (2016), Cao et al. (2009), Yang and Zhu (2016), Cartea et al. (2018), Stoikov and Waeber (2016), and Lipton et al. (2013).”