Reinforcement Learning For Trade Execution: Empirical Evidence Based On Simulations

We are interested in developing trade execution algorithms that minimize losses and limit risk across a variety of markets (e.g., cash, futures), market regimes (efficient, challenging), benchmarks (arrival price, volume-weighted average price), and risk measures (variance, expected shortfall). Traditionally, execution algorithms are often developed using some combination of optimal solutions to stylized versions of the problem expressed using market models and heuristic algorithms. Here, we explore an alternate strategy: training execution policies using deep reinforcement learning.

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