Abstract: A method for creating a realistic agent-based simulation of an over-the-counter market and using data from the real market to calibrate the simulator is provided. The method includes: assigning, to each of a group of agents, a type value that relates to a state of the respective agent; receiving agent-specific data that relates to market-based observations, market-based actions, and market-based rewards; generating a market simulation based on the assigned type values and the received agent-specific data; and calibrating the market simulation based on actual market data.

Abstract: Systems and methods for risk-sensitive reinforcement learning are disclosed. In one embodiment, a method for a method for training a risk-sensitive reinforcement learning policy may include: (1) receiving, from a data source, a plurality of sets of training data for a plurality of time steps; (2) receiving a training budget comprising a plurality of episodes, a risk aversion coefficient, and an end state; and (3) calculating a correction factor using the training data. The correction factor may minimize stochasticity based on a risk aversion coefficient.