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: A method for providing a synthetic neural data model is disclosed. The method includes generating a model that simulates an electronic communication network; appending agents to the model, the agents relating to a software component that sends orders to the model based on a predetermined timestep; assigning a fixed grid to the model, the fixed grid including a tick size that relates to a fixed granularity; calibrating each of the agents by using a calibration data set; inputting the model and historical book data to a neural network; and training, via the neural network, a neural network extension of the model by using an optimizer.

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.