Adaptive trading strategies across liquidity pools

TL;DR

This paper introduces a flexible, adaptive framework for optimal trading across multiple liquidity pools, incorporating market dynamics, partial executions, and advanced computational methods like Bayesian updates, finite difference solutions, and deep reinforcement learning.

Contribution

It presents a novel, comprehensive approach combining stochastic control, Bayesian modeling, and deep learning for multi-venue trading optimization.

Findings

Effective Bayesian parameter updates for changing market conditions

Development of a deep reinforcement learning algorithm for complex trading scenarios

Framework accommodates partial executions, market impact, and hidden liquidity

Abstract

In this article, we provide a flexible framework for optimal trading in an asset listed on different venues. We take into account the dependencies between the imbalance and spread of the venues, and allow for partial execution of limit orders at different limits as well as market orders. We present a Bayesian update of the model parameters to take into account possibly changing market conditions and propose extensions to include short/long trading signals, market impact or hidden liquidity. To solve the stochastic control problem of the trader we apply the finite difference method and also develop a deep reinforcement learning algorithm allowing to consider more complex settings.