Jun 19, 2023 · This work is about optimal order execution, where a large order is split into several small orders to maximize the implementation shortfall.

7 days ago · This study investigates the development of an optimal execution strategy through reinforcement learning, aiming to determine the most effective ...

Abstract. We present the first large-scale empirical application of reinforcement learning to the important problem of optimized trade execution.

This example uses deep reinforcement learning to design and train two deep Q-network (DQN) agents for optimal trade execution. One DQN agent is for sell trades ...

The overarching goal of an optimal execution algorithm is to place limit orders in order to buy/sell a set number of units of an asset over a fixed time horizon ...

2 days ago · PDF | This study investigates the development of an optimal execution strategy through reinforcement learning, aiming to determine the most ...

7 days ago · This study investigates the development of an optimal execution strategy through reinforcement learning, aiming to determine the most ...

In this section, we will formulate this intuition more formally and construct a price prediction approach to optimal execution via supervised learning.

Jul 4, 2024 · This post makes a connection between optimal trade execution and dynamic pricing by using reinforcement learning to solve both problems.

5 days ago · We develop a reinforcement learning algorithm for optimal execution problems. Using Chinese stock market data, we find that the intraday ...