Optimal Trading with Alpha Predictors

TL;DR

This paper develops a stochastic optimization framework for optimal trading with alpha predictors, incorporating linear costs and impact, and proposes practical algorithms that improve profit and loss through strategic order placement.

Contribution

It introduces explicit, simple analytical recipes for trading zones combining market and limit orders, enhancing practical implementation of optimal trading strategies.

Findings

Presence of no-trading and market-making zones due to linear costs

Algorithms improve Profit and Losses in Monte Carlo simulations

Practical recipes approximate full optimization effectively

Abstract

We study the problem of optimal trading using general alpha predictors with linear costs and temporary impact. We do this within the framework of stochastic optimization with finite horizon using both limit and market orders. Consistently with other studies, we find that the presence of linear costs induces a no-trading zone when using market orders, and a corresponding market-making zone when using limit orders. We show that, when combining both market and limit orders, the problem is further divided into zones in which we trade more aggressively using market orders. Even though we do not solve analytically the full optimization problem, we present explicit and simple analytical recipes which approximate the full solution and are easy to implement in practice. We test the algorithms using Monte Carlo simulations and show how they improve our Profit and Losses.