Journal of Investment Strategies

Optimal trading with alpha predictors

Filippo Passerini and Samuel Vazquez

  • We have developed a general framework to think about optimal algorithmic trading using Hamilton-Jacobi-Bellman (HJB) theory.
  • Even tough the HJB equations cannot be solved exactly, we have presented various analytic "recipes" or algorithms which are inspired by general features of the exact solution.
  • Our framework has allowed us to unify, not only daily and intraday alpha signals, but also market and limit orders.


In this paper, 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 a 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 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" that 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.

Sorry, our subscription options are not loading right now

Please try again later. Get in touch with our customer services team if this issue persists.

New to View our subscription options

If you already have an account, please sign in here.

You need to sign in to use this feature. If you don’t have a account, please register for a trial.

Sign in
You are currently on corporate access.

To use this feature you will need an individual account. If you have one already please sign in.

Sign in.

Alternatively you can request an individual account here