A posteriori multi-stage optimal trading under transaction costs and a diversification constraint

TL;DR

This paper introduces a simple, graph-based method for multi-stage optimal trading with transaction costs and diversification constraints, aimed at labeling financial data for machine learning.

Contribution

It proposes a novel a posteriori approach for multi-variate, multi-stage trading optimization considering various trading constraints.

Findings

Method effectively handles multiple assets and constraints.

Quantitative evaluation on real-world data demonstrates practical viability.

Supports preparatory labeling for supervised machine learning.

Abstract

This paper presents a simple method for a posteriori (historical) multi-variate multi-stage optimal trading under transaction costs and a diversification constraint. Starting from a given amount of money in some currency, we analyze the stage-wise optimal allocation over a time horizon with potential investments in multiple currencies and various assets. Three variants are discussed, including unconstrained trading frequency, a fixed number of total admissable trades, and the waiting of a specific time-period after every executed trade until the next trade. The developed methods are based on efficient graph generation and consequent graph search, and are evaluated quantitatively on real-world data. The fundamental motivation of this work is preparatory labeling of financial time-series data for supervised machine learning.