Efficient Computation of Optimal Trading Strategies

TL;DR

This paper introduces efficient algorithms for computing ex-post optimal trading strategies based on return series, optimizing for total return, Sterling ratio, and Sharpe ratio, aiding market analysis and predictive modeling.

Contribution

It provides novel algorithms for constructing ex-post optimal trading strategies, enhancing analysis, benchmarking, and learning in financial markets.

Findings

Algorithms efficiently compute optimal strategies

Strategies optimize for multiple performance metrics

Applications include market analysis and predictive systems

Abstract

Given the return series for a set of instruments, a \emph{trading strategy} is a switching function that transfers wealth from one instrument to another at specified times. We present efficient algorithms for constructing (ex-post) trading strategies that are optimal with respect to the total return, the Sterling ratio and the Sharpe ratio. Such ex-post optimal strategies are useful analysis tools. They can be used to analyze the "profitability of a market" in terms of optimal trading; to develop benchmarks against which real trading can be compared; and, within an inductive framework, the optimal trades can be used to to teach learning systems (predictors) which are then used to identify future trading opportunities.