Local Risk Decomposition for High-frequency Trading Systems

TL;DR

This paper introduces the local risk decomposition (LRD) framework, a novel method for analyzing the dynamic performance of high-frequency trading strategies, addressing the limitations of traditional global risk metrics.

Contribution

The paper presents the LRD formalism, which captures local performance dynamics and is suitable for optimizing high-frequency trading strategies, unlike traditional global risk measures.

Findings

LRD retains dynamical performance information over time.

LRD is effective for high-frequency trading system analysis.

The framework aids in strategy optimization.

Abstract

In the present work we address the problem of evaluating the historical performance of a trading strategy or a certain portfolio of assets. Common indicators such as the Sharpe ratio and the risk adjusted return have significant drawbacks. In particular, they are global indices, that is they do not preserve any 'local' information about the performance dynamics either in time or for a particular investment horizon. This information could be fundamental for practitioners as the past performance can be affected by the non-stationarity of financial market. In order to highlight this feature, we introduce the 'local risk decomposition' (LRD) formalism, where dynamical information about a strategy's performance is retained. This framework, motivated by the multi-scaling techniques used in complex system theory, is particularly suitable for high-frequency trading systems and can be applied into problems of strategy optimization.