A Generalized Framework for Simultaneous Long-Short Feedback Trading

TL;DR

This paper introduces a generalized simultaneous long-short trading strategy with different parameters for each side, optimizing control parameters based on historical data, and demonstrates its superior performance over traditional strategies through extensive testing.

Contribution

It proposes the Generalized SLS (GSLS) framework with parameter optimization techniques and validates its effectiveness on real stock data, enhancing practical applicability.

Findings

Optimization improves trading performance significantly.

GSLS outperforms traditional SLS in empirical tests.

Flexible parameters enable better adaptation to market conditions.

Abstract

We present a generalization of the Simultaneous Long-Short (SLS) trading strategy described in recent control literature wherein we allow for different parameters across the short and long sides of the controller; we refer to this new strategy as Generalized SLS (GSLS). Furthermore, we investigate the conditions under which positive gain can be assured within the GSLS setup for both deterministic stock price evolution and geometric Brownian motion. In contrast to existing literature in this area (which places little emphasis on the practical application of SLS strategies), we suggest optimization procedures for selecting the control parameters based on historical data, and we extensively test these procedures across a large number of real stock price trajectories (495 in total). We find that the implementation of such optimization procedures greatly improves the performance compared with fixing control parameters, and, indeed, the GSLS strategy outperforms the simpler SLS strategy in general.