Dynamic Weight Optimization for Double Linear Policy: A Stochastic Model Predictive Control Approach

TL;DR

This paper introduces a stochastic model predictive control framework to optimize dynamic weights in the Double Linear Policy, enhancing risk-adjusted returns and drawdown control.

Contribution

It formulates weight selection as a receding-horizon control problem with an analytical gradient, enabling efficient optimization of non-convex objectives.

Findings

Dynamic approach improves risk-adjusted performance.

Closed-loop optimization outperforms static and preset policies.

Analytical gradient enables efficient non-convex optimization.

Abstract

The Double Linear Policy (DLP) framework guarantees a Robust Positive Expectation (RPE) under optimized constant-weight designs or admissible prespecified time-varying policies. However, the sequential optimization of these time-varying weights remains an open challenge. To address this gap, we propose a Stochastic Model Predictive Control (SMPC) framework. We formulate weight selection as a receding-horizon optimal control problem that explicitly maximizes risk-adjusted returns while enforcing survivability and predicted positive expectation constraints. Notably, an analytical gradient is derived for the non-convex objective function, enabling efficient optimization via the L-BFGS-B algorithm. Empirical results demonstrate that this dynamic, closed-loop approach improves risk-adjusted performance and drawdown control relative to constant-weight and prescribed time-varying DLP baselines.