Determining Optimal Trading Rules without Backtesting

TL;DR

This paper introduces a method to determine optimal trading rules without backtesting, addressing overfitting issues, and provides empirical evidence for solutions in a specific stochastic price model.

Contribution

It proposes a novel procedure for finding optimal trading rules without backtesting, supported by empirical analysis of a discrete Ornstein-Uhlenbeck process.

Findings

Existence of optimal solutions for specific price models

Numerical computation methods for OTRs demonstrated

Empirical evidence supports the conjecture of solution existence

Abstract

Calibrating a trading rule using a historical simulation (also called backtest) contributes to backtest overfitting, which in turn leads to underperformance. In this paper we propose a procedure for determining the optimal trading rule (OTR) without running alternative model configurations through a backtest engine. We present empirical evidence of the existence of such optimal solutions for the case of prices following a discrete Ornstein-Uhlenbeck process, and show how they can be computed numerically. Although we do not derive a closed-form solution for the calculation of OTRs, we conjecture its existence on the basis of the empirical evidence presented.