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Optimal Trading Rules Without Backtesting

Marcos Lopez de Prado, Guggenheim Partners, LLC, Lawrence Berkeley National Laboratory, Harvard University - RCC


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MARCOS LOPEZ DE PRADO, Guggenheim Partners, LLC, Lawrence Berkeley National Laboratory, Harvard University - RCC
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Calibrating a trading rule using a historical simulation (also called backtest) contributes to backtest overfitting, which in turn leads to underperformance. 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.

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