getExpectedMaxSR: Expected Maximum Sharpe Ratio

In barryquinn1/ATI: Algorithmic Trading and Investment

Description

Calculate the theoretical Maximum Sharpe Ratio according to the False Discovery theorem first proposed by Bailey et al. 2014. Its returns values of the expected maximum Sharpe ratio controlling for the selection bias under multiple testing (SBuMT).

Usage

getExpectedMaxSR(nTrails, meanSR = 0, stdSR = 1)

Arguments

nTrails	double the number of trails (note that 1 trail will produce a -Inf results)
meanSR	mean of the Sharpe ratio of the null false strategy
stdSR	standard deviation of the Sharpe ratio of the null false strategy

Value

numeric

References

Bailey, D., J. Borwein, M. López de Prado, and J. Zhu 2014: “Pseudo- mathematics and financial charlatanism: The effects of backtest overfitting on out-of-sample performance.” Notices of the American Mathematical Society, Vol. 61, No. 5, pp. 458–471. Available at http://ssrn.com/abstract=2308659

Examples

getExpectedMaxSR(nTrails=100)

barryquinn1/ATI documentation built on May 10, 2021, 10:47 a.m.