profitHurdle: Profit Hurdle function - A Minimum Profitability Method for...

In marsanul/quantstrat: Quantitative Strategy Model Framework

Description

Based on their 2015 JPM paper "Backtesting", Campbell Harvey and Yan Liu (HL) propose and demonstrate three methods of adjusting for potential multiple testing bias. Using their model they propose haircuts for Sharpe ratios returned by trading strategies (see haircutSharpe). HL pose another way of viewing the problem is to ascertain a minimum average monthly return for a given significance level. This is replicated in quantstrat with the profitHurdle function.

Usage

profitHurdle(portfolios, ..., strategy = NULL, trials = NULL,
  alpha_sig = 0.05, vol_annual = 0.1, RHO = 0.2, audit = NULL,
  env = .GlobalEnv)

Arguments

portfolios	string name of portfolio, or optionally a vector of portfolios, see DETAILS
...	any other passthrough parameters
strategy	optional strategy specification that would contain more information on the process, default NULL
trials	optional number of trials,default NULL
alpha_sig	Significance level e.g. 0.05 ie. 5%, default 0.05
vol_annual	Annual return volatility e.g. 0.10 ie. 10%, default 0.10
RHO	Assumed average correlation, default 0.2
audit	optional audit environment containing the results of parameter optimization or walk forward, default NULL
env	optional environment to find market data in, if required

Value

an object of type profitHurdle containing:

• Significance Level: the significance level used to determine the minimum required average monthly return

• Number of Monthly Observations: the number of monthly observations in the strategy, converted from one of either daily, weekly, monthly, quarterly or yearly

• Annualized Return Volatility: the annualized volatility of the strategy returns

• Assumed Number of Tests: the number of tests assumed and needed to account for in multiple testing adjustment

• Assumed Average Correlation: assumed average level of correlation among strategy returns

• Independent: unadjusted return hurdle, implying single test significance

• Bonferroni: the Bonferroni adjusted return hurdle for the given level of significance

• Holm: the Holm adjusted return hurdle for the given level of significance

• BHY: the BHY adjusted return hurdle for the given level of significance

• Average for Multiple Tests: the average multiple testing return hurdle for the given level of significance

Author(s)

Jasen Mackie, Brian G. Peterson

References

Harvey, Campbell R. and Yan Liu. 2015. Backtesting The Journal of Portfolio Management. 41:1 pp. 13-28.

Harvey, Campbell R., Yan Liu, and Heqing Zhu. 2016. "... and the cross-section of expected returns." The Review of Financial Studies 29, no. 1 (2016): 5-68.

Mackie, Jasen. 2016. R-view: Backtesting - Harvey & Liu (2015). https://opensourcequant.wordpress.com/2016/11/17/r-view-backtesting-harvey-liu-2015/

See Also

SharpeRatio.haircut

marsanul/quantstrat documentation built on Aug. 10, 2020, 12:45 a.m.