Re: [R] Adjusting OHCL data via quantmod

head(merge(close_av, close_av_adj, Ad(AAPL))) #Adjust all data for splits, and split-adjusted dividends (I think) AAPL_a <- adjustOHLC(AAPL, use.Adjusted = TRUE) head(AAPL_a) ### On Thu, Mar 15, 2018 at 4:16 AM, Joe O wrote: > Hello, > > I'm trying to do two things: > -

[R] Adjusting OHCL data via quantmod

Hello, I'm trying to do two things: -1. Ensure that I understand how quantmod adjust's OHLC data -2. Determine how I ought to adjust my data. My overarching-goal is to adjust my OHLC data appropriately to minimize the difference between my backtest returns, and the returns I would get if I was tr

Re: [R] Do I need to transform backtest returns before using pbo (probability of backtest overfitting) package functions?

Fantastic! Thank you for your help, -Joe On Tue, Nov 21, 2017 at 2:17 PM, Eric Berger wrote: > Correct > > Sent from my iPhone > > On 21 Nov 2017, at 22:42, Joe O wrote: > > Hi Eric, > > Thank you, that helps a lot. If I'm understanding correctly, if I’m >

Re: [R] Do I need to transform backtest returns before using pbo (probability of backtest overfitting) package functions?

d(m[,i]) # re-scale >> m[,i] = m[,i] + mu_base - mean(m[,i]) # re-center} >> >> On Tue, Nov 21, 2017 at 2:10 PM, Bert Gunter >> wrote: >> >>> Wrong list. >>> >>> Post on r-sig-finance instead. >>> >>> Cheers, >>>

[R] Do I need to transform backtest returns before using pbo (probability of backtest overfitting) package functions?

Hello, I'm trying to understand how to use the pbo package by looking at a vignette. I'm curious about a part of the vignette that creates simulated returns data. The package author transforms his simulated returns in a way that I'm unfamiliar with, and that I haven't been able to find an explanat