search for: sigma_base

Displaying 6 results from an estimated 6 matches for "sigma_base".

2017 Nov 21
2
Do I need to transform backtest returns before using pbo (probability of backtest overfitting) package functions?
...trial has the same length T. This example #is random data so the backtest should be overfit.` set.seed(765) n <- 100 t <- 2400 m <- data.frame(matrix(rnorm(n*t),nrow=t,ncol=n, dimnames=list(1:t,1:n)), check.names=FALSE) sr_base <- 0 mu_base <- sr_base/(252.0) sigma_base <- 1.00/(252.0)**0.5 for ( i in 1:n ) { m[,i] = m[,i] * sigma_base / sd(m[,i]) # re-scale m[,i] = m[,i] + mu_base - mean(m[,i]) # re-center} #We can use any performance evaluation function that can work with the #reassembled sub-matrices during the cross validation iterations. #Following the...
2017 Nov 21
2
Do I need to transform backtest returns before using pbo (probability of backtest overfitting) package functions?
...random data from a distribution of returns with ANNUAL mean MU_A and ANNUAL std deviation SIGMA_A, both stated in decimal. The equivalent DAILY returns would have mean MU_D = MU_A / 252 and standard deviation SIGMA_D = SIGMA_A/SQRT(252). He calls MU_D by the name mu_base and SIGMA_D by the name sigma_base. His loop now converts the random numbers in his matrix so that each column has mean MU_D and std deviation SIGMA_D. HTH, Eric On Tue, Nov 21, 2017 at 2:33 PM, Eric Berger <ericjberger at gmail.com> wrote: > Hi Joe, > The centering and re-scaling is done for the purposes of his ex...
2017 Nov 21
0
Do I need to transform backtest returns before using pbo (probability of backtest overfitting) package functions?
...ecimal. The equivalent DAILY Then he does two steps: (1) generate a matrix of random values from the N(0,1) distribution. (2) convert them to DAILY After initializing the matrix with random values (from N(0,1)), he now wants to create a series of DAILY sr_base <- 0 mu_base <- sr_base/(252.0) sigma_base <- 1.00/(252.0)**0.5 for ( i in 1:n ) { m[,i] = m[,i] * sigma_base / sd(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 <bgunter.4567 at gmail.com> wrote: > Wrong list. > > Post on r-sig-finance instead. >...
2017 Nov 21
0
Do I need to transform backtest returns before using pbo (probability of backtest overfitting) package functions?
...on of returns > with ANNUAL mean MU_A and ANNUAL std deviation SIGMA_A, both stated in > decimal. > The equivalent DAILY returns would have mean MU_D = MU_A / 252 and > standard deviation SIGMA_D = SIGMA_A/SQRT(252). > > He calls MU_D by the name mu_base and SIGMA_D by the name sigma_base. > > His loop now converts the random numbers in his matrix so that each column > has mean MU_D and std deviation SIGMA_D. > > HTH, > Eric > > > > On Tue, Nov 21, 2017 at 2:33 PM, Eric Berger <ericjberger at gmail.com> > wrote: > >> Hi Joe, >> T...
2017 Nov 21
1
Do I need to transform backtest returns before using pbo (probability of backtest overfitting) package functions?
...ion of returns with ANNUAL mean MU_A and ANNUAL std deviation SIGMA_A, both stated in decimal. >> The equivalent DAILY returns would have mean MU_D = MU_A / 252 and standard deviation SIGMA_D = SIGMA_A/SQRT(252). >> >> He calls MU_D by the name mu_base and SIGMA_D by the name sigma_base. >> >> His loop now converts the random numbers in his matrix so that each column has mean MU_D and std deviation SIGMA_D. >> >> HTH, >> Eric >> >> >> >>> On Tue, Nov 21, 2017 at 2:33 PM, Eric Berger <ericjberger at gmail.com> wrot...
2017 Nov 21
0
Do I need to transform backtest returns before using pbo (probability of backtest overfitting) package functions?
...trial has the same length T. This example #is random data so the backtest should be overfit.` set.seed(765) n <- 100 t <- 2400 m <- data.frame(matrix(rnorm(n*t),nrow=t,ncol=n, dimnames=list(1:t,1:n)), check.names=FALSE) sr_base <- 0 mu_base <- sr_base/(252.0) sigma_base <- 1.00/(252.0)**0.5 for ( i in 1:n ) { m[,i] = m[,i] * sigma_base / sd(m[,i]) # re-scale m[,i] = m[,i] + mu_base - mean(m[,i]) # re-center} #We can use any performance evaluation function that can work with the #reassembled sub-matrices during the cross validation iterations. #Following the...