Displaying 6 results from an estimated 6 matches for "my_pbo".
2017 Nov 21
2
Do I need to transform backtest returns before using pbo (probability of backtest overfitting) package functions?
...rations.
#Following the original paper we can use the Sharpe ratio as
sharpe <- function(x,rf=0.03/252) {
sr <- apply(x,2,function(col) {
er = col - rf
return(mean(er)/sd(er))
})
return(sr)}
#Now that we have the trials matrix we can pass it to the pbo function
#for analysis.
my_pbo <- pbo(m,s=8,f=sharpe,threshold=0)
summary(my_pbo)
Here's the portion i'm curious about:
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...
2017 Nov 21
0
Do I need to transform backtest returns before using pbo (probability of backtest overfitting) package functions?
...Sharpe ratio as
>
> sharpe <- function(x,rf=0.03/252) {
> sr <- apply(x,2,function(col) {
> er = col - rf
> return(mean(er)/sd(er))
> })
> return(sr)}
> #Now that we have the trials matrix we can pass it to the pbo function
> #for analysis.
>
> my_pbo <- pbo(m,s=8,f=sharpe,threshold=0)
>
> summary(my_pbo)
>
> Here's the portion i'm curious about:
>
> 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-sca...
2017 Nov 21
2
Do I need to transform backtest returns before using pbo (probability of backtest overfitting) package functions?
...t;- function(x,rf=0.03/252) {
>> sr <- apply(x,2,function(col) {
>> er = col - rf
>> return(mean(er)/sd(er))
>> })
>> return(sr)}
>> #Now that we have the trials matrix we can pass it to the pbo function
>> #for analysis.
>>
>> my_pbo <- pbo(m,s=8,f=sharpe,threshold=0)
>>
>> summary(my_pbo)
>>
>> Here's the portion i'm curious about:
>>
>> sr_base <- 0
>> mu_base <- sr_base/(252.0)
>> sigma_base <- 1.00/(252.0)**0.5
>> for ( i in 1:n ) {
>> m[,i] =...
2017 Nov 21
0
Do I need to transform backtest returns before using pbo (probability of backtest overfitting) package functions?
...rations.
#Following the original paper we can use the Sharpe ratio as
sharpe <- function(x,rf=0.03/252) {
sr <- apply(x,2,function(col) {
er = col - rf
return(mean(er)/sd(er))
})
return(sr)}
#Now that we have the trials matrix we can pass it to the pbo function
#for analysis.
my_pbo <- pbo(m,s=8,f=sharpe,threshold=0)
summary(my_pbo)
Here's the portion i'm curious about:
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...
2017 Nov 21
0
Do I need to transform backtest returns before using pbo (probability of backtest overfitting) package functions?
...t;> sr <- apply(x,2,function(col) {
>>> er = col - rf
>>> return(mean(er)/sd(er))
>>> })
>>> return(sr)}
>>> #Now that we have the trials matrix we can pass it to the pbo function
>>> #for analysis.
>>>
>>> my_pbo <- pbo(m,s=8,f=sharpe,threshold=0)
>>>
>>> summary(my_pbo)
>>>
>>> Here's the portion i'm curious about:
>>>
>>> sr_base <- 0
>>> mu_base <- sr_base/(252.0)
>>> sigma_base <- 1.00/(252.0)**0.5
>>> f...
2017 Nov 21
1
Do I need to transform backtest returns before using pbo (probability of backtest overfitting) package functions?
...ion(col) {
>>>> er = col - rf
>>>> return(mean(er)/sd(er))
>>>> })
>>>> return(sr)}
>>>> #Now that we have the trials matrix we can pass it to the pbo function
>>>> #for analysis.
>>>>
>>>> my_pbo <- pbo(m,s=8,f=sharpe,threshold=0)
>>>>
>>>> summary(my_pbo)
>>>>
>>>> Here's the portion i'm curious about:
>>>>
>>>> sr_base <- 0
>>>> mu_base <- sr_base/(252.0)
>>>> sigma_base <...