Displaying 6 results from an estimated 6 matches for "sr_base".
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csr_base
2017 Nov 21
2
Do I need to transform backtest returns before using pbo (probability of backtest overfitting) package functions?
...here each column
#represents a trial and each 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 durin...
2017 Nov 21
0
Do I need to transform backtest returns before using pbo (probability of backtest overfitting) package functions?
...ANNUAL std deviation SIGMA_A, both stated in decimal.
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 lis...
2017 Nov 21
2
Do I need to transform backtest returns before using pbo (probability of backtest overfitting) package functions?
...ed in
> decimal.
> 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 gm...
2017 Nov 21
0
Do I need to transform backtest returns before using pbo (probability of backtest overfitting) package functions?
...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 G...
2017 Nov 21
1
Do I need to transform backtest returns before using pbo (probability of backtest overfitting) package functions?
...; 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...
2017 Nov 21
0
Do I need to transform backtest returns before using pbo (probability of backtest overfitting) package functions?
...here each column
#represents a trial and each 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 durin...