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How to read Excel into R and execute hurst(x)?


How to read Excel into R and execute hurst(x)?

By : Jimmy Alcala
Date : November 15 2020, 06:54 AM
it fixes the issue It looks like the link you gave points to an old version of the package; the hurst function no longer exists in the package. The current documentation is here; try
code :
library("gdata")  ## my favorite XLS-reader
x <- read.xls("hurstdata.xlsx",header=FALSE)
hurstexp(x[[1]])
## Simple R/S Hurst estimation:         0.5488892 
## Corrected R over S Hurst exponent:   0.6172157 
## Empirical Hurst exponent:            0.6796684 
## Corrected empirical Hurst exponent:  0.6438013 
## Theoretical Hurst exponent:          0.5316731 
R Under development (unstable) (2014-09-17 r66626)
Platform: i686-pc-linux-gnu (32-bit)

other attached packages:
[1] pracma_1.7.9 gdata_2.13.3


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Hurst exponent with R

Hurst exponent with R


By : Elias Mazzocco
Date : March 29 2020, 07:55 AM
it fixes the issue I would like calculate the Hurst exponent with R. Is there a library or built in function that can do this? any suggestion will be appreciated (even weblinks to references). thanx , (Converted from a comment.)
This isn't exactly an answer, but
code :
install.packages("sos")
library("sos")
findFn("hurst exponent")
How to Execute the user defined python methods using variables, read from Excel sheet

How to Execute the user defined python methods using variables, read from Excel sheet


By : LJP-TW
Date : March 29 2020, 07:55 AM
I think the issue was by ths following , I tried using the globals() keyword and it worked :)
Where User_Keyword variable will evaluate to a string equivalent to the method name in your package.
How to automate read an attachment from an outlook email, execute some Excel/VBA logic on it, then perform FTP upload?

How to automate read an attachment from an outlook email, execute some Excel/VBA logic on it, then perform FTP upload?


By : user2094321
Date : March 29 2020, 07:55 AM
wish helps you I'd use pentaho data integration for everything after the file is saved down. It is an open source ETL tool available here: http://community.pentaho.com/projects/data-integration/
It can be listen on a particular directory, and when a file appears, it can process the file, via either importing it & transforming inside the ETL, or running a shell script (which might grab excel to do the processing), it can move, zip, combine files, and it can upload to FTP server.
Talend Open Data Integration: Read expressions from excel file and then execute them in tMap

Talend Open Data Integration: Read expressions from excel file and then execute them in tMap


By : Bhavesh Nakum
Date : March 29 2020, 07:55 AM
hope this fix your issue What I would usually do is to have before the tFlowToIerate a tJavaRow Component where I would assign the values into context variables and use them in the tPostgresqlInput directly from the context
Hurst Exponent in python

Hurst Exponent in python


By : Santiago Perez
Date : March 29 2020, 07:55 AM
it helps some times I'm just as confused. I don't understand where the sqrt of std comes from either, and have spent 3 days trying to figure it out. In the end I noticed QuantStart credits Dr Tom Starke who uses a slightly different code. Dr Tom Starke credits Dr Ernie Chan, and going to his blog. I was able to find enough information to put together my own code from his principles. This doesn't use sqrt, uses variance instead of std and uses a 2.0 divisor at the end instead of a 2.0 multiplier. In the end, it seems to give the same results as the quantstart code you post, but I am able to understand it from first principles, which I guess is important. I put together a Jupyter Notebook which makes it clearer, but I'm not sure if I can post that here, so I will try to explain as best I can here. Code is pasted first, then an explanation.
code :
lags = range(2,100)
def hurst_ernie_chan(p):

    variancetau = []; tau = []

    for lag in lags: 

        #  Write the different lags into a vector to compute a set of tau or lags
        tau.append(lag)

        # Compute the log returns on all days, then compute the variance on the difference in log returns
        # call this pp or the price difference
        pp = subtract(p[lag:], p[:-lag])
        variancetau.append(var(pp))

    # we now have a set of tau or lags and a corresponding set of variances.
    #print tau
    #print variancetau

    # plot the log of those variance against the log of tau and get the slope
    m = polyfit(log10(tau),log10(variancetau),1)

    hurst = m[0] / 2

    return hurst
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