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
By : LJPTW
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?
By : user2094321
Date : March 29 2020, 07:55 AM

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
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

