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Steven Pav shabbychef

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shabbychef / monthly_spy_anova.R
Created May 20, 2012 04:12
ANOVA on monthly GSPC returns
# * Sat May 19 2012 21:04:56 Steven E. Pav <shabbychef@gmail.com>
# from https://github.com/milktrader/rfinance2012/blob/94e6dd0452b16697ad10f9bc7e0e7431c95d9020/xts/.Rhistory
require(quantmod)
getSymbols('^GSPC', from='1950-01-01')
G <- monthlyReturn(Cl(GSPC))
G$monnum <- .indexmon(G)
# try ANOVA on the monthly returns
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shabbychef / ipython_magics.py
Created December 12, 2012 21:06
demonstrate R and octave 'magics' in ipython.
# to get the R<->python magic
%load_ext rmagic
# now you can code in R:
%%R
library(survival)
amod <- coxph(Surv(lung$time,lung$status) ~ lung$sex + lung$ph.karno)
print(summary(amod))
# done
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shabbychef / ipython_test.ipynb
Created December 12, 2012 21:22
testing ipython notebook and magic.
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shabbychef / survival_demo.ipynb
Created December 26, 2012 22:44 — forked from anonymous/survival_demo.ipynb
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shabbychef / ipytable_introduction.ipynb
Created January 3, 2013 17:50
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shabbychef / ipy_table_Reference.ipynb
Created January 3, 2013 17:52
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shabbychef / moy_effect.ipynb
Created January 4, 2013 01:38
month of year effects in SPY since 1950, lack thereof.
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shabbychef / moments.jl
Created January 15, 2013 00:50
# * Fri Feb 24 2012 09:09:10 PM Steven E. Pav <shabbychef@gmail.com>
#
# computation of moments and moment-based statistics
#
# see also:
# * http://www.johndcook.com/blog/2008/09/26/comparing-three-methods-of-computing-standard-deviation/
# * http://www.johndcook.com/standard_deviation.html
# * J. Bennett, et. al., 'Numerically Stable, Single-Pass,
# Parallel Statistics Algorithms,' Proceedings of IEEE
# International Conference on Cluster Computing, 2009.
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shabbychef / realworld_hotelling.R
Last active December 12, 2015 06:28
check out SPY, TLT, GLD, AAPL...
require(quantmod)
getSymbols(c("SPY","TLT","GLD","AAPL"))
allofem <- merge(SPY[,"SPY.Adjusted"],TLT[,"TLT.Adjusted"],GLD[,"GLD.Adjusted"],AAPL[,"AAPL.Adjusted"])
mo.pry <- to.monthly(allofem,OHLC=FALSE)
mo.ret <- diff(log(mo.pry))
sub.ret <- mo.ret[(dim(mo.ret)[1]-59):dim(mo.ret)[1],]
mu <- apply(sub.ret,2,mean,na.rm=TRUE)
Sg <- cov(sub.ret,use="complete.obs")
w <- solve(Sg,mu)
n <- dim(sub.ret)[1]
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shabbychef / replicating_google_trends.ipynb
Created June 18, 2013 20:17
iPython notebook to replicate the paper 'Quantifying Trading Behavior in Financial Markets Using Google Trends.'