fast.jl

using DataFrames
using DataFramesMeta
using GLM


using Base.Collections

function f(ds)
	DPY = 252  ## days per year
	NWINDOW = 126  ## can be smaller or larger than 252
	FMCOL = 1
	DAYCOL = 2

	# create two empty arrays to store b_ols and sd_ols value
	b_ols = DataArray(Float64, size(ds)[1])
	sd_ols = DataArray(Float64, size(ds)[1])

	for i = 1:size(ds)[1]
		thisDay = ds[i, :day] ## Julia DataFrame way of accessing data, in R: ds$day[i]
		if mod(thisDay, DPY) != 0
			continue
		end
		if thisDay < DPY
			continue
		end
		thisFm = ds[i, :fm]
		# Find the rows that fits with given Fm
		cands_fm = searchsorted(ds[FMCOL].data, thisFm)

		# Find the last index where the day is >= thisDay - 1
		start_days = searchsortedfirst(ds[DAYCOL].data, thisDay - NWINDOW, 
			                          cands_fm[1], cands_fm[end], Forward)

		# Find the first index where day is < thisDay - 1
		end_days = searchsortedlast(ds[DAYCOL].data, thisDay - 1, 
			                          cands_fm[1], cands_fm[end], Forward)

		# Extract subset from data
		dataSubset = ds[start_days:end_days, :]

		olsReg = fit(LinearModel, xr ~ xm, dataSubset) ## OLS from package GLM
		b_ols[i] = coef(olsReg)[2] ## returns the OLS coefficients
		sd_ols[i] = stderr(olsReg)[2] ## returns the OLS coefficients' standard error
		print(i, " ")
	end

	ds[:b_ols] = b_ols
	ds[:sd_ols] = sd_ols

	print("\nOLS Beta Regressions are Done\n")

	return ds

end

ds = readtable("xri.csv")  ## a sample data set

@time f(ds);