#  NOTEBOOK settings and system details:   [00-tpl v14.12.21]

#  Assume that the backend is LINUX (our particular distro is Ubuntu, running bash shell):
print '\n ::  TIMESTAMP of last notebook execution:'
!date
print '\n ::  IPython version:'
!ipython --version

#  Automatically reload modified modules:
%load_ext autoreload
%autoreload 2
#           0 disables autoreload.
#  Generate plots inside notebook:
%matplotlib inline

#  DISPLAY options
from IPython.display import Image 
#  e.g. Image(filename='holt-winters-equations.png', embed=True) # url= also works
from IPython.display import YouTubeVideo
#  e.g. YouTubeVideo('1j_HxD4iLn8', start='43', width=600, height=400)
from IPython.display import HTML # useful for snippets
#  e.g. HTML('<iframe src=http://en.mobile.wikipedia.org/?useformat=mobile width=700 height=350></iframe>')

import pandas as pd
print '\n ::  pandas version:'
print pd.__version__
#      pandas DataFrames are represented as text by default; enable HTML representation:
#      [Deprecated: pd.core.format.set_printoptions( notebook_repr_html=True ) ]
pd.set_option( 'display.notebook_repr_html', False )

#  MATH display, use %%latex, rather than the following:
#                from IPython.display import Math
#                from IPython.display import Latex

print '\n ::  Working directory (set as $workd):'
workd, = !pwd
print workd + '\n'

from yi_1tools import *
from yi_plot import *
from yi_timeseries import *
from yi_fred import *

#  Retreive daily data:
brent = getfred( d4brent )
wti   = getfred( d4wti )

#  BoW: Brent over WTI spread:
bow = todf( brent - wti )

plotfred( bow )

#  Let's look at BoW trend since 1999:
plotfred( trend( bow ) )

#  Since the slope uses daily data, we annualize it:
0.0014023 * 256

#  Set wtbrent, the primary weight for OIL:
wtbrent = 0.50
#         0.50 represents the mean
#  ==================================
wtwti   = 1 - wtbrent
oil = todf( (wtbrent * brent) + (wtwti * wti) )

plotfred( oil )

stats( oil )

tmin = '1998-12-10'

georet( oil[tmin:] )

oiltrend = trend( oil[tmin:] )
plotfred( oiltrend )

plotfred( detrendpc(oil[tmin:]) )

#  So discount 40% off the trend, 
#  to illustrate statistical downside:
tailvalue( oiltrend ) * 0.60

#  We change the sampling frequency to match inflation data:
oilmth = monthly( oil )
defl = getfred( m4defl )
oildefl = todf( oilmth * defl )

plotfred( oildefl )

#  rtb is the real trade-weighted USD index,
#      computed monthly by the Federal Reserve.
rtb = getfred( m4usdrtb )
oilrtb = todf( oil / rtb )

#  cf. "oil" in dollars
plotfred( oilrtb )
#         ^in index units

#  Do the regression:
stat2( bow['Y'], oil['Y'] )