#  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 *

#  Specify monthly series of interest as ms list:
ms = [ m4infl, m4zero10, m4spx, m4xau, m4usdrtb, m4homepx ]
names = ['Infl', 'Zero10', 'SPX', 'XAU', 'USD', 'Homes']

#  Download into a dictionary:
msd = {}
for i in ms:
    msd[i] = getfred(i)

#  Compute the YoY percentage change:
msdc = {}
for i in ms:
    msdc[i] = pcent( msd[i], 12 )

#  Construct the mega YoY dataframe:
mega = paste( [ msdc[i] for i in ms ] )
#  Give names to the columns for mega:
mega.columns = names

#  Start time given by t0
t0 = '1988'

stats( mega[t0:] )

#  Overlapping YoY percentage change, recently:
boxplot(mega[t0:], 'Assets YoYm')
#  where the red dot represents the latest point.

#  Geometric mean returns, non-overlapping, annualized:
for i in range(len(ms)):
    print names[i], georet(msd[ms[i]][t0:], 12)

#  12-periods ahead forecasts use default alpha and beta values (robust update):
for i in range(len(ms)):
    print names[i] 
    print holtfred(msd[ ms[i]], h=12 )
    #                   ^we use all available data to forecast
    print '------------------------------------'

#  Specify daily series of interest:
ds = [ d4zero10, d4spx, d4xau, d4eurusd, d4usdjpy ]
names = [ 'Zero10', 'SPX', 'XAU', 'EURUSD', 'USDJPY' ]

#  Download into a dictionary:
dsd = {}
for i in ds:
    dsd[i] = getfred(i)

#  Compute the YoY percentage change (overlaping):
dsdc = {}
for i in ds:
    dsdc[i] = pcent( dsd[i], 256 )

#  Construct the YoY dataframe:
dega = paste( [ dsdc[i] for i in ds ] )
#  Give names to the columns for mega:
dega.columns = names

#  Start from the recent:
u0 = '2010-01-01'

#  Overlapping YoY percentage change, recently:
boxplot( dega[u0:], 'Assets YoYd' )

#  Geometric mean returns, non-overlapping, annualized:
for i in range(len(ds)):
    print names[i], georet( dsd[ds[i]][u0:] )

#  Show esp. correlations:
stats( dega[u0:] )