#  NOTEBOOK settings and system details:   [00-tpl v14.09.28]

#  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 will disable autoreload.
#  Generate plots inside notebook:
%matplotlib inline

#  DISPLAY options
from IPython.display import Image 
#  e.g. Image(filename='holt-winters-equations.png', embed=True)
from IPython.display import YouTubeVideo
#  e.g. YouTubeVideo('1j_HxD4iLn8')
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_fred import *
from yi_plot import *

#  Get various inflation and bond statistics:
ts = {}
for i in ml_infl + ml_long:
    ts[i] = getfred(i)

#  Collection of inflation levels
tsinf = [ ts[i] for i in ml_infl ]

#  Make a dataframe consists of those inf levels:
inf_levels = paste( tsinf )

#  Label the column names:
inf_levels.columns = ['CPI', 'CPIc', 'PCE', 'PCEc']

#  We are interested in year-over-year inflation rates, expressed in percent:
inf = pcent( inf_levels, 12 ).dropna()

boxplot( inf, 'Inflation' )

stats(inf)

inf_av = todf(( inf['CPI'] + inf['CPIc'] + inf['PCE'] + inf['PCEc'] ) / 4 )

stats( inf_av )

#  The shortest of our time series under consideration, m4tips10, 
#  starts at 2003-01-01, so let
start = '2003-01-01'

plotdf( inf_av[start:], 'Inflation mean' )

bei = todf( ts[m4bond10] - ts[m4tips10] )

plotdf(bei[start:], 'Inflation BEI')

tail(bei)

bei_inf_av = todf((bei + inf_av) / 2) 

plotdf(bei_inf_av[start:], 'Inflation BEI av')

tail(bei_inf_av)

gold_daily = getfred( d4xau )
gold = monthly(gold_daily)
#  Monthly gold data since 1968-04-01

#  Compute annualized returns:
gold_ret = pcent(gold, 12)

plotdf( gold_ret[start:] )

#  Conversions to dataframe with label before paste:
dfa = todf( inf_av, 'Iav')
dfb = todf( bei, 'BEI' )
dfc = todf( bei_inf_av, 'BEI_Iav')
dfd = todf( gold_ret, 'Gold' )

#  Mother of all annualized inflation rates:
infall = paste( [inf, dfa, dfb, dfc, dfd] )

#  Correlation matrix going back to 2003-01-01 (start of BEI)
cormatrix(infall)

#  Most recent values:
tail(infall)

#  Let's see if our two different methods agree:
infl = getfred(m4infl)

inflrate = pcent(infl, 12)

stat2( dfa['Iav'], inflrate['Y'] )

#  Let's forecast 12 periods ahead:
holtfred( infl, 12 )