#!/usr/bin/env python
# coding: utf-8

# In[1]:


import pandas as pd
import pandas_datareader.data as web
import pandas_datareader as pdr
import os
import numpy as np
from bokeh.io import output_notebook
from bokeh.plotting import figure, show
from bokeh.palettes import Spectral8 # 色を考えなくても良い　地味に素敵 https://bokeh.pydata.org/en/latest/docs/reference/palettes.html
from datetime import datetime
from datetime import timedelta
get_ipython().run_line_magic('matplotlib', 'inline')
output_notebook()


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start = datetime(2008, 9, 11)
end = datetime(2018, 2, 9)
mstar_spx = web.DataReader('SPX', 'morningstar', start, end)


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mstar_spx.head()


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mstar_spx.tail()


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mstar_spx2 = web.DataReader('SPX', 'morningstar', '1900-01-01')


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mstar_spx2.head()


# ### 株価以外のデータ・ソース
# * Enigma / https://public.enigma.com/    
# API KEYがいるみたいなのでパス   
# * FRED / https://fred.stlouisfed.org/   
# セントルイス連銀が出しているデータ
# * WORLD BANK / https://data.worldbank.org/
# * OECD / http://stats.oecd.org/
# * Eurostats / http://ec.europa.eu/eurostat/   
#    
# 色々とあります。データありすぎ問題・・・

# In[7]:


# FRED のデータを取得してみる。注目されている米国金利 FF、２年、１０年、３０年
start_f = datetime(1980,1,1)
end_f = datetime(2018,2,9)
# FF https://fred.stlouisfed.org/series/FF ２年 https://fred.stlouisfed.org/series/GS2
# 10年　https://fred.stlouisfed.org/series/DGS10 30年 https://fred.stlouisfed.org/series/DGS30
# Dがつくとdailyのよう。つけないとmonthly
f_ff = web.DataReader('FF', 'fred', start_f, end_f) 
f_2 = web.DataReader('GS2', 'fred', start_f, end_f)
f_10 = web.DataReader('GS10', 'fred', start_f, end_f)
f_30 = web.DataReader('GS30', 'fred', start_f, end_f)


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f_2.plot()


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f_10.plot()


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f_30.plot()


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f_ff.plot()


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# 負債の状況 自動車ローン https://fred.stlouisfed.org/series/MVLOAS
f_car = web.DataReader('MVLOAS', 'fred', start_f, end_f)


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f_car.plot()


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# 不動産ローン
f_re = web.DataReader('RHEACBW027SBOG', 'fred', start_f, end_f)
f_re.plot()


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# 全産業ローン
f_loan = web.DataReader('BUSLOANS', 'fred', start_f, end_f)
f_loan.plot()


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# FED TOTAL ASSET 
f_fed_asset = web.DataReader('WALCL', 'fred', start_f, end_f)
f_fed_asset.plot()


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f_fed_asset['2014':].plot()


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# BOJ TOTAL ASSET
f_boj = web.DataReader('JPNASSETS', 'fred', start_f, end_f)
f_boj.plot()


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# World Bank
from pandas_datareader import wb


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mat = wb.search('elect')


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mat


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dat = wb.download(indicator='EG.ELC.ACCS.UR.ZS')


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dat


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eu = web.DataReader('teimf060', 'eurostat', start_f)


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eu


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eu2 = web.DataReader('teimf050', 'eurostat', '1900')


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eu2


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eu2.columns


# ## pandas_datareaderを使って分析を作ってみる
# * FRB議長が交代する時に、新議長を試すために市場が荒れることがよくあるそうな。
# * 幾つかの指標を使ってみてみます！
# * 参考　　wikipedia 連邦準備制度　https://ja.wikipedia.org/wiki/%E9%80%A3%E9%82%A6%E6%BA%96%E5%82%99%E5%88%B6%E5%BA%A6

# In[29]:


chair_p = {}
chair_p = pd.DataFrame(chair_p)
chair_p['name'] = ['Charles Hamlin', 'William Harding', 'Daniel Crissinger', 'Roy Young', 'Eugene Meyer', \
                  'Eugene Black', 'Marriner Eccles', 'Thomas McCabe', 'William Martin', 'Authur Burns', \
                  'William Miller', 'Paul Volker', 'Alan Greenspan', 'Ben Bernanke', 'Janet Yellen', 'Jerome Powell']
chair_p['start'] = [datetime(1914,8,10), datetime(1916,8,10), datetime(1923,5,1), datetime(1927,10,4), \
                   datetime(1930,9,16), datetime(1933,5,19), datetime(1934,11,15), datetime(1948,4,15), \
                   datetime(1951,4,2), datetime(1970,2,2), datetime(1978,3,8), datetime(1979,8,6), \
                   datetime(1987,8,11), datetime(2006,2,1), datetime(2014,2,3), datetime(2018,2,5)]
chair_p['end'] = [datetime(1916,8,10), datetime(1922,8,9), datetime(1927,9,15), datetime(1930,8,30), \
                 datetime(1933,5,10),datetime(1934,8,15), datetime(1948,1,31), datetime(1951,3,31), \
                 datetime(1970,1,31), datetime(1978,1,31), datetime(1979,8,6), datetime(1987,8,11), \
                 datetime(2006,1,31), datetime(2014,1,31), datetime(2018,2,5), '-']


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chair_p


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chair_p['before_365'] = 0
chair_p['after_365'] = 0


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for i in range(len(chair_p)):
    a = chair_p.iloc[i, 1] - timedelta(days=365)
    b = chair_p.iloc[i, 1] + timedelta(days=365)
    chair_p.iloc[i, 3] = '{}-{}'.format(a.year, a.month)
    chair_p.iloc[i, 4] = '{}-{}'.format(b.year, b.month)


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chair_p


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spx = web.DataReader('^SPX', 'stooq')
spx = spx.sort_index()


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spx.loc['2014-02-03']


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spx.head()


# ## S&P500指数の1941年からの算出なのになんてこった！！
# https://ja.wikipedia.org/wiki/S%26P_500

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spx = spx['1941':]


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spx.head()


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spx7 = spx[chair_p.iloc[7,3]:chair_p.iloc[7,4]] / spx.loc[chair_p.iloc[7,1], 'Close'] * 100
spx8 = spx[chair_p.iloc[8,3]:chair_p.iloc[8,4]] / spx.loc[chair_p.iloc[8,1], 'Close'] * 100
spx9 = spx[chair_p.iloc[9,3]:chair_p.iloc[9,4]] / spx.loc[chair_p.iloc[9,1], 'Close'] * 100
spx10 = spx[chair_p.iloc[10,3]:chair_p.iloc[10,4]] / spx.loc[chair_p.iloc[10,1], 'Close'] * 100
spx11 = spx[chair_p.iloc[11,3]:chair_p.iloc[11,4]] / spx.loc[chair_p.iloc[11,1], 'Close'] * 100
spx12 = spx[chair_p.iloc[12,3]:chair_p.iloc[12,4]] / spx.loc[chair_p.iloc[12,1], 'Close'] * 100
spx13 = spx[chair_p.iloc[13,3]:chair_p.iloc[13,4]] / spx.loc[chair_p.iloc[13,1], 'Close'] * 100
spx14 = spx[chair_p.iloc[14,3]:chair_p.iloc[14,4]] / spx.loc[chair_p.iloc[14,1], 'Close'] * 100
spx15 = spx[chair_p.iloc[15,3]:chair_p.iloc[15,4]] / spx.loc[chair_p.iloc[15,1], 'Close'] * 100


# In[40]:


spx7.index = spx7.index - chair_p.iloc[7,1]
spx8.index = spx8.index - chair_p.iloc[8,1]
spx9.index = spx9.index - chair_p.iloc[9,1]
spx10.index = spx10.index - chair_p.iloc[10,1]
spx11.index = spx11.index - chair_p.iloc[11,1]
spx12.index = spx12.index - chair_p.iloc[12,1]
spx13.index = spx13.index - chair_p.iloc[13,1]
spx14.index = spx14.index - chair_p.iloc[14,1]
spx15.index = spx15.index - chair_p.iloc[15,1]


# In[41]:


p = figure(width=700, height = 500, title='FRB議長交代１年前後の株価の動き')

p.line(spx7.index, spx7.Close, legend='McCabe', line_color=Spectral8[0])
p.line(spx8.index, spx8.Close, legend='Martin', line_color=Spectral8[1])
p.line(spx9.index, spx9.Close, legend='Burns', line_color=Spectral8[2])
p.line(spx10.index, spx10.Close, legend='Miller', line_color=Spectral8[3])
p.line(spx11.index, spx11.Close, legend='Volker', line_color=Spectral8[4])
p.line(spx12.index, spx12.Close, legend='Greenspan', line_color=Spectral8[5])
p.line(spx13.index, spx13.Close, legend='Bernanke',line_color=Spectral8[6])
p.line(spx14.index, spx14.Close, legend='Yellen', line_color=Spectral8[7])
p.line(spx15.index, spx15.Close, legend='Powell', line_color='black', line_width=3)

p.legend.location = 'bottom_left'
show(p)


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