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

# In[1]:


import pandas as pd
from bokeh.charts import TimeSeries, show, output_file, vplot, output_notebook
output_notebook()


# In[2]:


# read in the S&P 500 Index Fund from the Yahoo Finance API
GSPC = pd.read_csv("https://ichart.yahoo.com/table.csv?s=^GSPC",
        parse_dates=['Date'])
FBIDX = pd.read_csv("https://ichart.yahoo.com/table.csv?s=FBIDX",
        parse_dates=['Date'])
NDX = pd.read_csv("https://ichart.yahoo.com/table.csv?s=^NDX",
        parse_dates=['Date'])
SO = pd.read_csv("https://ichart.yahoo.com/table.csv?s=SO", 
        parse_dates=['Date'])

# add column data for correct printing of dates in hovertool
GSPC['Str Date'] = GSPC['Date'].map(lambda x: x.strftime('%Y-%m-%d'))
FBIDX['Str Date'] = FBIDX['Date'].map(lambda x: x.strftime('%Y-%m-%d'))
NDX['Str Date'] = NDX['Date'].map(lambda x: x.strftime('%Y-%m-%d'))
SO['Str Date'] = SO['Date'].map(lambda x: x.strftime('%Y-%m-%d'))

# show sample data
print('GSPC')
print(GSPC.head())
print()
print('FBIDX')
print(FBIDX.head())
print()
print('NDX')
print(NDX.head())
print()
print('SO')
print(SO.head())


# In[3]:


data = dict(
    Date = GSPC['Date'],
    GSPC = GSPC['Adj Close'],
    FBIDX = FBIDX['Adj Close'],
    NDX = NDX['Adj Close'],
    SO = SO['Adj Close'],
    SDate = GSPC['Str Date'],
)
print(type(data['Date'][0]))
print(type(data['GSPC'][0]))


# In[4]:


from bokeh.models import HoverTool

hover = HoverTool(
        tooltips = [
            ("Date", "@SDate"),
            ("Adj Close", " $y{$0.00}"),
        ]
)

ts_stocks = TimeSeries(data,
        x = 'Date', y = ['GSPC', 'FBIDX', 'NDX', 'SO'],
        color = ['cadetblue', 'firebrick', 'limegreen', 'hotpink'], 
        tools=['box_zoom', 'pan', 'save', hover, 'resize', 'reset', 'wheel_zoom'],
        title = 'Stock Time Series', 
        ylabel = 'Stock Prices', legend=True
)

show(ts_stocks)


# In[ ]: