import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.graph_objs as go
import plotly.plotly as py
sns.set_style('whitegrid')
import cufflinks as cf
import datetime
import quandl
from pandas_datareader import data, wb

from scipy import stats
%matplotlib inline

py.sign_in("dattatele","93reCjIosFgiul4JuaIb")

# Widget related imports
from ipywidgets import interactive
from IPython.display import Audio, display
import ipywidgets as widgets
from IPython.display import display, clear_output, Javascript
from traitlets import Unicode

# nbconvert related imports
from nbconvert import get_export_names, export_by_name
from nbconvert.writers import FilesWriter
from nbformat import read, NO_CONVERT
from nbconvert.utils.exceptions import ConversionException

import pandas_datareader as web

notebook_name = widgets.Text()

js = """IPython.notebook.kernel.widget_manager.get_model('%s').then(function(model) {
      model.set('value', IPython.notebook.notebook_name);
    model.save();
});
""" % notebook_name.model_id
display(Javascript(data=js))

filename = notebook_name.value
filename

exporter_names = widgets.Dropdown(options=get_export_names(), value='html')
export_button = widgets.Button(description="Export")
download_link = widgets.HTML(visible=False)

quandl.ApiConfig.api_key = 'ygw7cuU2fx8t7pgxAtcm'

cof = quandl.get_table('WIKI/PRICES', ticker = 'COF', date = { 'gte': '2007-08-07', 'lte': '2017-08-07' })

cof['date'] = pd.to_datetime(cof['date'])

cof.set_index('date',inplace=True)

cof.head()

jpm = quandl.get_table('WIKI/PRICES', ticker = ['JPM'], date = { 'gte': '2007-08-07', 'lte': '2017-08-07' })

jpm['date'] = pd.to_datetime(jpm['date'])

jpm.set_index('date',inplace=True)

jpm.head()

tickers = ['JPM', 'COF']

bank_stocks = pd.concat([cof, jpm],axis=1,keys=tickers)

bank_stocks.head()

bank_stocks.columns.names = ['Bank Ticker','Stock Info']

bank_stocks.head()

bank_stocks.xs(key='close',axis=1,level='Stock Info').max()

bank_stocks['JPM'].max()

returns = pd.DataFrame()

for tick in tickers:
    returns[tick+' Return'] = bank_stocks[tick]['close'].pct_change()
returns.head()

sns.pairplot(returns[1:],size=3.5)

returns.min()

returns.idxmin()

returns.idxmax()

returns.std()

for tick in tickers:
    bank_stocks[tick]['close'].plot(figsize=(16,6),label=tick)
plt.legend(loc=2)

bank_stocks.xs(key='close',axis=1,level='Stock Info').iplot()

plt.figure(figsize=(16,6))
cof['close'].loc['2008-01-01':'2010-01-01'].rolling(window=30).mean().plot(label='30 Day Avg')
cof['close'].loc['2008-01-01':'2010-01-01'].plot(label='COF CLOSE')
plt.legend()

plt.figure(figsize=(16,6))
jpm['close'].loc['2008-01-01':'2010-01-01'].rolling(window=30).mean().plot(label='30 Day Avg')
jpm['close'].loc['2008-01-01':'2010-01-01'].plot(label='JPM CLOSE')
plt.legend()

plt.figure(figsize=(16,6))
cof['close'].loc['2016-01-01':'2017-07-07'].rolling(window=30).mean().plot(label='30 Day Avg')
cof['close'].loc['2016-01-01':'2017-07-07'].plot(label='COF CLOSE')
plt.legend()

plt.figure(figsize=(16,6))
jpm['close'].loc['2016-01-01':'2017-07-07'].rolling(window=30).mean().plot(label='30 Day Avg')
jpm['close'].loc['2016-01-01':'2017-07-07'].plot(label='JPM CLOSE')
plt.legend()

cof[['open', 'high', 'low', 'close']].loc['2016-01-01':'2017-07-07'].iplot(kind='candle')

cof['close'].loc['2016-01-01':'2017-07-017'].ta_plot(study='sma',title='Simple Moving Averages')

cof['close'].loc['2016-01-01':'2017-07-07'].ta_plot(study='boll')

jpm['close'].loc['2016-01-01':'2017-07-07'].ta_plot(study='boll')

jpm['close'].resample('M').mean().plot(kind='bar', figsize=(16,6))

cof['close'].resample('M').mean().plot(kind='bar', figsize=(16,6))

bank_stocks[['JPM', 'COF']].expanding().mean().plot(figsize=(16,6))

bank_stocks[['JPM', 'COF']].expanding().mean().iplot()

jpm['First Difference'] = jpm['open'] - jpm['open'].shift(1)

jpm['First Difference'].plot(figsize=(16,6))

cof['First Difference'] = cof['open'] - cof['open'].shift(1)

cof['First Difference'].plot(figsize=(16,6))

# Normalized return - like Cumulative daily return 
for stock_df in (cof,jpm):
    stock_df['Normed Return'] = stock_df['adj_close'] / stock_df.iloc[0]['adj_close']

cof.head()

cof.tail()

jpm.head()

jpm.tail()

import pandas_datareader as web

start = pd.to_datetime('2007-08-07')
end = pd.to_datetime('2017-08-07')

spy_etf = web.DataReader('SPY', 'google',start,end)

spy_etf.head()

jpm['close'].plot(label='JPM', figsize=(10,8))
cof['close'].plot(label='COF')
spy_etf['Close'].plot(label='SPY Index')
plt.legend()

jpm['cumulative'] = jpm['close']/jpm['close'].iloc[0]
cof['cumulative'] = cof['close']/cof['close'].iloc[0]
spy_etf['cumulative'] = spy_etf['Close']/spy_etf['Close'].iloc[0]

jpm['cumulative'].plot(label='JPM',figsize = (16,6))
cof['cumulative'].plot(label='COF')
spy_etf['cumulative'].plot(label='SPY')
plt.legend()

jpm['Daily Return'] = jpm['close'].pct_change(1)
cof['Daily Return'] = cof['close'].pct_change(1)
spy_etf['Daily Return'] = spy_etf['Close'].pct_change(1)

plt.scatter(jpm['Daily Return'],cof['Daily Return'],alpha=0.25)

beta,alpha,r_value,p_value,std_err = stats.linregress(jpm['Daily Return'].iloc[1:],
                                                     cof['Daily Return'].iloc[1:])

beta

alpha

r_value

file_writer = FilesWriter()

def export(name, nb):
    
    # Get a unique key for the notebook and set it in the resources object.
    notebook_name = name[:name.rfind('.')]
    resources = {}
    resources['unique_key'] = notebook_name
    resources['output_files_dir'] = '%s_files' % notebook_name

    # Try to export
    try:
        output, resources = export_by_name(exporter_names.value, nb)
    except ConversionException as e:
        download_link.value = "<br>Could not export notebook!"
    else:
        write_results = file_writer.write(output, resources, notebook_name=notebook_name)
    
        download_link.value = "<br>Results: <a href='files/{filename}'><i>\"{filename}\"</i></a>".format(filename=write_results)
        download_link.visible = True
        
def handle_export(widget):
    with open(filename, 'r') as f:
        export(filename, read(f, NO_CONVERT))
        
export_button.on_click(handle_export)

display(exporter_names, export_button, download_link)