Python HTML Reports in Python/v3

import plotly as py
import pandas as pd
import numpy as np

from datetime import datetime
from datetime import time as dt_tm
from datetime import date as dt_date

import plotly.plotly as py
import plotly.tools as plotly_tools
import plotly.graph_objs as go

import os
import tempfile
os.environ['MPLCONFIGDIR'] = tempfile.mkdtemp()
from matplotlib.finance import quotes_historical_yahoo
import matplotlib.pyplot as plt

from scipy.stats import gaussian_kde

from IPython.display import HTML

x = []
y = []
ma = []

def moving_average(interval, window_size):
    window = np.ones(int(window_size))/float(window_size)
    return np.convolve(interval, window, 'same')

date1 = dt_date( 2014, 1, 1 )
date2 = dt_date( 2014, 12, 12 )
quotes = quotes_historical_yahoo('AAPL', date1, date2)
if len(quotes) == 0:
    print "Couldn't connect to yahoo trading database"
else:
    dates = [q[0] for q in quotes]
    y = [q[1] for q in quotes]
    for date in dates:
        x.append(datetime.fromordinal(int(date))\
                .strftime('%Y-%m-%d')) # Plotly timestamp format
    ma = moving_average(y, 10)

xy_data = go.Scatter( x=x, y=y, mode='markers', marker=dict(size=4), name='AAPL' )
# vvv clip first and last points of convolution
mov_avg = go.Scatter( x=x[5:-4], y=ma[5:-4], \
                  line=dict(width=2,color='red',opacity=0.5), name='Moving average' )
data = [xy_data, mov_avg]

py.iplot(data, filename='apple stock moving average')

first_plot_url = py.plot(data, filename='apple stock moving average', auto_open=False,)
print first_plot_url

tickers = ['AAPL', 'GE', 'IBM', 'KO', 'MSFT', 'PEP']
prices = []
for ticker in tickers:
    quotes = quotes_historical_yahoo(ticker, date1, date2)
    prices.append( [q[1] for q in quotes] )

df = pd.DataFrame( prices ).transpose()
df.columns = tickers
df.head()

fig = plotly_tools.get_subplots(rows=6, columns=6, print_grid=True, horizontal_spacing= 0.05, vertical_spacing= 0.05)

def kde_scipy(x, x_grid, bandwidth=0.4, **kwargs):
    """Kernel Density Estimation with Scipy"""
    # From https://jakevdp.github.io/blog/2013/12/01/kernel-density-estimation/
    # Note that scipy weights its bandwidth by the covariance of the
    # input data.  To make the results comparable to the other methods,
    # we divide the bandwidth by the sample standard deviation here.
    kde = gaussian_kde(x, bw_method=bandwidth / x.std(ddof=1), **kwargs)
    return kde.evaluate(x_grid)

subplots = range(1,37)
sp_index = 0
data = []
for i in range(1,7):
    x_ticker = df.columns[i-1]
    for j in range(1,7):
        y_ticker = df.columns[j-1]
        if i==j:
            x = df[x_ticker]
            x_grid = np.linspace(x.min(), x.max(), 100)
            sp = [ go.Histogram( x=x, histnorm='probability density' ), \
                  go.Scatter( x=x_grid, y=kde_scipy( x.as_matrix(), x_grid ), \
                          line=dict(width=2,color='red',opacity='0.5') ) ]
        else:
            sp = [ go.Scatter( x=df[x_ticker], y=df[y_ticker], mode='markers', marker=dict(size=3) ) ]

        for ea in sp:
            ea.update( name='{0} vs {1}'.format(x_ticker,y_ticker),\
                      xaxis='x{}'.format(subplots[sp_index]),\
                      yaxis='y{}'.format(subplots[sp_index])
            )
        sp_index+=1
        data += sp

# Add x and y labels
left_index = 1
bottom_index = 1
for tk in tickers:
    fig['layout']['xaxis{}'.format(left_index)].update( title=tk )
    fig['layout']['yaxis{}'.format(bottom_index)].update( title=tk )
    left_index=left_index+1
    bottom_index=bottom_index+6

# Remove legend by updating 'layout' key
fig['layout'].update(showlegend=False,height=1000,width=1000, title='Major technology and CPG stock prices in 2014')
fig['data'] = data
py.iplot(fig, height=1000, width=1000, filename='Major technology and CPG stock prices in 2014 - scatter matrix')

second_plot_url = py.plot(fig, height=1000, width=1000, auto_open=False,\
                          filename='Major technology and CPG stock prices in 2014 - scatter matrix')
print second_plot_url

summary_table_1 = df.describe()
summary_table_1 = summary_table_1\
    .to_html()\
    .replace('<table border="1" class="dataframe">','<table class="table table-striped">') # use bootstrap styling

summary_table_2 = '''<table class="table table-striped">
<th>Ticker</th><th>Full name</th>
<tr>
    <td>AAPL</td>
    <td><a href="http://finance.yahoo.com/q?s=AAPL">Apple Inc</a></td>
</tr>
<tr>
    <td>GE</td>
    <td><a href="http://finance.yahoo.com/q?s=GE">General Electric Company</a></td>
</tr>
<tr>
    <td>IBM</td>
    <td><a href="http://finance.yahoo.com/q?s=IBM">International Business Machines Corp.</a></td>
</tr>
<tr>
    <td>KO</td>
    <td><a href="http://finance.yahoo.com/q?s=KO">The Coca-Cola Company</a></td>
</tr>
<tr>
    <td>MSFT</td>
    <td><a href="http://finance.yahoo.com/q?s=MSFT">Microsoft Corporation</a></td>
</tr>
<tr>
    <td>PEP</td>
    <td><a href="http://finance.yahoo.com/q?s=PEP">Pepsico, Inc.</a></td>
</tr>
</table>
'''
HTML(summary_table_2)

html_string = '''
<html>
    <head>
        <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.1/css/bootstrap.min.css">
        <style>body{ margin:0 100; background:whitesmoke; }</style>
    </head>
    <body>
        <h1>2014 technology and CPG stock prices</h1>

        <!-- *** Section 1 *** --->
        <h2>Section 1: Apple Inc. (AAPL) stock in 2014</h2>
        <iframe width="1000" height="550" frameborder="0" seamless="seamless" scrolling="no" \
src="''' + first_plot_url + '''.embed?width=800&height=550"></iframe>
        <p>Apple stock price rose steadily through 2014.</p>
        
        <!-- *** Section 2 *** --->
        <h2>Section 2: AAPL compared to other 2014 stocks</h2>
        <iframe width="1000" height="1000" frameborder="0" seamless="seamless" scrolling="no" \
src="''' + second_plot_url + '''.embed?width=1000&height=1000"></iframe>
        <p>GE had the most predictable stock price in 2014. IBM had the highest mean stock price. \
The red lines are kernel density estimations of each stock price - the peak of each red lines \
corresponds to its mean stock price for 2014 on the x axis.</p>
        <h3>Reference table: stock tickers</h3>
        ''' + summary_table_2 + '''
        <h3>Summary table: 2014 stock statistics</h3>
        ''' + summary_table_1 + '''
    </body>
</html>'''

f = open('/home/jack/report.html','w')
f.write(html_string)
f.close()