from IPython.core.display import HTML
HTML("<iframe src=http://pandas.pydata.org width=800 height=350></iframe>")

import datetime

import pandas as pd
import pandas.io.data
from pandas import Series, DataFrame
pd.__version__

import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.rc('figure', figsize=(8, 7))
mpl.__version__

labels = ['a', 'b', 'c', 'd', 'e']
s = Series([1, 2, 3, 4, 5], index=labels)
s

'b' in s

s['b']

mapping = s.to_dict()
mapping

Series(mapping)

aapl = pd.io.data.get_data_yahoo('AAPL', 
                                 start=datetime.datetime(2006, 10, 1), 
                                 end=datetime.datetime(2012, 1, 1))
aapl.head()

aapl.to_csv('data/aapl_ohlc.csv')
!head data/aapl_ohlc.csv

df = pd.read_csv('data/aapl_ohlc.csv', index_col='Date', parse_dates=True)
df.head()

df.index

ts = df['Close'][-10:]
ts

type(ts)

date = ts.index[5]
date

ts[date]

ts[5]

df[['Open', 'Close']].head()

df['diff'] = df.Open - df.Close
df.head()

del df['diff']
df.head()

close_px = df['Adj Close']

mavg = pd.rolling_mean(close_px, 40)
mavg[-10:]

rets = close_px / close_px.shift(1) - 1
rets.head()

close_px.pct_change().head()

close_px.plot(label='AAPL')
mavg.plot(label='mavg')
plt.legend()

df = pd.io.data.get_data_yahoo(['AAPL', 'GE', 'GOOG', 'IBM', 'KO', 'MSFT', 'PEP'], 
                               start=datetime.datetime(2010, 1, 1), 
                               end=datetime.datetime(2013, 1, 1))['Adj Close']
df.head()

rets = df.pct_change()

plt.scatter(rets.PEP, rets.KO)
plt.xlabel('Returns PEP')
plt.ylabel('Returns KO')

pd.scatter_matrix(rets, diagonal='kde', figsize=(10, 10));

corr = rets.corr()
corr

plt.imshow(corr, cmap='hot', interpolation='none')
plt.colorbar()
plt.xticks(range(len(corr)), corr.columns)
plt.yticks(range(len(corr)), corr.columns);

plt.scatter(rets.mean(), rets.std())
plt.xlabel('Expected returns')
plt.ylabel('Risk')
for label, x, y in zip(rets.columns, rets.mean(), rets.std()):
    plt.annotate(
        label, 
        xy = (x, y), xytext = (20, -20),
        textcoords = 'offset points', ha = 'right', va = 'bottom',
        bbox = dict(boxstyle = 'round,pad=0.5', fc = 'yellow', alpha = 0.5),
        arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0'))

series_list = []
securities = ['AAPL', 'GOOG', 'IBM', 'MSFT']
for security in securities:
    s = pd.io.data.get_data_yahoo(security, start=datetime.datetime(2011, 10, 1), end=datetime.datetime(2013, 1, 1))['Adj Close']
    s.name = security # Rename series to match security name
    series_list.append(s)

df = pd.concat(series_list, axis=1)
df.head()

df.ix[0, 'AAPL'] = np.nan
df.ix[1, ['GOOG', 'IBM']] = np.nan
df.ix[[1, 2, 3], 'MSFT'] = np.nan

df.head()

(df.AAPL + df.GOOG).head()

df.ffill().head()