kanzitelli

# 1. Apple - AAPL
AAPL_p_01_01_2016 = 4000
number_of_shares_start = math.floor(AAPL_p_01_01_2016 / close_prices_2016.AAPL[0])
AAPL_p_12_31_2016 = number_of_shares_start * close_prices_2016.AAPL[-1]
relative_return = (AAPL_p_12_31_2016 - AAPL_p_01_01_2016) / AAPL_p_01_01_2016 * 100
print "1. Relative return for Apple = %.2f%%" % relative_return

# 2. Amazon - AMZN
AMZN_p_01_01_2016 = 3000
number_of_shares_start = math.floor(AMZN_p_01_01_2016 / close_prices_2016.AMZN[0])

kanzitelli

AAPL_p_01_01_2016 = 5000
number_of_shares_start = math.floor(AAPL_p_01_01_2016 / close_prices_2016.AAPL[0])
AAPL_p_12_31_2016 = number_of_shares_start * close_prices_2016.AAPL[-1]
print "1. Relative return for Apple = %.2f%%" % ( (AAPL_p_12_31_2016 - AAPL_p_01_01_2016) / AAPL_p_01_01_2016 * 100 )

AMZN_p_01_01_2016 = 5000
number_of_shares_start = math.floor(AMZN_p_01_01_2016 / close_prices_2016.AMZN[0])
AMZN_p_12_31_2016 = number_of_shares_start * close_prices_2016.AMZN[-1]
print "2. Relative return for Amazon = %.2f%%" % ( (AMZN_p_12_31_2016 - AMZN_p_01_01_2016) / AMZN_p_01_01_2016 * 100 )

kanzitelli

box = sbn.boxplot(x='AMZN', data=AAPL_AMZN_log_returns)
box.set_title('Box plot for Amazon')
box.set_ylabel('Total relative returns (%)')

plt.show()

kanzitelli

box = sbn.boxplot(x='AAPL', data=AAPL_AMZN_log_returns)
box.set_title('Box plot for Apple')
box.set_ylabel('Total relative returns (%)')

plt.show()

kanzitelli

aapl_amzn_dict = { 'AAPL': log_returns.AAPL, 'AMZN': log_returns.AMZN }
AAPL_AMZN_log_returns = pd.DataFrame.from_dict(aapl_amzn_dict)

fig = plt.figure(figsize=[16,9])
ax = fig.add_subplot(2,1,2)

for c in AAPL_AMZN_log_returns:
    ax.plot(AAPL_AMZN_log_returns.index, 100*(np.exp(AAPL_AMZN_log_returns[c].cumsum()) - 1), label=str(c))

ax.set_ylabel('Total relative returns (%)')

kanzitelli

desc = close_prices_2016.describe()
desc.loc['min-max diff'] = desc.loc['max'] - desc.loc['min']

desc

kanzitelli

log_returns = np.log(close_prices_2016).diff()

log_returns.head()

kanzitelli

returns = close_prices_2016.pct_change(1)

returns.head()

kanzitelli

tickers = ['AAPL', 'AMZN', 'GOOGL', 'IBM']
data_source = 'yahoo'

date_2016 = (datetime.datetime(2016, 1, 1), datetime.datetime(2016, 12, 31))
all_weekdays_2016 = pd.date_range(start = date_2016[0], end = date_2016[1], freq='B') # here we get all business days from 1.JAN.2016 to 31.DEC.2016

data_2016 = data.DataReader(tickers, data_source, date_2016[0], date_2016[1])
close_prices_2016 = data_2016.ix['Adj Close'].reindex(all_weekdays_2016).dropna() # here we take Adjusted Close Price, take business days and drop NA values

close_prices_2016.head()

kanzitelli

list_of_traded_corps = symbols.loc[:, ['NASDAQ Symbol', 'Security Name', 'Listing Exchange']]

listing_exchange = {
    'A': 'NYSE MKT',
    'N': 'New York Stock Exchange (NYSE)',
    'P': 'NYSE ARCA',
    'Q': 'Q - NASDAQ?', # not sure about this one
    'V': 'Investors\' Exchange, LLC (IEXG)',
    'Z': 'BATS Global Markets (BATS)'
}