kanzitelli

// @flow

import React, { Component } from 'react';
import {
  WebView,
} from 'react-native';

class LoginScreen extends Component {
  state = {
    cookies    : {},

kanzitelli

from pandas_datareader.nasdaq_trader import get_nasdaq_symbols
from pandas_datareader import data
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sbn
import datetime
import math

symbols = get_nasdaq_symbols()

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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)'
}

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()

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returns = close_prices_2016.pct_change(1)

returns.head()

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

log_returns.head()

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

desc

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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 (%)')

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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

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()