def addCustomColumns(df, market_upd=False):
start = datetime.date(
int(df.index.get_level_values('date')[0]) - 10,
int(df['month'].values[0]), 1)
end_date_ls = [
int(d) for d in datetime.date.today().strftime('%Y-%m-%d').split("-")
]
end = datetime.date(end_date_ls[0], end_date_ls[1], end_date_ls[2])
try:
url = "https://www.quandl.com/api/v1/datasets/WIKI/{0}.csv?column=4&sort_order=asc&trim_start={1}&trim_end={2}".format(
df.index.get_level_values('ticker')[0], start, end)
qr = pd.read_csv(url)
qr['Date'] = qr['Date'].astype('datetime64[ns]')
# quotes = DataReader(df.index.get_level_values('ticker')[0], 'yahoo', start, end, pause=1)['Close']
# quotes = DataReader(df.index.get_level_values('ticker')[0], 'yahoo', start, end, pause=1)['Close']
except:
print("Could not read time series data for %s" %
df.index.get_level_values('ticker')[0])
exc_type, exc_obj, exc_tb = sys.exc_info()
app.logger.info(
"Could not read time series data for {3}: {0}, {1}, {2}".format(
exc_type, exc_tb.tb_lineno, exc_obj,
df.index.get_level_values('ticker')[0]))
raise
df = addBasicCustomCols(df, qr)
df = addGrowthCustomCols(df, qr)
df = addTimelineCustomCols(df, qr)
pdb.set_trace()
if market_upd:
# market = DataReader(".INX", 'google', start, end, pause=1)['Close']
market = DataReader('^GSPC', 'yahoo', start, end, pause=1)['Close']
market.to_csv(
'/home/ubuntu/workspace/finance/app/static/docs/market.csv')
quotes = pd.DataFrame(quotes)
market.columns = ['Date', 'market']
market.set_index('Date')
quotes['market'] = market
else:
market = pd.read_csv(
'/home/ubuntu/workspace/finance/app/static/docs/market.csv')
market.columns = ['Date', 'market']
market['Date'] = market['Date'].apply(pd.to_datetime)
market = market.set_index('Date')
qr = pd.merge(qr.set_index('Date'),
market,
left_index=True,
right_index=True)
df = calcBetas(df, qr)
'''
Still need to do:
'enterpriseToEbitda'
'ebitdaMargins'
'ebitda',
'shortRatio'
'''
return df
def get_data_for_multiple_stocks(tickers, start_date, end_date):
'''
Obtain stocks information (Date, OHLC, Volume and Adjusted Close).
Uses Pandas DataReader to make an API Call to Yahoo Finance and download the data directly.
Computes other values - Log Return and Arithmetic Return.
Args:
tickers: List of Stock Tickers
start_date: Start Date of the stock data
end_date: End Date of the stock data
Returns:
A dictionary of dataframes for each stock
'''
stocks = dict()
for ticker in tickers:
s = DataReader(ticker, 'yahoo', start_date, end_date)
s.insert(
0, "Ticker",
ticker) #insert ticker column so you can reference better later
s['Date'] = pd.to_datetime(s.index) #useful for transformation later
s['Prev Adj Close'] = s['Adj Close'].shift(1)
s['Log Return'] = np.log(s['Adj Close'] / s['Prev Adj Close'])
s['Return'] = (s['Adj Close'] / s['Prev Adj Close'] - 1)
s = s.reset_index(drop=True)
cols = list(s.columns.values) # re-arrange columns
cols.remove("Date")
s = s[["Date"] + cols]
s["Date"] = pd.to_datetime(s["Date"])
s = s.set_index("Date")
stocks[ticker] = s
return stocks
def web_plotting_plots():
# static
start = dt.datetime(2010, 1, 1)
end = dt.datetime(2013, 1, 27)
data = DataReader("MSFT", 'google', start, end)
data = data.reset_index()
fig, ax = plt.subplots()
data.plot(x='Date', y='Close', grid=True, ax=ax)
plt.savefig(PATH + 'MSFT.png', dpi=300)
# interactive - may not work in cloud 9
warnings.simplefilter('ignore')
py.sign_in('Python-Demo-Account', 'gwt101uhh0')
# to interactive D3.js plot
py.iplot_mpl(fig)
# direct approach with Cufflinks
data.set_index('Date')['Close'].iplot(world_readable=True)
'text': 'Start of the Recession'
}]
}
fig = dict(data=data, layout=layout)
plotly.offline.plot(fig, filename='google-recession-candlestick')
# Grab all the closing prices for the tech stock list into one DataFrame
stocks = ['AAPL','GOOG','MSFT','AMZN','GE']
closing_df = DataReader(stocks,'google',start,end)['Close']
closing_df_2 = DataReader(stocks,'google',start,end)['Close']
# change the timestamp to date
closing_df["Date"] = closing_df.index.date
closing_df.set_index("Date", drop=True, inplace=True)
# Let's compare the closing prices(Close) of 4 tech stocks
#fig1 = plot(closing_df.iplot(fill=True, asFigure=True, title="Daily Closing Price"))
fig1 = plot(closing_df.iplot(fill=True,asFigure = True, subplots = True, title='Compare Closing Prices'))
#plotly.offline.iplot(fig1, filename = 'compare-closing-prices')
### CORRELATION ANALYSIS ###
# Correlation is a statistical measure to analyze of how stocks move in
# relation to one another.
# Correlation is represented by correlation coefficient - PEARSON COEFFICIENT r
# ranging between -1 and +1
# When the prices of two stocks usually move in a similar direction,
