# 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,
# the stocks are considered positively correlated. The amount of
# correlation ranges from 0, which means no correlation, to 1,
# which means perfect correlation. Perfect correlation means the
# relationship that appears to exist between two stocks is positive 100%
# of the time.
}
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']
closing_df = DataReader(stocks, 'yahoo', start, end)['Adj Close']
closing_df_2 = DataReader(stocks, 'yahoo', start, end)['Adj 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(Adj Close) of 4 tech stocks
fig1 = closing_df.iplot(asFigure=True,
subplots=True,
title='Compare Closing Prices')
plotly.offline.plot(fig1, filename='compare-closing-prices')
# Stacking the stocks on a single plot
#figstack = closing_df.iplot(fill=True, asFigure=True, title="Tech Giants Closing Price", filename = 'stacked closing prices')
closing_df_2["Date"] = closing_df_2.index.date
closing_df_2.set_index("Date", drop=True, inplace=True)
figstack = closing_df_2.iplot(fill=True,
asFigure=True,
title="Tech Giants Closing Price")
plotly.offline.plot(figstack, filename='stacked closing prices')
### CORRELATION ANALYSIS ###
