def plot_return_vs_beta():
Coef = ReadData.GetCoef(START_TRAIN, END_TRAIN, StockReturns, CarhartDaily,
SP500Returns, DataFolder)
avaTickers = pd.merge(
Coef[['ticker']],
StockBeta[np.isnan(StockBeta.cqaBeta) == False][['ticker']])
avaTickers = pd.merge(
avaTickers,
StockPrices[StockPrices.date == START_TRAIN][['ticker']],
on='ticker')
avaTickers = pd.merge(
avaTickers,
StockPrices[StockPrices.date == END_TRAIN][['ticker']],
on='ticker')
avaTickers = pd.merge(avaTickers,
StockPrices[StockPrices.date == START][['ticker']],
on='ticker')
avaTickers = pd.merge(avaTickers,
StockPrices[StockPrices.date == END][['ticker']],
on='ticker')
Coef = Coef.merge(avaTickers, on='ticker')
stock_returns_sample = StockReturns[(StockReturns['endDate'] >= START)
& (StockReturns['endDate'] <= END)]
stock_annual_returns = stock_returns_sample.groupby(
'ticker').sum().reset_index()
stock_annual_returns['return'] = stock_annual_returns['return'].map(
math.exp) - 1
beta_return_comp = Coef[['ticker', 'Mkt-RF']].merge(stock_annual_returns)
# plt.figure()
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
fmt = '%.0f%%' # Format you want the ticks, e.g. '40%'
yticks = mtick.FormatStrFormatter(fmt)
ax.yaxis.set_major_formatter(yticks)
# plt.scatter(beta_return_comp['Mkt-RF'], beta_return_comp[ 'return'], marker='x')
ax.scatter(beta_return_comp['Mkt-RF'],
beta_return_comp['return'] * 100,
marker='o',
s=40,
color='#FF6E1E')
matplotlib.rc('xtick', labelsize=36)
matplotlib.rc('ytick', labelsize=36)
plt.xlabel('Stock Beta', fontsize=36)
plt.ylabel('Annual Return (in percentage)', fontsize=36)
plt.grid()
plt.title('Return vs Beta (Nov 2013-Oct 2014)', fontsize=36)
plt.gcf().subplots_adjust(left=0.15, bottom=0.14)
plt.show()
##beta_corr2 = np.corrcoef(aapl3y['return-RF'], aapl3y['SP500-RF'])[1,0]
##y = aapl3y['return-RF']
##X = aapl3y['SP500-RF']
##
##def fun1(y, X):
## model = sm.OLS(y, X).fit()
## print aaplModel.summary()
##fun1(y, X) # this returns 0.84, which is very close to beta_CQA = 0.83
##fun1(y, sm.add_constant(X))
##############################################################################
# Calculate or Read regression coefficients, depending on your own needs
# Coef = ReadData.ReadCoef(START_TRAIN, END_TRAIN, DataFolder)
# Coef2 = ReadData.ReadCoef(datetime.datetime(2011,11,1),
# datetime.datetime(2014,10,31), DataFolder)
Coef = ReadData.GetCoef(START_TRAIN, END_TRAIN, StockReturns, CarhartDaily,
SP500Returns, DataFolder)
# Coef2 = ReadData.GetCoef(datetime.datetime(2011,11,1),
# datetime.datetime(2014,10,31),
# StockReturns, CarhartDaily, SP500Returns, DataFolder)
avaTickers = pd.merge(
Coef[['ticker']],
StockBeta[np.isnan(StockBeta.cqaBeta) == False][['ticker']])
avaTickers = pd.merge(avaTickers,
StockPrices[StockPrices.date == START_TRAIN][['ticker']],
on='ticker')
avaTickers = pd.merge(avaTickers,
StockPrices[StockPrices.date == END_TRAIN][['ticker']],
on='ticker')
avaTickers = pd.merge(avaTickers,
StockPrices[StockPrices.date == START][['ticker']],
