def get_bab_pf_values(StockReturns):
first_date = StockPrices['date'].min()
last_date = StockPrices['date'].max()
stock_first = StockPrices[StockPrices['date'] == first_date][['ticker']].reset_index(drop=True)
stock_last = StockPrices[StockPrices['date'] == last_date][['ticker']].reset_index(drop=True)
stock_universe = stock_first.merge(stock_last).merge(StockBeta)
tickers = stock_universe[['ticker']]
# test_dates = np.sort(StockPrices['date'].astype(datetime.datetime).unique())
build_start_date = datetime.datetime(2011, 11, 1)
build_end_date = datetime.datetime(2014, 11, 7)
test_start_date = datetime.datetime(2014, 11, 10)
test_end_date = datetime.datetime(2015, 1, 31)
decay = 0.003
prices_build = StockPrices[StockPrices['date'] == build_end_date][['ticker', 'adj_close']]
(Coef, Res) = functions.wReg(StockReturns, CarhartDaily, tickers, decay,
build_start_date, build_end_date)
betas = Coef.merge(tickers).rename(columns={'Mkt-RF': 'beta'})[['ticker', 'beta']].reset_index(drop=True)
sortCoef = betas.sort('beta').reset_index(drop=True)
N = sortCoef.shape[0] / 10
font_size = 20
# color_cycle = ['b', 'g', 'r', 'c', 'm', 'y', 'k', 'lightblue', 'gold', 'pink']
color_cycle = []
for n in range(10):
color_cycle.append((n * 0.1, 1.0 - n * 0.1, 0.0))
pfValuesAll = pd.DataFrame()
for n in range(10):
tmpData = sortCoef.head(N * (n + 1)).tail(N)
mean_beta = tmpData['beta'].mean()
wTickers = tmpData[['ticker']].reset_index(drop=True)
wTickers['weight'] = 1 / np.float64(N)
portfolio = wTickers.merge(prices_build).rename(columns={'adj_close': 'price'})
portfolio['nShare'] = (CAPITAL * portfolio['weight'] / portfolio['price']).map(functions.intPart)
pfValues = functions.testPortfolio_simple(StockPrices, StockReturns, SP500Prices, StockBeta,
portfolio, test_start_date, test_end_date)
pfValues['beta group'] = n
pfValues['mean beta'] = mean_beta
pfValues['value'] = pfValues['value'] + CAPITAL - pfValues['value'][0]
plt.plot(pfValues['date'], pfValues['value'],
linewidth=2.0, color=color_cycle[n],
label='mean beta %0.2f' % mean_beta)
pfValuesAll = pfValuesAll.append(pfValues)
matplotlib.rc('xtick', labelsize=font_size)
matplotlib.rc('ytick', labelsize=font_size)
# plt.title('Performance of Beta Groups', size=font_size)
plt.ylabel('Account Value', size=font_size)
plt.xlabel('Date', size=font_size)
plt.grid()
plt.legend(loc=3)
plt.show()
return pfValuesAll
sol = solvers.lp(-mBeta, A, b)
w_res = sol['x']
print 'cqaBeta = %.4f' % np.float64(w_res.trans() * mCqaBeta)[0,0]
print 'beta = %.4f' % np.float64(w_res.trans() * mBeta)[0,0]
wTickers = pfTickers
wTickers['weight'] = w_res
return wTickers
font_size = 20
color_cycle = ['b', 'g', 'r', 'c', 'm', 'y', 'k', 'lightblue', 'gold', 'pink']
k = 0
for SECTOR_WEIGHT in [0.1, 0.05, 0.04, 0.03, 0.02, 0.01, 0.005, 0.001]:
wTickers = pfOptimizer_sector(long_tickers, short_tickers,
Coef, StockBeta, industry_data,
BETA_BOUND, WEIGHT_BOUND, SECTOR_WEIGHT)
wTickers = wTickers[abs(wTickers['weight']) > 0.002]
portfolio = wTickers.merge(prices_build).rename(columns={'adj_close': 'price'})
portfolio['nShare'] = (portfolio['weight'] * CAPITAL / portfolio['price']).map(functions.intPart)
pfValues = functions.testPortfolio_simple(StockPrices, StockReturns, SP500Prices, StockBeta, portfolio, test_start_date)
pfValues['value'] = pfValues['value'] + CAPITAL - pfValues['value'][0]
plt.plot(pfValues['date'], pfValues['value'],
linewidth=2.0, color=color_cycle[k],
label='max sector weight %s' % SECTOR_WEIGHT)
k += 1
plt.title('Account Value vs Time', size=font_size)
plt.ylabel('Account Value', size=font_size)
plt.xlabel('Date', size=font_size)
plt.grid()
plt.legend(loc=2)
plt.show()
def get_bab_pf_values(StockReturns):
first_date = StockPrices['date'].min()
last_date = StockPrices['date'].max()
stock_first = StockPrices[StockPrices['date'] == first_date][[
'ticker'
]].reset_index(drop=True)
stock_last = StockPrices[StockPrices['date'] == last_date][[
'ticker'
]].reset_index(drop=True)
stock_universe = stock_first.merge(stock_last).merge(StockBeta)
tickers = stock_universe[['ticker']]
# test_dates = np.sort(StockPrices['date'].astype(datetime.datetime).unique())
build_start_date = datetime.datetime(2011, 11, 1)
build_end_date = datetime.datetime(2014, 11, 7)
test_start_date = datetime.datetime(2014, 11, 10)
test_end_date = datetime.datetime(2015, 1, 31)
decay = 0.003
prices_build = StockPrices[StockPrices['date'] == build_end_date][[
'ticker', 'adj_close'
]]
(Coef, Res) = functions.wReg(StockReturns, CarhartDaily, tickers, decay,
build_start_date, build_end_date)
betas = Coef.merge(tickers).rename(columns={'Mkt-RF': 'beta'})[[
'ticker', 'beta'
]].reset_index(drop=True)
sortCoef = betas.sort('beta').reset_index(drop=True)
N = sortCoef.shape[0] / 10
font_size = 20
# color_cycle = ['b', 'g', 'r', 'c', 'm', 'y', 'k', 'lightblue', 'gold', 'pink']
color_cycle = []
for n in range(10):
color_cycle.append((n * 0.1, 1.0 - n * 0.1, 0.0))
pfValuesAll = pd.DataFrame()
for n in range(10):
tmpData = sortCoef.head(N * (n + 1)).tail(N)
mean_beta = tmpData['beta'].mean()
wTickers = tmpData[['ticker']].reset_index(drop=True)
wTickers['weight'] = 1 / np.float64(N)
portfolio = wTickers.merge(prices_build).rename(
columns={'adj_close': 'price'})
portfolio['nShare'] = (CAPITAL * portfolio['weight'] /
portfolio['price']).map(functions.intPart)
pfValues = functions.testPortfolio_simple(StockPrices, StockReturns,
SP500Prices, StockBeta,
portfolio, test_start_date,
test_end_date)
pfValues['beta group'] = n
pfValues['mean beta'] = mean_beta
pfValues['value'] = pfValues['value'] + CAPITAL - pfValues['value'][0]
plt.plot(pfValues['date'],
pfValues['value'],
linewidth=2.0,
color=color_cycle[n],
label='mean beta %0.2f' % mean_beta)
pfValuesAll = pfValuesAll.append(pfValues)
matplotlib.rc('xtick', labelsize=font_size)
matplotlib.rc('ytick', labelsize=font_size)
# plt.title('Performance of Beta Groups', size=font_size)
plt.ylabel('Account Value', size=font_size)
plt.xlabel('Date', size=font_size)
plt.grid()
plt.legend(loc=3)
plt.show()
return pfValuesAll
N = sortCoef.shape[0] / 10
font_size = 20
color_cycle = ['b', 'g', 'r', 'c', 'm', 'y', 'k', 'lightblue', 'gold', 'pink']
pfValuesAll = pd.DataFrame()
for n in range(10):
tmpData = sortCoef.head(N * (n + 1)).tail(N)
mean_beta = tmpData['beta'].mean()
wTickers = tmpData[['ticker']].reset_index(drop=True)
wTickers['weight'] = 1 / np.float64(N)
portfolio = wTickers.merge(prices_build).rename(
columns={'adj_close': 'price'})
portfolio['nShare'] = (CAPITAL * portfolio['weight'] /
portfolio['price']).map(functions.intPart)
pfValues = functions.testPortfolio_simple(StockPrices, StockReturns,
SP500Prices, StockBeta,
portfolio, test_start_date)
pfValues['beta group'] = n
pfValues['mean beta'] = mean_beta
pfValues['value'] = pfValues['value'] + CAPITAL - pfValues['value'][0]
plt.plot(pfValues['date'],
pfValues['value'],
linewidth=2.0,
color=color_cycle[n],
label='mean beta %s' % mean_beta)
pfValuesAll = pfValuesAll.append(pfValues)
plt.title('Performance of Beta Groups', size=font_size)
plt.ylabel('Account Value', size=font_size)
plt.xlabel('Date', size=font_size)
plt.grid()
