def testPortfolio2Test():
pfValues = functions.testPortfolio2(StockPrices, SP500Prices, StockBeta, wTickers, CAPITAL, START, END)
plt.subplot(121)
plt.title('Portfolio Value vs Date')
plt.plot(pfValues['date'], pfValues['value'],
label='Portfolio Value', color='b')
plt.plot(pfValues['date'], pfValues['SP500'] * (pfValues['value'].iloc[0]/pfValues['SP500'].iloc[0]),
label='S&P500', color='r')
plt.legend()
plt.grid()
plt.subplot(122)
plt.title('beta_CQA vs Date')
plt.plot(pfValues['date'], pfValues['beta_CQA'])
plt.grid()
plt.show()
return pfValues
def testPortfolio2Test():
pfValues = functions.testPortfolio2(StockPrices, SP500Prices, StockBeta,
wTickers, CAPITAL, START, END)
plt.subplot(121)
plt.title('Portfolio Value vs Date')
plt.plot(pfValues['date'],
pfValues['value'],
label='Portfolio Value',
color='b')
plt.plot(pfValues['date'],
pfValues['SP500'] *
(pfValues['value'].iloc[0] / pfValues['SP500'].iloc[0]),
label='S&P500',
color='r')
plt.legend()
plt.grid()
plt.subplot(122)
plt.title('beta_CQA vs Date')
plt.plot(pfValues['date'], pfValues['beta_CQA'])
plt.grid()
plt.show()
return pfValues
shortTickersLow = sortCoef.head(N)[['ticker']].sort('ticker').reset_index(drop=True)
mean_smb_40 = CarhartDaily[CarhartDaily['date'] <= start_date]['SMB'].tail(40).mean()
mean_smb_130 = CarhartDaily[CarhartDaily['date'] <= start_date]['SMB'].tail(130).mean()
diff_mean = mean_smb_40 - mean_smb_130
try:
(opt_comp, wTickers) = functions.pfOptimizer8(longTickers, shortTickersHigh, shortTickersLow,
Coef, Res, CarhartDaily, StockBeta,
BETA_BOUND, WEIGHT_BOUND, back_date, start_date,
diff_mean, diff_threshold)
except ValueError:
value_error_count += 1
pass
wTickers = wTickers[(wTickers['weight'] >= 0.001) | (wTickers['weight'] <= -0.001)]
# test portfolio
pfValues = pfValues.append(
functions.testPortfolio2(StockPrices, SP500Prices, StockBeta, Coef, wTickers, total_value, sp500_value, start_date, end_date))
total_value = pfValues['value'].iloc[-1]
sp500_value = pfValues['SP500value'].iloc[-1]
pfValues.reset_index(drop=True)
# analyze portfolio performance in each month
def pfValuesPlot(pfValues, BETA_BOUND):
line_width = 1.0
plt.subplot(221)
plt.plot(pfValues['date'], pfValues['value'], label='Portfolio Value', color='r', linewidth=line_width)
plt.plot(pfValues['date'], pfValues['SP500value'], label='S&P500', color='b', linewidth=line_width)
plt.title('Portfolio Value vs S&P500')
plt.legend(bbox_to_anchor=(0.2, 1))
plt.grid()
plt.subplot(222)
def risk_return_expectation():
back_date = datetime.datetime(2010, 11, 1)
start_date = datetime.datetime(2013, 11, 1)
end_date = datetime.datetime(2014, 10, 31)
tickers = ReadData.ReadTickers(DataFolder)
BETA_BOUND = 0.4
WEIGHT_BOUND = 0.02
decay = 0.003
(Coef, Res) = functions.wReg(StockReturns, CarhartDaily, tickers, decay,
back_date, start_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
longTickers = sortCoef.head(4 * N).tail(
3 * N)[['ticker']].sort('ticker').reset_index(drop=True)
shortTickers = sortCoef.tail(
3 * N)[['ticker']].sort('ticker').reset_index(drop=True)
wTickers = functions.pfOptimizer(longTickers, shortTickers, Coef,
StockBeta, BETA_BOUND, WEIGHT_BOUND)
wTickers = wTickers[(wTickers['weight'] >= 0.001) |
(wTickers['weight'] <= -0.001)]
total_value = 1e7
sp500_value = 1e7
# Initial Exposures
Exposure = wTickers.merge(Coef)
ini_beta = (Exposure['weight'] * Exposure['Mkt-RF']).sum()
ini_smb = (Exposure['weight'] * Exposure['SMB']).sum()
ini_hml = (Exposure['weight'] * Exposure['HML']).sum()
ini_umd = (Exposure['weight'] * Exposure['UMD']).sum()
print 'ini_beta = %.2f' % ini_beta
print 'ini_smb = %.2f' % ini_smb
print 'ini_hml = %.2f' % ini_hml
print 'ini_umd = %.2f' % ini_umd
# test portfolio
pfValues = functions.testPortfolio2(
StockPrices, SP500Prices, StockBeta, Coef, wTickers, total_value,
sp500_value, start_date,
end_date).rename(columns={'SP500value': 'sp500value'})
pfValues['return'] = (pfValues['value'] - total_value) / total_value
pfValues['sp500return'] = (pfValues['sp500value'] -
total_value) / total_value
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
matplotlib.rc('xtick', labelsize=36)
matplotlib.rc('ytick', labelsize=36)
plt.plot(pfValues['date'],
pfValues['return'] * 100,
color=cit_color,
linewidth=6,
label='Portfolio Value')
plt.plot(pfValues['date'],
pfValues['sp500return'] * 100,
color='b',
linewidth=6,
label='S&P 500')
plt.legend(loc=2, prop={'size': 36})
fmt = '%.2f%%' # Format you want the ticks, e.g. '40%'
yticks = mtick.FormatStrFormatter(fmt)
plt.xticks(rotation=25)
ax.yaxis.set_major_formatter(yticks)
plt.gcf().subplots_adjust(bottom=0.18)
plt.grid()
plt.show()
breakpoints = [
datetime.datetime(2013, 11, 30),
datetime.datetime(2013, 12, 31),
datetime.datetime(2014, 1, 31),
datetime.datetime(2014, 2, 28),
datetime.datetime(2014, 3, 31),
datetime.datetime(2014, 4, 30),
datetime.datetime(2014, 5, 31),
datetime.datetime(2014, 6, 30),
datetime.datetime(2014, 7, 31),
datetime.datetime(2014, 8, 31),
datetime.datetime(2014, 9, 30),
datetime.datetime(2014, 10, 31),
]
comparison = pd.DataFrame()
tmp_start = datetime.datetime(2013, 10, 30)
for tmp_end in breakpoints:
print tmp_end
tmp_pfValues = pfValues[(pfValues['date'] > tmp_start)
& (pfValues['date'] <= tmp_end)]
# tmp_return = tmp_pfValues['return'].sum()
# tmp_sp500_return = tmp_pfValues['sp500return'].sum()
start_value = tmp_pfValues['value'].iloc[0]
end_value = tmp_pfValues['value'].iloc[-1]
tmp_return = ((end_value - start_value) / start_value) * 100
start_sp500 = tmp_pfValues['sp500value'].iloc[0]
end_sp500 = tmp_pfValues['sp500value'].iloc[-1]
tmp_sp500_return = ((end_sp500 - start_sp500) / start_value) * 100
comparison = comparison.append(
pd.DataFrame([{
'return': tmp_return,
'sp500return': tmp_sp500_return
}]))
tmp_start = tmp_end
comparison
pfReturns = pfValues[['date', 'value', 'sp500value']]
pfReturns['daily_return'] = (
pfReturns['value'] -
pfReturns['value'].shift(1)) / pfReturns['value'].shift(1)
pfReturns['daily_sp500return'] = (
pfReturns['sp500value'] -
pfReturns['sp500value'].shift(1)) / pfReturns['value'].shift(1)
pfReturns = pfReturns[1:]
# Annualized volatility
vol_portfolio = pfReturns['daily_return'].std() * math.sqrt(252)
vol_sp500 = pfReturns['daily_sp500return'].std() * math.sqrt(252)
n_days = np.float(pfReturns.shape[0])
ave_return = ((pfValues['value'].iloc[-1] / pfValues['value'].iloc[0])**
(1 / n_days) - 1) * 100
ave_sp500_return = (
(pfValues['sp500value'].iloc[-1] / pfValues['sp500value'].iloc[0])**
(1 / n_days) - 1) * 100
cum_return = (pfValues['value'].iloc[-1] -
pfValues['value'].iloc[0]) / pfValues['value'].iloc[0] * 100
cum_sp500return = (
pfValues['sp500value'].iloc[-1] -
pfValues['sp500value'].iloc[0]) / pfValues['sp500value'].iloc[0] * 100
print 'cum_return = %.2f' % cum_return
print 'cum_sp500return = %.2f' % cum_sp500return
print 'vol_portfolio = %.4f' % vol_portfolio
print 'vol_sp500 = %.4f' % vol_sp500
sharpe_portfolio = pfReturns['daily_return'].mean() / vol_portfolio * 252
sharpe_sp500 = pfReturns['daily_sp500return'].mean() / vol_sp500 * 252
down_dev_portfolio = math.sqrt(
pfReturns[pfReturns['daily_return'] < 0]['daily_return'].map(
lambda x: x**2).sum() / n_days)
down_dev_sp500 = math.sqrt(
pfReturns[pfReturns['daily_sp500return'] < 0]['daily_sp500return'].map(
lambda x: x**2).sum() / n_days)
print 'down_dev_portfolio = %.4f' % down_dev_portfolio
print 'down_dev_sp500 = %.4f' % down_dev_sp500
max_drawdown = 0
max_drawdown_sp500 = 0
for i in range(pfValues.shape[0] - 1):
for j in range(i + 1, pfValues.shape[0]):
tmp_drawdown = (
pfValues['value'].iloc[j] -
pfValues['value'].iloc[i]) / pfValues['value'].iloc[i] * 100
tmp_drawdown_sp500 = (pfValues['sp500value'].iloc[j] -
pfValues['sp500value'].iloc[i]
) / pfValues['sp500value'].iloc[i] * 100
max_drawdown = min(max_drawdown, tmp_drawdown)
max_drawdown_sp500 = min(max_drawdown_sp500, tmp_drawdown_sp500)
print 'max_drawdown = %.2f' % max_drawdown
print 'max_drawdown_sp500 = %.2f' % max_drawdown_sp500
pfReturns = pfReturns.merge(CarhartDaily, on='date')
y = pfReturns['daily_return'] * 100 - pfReturns['RF']
X = sm.add_constant(pfReturns[['Mkt-RF', 'SMB', 'HML', 'UMD']])
model = sm.OLS(y, X).fit()
print model.params
CarhartDaily['date'] <= start_date]['SMB'].tail(130).mean()
diff_mean = mean_smb_40 - mean_smb_130
try:
(opt_comp, wTickers) = functions.pfOptimizer8(
longTickers, shortTickersHigh, shortTickersLow, Coef, Res,
CarhartDaily, StockBeta, BETA_BOUND, WEIGHT_BOUND, back_date,
start_date, diff_mean, diff_threshold)
except ValueError:
value_error_count += 1
pass
wTickers = wTickers[(wTickers['weight'] >= 0.001) |
(wTickers['weight'] <= -0.001)]
# test portfolio
pfValues = pfValues.append(
functions.testPortfolio2(StockPrices, SP500Prices, StockBeta, Coef,
wTickers, total_value, sp500_value,
start_date, end_date))
total_value = pfValues['value'].iloc[-1]
sp500_value = pfValues['SP500value'].iloc[-1]
pfValues.reset_index(drop=True)
# analyze portfolio performance in each month
def pfValuesPlot(pfValues, BETA_BOUND):
line_width = 1.0
plt.subplot(221)
plt.plot(pfValues['date'],
pfValues['value'],
label='Portfolio Value',
color='r',
linewidth=line_width)
def risk_return_expectation():
back_date = datetime.datetime(2010, 11, 1)
start_date = datetime.datetime(2013, 11, 1)
end_date = datetime.datetime(2014, 10, 31)
tickers = ReadData.ReadTickers(DataFolder)
BETA_BOUND = 0.4
WEIGHT_BOUND = 0.02
decay = 0.003
(Coef, Res) = functions.wReg(StockReturns, CarhartDaily, tickers, decay, back_date, start_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
longTickers = sortCoef.head(4*N).tail(3*N)[['ticker']].sort('ticker').reset_index(drop=True)
shortTickers = sortCoef.tail(3*N)[['ticker']].sort('ticker').reset_index(drop=True)
wTickers = functions.pfOptimizer(longTickers, shortTickers,
Coef, StockBeta,
BETA_BOUND, WEIGHT_BOUND)
wTickers = wTickers[(wTickers['weight'] >= 0.001) | (wTickers['weight'] <= -0.001)]
total_value = 1e7
sp500_value = 1e7
# Initial Exposures
Exposure = wTickers.merge(Coef)
ini_beta = (Exposure['weight']*Exposure['Mkt-RF']).sum()
ini_smb = (Exposure['weight']*Exposure['SMB']).sum()
ini_hml = (Exposure['weight']*Exposure['HML']).sum()
ini_umd = (Exposure['weight']*Exposure['UMD']).sum()
print 'ini_beta = %.2f' % ini_beta
print 'ini_smb = %.2f' % ini_smb
print 'ini_hml = %.2f' % ini_hml
print 'ini_umd = %.2f' % ini_umd
# test portfolio
pfValues = functions.testPortfolio2(StockPrices, SP500Prices, StockBeta, Coef, wTickers,
total_value, sp500_value, start_date, end_date).rename(columns={'SP500value': 'sp500value'})
pfValues['return'] = (pfValues['value']-total_value)/total_value
pfValues['sp500return'] = (pfValues['sp500value']-total_value)/total_value
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
matplotlib.rc('xtick', labelsize=36)
matplotlib.rc('ytick', labelsize=36)
plt.plot(pfValues['date'], pfValues['return']*100, color=cit_color, linewidth=6,
label='Portfolio Value')
plt.plot(pfValues['date'], pfValues['sp500return']*100, color='b', linewidth=6,
label='S&P 500')
plt.legend(loc=2, prop={'size': 36})
fmt = '%.2f%%' # Format you want the ticks, e.g. '40%'
yticks = mtick.FormatStrFormatter(fmt)
plt.xticks(rotation=25)
ax.yaxis.set_major_formatter(yticks)
plt.gcf().subplots_adjust(bottom=0.18)
plt.grid()
plt.show()
breakpoints = [datetime.datetime(2013, 11, 30),
datetime.datetime(2013, 12, 31),
datetime.datetime(2014, 1, 31),
datetime.datetime(2014, 2, 28),
datetime.datetime(2014, 3, 31),
datetime.datetime(2014, 4, 30),
datetime.datetime(2014, 5, 31),
datetime.datetime(2014, 6, 30),
datetime.datetime(2014, 7, 31),
datetime.datetime(2014, 8, 31),
datetime.datetime(2014, 9, 30),
datetime.datetime(2014, 10, 31),
]
comparison = pd.DataFrame()
tmp_start = datetime.datetime(2013, 10, 30)
for tmp_end in breakpoints:
print tmp_end
tmp_pfValues = pfValues[(pfValues['date'] > tmp_start) &
(pfValues['date'] <= tmp_end)]
# tmp_return = tmp_pfValues['return'].sum()
# tmp_sp500_return = tmp_pfValues['sp500return'].sum()
start_value = tmp_pfValues['value'].iloc[0]
end_value = tmp_pfValues['value'].iloc[-1]
tmp_return = ((end_value - start_value) / start_value) * 100
start_sp500 = tmp_pfValues['sp500value'].iloc[0]
end_sp500 = tmp_pfValues['sp500value'].iloc[-1]
tmp_sp500_return = ((end_sp500 - start_sp500) / start_value) * 100
comparison = comparison.append(pd.DataFrame([{'return': tmp_return,
'sp500return': tmp_sp500_return
}]))
tmp_start = tmp_end
comparison
pfReturns = pfValues[['date', 'value', 'sp500value']]
pfReturns['daily_return'] = (pfReturns['value']-pfReturns['value'].shift(1))/pfReturns['value'].shift(1)
pfReturns['daily_sp500return'] = (pfReturns['sp500value']-pfReturns['sp500value'].shift(1))/pfReturns['value'].shift(1)
pfReturns = pfReturns[1:]
# Annualized volatility
vol_portfolio = pfReturns['daily_return'].std()*math.sqrt(252)
vol_sp500 = pfReturns['daily_sp500return'].std()*math.sqrt(252)
n_days = np.float(pfReturns.shape[0])
ave_return = ((pfValues['value'].iloc[-1]/pfValues['value'].iloc[0])**(1/n_days)-1)*100
ave_sp500_return = ((pfValues['sp500value'].iloc[-1]/pfValues['sp500value'].iloc[0])**(1/n_days)-1)*100
cum_return = (pfValues['value'].iloc[-1]-pfValues['value'].iloc[0])/pfValues['value'].iloc[0]*100
cum_sp500return = (pfValues['sp500value'].iloc[-1]-pfValues['sp500value'].iloc[0])/pfValues['sp500value'].iloc[0]*100
print 'cum_return = %.2f' % cum_return
print 'cum_sp500return = %.2f' % cum_sp500return
print 'vol_portfolio = %.4f' % vol_portfolio
print 'vol_sp500 = %.4f' % vol_sp500
sharpe_portfolio = pfReturns['daily_return'].mean()/vol_portfolio * 252
sharpe_sp500 = pfReturns['daily_sp500return'].mean()/vol_sp500 * 252
down_dev_portfolio = math.sqrt(pfReturns[pfReturns['daily_return']<0]['daily_return'].map(lambda x: x**2).sum()/n_days)
down_dev_sp500 = math.sqrt(pfReturns[pfReturns['daily_sp500return']<0]['daily_sp500return'].map(lambda x: x**2).sum()/n_days)
print 'down_dev_portfolio = %.4f' % down_dev_portfolio
print 'down_dev_sp500 = %.4f' % down_dev_sp500
max_drawdown = 0
max_drawdown_sp500 = 0
for i in range(pfValues.shape[0]-1):
for j in range(i+1, pfValues.shape[0]):
tmp_drawdown = (pfValues['value'].iloc[j]-pfValues['value'].iloc[i])/pfValues['value'].iloc[i]*100
tmp_drawdown_sp500 = (pfValues['sp500value'].iloc[j]-pfValues['sp500value'].iloc[i])/pfValues['sp500value'].iloc[i]*100
max_drawdown = min(max_drawdown, tmp_drawdown)
max_drawdown_sp500 = min(max_drawdown_sp500, tmp_drawdown_sp500)
print 'max_drawdown = %.2f' % max_drawdown
print 'max_drawdown_sp500 = %.2f' % max_drawdown_sp500
pfReturns = pfReturns.merge(CarhartDaily, on='date')
y = pfReturns['daily_return'] * 100 - pfReturns['RF']
X = sm.add_constant(pfReturns[['Mkt-RF', 'SMB', 'HML', 'UMD']])
model = sm.OLS(y, X).fit()
print model.params