def stratMark( dtStart, dtEnd, dFuncArgs ):
"""
@summary Markovitz strategy, generates a curve and then chooses a point on it.
@param dtStart: Start date for portfolio
@param dtEnd: End date for portfolio
@param dFuncArgs: Dict of function args passed to the function
@return DataFrame corresponding to the portfolio allocations
"""
if not dFuncArgs.has_key('dmPrice'):
print 'Error:', stratMark.__name__, 'requires dmPrice information'
return
if not dFuncArgs.has_key('sPeriod'):
print 'Error:', stratMark.__name__, 'requires rebalancing period'
return
if not dFuncArgs.has_key('lLookback'):
print 'Error:', stratMark.__name__, 'requires lookback'
return
if not dFuncArgs.has_key('sMarkPoint'):
print 'Error:', stratMark.__name__, 'requires markowitz point to choose'
return
''' Optional variables '''
if not dFuncArgs.has_key('bAddAlpha'):
bAddAlpha = False
else:
bAddAlpha = dFuncArgs['bAddAlpha']
dmPrice = dFuncArgs['dmPrice']
sPeriod = dFuncArgs['sPeriod']
lLookback = dFuncArgs['lLookback']
sMarkPoint = dFuncArgs['sMarkPoint']
''' Select rebalancing dates '''
drNewRange = pand.DateRange(dtStart, dtEnd, timeRule=sPeriod) + pand.DateOffset(hours=16)
dfAlloc = pand.DataMatrix()
''' Go through each rebalance date and calculate an efficient frontier for each '''
for i, dtDate in enumerate(drNewRange):
dtStart = dtDate - pand.DateOffset(days=lLookback)
if( dtStart < dmPrice.index[0] ):
print 'Error, not enough data to rebalance'
continue
naRets = dmPrice.ix[ dtStart:dtDate ].values.copy()
tsu.returnize1(naRets)
tsu.fillforward(naRets)
tsu.fillbackward(naRets)
''' Add alpha to returns '''
if bAddAlpha:
if i < len(drNewRange) - 1:
naFutureRets = dmPrice.ix[ dtDate:drNewRange[i+1] ].values.copy()
tsu.returnize1(naFutureRets)
tsu.fillforward(naFutureRets)
tsu.fillbackward(naFutureRets)
naAvg = np.mean( naFutureRets, axis=0 )
''' make a mix of past/future rets '''
for i in range( naRets.shape[0] ):
naRets[i,:] = (naRets[i,:] + (naAvg*0.05)) / 1.05
''' Generate the efficient frontier '''
(lfReturn, lfStd, lnaPortfolios) = getFrontier( naRets, fUpper=0.2, fLower=0.01 )
lInd = 0
'''
plt.clf()
plt.plot( lfStd, lfReturn)'''
if( sMarkPoint == 'Sharpe'):
''' Find portfolio with max sharpe '''
fMax = -1E300
for i in range( len(lfReturn) ):
fShrp = (lfReturn[i]-1) / (lfStd[i])
if fShrp > fMax:
fMax = fShrp
lInd = i
'''
plt.plot( [lfStd[lInd]], [lfReturn[lInd]], 'ro')
plt.draw()
time.sleep(2)
plt.show()'''
elif( sMarkPoint == 'MinVar'):
''' use portfolio with minimum variance '''
fMin = 1E300
for i in range( len(lfReturn) ):
if lfStd[i] < fMin:
fMin = lfStd[i]
lInd = i
elif( sMarkPoint == 'MaxRet'):
''' use Portfolio with max returns (not really markovitz) '''
lInd = len(lfReturn)-1
elif( sMarkPoint == 'MinRet'):
''' use Portfolio with min returns (not really markovitz) '''
lInd = 0
else:
print 'Warning: invalid sMarkPoint'''
return
''' Generate allocation based on selected portfolio '''
naAlloc = (np.array( lnaPortfolios[lInd] ).reshape(1,-1) )
dmNew = pand.DataMatrix( index=[dtDate], data=naAlloc, columns=(dmPrice.columns) )
dfAlloc = dfAlloc.append( dmNew )
dfAlloc['_CASH'] = 0.0
return dfAlloc
dtEnd = dt.datetime(lYear + 1, 1, 1) dtStart = dtEnd - dt.timedelta(days=365) dtTest = dtEnd + dt.timedelta(days=365) timeofday = dt.timedelta(hours=16) ldtTimestamps = du.getNYSEdays(dtStart, dtEnd, timeofday) ldtTimestampTest = du.getNYSEdays(dtEnd, dtTest, timeofday) dmClose = norgateObj.get_data(ldtTimestamps, lsSymbols, "close") dmTest = norgateObj.get_data(ldtTimestampTest, lsSymbols, "close") naData = dmClose.values.copy() naDataTest = dmTest.values.copy() tsu.fillforward(naData) tsu.fillbackward(naData) tsu.returnize1(naData) tsu.fillforward(naDataTest) tsu.fillbackward(naDataTest) tsu.returnize1(naDataTest) lPeriod = 21 ''' Get efficient frontiers ''' (lfReturn, lfStd, lnaPortfolios, naAvgRets, naStd) = getFrontier(naData, lPeriod) (lfReturnTest, lfStdTest, unused, unused, unused) = getFrontier(naDataTest, lPeriod) plt.clf() fig = plt.figure()
dtEnd = dt.datetime(lYear+1,1,1) dtStart = dtEnd - dt.timedelta(days=365) dtTest = dtEnd + dt.timedelta(days=365) timeofday=dt.timedelta(hours=16) ldtTimestamps = du.getNYSEdays( dtStart, dtEnd, timeofday ) ldtTimestampTest = du.getNYSEdays( dtEnd, dtTest, timeofday ) dmClose = norgateObj.get_data(ldtTimestamps, lsSymbols, "close") dmTest = norgateObj.get_data(ldtTimestampTest, lsSymbols, "close") naData = dmClose.values.copy() naDataTest = dmTest.values.copy() tsu.fillforward(naData) tsu.fillbackward(naData) tsu.returnize1(naData) tsu.fillforward(naDataTest) tsu.fillbackward(naDataTest) tsu.returnize1(naDataTest) lPeriod = 21 ''' Get efficient frontiers ''' (lfReturn, lfStd, lnaPortfolios, naAvgRets, naStd) = getFrontier( naData, lPeriod ) (lfReturnTest, lfStdTest, unused, unused, unused) = getFrontier( naDataTest, lPeriod ) plt.clf() fig = plt.figure()
def stratMark(dtStart, dtEnd, dFuncArgs):
"""
@summary Markovitz strategy, generates a curve and then chooses a point on it.
@param dtStart: Start date for portfolio
@param dtEnd: End date for portfolio
@param dFuncArgs: Dict of function args passed to the function
@return DataFrame corresponding to the portfolio allocations
"""
if not dFuncArgs.has_key('dmPrice'):
print 'Error:', stratMark.__name__, 'requires dmPrice information'
return
if not dFuncArgs.has_key('sPeriod'):
print 'Error:', stratMark.__name__, 'requires rebalancing period'
return
if not dFuncArgs.has_key('lLookback'):
print 'Error:', stratMark.__name__, 'requires lookback'
return
if not dFuncArgs.has_key('sMarkPoint'):
print 'Error:', stratMark.__name__, 'requires markowitz point to choose'
return
''' Optional variables '''
if not dFuncArgs.has_key('bAddAlpha'):
bAddAlpha = False
else:
bAddAlpha = dFuncArgs['bAddAlpha']
dmPrice = dFuncArgs['dmPrice']
sPeriod = dFuncArgs['sPeriod']
lLookback = dFuncArgs['lLookback']
sMarkPoint = dFuncArgs['sMarkPoint']
''' Select rebalancing dates '''
drNewRange = pand.DateRange(dtStart, dtEnd,
timeRule=sPeriod) + pand.DateOffset(hours=16)
dfAlloc = pand.DataMatrix()
''' Go through each rebalance date and calculate an efficient frontier for each '''
for i, dtDate in enumerate(drNewRange):
dtStart = dtDate - pand.DateOffset(days=lLookback)
if (dtStart < dmPrice.index[0]):
print 'Error, not enough data to rebalance'
continue
naRets = dmPrice.ix[dtStart:dtDate].values.copy()
tsu.returnize1(naRets)
tsu.fillforward(naRets)
tsu.fillbackward(naRets)
''' Add alpha to returns '''
if bAddAlpha:
if i < len(drNewRange) - 1:
naFutureRets = dmPrice.ix[dtDate:drNewRange[i +
1]].values.copy()
tsu.returnize1(naFutureRets)
tsu.fillforward(naFutureRets)
tsu.fillbackward(naFutureRets)
naAvg = np.mean(naFutureRets, axis=0)
''' make a mix of past/future rets '''
for i in range(naRets.shape[0]):
naRets[i, :] = (naRets[i, :] + (naAvg * 0.05)) / 1.05
''' Generate the efficient frontier '''
(lfReturn, lfStd, lnaPortfolios) = getFrontier(naRets,
fUpper=0.2,
fLower=0.01)
lInd = 0
'''
plt.clf()
plt.plot( lfStd, lfReturn)'''
if (sMarkPoint == 'Sharpe'):
''' Find portfolio with max sharpe '''
fMax = -1E300
for i in range(len(lfReturn)):
fShrp = (lfReturn[i] - 1) / (lfStd[i])
if fShrp > fMax:
fMax = fShrp
lInd = i
'''
plt.plot( [lfStd[lInd]], [lfReturn[lInd]], 'ro')
plt.draw()
time.sleep(2)
plt.show()'''
elif (sMarkPoint == 'MinVar'):
''' use portfolio with minimum variance '''
fMin = 1E300
for i in range(len(lfReturn)):
if lfStd[i] < fMin:
fMin = lfStd[i]
lInd = i
elif (sMarkPoint == 'MaxRet'):
''' use Portfolio with max returns (not really markovitz) '''
lInd = len(lfReturn) - 1
elif (sMarkPoint == 'MinRet'):
''' use Portfolio with min returns (not really markovitz) '''
lInd = 0
else:
print 'Warning: invalid sMarkPoint' ''
return
''' Generate allocation based on selected portfolio '''
naAlloc = (np.array(lnaPortfolios[lInd]).reshape(1, -1))
dmNew = pand.DataMatrix(index=[dtDate],
data=naAlloc,
columns=(dmPrice.columns))
dfAlloc = dfAlloc.append(dmNew)
dfAlloc['_CASH'] = 0.0
return dfAlloc
import matplotlib.pyplot as plt
from pylab import *
import pandas
print pandas.__version__
#
# Prepare to read the data
startday = dt.datetime(2011, 1, 1)
endday = dt.datetime(2011, 12, 31)
timeofday=dt.timedelta(hours=16)
timestamps = du.getNYSEdays(startday, endday, timeofday)
#
#Read close data from symbols list
dataobj = da.DataAccess('Yahoo')
symbols = dataobj.get_all_symbols()
close = dataobj.get_data(timestamps, symbols, "close", verbose=True)
#
#Calculate daily_return
trading_date = close.index
daily_price = close.values.copy()
daily_rets = close.values.copy()
tsu.fillforward(daily_rets)
tsu.fillbackward(daily_rets)
tsu.returnize0(daily_rets)
sharpe_list = sorted(zip(tsu.get_sharpe_ratio(daily_rets), daily_price[-1, :] / daily_price[0, :] - 1, symbols), reverse=True)
#Filter NaN value
sharpe_list = [f for f in sharpe_list if not math.isnan(f[0]) and not math.isnan(f[1])]
print sharpe_list