def featBeta( dData, lLookback=14, sMarket='$SPX', b_human=False ):
'''
@summary: Calculate beta relative to a given stock/index.
@param dData: Dictionary of data to use
@param sStock: Stock to calculate beta relative to
@param b_human: if true return dataframe to plot
@return: DataFrame array containing feature values
'''
dfPrice = dData['close']
#''' Calculate returns '''
dfRets = dfPrice.copy()
tsu.returnize1(dfRets.values)
tsMarket = dfRets[sMarket]
dfRet = pand.rolling_cov(tsMarket, dfRets, lLookback)
dfRet /= dfRet[sMarket]
if b_human:
for sym in dData['close']:
x=1000/dData['close'][sym][0]
dData['close'][sym]=dData['close'][sym]*x
return dData['close']
return dfRet
def featBeta(dData, lLookback=14, sMarket='$SPX', b_human=False):
'''
@summary: Calculate beta relative to a given stock/index.
@param dData: Dictionary of data to use
@param sStock: Stock to calculate beta relative to
@param b_human: if true return dataframe to plot
@return: DataFrame array containing feature values
'''
dfPrice = dData['close']
#''' Calculate returns '''
dfRets = dfPrice.copy()
tsu.returnize1(dfRets.values)
tsMarket = dfRets[sMarket]
dfRet = pand.rolling_cov(tsMarket, dfRets, lLookback)
dfRet /= dfRet[sMarket]
if b_human:
for sym in dData['close']:
x = 1000 / dData['close'][sym][0]
dData['close'][sym] = dData['close'][sym] * x
return dData['close']
return dfRet
def featCorrelation(dData, lLookback=20, sRel='$SPX', b_human=False):
'''
@summary: Calculate correlation of two stocks.
@param dData: Dictionary of data to use
@param lLookback: Number of days to calculate moving average over
@param b_human: if true return dataframe to plot
@return: DataFrame array containing feature values
'''
dfPrice = dData['close']
if sRel not in dfPrice.columns:
raise KeyError("%s not found in data provided to featCorrelation" %
sRel)
#''' Calculate returns '''
naRets = dfPrice.values.copy()
tsu.returnize1(naRets)
dfHistReturns = pand.DataFrame(index=dfPrice.index,
columns=dfPrice.columns,
data=naRets)
#''' Feature DataFrame will be 1:1, we can use the price as a template '''
dfRet = pand.DataFrame(index=dfPrice.index,
columns=dfPrice.columns,
data=np.zeros(dfPrice.shape))
#''' Loop through stocks '''
for sStock in dfHistReturns.columns:
tsHistReturns = dfHistReturns[sStock]
tsRelativeReturns = dfHistReturns[sRel]
tsRet = dfRet[sStock]
#''' Loop over time '''
for i in range(len(tsHistReturns.index)):
#''' NaN if not enough data to do lookback '''
if i < lLookback - 1:
tsRet[i] = float('nan')
continue
naCorr = np.corrcoef(tsHistReturns[i - (lLookback - 1):i + 1],
tsRelativeReturns[i - (lLookback - 1):i + 1])
tsRet[i] = naCorr[0, 1]
if b_human:
for sym in dData['close']:
x = 1000 / dData['close'][sym][0]
dData['close'][sym] = dData['close'][sym] * x
return dData['close']
return dfRet
def featCorrelation( dData, lLookback=20, sRel='$SPX', b_human=False ):
'''
@summary: Calculate correlation of two stocks.
@param dData: Dictionary of data to use
@param lLookback: Number of days to calculate moving average over
@param b_human: if true return dataframe to plot
@return: DataFrame array containing feature values
'''
dfPrice = dData['close']
if sRel not in dfPrice.columns:
raise KeyError( "%s not found in data provided to featCorrelation"%sRel )
#''' Calculate returns '''
naRets = dfPrice.values.copy()
tsu.returnize1(naRets)
dfHistReturns = pand.DataFrame( index=dfPrice.index, columns=dfPrice.columns, data=naRets )
#''' Feature DataFrame will be 1:1, we can use the price as a template '''
dfRet = pand.DataFrame( index=dfPrice.index, columns=dfPrice.columns, data=np.zeros(dfPrice.shape) )
#''' Loop through stocks '''
for sStock in dfHistReturns.columns:
tsHistReturns = dfHistReturns[sStock]
tsRelativeReturns = dfHistReturns[sRel]
tsRet = dfRet[sStock]
#''' Loop over time '''
for i in range(len(tsHistReturns.index)):
#''' NaN if not enough data to do lookback '''
if i < lLookback - 1:
tsRet[i] = float('nan')
continue
naCorr = np.corrcoef( tsHistReturns[ i-(lLookback-1):i+1 ], tsRelativeReturns[ i-(lLookback-1):i+1 ] )
tsRet[i] = naCorr[0,1]
if b_human:
for sym in dData['close']:
x=1000/dData['close'][sym][0]
dData['close'][sym]=dData['close'][sym]*x
return dData['close']
return dfRet
def testSTD(self):
npData = np.random.random(10000)
dData = {}
dData['close'] = pand.DataFrame(npData)
feat = price.featSTDReturn(dData, lLookback = 10, bRel = False).values.ravel() * np.sqrt( 0.9 ) #correction for Degrees of Freedom
import QSTK.qstkutil.tsutil as tsu
npData2 = np.copy(npData)
npData2 = tsu.returnize1(npData2).ravel()
tastd = ta.STDDEV(real = npData2, timeperiod = 10)
np.testing.assert_array_almost_equal(feat, tastd, err_msg = "values not equal", verbose = True)
def simulate(startdate, enddate, symbols, allocations):
# We need closing prices so the timestamp should be hours=16.
dt_timeofday = dt.timedelta(hours=16)
# Get a list of trading days between the start and the end.
ldt_timestamps = du.getNYSEdays(startdate, enddate, dt_timeofday)
# Creating an object of the dataaccess class with Yahoo as the source.
c_dataobj = da.DataAccess('Yahoo')
# Keys to be read from the data, it is good to read everything in one go.
ls_keys = ['close']
# Reading the data, now d_data is a dictionary with the keys above.
# Timestamps and symbols are the ones that were specified before.
data = c_dataobj.get_data(ldt_timestamps, symbols, ls_keys)[0]
returns = tsu.returnize1(data)
previous_day = None
for row in data.iterrows():
day = row[0]
series = row[1]
if previous_day is None:
for symbol in symbols:
index = symbols.index(symbol)
allocation = allocations[index]
series[symbol] = allocation
else:
daily_returns = returns.loc[day]
for (symbol, price) in series.iteritems():
series[symbol] = previous_day[symbol] * daily_returns[symbol]
previous_day = series
daily_sum = data.apply(lambda row : row[0] + row[1] + row[2] + row[3], 1)
tsu.returnize0(daily_sum)
stats = daily_sum.describe()
std_dr = stats['std']
mean_dr = stats['mean']
sharpe = tsu.get_sharpe_ratio(daily_sum.values)
cumulative_return = 1
for value in daily_sum:
value += 1
cumulative_return = cumulative_return * value
return std_dr, mean_dr, sharpe, cumulative_return
def featSTD( dData, lLookback=20, bRel=True, b_human=False ):
'''
@summary: Calculate standard deviation
@param dData: Dictionary of data to use
@param lLookback: Number of days to look in the past
@param b_human: if true return dataframe to plot
@return: DataFrame array containing values
'''
dfPrice = dData['close'].copy()
tsu.returnize1(dfPrice.values)
dfRet = pand.rolling_std(dfPrice, lLookback)
if bRel:
dfRet = dfRet / dfPrice
if b_human:
for sym in dData['close']:
x=1000/dData['close'][sym][0]
dData['close'][sym]=dData['close'][sym]*x
return dData['close']
return dfRet
def featSTD(dData, lLookback=20, bRel=True, b_human=False):
'''
@summary: Calculate standard deviation
@param dData: Dictionary of data to use
@param lLookback: Number of days to look in the past
@param b_human: if true return dataframe to plot
@return: DataFrame array containing values
'''
dfPrice = dData['close'].copy()
tsu.returnize1(dfPrice.values)
dfRet = pand.rolling_std(dfPrice, lLookback)
if bRel:
dfRet = dfRet / dfPrice
if b_human:
for sym in dData['close']:
x = 1000 / dData['close'][sym][0]
dData['close'][sym] = dData['close'][sym] * x
return dData['close']
return dfRet
def main():
(symbols, signals, dataAll, index2) = marketSimulator.main()
curr_ownership = {} # KEY: TICKER, VALUE: NUMBER OF SHARES SOLD
curr_cash = 10000
fundValue = pd.Series(index = index2)
for timestamp in index2:
for (symbol, rating) in signals[timestamp]:
owned = curr_ownership.get(symbol, 0)
if rating == 0:
if owned == 0:
pass
elif owned > 0:# if we own that stock, sell all of it
curr_price = dataAll[symbol].Close[timestamp]
curr_cash += curr_price * owned
curr_ownership[symbol] = 0
elif owned < 0:# if we owe that stock to someone else
curr_price = dataAll[symbol].Close[timestamp]
curr_cash -= curr_price * owned
curr_ownership[symbol] = 0
elif (rating > 0) or (rating > 0):# buy signal
amt = rating * 100
# print "BOUGHT ", amt ,"", symbol, "SHARES ON ", timestamp
curr_price = dataAll[symbol].Close[timestamp]
curr_ownership[symbol] = owned + amt
curr_cash -= amt * curr_price
fundValue[timestamp] = computeStockValue(curr_ownership, dataAll, timestamp, sorted(list(index2))) + curr_cash
print fundValue
plt.clf()
plt.plot(fundValue)
plt.savefig('fund.pdf', format='pdf')
plt.clf()
tsu.returnize1(fundValue.values)
plt.plot(fundValue.index, fundValue.values)
plt.xlabel("date")
plt.ylabel("returns",)
plt.savefig("returns.pdf", format = 'pdf')
return fundValue
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
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() ''' Plot efficient frontiers '''
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
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() ''' Plot efficient frontiers '''
from QSTK.qstkutil.DataAccess import DataAccess from QSTK.qstkutil.qsdateutil import getNYSEdays from QSTK.qstkutil.tsutil import returnize1 startDate = datetime(2010, 8, 30) endDate = datetime(2012, 8, 30) symbols = ['AAPL', 'GLD', 'MCD', '$SPX'] forecastLength = 21 numPaths = 1000 origPrices = getPrices(startDate, endDate, symbols, 'close') # get log returns origLogRets = deepcopy(origPrices) for col in origLogRets.columns: returnize1(origLogRets[col]) origLogRets = np.log(origLogRets) print 'first 10 rows of origPrices:\n', origPrices[:10] # print origLogRets print '\nCorrelation matrix:\n', origLogRets.corr() print '\nCholesky decomposition:\n', np.linalg.cholesky(origLogRets.corr()) # get correlated future log returns meanVec = origLogRets.mean() covMat = np.cov(origLogRets.T) futureLogRets = multivariate_normal(meanVec, covMat, (forecastLength, numPaths)) # forecastLength x numpaths x numStocks # print futureLogRets
from QSTK.qstkutil.tsutil import returnize1
t0 = time()
startDate = datetime(2012, 9, 1)
endDate = datetime(2012, 9, 12)
symbols = ['AAPL', 'GLD', 'MCD', 'SPY']
forecastLength = 21
numPaths = 1000
origPrices = getPrices(startDate, endDate, symbols, 'close')
# get log returns
origLogRets = deepcopy(origPrices)
for col in origLogRets.columns:
returnize1(origLogRets[col])
origLogRets = np.log(origLogRets)
print 'first 10 rows of origPrices:\n', origPrices[:10]
# print origLogRets
print '\nCorrelation matrix:\n', origLogRets.corr()
print '\nCholesky decomposition:\n', np.linalg.cholesky(origLogRets.corr())
# get correlated future log returns
meanVec = origLogRets.mean()
covMat = np.cov(origLogRets.T)
futureLogRets = multivariate_normal(
meanVec, covMat,
(forecastLength, numPaths)) # forecastLength x numpaths x numStocks
