def getGCPvalues(aDate):
tick = time()
npNArray, npDArray = getNames(aDate)
p_values = grangerCausality(npDArray)
tock = time()
print 'getting p-values takes %f' %(tock-tick)
return npNArray, p_values
def getGCPvalues(aDate):
tick = time()
npNArray, npDArray = getNames(aDate)
p_values = grangerCausality(npDArray)
tock = time()
print 'getting p-values takes %f' % (tock - tick)
return npNArray, p_values
def AggregateIlliquidity(endDate, n=25):
nameArray, dataArray = getNames(endDate, inst=['hedgefunds'], numComps=n)
k = 1 #lag steps we want to analyze
sumAUM = 0.0
for name in nameArray:
sumAUM += float(name[1])
rho_prime = 0.0
for ind, data in enumerate(dataArray):
r_arr, se = autocorr(data, k, SE=True)
rho = r_arr[-1]
weight = float(nameArray[ind][1]) / sumAUM
rho_prime += weight * rho
return rho_prime
def AggregateIlliquidity(endDate, n=25):
nameArray, dataArray = getNames(endDate, inst=['hedgefunds'], numComps = n)
k = 1 #lag steps we want to analyze
sumAUM = 0.0
for name in nameArray:
sumAUM += float(name[1])
rho_prime = 0.0
for ind, data in enumerate(dataArray):
r_arr, se = autocorr(data, k, SE=True)
rho = r_arr[-1]
weight = float(nameArray[ind][1])/sumAUM
rho_prime += weight*rho
return rho_prime
def Qstatistic(endDate, n=25):
Qarray = []
nameArray, dataArray = getNames(endDate, inst=['hedgefunds'], numComps=n)
k = 6 #lag steps we want to analyze
for data in dataArray:
r_arr, se = autocorr(data, k, SE=True)
T = len(data)
sum1 = 0.0
for i in range(1, k + 1):
#print r_arr
sum1 += r_arr[i]**2 / (T - i)
sum1 = sum1 * T * (T + 2)
Qarray.append(sum1)
print Qarray
return nameArray, Qarray
def Qstatistic(endDate, n=25):
Qarray = []
nameArray, dataArray = getNames(endDate, inst=['hedgefunds'], numComps = n)
k = 6 #lag steps we want to analyze
for data in dataArray:
r_arr, se = autocorr(data, k, SE=True)
T = len(data)
sum1 = 0.0
for i in range(1, k+1):
#print r_arr
sum1+= r_arr[i]**2/(T-i)
sum1 = sum1*T*(T+2)
Qarray.append(sum1)
print Qarray
return nameArray, Qarray
'''
def PCA(index_names=[], window = 36, index=False, startDate = dt.date(1994, 01, 01), endDate = dt.date(2013, 01, 01)):
if index:
for i, ind in enumerate(index_names):
ROR, newInd = getIndData(ind, 'ROR', startDate, endDate)
#print newInd
listtmp = np.ndarray.tolist(ROR)
#print len(listtmp)
if i==0:
RORs = np.array([listtmp])
else:
RORs = np.append(RORs, [listtmp], axis= 0)
#print RORs
covMat = np.cov(RORs)
else:
npNarr, npDarr = getNames(endDate, window=window)
covMat = np.cov(npDarr)
eigVal, eigVec = np.linalg.eig(covMat)
return eigVal, eigVec
'''
given eigenvalues, return the cumulative risk fraction
===Input===
eigVal: The eigenvalues
===Output===
returns the CRF for the given eigVal
'''
def CumulRiskFrac(eigVal):
sort1 = sorted(eigVal, reverse=True)
return sort1[0]/sum(sort1)
startDate=dt.date(1994, 01, 01),
endDate=dt.date(2013, 01, 01)):
if index:
for i, ind in enumerate(index_names):
ROR, newInd = getIndData(ind, 'ROR', startDate, endDate)
#print newInd
listtmp = np.ndarray.tolist(ROR)
#print len(listtmp)
if i == 0:
RORs = np.array([listtmp])
else:
RORs = np.append(RORs, [listtmp], axis=0)
#print RORs
covMat = np.cov(RORs)
else:
npNarr, npDarr = getNames(endDate, window=window)
covMat = np.cov(npDarr)
eigVal, eigVec = np.linalg.eig(covMat)
return eigVal, eigVec
'''
given eigenvalues, return the cumulative risk fraction
===Input===
eigVal: The eigenvalues
===Output===
returns the CRF for the given eigVal
'''