def genRollAutocorr():
output = {}
for ind in standardIndices+HFIndices_raw:
ROR, indNew = getIndData(ind, 'ROR')
Date, indNew = getIndData(ind, 'Date')
end = len(ROR)
rollingac = []
upbound = []
lowbound = []
for i in range(36, end):
sampleY = ROR[i-36:i]
#sampleX = Date[i]
r, se = autocorr(sampleY, 1)
rollingac.append(r[-1])
upbound.append(r+1.96*se)
lowbound.append(r-1.96*se)
rAC = np.array(rollingac)
uB = np.array(upbound)
lB = np.array(lowbound)
tAxis = Date[36:]
print len(tAxis), len(rAC)
#fig, ax = plt.subplots()
#ax.plot_date(tAxis, rAC, linestyle='--')
#ax.set_title('%s'%(indNew))
#ax.annotate('Test', (mdates.date2num(tAxis[1]), rAC[1]), xytext=(15, 15), textcoords='offset points', arrowprops=dict(arrowstyle='-|>'))
#fig.autofmt_xdate()
#plt.show()
output[indNew] = [rAC, uB, lB]
def genRollAutocorr():
output = {}
for ind in standardIndices + HFIndices_raw:
ROR, indNew = getIndData(ind, 'ROR')
Date, indNew = getIndData(ind, 'Date')
end = len(ROR)
rollingac = []
upbound = []
lowbound = []
for i in range(36, end):
sampleY = ROR[i - 36:i]
#sampleX = Date[i]
r, se = autocorr(sampleY, 1)
rollingac.append(r[-1])
upbound.append(r + 1.96 * se)
lowbound.append(r - 1.96 * se)
rAC = np.array(rollingac)
uB = np.array(upbound)
lB = np.array(lowbound)
tAxis = Date[36:]
print len(tAxis), len(rAC)
#fig, ax = plt.subplots()
#ax.plot_date(tAxis, rAC, linestyle='--')
#ax.set_title('%s'%(indNew))
#ax.annotate('Test', (mdates.date2num(tAxis[1]), rAC[1]), xytext=(15, 15), textcoords='offset points', arrowprops=dict(arrowstyle='-|>'))
#fig.autofmt_xdate()
#plt.show()
output[indNew] = [rAC, uB, lB]
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
for ind in standardIndices+HFIndices_raw:
ROR, indNew = getIndData(ind, 'ROR', aDate, bDate)
rorDataDict[indNew] = ROR
outputDict = {}
for ind in rorDataDict.keys():
print ind
#print rorDataDict[ind]
ror = rorDataDict[ind]
stats1 = stats.describe(ror)
#stats1 is the output defined in scipy.stats.describe.html
rho_arr = []
pval_arr = []
for i in range(1, 4):
r, p = autocorr(ror, i)
rho_arr.append(r)
pval_arr.append(p)
output = [stats1[0], stats1[2]*12,np.sqrt(12*stats1[3]), stats1[1][0], stats1[1][1],
np.median(ror), stats1[4], stats1[5],
rho_arr[0], pval_arr[0],
rho_arr[1], pval_arr[1],
rho_arr[2], pval_arr[2]]
outputDict[ind] = output
print output
Dateoutput = open('SummaryStatistic.pkl', 'wb')
pickle.dump(outputDict, Dateoutput)
return outputDict
if __name__ == '__main__':
for ind in standardIndices + HFIndices_raw:
ROR, indNew = getIndData(ind, 'ROR', aDate, bDate)
rorDataDict[indNew] = ROR
outputDict = {}
for ind in rorDataDict.keys():
print ind
#print rorDataDict[ind]
ror = rorDataDict[ind]
stats1 = stats.describe(ror)
#stats1 is the output defined in scipy.stats.describe.html
rho_arr = []
pval_arr = []
for i in range(1, 4):
r, p = autocorr(ror, i)
rho_arr.append(r)
pval_arr.append(p)
output = [
stats1[0], stats1[2] * 12,
np.sqrt(12 * stats1[3]), stats1[1][0], stats1[1][1],
np.median(ror), stats1[4], stats1[5], rho_arr[0], pval_arr[0],
rho_arr[1], pval_arr[1], rho_arr[2], pval_arr[2]
]
outputDict[ind] = output
print output
Dateoutput = open('SummaryStatistic.pkl', 'wb')
pickle.dump(outputDict, Dateoutput)
return outputDict