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]
'''
for the given index names and time window, calculates the eigVal and eigVectors
===Input===
index_names: the name of the indices
index: if true, then perform analysis on indices. If false, the perform analysis on top 100 companies
startDate: Datetime object, which specifies the beginning of time to select data
endDate: Datetime object, which specifies the end of time to select data
===Output===
eigVal: the eigenvalues of the PCA analysis
eigVec: the eigenvectors of the PCA analysis
'''
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
def rollCrossCorr():
Indices = ["", 'brokers', 'banks', 'insurers']
#note, ROR of hedge fund starts one month before the other 3 indices
RORs = []
newIndices = []
for ind in Indices:
ROR, newInd = getIndData(ind, 'ROR')
newIndices.append(newInd)
if ind == "":
ROR = ROR[1:]
RORs.append(ROR)
end = len(ROR)
print end
Date, indNew = getIndData(ind, 'Date')
resultsDict = {}
for i in range(len(Indices)-1):
for j in range(i+1, len(Indices)):
rollcc = []
rollcc_lag1 = [] #-1 lag
rollcc_flag1 = [] #+1 lag
ror1 = RORs[i]
ror2 = RORs[j]
for k in range(37, end-1):
Y1 = ror1[k-36:k]
Y20 = ror2[k-36:k] #lag 0
Y21 = ror2[k-37:k-1] #lag 1
Y22 = ror2[k-35:k+1] #lag -1
set1 = np.array([Y1, Y20])
set2 = np.array([Y1, Y21])
set3 = np.array([Y1, Y22])
#print i, set1
r1 = np.corrcoef(set1)
r2 = np.corrcoef(set2)
r3 = np.corrcoef(set3)
rollcc.append(r1[0, 1])
rollcc_lag1.append(r2[0, 1])
rollcc_flag1.append(r3[0, 1])
rcc = np.array(rollcc)
rccl1 = np.array(rollcc_lag1)
rccfl1 = np.array(rollcc_flag1)
resultsDict['%s vs. %s'%(newIndices[i], newIndices[j])] = [rcc, rccl1, rccfl1]
tAxis = Date[37:-1]
for title in resultsDict.keys():
fig, ax = plt.subplots()
ax.plot_date(tAxis, resultsDict[title][0], linestyle='--', color='r', fillstyle='none', label="No Lag")
ax.plot_date(tAxis, resultsDict[title][1], linestyle='--', color = 'b',fillstyle='none', label="Lag = 1")
ax.plot_date(tAxis, resultsDict[title][2], linestyle='--', color = 'g', fillstyle='none',label="Lag = -1")
ax.set_title('%s'%(title))
handles, labels = ax.get_legend_handles_labels()
# reverse the order
ax.legend(handles, labels)
ax.legend(bbox_to_anchor=(0., -.1, 1., .102), loc=2,ncol=3, mode="expand", borderaxespad=0.)
#ax.annotate('Test', (mdates.date2num(tAxis[1]), rAC[1]), xytext=(15, 15), textcoords='offset points', arrowprops=dict(arrowstyle='-|>'))
fig.autofmt_xdate()
plt.show()
return resultsDict
def rollCrossCorr():
Indices = ["", 'brokers', 'banks', 'insurers']
#note, ROR of hedge fund starts one month before the other 3 indices
RORs = []
newIndices = []
for ind in Indices:
ROR, newInd = getIndData(ind, 'ROR')
newIndices.append(newInd)
if ind == "":
ROR = ROR[1:]
RORs.append(ROR)
end = len(ROR)
print end
Date, indNew = getIndData(ind, 'Date')
resultsDict = {}
for i in range(len(Indices) - 1):
for j in range(i + 1, len(Indices)):
rollcc = []
rollcc_lag1 = [] #-1 lag
rollcc_flag1 = [] #+1 lag
ror1 = RORs[i]
ror2 = RORs[j]
for k in range(37, end - 1):
Y1 = ror1[k - 36:k]
Y20 = ror2[k - 36:k] #lag 0
Y21 = ror2[k - 37:k - 1] #lag 1
Y22 = ror2[k - 35:k + 1] #lag -1
set1 = np.array([Y1, Y20])
set2 = np.array([Y1, Y21])
set3 = np.array([Y1, Y22])
#print i, set1
r1 = np.corrcoef(set1)
r2 = np.corrcoef(set2)
r3 = np.corrcoef(set3)
rollcc.append(r1[0, 1])
rollcc_lag1.append(r2[0, 1])
rollcc_flag1.append(r3[0, 1])
rcc = np.array(rollcc)
rccl1 = np.array(rollcc_lag1)
rccfl1 = np.array(rollcc_flag1)
resultsDict['%s vs. %s' %
(newIndices[i], newIndices[j])] = [rcc, rccl1, rccfl1]
tAxis = Date[37:-1]
for title in resultsDict.keys():
fig, ax = plt.subplots()
ax.plot_date(tAxis,
resultsDict[title][0],
linestyle='--',
color='r',
fillstyle='none',
label="No Lag")
ax.plot_date(tAxis,
resultsDict[title][1],
linestyle='--',
color='b',
fillstyle='none',
label="Lag = 1")
ax.plot_date(tAxis,
resultsDict[title][2],
linestyle='--',
color='g',
fillstyle='none',
label="Lag = -1")
ax.set_title('%s' % (title))
handles, labels = ax.get_legend_handles_labels()
# reverse the order
ax.legend(handles, labels)
ax.legend(bbox_to_anchor=(0., -.1, 1., .102),
loc=2,
ncol=3,
mode="expand",
borderaxespad=0.)
#ax.annotate('Test', (mdates.date2num(tAxis[1]), rAC[1]), xytext=(15, 15), textcoords='offset points', arrowprops=dict(arrowstyle='-|>'))
fig.autofmt_xdate()
plt.show()
return resultsDict
startDate: Datetime object, which specifies the beginning of time to select data
endDate: Datetime object, which specifies the end of time to select data
===Output===
eigVal: the eigenvalues of the PCA analysis
eigVec: the eigenvectors of the PCA analysis
'''
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