def build_scenario(overwrite=True):
print('stepdown test...', filename)
if not overwrite:
return
initialValues = [387833, 27450]
dividends = [0.0247, 0.0181]
volatilities = [0.2809, 0.5795]
gbmconst1 = xen.GBMConst('gbmconst1', initialValues[0], riskFree,
dividends[0], volatilities[0])
gbmconst2 = xen.GBMConst('gbmconst2', initialValues[1], riskFree,
dividends[1], volatilities[1])
models = [gbmconst1, gbmconst2]
corr = 0.6031
corrMatrix = mx.IdentityMatrix(len(models))
corrMatrix[0][1] = corr
corrMatrix[1][0] = corr
timeGrid = mx.TimeEqualGrid(refDate, 3, 365)
# random
rsg = xen.Rsg(sampleNum=5000)
xen.generate(models, None, corrMatrix, timeGrid, rsg, filename, False)
def test():
print('vasicek1f test...', filename)
m = model()
timeGrid = mx.TimeEqualGrid(ref_date, 3, 365)
# random
rsg = xen.Rsg(sampleNum=5000)
results = xen.generate1d(m, None, timeGrid, rsg, filename, False)
def test():
print('gtwoext test...', filename)
ref_date = mx.Date(2018, 9, 9)
tenor_rates = [('3M', 0.0151), ('6M', 0.0152), ('9M', 0.0153),
('1Y', 0.0154), ('2Y', 0.0155), ('3Y', 0.0156),
('4Y', 0.0157), ('5Y', 0.0158), ('7Y', 0.0159),
('10Y', 0.016), ('15Y', 0.0161), ('20Y', 0.0162)]
tenors = []
zerorates = []
interpolator1DType = mx.Interpolator1D.Linear
extrapolator1DType = mx.Extrapolator1D.FlatForward
for tr in tenor_rates:
tenors.append(tr[0])
zerorates.append(tr[1])
fittingCurve = mx.ZeroYieldCurveExt(ref_date, tenors, zerorates,
interpolator1DType, extrapolator1DType,
'irskrw_krccp')
alpha1 = 0.1
sigma1 = 0.01
alpha2 = 0.2
sigma2 = 0.02
corr = 0.5
g2ext = mx.GTwoExtModel('g2ext', fittingCurve, alpha1, sigma1, alpha2,
sigma2, corr)
models = [g2ext]
corrMatrix = mx.Matrix([[1.0]])
timeGrid = mx.TimeEqualGrid(ref_date, 3, 365)
# random
scenario_num = 5000
dimension = (len(timeGrid) - 1) * len(models)
seed = 100
skip = 0
isMomentMatching = False
randomType = "crude"
subType = "mersennetwister"
randomTransformType = "invnormal"
rsg = mx.Rsg(scenario_num, dimension, seed, skip, isMomentMatching,
randomType, subType, randomTransformType)
scen = mx.ScenarioGenerator(models, corrMatrix, timeGrid, rsg, False,
filename, False)
scen.generate()
results = mx.ScenarioResult(filename)
print(results.multiPath(scenCount=10))
def test():
print('hw1f test...', filename)
m = model()
# timegrid
timeGrid = mx.TimeEqualGrid(ref_date, 3, 365)
# random sequence
rsg = xen.Rsg(sampleNum=5000)
# generate scenario of 1 dimension
results = xen.generate1d(m, None, timeGrid, rsg, filename, False)
def timegrid_test():
ref_date = mx.Date.todaysDate()
# TimeGrid
timeGrid1 = mx.TimeEqualGrid(ref_date, 3, 365)
print(timeGrid1.times())
timeGrid2 = mx.TimeArrayGrid(
ref_date, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])
print(timeGrid2.times())
timeGrid4 = mx.TimeGrid(refDate=ref_date,
maxYear=10,
frequency='custom',
frequency_month=8,
frequency_day=10)
print(timeGrid4.times())
timeGrid3 = mx.TimeGrid(ref_date, maxYear=10, frequency='endofmonth')
print(timeGrid3.times())
def build_scenario(overwrite=True):
print('stepdown test...', filename)
if not overwrite:
return
initialValues = [387833, 27450]
dividends = [0.0247, 0.0181]
volatilities = [0.2809, 0.5795]
gbmconst1 = mx.GBMConstModel('gbmconst', initialValues[0], riskFree,
dividends[0], volatilities[0])
gbmconst2 = mx.GBMConstModel('gbmconst', initialValues[1], riskFree,
dividends[1], volatilities[1])
models = [gbmconst1, gbmconst2]
corr = 0.6031
corrMatrix = mx.Matrix([[1.0, corr], [corr, 1.0]])
timeGrid = mx.TimeEqualGrid(refDate, 3, 365)
# random
scenario_num = 30000
dimension = (len(timeGrid) - 1) * len(models)
seed = 100
skip = 0
isMomentMatching = False
randomType = "crude"
subType = "mersennetwister"
randomTransformType = "invnormal"
rsg = mx.Rsg(scenario_num, dimension, seed, skip, isMomentMatching,
randomType, subType, randomTransformType)
scen = mx.ScenarioGenerator(models, corrMatrix, timeGrid, rsg, False,
filename, False)
scen.generate()
results = mx.ScenarioResult(filename)
def test():
print('multiplemodels test...', filename)
ref_date = mx.Date(2018, 9, 9)
initialValue = 10000
riskFree = 0.032
dividend = 0.01
volatility = 0.15
gbmconst1 = mx.GBMConstModel('gbmconst', initialValue, riskFree, dividend,
volatility)
gbmconst2 = mx.GBMConstModel('gbmconst', initialValue, riskFree, dividend,
volatility)
models = [gbmconst1, gbmconst2]
corrMatrix = mx.Matrix([[1.0, 0.0], [0.0, 1.0]])
timeGrid = mx.TimeEqualGrid(ref_date, 3, 365)
# random
scenario_num = 5000
dimension = (len(timeGrid) - 1) * len(models)
seed = 100
skip = 0
isMomentMatching = False
randomType = "crude"
subType = "mersennetwister"
randomTransformType = "invnormal"
rsg = mx.Rsg(scenario_num, dimension, seed, skip, isMomentMatching,
randomType, subType, randomTransformType)
scen = mx.ScenarioGenerator(models, corrMatrix, timeGrid, rsg, False,
filename, False)
scen.generate()
results = mx.ScenarioResult(filename)
print(results.multiPath(scenCount=10))
def test():
print('multiplemodels test...', filename)
initialValue = 10000
riskFree = 0.032
dividend = 0.01
volatility = 0.15
gbmconst1 = xen.GBMConst('gbmconst1', initialValue, riskFree, dividend,
volatility)
gbmconst2 = xen.GBMConst('gbmconst2', initialValue, riskFree, dividend,
volatility)
models = [gbmconst1, gbmconst2]
# corrMatrix = mx.Matrix([[1.0, 0.0],[0.0, 1.0]])
corrMatrix = mx.IdentityMatrix(len(models))
timeGrid = mx.TimeEqualGrid(ref_date, 3, 365)
# random
rsg = xen.Rsg(sampleNum=5000)
results = xen.generate(models, None, corrMatrix, timeGrid, rsg, filename,
False)
def test():
print('cir1f test...', filename)
ref_date = mx.Date(2018, 10, 10)
initialValue = 0.02
meanReverting = 0.1
longTermRate = 0.042
volatility = 0.03
cir1f = mx.CIR1FModel('cir1f', initialValue, meanReverting, longTermRate,
volatility)
models = [cir1f]
corrMatrix = mx.Matrix([[1.0]])
timeGrid = mx.TimeEqualGrid(ref_date, 3, 365)
# random
scenario_num = 5000
dimension = (len(timeGrid) - 1) * len(models)
seed = 100
skip = 0
isMomentMatching = False
randomType = "crude"
subType = "mersennetwister"
randomTransformType = "invnormal"
rsg = mx.Rsg(scenario_num, dimension, seed, skip, isMomentMatching,
randomType, subType, randomTransformType)
scen = mx.ScenarioGenerator(models, corrMatrix, timeGrid, rsg, False,
filename, False)
scen.generate()
results = mx.ScenarioResult(filename)
print(results.multiPath(scenCount=10))
def timegrid_test():
ref_date = mx.Date.todaysDate()
maxYear = 10
# TimeGrid
timeGrid1 = mx.TimeEqualGrid(ref_date, 3, 365)
timeGrid2 = mx.TimeArrayGrid(
ref_date, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])
timeGrid3 = mx.TimeGrid(refDate=ref_date,
maxYear=maxYear,
frequency_type='endofmonth')
timeGrid4 = mx.TimeGrid(refDate=ref_date,
maxYear=maxYear,
frequency_type='day')
timeGrid4 = mx.TimeGrid(refDate=ref_date,
maxYear=maxYear,
frequency_type='week')
timeGrid5 = mx.TimeGrid(refDate=ref_date,
maxYear=maxYear,
frequency_type='month',
frequency_day=10)
timeGrid6 = mx.TimeGrid(refDate=ref_date,
maxYear=maxYear,
frequency_type='quarter',
frequency_day=10)
timeGrid7 = mx.TimeGrid(refDate=ref_date,
maxYear=maxYear,
frequency_type='semiannual',
frequency_day=10)
timeGrid8 = mx.TimeGrid(refDate=ref_date,
maxYear=maxYear,
frequency_type='annual',
frequency_month=8,
frequency_day=10)
timeGrid9 = mx.TimeGrid(refDate=ref_date,
maxYear=maxYear,
frequency_type='firstofmonth')
timeGrid10 = mx.TimeGrid(refDate=ref_date,
maxYear=maxYear,
frequency_type='firstofquarter')
timeGrid11 = mx.TimeGrid(refDate=ref_date,
maxYear=maxYear,
frequency_type='firstofsemiannual')
timeGrid12 = mx.TimeGrid(refDate=ref_date,
maxYear=maxYear,
frequency_type='firstofannual')
timeGrid13 = mx.TimeGrid(refDate=ref_date,
maxYear=maxYear,
frequency_type='endofmonth')
timeGrid14 = mx.TimeGrid(refDate=ref_date,
maxYear=maxYear,
frequency_type='endofquarter')
timeGrid15 = mx.TimeGrid(refDate=ref_date,
maxYear=maxYear,
frequency_type='endofsemiannual')
timeGrid16 = mx.TimeGrid(refDate=ref_date,
maxYear=maxYear,
frequency_type='endofannual')
timeGrids = [
timeGrid1, timeGrid2, timeGrid3, timeGrid4, timeGrid5, timeGrid6,
timeGrid7, timeGrid8, timeGrid9, timeGrid10, timeGrid11, timeGrid12,
timeGrid13, timeGrid14, timeGrid15, timeGrid16
]
for tg, i in zip(timeGrids, range(len(timeGrids))):
print('timeGrid{0} :'.format(i + 1), tg.dates()[:10])
def test():
ref_date = mx.Date.todaysDate()
null_calendar = mx.NullCalendar()
# (period, rf, div)
tenor_rates = [('3M', 0.0151, 0.01), ('6M', 0.0152, 0.01),
('9M', 0.0153, 0.01), ('1Y', 0.0154, 0.01),
('2Y', 0.0155, 0.01), ('3Y', 0.0156, 0.01),
('4Y', 0.0157, 0.01), ('5Y', 0.0158, 0.01),
('7Y', 0.0159, 0.01), ('10Y', 0.016, 0.01),
('15Y', 0.0161, 0.01), ('20Y', 0.0162, 0.01)]
tenors = []
rf_rates = []
div_rates = []
vol = 0.2
interpolator1DType = mx.Interpolator1D.Linear
extrapolator1DType = mx.Extrapolator1D.FlatForward
for tr in tenor_rates:
tenors.append(tr[0])
rf_rates.append(tr[1])
div_rates.append(tr[2])
x0 = 420
# yieldCurve
rfCurve = ts.ZeroYieldCurve(ref_date, tenors, rf_rates, interpolator1DType,
extrapolator1DType)
divCurve = ts.ZeroYieldCurve(ref_date, tenors, div_rates,
interpolator1DType, extrapolator1DType)
utils.check_hashCode(rfCurve, divCurve)
# variance termstructure
const_vts = ts.BlackConstantVol(refDate=ref_date, vol=vol)
periods = [str(i + 1) + 'm' for i in range(0, 24)] # monthly upto 2 years
expirydates = [null_calendar.advance(ref_date, p) for p in periods]
volatilities = [
0.260, 0.223, 0.348, 0.342, 0.328, 0.317, 0.310, 0.302, 0.296, 0.291,
0.286, 0.282, 0.278, 0.275, 0.273, 0.270, 0.267, 0.263, 0.261, 0.258,
0.255, 0.253, 0.252, 0.251
]
curve_vts = ts.BlackVarianceCurve(refDate=ref_date,
dates=expirydates,
volatilities=volatilities)
utils.check_hashCode(const_vts, curve_vts)
# models
gbmconst = xen.GBMConst('gbmconst', x0=x0, rf=0.032, div=0.01, vol=0.15)
gbm = xen.GBM('gbm',
x0=x0,
rfCurve=rfCurve,
divCurve=divCurve,
volTs=curve_vts)
heston = xen.Heston('heston',
x0=x0,
rfCurve=rfCurve,
divCurve=divCurve,
v0=0.2,
volRevertingSpeed=0.1,
longTermVol=0.15,
volOfVol=0.1,
rho=0.3)
alphaPara = xen.DeterministicParameter(['1y', '20y', '100y'],
[0.1, 0.15, 0.15])
sigmaPara = xen.DeterministicParameter(['20y', '100y'], [0.01, 0.015])
hw1f = xen.HullWhite1F('hw1f',
fittingCurve=rfCurve,
alphaPara=alphaPara,
sigmaPara=sigmaPara)
bk1f = xen.BK1F('bk1f',
fittingCurve=rfCurve,
alphaPara=alphaPara,
sigmaPara=sigmaPara)
cir1f = xen.CIR1F('cir1f', r0=0.02, alpha=0.1, longterm=0.042, sigma=0.03)
vasicek1f = xen.Vasicek1F('vasicek1f',
r0=0.02,
alpha=0.1,
longterm=0.042,
sigma=0.03)
g2ext = xen.G2Ext('g2ext',
fittingCurve=rfCurve,
alpha1=0.1,
sigma1=0.01,
alpha2=0.2,
sigma2=0.02,
corr=0.5)
# calcs in models
hw1f_spot3m = hw1f.spot('hw1f_spot3m',
maturityTenor=mx.Period(3, mx.Months),
compounding=mx.Compounded)
hw1f_forward6m3m = hw1f.forward('hw1f_forward6m3m',
startTenor=mx.Period(6, mx.Months),
maturityTenor=mx.Period(3, mx.Months),
compounding=mx.Compounded)
hw1f_discountFactor = hw1f.discountFactor('hw1f_discountFactor')
hw1f_discountBond3m = hw1f.discountBond('hw1f_discountBond3m',
maturityTenor=mx.Period(
3, mx.Months))
# calcs
constantValue = xen.ConstantValue('constantValue', 15)
constantArr = xen.ConstantArray('constantArr', [15, 14, 13])
oper1 = gbmconst + gbm
oper2 = gbmconst - gbm
oper3 = (gbmconst * gbm).withName('multiple_gbmconst_gbm')
oper4 = gbmconst / gbm
oper5 = gbmconst + 10
oper6 = gbmconst - 10
oper7 = gbmconst * 1.1
oper8 = gbmconst / 1.1
oper9 = 10 + gbmconst
oper10 = 10 - gbmconst
oper11 = 1.1 * gbmconst
oper12 = 1.1 / gbmconst
linearOper1 = xen.LinearOper('linearOper1',
gbmconst,
multiple=1.1,
spread=10)
linearOper2 = gbmconst.linearOper('linearOper2', multiple=1.1, spread=10)
shiftRight1 = xen.Shift('shiftRight1', hw1f, shift=5)
shiftRight2 = hw1f.shift('shiftRight2', shift=5, fill_value=0.0)
shiftLeft1 = xen.Shift('shiftLeft1', cir1f, shift=-5)
shiftLeft2 = cir1f.shift('shiftLeft2', shift=-5, fill_value=0.0)
returns1 = xen.Returns('returns1', gbm, 'return')
returns2 = gbm.returns('returns2', 'return')
logreturns1 = xen.Returns('logreturns1', gbmconst, 'logreturn')
logreturns2 = gbmconst.returns('logreturns2', 'logreturn')
cumreturns1 = xen.Returns('cumreturns1', heston, 'cumreturn')
cumreturns2 = heston.returns('cumreturns2', 'cumreturn')
cumlogreturns1 = xen.Returns('cumlogreturns1', gbm, 'cumlogreturn')
cumlogreturns2 = gbm.returns('cumlogreturns2', 'cumlogreturn')
fixedRateBond = xen.FixedRateBond('fixedRateBond',
vasicek1f,
notional=10000,
fixedRate=0.0,
couponTenor=mx.Period(3, mx.Months),
maturityTenor=mx.Period(3, mx.Years),
discountCurve=rfCurve)
# timegrid
maxYear = 10
timegrid1 = mx.TimeEqualGrid(refDate=ref_date, maxYear=3, nPerYear=365)
timegrid2 = mx.TimeArrayGrid(
refDate=ref_date,
times=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])
timegrid3 = mx.TimeGrid(refDate=ref_date,
maxYear=maxYear,
frequency_type='day')
timegrid4 = mx.TimeGrid(refDate=ref_date,
maxYear=maxYear,
frequency_type='week')
timegrid5 = mx.TimeGrid(refDate=ref_date,
maxYear=maxYear,
frequency_type='month',
frequency_day=10)
timegrid6 = mx.TimeGrid(refDate=ref_date,
maxYear=maxYear,
frequency_type='quarter',
frequency_day=10)
timegrid7 = mx.TimeGrid(refDate=ref_date,
maxYear=maxYear,
frequency_type='semiannual',
frequency_day=10)
timegrid8 = mx.TimeGrid(refDate=ref_date,
maxYear=maxYear,
frequency_type='annual',
frequency_month=8,
frequency_day=10)
timegrid9 = mx.TimeGrid(refDate=ref_date,
maxYear=maxYear,
frequency_type='firstofmonth')
timegrid10 = mx.TimeGrid(refDate=ref_date,
maxYear=maxYear,
frequency_type='firstofquarter')
timegrid11 = mx.TimeGrid(refDate=ref_date,
maxYear=maxYear,
frequency_type='firstofsemiannual')
timegrid12 = mx.TimeGrid(refDate=ref_date,
maxYear=maxYear,
frequency_type='firstofannual')
timegrid13 = mx.TimeGrid(refDate=ref_date,
maxYear=maxYear,
frequency_type='endofmonth')
timegrid14 = mx.TimeGrid(refDate=ref_date,
maxYear=maxYear,
frequency_type='endofquarter')
timegrid15 = mx.TimeGrid(refDate=ref_date,
maxYear=maxYear,
frequency_type='endofsemiannual')
timegrid16 = mx.TimeGrid(refDate=ref_date,
maxYear=maxYear,
frequency_type='endofannual')
# random
pseudo_rsg = xen.Rsg(sampleNum=1000,
dimension=365,
seed=1,
skip=0,
isMomentMatching=False,
randomType='pseudo',
subType='mersennetwister',
randomTransformType='boxmullernormal')
sobol_rsg = xen.Rsg(sampleNum=1000,
dimension=365,
seed=1,
skip=0,
isMomentMatching=False,
randomType='sobol',
subType='joekuod7',
randomTransformType='invnormal')
# single model
filename1 = './single_model.npz'
results1 = xen.generate1d(model=gbm,
calcs=None,
timegrid=timegrid1,
rsg=pseudo_rsg,
filename=filename1,
isMomentMatching=False)
# multiple model
filename2 = './multiple_model.npz'
models = [gbmconst, gbm, hw1f, cir1f, vasicek1f]
corrMatrix = mx.IdentityMatrix(len(models))
results2 = xen.generate(models=models,
calcs=None,
corr=corrMatrix,
timegrid=timegrid3,
rsg=sobol_rsg,
filename=filename2,
isMomentMatching=False)
# multiple model with calc
filename3 = './multiple_model_with_calc.npz'
calcs = [
oper1, oper3, linearOper1, linearOper2, shiftLeft2, returns1,
fixedRateBond, hw1f_spot3m
]
results3 = xen.generate(models=models,
calcs=calcs,
corr=corrMatrix,
timegrid=timegrid4,
rsg=sobol_rsg,
filename=filename3,
isMomentMatching=False)
all_models = [gbmconst, gbm, heston, hw1f, bk1f, cir1f, vasicek1f, g2ext]
all_calcs = [
hw1f_spot3m, hw1f_forward6m3m, hw1f_discountFactor,
hw1f_discountBond3m, constantValue, constantArr, oper1, oper2, oper3,
oper4, oper5, oper6, oper7, oper8, oper9, oper10, oper11, oper12,
linearOper1, linearOper2, shiftRight1, shiftRight2, shiftLeft1,
shiftLeft2, returns1, returns2, logreturns1, logreturns2, cumreturns1,
cumreturns2, cumlogreturns1, cumlogreturns2, fixedRateBond
]
filename4 = './multiple_model_with_calc_all.npz'
corrMatrix2 = mx.IdentityMatrix(len(all_models))
corrMatrix2[1][0] = 0.5
corrMatrix2[0][1] = 0.5
results4 = xen.generate(models=all_models,
calcs=all_calcs,
corr=corrMatrix2,
timegrid=timegrid4,
rsg=sobol_rsg,
filename=filename4,
isMomentMatching=False)
# results
results = results3
resultsInfo = (results.genInfo, results.refDate, results.maxDate,
results.maxTime, results.randomMomentMatch,
results.randomSubtype, results.randomType, results.seed,
results.shape)
ndarray = results.toNumpyArr() # pre load all scenario data to ndarray
t_pos = 1
scenCount = 15
# scenario path of selected scenCount
# ((100.0, 82.94953421561434, 110.87375162324332, 91.96798678908293, 70.29920544659505, ... ),
# (100.0, 96.98838977927142, 97.0643112022828, 91.19803393176569, 104.94407125936456, ... ),
# ...
# (200.0, 179.93792399488575, 207.93806282552612, 183.16602072084862, ... ),
# (9546.93761943355, 9969.778029330208, 10758.449206155927, 11107.968356394866, ... ))
multipath = results[scenCount]
multipath_arr = ndarray[scenCount]
# t_pos data
multipath_t_pos = results.tPosSlice(
t_pos=t_pos, scenCount=scenCount
) # (82.94953421561434, 96.98838977927142, 0.015097688448292656, 0.02390612251701627, ... )
multipath_t_pos_arr = ndarray[scenCount, :, t_pos]
multipath_all_t_pos = results.tPosSlice(t_pos=t_pos) # all t_pos data
# t_pos data of using date
t_date = ref_date + 10
multipath_using_date = results.dateSlice(
date=t_date, scenCount=scenCount
) # (99.5327905069975, 99.91747715856324, 0.015099936660211026, 0.020107033880707947, ... )
multipath_all_using_date = results.dateSlice(date=t_date) # all t_pos data
# t_pos data of using time
t_time = 1.32
multipath_using_time = results.timeSlice(
time=t_time, scenCount=scenCount
) # (91.88967340028992, 97.01269656928498, 0.018200574048792405, 0.02436896520516243, ... )
multipath_all_using_time = results.timeSlice(time=t_time) # all t_pos data
# analyticPath and test calculation
all_pv_list = []
all_pv_list.extend(all_models)
all_pv_list.extend(all_calcs)
for pv in all_pv_list:
analyticPath = pv.analyticPath(timegrid2)
input_arr = [0.01, 0.02, 0.03, 0.04, 0.05]
input_arr2d = [[0.01, 0.02, 0.03, 0.04, 0.05],
[0.06, 0.07, 0.08, 0.09, 0.1]]
for pv in all_calcs:
if pv.sourceNum == 1:
calculatePath = pv.calculatePath(input_arr, timegrid1)
elif pv.sourceNum == 2:
calculatePath = pv.calculatePath(input_arr2d, timegrid1)
else:
pass
# repository
repo_path = './xenrepo'
repo_config = {'location': repo_path}
repo = mx_dr.FolderRepository(repo_config)
mx_dr.settings.set_repo(repo)
# xenarix manxager
xm = repo.xenarix_manager
filename5 = 'scen_all.npz'
scen_all = xen.Scenario(models=all_models,
calcs=all_calcs,
corr=corrMatrix2,
timegrid=timegrid4,
rsg=sobol_rsg,
filename=filename5,
isMomentMatching=False)
filename6 = 'scen_multiple.npz'
scen_multiple = xen.Scenario(models=models,
calcs=[],
corr=corrMatrix,
timegrid=timegrid4,
rsg=pseudo_rsg,
filename=filename6,
isMomentMatching=False)
utils.check_hashCode(scen_all)
# scenario - save, load, list
name1 = 'name1'
xm.save_xen(name1, scen_all) #
scen_name1 = xm.load_xen(name=name1)
scen_name1.filename = './reloaded_scenfile.npz'
scen_name1.generate()
name2 = 'name2'
xm.save_xens(name=name2, scen_all=scen_all, scen_multiple=scen_multiple)
scen_name2 = xm.load_xens(name=name2)
scenList = xm.scenList() # ['name1', 'name2']
# generate in result directory
xm.generate_xen(scenList[0])
# scenario template builder using market data
sb = xen.ScenarioBuilder()
sb.addModel(xen.GBMConst.__name__,
'gbmconst',
x0='kospi2',
rf='cd91',
div=0.01,
vol=0.3)
sb.addModel(xen.GBM.__name__,
'gbm',
x0=100,
rfCurve='zerocurve1',
divCurve=divCurve,
volTs=const_vts)
sb.addModel(xen.Heston.__name__,
'heston',
x0='ni225',
rfCurve='zerocurve1',
divCurve=divCurve,
v0=0.2,
volRevertingSpeed=0.1,
longTermVol=0.15,
volOfVol=0.1,
rho=0.3)
sb.addModel(xen.HullWhite1F.__name__,
'hw1f',
fittingCurve='zerocurve2',
alphaPara=alphaPara,
sigmaPara=sigmaPara)
sb.addModel(xen.BK1F.__name__,
'bk1f',
fittingCurve='zerocurve2',
alphaPara=alphaPara,
sigmaPara=sigmaPara)
sb.addModel(xen.CIR1F.__name__,
'cir1f',
r0='cd91',
alpha=0.1,
longterm=0.042,
sigma=0.03)
sb.addModel(xen.Vasicek1F.__name__,
'vasicek1f',
r0='cd91',
alpha='alpha1',
longterm=0.042,
sigma=0.03)
sb.addModel(xen.G2Ext.__name__,
'g2ext',
fittingCurve=rfCurve,
alpha1=0.1,
sigma1=0.01,
alpha2=0.2,
sigma2=0.02,
corr=0.5)
sb.corr[1][0] = 0.5
sb.corr[0][1] = 0.5
sb.corr[0][2] = 'kospi2_ni225_corr'
sb.corr[2][0] = 'kospi2_ni225_corr'
sb.addCalc(xen.SpotRate.__name__,
'hw1f_spot3m',
ir_pc='hw1f',
maturityTenor='3m',
compounding=mx.Compounded)
sb.addCalc(xen.ForwardRate.__name__,
'hw1f_forward6m3m',
ir_pc='hw1f',
startTenor=mx.Period(6, mx.Months),
maturityTenor=mx.Period(3, mx.Months),
compounding=mx.Compounded)
sb.addCalc(xen.DiscountFactor.__name__,
'hw1f_discountFactor',
ir_pc='hw1f')
sb.addCalc(xen.DiscountBond.__name__,
'hw1f_discountBond3m',
ir_pc='hw1f',
maturityTenor=mx.Period(3, mx.Months))
sb.addCalc(xen.ConstantValue.__name__, 'constantValue', v=15)
sb.addCalc(xen.ConstantArray.__name__, 'constantArr', arr=[15, 14, 13])
sb.addCalc(xen.AdditionOper.__name__,
'addOper1',
pc1='gbmconst',
pc2='gbm')
sb.addCalc(xen.SubtractionOper.__name__,
'subtOper1',
pc1='gbmconst',
pc2='gbm')
sb.addCalc(xen.MultiplicationOper.__name__,
'multiple_gbmconst_gbm',
pc1='gbmconst',
pc2='gbm')
sb.addCalc(xen.DivisionOper.__name__,
'divOper1',
pc1='gbmconst',
pc2='gbm')
sb.addCalc(xen.AdditionOper.__name__, 'addOper2', pc1='gbmconst', pc2=10)
sb.addCalc(xen.SubtractionOper.__name__,
'subtOper2',
pc1='gbmconst',
pc2=10)
sb.addCalc(xen.MultiplicationOper.__name__,
'mulOper2',
pc1='gbmconst',
pc2=1.1)
sb.addCalc(xen.DivisionOper.__name__, 'divOper1', pc1='gbmconst', pc2=1.1)
sb.addCalc(xen.AdditionOper.__name__, 'addOper2', pc1=10, pc2='gbmconst')
sb.addCalc(xen.SubtractionOper.__name__,
'subtOper2',
pc1=10,
pc2='gbmconst')
sb.addCalc(xen.MultiplicationOper.__name__,
'mulOper2',
pc1=1.1,
pc2='gbmconst')
sb.addCalc(xen.DivisionOper.__name__, 'divOper1', pc1=1.1, pc2='gbmconst')
sb.addCalc(xen.LinearOper.__name__,
'linearOper1',
pc='gbm',
multiple=1.1,
spread=10)
sb.addCalc(xen.Shift.__name__,
'shiftRight1',
pc='hw1f',
shift=5,
fill_value=0.0)
sb.addCalc(xen.Shift.__name__,
'shiftLeft1',
pc='cir1f',
shift=-5,
fill_value=0.0)
sb.addCalc(xen.Returns.__name__,
'returns1',
pc='gbm',
return_type='return')
sb.addCalc(xen.Returns.__name__,
'logreturns1',
pc='gbmconst',
return_type='logreturn')
sb.addCalc(xen.Returns.__name__,
'cumreturns1',
pc='heston',
return_type='cumreturn')
sb.addCalc(xen.Returns.__name__,
'cumlogreturns1',
pc='gbm',
return_type='cumlogreturn')
sb.addCalc(xen.FixedRateBond.__name__,
'fixedRateBond',
ir_pc='vasicek1f',
notional=10000,
fixedRate=0.0,
couponTenor=mx.Period(3, mx.Months),
maturityTenor=mx.Period(3, mx.Years),
discountCurve=rfCurve)
sb.addCalc(xen.AdditionOper.__name__,
'addOper_for_remove',
pc1='gbmconst',
pc2='gbm')
sb.removeCalc('addOper_for_remove')
# scenarioBuilder - save, load, list
mdp = mx_dp.SampleMarketDataProvider()
mrk = mdp.get_data()
xm.save_xnb('sb1', sb=sb)
sb.setTimeGridCls(timegrid3)
sb.setRsgCls(pseudo_rsg)
xm.save_xnb('sb2', sb=sb)
sb.setTimeGrid(mx.TimeGrid.__name__,
refDate=ref_date,
maxYear=10,
frequency_type='endofmonth')
sb.setRsg(xen.Rsg.__name__, sampleNum=1000)
xm.save_xnb('sb3', sb=sb)
xm.scenBuilderList() # ['sb1', 'sb2', 'sb3']
sb1_reload = xm.load_xnb('sb1')
sb2_reload = xm.load_xnb('sb2')
sb3_reload = xm.load_xnb('sb3')
utils.compare_hashCode(sb, sb3_reload)
utils.check_hashCode(sb, sb1_reload, sb2_reload, sb3_reload)
xm.generate_xnb('sb1', mrk)
xm.load_results_xnb('sb1')
scen = sb.build_scenario(mrk)
utils.check_hashCode(scen, sb)
res = scen.generate()
res1 = scen.generate_clone(
filename='new_temp.npz') # clone generate with some change
# res.show()
# marketdata
mrk_clone = mrk.clone()
utils.compare_hashCode(mrk, mrk_clone)
zerocurve1 = mrk.get_yieldCurve('zerocurve1')
zerocurve2 = mrk.get_yieldCurve('zerocurve2')
# shock definition
quote1 = mx_q.SimpleQuote('quote1', 100)
qst_add = mx_s.QuoteShockTrait(name='add_up1', value=10, operand='add')
qst_mul = mx_s.QuoteShockTrait('mul_up1', 1.1, 'mul')
qst_ass = mx_s.QuoteShockTrait('assign_up1', 0.03, 'assign')
qst_add2 = mx_s.QuoteShockTrait('add_down1', 15, 'add')
qst_mul2 = mx_s.QuoteShockTrait('mul_down2', 0.9, 'mul')
quoteshocktrait_list = [qst_add, qst_mul, qst_ass, qst_add2, qst_mul2]
quoteshocktrait_results = [
100 + 10, (100 + 10) * 1.1, 0.03, 0.03 + 15, (0.03 + 15) * 0.9
]
quote1_d = quote1.toDict()
for st, res in zip(quoteshocktrait_list, quoteshocktrait_results):
st.calculate(quote1_d)
assert res == quote1_d['v']
qcst = mx_s.CompositeQuoteShockTrait('comp1', [qst_add2, qst_mul2])
ycps = mx_s.YieldCurveParallelBpShockTrait('parallel_up1', 10)
vcps = mx_s.VolTsParallelShockTrait('vol_up1', 0.1)
shocktrait_list = quoteshocktrait_list + [qcst, ycps, vcps]
# qcst = mx_s.CompositeQuoteShockTrait('comp2', [qst_add2, vcps])
# build shock from shocktraits
shock1 = mx_s.Shock(name='shock1')
shock1.addShockTrait(target='kospi2', shocktrait=qst_add)
shock1.addShockTrait(target='spx', shocktrait=qst_add)
shock1.addShockTrait(target='ni*', shocktrait=qst_add) # filter expression
shock1.addShockTrait(target='*', shocktrait=qst_mul)
shock1.addShockTrait(target='cd91', shocktrait=qst_ass)
shock1.addShockTrait(target='alpha1', shocktrait=qcst)
shock1.removeShockTrait(target='cd91')
shock1.removeShockTrait(shocktrait=qst_mul)
shock1.removeShockTrait(target='target2', shocktrait=ycps)
shock1.removeShockTraitAt(3)
# build shocked market data from shock
shocked_mrk1 = mx_s.build_shockedMrk(shock1, mrk)
shock2 = shock1.clone(name='shock2')
shocked_mrk2 = mx_s.build_shockedMrk(shock2, mrk)
utils.check_hashCode(shock1, shock2, shocked_mrk1, shocked_mrk2)
shockedScen_list = mx_s.build_shockedScen([shock1, shock2], sb, mrk)
shm = mx_s.ShockScenarioModel('shm1',
basescen=scen,
s_up=shockedScen_list[0],
s_down=shockedScen_list[1])
basescen_name = 'basescen'
shm.addCompositeScenRes(name='compscen1',
basescen_name=basescen_name,
gbmconst='s_down')
# shm.removeCompositeScenRes(name='compscen1')
shm.compositeScenResList() # ['compscen1']
# compare ?
csr = xen.CompositeScenarioResults(shm.shocked_scen_res_d,
basescen_name,
gbmconst='s_down')
csr_arr = csr.toNumpyArr()
base_arr = scen.getResults().toNumpyArr()
assert base_arr[0][0][0] + qst_add.value == csr_arr[0][0][
0] # replaced(gbmconst)
assert base_arr[0][1][0] == csr_arr[0][1][0] # not replaced(gbm)
# shock manager - save, load, list
# extensions : shock(.shk), shocktrait(.sht), shockscenariomodel(.shm)
sfm = repo.shock_manager
# shocktrait
sht_name = 'shocktraits'
sfm.save_shts(sht_name, *shocktrait_list)
reloaded_sht_d = sfm.load_shts(sht_name)
for s in shocktrait_list:
utils.check_hashCode(s, reloaded_sht_d[s.name])
utils.compare_hashCode(s, reloaded_sht_d[s.name])
# shock
shk_name = 'shocks'
sfm.save_shks(shk_name, shock1, shock2)
reloaded_shk_d = sfm.load_shks(shk_name)
for s in [shock1, shock2]:
utils.check_hashCode(s, reloaded_shk_d[s.name])
utils.compare_hashCode(s, reloaded_shk_d[s.name])
# shock scenario model
shm_name = 'shockmodel'
sfm.save_shm(shm_name, shm)
reloaded_shm = sfm.load_shm(shm_name)
utils.check_hashCode(shm, reloaded_shm)
utils.compare_hashCode(shm, reloaded_shm)
shocked_scen_list = mx_s.build_shockedScen([shock1, shock2], sb, mrk)
for i, scen in enumerate(shocked_scen_list):
name = 'shocked_scen{0}'.format(i)
xm.save_xen(name, scen)
res = scen.generate_clone(filename=name)
# bloomberg provider(blpapi) checking to request sample if available
try:
mx_dp.check_bloomberg()
except:
print('fail to check bloomberg')
# instruments pricing
# this is built-in instruments
# option1 = mx_i.EuropeanOption(option_type='c', strike=400, maturityDate=ref_date + 365)
# this is inherit instrument for user output
class EuropeanOptionForUserOutput(mx_i.EuropeanOption):
def userfunc_test(self, scen_data_d, calc_kwargs):
v = calc_kwargs['calc_arg1']
return v + 99
option = EuropeanOptionForUserOutput(option_type='c',
strike=400,
maturityDate=ref_date + 365)
# outputs
delta = mx_io.Delta(up='s_up', down='s_down')
gamma = mx_io.Gamma(up='s_up', center='basescen', down='s_down')
npv = mx_io.Npv(scen='basescen', currency='krw')
discount_cf = mx_io.CashFlow(scen='basescen',
currency='krw',
discount=None)
test_output = mx_io.UserFunc(scen='basescen',
userfunc=option.userfunc_test,
abc=10)
# calculate from scenario
results1 = option.calculateScen(
outputs=[npv, discount_cf, delta, gamma, test_output],
shm=shm,
reduce='aver',
path_kwargs={
's1': 'gbmconst',
'discount': 'hw1f_discountFactor'
},
calc_kwargs={'calc_arg1': 10})
# calculate from model
basescen = shm.getScenario('basescen')
gbmconst_basescen = basescen.getModel('gbmconst')
arg_d = {
'x0': gbmconst_basescen._x0,
'rf': gbmconst_basescen._rf,
'div': gbmconst_basescen._div,
'vol': gbmconst_basescen._vol
}
assert option.setPricingParams_GBMConst(**arg_d).NPV(
) == option.setPricingParams_Model(gbmconst_basescen).NPV()
# calendar holiday
mydates = [
mx.Date(2022, 10, 11),
mx.Date(2022, 10, 12),
mx.Date(2022, 10, 13),
mx.Date(2022, 11, 11)
]
kr_cal = mx.SouthKorea()
user_cal = mx.UserCalendar('testcal')
for cal in [kr_cal, user_cal]:
repo.addHolidays(cal, mydates, onlyrepo=False)
# repo.removeHolidays(cal, mydates, onlyrepo=False)
# graph
# rfCurve.graph_view(show=False)
# report
html_template = '''
<!DOCTYPE html>
<html>
<head><title>{{ name }}</title></head>
<body>
<h1>Scenario Summary - Custom Template</h1>
<p>models : {{ models_num }} - {{ model_names }}</p>
<p>calcs : {{ calcs_num }} - {{ calc_names }}</p>
<p>corr : {{ corr }}</p>
<p>timegrid : {{ timegrid_items }}</p>
<p>filename : {{ scen.filename }}</p>
<p>ismomentmatch : {{ scen.isMomentMatching }}</p>
</body>
'''
html = scen.report(typ='html',
html_template=html_template,
browser_isopen=False)
def test():
ref_date = mx.Date.todaysDate()
# (period, rf, div)
tenor_rates = [('3M', 0.0151, 0.01), ('6M', 0.0152, 0.01),
('9M', 0.0153, 0.01), ('1Y', 0.0154, 0.01),
('2Y', 0.0155, 0.01), ('3Y', 0.0156, 0.01),
('4Y', 0.0157, 0.01), ('5Y', 0.0158, 0.01),
('7Y', 0.0159, 0.01), ('10Y', 0.016, 0.01),
('15Y', 0.0161, 0.01), ('20Y', 0.0162, 0.01)]
tenors = []
rf_rates = []
div_rates = []
vol = 0.2
interpolator1DType = mx.Interpolator1D.Linear
extrapolator1DType = mx.Extrapolator1D.FlatForward
for tr in tenor_rates:
tenors.append(tr[0])
rf_rates.append(tr[1])
div_rates.append(tr[2])
rfCurve = ts.ZeroYieldCurve(ref_date, tenors, rf_rates, interpolator1DType,
extrapolator1DType)
divCurve = ts.ZeroYieldCurve(ref_date, tenors, div_rates,
interpolator1DType, extrapolator1DType)
volTs = ts.BlackConstantVol(ref_date, vol)
# models
gbmconst = xen.GBMConst('gbmconst', x0=100, rf=0.032, div=0.01, vol=0.15)
gbm = xen.GBM('gbm',
x0=100,
rfCurve=rfCurve,
divCurve=divCurve,
volTs=volTs)
heston = xen.Heston('heston',
x0=100,
rfCurve=rfCurve,
divCurve=divCurve,
v0=0.2,
volRevertingSpeed=0.1,
longTermVol=0.15,
volOfVol=0.1,
rho=0.3)
alphaPara = xen.DeterministicParameter(['1y', '20y', '100y'],
[0.1, 0.15, 0.15])
sigmaPara = xen.DeterministicParameter(['20y', '100y'], [0.01, 0.015])
hw1f = xen.HullWhite1F('hw1f',
fittingCurve=rfCurve,
alphaPara=alphaPara,
sigmaPara=sigmaPara)
bk1f = xen.BK1F('bk1f',
fittingCurve=rfCurve,
alphaPara=alphaPara,
sigmaPara=sigmaPara)
cir1f = xen.CIR1F('cir1f', r0=0.02, alpha=0.1, longterm=0.042, sigma=0.03)
vasicek1f = xen.Vasicek1F('vasicek1f',
r0=0.02,
alpha=0.1,
longterm=0.042,
sigma=0.03)
g2ext = xen.G2Ext('g2ext',
fittingCurve=rfCurve,
alpha1=0.1,
sigma1=0.01,
alpha2=0.2,
sigma2=0.02,
corr=0.5)
# calcs in models
hw1f_spot3m = hw1f.spot('hw1f_spot3m',
maturityTenor=mx.Period(3, mx.Months),
compounding=mx.Compounded)
hw1f_forward6m3m = hw1f.forward('hw1f_forward6m3m',
startTenor=mx.Period(6, mx.Months),
maturityTenor=mx.Period(3, mx.Months),
compounding=mx.Compounded)
hw1f_discountFactor = hw1f.discountFactor('hw1f_discountFactor')
hw1f_discountBond3m = hw1f.discountBond('hw1f_discountBond3m',
maturityTenor=mx.Period(
3, mx.Months))
# calcs
constantValue = xen.ConstantValue('constantValue', 15)
constantArr = xen.ConstantArray('constantArr', [15, 14, 13])
oper1 = gbmconst + gbm
oper2 = gbmconst - gbm
oper3 = (gbmconst * gbm).withName('multiple_gbmconst_gbm')
oper4 = gbmconst / gbm
oper5 = gbmconst + 10
oper6 = gbmconst - 10
oper7 = gbmconst * 1.1
oper8 = gbmconst / 1.1
oper9 = 10 + gbmconst
oper10 = 10 - gbmconst
oper11 = 1.1 * gbmconst
oper12 = 1.1 / gbmconst
linearOper1 = xen.LinearOper('linearOper1',
gbmconst,
multiple=1.1,
spread=10)
linearOper2 = gbmconst.linearOper('linearOper2', multiple=1.1, spread=10)
shiftRight1 = xen.Shift('shiftRight1', hw1f, shift=5)
shiftRight2 = hw1f.shift('shiftRight2', shift=5, fill_value=0.0)
shiftLeft1 = xen.Shift('shiftLeft1', cir1f, shift=-5)
shiftLeft2 = cir1f.shift('shiftLeft2', shift=-5, fill_value=0.0)
returns1 = xen.Returns('returns1', gbm, 'return')
returns2 = gbm.returns('returns2', 'return')
logreturns1 = xen.Returns('logreturns1', gbmconst, 'logreturn')
logreturns2 = gbmconst.returns('logreturns2', 'logreturn')
cumreturns1 = xen.Returns('cumreturns1', heston, 'cumreturn')
cumreturns2 = heston.returns('cumreturns2', 'cumreturn')
cumlogreturns1 = xen.Returns('cumlogreturns1', gbm, 'cumlogreturn')
cumlogreturns2 = gbm.returns('cumlogreturns2', 'cumlogreturn')
fixedRateBond = xen.FixedRateBond('fixedRateBond',
vasicek1f,
notional=10000,
fixedRate=0.0,
couponTenor=mx.Period(3, mx.Months),
maturityTenor=mx.Period(3, mx.Years),
discountCurve=rfCurve)
# timegrid
timegrid1 = mx.TimeEqualGrid(refDate=ref_date, maxYear=3, nPerYear=365)
timegrid2 = mx.TimeArrayGrid(
refDate=ref_date,
times=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])
timegrid3 = mx.TimeGrid(refDate=ref_date,
maxYear=10,
frequency='endofmonth')
timegrid4 = mx.TimeGrid(refDate=ref_date,
maxYear=10,
frequency='custom',
frequency_month=8,
frequency_day=10)
# random
pseudo_rsg = xen.Rsg(sampleNum=1000,
dimension=365,
seed=0,
skip=0,
isMomentMatching=False,
randomType='pseudo',
subType='mersennetwister',
randomTransformType='boxmullernormal')
sobol_rsg = xen.Rsg(sampleNum=1000,
dimension=365,
seed=0,
skip=0,
isMomentMatching=False,
randomType='sobol',
subType='joekuod7',
randomTransformType='invnormal')
# single model
filename1 = './single_model.npz'
results1 = xen.generate1d(model=gbm,
calcs=None,
timegrid=timegrid1,
rsg=pseudo_rsg,
filename=filename1,
isMomentMatching=False)
# multiple model
filename2 = './multiple_model.npz'
models = [gbmconst, gbm, hw1f, cir1f, vasicek1f]
corrMatrix = mx.IdentityMatrix(len(models))
results2 = xen.generate(models=models,
calcs=None,
corr=corrMatrix,
timegrid=timegrid3,
rsg=sobol_rsg,
filename=filename2,
isMomentMatching=False)
# multiple model with calc
filename3 = './multiple_model_with_calc.npz'
calcs = [
oper1, oper3, linearOper1, linearOper2, shiftLeft2, returns1,
fixedRateBond, hw1f_spot3m
]
results3 = xen.generate(models=models,
calcs=calcs,
corr=corrMatrix,
timegrid=timegrid4,
rsg=sobol_rsg,
filename=filename3,
isMomentMatching=False)
all_models = [gbmconst, gbm, heston, hw1f, bk1f, cir1f, vasicek1f, g2ext]
all_calcs = [
hw1f_spot3m, hw1f_forward6m3m, hw1f_discountFactor,
hw1f_discountBond3m, constantValue, constantArr, oper1, oper2, oper3,
oper4, oper5, oper6, oper7, oper8, oper9, oper10, oper11, oper12,
linearOper1, linearOper2, shiftRight1, shiftRight2, shiftLeft1,
shiftLeft2, returns1, returns2, logreturns1, logreturns2, cumreturns1,
cumreturns2, cumlogreturns1, cumlogreturns2, fixedRateBond
]
filename4 = './multiple_model_with_calc_all.npz'
corrMatrix2 = mx.IdentityMatrix(len(all_models))
results4 = xen.generate(models=all_models,
calcs=all_calcs,
corr=corrMatrix2,
timegrid=timegrid4,
rsg=sobol_rsg,
filename=filename4,
isMomentMatching=False)
# results
results = results3
genInfo = results.genInfo
refDate = results.refDate
maxDate = results.maxDate
maxTime = results.maxTime
randomMomentMatch = results.randomMomentMatch
randomSubtype = results.randomSubtype
randomType = results.randomType
seed = results.seed
shape = results.shape
ndarray = results.toNumpyArr() # pre load all scenario data to ndarray
t_pos = 1
scenCount = 15
# scenario path of selected scenCount
# ((100.0, 82.94953421561434, 110.87375162324332, 91.96798678908293, 70.29920544659505, ... ),
# (100.0, 96.98838977927142, 97.0643112022828, 91.19803393176569, 104.94407125936456, ... ),
# ...
# (200.0, 179.93792399488575, 207.93806282552612, 183.16602072084862, ... ),
# (9546.93761943355, 9969.778029330208, 10758.449206155927, 11107.968356394866, ... ))
multipath = results[scenCount]
multipath_arr = ndarray[scenCount]
# t_pos data
multipath_t_pos = results.tPosSlice(
t_pos=t_pos, scenCount=scenCount
) # (82.94953421561434, 96.98838977927142, 0.015097688448292656, 0.02390612251701627, ... )
multipath_t_pos_arr = ndarray[scenCount, :, t_pos]
multipath_all_t_pos = results.tPosSlice(t_pos=t_pos) # all t_pos data
# t_pos data of using date
t_date = ref_date + 10
multipath_using_date = results.dateSlice(
date=t_date, scenCount=scenCount
) # (99.5327905069975, 99.91747715856324, 0.015099936660211026, 0.020107033880707947, ... )
multipath_all_using_date = results.dateSlice(date=t_date) # all t_pos data
# t_pos data of using time
t_time = 1.32
multipath_using_time = results.timeSlice(
time=t_time, scenCount=scenCount
) # (91.88967340028992, 97.01269656928498, 0.018200574048792405, 0.02436896520516243, ... )
multipath_all_using_time = results.timeSlice(time=t_time) # all t_pos data
# analyticPath and test calculation
all_pv_list = []
all_pv_list.extend(all_models)
all_pv_list.extend(all_calcs)
for pv in all_pv_list:
analyticPath = pv.analyticPath(timegrid2)
input_arr = [0.01, 0.02, 0.03, 0.04, 0.05]
input_arr2d = [[0.01, 0.02, 0.03, 0.04, 0.05],
[0.06, 0.07, 0.08, 0.09, 0.1]]
for pv in all_calcs:
if pv.sourceNum == 1:
calculatePath = pv.calculatePath(input_arr, timegrid1)
elif pv.sourceNum == 2:
calculatePath = pv.calculatePath(input_arr2d, timegrid1)
else:
pass
# Xenarix Manager
xfm_config = {'location': 'd:/mxdevtool'}
xm = xen.XenarixFileManager(xfm_config)
filename5 = 'scen_all.npz'
scen_all = xen.Scenario(models=all_models,
calcs=all_calcs,
corr=corrMatrix2,
timegrid=timegrid4,
rsg=sobol_rsg,
filename=filename5,
isMomentMatching=False)
filename6 = 'scen_multiple.npz'
scen_multiple = xen.Scenario(models=models,
calcs=[],
corr=corrMatrix,
timegrid=timegrid4,
rsg=pseudo_rsg,
filename=filename6,
isMomentMatching=False)
scen_all_hashCode = scen_all.hashCode()
scen_all_hashCode2 = scen_all.fromDict(scen_all.toDict()).hashCode()
if scen_all_hashCode != scen_all_hashCode2:
raise Exception('hashcode is not same')
# save, load, scenario list
name1 = 'name1'
xm.save(name=name1, scen=scen_all)
scen_name1 = xm.load(name=name1)
scen_name1['scen0'].filename = './reloaded_scenfile.npz'
scen_name1['scen0'].generate()
name2 = 'name2'
xm.save(name=name2, scen=[scen_all, scen_multiple])
scen_name2 = xm.load(name=name2)
name3 = 'name3'
xm.save(name=name3,
scen={
'scen_all': scen_all,
'scen_multiple': scen_multiple
})
scen_name3 = xm.load(name=name3)
scenList = xm.scenList() # ['name1', 'name2', 'name3']
def test():
print('gbm test...', filename)
ref_date = mx.Date(2018, 9, 9)
initialValue = 10000
# (period, rf, div)
tenor_rates = [('3M', 0.0151, 0.01), ('6M', 0.0152, 0.01),
('9M', 0.0153, 0.01), ('1Y', 0.0154, 0.01),
('2Y', 0.0155, 0.01), ('3Y', 0.0156, 0.01),
('4Y', 0.0157, 0.01), ('5Y', 0.0158, 0.01),
('7Y', 0.0159, 0.01), ('10Y', 0.016, 0.01),
('15Y', 0.0161, 0.01), ('20Y', 0.0162, 0.01)]
tenors = []
rf_rates = []
div_rates = []
vols = []
interpolator1DType = mx.Interpolator1D.Linear
extrapolator1DType = mx.Extrapolator1D.FlatForward
for tr in tenor_rates:
tenors.append(tr[0])
rf_rates.append(tr[1])
div_rates.append(tr[2])
rfCurve = mx.ZeroYieldCurveExt(ref_date, tenors, rf_rates,
interpolator1DType, extrapolator1DType,
'irskrw_krccp')
divCurve = mx.ZeroYieldCurveExt(ref_date, tenors, div_rates,
interpolator1DType, extrapolator1DType,
'irskrw_krccp')
volCurve = mx.BlackConstantVol(ref_date, 0.2, 'irskrw_krccp')
gbm = mx.GBMModel('gbm', initialValue, rfCurve, divCurve, volCurve)
models = [gbm]
corrMatrix = mx.Matrix([[1.0]])
timeGrid = mx.TimeEqualGrid(ref_date, 3, 365)
# random
scenario_num = 5000
dimension = (len(timeGrid) - 1) * len(models)
seed = 100
skip = 0
isMomentMatching = False
randomType = "crude"
subType = "mersennetwister"
randomTransformType = "invnormal"
rsg = mx.Rsg(scenario_num, dimension, seed, skip, isMomentMatching,
randomType, subType, randomTransformType)
scen = mx.ScenarioGenerator(models, corrMatrix, timeGrid, rsg, False,
filename, False)
scen.generate()
results = mx.ScenarioResult(filename)
print(results.multiPath(scenCount=10))
r0=0.015,
alpha=0.1,
longterm=0.04,
sigma=0.01)
models = [gbmconst1, gbmconst2, vasicek]
# 모델간 상관계수 설정
corrMatrix = mx.IdentityMatrix(len(models))
gbmconst1_gbmconst2_corr = 0.3
corrMatrix[1][0] = gbmconst1_gbmconst2_corr
corrMatrix[0][1] = gbmconst1_gbmconst2_corr
gbmconst1_vasicek_corr = 0.1
corrMatrix[2][0] = gbmconst1_vasicek_corr
corrMatrix[0][2] = gbmconst1_vasicek_corr
# 시간 간격 및 최대 생성 구간 설정
timeGrid = mx.TimeEqualGrid(ref_date, 3, 365)
# random
filename = './multipleassets.npz'
rsg = xen.Rsg(sampleNum=5000)
results = xen.generate(models, None, corrMatrix, timeGrid, rsg, filename,
False)
# 결과 로드
results = xen.ScenarioResults(filename)
data = results.toNumpyArr()
