import sys, os import mxdevtool as mx import mxdevtool.xenarix as xen import mxdevtool.termstructures as ts import numpy as np filename = './test_hw1f.npz' ref_date = mx.Date.todaysDate() def model(): 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 = ts.ZeroYieldCurve(ref_date, tenors, zerorates, interpolator1DType, extrapolator1DType) 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, alphaPara, sigmaPara) return hw1f def test(): print('hw1f test...', filename) m = model() timeGrid = mx.TimeDateGrid_Equal(ref_date, 3, 365) rsg = xen.Rsg(sampleNum=5000) results = xen.generate1d(m, None, timeGrid, rsg, filename, False) if __name__ == "__main__": test()

import os, time, platform import numpy as np import mxdevtool as mx import mxdevtool.shock as mx_s import mxdevtool.xenarix as xen import mxdevtool.termstructures as ts import mxdevtool.quotes as mx_q import mxdevtool.marketconvension as mx_m import mxdevtool.data.providers as mx_dp import mxdevtool.data.repositories as mx_dr import mxdevtool.utils as utils

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', 0.0160, 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)

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)

hw1f_spot3m = hw1f.spot('hw1f_spot3m', maturity=mx.Period(3, mx.Months), compounding=mx.Compounded) hw1f_overnight = hw1f.overnight('hw1f_sofr', mx_m.IndexFactory().get_overnightIndex('sofr')) hw1f_libor = hw1f.ibor('libor3m', mx_m.IndexFactory().get_iborIndex('libor', mx.Period(3, mx.Months))) hw1f_swap = hw1f.swaprate('cms5y', mx_m.IndexFactory().get_swapIndex('krwirs', mx.Period(5, mx.Years), mx.Period(3, mx.Months))) hw1f_bond = hw1f.bondrate('cmt10y', mx_m.IndexFactory().get_bondIndex('ktb', mx.Period(5, mx.Years), mx.Period(6, mx.Months))) # hw1f_forward6m3m = hw1f.forward('hw1f_forward6m3m', startTenor=mx.Period(6, mx.Months), maturityTenor=mx.Period(3, mx.Months), compounding=mx.Compounded) hw1f_forward6m3m = hw1f.forward('hw1f_forward6m3m', startTenor=0.5, maturityTenor=3.0, compounding=mx.Compounded) hw1f_discountFactor = hw1f.discountFactor('hw1f_discountFactor') hw1f_discountBond3m = hw1f.discountBond('hw1f_discountBond3m', maturity=mx.Period(3, mx.Months)) r_t = 0.02 # short rate hw1f.model_discountBond(0.0, 1.0, r_t) hw1f.model_spot(1.0, 2.0, r_t) # continuous compounding hw1f.model_forward(1.0, 2.0, 3.0, r_t) # continuous compounding hw1f.model_discount(1.0) #

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)

timegrid1 = mx.TimeDateGrid_Equal(refDate=ref_date, maxYear=3, nPerYear=365) timegrid2 = mx.TimeDateGrid_Times(refDate=ref_date, times=[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]) timegrid3 = mx.TimeDateGrid_Custom(refDate=ref_date, maxYear=maxYear, frequency_type='day') timegrid4 = mx.TimeDateGrid_Custom(refDate=ref_date, maxYear=maxYear, frequency_type='week') timegrid5 = mx.TimeDateGrid_Custom(refDate=ref_date, maxYear=maxYear, frequency_type='month', frequency_day=10) timegrid6 = mx.TimeDateGrid_Custom(refDate=ref_date, maxYear=maxYear, frequency_type='quarter', frequency_day=10) timegrid7 = mx.TimeDateGrid_Custom(refDate=ref_date, maxYear=maxYear, frequency_type='semiannual', frequency_day=10) timegrid8 = mx.TimeDateGrid_Custom(refDate=ref_date, maxYear=maxYear, frequency_type='annual', frequency_month=8, frequency_day=10) timegrid9 = mx.TimeDateGrid_Custom(refDate=ref_date, maxYear=maxYear, frequency_type='firstofmonth') timegrid10 = mx.TimeDateGrid_Custom(refDate=ref_date, maxYear=maxYear, frequency_type='firstofquarter') timegrid11 = mx.TimeDateGrid_Custom(refDate=ref_date, maxYear=maxYear, frequency_type='firstofsemiannual') timegrid12 = mx.TimeDateGrid_Custom(refDate=ref_date, maxYear=maxYear, frequency_type='firstofannual') timegrid13 = mx.TimeDateGrid_Custom(refDate=ref_date, maxYear=maxYear, frequency_type='endofmonth') timegrid14 = mx.TimeDateGrid_Custom(refDate=ref_date, maxYear=maxYear, frequency_type='endofquarter') timegrid15 = mx.TimeDateGrid_Custom(refDate=ref_date, maxYear=maxYear, frequency_type='endofsemiannual') timegrid16 = mx.TimeDateGrid_Custom(refDate=ref_date, maxYear=maxYear, frequency_type='endofannual')

pseudo_rsg = xen.Rsg(sampleNum=1000, dimension=365, seed=1, skip=0, isMomentMatching=False, randomType='pseudo', subType='mersennetwister', randomTransformType='boxmullernormal') pseudo_rsg2 = xen.RsgPseudo(sampleNum=1000, dimension=365, randomTransformType='uniform') halton_rsg = xen.RsgHalton(sampleNum=1000, dimension=365) sobol_rsg = xen.Rsg(sampleNum=1000, dimension=365, seed=1, skip=2048, isMomentMatching=False, randomType='sobol', subType='joekuod7', randomTransformType='invnormal') sobol_rsg2 = xen.RsgSobol(sampleNum=1000, dimension=365, skip=2048) latinhs_rsg = xen.RsgLatinHs(pseudo_rsg2) arr = np.random.random((1000, 365 * 3)) # timegrid1, rand is not fixed np.save('./external_rsg.npy', arr) external_rsg = xen.RsgExternal(sampleNum=1000, dimension=365 * 3, filename='./external_rsg.npy') rsg_list = [pseudo_rsg, pseudo_rsg2, halton_rsg, faure_rsg, sobol_rsg, sobol_rsg2, latinhs_rsg, external_rsg] # for rsg in rsg_list: # print(rsg.type(), rsg.nextSequence()[0:3], rsg.nextSequence()[0:3]) 

# 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 # correlation matrix should be positive semidefinite 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 = 264 scenCount = 15 calculated_tpos_264 = results.tPosSlice(t_pos, scenCount) multipath = results[scenCount] multipath_arr = ndarray[scenCount] # t_pos data multipath_t_pos = results.tPosSlice(t_pos=t_pos, scenCount=scenCount) 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) 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) multipath_all_using_time = results.timeSlice(time=t_time) # all t_pos data

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

repo_path = './xenrepo' repo_config = { 'location': repo_path } repo = mx_dr.FolderRepository(repo_config) mx_dr.settings.set_repo(repo)

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) # single contents 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) # multiple contents scen_name2 = xm.load_xens(name=name2) scenList = xm.scenList() # ['name1', 'name2'] # generate in result directory xm.generate_xen(scenList[0])

mdp = mx_dp.SampleMarketDataProvider() mrk = mdp.get_data() mrk_clone = mrk.clone() utils.compare_hashCode(mrk, mrk_clone) zerocurve1 = mrk.get_yieldCurve('zerocurve1') zerocurve2 = mrk.get_yieldCurve('zerocurve2')

sb = xen.ScenarioJsonBuilder() # the string parameters is converted by value in market data 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=volTs) sb.addModel(xen.Heston.__name__, 'heston', x0=100, 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_pv='hw1f', maturityTenor='3m', compounding=mx.Compounded) sb.addCalc(xen.ForwardRate.__name__, 'hw1f_forward6m3m', ir_pv='hw1f', startTenor=mx.Period(6, mx.Months), maturityTenor=mx.Period(3, mx.Months), compounding=mx.Compounded) sb.addCalc(xen.ForwardRate.__name__, 'hw1f_forward6m3m_2', ir_pv='hw1f', startTenor=0.5, maturityTenor=0.25, compounding=mx.Compounded) sb.addCalc(xen.DiscountFactor.__name__, 'hw1f_discountFactor', ir_pv='hw1f') sb.addCalc(xen.DiscountBond.__name__, 'hw1f_discountBond3m', ir_pv='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', pv1='gbmconst', pv2='gbm') sb.addCalc(xen.SubtractionOper.__name__, 'subtOper1', pv1='gbmconst', pv2='gbm') sb.addCalc(xen.MultiplicationOper.__name__, 'multiple_gbmconst_gbm', pv1='gbmconst', pv2='gbm') sb.addCalc(xen.DivisionOper.__name__, 'divOper1', pv1='gbmconst', pv2='gbm') sb.addCalc(xen.AdditionConstOper.__name__, 'addOper2', pv1='gbmconst', v=10) sb.addCalc(xen.SubtractionConstOper.__name__, 'subtOper2', pv1='gbmconst', v=10) sb.addCalc(xen.MultiplicationConstOper.__name__, 'mulOper2', pv1='gbmconst', v=1.1) sb.addCalc(xen.DivisionConstOper.__name__, 'divOper1', pv1='gbmconst', v=1.1) sb.addCalc(xen.AdditionConstReverseOper.__name__, 'addOper2', v=10, pv2='gbmconst') sb.addCalc(xen.SubtractionConstReverseOper.__name__, 'subtOper2', v=10, pv2='gbmconst') sb.addCalc(xen.MultiplicationConstReverseOper.__name__, 'mulOper2', v=1.1, pv2='gbmconst') sb.addCalc(xen.DivisionConstReverseOper.__name__, 'divOper1', v=1.1, pv2='gbmconst') sb.addCalc(xen.LinearOper.__name__, 'linearOper1', pv='gbm', multiple=1.1, spread=10) sb.addCalc(xen.Shift.__name__, 'shiftRight1', pv='hw1f', shift=5, fill_value=0.0) sb.addCalc(xen.Shift.__name__, 'shiftLeft1', pv='cir1f', shift=-5, fill_value=0.0) sb.addCalc(xen.Returns.__name__, 'returns1', pv='gbm', return_type='return') sb.addCalc(xen.Returns.__name__, 'logreturns1', pv='gbmconst', return_type='logreturn') sb.addCalc(xen.Returns.__name__, 'cumreturns1', pv='heston', return_type='cumreturn') sb.addCalc(xen.Returns.__name__, 'cumlogreturns1', pv='gbm', return_type='cumlogreturn') sb.addCalc(xen.FixedRateBond.__name__, 'fixedRateBond', ir_pv='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', pv1='gbmconst', pv2='gbm') sb.removeCalc('addOper_for_remove') # scenarioBuilder - save, load, list xm.save_xnb('sb1', sb=sb) sb.setTimeGridCls(timegrid3) sb.setRsgCls(pseudo_rsg) xm.save_xnb('sb2', sb=sb) sb.setTimeGrid(mx.TimeDateGrid_Custom.__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()

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) # qcst = mx_s.CompositeQuoteShockTrait('comp2', [qst_add2, vcps]) shocktrait_list = quoteshocktrait_list + [qcst, ycps, 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'] 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')

# calendar holiday mydates = [mx.Date(11, 10, 2022), mx.Date(12, 10, 2022), mx.Date(13, 10, 2022), mx.Date(11, 11, 2022)] 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>  ''' html = scen.report(typ='html', html_template=html_template, browser_isopen=False)