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Test.py
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Test.py
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#%%
# -*- coding: utf-8 -*-
import numpy as np
np.random.seed(752)
from Regression import *
from Exposures import *
#############################
# Setup
#############################
# Model setup
S = 1.0
model = 'ln'
sigma = 0.2
# Simulation setup
T = 5.0
step = .5
nbPreSimSamples = 100000
nbScenariosSamples = 100000
def Digit(Call,strike,overhedge): # digit with overhedges
return ( Call(strike-.5*overhedge) - Call(strike+.5*overhedge) ) / overhedge
def Range(Call,strikeDown,strikeUp,overhedge): # range with overhedges
return Digit(Call,strikeDown,overhedge) - Digit(Call,strikeUp,overhedge)
# Write payoff here
def payoffFn(Fwd,Call,Put):
# return Call(1.)
# return Put(1.)
# return Call(.9) - Call(1.1)
# return Fwd
# return Call(1.) + Put(1.)
return Range(Call,0.8,1.2,0.1)
# return Range(Call,1.2,1.6,0.1)
# return Range(Call,0.4,0.8,0.1)
# return Range(Call,0.7,0.9,0.1) - Range(Call,1.1,1.3,0.1)
# Regression setup
projectedRegression = True # project on a set of calls on udl factor and regress only the remainder
projectionNbPayoffs = 11
projectionPruning = 1.
projectionRegressRemainder = True
projectionType = 'calls' # project on 'calls' or 'moments'
projectionDegree = 3
chainedRegression = True
densifiedChainedRegression = False
densificationNbSamples = 10
testRegressorName = 'c' # 'c' for clustered, 'p' for polynomial, 'rf' for random forest...
testRegressorParams = {
# polynomial regressor
'p': {
'alpha': 1e-8,
'd': 2
},
# chebyshev regressor
'ch': {
'd': 10
},
# clustered regressor
'c': {
'nbClusters': 10,
'clusteringFraction': .1,
'smoothing': False,
'smoothingNbNeighbors': 1,
'smoothingGamma': 10.,
'regressorName': 'p',
'regressorParams': {
'alpha': 1e-15,
'd': 3
}
},
# random forest regressor
'rf': {
'max_depth': 5
},
# support vector regressor
'svr': {
'C': 1.0,
'kernel': 'rbf'
},
# kernel ridge regressor
'kr': {
'alpha': 1e-8,
'kernel': 'rbf'
},
# averaged regressor
'a': {
'subRegressorsSetup': [
{
'regressorWeight': 0.75,
'regressorName': 'c',
'regressorParams': {
'nbClusters': 10,
'clusteringFraction': .1,
'regressorName': 'p',
'regressorParams': {
'alpha': 1e-8,
'd': 3
}
}
},
{
'regressorWeight': 0.25,
'regressorName': 'c',
'regressorParams': {
'nbClusters': 25,
'clusteringFraction': .1,
'regressorName': 'p',
'regressorParams': {
'alpha': 1e-8,
'd': 3
}
}
}
]
}
}
testRegressOnFormula = False # regress directly on the formula provided eg to validate the functional fit of the regressor
# Plot setup
# During regression
plotPayoffDistribution = False
plotBinnedStatistics = False
plotClusterLabels = False
plotRegressions = False
plotFormulaPayoffWithRegressions = True
# During exposures monitoring
plotForecasts = True
plotForecastsVsFormulaRegression = False
# Exposures analysis setup
useControlVariate = False
thresholdsCoverage = 1.0 # coverage of formula span
nbThresholdsSteps = 100 # nb of thresholds steps to split forumla span
thresholdsEpeRelativeDiffs = False # Compute relative (vs absolute) differences vs EPE with formula
addDiffsWithCoupons = False # Also compute diffs for the method using 'Coupons' payoff and regression only in the indicator
#############################
# Regression step
#############################
testRegressors = runRegression(payoffFn,S,T,step,model,sigma,nbPreSimSamples,projectedRegression,projectionNbPayoffs,projectionPruning,projectionRegressRemainder,projectionType,projectionDegree,chainedRegression,densifiedChainedRegression,densificationNbSamples,testRegressorName,testRegressorParams.get(testRegressorName,{}),testRegressOnFormula,plotPayoffDistribution,plotBinnedStatistics,plotClusterLabels,plotRegressions,plotFormulaPayoffWithRegressions)
#############################
# Exposures step
#############################
exposures = runExposuresAnalysis( payoffFn, S, T, step, model, sigma, nbScenariosSamples, useControlVariate, testRegressors, testRegressorName, thresholdsCoverage, nbThresholdsSteps, plotForecasts, plotForecastsVsFormulaRegression)
#############################
# Exposures at Regression Dates
#############################
#plotAllExposures( exposures, addDiffsWithCoupons, testRegressorName)
#############################
# Exposures with Thresholds at Regression Dates
#############################
plotThresholdExposures(exposures, thresholdsEpeRelativeDiffs, addDiffsWithCoupons, testRegressorName)
#############################
# Exposures with Thresholds at a specific Date
#############################
atDate = 4
#plotThresholdExposuresAtDate( exposures, atDate, addDiffsWithCoupons, testRegressorName)