def build_rating_to_edf_struct(migration_matrix_struct,rating_scale_set_struct, method="default"):
if method not in ["default", "aggregate"]:
raise ValueError("Illegal method : {}".format(method))
performing_ratings = lp_rs.list_ratings(rating_scale_set_struct,
migration_matrix_struct['rating_scale_name'],
"performing", "dict")
nonperforming_ratings = lp_rs.list_ratings(rating_scale_set_struct,
migration_matrix_struct['rating_scale_name'],
"nonperforming", "dict")
edf_list = []
for rating in performing_ratings:
pd = None
if len(nonperforming_ratings) == 1:
non_performing_rating = nonperforming_ratings[0]['rating_code']
pd = find_probability(migration_matrix_struct, rating['rating_code'], non_performing_rating)
else:
if method == "default":
# We use the worst non performing rating
non_performing_rating = nonperforming_ratings[len(nonperforming_ratings) - 1]['rating_code']
pd = find_probability(migration_matrix_struct, rating['rating_code'], non_performing_rating)
if method == "aggregate":
# We sum the probabilities of transition towards a nonperforming rating
pd = float(0)
for non_performing_rating in nonperforming_ratings:
pd = pd + find_probability(migration_matrix_struct,
rating['rating_code'],
non_performing_rating['rating_code'])
item = rating.copy()
item['edf'] = pd
edf_list.append(item)
# the last point
item = nonperforming_ratings[len(nonperforming_ratings) - 1].copy()
item['edf'] = float(1.0)
edf_list.append(item)
return {migration_matrix_struct['rating_scale_name']: {'edf': edf_list}}
def build_default_probabilities(migration_matrix_struct, rating_scale_set_struct, rating_default):
"""
:return: an ordered array of tuple (rating, probability) corrresponding to the default rating
"""
final_ratings = lp_rs.list_ratings(rating_scale_set_struct, migration_matrix_struct['rating_scale_name'])
if final_ratings:
probabilities = []
for rating in final_ratings:
if rating != rating_default:
probabilities.append((rating, float(0)))
else:
probabilities.append((rating, float(1)))
return probabilities
def extract_probabilities(migration_matrix_struct, rating_scale_set_struct, rating_from):
"""
return the rows of the matrix containing all the probabilities of transition for a rating
:param migration_matrix_struct: a dictionary
:param rating_scale_set_struct: a dictionary
:param rating_from: string
:return: an ordered array of tuple (rating, probability). The sort order is given by the rating scale
"""
final_ratings = lp_rs.list_ratings(rating_scale_set_struct, migration_matrix_struct['rating_scale_name'])
if final_ratings:
probabilities = []
for rating in final_ratings:
probabilities.append((rating, find_probability(migration_matrix_struct, rating_from, rating)))
return probabilities
def apply_montecarlo_migration(scenario,bucket,prevBucket,migration_matrix_struct, rating_scale_set_struct, exposure, alea, stage_fct = None):
"""
return a new exposure according to the alea
"""
probabilities = []
denotch_params = {}
denotch_indicator = 0
denotch_degradation = 0
final_matrix = migration_matrix_struct
#Adjust the rating,
all_ratings = lp_rs.list_ratings(rating_scale_set_struct, migration_matrix_struct['rating_scale_name'])
rating_code = exposure['rating_code']
if not rating_code in all_ratings:
rating_code = rating_code.replace('+','').replace('-','').strip()
if not rating_code in all_ratings:
raise ValueError("Unknown rating {} => {} for rating scale : {}".format(exposure['rating_code'], rating_code,migration_matrix_struct['rating_scale_name']))
#if denotch_fct:
# denotch_params = denotch_fct(scenario, bucket)
# denotch_indicator = denotch_params['denotch_indicator']
# denotch_degradation = denotch_params['degradation']
#if ( denotch_indicator != 0) and denotch_degradation:
# final_matrix = lp_denotch.apply_denotching(denotch_params['denotch_indicator'],denotch_params['degradation'],denotch_params['pd_imposee'],
# rating_scale_set_struct,migration_matrix_struct)
is_non_performing = lp_rs.is_nonperforming(rating_scale_set_struct,final_matrix['rating_scale_name'],rating_code)
if is_non_performing:
probabilities = build_default_probabilities(final_matrix, rating_scale_set_struct, rating_code)
else:
probabilities = extract_probabilities(final_matrix, rating_scale_set_struct, rating_code)
cumulated_edf = float(0)
for i,probability in enumerate(probabilities):
new_rating = probability[0]
pd = probability[1]
cumulated_edf = cumulated_edf + pd
#calcul lgd
#lgd_ifrs9, lgd_reg, lgd_pit = None, None, None
#if(lgd_fct):
# lgdValues = lgd_fct(scenario,bucket,exposure)
# lgd_ifrs9 =lgdValues['lgd_ifrs9']
# lgd_reg =lgdValues['lgd_reg']
# lgd_pit = lgdValues['lgd_pit']
#Floor S2
floor_s2 = False
if (exposure['stage']=='2'):
if (lp_rs.compareRatings(new_rating,rating_code,rating_scale_set_struct,migration_matrix_struct['rating_scale_name']) <= 0):
#dégradation
floor_s2 = True
if alea<=cumulated_edf or i == (len(probabilities)-1) :
new_exposure = {}
if stage_fct:
s1, s2, s3 = stage_fct(new_rating, exposure['rating_code_origin'], rating_code, exposure['stage'])
stage = ""
if s3 >0:
stage = "3"
else:
if (floor_s2):
s2 = s2 + s1
s1 = 0
if s1>s2 :
stage= "1"
else:
stage= "2"
new_exposure={'rating_code': new_rating ,
'stage' : stage,
'ead_reg' : exposure['ead_reg'],
'lgd_ifrs9' : exposure['lgd_ifrs9'],
'lgd_reg' : exposure['lgd_reg'],
'lgd_pit' : exposure['lgd_pit'],
'previous_rating_code': rating_code,
'previous_stage' : exposure['stage'],
'previous_lgd_ifrs9' : exposure['lgd_ifrs9'],
'previous_lgd_reg' : exposure['lgd_reg'],
'previous_lgd_pit' : exposure['lgd_pit'],
'rating_code_origin' : exposure['rating_code_origin']}
else:
new_exposure={'rating_code': new_rating ,
'stage' : exposure['stage'],
'ead_reg' : exposure['ead_reg'],
'lgd_ifrs9' : exposure['lgd_ifrs9'],
'lgd_reg' : exposure['lgd_reg'],
'lgd_pit' : exposure['lgd_pit'],
'previous_rating_code': rating_code,
'previous_stage' : exposure['stage'],
'previous_lgd_ifrs9' : exposure['lgd_ifrs9'],
'previous_lgd_reg' : exposure['lgd_reg'],
'previous_lgd_pit' : exposure['lgd_pit'],
'rating_code_origin' : exposure['rating_code_origin']}
#ifrs9 computation
#if ecl_fct:
# eclValues = ecl_fct(scenario,bucket,prevBucket,new_exposure)
# if eclValues:
# new_exposure['el_final'] = eclValues['elFinal']
# else:
# new_exposure['el_final'] = None
return new_exposure
raise ValueError("Sum(pd) < 1 {} {}".format(migration_matrix_struct['name'], rating_code ))
def apply_analytic_migration(scenario, bucket, prevBucket, migration_matrix_struct, rating_scale_set_struct, exposure,stage_fct = None,denotched = False):
"""
return an array of exposure created by the usage of the migration matrix
"""
probabilities = []
denotch_params = {}
denotch_indicator = 0
final_matrix = migration_matrix_struct
#Adjust the rating,
all_ratings = lp_rs.list_ratings(rating_scale_set_struct, migration_matrix_struct['rating_scale_name'])
rating_code = exposure['rating_code']
if not rating_code in all_ratings:
rating_code = rating_code.replace('+','').replace('-','').strip()
if not rating_code in all_ratings:
raise ValueError("Unknown rating {} => {} for rating scale : {}".format(exposure['rating_code'], rating_code,migration_matrix_struct['rating_scale_name']))
#if denotch_fct:
# denotch_params = denotch_fct(scenario, bucket)
# denotch_indicator = denotch_params['denotch_indicator']
# denotch_degradation = denotch_params['degradation']
#if ( denotch_indicator != 0) and denotch_degradation:
# final_matrix = lp_denotch.apply_denotching(denotch_params['denotch_indicator'],denotch_params['degradation'],denotch_params['pd_imposee'],
# rating_scale_set_struct,migration_matrix_struct)
is_non_performing = lp_rs.is_nonperforming(rating_scale_set_struct,final_matrix['rating_scale_name'],rating_code)
if is_non_performing:
probabilities = build_default_probabilities(final_matrix, rating_scale_set_struct, rating_code)
else:
probabilities = extract_probabilities(final_matrix, rating_scale_set_struct, rating_code)
exposures = []
for probability in probabilities:
new_exposure = {}
new_rating = probability[0]
pd = probability[1]
#if abs(pd)>0:#0.000000001:
lgd_ifrs9, lgd_reg, lgd_pit = None, None, None
#if(lgd_fct):
# lgdValues = lgd_fct(scenario,bucket,exposure)
# lgd_ifrs9 =lgdValues['lgd_ifrs9']
# lgd_reg =lgdValues['lgd_reg']
# lgd_pit = lgdValues['lgd_pit']
#Floor S2
floor_s2 = False
if (exposure['stage']=='2'):
if (lp_rs.compareRatings(new_rating,rating_code,rating_scale_set_struct,migration_matrix_struct['rating_scale_name']) <= 0):
#dégradation
floor_s2 = True
if stage_fct:
s1,s2,s3 = stage_fct(new_rating, exposure['rating_code_origin'], rating_code, exposure['stage'])
ead_reg = pd * exposure['ead_reg']
ead_s1 = ead_reg * s1
ead_s2 = ead_reg * s2
ead_s3 = ead_reg * s3
if (floor_s2):
ead_s2 = ead_s2 + ead_s1
ead_s1 = 0.0
s2 = s2 + s1
s1 = 0
if abs(s1)>0.00000000001:
new_exposure={'rating_code': new_rating ,
'stage' : "1",
'ead_reg' : ead_reg,
'ead_s1': ead_s1,
'ead_s2': ead_s2,
'ead_s3': ead_s3,
'lgd_ifrs9' : exposure['lgd_ifrs9'],
'lgd_reg' : exposure['lgd_reg'],
'lgd_pit' : exposure['lgd_pit'],
'previous_rating_code': rating_code,
'previous_stage' : exposure['stage'],
'previous_lgd_ifrs9' : exposure['lgd_ifrs9'],
'previous_lgd_reg' : exposure['lgd_reg'],
'previous_lgd_pit' : exposure['lgd_pit'],
'rating_code_origin' : exposure['rating_code_origin']}
if abs(s2)>0.00000000001:
new_exposure={'rating_code': new_rating ,
'stage' : "2",
'ead_reg' : ead_reg,
'ead_s1': ead_s1,
'ead_s2': ead_s2,
'ead_s3': ead_s3,
'lgd_ifrs9' : exposure['lgd_ifrs9'],
'lgd_reg' : exposure['lgd_reg'],
'lgd_pit' : exposure['lgd_pit'],
'previous_rating_code': rating_code,
'previous_stage' : exposure['stage'],
'previous_lgd_ifrs9' : exposure['lgd_ifrs9'],
'previous_lgd_reg' : exposure['lgd_reg'],
'previous_lgd_pit' : exposure['lgd_pit'],
'rating_code_origin' : exposure['rating_code_origin']}
if abs(s3)>0.00000000001:
new_exposure={'rating_code': new_rating ,
'stage' : "3",
'ead_reg' : ead_reg,
'ead_s1': ead_s1,
'ead_s2': ead_s2,
'ead_s3': ead_s3,
'lgd_ifrs9' : exposure['lgd_ifrs9'],
'lgd_reg' : exposure['lgd_reg'],
'lgd_pit' : exposure['lgd_pit'],
'previous_rating_code': rating_code,
'previous_stage' : exposure['stage'],
'previous_lgd_ifrs9' : exposure['lgd_ifrs9'],
'previous_lgd_reg' : exposure['lgd_reg'],
'previous_lgd_pit' : exposure['lgd_pit'],
'rating_code_origin' : exposure['rating_code_origin']}
else:
new_exposure={'rating_code': new_rating ,
'stage' : None,
'ead_reg' : pd * exposure['ead_reg'],
'lgd_ifrs9' : exposure['lgd_ifrs9'],
'lgd_reg' : exposure['lgd_reg'],
'lgd_pit' : exposure['lgd_pit'],
'previous_rating_code': rating_code,
'previous_stage' : exposure['stage'],
'previous_lgd_ifrs9' : exposure['lgd_ifrs9'],
'previous_lgd_reg' : exposure['lgd_reg'],
'previous_lgd_pit' : exposure['lgd_pit'],
'rating_code_origin' : exposure['rating_code_origin']}
#ifrs9 computation
#if ecl_fct:
# eclValues = ecl_fct(scenario,bucket,prevBucket,new_exposure)
# if eclValues:
# new_exposure['el_final'] = eclValues['elFinal']
# else:
# new_exposure['el_final'] = None
if denotched:
#up_date previous state to before denotching
new_exposure['previous_rating_code'] = exposure['previous_rating_code']
new_exposure['previous_stage'] = exposure['previous_stage']
new_exposure['previous_lgd_ifrs9'] = exposure['previous_lgd_ifrs9']
new_exposure['previous_lgd_reg'] = exposure['previous_lgd_reg']
new_exposure['previous_lgd_pit'] = exposure['previous_lgd_pit']
exposures.append(new_exposure)
return exposures