def __init__(self,
tranche_list,
tranche_principal,
bond_spread,
base_coupon_rate,
rev_percentage,
simulated_rates,
maturity,
tables_file,
show_prints=False,
show_plots=False):
self.tranche_list = tranche_list
self.tranche_principal = tranche_principal
self.bond_spread = bond_spread
self.rev_percentage = rev_percentage
self.base_coupon_rate = base_coupon_rate
self.simulated_rates = simulated_rates
self.T = maturity #the input should be already in the form of Months
self.show_prints = show_prints
self.show_plots = show_plots
self.tables_file = tables_file
self.fi = fixed_income.FixedIncome()
#Processed Values
self.N = simulated_rates.shape[0] # Number of simulations
self.regular_classes = self.tranche_list[:]
if 'R' in self.regular_classes: #might be kept
self.regular_classes.remove('R')
def __init__(self):
self.fi = fixed_income.FixedIncome()
home_dir = os.path.dirname('__file__')
self.libor_data = self.libor_data_func()
self.cap_master_df = self.cap_data_func()
#-----------------------------------------
# self.opti_params = self.opti_func() #uncomment this section out to run optimization
# self.opti_kap = self.opti_params[0]
# self.opti_vol = self.opti_params[1]
#---------------------------------------------
self.opti_kap = 0.11631394289351267 #comment out hard coded values is using optimization
self.opti_vol = 0.017245499818435216
self.cap_pricing_df = self.cap_pricing()
self.theta = self.find_theta()
def __init__(self,
start_date,
first_payment_date,
pool_interest_rate,
pools_info,
classes_info,
principal_sequential_pay,
accruals_sequential_pay,
simulated_rates,
tables_file,
show_prints=False,
show_plots=False):
# Direct inputs
self.start_date = datetime.strptime(start_date, "%m/%d/%Y")
self.first_payment_date = datetime.strptime(first_payment_date,
"%m/%d/%Y")
self.pool_interest_rate = pool_interest_rate
self.pools_info = pools_info
self.classes_info = classes_info
self.principal_sequential_pay = principal_sequential_pay
self.accruals_sequential_pay = accruals_sequential_pay
self.simulated_rates = simulated_rates
self.show_prints = show_prints
self.show_plots = show_plots
self.tables_file = tables_file
self.fi = fixed_income.FixedIncome()
# Processed attributes
self.maturity = np.max(self.pools_info['Term'])
self.n_pools = self.pools_info.shape[0]
self.classes = list(self.classes_info['REMIC Classes'])
self.regular_classes = self.classes[:]
self.regular_classes.remove('R')
self.accrual_classes = list(self.accruals_sequential_pay.keys())
self.principal_classes = list(
set(self.regular_classes) - set(self.accrual_classes))
self.pools_info['Original Balance'] = self.pools_info[
'Original Balance'].astype(float)
self.pools_info['WAC'] = self.pools_info['WAC'].astype(float)
self.classes_info['Original Balance'] = self.classes_info[
'Original Balance'].astype(float)
self.classes_info['Class Coupon'] = self.classes_info[
'Original Balance'].astype(float)
self.classes_info.index = self.classes_info['REMIC Classes']
self.calculate_pool_groups_proportions()
def __init__(self, show_prints=False, show_plots=False):
self.show_prints = show_prints
self.show_plots = show_plots
self.fi = fixed_income.FixedIncome()
def __init__(self,
pool_origination_date,
evaluation_date,
pools_info,
classes_info,
principal_sequential_pay,
accruals_sequential_pay,
simulated_rates,
tables_file,
show_prints=False,
show_plots=False):
# Direct inputs
self.pool_origination_date = datetime.strptime(pool_origination_date,
"%m/%d/%Y")
self.evaluation_date = datetime.strptime(evaluation_date, "%m/%d/%Y")
self.pools_info = pools_info
self.classes_info = classes_info
self.principal_sequential_pay = principal_sequential_pay
self.accruals_sequential_pay = accruals_sequential_pay
self.simulated_rates_cont = simulated_rates
self.simulated_rates_APR = 12 * (np.exp(simulated_rates * 1 / 12) -
1) * 100 # Monthly APR in %
self.show_prints = show_prints
self.show_plots = show_plots
self.tables_file = tables_file
self.fi = fixed_income.FixedIncome()
# Processed attributes
self.maturity = np.max(self.pools_info['Term'])
self.T = min(
self.maturity + 1,
simulated_rates.shape[1]) # Months remaining counting month 0
self.N = simulated_rates.shape[0] # Number of simulations
self.evaluation_lag = (12 * self.evaluation_date.year +
self.evaluation_date.month) - (
12 * self.pool_origination_date.year +
self.pool_origination_date.month)
self.n_pools = self.pools_info.shape[0]
self.classes = list(self.classes_info['REMIC Classes'])
self.classes_ordered = principal_sequential_pay['1']
self.regular_classes = self.classes[:]
if 'R' in self.regular_classes:
self.regular_classes.remove('R')
self.accrual_classes = list(self.accruals_sequential_pay.keys())
self.principal_classes = list(
set(self.regular_classes) - set(self.accrual_classes))
self.pools_info.index = self.pools_info['Pool ID']
self.pools_info['Balance'] = self.pools_info['Balance'].astype(float)
self.pools_info['WAC'] = self.pools_info['WAC'].astype(float)
self.pools_info['Spread'] = self.pools_info['Spread'].astype(float)
self.pools_info['LOL Cap'] = self.pools_info['LOL Cap'].astype(float)
self.pools_info['Periodic Cap'] = self.pools_info[
'Periodic Cap'].astype(float)
self.pools_info['LTV'] = self.pools_info['LTV'].astype(float)
self.pool_frm_balance = self.pools_info.loc['FRM', 'Balance']
self.pool_frm_wac = self.pools_info.loc['FRM', 'WAC']
self.pool_frm_ltv = self.pools_info.loc['FRM', 'LTV']
self.pool_arm_balance = self.pools_info.loc['ARM', 'Balance']
self.pool_arm_spread = self.pools_info.loc['ARM', 'Spread']
self.pool_arm_lol_cap = self.pools_info.loc['ARM', 'LOL Cap']
self.pool_arm_periodic_cap = self.pools_info.loc['ARM', 'Periodic Cap']
self.pool_arm_ltv = self.pools_info.loc['ARM', 'LTV']
self.classes_info.index = self.classes_info['REMIC Classes']
self.classes_info['Balance'] = self.classes_info['Balance'].astype(
float)
self.classes_info['Spread'] = self.classes_info['Spread'].astype(float)
self.calculate_ARM_simulated_rates()