testRunMonte_main.py

'''
Purpose: This program is to test waterfall with random loan default model
Author: Mengheng
Date: 11/26/2018
'''

import csv
from loan.loan_pool import LoanPool
from loan.autoloan import AutoLoan
from asset.car_base import Car
from tranche.structured_securities import StructuredSecurities
from usefulFunctions.doWaterfall import doWaterfall
import itertools


def main():
    # logging.basicConfig(level=logging.INFO)
    # create a loan_pool object by loading the loan data in the csv file
    loan_pool = LoanPool([])
    with open('Loans.csv', 'r') as fp:
        reader = csv.reader(fp, delimiter=',')
        header = next(reader, None)  # skip first row
        for row in reader:
            asset_type = row[5]
            asset_value = float(row[6])
            if asset_type == 'Car':  # in the csv file we only have car type asset
                car = Car(asset_value)
                loan_type = row[1]
                face = float(row[2])
                rate = float(row[3])
                term = int(row[4])
                if loan_type == 'Auto Loan':  # in the cvs file we only have auto loan type
                    loan = AutoLoan(term, rate, face, car)  # create loan object
                    loan_pool.loan_list.append(loan)  # add it to loan_pool

    # set tranche A and tranche B information
    tranche_percents = [0.6, 0.4]
    tranche_rates = [0.02, 0.03]  # subordinated tranche should have higher rate since increased risk
    tranche_levels = [0, 1]  # 0 is higher class than 1
    structured_securities = StructuredSecurities.constructSecurities(loan_pool.totalPrincipal(), tranche_percents,
                                                                     tranche_rates, tranche_levels)
    structured_securities.mode = 'Sequential'  # set mode Sequential or Pro Rata
    loan_pool_waterfall, structured_securities_waterfall, reserve_amount, tranche_metrics = doWaterfall(loan_pool,
                                                                                                        structured_securities)

    with open('loan pool waterfall.csv', 'w') as lp_fp:
        header_list = []
        # add header line for each loan
        for i in range(len(loan_pool_waterfall[0])):
            header = ['Loan ' + str(i + 1) + ' Monthly Payment', 'Loan ' + str(i + 1) + ' Principal Due',
                      'Loan ' + str(i + 1) + ' Interest Due', 'Loan ' + str(i + 1) + ' Balance']
            header_list.append(header)
        flattened_headers = [item for sublist in header_list for item in sublist]
        lp_fp.write(','.join(map(str, list(flattened_headers))) + '\n')

        # do loan waterfall and save results in one csv file
        for loans in loan_pool_waterfall:
            # since it is list of list, we need to flatten it first
            flattened_loans = [item for sublist in loans for item in sublist]
            lp_fp.write(','.join(map(str, list(itertools.chain(flattened_loans)))) + '\n')

    with open('securities_waterfall.csv', 'w') as sc_fp:
        header_list = []
        # add header for each structured security
        for i in range(len(structured_securities_waterfall[0])):
            header = ['Tranche ' + str(i + 1) + ' Interest Due', 'Tranche ' + str(i + 1) + ' Interest Paid',
                      'Tranche ' + str(i + 1) + ' Interest Shortfall', ' Tranche ' + str(i + 1) + ' Principal Paid',
                      'Tranche ' + str(i + 1) + ' Balance']
            header_list.append(header)
        header_list.append(['Reserve Account'])
        flattened_headers = [item for sublist in header_list for item in sublist]
        sc_fp.write(','.join(map(str, list(flattened_headers))) + '\n')

        # do securities waterfall and save results in one csv file
        for tranches, res_cash in zip(structured_securities_waterfall, reserve_amount):
            flattened_tranches = [item for sublist in tranches for item in sublist]  # flatten the list first
            sc_fp.write(','.join(map(str, list(itertools.chain(flattened_tranches))) +
                                 [str(res_cash)]) + '\n')

    # output the waterfall metrics and letter rating to the screen
    print '{0:<10s} {1:<10s} {2:<10s} {3:<10s}'.format('IRR', 'DIRR', 'AL', 'LetterRating')
    for tranche_metric in tranche_metrics:
        print '{0:<11f}{1:<11f}{2:<11f}{3:<11s}'.format(*tranche_metric)

    '''
    You could run waterfall several times, and each run results in a different DIRR and AL for a given set of rates. 
    This is because I am using a random component to the loan default model. 
    '''


if __name__ == '__main__':
    main()