def create_aggregate_cash_flows(analysis_results_name, cash_flow_results, discount_rate):
loan_df = pd.read_pickle(analysis_results_name + '_loans.pk')
cash_flow_df = pd.read_pickle(analysis_results_name + '_cash_flows.pk')
analysis_portfolio = LoanPortfolio(
discount_rate=discount_rate, loan_df=loan_df, cash_flow_df=cash_flow_df
)
aggregate_cash_flow_df = analysis_portfolio.cash_flows_aggregate_for_portfolio()
analysis_portfolio.cash_flows_df.to_pickle(analysis_results_name + '_aggregate_flows.pk')
cash_flow_results.aggregated = True
cash_flow_results.save()
return aggregate_cash_flow_df
def create_aggregate_cash_flows(analysis_results_name, cash_flow_results,
discount_rate):
loan_df = pd.read_pickle(analysis_results_name + '_loans.pk')
cash_flow_df = pd.read_pickle(analysis_results_name + '_cash_flows.pk')
analysis_portfolio = LoanPortfolio(discount_rate=discount_rate,
loan_df=loan_df,
cash_flow_df=cash_flow_df)
aggregate_cash_flow_df = analysis_portfolio.cash_flows_aggregate_for_portfolio(
)
analysis_portfolio.cash_flows_df.to_pickle(analysis_results_name +
'_aggregate_flows.pk')
cash_flow_results.aggregated = True
cash_flow_results.save()
return aggregate_cash_flow_df
def post(self, request):
""" Conducts new analysis on portfolio, scenario combinations.
If cash_flow_results do not exist with the same scenario_id, portfolio_id and analysis_results_name,
a new cash flow is generated.
Args:
request: Request
Returns: Status and message indicating if there it is a new analysis.
"""
request_dict = request.POST.dict()
scenario_id = request_dict['scenario_id']
portfolio_id = request_dict['portfolio_id']
discount_rate = float(request_dict['discount_rate']) / 100
analysis_results_name = "scenario_{}_portfolio_{}_discount_{}_analysis".format(
scenario_id, portfolio_id, discount_rate)
cash_flow_results = self.model.objects.filter(
scenario_id=scenario_id,
portfolio_id=portfolio_id,
analysis_results_name=analysis_results_name,
discount_rate=discount_rate)
if not cash_flow_results.exists():
loan_df = generate_adjusted_assumptions(portfolio_id, scenario_id)
loan_df.to_pickle(analysis_results_name + '_loans.pk')
analysis_portfolio = LoanPortfolio(discount_rate=discount_rate,
loan_df=loan_df)
analysis_portfolio.cash_flows_df.to_pickle(analysis_results_name +
'_cash_flows.pk')
analysis_results = self.model.objects.create(
discount_rate=discount_rate,
scenario_id=scenario_id,
portfolio_id=portfolio_id,
analysis_results_name=analysis_results_name)
analysis_results.save()
message = 'New Analysis has been run.'
else:
message = 'Analysis has already been run.'
return JsonResponse({'status': 'PASS', 'message': message})
def get(self, request):
request_dict = request.GET.dict()
scenario_id = request_dict['scenario_id']
portfolio_id = request_dict['portfolio_id']
discount_rate = float(request_dict['discount_rate']) / 100
analysis_results_name = "scenario_{}_portfolio_{}_discount_{}_analysis".format(
scenario_id, portfolio_id, discount_rate
)
cash_flow_results = self.model.objects.filter(
scenario_id=scenario_id,
portfolio_id=portfolio_id,
discount_rate=discount_rate,
analysis_results_name=analysis_results_name
)
if cash_flow_results.exists():
cash_flow_results = cash_flow_results[0]
loan_df = pd.read_pickle(analysis_results_name + '_loans.pk')
cash_flow_df = pd.read_pickle(analysis_results_name + '_cash_flows.pk')
if cash_flow_results.aggregated:
aggregate_flows_df = pd.read_pickle(analysis_results_name + '_aggregate_flows.pk')
else:
aggregate_flows_df = create_aggregate_cash_flows(
analysis_results_name, cash_flow_results, discount_rate
)
analysis_portfolio = LoanPortfolio(
discount_rate=discount_rate,
loan_df=loan_df,
cash_flow_df=cash_flow_df,
aggregate_flows_df=aggregate_flows_df
)
remaining_balance = analysis_portfolio.current_balance_aggregate_for_portfolio()
npv = analysis_portfolio.net_present_value_aggregate_for_portfolio()
current_balance = analysis_portfolio.current_balance_aggregate_for_portfolio()
price = npv / current_balance * 100
yield_irr = float(analysis_portfolio.internal_rate_of_return_aggregate_for_portfolio()) * 100
weighted_average_life = analysis_portfolio.weighted_average_life_for_portfolio()
original_cdr = float(cash_flow_results.scenario.assumption_profile.constant_default_rate)
original_cpr = float(cash_flow_results.scenario.assumption_profile.constant_prepayment_rate)
original_recovery_percentage = float(cash_flow_results.scenario.assumption_profile.recovery_percentage)
weighted_average_cdr = analysis_portfolio.weighted_average_cdr_for_portfolio()
weighted_average_cpr = analysis_portfolio.weighted_average_cpr_for_portfolio()
weighted_average_recovery = float(analysis_portfolio.weighted_average_recovery_for_portfolio()) * 100
return JsonResponse(dict(
portfolio_balance='$ {:,.2f}'.format(remaining_balance),
npv='$ {:,.2f}'.format(npv),
price='$ {:,.2f}'.format(price),
yield_irr='{:.2%}'.format(yield_irr),
weighted_average_life='{:,.2f}'.format(weighted_average_life),
original_cdr='{:.2%}'.format(original_cdr),
original_cpr='{:.2%}'.format(original_cpr),
original_recovery='{:.2%}'.format(original_recovery_percentage),
weighted_average_cdr='{:.2%}'.format(weighted_average_cdr),
weighted_average_cpr='{:.2%}'.format(weighted_average_cpr),
weighted_average_recovery='{:.2%}'.format(weighted_average_recovery)
))
def get(self, request):
request_dict = request.GET.dict()
scenario_id = request_dict['scenario_id']
portfolio_id = request_dict['portfolio_id']
discount_rate = float(request_dict['discount_rate']) / 100
analysis_results_name = "scenario_{}_portfolio_{}_discount_{}_analysis".format(
scenario_id, portfolio_id, discount_rate)
cash_flow_results = self.model.objects.filter(
scenario_id=scenario_id,
portfolio_id=portfolio_id,
discount_rate=discount_rate,
analysis_results_name=analysis_results_name)
if cash_flow_results.exists():
cash_flow_results = cash_flow_results[0]
loan_df = pd.read_pickle(analysis_results_name + '_loans.pk')
cash_flow_df = pd.read_pickle(analysis_results_name +
'_cash_flows.pk')
if cash_flow_results.aggregated:
aggregate_flows_df = pd.read_pickle(analysis_results_name +
'_aggregate_flows.pk')
else:
aggregate_flows_df = create_aggregate_cash_flows(
analysis_results_name, cash_flow_results, discount_rate)
analysis_portfolio = LoanPortfolio(
discount_rate=discount_rate,
loan_df=loan_df,
cash_flow_df=cash_flow_df,
aggregate_flows_df=aggregate_flows_df)
remaining_balance = analysis_portfolio.current_balance_aggregate_for_portfolio(
)
npv = analysis_portfolio.net_present_value_aggregate_for_portfolio(
)
current_balance = analysis_portfolio.current_balance_aggregate_for_portfolio(
)
price = npv / current_balance * 100
yield_irr = float(
analysis_portfolio.
internal_rate_of_return_aggregate_for_portfolio()) * 100
weighted_average_life = analysis_portfolio.weighted_average_life_for_portfolio(
)
original_cdr = float(cash_flow_results.scenario.assumption_profile.
constant_default_rate)
original_cpr = float(cash_flow_results.scenario.assumption_profile.
constant_prepayment_rate)
original_recovery_percentage = float(
cash_flow_results.scenario.assumption_profile.
recovery_percentage)
weighted_average_cdr = analysis_portfolio.weighted_average_cdr_for_portfolio(
)
weighted_average_cpr = analysis_portfolio.weighted_average_cpr_for_portfolio(
)
weighted_average_recovery = float(
analysis_portfolio.weighted_average_recovery_for_portfolio(
)) * 100
return JsonResponse(
dict(
portfolio_balance='$ {:,.2f}'.format(remaining_balance),
npv='$ {:,.2f}'.format(npv),
price='$ {:,.2f}'.format(price),
yield_irr='{:.2%}'.format(yield_irr),
weighted_average_life='{:,.2f}'.format(
weighted_average_life),
original_cdr='{:.2%}'.format(original_cdr),
original_cpr='{:.2%}'.format(original_cpr),
original_recovery='{:.2%}'.format(
original_recovery_percentage),
weighted_average_cdr='{:.2%}'.format(weighted_average_cdr),
weighted_average_cpr='{:.2%}'.format(weighted_average_cpr),
weighted_average_recovery='{:.2%}'.format(
weighted_average_recovery)))
