def test_recommend_risk_fully_answered(self):
# Fully populate the answers, we should get 0.5
goal = Fixture1.goal1()
settings = Fixture1.settings1()
Fixture1.populate_risk_profile_questions() # Also populates all possible answers.
Fixture1.populate_risk_profile_responses()
self.assertEqual(recommend_risk(settings), 1.0)
def test_max_risk(self):
goal = Fixture1.goal1()
settings = Fixture1.settings1()
client = goal.account.primary_owner
# Add the weights for the risk factors
Fixture1.populate_risk_profile_questions() # Also populates all possible answers.
Fixture1.populate_risk_profile_responses()
# we haven't set a net worth or a target, so worth_score isn't a factor
# An all-9s account will have a max_risk of 1
self.assertEqual(max_risk(settings), 1.0)
# and if they are low risk behavior, high Ability + Sophistication
# the max risk is still 1
client.risk_profile_responses.clear()
client.risk_profile_responses.add(Fixture1.risk_profile_answer1c())
client.risk_profile_responses.add(Fixture1.risk_profile_answer2c())
self.assertEqual(max_risk(settings), 1.0)
# but if they are risky, new and unskilled, recommend no risk
client.risk_profile_responses.clear()
client.risk_profile_responses.add(Fixture1.risk_profile_answer1d())
client.risk_profile_responses.add(Fixture1.risk_profile_answer2d())
self.assertAlmostEqual(recommend_risk(settings), 0.1, 1)
self.assertAlmostEqual(max_risk(settings), 0.1, 1)
def test_recommend_risk_fully_answered_bad_questions(self):
# Fully populate the answers, but no range in the available question responses, we should get 0.5
goal = Fixture1.goal1()
settings = Fixture1.settings1()
account = settings.goal.account
Fixture1.populate_risk_profile_questions() # Also populates all possible answers.
Fixture1.populate_risk_profile_responses()
Fixture1.risk_profile_question3() # Add a question we don't have an answer for
self.assertEqual(recommend_risk(settings), MINIMUM_RISK)
# Now answer the question, we shouldn't get MINIMUM_RISK
account.primary_owner.risk_profile_responses.add(Fixture1.risk_profile_answer3a())
self.assertNotEqual(recommend_risk(settings), MINIMUM_RISK)
def test_recommend_risk(self):
goal = Fixture1.goal1()
settings = Fixture1.settings1()
client = goal.account.primary_owner
# Add the weights for the risk factors
Fixture1.populate_risk_profile_questions() # Also populates all possible answers.
Fixture1.populate_risk_profile_responses()
# First lets start with the test_client, who scored 9 for all B,A,S
# A goal of 80% of the value on a all-9s account is a bad idea
# It's the lowest possible score
settings.goal.account.primary_owner.net_worth = 100
settings.goal.cash_balance = 80
self.assertAlmostEqual(recommend_risk(settings), 0.10, 2)
# A goal of 50% of the value is just as bad
settings.goal.account.primary_owner.net_worth = 100
settings.goal.cash_balance = 50
self.assertAlmostEqual(recommend_risk(settings), 0.10, 2)
# A goal of 10% of the value on a all-9s account is 1.0
# meaning this is the safest possible bet
settings.goal.account.primary_owner.net_worth = 100
settings.goal.cash_balance = 10
self.assertAlmostEqual(recommend_risk(settings), 1.0, 2)
# A goal of 33% of the value on a all-9s account is about 0.5
# Even if you are risky, sophisticated and rich, 30% is a lot
settings.goal.account.primary_owner.net_worth = 100
settings.goal.cash_balance = 33
self.assertAlmostEqual(recommend_risk(settings), 0.5, 1)
# For a new investor, the best possible suggestion is 10% or less
settings.goal.account.primary_owner.net_worth = 100
settings.goal.cash_balance = 10
client.risk_profile_responses.clear()
client.risk_profile_responses.add(Fixture1.risk_profile_answer1b())
client.risk_profile_responses.add(Fixture1.risk_profile_answer2b())
self.assertAlmostEqual(recommend_risk(settings), 0.2, 1)