def test_rank_ROE_and_PE_returns_correct_top(self):
roe_rank = {
'name': 'roe',
'ascending': False
}
pe_rank = {
'name': 'pe',
'ascending': True
}
data = pd.DataFrame({
'code': ['ANZ', 'CBA', 'NAB', 'WST'],
'pe': [3, 4, 5, 6],
'roe': [0.30, 0.50, 0.80, 0.70]},
index=pd.to_datetime(
[datetime.date(self.current_year, 6, 20)] * 4
), dtype=float
)
ranker = Ranker(data, [pe_rank, roe_rank], [], limit=50)
results = ranker.process()
# Output should look like this:
# code pe_rank roe_rank total_rank
# ANZ 1 4 5
# CBA 2 3 5
# NAB 3 1 4 -- first pick
# WST 4 2 6 -- last pick
self.assertTrue(results[0:1]['code'][0] == 'NAB')
self.assertTrue(results[-1:]['code'][0] == 'WST')
def test_rank_ROE_avg_3_returns_correct_top(self):
roe_rank = {
'name': 'roe',
'max': 0.8,
'average': 3,
'ascending': False
}
# Push last 3 years into a list
date_array = [
datetime.date(self.current_year - i, 6, 20) for i in range(3)
]
data = pd.DataFrame(
{
'code': ['ANZ'] * 3 + ['CBA'] * 3 + ['NAB'] * 3,
'roe': [0.1, 0.2, 0.5] + [0.7, 0.1, 0.2] + [0.1, 0.2, 0.4]
},
index=pd.to_datetime(date_array * 3),
dtype=float)
ranker = Ranker(data, [roe_rank], [], limit=50)
results = ranker.process()
self.assertTrue(results[0:1]['code'][0] == 'CBA')
self.assertTrue(results[-1:]['code'][0] == 'NAB')
def test_rank_ROE_and_PE_returns_correct_top(self):
roe_rank = {'name': 'roe', 'ascending': False}
pe_rank = {'name': 'pe', 'ascending': True}
data = pd.DataFrame(
{
'code': ['ANZ', 'CBA', 'NAB', 'WST'],
'pe': [3, 4, 5, 6],
'roe': [0.30, 0.50, 0.80, 0.70]
},
index=pd.to_datetime([datetime.date(self.current_year, 6, 20)] *
4),
dtype=float)
ranker = Ranker(data, [pe_rank, roe_rank], [], limit=50)
results = ranker.process()
# Output should look like this:
# code pe_rank roe_rank total_rank
# ANZ 1 4 5
# CBA 2 3 5
# NAB 3 1 4 -- first pick
# WST 4 2 6 -- last pick
self.assertTrue(results[0:1]['code'][0] == 'NAB')
self.assertTrue(results[-1:]['code'][0] == 'WST')
def test_rank_by_ROE_return_highest_first_after_filtering(self):
roe_rank = {'name': 'roe', 'max': 0.70, 'ascending': False}
data = pd.DataFrame(
{
'code': ['ANZ', 'CBA', 'NAB'],
'roe': [0.70, 0.71, 0.69]
},
index=pd.to_datetime([datetime.date(self.current_year, 6, 20)] *
3),
dtype=float)
ranker = Ranker(data, [roe_rank], [], limit=50)
results = ranker.process()
self.assertTrue(results[0:1]['code'][0] == 'ANZ')
def test_rank_by_PE_returns_lowest_first(self):
pe_rank = {'name': 'pe', 'ascending': True}
data = pd.DataFrame(
{
'code': ['ANZ', 'CBA', 'NAB'],
'pe': [3.0, 1.0, 2.0],
},
index=pd.to_datetime([datetime.date(self.current_year, 6, 20)] *
3),
dtype=float)
ranker = Ranker(data, [pe_rank], [], limit=50)
results = ranker.process()
self.assertTrue(results[0:1]['code'][0] == 'CBA')
def test_rank_by_PE_returns_lowest_first(self):
pe_rank = {
'name': 'pe',
'ascending': True
}
data = pd.DataFrame({
'code': ['ANZ', 'CBA', 'NAB'],
'pe': [3.0, 1.0, 2.0],
}, index=pd.to_datetime(
[datetime.date(self.current_year, 6, 20)] * 3), dtype=float
)
ranker = Ranker(data, [pe_rank], [], limit=50)
results = ranker.process()
self.assertTrue(results[0:1]['code'][0] == 'CBA')
def test_rank_and_filter_removes_too_small_companies(self):
market_cap_filter = {'name': 'market_cap', 'min': 5000000}
roe_rank = {'name': 'roe', 'max': 0.70, 'ascending': False}
data = pd.DataFrame(
{
'code': ['SMALL', 'ANZ', 'CBA', 'NAB'],
'roe': [0.50, 0.40, 0.41, 0.39],
'market_cap': [1000000] + [6000000] * 3
},
index=pd.to_datetime([datetime.date(self.current_year, 6, 20)] *
4),
dtype=float)
ranker = Ranker(data, [roe_rank], [market_cap_filter], limit=50)
results = ranker.process()
self.assertTrue(results[0:1]['code'][0] == 'CBA')
def test_rank_by_ROE_return_highest_first_after_filtering(self):
roe_rank = {
'name': 'roe',
'max': 0.70,
'ascending': False
}
data = pd.DataFrame({
'code': ['ANZ', 'CBA', 'NAB'],
'roe': [0.70, 0.71, 0.69]},
index=pd.to_datetime(
[datetime.date(self.current_year, 6, 20)] * 3
), dtype=float
)
ranker = Ranker(data, [roe_rank], [], limit=50)
results = ranker.process()
self.assertTrue(results[0:1]['code'][0] == 'ANZ')
def test_rank_and_filter_removes_too_small_companies(self):
market_cap_filter = {
'name': 'market_cap',
'min': 5000000
}
roe_rank = {
'name': 'roe',
'max': 0.70,
'ascending': False
}
data = pd.DataFrame({
'code': ['SMALL', 'ANZ', 'CBA', 'NAB'],
'roe': [0.50, 0.40, 0.41, 0.39],
'market_cap': [1000000] + [6000000] * 3},
index=pd.to_datetime(
[datetime.date(self.current_year, 6, 20)] * 4
), dtype=float
)
ranker = Ranker(data, [roe_rank], [market_cap_filter], limit=50)
results = ranker.process()
self.assertTrue(results[0:1]['code'][0] == 'CBA')
def test_rank_ROE_avg_3_returns_correct_top(self):
roe_rank = {
'name': 'roe',
'max': 0.8,
'average': 3,
'ascending': False
}
# Push last 3 years into a list
date_array = [
datetime.date(self.current_year - i, 6, 20) for i in range(3)]
data = pd.DataFrame({
'code': ['ANZ'] * 3 + ['CBA'] * 3 + ['NAB'] * 3,
'roe': [0.1, 0.2, 0.5] + [0.7, 0.1, 0.2] + [0.1, 0.2, 0.4]},
index=pd.to_datetime(date_array * 3), dtype=float
)
ranker = Ranker(data, [roe_rank], [], limit=50)
results = ranker.process()
self.assertTrue(results[0:1]['code'][0] == 'CBA')
self.assertTrue(results[-1:]['code'][0] == 'NAB')
