def calculate(model_name, portfolio_url):
print(model_name)
# Exctract exposure data for calculation
portfolio_raw = get_data(portfolio_url)
portfolio_list = []
for entry in portfolio_raw['_items']:
portfolio_list.append(entry['EAD'])
portfolio = np.array(portfolio_list)
weights = cl.weights(portfolio)
if model_name == 'shannon':
value = cl.shannon(weights)
elif model_name == 'hhi':
value = cl.hhi(weights)
return {"result": value}
def test_hk(self):
portfolio = np.ones(10)
portfolio = sorted(portfolio, reverse=True)
weights = cl.weights(portfolio)
self.assertTrue(abs(cl.hk(weights,3) - 0.0) < ERROR_MARGIN)
def test_cl(self):
portfolio = np.ones(10)
portfolio = sorted(portfolio, reverse=True)
weights = cl.weights(portfolio)
self.assertTrue(abs(cl.cr(weights,1) - 0.1) < ERROR_MARGIN)
def test_hk(self):
portfolio = np.ones(10)
portfolio = sorted(portfolio, reverse=True)
weights = cl.weights(portfolio)
self.assertTrue(abs(cl.hk(weights, 3) - 0.0) < ERROR_MARGIN)
def test_cl(self):
portfolio = np.ones(10)
portfolio = sorted(portfolio, reverse=True)
weights = cl.weights(portfolio)
self.assertTrue(abs(cl.cr(weights, 1) - 0.1) < ERROR_MARGIN)