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.get_weights(portfolio)
if model_name == 'shannon':
value = cl.shannon(weights)
elif model_name == 'hhi':
value = cl.hhi(weights)
return {"result": value}
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.get_weights(portfolio)
if model_name == 'shannon':
value = cl.shannon(weights)
elif model_name == 'hhi':
value = cl.hhi(weights)
return {"result": value}
def calculate_index(model_name, exposures):
# Step 1: Calculate positions weights
weights = cl.get_weights(exposures)
# Step 2: Calculate index
if model_name == 'Shannon_Index':
value = cl.shannon(weights)
elif model_name == 'HHI_Index':
value = cl.hhi(weights)
elif model_name == 'Gini_Index':
value = cl.gini(weights)
return {model_name: value}
def test_hhi(self):
vector = np.ones(10)
self.assertTrue(abs(cl.hhi(vector) - 0.0) < ERROR_MARGIN)
def test_hhi(self):
portfolio = np.ones(10)
portfolio = sorted(portfolio, reverse=True)
weights = cl.weights(portfolio)
self.assertTrue(abs(cl.hhi(weights) - 0.0) < ERROR_MARGIN)
from scipy import special
import numpy as np
import matplotlib.pyplot as plt
import concentration_library as cl
if __name__ == "__main__":
# generate portfolio data (list)
# for some realism we use the Zipf power law
a = 1.7 # zipf parameter
x = []
y = []
for iter in range(0, 10000): # portfolio simulations
# Generate a portfolio of 100 entities
portfolio = np.random.zipf(a, 100)
portfolio = sorted(portfolio, reverse=True)
weights = cl.get_weights(portfolio)
# Compute HHI and Gini indexes
hhi = cl.hhi(weights)
gini = cl.gini(weights)
x.append(hhi)
y.append(gini)
# Plot values of HHI against Gini
plt.style.use(['dark_background', 'ggplot'])
plt.title('HHI versus Gini for random portfolios')
plt.ylabel('Gini Index')
plt.xlabel('Herfindahl-Hirschman Index')
plt.scatter(y, x)
plt.show()
def test_hhi(self):
portfolio = np.ones(10)
portfolio = sorted(portfolio, reverse=True)
weights = cl.weights(portfolio)
self.assertTrue(abs(cl.hhi(weights) - 0.0) < ERROR_MARGIN)