def setUp(self):
self.y1 = NelsonSiegelCurve(0.017, -0.023, 0.24, 2.2)
self.y2 = NelsonSiegelSvenssonCurve(0.017, -0.023, 0.24, 0.1, 2.2, 3.1)
self.t = [
0.0, 0.5, 1.0, 2.0, 3.0, 4.0, 5.0, 10.0, 15.0, 20.0, 25.0, 30.0
]
self.y = [
0.01, 0.011, 0.013, 0.016, 0.019, 0.021, 0.026, 0.03, 0.035, 0.037,
0.038, 0.04
]
self.runner = CliRunner()
def test_gilli_grosse_schumann_parameters(self):
'''Test parameters from Gilli, Grosse, Schumann paper (Table 1)'''
y = NelsonSiegelSvenssonCurve(2.05, -1.82, -2.03, 8.25, 0.87, 14.38)
t = np.array(
[1 / 4, 1 / 2, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30])
y_expected = np.array([
0.3, 0.4, 0.68, 1.27, 1.78, 2.2, 2.53, 2.8, 3.03, 3.23, 3.4, 3.54,
4.04, 4.28, 4.38, 4.38
])
y_actual = y(t).round(2)
self.assertTrue(np.allclose(y_expected, y_actual),
'calculated yields differ from expected ones')
def test_ecb_2016_01_04_parameters(self):
'''Test curve evaluation for ECB AAA curve parameters on 2016-01-04'''
y = NelsonSiegelSvenssonCurve(2.142562216, -2.649562216, 19.9532384206,
-24.0677865973, 1.6568604918,
1.8145254889)
y_expected = np.array([
-0.410613, -0.366505, -0.290997, -0.174151, -0.028807, 0.129236,
0.287602, 0.438365, 0.577291, 0.702713, 0.814554, 0.913619,
1.001124, 1.078417, 1.146814, 1.207524, 1.261617, 1.310018,
1.353515, 1.392777, 1.428369, 1.460766, 1.490370, 1.517522,
1.542510, 1.565581, 1.586946, 1.606787, 1.625260, 1.642502
])
y_actual = y(np.arange(1, 31)).round(6)
self.assertTrue(np.allclose(y_expected, y_actual),
'calculated yields differ from expected ones')
def setUp(self):
self.y = NelsonSiegelSvenssonCurve(0.017, -0.023, 0.24, 0.1, 2.2, 3.1)
class TestNelsonSiegelSvenssonCurveImplementation(unittest.TestCase):
'''Tests for Nelson-Siegel-Svensson curve implementation'''
def setUp(self):
self.y = NelsonSiegelSvenssonCurve(0.017, -0.023, 0.24, 0.1, 2.2, 3.1)
def test_curve_init(self):
NelsonSiegelSvenssonCurve(0, 0, 0, 0, 0, 0)
def test_nelson_siegel_svensson_curve_array_vs_individual(self):
'''Test curve evaluation on array vs individual elements'''
y = self.y
t = np.linspace(0, 10, 11)
y_array = y(t)
y_individual = np.array([y(t_i) for t_i in t])
self.assertTrue(np.allclose(y_array, y_individual),
'array valued yields differ from individual ones')
def test_nelson_siegel_svensson_at_0(self):
'''Test curve evaluation at time 0'''
y = self.y
y_actual = y(0.0) # float time
self.assertEqual(y.beta0 + y.beta1, y_actual)
y_actual = y(0) # int time
self.assertEqual(y.beta0 + y.beta1, y_actual)
y_actual = y(np.array([0.0])) # array time
self.assertEqual(y.beta0 + y.beta1, y_actual[0])
def test_mithril_parameters(self):
y = NelsonSiegelSvenssonCurve(0.038, -0.032, -0.019, -0.02, 2.1, 1.04)
y_expected = np.array([0.38, 0.63, 1.65, 2.58, 3.32]) / 100
y_actual = np.array([y(1), y(2), y(5), y(10), y(25)]).round(4)
self.assertTrue(np.allclose(y_expected, y_actual, atol=5e-4),
'calculated yields differ from expected ones')
def test_ecb_2016_01_04_parameters(self):
'''Test curve evaluation for ECB AAA curve parameters on 2016-01-04'''
y = NelsonSiegelSvenssonCurve(2.142562216, -2.649562216, 19.9532384206,
-24.0677865973, 1.6568604918,
1.8145254889)
y_expected = np.array([
-0.410613, -0.366505, -0.290997, -0.174151, -0.028807, 0.129236,
0.287602, 0.438365, 0.577291, 0.702713, 0.814554, 0.913619,
1.001124, 1.078417, 1.146814, 1.207524, 1.261617, 1.310018,
1.353515, 1.392777, 1.428369, 1.460766, 1.490370, 1.517522,
1.542510, 1.565581, 1.586946, 1.606787, 1.625260, 1.642502
])
y_actual = y(np.arange(1, 31)).round(6)
self.assertTrue(np.allclose(y_expected, y_actual),
'calculated yields differ from expected ones')
def test_gilli_grosse_schumann_parameters(self):
'''Test parameters from Gilli, Grosse, Schumann paper (Table 1)'''
y = NelsonSiegelSvenssonCurve(2.05, -1.82, -2.03, 8.25, 0.87, 14.38)
t = np.array(
[1 / 4, 1 / 2, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30])
y_expected = np.array([
0.3, 0.4, 0.68, 1.27, 1.78, 2.2, 2.53, 2.8, 3.03, 3.23, 3.4, 3.54,
4.04, 4.28, 4.38, 4.38
])
y_actual = y(t).round(2)
self.assertTrue(np.allclose(y_expected, y_actual),
'calculated yields differ from expected ones')
def test_forward_against_zero_curve(self):
'''Test forward against zero curve implementation by integrating'''
t = np.linspace(0.001, 25, 500)
dt = t[1] - t[0]
y_by_fw_integration = np.cumsum(self.y.forward(t)) * dt / t
y_actual = self.y(t)
self.assertTrue(
np.allclose(y_actual[100:], y_by_fw_integration[100:], atol=1e-3))
# todo: numerical issue closer to 0?
def test_factor_matrix(self):
'''Test shape of factor matrix'''
n = 50
t = np.linspace(0, 25, n)
fmat = self.y.factor_matrix(t)
self.assertEqual((n, 4), fmat.shape)
self.assertEqual((4, ), self.y.factor_matrix(0.123).shape)
def test_mithril_parameters(self):
y = NelsonSiegelSvenssonCurve(0.038, -0.032, -0.019, -0.02, 2.1, 1.04)
y_expected = np.array([0.38, 0.63, 1.65, 2.58, 3.32]) / 100
y_actual = np.array([y(1), y(2), y(5), y(10), y(25)]).round(4)
self.assertTrue(np.allclose(y_expected, y_actual, atol=5e-4),
'calculated yields differ from expected ones')
def test_curve_init(self):
NelsonSiegelSvenssonCurve(0, 0, 0, 0, 0, 0)
def load_csv(csv_file):
eur_hist = pd.read_csv(csv_file, parse_dates=['date'], index_col=0)
for ts, param in eur_hist.iterrows():
yield ts.date(), NelsonSiegelSvenssonCurve(*param)
from nelson_siegel_svensson import NelsonSiegelSvenssonCurve
import numpy as np
from matplotlib.pyplot import plot
y = NelsonSiegelSvenssonCurve(0.028, -0.03, -0.04, -0.015, 1.1, 4.0)
t = np.linspace(0, 20, 100)
plot(t, y(t))
import numpy as np
from nelson_siegel_svensson.calibrate import calibrate_ns_ols
from nelson_siegel_svensson.calibrate import calibrate_nss_ols
t = np.array([0.0, 0.5, 1.0, 2.0, 3.0, 4.0, 5.0, 10.0, 15.0, 20.0, 25.0, 30.0])
y = np.array([
0.01, 0.011, 0.013, 0.014, 0.018, 0.020, 0.025, 0.03, 0.035, 0.037, 0.038,
0.04
])
curve, status = calibrate_ns_ols(
t, y, tau0=1.0) # starting value of 1.0 for the optimization of tau
assert status.success
print(curve)
curve.beta
# NSS Model fit
curve, status = calibrate_nss_ols(t, y)
import plotly.graph_objects as go
fig = go.Figure(data=go.Bar(y=[2, 3, 1]))
fig.write_html('first_figure.html', auto_open=True)
