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FMT: fix travis tests (#90)
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@pepicello
pepicello committed Aug 9, 2020
1 parent 29cbf6f commit bfc8a16
Showing 1 changed file with 51 additions and 50 deletions.
101 changes: 51 additions & 50 deletions empyrical/stats.py
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Original file line number Diff line number Diff line change
Expand Up @@ -409,7 +409,7 @@ def expected_max_drawdown(mu, sigma, t, gbm=False):
"""
Determines the expected maximum drawdown of a brownian motion,
given drift and diffusion
If a geometric Brownian motion with stochastic differential equation
dS(t) = Mu0 * S(t) * dt + Sigma0 * S(t) * dW(t) ,
it converts to the form here by Ito's Lemma with X(t) = log(S(t)) such that
Expand All @@ -433,7 +433,8 @@ def expected_max_drawdown(mu, sigma, t, gbm=False):
Note
-----
See http://www.cs.rpi.edu/~magdon/ps/journal/drawdown_journal.pdf for more details.
See http://www.cs.rpi.edu/~magdon/ps/journal/drawdown_journal.pdf
for more details.
"""

if gbm:
Expand All @@ -442,33 +443,33 @@ def expected_max_drawdown(mu, sigma, t, gbm=False):
new_mu = mu

def emdd_qp(x):
""" Return value for Q functions based on lookup table, for positive drifts"""
A = [ 0.0005, 0.001, 0.0015, 0.002, 0.0025, 0.005,
0.0075, 0.01, 0.0125, 0.015, 0.0175, 0.02,
0.0225, 0.025, 0.0275, 0.03, 0.0325, 0.035,
0.0375, 0.04, 0.0425, 0.045, 0.0475, 0.05,
0.055, 0.06, 0.065, 0.07, 0.075, 0.08,
0.085, 0.09, 0.095, 0.1, 0.15, 0.2,
0.25, 0.3, 0.35, 0.4, 0.45, 0.5,
1.0, 1.5, 2.0, 2.5, 3.0, 3.5,
4.0, 4.5, 5.0, 10.0, 15.0, 20.0,
25.0, 30.0, 35.0, 40.0, 45.0, 50.0,
100.0, 150.0, 200.0, 250.0, 300.0, 350.0,
400.0, 450.0, 500.0, 1000.0, 1500.0, 2000.0,
2500.0, 3000.0, 3500.0, 4000.0, 4500.0, 5000.0 ]
B = [ 0.01969, 0.027694, 0.033789, 0.038896, 0.043372, 0.060721,
0.073808, 0.084693, 0.094171, 0.102651, 0.110375, 0.117503,
0.124142, 0.130374, 0.136259, 0.141842, 0.147162, 0.152249,
0.157127, 0.161817, 0.166337, 0.170702, 0.174924, 0.179015,
0.186842, 0.194248, 0.201287, 0.207999, 0.214421, 0.220581,
0.226505, 0.232212, 0.237722, 0.24305, 0.288719, 0.325071,
0.355581, 0.382016, 0.405415, 0.426452, 0.445588, 0.463159,
0.588642, 0.668992, 0.72854, 0.775976, 0.815456, 0.849298,
0.878933, 0.905305, 0.92907, 1.088998, 1.184918, 1.253794,
1.307607, 1.351794, 1.389289, 1.42186, 1.450654, 1.476457,
1.647113, 1.747485, 1.818873, 1.874323, 1.919671, 1.958037,
1.991288, 2.02063, 2.046885, 2.219765, 2.320983, 2.392826,
2.448562, 2.494109, 2.532622, 2.565985, 2.595416, 2.621743 ]
""" Q function based on lookup table, for positive drifts """
A = [0.0005, 0.001, 0.0015, 0.002, 0.0025, 0.005,
0.0075, 0.01, 0.0125, 0.015, 0.0175, 0.02,
0.0225, 0.025, 0.0275, 0.03, 0.0325, 0.035,
0.0375, 0.04, 0.0425, 0.045, 0.0475, 0.05,
0.055, 0.06, 0.065, 0.07, 0.075, 0.08,
0.085, 0.09, 0.095, 0.1, 0.15, 0.2,
0.25, 0.3, 0.35, 0.4, 0.45, 0.5,
1.0, 1.5, 2.0, 2.5, 3.0, 3.5,
4.0, 4.5, 5.0, 10.0, 15.0, 20.0,
25.0, 30.0, 35.0, 40.0, 45.0, 50.0,
100.0, 150.0, 200.0, 250.0, 300.0, 350.0,
400.0, 450.0, 500.0, 1000.0, 1500.0, 2000.0,
2500.0, 3000.0, 3500.0, 4000.0, 4500.0, 5000.0]
B = [0.01969, 0.027694, 0.033789, 0.038896, 0.043372, 0.060721,
0.073808, 0.084693, 0.094171, 0.102651, 0.110375, 0.117503,
0.124142, 0.130374, 0.136259, 0.141842, 0.147162, 0.152249,
0.157127, 0.161817, 0.166337, 0.170702, 0.174924, 0.179015,
0.186842, 0.194248, 0.201287, 0.207999, 0.214421, 0.220581,
0.226505, 0.232212, 0.237722, 0.24305, 0.288719, 0.325071,
0.355581, 0.382016, 0.405415, 0.426452, 0.445588, 0.463159,
0.588642, 0.668992, 0.72854, 0.775976, 0.815456, 0.849298,
0.878933, 0.905305, 0.92907, 1.088998, 1.184918, 1.253794,
1.307607, 1.351794, 1.389289, 1.42186, 1.450654, 1.476457,
1.647113, 1.747485, 1.818873, 1.874323, 1.919671, 1.958037,
1.991288, 2.02063, 2.046885, 2.219765, 2.320983, 2.392826,
2.448562, 2.494109, 2.532622, 2.565985, 2.595416, 2.621743]
if x > 5000:
return 0.25 * np.log(x) + 0.49088
elif x < 0.0005:
Expand All @@ -477,27 +478,27 @@ def emdd_qp(x):
return interpolate.interp1d(A, B)([x])[0]

def emdd_qn(x):
""" Return value for Q functions based on lookup table, for negative drifts"""
A = [ 0.0005, 0.001, 0.0015, 0.002, 0.0025, 0.005,
0.0075, 0.01, 0.0125, 0.015, 0.0175, 0.02,
0.0225, 0.025, 0.0275, 0.03, 0.0325, 0.035,
0.0375, 0.04, 0.0425, 0.045, 0.0475, 0.05,
0.055, 0.06, 0.065, 0.07, 0.075, 0.08,
0.085, 0.09, 0.095, 0.1, 0.15, 0.2,
0.25, 0.3, 0.35, 0.4, 0.45, 0.5,
0.75, 1.0, 1.25, 1.5, 1.75, 2.0,
2.25, 2.5, 2.75, 3.0, 3.25, 3.5,
3.75, 4.0, 4.25, 4.5, 4.75, 5.0 ]
B = [ 0.019965, 0.028394, 0.034874, 0.040369, 0.045256, 0.064633,
0.079746, 0.092708, 0.104259, 0.114814, 0.124608, 0.133772,
0.142429, 0.150739, 0.158565, 0.166229, 0.173756, 0.180793,
0.187739, 0.194489, 0.201094, 0.207572, 0.213877, 0.220056,
0.231797, 0.243374, 0.254585, 0.265472, 0.27607, 0.286406,
0.296507, 0.306393, 0.316066, 0.325586, 0.413136, 0.491599,
0.564333, 0.633007, 0.698849, 0.762455, 0.824484, 0.884593,
1.17202, 1.44552, 1.70936, 1.97074, 2.22742, 2.48396,
2.73676, 2.99094, 3.24354, 3.49252, 3.74294, 3.99519,
4.24274, 4.49238, 4.73859, 4.99043, 5.24083, 5.49882 ]
""" Q function based on lookup table, for negative drifts """
A = [0.0005, 0.001, 0.0015, 0.002, 0.0025, 0.005,
0.0075, 0.01, 0.0125, 0.015, 0.0175, 0.02,
0.0225, 0.025, 0.0275, 0.03, 0.0325, 0.035,
0.0375, 0.04, 0.0425, 0.045, 0.0475, 0.05,
0.055, 0.06, 0.065, 0.07, 0.075, 0.08,
0.085, 0.09, 0.095, 0.1, 0.15, 0.2,
0.25, 0.3, 0.35, 0.4, 0.45, 0.5,
0.75, 1.0, 1.25, 1.5, 1.75, 2.0,
2.25, 2.5, 2.75, 3.0, 3.25, 3.5,
3.75, 4.0, 4.25, 4.5, 4.75, 5.0]
B = [0.019965, 0.028394, 0.034874, 0.040369, 0.045256, 0.064633,
0.079746, 0.092708, 0.104259, 0.114814, 0.124608, 0.133772,
0.142429, 0.150739, 0.158565, 0.166229, 0.173756, 0.180793,
0.187739, 0.194489, 0.201094, 0.207572, 0.213877, 0.220056,
0.231797, 0.243374, 0.254585, 0.265472, 0.27607, 0.286406,
0.296507, 0.306393, 0.316066, 0.325586, 0.413136, 0.491599,
0.564333, 0.633007, 0.698849, 0.762455, 0.824484, 0.884593,
1.17202, 1.44552, 1.70936, 1.97074, 2.22742, 2.48396,
2.73676, 2.99094, 3.24354, 3.49252, 3.74294, 3.99519,
4.24274, 4.49238, 4.73859, 4.99043, 5.24083, 5.49882]
if x > 5:
return x + 0.5
elif x < 0.0005:
Expand Down

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