robertmartin8
commented
2 years ago I can't reproduce this: it works for me
Closed acastiel closed 1 year ago
robertmartin8
commented
2 years ago I can't reproduce this: it works for me
yosukesan
commented
2 years ago I had probably the same issue on my host. Is this reproducible for you ? I also found pytest failed in my host env. Please check my following report after this.
env
Input
import pandas as pd
import matplotlib.pyplot as plt
from pypfopt import EfficientFrontier
from pypfopt import risk_models
from pypfopt import expected_returns
from pypfopt import plotting
import copydf = pd.read_csv("PyPortfolioOpt/tests/resources/stock_prices.csv", parse_dates=True, index_col="date")
mu = expected_returns.mean_historical_return(df) S = risk_models.sample_cov(df)
ef = EfficientFrontier(mu, S) raw_weights = ef.max_sharpe() cleaned_weights = ef.clean_weights() ef.save_weights_to_file("weights.csv") # saves to file print(cleaned_weights) ef.portfolio_performance(verbose=True)
fig, ax = plt.subplots() plotting.plot_efficient_frontier(ef, ax=ax, show_assets=True)
* Err logpython efficent_front_plot_sample.py
OrderedDict([('GOOG', 0.03835), ('AAPL', 0.0689), ('FB', 0.20603), ('BABA', 0.07315), ('AMZN', 0.04033), ('GE', 0.0), ('AMD', 0.0), ('WMT', 0.0), ('BAC', 0.0), ('GM', 0.0), ('T', 0.0), ('UAA', 0.0), ('SHLD', 0.0), ('XOM', 0.0), ('RRC', 0.0), ('BBY', 0.01324), ('MA', 0.35349), ('PFE', 0.1957), ('JPM', 0.0), ('SBUX', 0.01082)])
Expected annual return: 30.5%
Annual volatility: 22.2%
Sharpe Ratio: 1.28
Traceback (most recent call last):
File "/home/yosuke/Data/finance/py_portfolio_opt/PyPortfolioOpt/efficent_front_plot_sample.py", line 26, in
Here is my test result.
============================= test session starts ==============================
platform linux -- Python 3.9.2, pytest-6.0.2, py-1.10.0, pluggy-0.13.0
rootdir: /home/yosuke/Data/finance/py_portfolio_opt/PyPortfolioOpt
collected 312 items
tests/test_base_optimizer.py ................... [ 6%]
tests/test_black_litterman.py ..................... [ 12%]
tests/test_cla.py ............ [ 16%]
tests/test_custom_objectives.py .................. [ 22%]
tests/test_discrete_allocation.py ................. [ 27%]
tests/test_efficient_cdar.py .................... [ 34%]
tests/test_efficient_cvar.py ..................... [ 41%]
tests/test_efficient_frontier.py ....................................... [ 53%]
................................... [ 64%]
tests/test_efficient_semivariance.py ............................ [ 73%]
tests/test_expected_returns.py ................... [ 79%]
tests/test_hrp.py ...... [ 81%]
tests/test_imports.py ... [ 82%]
tests/test_objective_functions.py ........... [ 86%]
tests/test_plotting.py ..FFFFF.FFFFF.... [ 91%]
tests/test_risk_models.py .......................... [100%]
=================================== FAILURES ===================================
________________________________ test_cla_plot _________________________________
def test_cla_plot():
plt.figure()
df = get_data()
rets = expected_returns.mean_historical_return(df)
S = risk_models.exp_cov(df)
cla = CLA(rets, S)
ax = plotting.plot_efficient_frontier(cla, showfig=False)
> assert len(ax.findobj()) == 142
E AssertionError: assert 143 == 142
E + where 143 = len([<matplotlib.collections.PathCollection object at 0x7f6e27b28af0>, <matplotlib.collections.PathCollection object at 0x...3424e700>, <matplotlib.spines.Spine object at 0x7f6e3424e220>, <matplotlib.spines.Spine object at 0x7f6e3424ea90>, ...])
E + where [<matplotlib.collections.PathCollection object at 0x7f6e27b28af0>, <matplotlib.collections.PathCollection object at 0x...3424e700>, <matplotlib.spines.Spine object at 0x7f6e3424e220>, <matplotlib.spines.Spine object at 0x7f6e3424ea90>, ...] = <bound method Artist.findobj of <AxesSubplot:xlabel='Volatility', ylabel='Return'>>()
E + where <bound method Artist.findobj of <AxesSubplot:xlabel='Volatility', ylabel='Return'>> = <AxesSubplot:xlabel='Volatility', ylabel='Return'>.findobj
tests/test_plotting.py:88: AssertionError
_______________________________ test_cla_plot_ax _______________________________
def test_cla_plot_ax():
plt.figure()
df = get_data()
rets = expected_returns.mean_historical_return(df)
S = risk_models.exp_cov(df)
cla = CLA(rets, S)
fig, ax = plt.subplots(figsize=(12, 10))
plotting.plot_efficient_frontier(cla, ax=ax)
> assert len(ax.findobj()) == 142
E AssertionError: assert 143 == 142
E + where 143 = len([<matplotlib.collections.PathCollection object at 0x7f6e26f99250>, <matplotlib.collections.PathCollection object at 0x...27bb6070>, <matplotlib.spines.Spine object at 0x7f6e27bb6e80>, <matplotlib.spines.Spine object at 0x7f6e27bb6ac0>, ...])
E + where [<matplotlib.collections.PathCollection object at 0x7f6e26f99250>, <matplotlib.collections.PathCollection object at 0x...27bb6070>, <matplotlib.spines.Spine object at 0x7f6e27bb6e80>, <matplotlib.spines.Spine object at 0x7f6e27bb6ac0>, ...] = <bound method Artist.findobj of <AxesSubplot:xlabel='Volatility', ylabel='Return'>>()
E + where <bound method Artist.findobj of <AxesSubplot:xlabel='Volatility', ylabel='Return'>> = <AxesSubplot:xlabel='Volatility', ylabel='Return'>.findobj
tests/test_plotting.py:106: AssertionError
_____________________________ test_default_ef_plot _____________________________
def test_default_ef_plot():
plt.figure()
ef = setup_efficient_frontier()
ax = plotting.plot_efficient_frontier(ef, show_assets=True)
> assert len(ax.findobj()) == 124
E AssertionError: assert 125 == 124
E + where 125 = len([<matplotlib.collections.PathCollection object at 0x7f6e26f1e370>, <matplotlib.lines.Line2D object at 0x7f6e26f2c9d0>,...26f70fd0>, <matplotlib.spines.Spine object at 0x7f6e26f36100>, <matplotlib.spines.Spine object at 0x7f6e26f361f0>, ...])
E + where [<matplotlib.collections.PathCollection object at 0x7f6e26f1e370>, <matplotlib.lines.Line2D object at 0x7f6e26f2c9d0>,...26f70fd0>, <matplotlib.spines.Spine object at 0x7f6e26f36100>, <matplotlib.spines.Spine object at 0x7f6e26f361f0>, ...] = <bound method Artist.findobj of <AxesSubplot:xlabel='Volatility', ylabel='Return'>>()
E + where <bound method Artist.findobj of <AxesSubplot:xlabel='Volatility', ylabel='Return'>> = <AxesSubplot:xlabel='Volatility', ylabel='Return'>.findobj
tests/test_plotting.py:115: AssertionError
_________________________ test_default_ef_plot_labels __________________________
def test_default_ef_plot_labels():
plt.figure()
ef = setup_efficient_frontier()
ax = plotting.plot_efficient_frontier(ef, show_assets=True, show_tickers=True)
> assert len(ax.findobj()) == 124 + len(ef.tickers)
E AssertionError: assert 145 == (124 + 20)
E + where 145 = len([<matplotlib.collections.PathCollection object at 0x7f6e26e41ee0>, <matplotlib.lines.Line2D object at 0x7f6e26e41340>,...AAPL'), Text(0.3704067363898078, 0.2840374345617638, 'FB'), Text(0.3177391875264159, 0.19231644713860208, 'BABA'), ...])
E + where [<matplotlib.collections.PathCollection object at 0x7f6e26e41ee0>, <matplotlib.lines.Line2D object at 0x7f6e26e41340>,...AAPL'), Text(0.3704067363898078, 0.2840374345617638, 'FB'), Text(0.3177391875264159, 0.19231644713860208, 'BABA'), ...] = <bound method Artist.findobj of <AxesSubplot:xlabel='Volatility', ylabel='Return'>>()
E + where <bound method Artist.findobj of <AxesSubplot:xlabel='Volatility', ylabel='Return'>> = <AxesSubplot:xlabel='Volatility', ylabel='Return'>.findobj
E + and 20 = len(['GOOG', 'AAPL', 'FB', 'BABA', 'AMZN', 'GE', ...])
E + where ['GOOG', 'AAPL', 'FB', 'BABA', 'AMZN', 'GE', ...] = <pypfopt.efficient_frontier.efficient_frontier.EfficientFrontier object at 0x7f6e26f16850>.tickers
tests/test_plotting.py:132: AssertionError
_____________________________ test_ef_plot_utility _____________________________
def test_ef_plot_utility():
plt.figure()
ef = setup_efficient_frontier()
delta_range = np.arange(0.001, 50, 1)
ax = plotting.plot_efficient_frontier(
ef, ef_param="utility", ef_param_range=delta_range, showfig=False
)
> assert len(ax.findobj()) == 124
E AssertionError: assert 125 == 124
E + where 125 = len([<matplotlib.collections.PathCollection object at 0x7f6e26db2bb0>, <matplotlib.lines.Line2D object at 0x7f6e26db2ee0>,...26ead430>, <matplotlib.spines.Spine object at 0x7f6e26ead520>, <matplotlib.spines.Spine object at 0x7f6e26ead610>, ...])
E + where [<matplotlib.collections.PathCollection object at 0x7f6e26db2bb0>, <matplotlib.lines.Line2D object at 0x7f6e26db2ee0>,...26ead430>, <matplotlib.spines.Spine object at 0x7f6e26ead520>, <matplotlib.spines.Spine object at 0x7f6e26ead610>, ...] = <bound method Artist.findobj of <AxesSubplot:xlabel='Volatility', ylabel='Return'>>()
E + where <bound method Artist.findobj of <AxesSubplot:xlabel='Volatility', ylabel='Return'>> = <AxesSubplot:xlabel='Volatility', ylabel='Return'>.findobj
tests/test_plotting.py:143: AssertionError
______________________________ test_ef_plot_risk _______________________________
def test_ef_plot_risk():
plt.figure()
ef = setup_efficient_frontier()
ef.min_volatility()
min_risk = ef.portfolio_performance()[1]
ef = setup_efficient_frontier()
risk_range = np.linspace(min_risk + 0.05, 0.5, 30)
ax = plotting.plot_efficient_frontier(
ef, ef_param="risk", ef_param_range=risk_range, showfig=False
)
> assert len(ax.findobj()) == 124
E AssertionError: assert 125 == 124
E + where 125 = len([<matplotlib.collections.PathCollection object at 0x7f6e26e41430>, <matplotlib.lines.Line2D object at 0x7f6e26e41460>,...27b7b850>, <matplotlib.spines.Spine object at 0x7f6e27b7b730>, <matplotlib.spines.Spine object at 0x7f6e27b47b20>, ...])
E + where [<matplotlib.collections.PathCollection object at 0x7f6e26e41430>, <matplotlib.lines.Line2D object at 0x7f6e26e41460>,...27b7b850>, <matplotlib.spines.Spine object at 0x7f6e27b7b730>, <matplotlib.spines.Spine object at 0x7f6e27b47b20>, ...] = <bound method Artist.findobj of <AxesSubplot:xlabel='Volatility', ylabel='Return'>>()
E + where <bound method Artist.findobj of <AxesSubplot:xlabel='Volatility', ylabel='Return'>> = <AxesSubplot:xlabel='Volatility', ylabel='Return'>.findobj
tests/test_plotting.py:177: AssertionError
_____________________________ test_ef_plot_return ______________________________
def test_ef_plot_return():
plt.figure()
ef = setup_efficient_frontier()
# Internally _max_return() is used, so subtract epsilon
max_ret = ef.expected_returns.max() - 0.0001
return_range = np.linspace(0, max_ret, 30)
ax = plotting.plot_efficient_frontier(
ef, ef_param="return", ef_param_range=return_range, showfig=False
)
> assert len(ax.findobj()) == 124
E AssertionError: assert 125 == 124
E + where 125 = len([<matplotlib.collections.PathCollection object at 0x7f6e27b590d0>, <matplotlib.lines.Line2D object at 0x7f6e27ad6ee0>,...34233910>, <matplotlib.spines.Spine object at 0x7f6e34233cd0>, <matplotlib.spines.Spine object at 0x7f6e34233e20>, ...])
E + where [<matplotlib.collections.PathCollection object at 0x7f6e27b590d0>, <matplotlib.lines.Line2D object at 0x7f6e27ad6ee0>,...34233910>, <matplotlib.spines.Spine object at 0x7f6e34233cd0>, <matplotlib.spines.Spine object at 0x7f6e34233e20>, ...] = <bound method Artist.findobj of <AxesSubplot:xlabel='Volatility', ylabel='Return'>>()
E + where <bound method Artist.findobj of <AxesSubplot:xlabel='Volatility', ylabel='Return'>> = <AxesSubplot:xlabel='Volatility', ylabel='Return'>.findobj
tests/test_plotting.py:191: AssertionError
__________________________ test_ef_plot_utility_short __________________________
def test_ef_plot_utility_short():
plt.figure()
ef = EfficientFrontier(
*setup_efficient_frontier(data_only=True), weight_bounds=(None, None)
)
delta_range = np.linspace(0.001, 20, 50)
ax = plotting.plot_efficient_frontier(
ef, ef_param="utility", ef_param_range=delta_range, showfig=False
)
> assert len(ax.findobj()) == 160
E AssertionError: assert 161 == 160
E + where 161 = len([<matplotlib.collections.PathCollection object at 0x7f6e3439d3d0>, <matplotlib.lines.Line2D object at 0x7f6e3439d1c0>,...34262970>, <matplotlib.spines.Spine object at 0x7f6e34262940>, <matplotlib.spines.Spine object at 0x7f6e34262610>, ...])
E + where [<matplotlib.collections.PathCollection object at 0x7f6e3439d3d0>, <matplotlib.lines.Line2D object at 0x7f6e3439d1c0>,...34262970>, <matplotlib.spines.Spine object at 0x7f6e34262940>, <matplotlib.spines.Spine object at 0x7f6e34262610>, ...] = <bound method Artist.findobj of <AxesSubplot:xlabel='Volatility', ylabel='Return'>>()
E + where <bound method Artist.findobj of <AxesSubplot:xlabel='Volatility', ylabel='Return'>> = <AxesSubplot:xlabel='Volatility', ylabel='Return'>.findobj
tests/test_plotting.py:205: AssertionError
_______________________ test_constrained_ef_plot_utility _______________________
def test_constrained_ef_plot_utility():
plt.figure()
ef = setup_efficient_frontier()
ef.add_constraint(lambda w: w[0] >= 0.2)
ef.add_constraint(lambda w: w[2] == 0.15)
ef.add_constraint(lambda w: w[3] + w[4] <= 0.10)
delta_range = np.linspace(0.001, 20, 50)
ax = plotting.plot_efficient_frontier(
ef, ef_param="utility", ef_param_range=delta_range, showfig=False
)
> assert len(ax.findobj()) == 124
E AssertionError: assert 125 == 124
E + where 125 = len([<matplotlib.collections.PathCollection object at 0x7f6e26d4ad00>, <matplotlib.lines.Line2D object at 0x7f6e26d4a070>,...27a9cd30>, <matplotlib.spines.Spine object at 0x7f6e27a9cf40>, <matplotlib.spines.Spine object at 0x7f6e27a9c940>, ...])
E + where [<matplotlib.collections.PathCollection object at 0x7f6e26d4ad00>, <matplotlib.lines.Line2D object at 0x7f6e26d4a070>,...27a9cd30>, <matplotlib.spines.Spine object at 0x7f6e27a9cf40>, <matplotlib.spines.Spine object at 0x7f6e27a9c940>, ...] = <bound method Artist.findobj of <AxesSubplot:xlabel='Volatility', ylabel='Return'>>()
E + where <bound method Artist.findobj of <AxesSubplot:xlabel='Volatility', ylabel='Return'>> = <AxesSubplot:xlabel='Volatility', ylabel='Return'>.findobj
tests/test_plotting.py:221: AssertionError
________________________ test_constrained_ef_plot_risk _________________________
def test_constrained_ef_plot_risk():
plt.figure()
ef = EfficientFrontier(
*setup_efficient_frontier(data_only=True), weight_bounds=(None, None)
)
ef.add_constraint(lambda w: w[0] >= 0.2)
ef.add_constraint(lambda w: w[2] == 0.15)
ef.add_constraint(lambda w: w[3] + w[4] <= 0.10)
# 100 portfolios with risks between 0.10 and 0.30
risk_range = np.linspace(0.157, 0.40, 50)
ax = plotting.plot_efficient_frontier(
ef, ef_param="risk", ef_param_range=risk_range, show_assets=True, showfig=False
)
> assert len(ax.findobj()) == 136
E AssertionError: assert 137 == 136
E + where 137 = len([<matplotlib.collections.PathCollection object at 0x7f6e26cfeaf0>, <matplotlib.lines.Line2D object at 0x7f6e26cfe190>,...26d802b0>, <matplotlib.spines.Spine object at 0x7f6e26d803a0>, <matplotlib.spines.Spine object at 0x7f6e26d80490>, ...])
E + where [<matplotlib.collections.PathCollection object at 0x7f6e26cfeaf0>, <matplotlib.lines.Line2D object at 0x7f6e26cfe190>,...26d802b0>, <matplotlib.spines.Spine object at 0x7f6e26d803a0>, <matplotlib.spines.Spine object at 0x7f6e26d80490>, ...] = <bound method Artist.findobj of <AxesSubplot:xlabel='Volatility', ylabel='Return'>>()
E + where <bound method Artist.findobj of <AxesSubplot:xlabel='Volatility', ylabel='Return'>> = <AxesSubplot:xlabel='Volatility', ylabel='Return'>.findobj
tests/test_plotting.py:241: AssertionError
=============================== warnings summary ===============================
tests/test_base_optimizer.py::test_exception_immutability
/home/yosuke/Data/finance/py_portfolio_opt/PyPortfolioOpt/pypfopt/efficient_frontier/efficient_frontier.py:176: RuntimeWarning: Market neutrality requires shorting - bounds have been amended
warnings.warn(
tests/test_black_litterman.py::test_input_errors
tests/test_black_litterman.py::test_parse_views
tests/test_black_litterman.py::test_dataframe_input
tests/test_black_litterman.py::test_default_omega
tests/test_black_litterman.py::test_bl_returns_no_prior
tests/test_black_litterman.py::test_bl_relative_views
tests/test_black_litterman.py::test_bl_cov_default
tests/test_black_litterman.py::test_bl_weights
tests/test_black_litterman.py::test_bl_no_uncertainty
/home/yosuke/Data/finance/py_portfolio_opt/PyPortfolioOpt/pypfopt/black_litterman.py:257: UserWarning: Running Black-Litterman with no prior.
warnings.warn("Running Black-Litterman with no prior.")
tests/test_black_litterman.py::test_bl_returns_all_views
tests/test_black_litterman.py::test_bl_returns_all_views
<frozen importlib._bootstrap>:228: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 216 from C header, got 232 from PyObject
tests/test_black_litterman.py::test_bl_returns_all_views
/usr/lib/python3/dist-packages/sklearn/linear_model/_least_angle.py:34: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
method='lar', copy_X=True, eps=np.finfo(np.float).eps,
tests/test_black_litterman.py::test_bl_returns_all_views
/usr/lib/python3/dist-packages/sklearn/linear_model/_least_angle.py:164: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
method='lar', copy_X=True, eps=np.finfo(np.float).eps,
tests/test_black_litterman.py::test_bl_returns_all_views
/usr/lib/python3/dist-packages/sklearn/linear_model/_least_angle.py:281: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
eps=np.finfo(np.float).eps, copy_Gram=True, verbose=0,
tests/test_black_litterman.py::test_bl_returns_all_views
/usr/lib/python3/dist-packages/sklearn/linear_model/_least_angle.py:865: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
eps=np.finfo(np.float).eps, copy_X=True, fit_path=True,
tests/test_black_litterman.py::test_bl_returns_all_views
/usr/lib/python3/dist-packages/sklearn/linear_model/_least_angle.py:1121: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
eps=np.finfo(np.float).eps, copy_X=True, fit_path=True,
tests/test_black_litterman.py::test_bl_returns_all_views
/usr/lib/python3/dist-packages/sklearn/linear_model/_least_angle.py:1149: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
eps=np.finfo(np.float).eps, positive=False):
tests/test_black_litterman.py::test_bl_returns_all_views
/usr/lib/python3/dist-packages/sklearn/linear_model/_least_angle.py:1379: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
max_n_alphas=1000, n_jobs=None, eps=np.finfo(np.float).eps,
tests/test_black_litterman.py::test_bl_returns_all_views
/usr/lib/python3/dist-packages/sklearn/linear_model/_least_angle.py:1621: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
max_n_alphas=1000, n_jobs=None, eps=np.finfo(np.float).eps,
tests/test_black_litterman.py::test_bl_returns_all_views
/usr/lib/python3/dist-packages/sklearn/linear_model/_least_angle.py:1755: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
eps=np.finfo(np.float).eps, copy_X=True, positive=False):
tests/test_discrete_allocation.py: 6 warnings
tests/test_efficient_cvar.py: 1 warning
tests/test_efficient_semivariance.py: 2 warnings
tests/test_plotting.py: 1 warning
/home/yosuke/.local/lib/python3.9/site-packages/cvxpy/problems/problem.py:1337: UserWarning: Solution may be inaccurate. Try another solver, adjusting the solver settings, or solve with verbose=True for more information.
warnings.warn(
tests/test_efficient_cdar.py::test_cdar_example
tests/test_efficient_cdar.py::test_cdar_example_weekly
tests/test_efficient_cdar.py::test_cdar_example_monthly
tests/test_efficient_cvar.py::test_cvar_example
tests/test_efficient_cvar.py::test_cvar_example_weekly
tests/test_efficient_cvar.py::test_cvar_example_monthly
tests/test_efficient_semivariance.py::test_es_example_short
/usr/lib/python3/dist-packages/pandas/core/nanops.py:1558: DeprecationWarning: the `interpolation=` argument to percentile was renamed to `method=`, which has additional options.
Users of the modes 'nearest', 'lower', 'higher', or 'midpoint' are encouraged to review the method they used. (Deprecated NumPy 1.22)
return np.percentile(values, q, axis=axis, interpolation=interpolation)
tests/test_efficient_frontier.py::test_min_volatility_sector_constraints
/home/yosuke/Data/finance/py_portfolio_opt/PyPortfolioOpt/pypfopt/base_optimizer.py:382: UserWarning: Sector constraints may not produce reasonable results if shorts are allowed.
warnings.warn(
tests/test_efficient_frontier.py::test_max_sharpe_L2_reg_different_gamma
tests/test_efficient_frontier.py::test_max_sharpe_L2_reg_different_gamma
tests/test_efficient_frontier.py::test_max_sharpe_L2_reg_reduces_sharpe
tests/test_efficient_frontier.py::test_max_sharpe_L2_reg_with_shorts
/home/yosuke/Data/finance/py_portfolio_opt/PyPortfolioOpt/pypfopt/efficient_frontier/efficient_frontier.py:257: UserWarning: max_sharpe transforms the optimization problem so additional objectives may not work as expected.
warnings.warn(
-- Docs: https://docs.pytest.org/en/stable/warnings.html
=========================== short test summary info ============================
FAILED tests/test_plotting.py::test_cla_plot - AssertionError: assert 143 == 142
FAILED tests/test_plotting.py::test_cla_plot_ax - AssertionError: assert 143 ...
FAILED tests/test_plotting.py::test_default_ef_plot - AssertionError: assert ...
FAILED tests/test_plotting.py::test_default_ef_plot_labels - AssertionError: ...
FAILED tests/test_plotting.py::test_ef_plot_utility - AssertionError: assert ...
FAILED tests/test_plotting.py::test_ef_plot_risk - AssertionError: assert 125...
FAILED tests/test_plotting.py::test_ef_plot_return - AssertionError: assert 1...
FAILED tests/test_plotting.py::test_ef_plot_utility_short - AssertionError: a...
FAILED tests/test_plotting.py::test_constrained_ef_plot_utility - AssertionEr...
FAILED tests/test_plotting.py::test_constrained_ef_plot_risk - AssertionError...
============ 10 failed, 302 passed, 43 warnings in 72.19s (0:01:12) ============In your second last comment, the error is reproducible to me but also there's a problem with the code:
One shouldn't call any optimisation method on the EfficientFrontier object before running the plotting function. So when I changed it to the below, it worked:
import pandas as pd
import matplotlib.pyplot as plt
from pypfopt import EfficientFrontier
from pypfopt import risk_models
from pypfopt import expected_returns
from pypfopt import plotting
import copy
# Read in price data
df = pd.read_csv("PyPortfolioOpt/tests/resources/stock_prices.csv", parse_dates=True, index_col="date")
# Calculate expected returns and sample covariance
mu = expected_returns.mean_historical_return(df)
S = risk_models.sample_cov(df)
# Optimize for maximal Sharpe ratio
ef = EfficientFrontier(mu, S)
# COMMENTING OUT BELOW
# raw_weights = ef.max_sharpe()
# cleaned_weights = ef.clean_weights()
# ef.save_weights_to_file("weights.csv") # saves to file
# print(cleaned_weights)
# ef.portfolio_performance(verbose=True)
fig, ax = plt.subplots()
plotting.plot_efficient_frontier(ef, ax=ax, show_assets=True)As for the testing issues you pointed out, these are indeed valid and I have observed them from time to time. I think it is a result of different plotting backends creating slightly different objects when plotting. So I will modify the tests slightly. Thanks for pointing this out!
yosukesan
commented
1 year ago Worked on my env. Thanks !
Just added plt.show() for completeness.
import pandas as pd
import matplotlib.pyplot as plt
from pypfopt import EfficientFrontier
from pypfopt import risk_models
from pypfopt import expected_returns
from pypfopt import plotting
import copy
# Read in price data
df = pd.read_csv("PyPortfolioOpt/tests/resources/stock_prices.csv", parse_dates=True, index_col="date")
# Calculate expected returns and sample covariance
mu = expected_returns.mean_historical_return(df)
S = risk_models.sample_cov(df)
# Optimize for maximal Sharpe ratio
ef = EfficientFrontier(mu, S)
# COMMENTING OUT BELOW
# raw_weights = ef.max_sharpe()
# cleaned_weights = ef.clean_weights()
# ef.save_weights_to_file("weights.csv") # saves to file
# print(cleaned_weights)
# ef.portfolio_performance(verbose=True)
fig, ax = plt.subplots()
plotting.plot_efficient_frontier(ef, ax=ax, show_assets=True)
plt.show()
It seems the Plotting module is not working properly:
using
from pypfopt import plotting ef = pf.EfficientFrontier(mu, S) plotting.plot_efficient_frontier(ef,ax=ax)
I have this message: TypeError: cannot pickle 'osqp.OSQP_results' object