isort; edit workflow

.flake8

@@ -0,0 +1,3 @@
+[flake8]
+max-line-length = 88
+extend-ignore = E203

.github/workflows/main.yml

@@ -1,9 +1,13 @@
+# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
+
 name: pytest
+
 on:
-    pull_request:
-    push:
-        branches:
-            - master
+  push:
+    branches: [ "master" ]
+  pull_request:
+    branches: [ "master" ]
 
 jobs:
     pytest:
@@ -28,7 +32,16 @@ jobs:
             - name: Install dependencies
               run: |
                   pip install -r requirements.txt
+                  pip install pytest isort black flake8
             - name: Test with pytest
               run: |
-                  pip install pytest
-                  pytest ./tests
+                  pytest ./tests
+            - name: Check with isort
+              run: |
+                  isort --check --diff .
+            - name: Check with black
+              run: |
+                  black --check --diff .
+            - name: Check with flake8
+              run: |
+                  flake8 --show-source --statistics .

.gitignore

@@ -1,3 +1,4 @@
+.git
 *.pyc
 .idea
 build

docs/conf.py

@@ -13,8 +13,8 @@
 # All configuration values have a default; values that are commented out
 # serve to show the default.
 
-import sys
 import os
+import sys
 
 # If extensions (or modules to document with autodoc) are in another directory,
 # add these directories to sys.path here. If the directory is relative to the

example/examples.py

@@ -1,17 +1,19 @@
-import pandas as pd
-import numpy as np
 import cvxpy as cp
-from pypfopt import risk_models
-from pypfopt import expected_returns
-from pypfopt import EfficientFrontier
-from pypfopt import objective_functions
-from pypfopt.discrete_allocation import DiscreteAllocation, get_latest_prices
-from pypfopt import HRPOpt
-from pypfopt import CLA
-from pypfopt import black_litterman
-from pypfopt import BlackLittermanModel
-from pypfopt import plotting
+import numpy as np
+import pandas as pd
 
+from pypfopt import (
+    CLA,
+    BlackLittermanModel,
+    EfficientFrontier,
+    HRPOpt,
+    black_litterman,
+    expected_returns,
+    objective_functions,
+    plotting,
+    risk_models,
+)
+from pypfopt.discrete_allocation import DiscreteAllocation, get_latest_prices
 
 # Reading in the data; preparing expected returns and a risk model
 df = pd.read_csv("tests/resources/stock_prices.csv", parse_dates=True, index_col="date")

pypfopt/__init__.py

@@ -1,20 +1,19 @@
 from .black_litterman import (
+    BlackLittermanModel,
     market_implied_prior_returns,
     market_implied_risk_aversion,
-    BlackLittermanModel,
 )
 from .cla import CLA
-from .discrete_allocation import get_latest_prices, DiscreteAllocation
+from .discrete_allocation import DiscreteAllocation, get_latest_prices
 from .efficient_frontier import (
+    EfficientCDaR,
+    EfficientCVaR,
     EfficientFrontier,
     EfficientSemivariance,
-    EfficientCVaR,
-    EfficientCDaR,
 )
 from .hierarchical_portfolio import HRPOpt
 from .risk_models import CovarianceShrinkage
 
-
 __version__ = "1.5.4"
 
 __all__ = [

pypfopt/base_optimizer.py

@@ -13,13 +13,12 @@ import warnings
 from collections.abc import Iterable
 from typing import List
 
+import cvxpy as cp
 import numpy as np
 import pandas as pd
-import cvxpy as cp
 import scipy.optimize as sco
 
-from . import objective_functions
-from . import exceptions
+from . import exceptions, objective_functions
 
 
 class BaseOptimizer:

pypfopt/black_litterman.py

@@ -8,8 +8,10 @@ views. In addition, two utility functions are defined, which calculate:
 """
 import sys
 import warnings
+
 import numpy as np
 import pandas as pd
+
 from . import base_optimizer
 
 

pypfopt/cla.py

@@ -6,6 +6,7 @@ by Marcos Lopez de Prado and David Bailey.
 
 import numpy as np
 import pandas as pd
+
 from . import base_optimizer
 
 

pypfopt/efficient_frontier/__init__.py

@@ -3,11 +3,10 @@ The ``efficient_frontier`` module houses the EfficientFrontier class and its des
 which generate optimal portfolios for various possible objective functions and parameters.
 """
 
-from .efficient_frontier import EfficientFrontier
-from .efficient_cvar import EfficientCVaR
-from .efficient_semivariance import EfficientSemivariance
 from .efficient_cdar import EfficientCDaR
-
+from .efficient_cvar import EfficientCVaR
+from .efficient_frontier import EfficientFrontier
+from .efficient_semivariance import EfficientSemivariance
 
 __all__ = [
     "EfficientFrontier",

pypfopt/efficient_frontier/efficient_cdar.py

@@ -4,8 +4,9 @@ generates portfolios along the mean-CDaR (conditional drawdown-at-risk) frontier
 """
 
 import warnings
-import numpy as np
+
 import cvxpy as cp
+import numpy as np
 
 from .. import objective_functions
 from .efficient_frontier import EfficientFrontier

pypfopt/efficient_frontier/efficient_cvar.py

@@ -4,8 +4,9 @@ generates portfolios along the mean-CVaR frontier.
 """
 
 import warnings
-import numpy as np
+
 import cvxpy as cp
+import numpy as np
 
 from .. import objective_functions
 from .efficient_frontier import EfficientFrontier

pypfopt/efficient_frontier/efficient_frontier.py

@@ -4,12 +4,11 @@ classical mean-variance optimal portfolios for a variety of objectives and const
 """
 import warnings
 
+import cvxpy as cp
 import numpy as np
 import pandas as pd
-import cvxpy as cp
 
-from .. import exceptions
-from .. import objective_functions, base_optimizer
+from .. import base_optimizer, exceptions, objective_functions
 
 
 class EfficientFrontier(base_optimizer.BaseConvexOptimizer):

pypfopt/efficient_frontier/efficient_semivariance.py

@@ -4,8 +4,8 @@ generates portfolios along the mean-semivariance frontier.
 """
 
 
-import numpy as np
 import cvxpy as cp
+import numpy as np
 
 from .. import objective_functions
 from .efficient_frontier import EfficientFrontier

pypfopt/hierarchical_portfolio.py

@@ -13,6 +13,7 @@ Currently implemented:
 """
 
 import collections
+
 import numpy as np
 import pandas as pd
 import scipy.cluster.hierarchy as sch

pypfopt/objective_functions.py

@@ -28,8 +28,8 @@ Currently implemented:
 - Ex-post (squared) tracking error
 """
 
-import numpy as np
 import cvxpy as cp
+import numpy as np
 
 
 def _objective_value(w, obj):

pypfopt/plotting.py

@@ -9,12 +9,13 @@ Currently implemented:
   - ``plot_weights`` - bar chart of weights
 """
 import copy
-import numpy as np
-from . import risk_models, exceptions
-from . import EfficientFrontier, CLA
-import scipy.cluster.hierarchy as sch
 import warnings
 
+import numpy as np
+import scipy.cluster.hierarchy as sch
+
+from . import CLA, EfficientFrontier, exceptions, risk_models
+
 try:
     import matplotlib.pyplot as plt
 

pypfopt/risk_models.py

@@ -22,8 +22,10 @@ The format of the data input is the same as that in :ref:`expected-returns`.
 """
 
 import warnings
+
 import numpy as np
 import pandas as pd
+
 from .expected_returns import returns_from_prices
 
 
@@ -486,13 +488,13 @@ class CovarianceShrinkage:
 
         # compute shrinkage parameters
         c = np.linalg.norm(sample - F, "fro") ** 2
-        y = Xm ** 2
-        p = 1 / t * np.sum(np.dot(y.T, y)) - np.sum(sample ** 2)
+        y = Xm**2
+        p = 1 / t * np.sum(np.dot(y.T, y)) - np.sum(sample**2)
 
         # r is divided into diagonal
         # and off-diagonal terms, and the off-diagonal term
         # is itself divided into smaller terms
-        rdiag = 1 / t * np.sum(y ** 2) - sum(np.diag(sample) ** 2)
+        rdiag = 1 / t * np.sum(y**2) - sum(np.diag(sample) ** 2)
         z = Xm * np.tile(xmkt, (n,))
         v1 = 1 / t * np.dot(y.T, z) - np.tile(betas, (n,)) * sample
         roff1 = (
@@ -501,8 +503,8 @@ class CovarianceShrinkage:
         )
         v3 = 1 / t * np.dot(z.T, z) - varmkt * sample
         roff3 = (
-            np.sum(v3 * np.dot(betas, betas.T)) / varmkt ** 2
-            - np.sum(np.diag(v3).reshape(-1, 1) * betas ** 2) / varmkt ** 2
+            np.sum(v3 * np.dot(betas, betas.T)) / varmkt**2
+            - np.sum(np.diag(v3).reshape(-1, 1) * betas**2) / varmkt**2
         )
         roff = 2 * roff1 - roff3
         r = rdiag + roff
@@ -540,12 +542,12 @@ class CovarianceShrinkage:
 
         # Estimate pi
         Xm = X - X.mean(axis=0)
-        y = Xm ** 2
-        pi_mat = np.dot(y.T, y) / t - 2 * np.dot(Xm.T, Xm) * S / t + S ** 2
+        y = Xm**2
+        pi_mat = np.dot(y.T, y) / t - 2 * np.dot(Xm.T, Xm) * S / t + S**2
         pi_hat = np.sum(pi_mat)
 
         # Theta matrix, expanded term by term
-        term1 = np.dot((Xm ** 3).T, Xm) / t
+        term1 = np.dot((Xm**3).T, Xm) / t
         help_ = np.dot(Xm.T, Xm) / t
         help_diag = np.diag(help_)
         term2 = np.tile(help_diag, (n, 1)).T * S

pyproject.toml

@@ -1,2 +1,5 @@
 [tool.black]
-line-length = 88
+line-length = 88
+
+[tool.isort]
+profile = "black"

setup.py

@@ -1,4 +1,5 @@
 import re
+
 import setuptools
 
 with open("README.md", "r") as f:

tests/test_black_litterman.py

@@ -2,11 +2,11 @@ import numpy as np
 import pandas as pd
 import pytest
 
-from pypfopt import risk_models, expected_returns
+from pypfopt import expected_returns, risk_models
 from pypfopt.black_litterman import (
     BlackLittermanModel,
-    market_implied_risk_aversion,
     market_implied_prior_returns,
+    market_implied_risk_aversion,
 )
 from tests.utilities_for_tests import get_data, get_market_caps, resource
 

tests/test_discrete_allocation.py

@@ -3,7 +3,7 @@ import pandas as pd
 import pytest
 from cvxpy.error import SolverError
 
-from pypfopt.discrete_allocation import get_latest_prices, DiscreteAllocation
+from pypfopt.discrete_allocation import DiscreteAllocation, get_latest_prices
 from tests.utilities_for_tests import get_data, setup_efficient_frontier
 
 

tests/test_efficient_cdar.py

@@ -1,9 +1,9 @@
 import numpy as np
 import pytest
 
-from pypfopt import expected_returns, EfficientCDaR, objective_functions
+from pypfopt import EfficientCDaR, expected_returns, objective_functions
 from pypfopt.exceptions import OptimizationError
-from tests.utilities_for_tests import setup_efficient_cdar, get_data
+from tests.utilities_for_tests import get_data, setup_efficient_cdar
 
 
 def test_cdar_example():

tests/test_efficient_cvar.py

@@ -1,9 +1,9 @@
 import numpy as np
 import pytest
 
-from pypfopt import expected_returns, EfficientCVaR, objective_functions
+from pypfopt import EfficientCVaR, expected_returns, objective_functions
 from pypfopt.exceptions import OptimizationError
-from tests.utilities_for_tests import setup_efficient_cvar, get_data
+from tests.utilities_for_tests import get_data, setup_efficient_cvar
 
 
 def test_cvar_example():

tests/test_expected_returns.py

@@ -3,7 +3,7 @@ import pandas as pd
 import pytest
 
 from pypfopt import expected_returns
-from tests.utilities_for_tests import get_data, get_benchmark_data
+from tests.utilities_for_tests import get_benchmark_data, get_data
 
 
 def test_returns_dataframe():

tests/test_hrp.py

@@ -2,7 +2,7 @@ import numpy as np
 import pandas as pd
 import pytest
 
-from pypfopt import HRPOpt, CovarianceShrinkage
+from pypfopt import CovarianceShrinkage, HRPOpt
 from tests.utilities_for_tests import get_data, resource
 
 

tests/test_imports.py

@@ -15,16 +15,16 @@ def test_import_modules():
 
 def test_explicit_import():
     from pypfopt.black_litterman import (
+        BlackLittermanModel,
         market_implied_prior_returns,
         market_implied_risk_aversion,
-        BlackLittermanModel,
     )
     from pypfopt.cla import CLA
-    from pypfopt.discrete_allocation import get_latest_prices, DiscreteAllocation
+    from pypfopt.discrete_allocation import DiscreteAllocation, get_latest_prices
     from pypfopt.efficient_frontier import (
+        EfficientCVaR,
         EfficientFrontier,
         EfficientSemivariance,
-        EfficientCVaR,
     )
     from pypfopt.hierarchical_portfolio import HRPOpt
     from pypfopt.risk_models import CovarianceShrinkage
@@ -32,15 +32,15 @@ def test_explicit_import():
 
 def test_import_toplevel():
     from pypfopt import (
-        market_implied_prior_returns,
-        market_implied_risk_aversion,
-        BlackLittermanModel,
         CLA,
-        get_latest_prices,
+        BlackLittermanModel,
+        CovarianceShrinkage,
         DiscreteAllocation,
+        EfficientCVaR,
         EfficientFrontier,
         EfficientSemivariance,
-        EfficientCVaR,
         HRPOpt,
-        CovarianceShrinkage,
+        get_latest_prices,
+        market_implied_prior_returns,
+        market_implied_risk_aversion,
     )

tests/test_plotting.py

@@ -6,16 +6,16 @@ import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import pytest
-from tests.utilities_for_tests import get_data, setup_efficient_frontier
 
 from pypfopt import (
     CLA,
     EfficientFrontier,
-    expected_returns,
     HRPOpt,
+    expected_returns,
     plotting,
     risk_models,
 )
+from tests.utilities_for_tests import get_data, setup_efficient_frontier
 
 
 def test_correlation_plot():

tests/test_risk_models.py

@@ -2,7 +2,7 @@ import numpy as np
 import pandas as pd
 import pytest
 
-from pypfopt import risk_models, expected_returns
+from pypfopt import expected_returns, risk_models
 from tests.utilities_for_tests import get_data
 
 

tests/utilities_for_tests.py

@@ -3,15 +3,14 @@ import os
 import numpy as np
 import pandas as pd
 
-from pypfopt import expected_returns
-from pypfopt import risk_models
+from pypfopt import expected_returns, risk_models
+from pypfopt.cla import CLA
 from pypfopt.efficient_frontier import (
+    EfficientCDaR,
+    EfficientCVaR,
     EfficientFrontier,
     EfficientSemivariance,
-    EfficientCVaR,
-    EfficientCDaR,
 )
-from pypfopt.cla import CLA
 
 
 def resource(name):