fix pytest badge using branch; slightly enhance code

Author: 88d52bdba0366127fffca9dfa93895

README.md

@@ -17,7 +17,7 @@
         <img src="https://img.shields.io/badge/license-MIT-brightgreen.svg"
             alt="MIT license"></a> &nbsp;
     <a href="https://github.com/robertmartin8/PyPortfolioOpt/actions">
-        <img src="https://github.com/robertmartin8/PyPortfolioOpt/workflows/pytest/badge.svg?event=push"
+        <img src="https://github.com/robertmartin8/PyPortfolioOpt/workflows/pytest/badge.svg?branch=master"
             alt="build"></a> &nbsp;
     <a href="https://app.codecov.io/gh/robertmartin8/PyPortfolioOpt">
         <img src="https://codecov.io/github/robertmartin8/PyPortfolioOpt/coverage.svg?branch=main"

pypfopt/efficient_frontier/efficient_frontier.py

@@ -83,10 +83,8 @@ def __init__(
         :raises TypeError: if ``cov_matrix`` is not a dataframe or array
         """
         # Inputs
-        self.cov_matrix = EfficientFrontier._validate_cov_matrix(cov_matrix)
-        self.expected_returns = EfficientFrontier._validate_expected_returns(
-            expected_returns
-        )
+        self.cov_matrix = self._validate_cov_matrix(cov_matrix)
+        self.expected_returns = self._validate_expected_returns(expected_returns)
         self._max_return_value = None
         self._market_neutral = None
 

pypfopt/expected_returns.py

@@ -21,8 +21,9 @@
 """
 
 import warnings
-import pandas as pd
+
 import numpy as np
+import pandas as pd
 
 
 def _check_returns(returns):
@@ -247,12 +248,12 @@ def capm_return(
             market_returns = market_prices
     else:
         returns = returns_from_prices(prices, log_returns)
-        
+
         if market_prices is not None:
             if not isinstance(market_prices, pd.DataFrame):
                 warnings.warn("market prices are not in a dataframe", RuntimeWarning)
                 market_prices = pd.DataFrame(market_prices)
-                
+
             market_returns = returns_from_prices(market_prices, log_returns)
     # Use the equally-weighted dataset as a proxy for the market
     if market_returns is None:

tests/test_efficient_semivariance.py

@@ -3,13 +3,13 @@
 from cvxpy.error import SolverError
 
 from pypfopt import (
-    risk_models,
-    expected_returns,
     EfficientFrontier,
     EfficientSemivariance,
+    expected_returns,
     objective_functions,
+    risk_models,
 )
-from tests.utilities_for_tests import setup_efficient_semivariance, get_data
+from tests.utilities_for_tests import get_data, setup_efficient_semivariance
 
 
 def test_es_example():

tests/utilities_for_tests.py

@@ -2,6 +2,7 @@
 
 import numpy as np
 import pandas as pd
+
 from pypfopt import expected_returns
 from pypfopt import risk_models
 from pypfopt.efficient_frontier import (
@@ -11,7 +12,6 @@
     EfficientCDaR,
 )
 from pypfopt.cla import CLA
-from pypfopt.expected_returns import returns_from_prices
 
 
 def resource(name):
@@ -72,7 +72,7 @@ def setup_efficient_frontier(
 def setup_efficient_semivariance(data_only=False, solver=None, verbose=False):
     df = get_data().dropna(axis=0, how="any")
     mean_return = expected_returns.mean_historical_return(df, compounding=False)
-    historic_returns = returns_from_prices(df)
+    historic_returns = expected_returns.returns_from_prices(df)
     if data_only:
         return mean_return, historic_returns
     return EfficientSemivariance(
@@ -85,7 +85,7 @@ def setup_efficient_cvar(
 ):
     df = get_data().dropna(axis=0, how="any")
     mean_return = expected_returns.mean_historical_return(df)
-    historic_returns = returns_from_prices(df)
+    historic_returns = expected_returns.returns_from_prices(df)
     if data_only:
         return mean_return, historic_returns
     return EfficientCVaR(
@@ -102,7 +102,7 @@ def setup_efficient_cdar(
 ):
     df = get_data().dropna(axis=0, how="any")
     mean_return = expected_returns.mean_historical_return(df)
-    historic_returns = returns_from_prices(df)
+    historic_returns = expected_returns.returns_from_prices(df)
     if data_only:
         return mean_return, historic_returns
     return EfficientCDaR(