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updated docs

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robertmartin8
2020-04-10 11:08:15 +08:00
parent 85f83ac6f8
commit 31684f211f
3 changed files with 9 additions and 4 deletions
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5
docs/Roadmap.rst
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@@ -45,6 +45,11 @@ have any other feature requests, please raise them using GitHub
Fixed minor issues in CLA: weight bound bug, ``efficient_frontier`` needed weights to be called, ``set_weights`` not needed. Fixed minor issues in CLA: weight bound bug, ``efficient_frontier`` needed weights to be called, ``set_weights`` not needed.
1.0.2
-----
Fixed small but important bug where passing ``expected_returns=None`` fails. According to the docs, users
should be able to only pass covariance if they want to only optimise min volatility.
0.5.0 0.5.0
===== =====

4
pypfopt/base_optimizer.py
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@@ -351,8 +351,8 @@ def portfolio_performance(
After optimising, calculate (and optionally print) the performance of the optimal After optimising, calculate (and optionally print) the performance of the optimal
portfolio. Currently calculates expected return, volatility, and the Sharpe ratio. portfolio. Currently calculates expected return, volatility, and the Sharpe ratio.
:param expected_returns: expected returns for each asset. Set to None if :param expected_returns: expected returns for each asset. Can be None if
optimising for volatility only. optimising for volatility only (but not recommended).
:type expected_returns: np.ndarray or pd.Series :type expected_returns: np.ndarray or pd.Series
:param cov_matrix: covariance of returns for each asset :param cov_matrix: covariance of returns for each asset
:type cov_matrix: np.array or pd.DataFrame :type cov_matrix: np.array or pd.DataFrame

4
pypfopt/efficient_frontier.py
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@@ -55,8 +55,8 @@ class EfficientFrontier(base_optimizer.BaseConvexOptimizer):
def __init__(self, expected_returns, cov_matrix, weight_bounds=(0, 1), gamma=0): def __init__(self, expected_returns, cov_matrix, weight_bounds=(0, 1), gamma=0):
""" """
:param expected_returns: expected returns for each asset. Set to None if :param expected_returns: expected returns for each asset. Can be None if
optimising for volatility only. optimising for volatility only (but not recommended).
:type expected_returns: pd.Series, list, np.ndarray :type expected_returns: pd.Series, list, np.ndarray
:param cov_matrix: covariance of returns for each asset :param cov_matrix: covariance of returns for each asset
:type cov_matrix: pd.DataFrame or np.array :type cov_matrix: pd.DataFrame or np.array