docs(various): Fix typos

cookbook/2-Mean-Variance-Optimisation.ipynb

@@ -1996,7 +1996,7 @@
         "# You must always create a new efficient frontier object\n",
         "ef = EfficientFrontier(mu, S)\n",
         "ef.add_sector_constraints(sector_mapper, sector_lower, sector_upper)\n",
-        "ef.add_objective(objective_functions.L2_reg, gamma=0.1)  # gamme is the tuning parameter\n",
+        "ef.add_objective(objective_functions.L2_reg, gamma=0.1)  # gamma is the tuning parameter\n",
         "ef.efficient_risk(0.15)\n",
         "weights = ef.clean_weights()\n",
         "weights"
@@ -2074,7 +2074,7 @@
       "source": [
         "ef = EfficientFrontier(mu, S)\n",
         "ef.add_sector_constraints(sector_mapper, sector_lower, sector_upper)\n",
-        "ef.add_objective(objective_functions.L2_reg, gamma=1)  # gamme is the tuning parameter\n",
+        "ef.add_objective(objective_functions.L2_reg, gamma=1)  # gamma is the tuning parameter\n",
         "ef.efficient_risk(0.15)\n",
         "weights = ef.clean_weights()\n",
         "weights"

docs/BlackLitterman.rst

@@ -49,7 +49,7 @@ but I ultimately decided that though BL is not technically an optimizer, it didn
 split up its methods into `expected_returns` or `risk_models`. I have thus made it an independent
 module and owing to the comparatively extensive theory, have given it a dedicated documentation page.
 I'd like to thank  `Felipe Schneider <https://github.com/schneiderfelipe>`_ for his multiple
-contributions to the Black-Litterman implementation. A full example of its usage, including the acquistion
+contributions to the Black-Litterman implementation. A full example of its usage, including the acquisition
 of market cap data for free, please refer to the `cookbook recipe <https://github.com/robertmartin8/PyPortfolioOpt/blob/master/cookbook/4-Black-Litterman-Allocation.ipynb>`_.
 
 .. tip:: 

docs/GeneralEfficientFrontier.rst

@@ -200,7 +200,7 @@ in anything related to ``max_sharpe()``, ``min_volatility()``, ``efficient_risk(
 set up a completely new problem to optimize for some custom objective?
 
 For example, perhaps our objective is to construct a basket of assets that best replicates a
-particular index, in otherwords, to minimise the **tracking error**. This does not fit within
+particular index, in other words, to minimise the **tracking error**. This does not fit within
 a mean-variance optimization paradigm, but we can still implement it in PyPortfolioOpt::
 
     from pypfopt.base_optimizer import BaseConvexOptimizer

pypfopt/base_optimizer.py

@@ -515,7 +515,7 @@ def portfolio_performance(
     :type verbose: bool, optional
     :param risk_free_rate: risk-free rate of borrowing/lending, defaults to 0.02
     :type risk_free_rate: float, optional
-    :raises ValueError: if weights have not been calcualted yet
+    :raises ValueError: if weights have not been calculated yet
     :return: expected return, volatility, Sharpe ratio.
     :rtype: (float, float, float)
     """

pypfopt/black_litterman.py

@@ -144,7 +144,7 @@ class BlackLittermanModel(base_optimizer.BaseOptimizer):
                    to be passed).
                    If pi="equal", use an equal-weighted prior.
         :type pi: np.ndarray, pd.Series, optional
-        :param absolute_views: a colleciton of K absolute views on a subset of assets,
+        :param absolute_views: a collection of K absolute views on a subset of assets,
                                defaults to None. If this is provided, we do not need P, Q.
         :type absolute_views: pd.Series or dict, optional
         :param Q: Kx1 views vector, defaults to None
@@ -469,7 +469,7 @@ class BlackLittermanModel(base_optimizer.BaseOptimizer):
                                The period of the risk-free rate should correspond to the
                                frequency of expected returns.
         :type risk_free_rate: float, optional
-        :raises ValueError: if weights have not been calcualted yet
+        :raises ValueError: if weights have not been calculated yet
         :return: expected return, volatility, Sharpe ratio.
         :rtype: (float, float, float)
         """

pypfopt/cla.py

@@ -103,7 +103,7 @@ class CLA(base_optimizer.BaseOptimizer):
 
         :param x: argument
         :type x: float
-        :return: infinity if the argmument was None otherwise x
+        :return: infinity if the argument was None otherwise x
         :rtype: float
         """
         return float("-inf") if x is None else x

pypfopt/efficient_frontier/efficient_cdar.py

@@ -216,7 +216,7 @@ class EfficientCDaR(EfficientFrontier):
 
         :param verbose: whether performance should be printed, defaults to False
         :type verbose: bool, optional
-        :raises ValueError: if weights have not been calcualted yet
+        :raises ValueError: if weights have not been calculated yet
         :return: expected return, CDaR.
         :rtype: (float, float)
         """

pypfopt/efficient_frontier/efficient_cvar.py

@@ -223,7 +223,7 @@ class EfficientCVaR(EfficientFrontier):
 
         :param verbose: whether performance should be printed, defaults to False
         :type verbose: bool, optional
-        :raises ValueError: if weights have not been calcualted yet
+        :raises ValueError: if weights have not been calculated yet
         :return: expected return, CVaR.
         :rtype: (float, float)
         """

pypfopt/efficient_frontier/efficient_frontier.py

@@ -429,7 +429,7 @@ class EfficientFrontier(base_optimizer.BaseConvexOptimizer):
                                The period of the risk-free rate should correspond to the
                                frequency of expected returns.
         :type risk_free_rate: float, optional
-        :raises ValueError: if weights have not been calcualted yet
+        :raises ValueError: if weights have not been calculated yet
         :return: expected return, volatility, Sharpe ratio.
         :rtype: (float, float, float)
         """

pypfopt/efficient_frontier/efficient_semivariance.py

@@ -261,7 +261,7 @@ class EfficientSemivariance(EfficientFrontier):
                                The period of the risk-free rate should correspond to the
                                frequency of expected returns.
         :type risk_free_rate: float, optional
-        :raises ValueError: if weights have not been calcualted yet
+        :raises ValueError: if weights have not been calculated yet
         :return: expected return, semideviation, Sortino ratio.
         :rtype: (float, float, float)
         """

tests/test_black_litterman.py

@@ -512,7 +512,7 @@ def test_bl_no_uncertainty():
     for k, v in viewdict.items():
         assert np.abs(rets[k] - v) < 1e-5
 
-    # If only one view has 100% confidencee, only that asset will have post = prior.
+    # If only one view has 100% confidence, only that asset will have post = prior.
     omega = np.diag([0, 0.2, 0.2, 0.2])
     bl = BlackLittermanModel(S, absolute_views=viewdict, omega=omega)
     rets = bl.bl_returns()

tests/test_discrete_allocation.py

@@ -240,7 +240,7 @@ def test_lp_portfolio_allocation():
     da = DiscreteAllocation(w, latest_prices, short_ratio=0.3)
     allocation, leftover = da.lp_portfolio()
 
-    #  Gives differnt answers on different machines
+    #  Gives different answers on different machines
     # assert allocation == {
     #     "AMD": 1,
     #     "GOOG": 1,

tests/test_efficient_frontier.py

@@ -1255,7 +1255,7 @@ def test_max_sharpe_short_semicovariance():
     )
 
 
-def test_min_volatilty_shrunk_L2_reg():
+def test_min_volatility_shrunk_L2_reg():
     df = get_data()
     ef = setup_efficient_frontier()
     ef.add_objective(objective_functions.L2_reg)