Merge pull request dcajasn#86 from ryanrussell/master

docs(various): Fix typos

README.md

@@ -188,7 +188,7 @@ The latest stable release (and older versions) can be installed from PyPI:
 
 ## Citing
 
-If you use Riskfolio-Lib for published work, please use the following BibTeX entrie:
+If you use Riskfolio-Lib for published work, please use the following BibTeX entry:
 
 ```
 @misc{riskfolio,

docs/source/index.rst

@@ -169,7 +169,7 @@ Some of key functionalities that Riskfolio-Lib offers:
 Citing
 ======
 
-If you use Riskfolio-Lib for published work, please use the following BibTeX entrie:
+If you use Riskfolio-Lib for published work, please use the following BibTeX entry:
 
 ::
 

docs/source/portfolio.rst

@@ -183,7 +183,7 @@ Where:
 
 :math:`\psi`: is the average risk of the portfolio.
 
-:math:`\gamma`: is the lower bound of each asset risk constribution.
+:math:`\gamma`: is the lower bound of each asset risk contribution.
 
 :math:`\zeta_{i}`: is the marginal risk of asset :math:`i`.
 

examples/README.md

@@ -9,7 +9,7 @@ Tutorials:
 - Index Tracking / Replicating Portfolios [Tutorial 7](https://github.com/microprediction/Riskfolio-Lib/blob/master/examples/Tutorial%207.ipynb)
 - Short and Leveraged Portfolios [Tutorial 8](https://github.com/microprediction/Riskfolio-Lib/blob/master/examples/Tutorial%208.ipynb)
 - Portfolio Optimization with Risk Factors and Principal Components Regression (PCR) [Tutorial 9](https://github.com/microprediction/Riskfolio-Lib/blob/master/examples/Tutorial%209.ipynb)
-- Risk Parity Portfolio Optimization [Tuturial 10](https://github.com/microprediction/Riskfolio-Lib/blob/master/examples/Tutorial%2010.ipynb)
+- Risk Parity Portfolio Optimization [Tutorial 10](https://github.com/microprediction/Riskfolio-Lib/blob/master/examples/Tutorial%2010.ipynb)
 - Risk Parity Portfolio Optimization with Risk Factors using Stepwise Regression [Tutorial 11](https://github.com/microprediction/Riskfolio-Lib/blob/master/examples/Tutorial%2011.ipynb)
 - Worst Case Mean Variance Portfolio Optimization [Tutorial 12](https://github.com/microprediction/Riskfolio-Lib/blob/master/examples/Tutorial%2012.ipynb)
 - Riskfolio-Lib and Xlwings [Tutorial 13](https://github.com/microprediction/Riskfolio-Lib/blob/master/examples/Tutorial%2013.ipynb)

examples/Tutorial 12.ipynb

@@ -1307,7 +1307,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Finally, you can try several combinations of differents uncertainty sets and estimation methods of parameters."
+    "Finally, you can try several combinations of different uncertainty sets and estimation methods of parameters."
    ]
   }
  ],

examples/Tutorial 3.ipynb

@@ -574,7 +574,7 @@
     "         'Set': ['Industry', 'Industry', 'Industry'],\n",
     "         'Position': ['Energy', 'Consumer Staples', 'Materials'],\n",
     "         'Sign': ['>=', '>=', '>='],\n",
-    "         'Weight': [0.08, 0.1, 0.09], # Anual terms \n",
+    "         'Weight': [0.08, 0.1, 0.09], # Annual terms \n",
     "         'Type Relative': ['Classes', 'Classes', 'Classes'],\n",
     "         'Relative Set': ['Industry', 'Industry', 'Industry'],\n",
     "         'Relative': ['Financials', 'Utilities', 'Industrials']}\n",
@@ -1279,7 +1279,7 @@
    "source": [
     "## 3. Estimating Black Litterman Mean Risk Portfolios\n",
     "\n",
-    "When we use risk measures differents than Standard Deviation, Riskfolio-Lib only considers the vector of expected returns, and use historical returns to calculate risk measures.\n",
+    "When we use risk measures different than Standard Deviation, Riskfolio-Lib only considers the vector of expected returns, and use historical returns to calculate risk measures.\n",
     "\n",
     "### 3.4 Calculate Black Litterman Portfolios for Several Risk Measures"
    ]

examples/Tutorial 4.ipynb

@@ -1580,7 +1580,7 @@
    "source": [
     "## 3. Optimization with Key Rate Durations Constraints\n",
     "\n",
-    "This part shows how __Riskfolio-Lib__ can be used to build immunized portfolios using __duration matching__ and __convexity matching__, however the example only use duration matching. More information about inmunization theory can be found in this __[link](https://www.investopedia.com/terms/i/immunization.asp)__.\n",
+    "This part shows how __Riskfolio-Lib__ can be used to build immunized portfolios using __duration matching__ and __convexity matching__, however the example only use duration matching. More information about immunization theory can be found in this __[link](https://www.investopedia.com/terms/i/immunization.asp)__.\n",
     "\n",
     "### 3.1 Statistics of Risk Factors"
    ]
@@ -3738,7 +3738,7 @@
    "source": [
     "## 5. Optimization of Equity and Bond Portfolio with Key Rate Durations Constraints\n",
     "\n",
-    "This part shows how __Riskfolio-Lib__ can be used to build immunized portfolios using __duration matching__ and __convexity matching__, however the example only use duration matching. More information about inmunization theory can be found in this __[link](https://www.investopedia.com/terms/i/immunization.asp)__.\n",
+    "This part shows how __Riskfolio-Lib__ can be used to build immunized portfolios using __duration matching__ and __convexity matching__, however the example only use duration matching. More information about immunization theory can be found in this __[link](https://www.investopedia.com/terms/i/immunization.asp)__.\n",
     "\n",
     "### 5.1 Statistics of Risk Factors"
    ]

riskfolio/ConstraintsFunctions.py

@@ -779,12 +779,12 @@ def assets_clusters(
         codep = af.ltdi_matrix(returns, alpha_tail).astype(float)
         dist = -np.log(codep)
 
-    # Hierarchcial clustering
+    # Hierarchical clustering
     dist = dist.to_numpy()
     dist = pd.DataFrame(dist, columns=codep.columns, index=codep.index)
     if linkage == "DBHT":
         # different choices for D, S give different outputs!
-        D = dist.to_numpy()  # dissimilatity matrix
+        D = dist.to_numpy()  # dissimilarity matrix
         if codependence in {"pearson", "spearman"}:
             S = (1 - dist**2).to_numpy()
         else:

riskfolio/HCPortfolio.py

@@ -187,12 +187,12 @@ def _hierarchical_clustering(
         elif codependence in {"tail"}:
             dist = -np.log(self.codep).astype(float)
 
-        # Hierarchcial clustering
+        # Hierarchical clustering
         dist = dist.to_numpy()
         dist = pd.DataFrame(dist, columns=self.codep.columns, index=self.codep.index)
         if linkage == "DBHT":
             # different choices for D, S give different outputs!
-            D = dist.to_numpy()  # dissimilatity matrix
+            D = dist.to_numpy()  # dissimilarity matrix
             if codependence in {"pearson", "spearman", "custom_cov"}:
                 codep = 1 - dist**2
                 S = codep.to_numpy()  # similarity matrix

riskfolio/ParamsEstimation.py

@@ -502,7 +502,7 @@ def backward_regression(X, y, criterion="pvalue", threshold=0.05, verbose=False)
 
 def PCR(X, y, n_components=0.95):
     r"""
-    Estimate the coeficients using Principal Components Regression (PCR).
+    Estimate the coefficients using Principal Components Regression (PCR).
 
     Parameters
     ----------
@@ -514,7 +514,7 @@ def PCR(X, y, n_components=0.95):
     n_components : int, float, None or str, optional
         if 1 < n_components (int), it represents the number of components that
         will be keep. if 0 < n_components < 1 (float), it represents the
-        percentage of variance that the is explained by the components keeped.
+        percentage of variance that the is explained by the components kept.
         See `PCA <https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html>`_
         for more details. The default is 0.95.
 
@@ -607,7 +607,7 @@ def loadings_matrix(
     n_components : int, float, None or str, optional
         if 1 < n_components (int), it represents the number of components that
         will be keep. if 0 < n_components < 1 (float), it represents the
-        percentage of variance that the is explained by the components keeped.
+        percentage of variance that the is explained by the components kept.
         See `PCA <https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html>`_
         for more details. The default is 0.95.
     verbose : bool, optional
@@ -741,7 +741,7 @@ def risk_factors(
     n_components : int, float, None or str, optional
         if 1 < n_components (int), it represents the number of components that
         will be keep. if 0 < n_components < 1 (float), it represents the
-        percentage of variance that the is explained by the components keeped.
+        percentage of variance that the is explained by the components kept.
         See `PCA <https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html>`_
         for more details. The default is 0.95.
     error : bool

riskfolio/PlotFunctions.py

@@ -2055,13 +2055,13 @@ def plot_clusters(
         codep = af.cov2corr(custom_cov).astype(float)
         dist = np.sqrt(np.clip((1 - codep) / 2, a_min=0.0, a_max=1.0))
 
-    # Hierarchcial clustering
+    # Hierarchical clustering
     dist = dist.to_numpy()
     dist = pd.DataFrame(dist, columns=codep.columns, index=codep.index)
     dim = len(dist)
     if linkage == "DBHT":
         # different choices for D, S give different outputs!
-        D = dist.to_numpy()  # dissimilatity matrix
+        D = dist.to_numpy()  # dissimilarity matrix
         if codependence in {"pearson", "spearman", "custom_cov"}:
             S = (1 - dist**2).to_numpy()
         else:
@@ -2378,12 +2378,12 @@ def plot_dendrogram(
         codep = af.cov2corr(custom_cov).astype(float)
         dist = np.sqrt(np.clip((1 - codep) / 2, a_min=0.0, a_max=1.0))
 
-    # Hierarchcial clustering
+    # Hierarchical clustering
     dist = dist.to_numpy()
     dist = pd.DataFrame(dist, columns=codep.columns, index=codep.index)
     if linkage == "DBHT":
         # different choices for D, S give different outputs!
-        D = dist.to_numpy()  # dissimilatity matrix
+        D = dist.to_numpy()  # dissimilarity matrix
         if codependence in {"pearson", "spearman", "custom_cov"}:
             S = (1 - dist**2).to_numpy()
         else:
@@ -2629,12 +2629,12 @@ def plot_network(
         codep = af.cov2corr(custom_cov).astype(float)
         dist = np.sqrt(np.clip((1 - codep) / 2, a_min=0.0, a_max=1.0))
 
-    # Hierarchcial clustering
+    # Hierarchical clustering
     dist = dist.to_numpy()
     dist = pd.DataFrame(dist, columns=codep.columns, index=codep.index)
     if linkage == "DBHT":
         # different choices for D, S give different outputs!
-        D = dist.to_numpy()  # dissimilatity matrix
+        D = dist.to_numpy()  # dissimilarity matrix
         if codependence in {"pearson", "spearman", "custom_cov"}:
             S = (1 - dist**2).to_numpy()
         else:

riskfolio/Portfolio.py

@@ -1033,7 +1033,7 @@ def optimization(
             - 'BLFM': use estimates of expected return vector and covariance matrix based on Black Litterman applied to a Risk Factor model specified by the user.
             
         rm : str, optional
-            The risk measure used to optimze the portfolio.
+            The risk measure used to optimize the portfolio.
             The default is 'MV'. Possible values are:
             
             - 'MV': Standard Deviation.
@@ -1083,7 +1083,7 @@ def optimization(
             If model = 'FM', True means historical covariance and returns and
             False Risk Factor model for covariance and returns.
             If model = 'BL_FM', True means historical covariance and returns,
-            False Black Litteram with Risk Factor model for covariance and
+            False Black Litterman with Risk Factor model for covariance and
             Risk Factor model for returns, and '2' Risk Factor model for
             covariance and returns. The default is True.
 
@@ -1822,7 +1822,7 @@ def rp_optimization(self, model="Classic", rm="MV", rf=0, b=None, hist=True):
             - 'FM': use estimates of expected return vector and covariance matrix based on a Risk Factor model specified by the user.
             
         rm : str, optional
-            The risk measure used to optimze the portfolio.
+            The risk measure used to optimize the portfolio.
             The default is 'MV'. Possible values are:
             
             - 'MV': Standard Deviation.
@@ -2187,7 +2187,7 @@ def rrp_optimization(self, model="Classic", version="A", l=1, b=None, hist=True)
         
         :math:`\psi`: is the average risk of the portfolio.
         
-        :math:`\gamma`: is the lower bound of each asset risk constribution.
+        :math:`\gamma`: is the lower bound of each asset risk contribution.
         
         :math:`\zeta_{i}`: is the marginal risk of asset :math:`i`.
         
@@ -2955,7 +2955,7 @@ def frontier_limits(self, model="Classic", rm="MV", kelly=False, rf=0, hist=True
             Methodology used to estimate input parameters.
             The default is 'Classic'.
         rm : str, optional
-            The risk measure used to optimze the portfolio.
+            The risk measure used to optimize the portfolio.
             The default is 'MV'. Possible values are:
 
             - 'MV': Standard Deviation.
@@ -2992,7 +2992,7 @@ def frontier_limits(self, model="Classic", rm="MV", kelly=False, rf=0, hist=True
             If model = 'FM', True means historical covariance and returns and
             False Risk Factor model for covariance and returns.
             If model = 'BL_FM', True means historical covariance and returns,
-            False Black Litteram with Risk Factor model for covariance and
+            False Black Litterman with Risk Factor model for covariance and
             Risk Factor model for returns, and '2' Risk Factor model for
             covariance and returns. The default is True.
 
@@ -3036,7 +3036,7 @@ def efficient_frontier(
             Methodology used to estimate input parameters.
             The default is 'Classic'.
         rm : str, optional
-            The risk measure used to optimze the portfolio.
+            The risk measure used to optimize the portfolio.
             The default is 'MV'. Possible values are:
 
             - 'MV': Standard Deviation.
@@ -3076,7 +3076,7 @@ def efficient_frontier(
             If model = 'FM', True means historical covariance and returns and
             False Risk Factor model for covariance and returns.
             If model = 'BL_FM', True means historical covariance and returns,
-            False Black Litteram with Risk Factor model for covariance and
+            False Black Litterman with Risk Factor model for covariance and
             Risk Factor model for returns, and '2' Risk Factor model for
             covariance and returns. The default is True.
 

riskfolio/Reports.py

@@ -70,9 +70,9 @@ def jupyter_report(
         - 'UCI': Ulcer Index of uncompounded cumulative returns.
 
     rf : float, optional
-        Risk free rate or minimum aceptable return. The default is 0.
+        Risk free rate or minimum acceptable return. The default is 0.
     alpha : float, optional
-        Significante level of VaR, CVaR, EVaR, DaR and CDaR.
+        Significant level of VaR, CVaR, EVaR, DaR and CDaR.
         The default is 0.05.
     others : float, optional
         Percentage of others section. The default is 0.05.

riskfolio/RiskFunctions.py

@@ -403,7 +403,7 @@ def EVaR_Hist(X, alpha=0.05):
 def MDD_Abs(X):
     r"""
     Calculate the Maximum Drawdown (MDD) of a returns series
-    using uncumpounded cumulative returns.
+    using uncompounded cumulative returns.
 
     .. math::
         \text{MDD}(X) = \max_{j \in (0,T)} \left [\max_{t \in (0,j)}
@@ -422,7 +422,7 @@ def MDD_Abs(X):
     Returns
     -------
     value : float
-        MDD of an uncumpounded cumulative returns.
+        MDD of an uncompounded cumulative returns.
 
     """
 
@@ -451,7 +451,7 @@ def MDD_Abs(X):
 def ADD_Abs(X):
     r"""
     Calculate the Average Drawdown (ADD) of a returns series
-    using uncumpounded cumulative returns.
+    using uncompounded cumulative returns.
 
     .. math::
         \text{ADD}(X) = \frac{1}{T}\sum_{j=0}^{T}\left [ \max_{t \in (0,j)}
@@ -470,7 +470,7 @@ def ADD_Abs(X):
     Returns
     -------
     value : float
-        ADD of an uncumpounded cumulative returns.
+        ADD of an uncompounded cumulative returns.
 
     """
 
@@ -505,7 +505,7 @@ def ADD_Abs(X):
 def DaR_Abs(X, alpha=0.05):
     r"""
     Calculate the Drawdown at Risk (DaR) of a returns series
-    using uncumpounded cumulative returns.
+    using uncompounded cumulative returns.
 
     .. math::
         \text{DaR}_{\alpha}(X) & = \max_{j \in (0,T)} \left \{ \text{DD}(X,j)
@@ -529,7 +529,7 @@ def DaR_Abs(X, alpha=0.05):
     Returns
     -------
     value : float
-        DaR of an uncumpounded cumulative returns series.
+        DaR of an uncompounded cumulative returns series.
 
     """
 
@@ -559,7 +559,7 @@ def DaR_Abs(X, alpha=0.05):
 def CDaR_Abs(X, alpha=0.05):
     r"""
     Calculate the Conditional Drawdown at Risk (CDaR) of a returns series
-    using uncumpounded cumulative returns.
+    using uncompounded cumulative returns.
 
     .. math::
         \text{CDaR}_{\alpha}(X) = \text{DaR}_{\alpha}(X) + \frac{1}{\alpha T}
@@ -569,7 +569,7 @@ def CDaR_Abs(X, alpha=0.05):
 
     Where:
 
-    :math:`\text{DaR}_{\alpha}` is the Drawdown at Risk of an uncumpound
+    :math:`\text{DaR}_{\alpha}` is the Drawdown at Risk of an uncompounded
     cumulated return series :math:`X`.
 
     Parameters
@@ -587,7 +587,7 @@ def CDaR_Abs(X, alpha=0.05):
     Returns
     -------
     value : float
-        CDaR of an uncumpounded cumulative returns series.
+        CDaR of an uncompounded cumulative returns series.
 
     """
 
@@ -620,7 +620,7 @@ def CDaR_Abs(X, alpha=0.05):
 def EDaR_Abs(X, alpha=0.05):
     r"""
     Calculate the Entropic Drawdown at Risk (EDaR) of a returns series
-    using uncumpounded cumulative returns.
+    using uncompounded cumulative returns.
 
     .. math::
         \text{EDaR}_{\alpha}(X) & = \inf_{z>0} \left \{ z
@@ -643,7 +643,7 @@ def EDaR_Abs(X, alpha=0.05):
     Returns
     -------
     (value, z) : tuple
-        EDaR of an uncumpounded cumulative returns series 
+        EDaR of an uncompounded cumulative returns series 
         and value of z that minimize EDaR.
         
     """
@@ -672,7 +672,7 @@ def EDaR_Abs(X, alpha=0.05):
 def UCI_Abs(X):
     r"""
     Calculate the Ulcer Index (UCI) of a returns series
-    using uncumpounded cumulative returns.
+    using uncompounded cumulative returns.
 
     .. math::
         \text{UCI}(X) =\sqrt{\frac{1}{T}\sum_{j=0}^{T} \left [ \max_{t \in
@@ -692,7 +692,7 @@ def UCI_Abs(X):
     Returns
     -------
     value : float
-        Ulcer Index of an uncumpounded cumulative returns.
+        Ulcer Index of an uncompounded cumulative returns.
 
     """