Auxiliary Functions

AuxFunctions.is_pos_def(cov, threshold=1e-08)[source]

Indicate if a matrix is positive (semi)definite.

Parameters

cov (nd-array of shape (n_features, n_features)) – Features covariance matrix, where n_features is the number of features.

Returns

value – True if matrix is positive (semi)definite.

Return type

bool

Raises

ValueError when the value cannot be calculated.

AuxFunctions.correl_matrix(cov)[source]

Generate a correlation matrix from a covariance matrix cov.

Parameters

cov (nd-array of shape (n_features, n_features)) – Assets covariance matrix, where n_features is the number of features.

Returns

corr – A correlation matrix.

Return type

nd-array

Raises

ValueError when the value cannot be calculated.

AuxFunctions.cov_fix(cov, method='clipped', **kwargs)[source]

Fix a covariance matrix to a positive definite matrix.

Parameters
  • cov (nd-array of shape (n_features, n_features)) – Features covariance matrix, where n_features is the number of features.

  • method (str) – The default value is ‘clipped’, see more in cov_nearest.

  • **kwargs

    Other parameters from cov_nearest.

Returns

cov_ – A positive definite covariance matrix.

Return type

bool

Raises

ValueError when the value cannot be calculated.

AuxFunctions.cov_returns(cov, seed=0)[source]

Generate a matrix of returns that have a covariance matrix cov.

Parameters

cov (nd-array of shape (n_features, n_features)) – Assets covariance matrix, where n_features is the number of features.

Returns

a – A matrix of returns that have a covariance matrix cov.

Return type

nd-array

Raises

ValueError when the value cannot be calculated.