ElasticNetFactorsIdentifierSimplified

class qf_lib.common.utils.factorization.factors_identification.elastic_net_factors_identifier_simplified.ElasticNetFactorsIdentifierSimplified(epsilon: float = 0.05, l1_ratio: float = 1, number_of_alphas: int = 75, is_fit_intercept: bool = True)[source]

Bases: FactorsIdentifier

Class used for identifying factors in the model with Elastic Net method (with Cross-validation). Implementation was simplified so that the stock ElasticNetCV optimizer was used.

Parameters:

epsilon – if abs(coefficient) is smaller than epsilon it is considered to be zero, thus won’t be included in the model
l1_ratio – value between [0,1] the higher the simpler and more sensitive model is to collinear factors
number_of_alphas – number of different lambda values tested
is_fit_intercept – True if intercept should be included in the model, False otherwise

Attributes:

`MIN_NUM_OF_1SE_REGRESSORS`	minimal number of regressors taken for the alpha_1se (max.
`NUMBER_OF_FOLDS`	number of folds in the k-fold cross-validation.

Methods:

select_best_factors(regressors_df, analysed_tms)

Returns the dataframe which is the subset of the original regressors_df but only contains rows for dates common for it and analysed_tms and only contains columns for coefficients which should be included in the model.

MIN_NUM_OF_1SE_REGRESSORS = 2: minimal number of regressors taken for the alpha_1se (max. alpha for which the MSE is within 1 std. from the min. MSE). If number of regressors is smaller, then coefficients for min. MSE are taken.

NUMBER_OF_FOLDS = 10: number of folds in the k-fold cross-validation.

select_best_factors(regressors_df: QFDataFrame, analysed_tms: QFSeries) → QFDataFrame[source]

Returns the dataframe which is the subset of the original regressors_df but only contains rows for dates common for it and analysed_tms and only contains columns for coefficients which should be included in the model. Factors are identified using Elastic Net method with Cross-Validation (for calculating the MSE).

Parameters:

regressors_df – dataframe containing data for regressors (e.g. daily log-returns)
analysed_tms – timeseries of analysed data (data which should be modeled with regressors, e.g. daily log-returns)

Returns:

Subset of the original regressors_df. Only contains rows corresponding to dates common for it and analysed_tms. Only contains columns corresponding to coefficients which should be included in the model

Return type:

QFDataFrame