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:
qf_lib.common.utils.factorization.factors_identification.factors_identifier.FactorsIdentifier
Class used for identifying factors in the model with Elastic Net method (with Crossvalidation). 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
minimal number of regressors taken for the alpha_1se (max. alpha for which the MSE is within 1 std.
number of folds in the kfold crossvalidation.
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 kfold crossvalidation.

select_best_factors
(regressors_df: qf_lib.containers.dataframe.qf_dataframe.QFDataFrame, analysed_tms: qf_lib.containers.series.qf_series.QFSeries) → qf_lib.containers.dataframe.qf_dataframe.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 CrossValidation (for calculating the MSE).
 Parameters
regressors_df – dataframe containing data for regressors (e.g. daily logreturns)
analysed_tms – timeseries of analysed data (data which should be modeled with regressors, e.g. daily logreturns)
 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