DataModel¶

class
qf_lib.common.utils.factorization.data_models.data_model.
DataModel
(data_model_input: qf_lib.common.utils.factorization.data_models.data_model_input.DataModelInput)[source]¶ Bases:
object
Class grouping the results of factorization.
 Parameters
data_model_input – data from which the model is built
Attributes
int
float
Structure with a result of multilinear regression (based on all data points and using OLS to calculate coefficients).
Fitted (predicted) response values based on input data.
Constant alpha (y = beta * x + constant).
Vector of coefficients [beta1, beta2, …].
TimeseriesAnalysis class based on returns of the fit.
TimeseriesAnalysis class based on returns of the analysed fund.
Vector containing normalised risk contribution of each factor.
Vector containing annualised performance attribution of each factor.
Scalar with annualised return unexplained by factors.
Used to test if linear regression residuals are uncorrelated.
Extension of DurbinWatson test to add many lags (15).
Probability of a hypothesis that the error variance doesn’t depend on input data (regressors).
Condition number of a matrix measures the sensitivity of the solution of a system of linear equations to errors in the data.
Concerns about collinearity can be ignored if rSquare is higher than rSquare of each predictor.
Returns of a fit based on insample coefficients.
Date on which the OutOfSample period started (InSample vs OutOfSample test).
Cooks distance.
Class for calculating outliers and influence measures for OLS result.

AUTOCORR_MAX_LAG
= 3¶ int maximal lag used during testing for autocorrelation of the fit; lags used for testing will be values 1, …, autocorr_max_lag

AUTOCORR_SIGNIFICANCE_LEVEL
= 0.05¶ float significance level for the autocorrelation of the fit test

autocorrelation
¶ Extension of DurbinWatson test to add many lags (15). 0  not autocorrelated, 1  autocorrelated.

coefficients
¶ Vector of coefficients [beta1, beta2, …].

condition_number
¶ Condition number of a matrix measures the sensitivity of the solution of a system of linear equations to errors in the data.

cooks_distance_tms
¶ Cooks distance. Used for checking the influence of outliers for the model.

durbin_watson_test
¶ Used to test if linear regression residuals are uncorrelated. Small pvalues indicate correlation among residuals.

factors_performance_attribution_ret
¶ Vector containing annualised performance attribution of each factor.

fit_model
¶ Structure with a result of multilinear regression (based on all data points and using OLS to calculate coefficients).

fit_tms_analysis
¶ TimeseriesAnalysis class based on returns of the fit.

fitted_tms
¶ Fitted (predicted) response values based on input data.

fund_tms_analysis
¶ TimeseriesAnalysis class based on returns of the analysed fund.

heteroskedasticity
¶ Probability of a hypothesis that the error variance doesn’t depend on input data (regressors).

in_sample_and_out_sample_returns
¶ Returns of a fit based on insample coefficients. Vector with insample and outofsample simple returns. Its length is equal to length of fitted returns.

intercept
¶ Constant alpha (y = beta * x + constant).

ols_influence
¶ Class for calculating outliers and influence measures for OLS result.

oos_start_date
¶ Date on which the OutOfSample period started (InSample vs OutOfSample test).

r_squared_of_each_predictor
¶ Concerns about collinearity can be ignored if rSquare is higher than rSquare of each predictor.

risk_contribution
¶ Vector containing normalised risk contribution of each factor.

unexplained_performance_attribution_ret
¶ Scalar with annualised return unexplained by factors.