DataModel
- class qf_lib.common.utils.factorization.data_models.data_model.DataModel(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 maximal lag used during testing for autocorrelation of the fit; lags used for testing will be values 1, ..., autocorr_max_lag
float significance level for the autocorrelation of the fit test
Extension of Durbin-Watson test to add many lags (1-5).
Vector of coefficients [beta1, beta2, ...].
Condition number of a matrix measures the sensitivity of the solution of a system of linear equations to errors in the data.
Cooks distance.
Used to test if linear regression residuals are uncorrelated.
Vector containing annualised performance attribution of each factor.
Structure with a result of multilinear regression (based on all data points and using OLS to calculate coefficients).
TimeseriesAnalysis class based on returns of the fit.
Fitted (predicted) response values based on input data.
TimeseriesAnalysis class based on returns of the analysed fund.
Probability of a hypothesis that the error variance doesn't depend on input data (regressors).
Returns of a fit based on in-sample coefficients.
Constant alpha (y = beta * x + constant).
Class for calculating outliers and influence measures for OLS result.
Date on which the Out-Of-Sample period started (In-Sample vs Out-Of-Sample test).
Concerns about collinearity can be ignored if rSquare is higher than rSquare of each predictor.
Vector containing normalised risk contribution of each factor.
Scalar with annualised return unexplained by factors.
- 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 Durbin-Watson test to add many lags (1-5). 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 p-values 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 in-sample coefficients. Vector with in-sample and out-of-sample 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 Out-Of-Sample period started (In-Sample vs Out-Of-Sample 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.