Hi author, I tried to do out-of-sample prediction for portfolio but realized that there is no data_type parameter in the predictOOS function, so I tried to run "Ypred = regr.predict(X=data_OOS, data_type='portfolio', mean_factor=True)". In your source code predict function exists such a parameter as data_type, but if you run the above code, it selects the elif that X is not None, and then runs pred = self.predict_portfolio(W, L, T, mean_factor), but the values of L and T are not returned in the elif. This makes it impossible to predict the out-of-sample portfolio. my solution is to calculate the values of W, L and T myself and then run the predict_portfolio function.
Hi author, I tried to do out-of-sample prediction for portfolio but realized that there is no data_type parameter in the predictOOS function, so I tried to run "Ypred = regr.predict(X=data_OOS, data_type='portfolio', mean_factor=True)". In your source code predict function exists such a parameter as data_type, but if you run the above code, it selects the elif that X is not None, and then runs pred = self.predict_portfolio(W, L, T, mean_factor), but the values of L and T are not returned in the elif. This makes it impossible to predict the out-of-sample portfolio. my solution is to calculate the values of W, L and T myself and then run the predict_portfolio function.