8 Commits

Author	SHA1	Message	Date
Mark Aron Szulyovszky	95573eb9dd	feat(Data): add option to predict 3 classes (#79) ... * feat(Data): add option to predict 3 classes * feat(Evaluation): added ability to evaluate 3 class predictions * chore(Config): set sensible config for regression models * feat(Data): added option to use balanced or imbalanced three-class data * feat(Evaluate): correctly track "no_of_samples" now that we have three classes * chore(Sweep): remove probably not useful scaler values from sweep	2021-12-23 13:24:56 +01:00
Mark Aron Szulyovszky	b6cd6b14fe	feat(Config): feature extractors are enabled one-by-one with a bool, added previous model to model.fit() (#77) ... * feat(Config): feature extractors are enabled one-by-one with a bool, added previous model to model.fit() * fix(Sweep): removed unused `other_features` parameter that fails sweep * feat(Config): using preset names for defining feature extractors again * fix(Tests): fixed model stub classes	2021-12-23 10:35:20 +01:00
Mark Aron Szulyovszky	6ae8acf70e	feat(Models): added debug_future_lookahead, sped up LogisticRegression & DecisionTreeClassifier (#74) ... * feat(Models): added `debug_future_lookahead`, sped up LogisticRegression & DecisionTreeClassifier * feat(Training): added ability to train on expanding_window * feat(Models): tuned some hyperparameters, added expanding_window to sweep config, fixed tests * feat(Models): tune parameters of ensemble models * fix(Config): use window size that works with ensembling	2021-12-22 16:59:03 +01:00
Mark Aron Szulyovszky	79d84cf0a3	feat(Model): added own Model class, SkLearnModel wrapper and StaticMomentumModel (#61) ... * feat(Model): added own `Model` class, SkLearnModel wrapper and StaticMomentumModel * fix(Tests): added missing Model variable * fix(Tests): added missing clone method()	2021-12-21 10:30:09 +01:00
Mark Aron Szulyovszky	b8b7375c30	feat(Tests): added test for evaluate_predictions() (#41)	2021-12-18 22:23:07 +01:00
Mark Aron Szulyovszky	0963df2087	refactor(Project): move out load_data to utils, rename fetch_data to run_fetch_data, got classifiers to work (#38)	2021-12-17 17:41:50 +01:00
Mark Aron Szulyovszky	cc7061b456	feat(Core): ensemble models, correct forward returns calculation, scaling, only train from when asset returns are available, major bug fixed in walk_forward_train_test (#35) ... * fix(Core): correct forward returns calculation, classifiers are now working again, only train from when asset returns are available * feat(Utils): added get_first_valid_return_index() * feat(Ensemble): return models from `run_whole_pipeline` * feat(Ensemble): added ensemble step, fixed walk_forward_train_test predictions index confusion, * chore(Pipeline): remove unnecessary extra ensemble results dataframe * refactor(Core): removed unnecessary ensemble_train_predict, moved run_single_asset_trainig_pipeline to a separate file * feat(Training): added scaling on expanding window (the past) to walk_forward_train_test(), now printing out mean sharpe ratio * feat(CI): added environment.yml file * chore(Environment): update env.yml * feat(CI): added testing workflow * fix(CI): renamed enviroment.yml * fix(Tests): added missing new parameter to walk_forward_train_test()	2021-12-17 14:32:17 +01:00
Mark Aron Szulyovszky	6440ced32c	feat(Tests): added basic unit tests for walk_forward_train_test() (#22) ... * feat(Tests): added basic unit tests for walk_forward_train_test() * fix(Tests): inherit from BaseEstimator, fix index problems in walk_forward_train_test * fix(WalkForward): predictions were mistakenly removed, oops * fix(WalkForward): mistakenly re-assiging model	2021-12-15 17:54:03 +01:00