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b6cd6b14fe6aac07e15d409ad74bc0751821db87
* 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
refactor(Project): move out load_data to utils, rename fetch_data to
run_fetch_data, got classifiers to work (#38)
refactor(Project): move out load_data to utils, rename fetch_data to
run_fetch_data, got classifiers to work (#38)
refactor(Project): move out load_data to utils, rename fetch_data to
run_fetch_data, got classifiers to work (#38)
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() (#77)
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() (#77)
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() (#77)
feat(Config): feature extractors are enabled one-by-one with a bool, added previous model to model.fit() (#77)
feat(Models): added
debug_future_lookahead, sped up LogisticRegression & DecisionTreeClassifier (#74)
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)
refactor(Project): move out load_data to utils, rename fetch_data to
run_fetch_data, got classifiers to work (#38)
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() (#77)
Financial time series prediction models
Installation
Use the conda environment file attached!:)
Description
Languages
Python
85%
Jupyter Notebook
15%