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* 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
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(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)
Financial time series prediction models
Installation
Use the conda environment file attached!:)
Description
Languages
Python
85%
Jupyter Notebook
15%