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* chore(Config): added more options to sweep.yaml * fix(Config): mistake with declaring `value` <> `values` in sweep.yaml * fix(Config): set the metric to optimize for
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(Data): added feature extractors, and feature extractor presets, removed a bunch of custom arguments from load_data (#42)
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%