gavindiaz/drift

mirror of https://github.com/webclinic017/drift.git synced 2026-07-27 18:57:55 +00:00

T

Mark Aron Szulyovszky 3b9d7f554a feature(Config): added transformation parameters to Config, removed expanding_window (it's ON now)

2022-02-19 14:44:49 +01:00

.github/workflows

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chore(Linter): reformatted code with black (#211 )

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feature(Config): added transformation parameters to Config, removed expanding_window (it's ON now)

2022-02-19 14:44:49 +01:00

feature(Config): added transformation parameters to Config, removed expanding_window (it's ON now)

2022-02-19 14:44:49 +01:00

feature(Config): added transformation parameters to Config, removed expanding_window (it's ON now)

2022-02-19 14:44:49 +01:00

chore(Linter): reformatted code with black (#211 )

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feature_extractors

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chore(Project): added output folder (#124 )

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feature(Config): added transformation parameters to Config, removed expanding_window (it's ON now)

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transformations

feature(Config): added transformation parameters to Config, removed expanding_window (it's ON now)

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.gitignore

feat(Project): use SKLearn models directly, removed custom ensembling, use 5 minute data, batch inference, numba cusum filter (#192 )

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.libcst.codemod.yaml

Create copy_repo.yml (#200 )

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conftest.py

feat(Tests): added basic unit tests for walk_forward_train_test() (#22 )

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environment-strip-action.yaml

Create copy_repo.yml (#200 )

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environment.yml

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exploration.ipynb

feat(Events): added EventFilter, EventLabeller (#186 )

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README.md

Update README.md

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run_clear_cache.py

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run_evaluate_robustness.py

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run_fetch_data_5min.py

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run_fetch_data_daily.py

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run_fetch_data_glassnode.py

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run_inference.py

feature(Config): added transformation parameters to Config, removed expanding_window (it's ON now)

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run_model_dev.py

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run_pipeline.py

feature(Config): added transformation parameters to Config, removed expanding_window (it's ON now)

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run_portfolio_reporting.py

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run_sweep.py

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strip_action.yaml

feat(Docs): prepare for sphinx-pages (#205 )

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wandb_setup.py

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README.md

Financial time series prediction

And end-to-end pipeline to train predictive Machine Learning models on financial (non-stationary, regime changing) time series. Includes feature selection and meta labelling.