gavindiaz/drift

mirror of https://github.com/webclinic017/drift.git synced 2026-07-28 03:08:01 +00:00

T

Mark Aron Szulyovszky f3fee4a4e1 fix(Codemod): restored original docstring replacement

2022-02-17 16:53:09 +01:00

.github/workflows

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

2022-02-17 16:36:35 +01:00

feat(Pipeline): save results, train on all assets

2021-12-14 22:59:44 +01:00

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

2022-01-26 23:22:43 +01:00

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

2022-02-17 16:36:35 +01:00

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

2022-02-17 16:36:35 +01:00

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

2022-02-17 16:36:35 +01:00

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

2022-02-02 17:56:25 +01:00

feature_extractors

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

2022-01-26 23:22:43 +01:00

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

2022-02-17 16:36:35 +01:00

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

2022-02-17 16:36:35 +01:00

fix(Codemod): restored original docstring replacement

2022-02-17 16:53:09 +01:00

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

2022-01-07 19:03:17 +01:00

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

2022-02-17 16:36:35 +01:00

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

2022-02-17 16:36:35 +01:00

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

2022-02-17 16:36:35 +01:00

transformations

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

2022-02-17 16:36:35 +01:00

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

2022-02-17 16:36:35 +01:00

.gitignore

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

2022-02-17 16:36:35 +01:00

.libcst.codemod.yaml

Create copy_repo.yml (#200 )

2022-02-02 17:23:16 +01:00

conftest.py

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

2021-12-15 17:54:03 +01:00

environment-strip-action.yaml

Create copy_repo.yml (#200 )

2022-02-02 17:23:16 +01:00

environment.yml

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

2022-02-17 16:36:35 +01:00

exploration.ipynb

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

2022-01-26 23:22:43 +01:00

README.md

Update README.md

2022-01-26 22:06:40 +01:00

run_clear_cache.py

fix(Selection): dynamic step size for feature selection (#123 )

2022-01-07 18:45:03 +01:00

run_evaluate_robustness.py

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

2022-01-26 23:22:43 +01:00

run_fetch_data_5min.py

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

2022-02-17 16:36:35 +01:00

run_fetch_data_daily.py

feat(Data): create DataSource, DataCollection, added hourly crypto data (#96 )

2021-12-31 19:04:27 +01:00

run_fetch_data_glassnode.py

feat(Data): added daily_glassnode DataCollection (#99 )

2022-01-03 13:57:36 +01:00

run_inference.py

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

2022-02-17 16:36:35 +01:00

run_model_dev.py

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

2022-01-26 23:22:43 +01:00

run_pipeline.py

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

2022-02-17 16:36:35 +01:00

run_portfolio_reporting.py

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

2022-01-26 23:22:43 +01:00

run_sweep.py

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

2022-01-26 23:22:43 +01:00

strip_action.yaml

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

2022-02-02 17:56:25 +01:00

wandb_setup.py

feature: Added Weights and Biases configuration to the repo. (#48 )

2021-12-20 14:01:14 +01:00

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.