gavindiaz/drift

mirror of https://github.com/webclinic017/drift.git synced 2026-07-31 20:57:51 +00:00

T

Mark Aron Szulyovszky 77206a5d0a fix(Config): adjusted parameters to 5 minute timeframe

2022-02-19 14:58:34 +01:00

.github/workflows

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

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

2022-02-17 19:22:17 +01:00

fix(Config): adjusted parameters to 5 minute timeframe

2022-02-19 14:58:34 +01:00

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

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

fix(Config): adjusted parameters to 5 minute timeframe

2022-02-19 14:58:34 +01:00

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

2022-02-17 19:22:17 +01:00

feature_extractors

fix(Config): adjusted parameters to 5 minute timeframe

2022-02-19 14:58:34 +01:00

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

2022-02-17 19:22:17 +01:00

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

2022-02-17 19:22:17 +01:00

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

2022-02-17 19:22:17 +01:00

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

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

2022-02-17 19:22:17 +01:00

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

2022-02-17 19:22:17 +01:00

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

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

transformations

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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.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

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

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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

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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

fix(Config): adjusted parameters to 5 minute timeframe

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run_model_dev.py

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run_pipeline.py

fix(Config): adjusted parameters to 5 minute timeframe

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run_portfolio_reporting.py

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

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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.