gavindiaz/drift

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

T

Mark Aron Szulyovszky 5b7bcbb5e9 fix(DataLoader): sort the data before merging (this corrupted the pipeline completely)

2022-03-12 22:22:10 +01:00

.github/workflows

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

feat(Data): resample exogenous/other datasource when their frequency is different (#234 )

2022-03-10 20:00:06 +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(DataLoader): sort the data before merging (this corrupted the pipeline completely)

2022-03-12 22:22:10 +01:00

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

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

feature_extractors

fix(DataLoader): sort the data before merging (this corrupted the pipeline completely)

2022-03-12 22:22:10 +01:00

fix(Labeling): use X.loc instead of X.filter, remove Nan from event_start_times

2022-03-12 13:27:44 +01:00

fix(Project): m1 support (#233 )

2022-03-10 13:07:41 +01:00

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

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

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

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

feat(Data): resample exogenous/other datasource when their frequency is different (#234 )

2022-03-10 20:00:06 +01:00

refactor(Evaluate): print out accuracy, f1, etc. for the final & meta predictions, separated out evaluation step (#228 )

2022-03-03 17:40:17 +01:00

feat(Data): resample exogenous/other datasource when their frequency is different (#234 )

2022-03-10 20:00:06 +01:00

transformations

feat(Transformations): added robust scaler (#223 )

2022-02-20 00:41:46 +01:00

fix(DataLoader): sort the data before merging (this corrupted the pipeline completely)

2022-03-12 22:22:10 +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

fix(Project): m1 support (#233 )

2022-03-10 13:07:41 +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

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

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

run_evaluate_robustness.py

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

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

run_fetch_data_5min.py

feat(Labeling): purge overlapping events, sort dataframe at loading time (#226 )

2022-03-02 00:26:33 +01:00

run_fetch_data_daily.py

feat(Data): added script to download data from binance (#224 )

2022-02-20 12:30:53 +01:00

run_fetch_data_glassnode.py

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

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

run_inference.py

refactor(Evaluate): print out accuracy, f1, etc. for the final & meta predictions, separated out evaluation step (#228 )

2022-03-03 17:40:17 +01:00

run_model_dev.py

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

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

run_multi_asset_pipeline.py

feat(Pipeline): added multi-asset pipeline, ensembling_method to config

2022-02-19 15:37:19 +01:00

run_pipeline.py

fix(Project): m1 support (#233 )

2022-03-10 13:07:41 +01:00

run_portfolio_reporting.py

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

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

run_sweep.py

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

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

strip_action.yaml

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

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

wandb_setup.py

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

2022-02-17 19:22:17 +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.