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T

Mark Aron Szulyovszky 2606639cc2 fix(Labelling): didn't forward shift forward returns previously, introduced major lookahead bias (#249 )

* fix(Labelling): didn't forward shift forward returns previously, introduced major lookahead bias

* fix(Baseline): use the same config

2022-03-15 19:12:40 +01:00

.github/workflows

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

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

.vscode

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

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

config

fix(Labelling): didn't forward shift forward returns previously, introduced major lookahead bias (#249 )

2022-03-15 19:12:40 +01:00

data

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

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

data_loader

feat(Baseline): added one-split baseline, pure sklearn (#247 )

2022-03-15 18:16:12 +01:00

docs

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

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

feature_extractors

fix(Linter): ran

2022-03-12 22:25:03 +01:00

labeling

fix(Labelling): didn't forward shift forward returns previously, introduced major lookahead bias (#249 )

2022-03-15 19:12:40 +01:00

models

feat(Models): added optional hpsklearn (#236 )

2022-03-13 14:59:42 +01:00

mycodemod

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

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

output

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

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

reporting

feat(HPO): added run_hpo script (#237 )

2022-03-15 14:43:16 +01:00

tests

fix(Config): set remove_overlapping_events=True (#246 )

2022-03-15 17:39:39 +01:00

training

refactor(Training): remove non-expanding window option (#244 )

2022-03-15 16:21:06 +01:00

transformations

feat(HPO): added run_hpo script (#237 )

2022-03-15 14:43:16 +01:00

utils

feat(HPO): added run_hpo script (#237 )

2022-03-15 14:43:16 +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(HPO): added run_hpo script (#237 )

2022-03-15 14:43:16 +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_baseline.py

fix(Labelling): didn't forward shift forward returns previously, introduced major lookahead bias (#249 )

2022-03-15 19:12:40 +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

feat(HPO): added run_hpo script (#237 )

2022-03-15 14:43:16 +01:00

run_fetch_data_5min.py

fix(DataLoader): sort the dataframe when fetching data (#239 )

2022-03-15 14:12:05 +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_hpo.py

feat(HPO): added run_hpo script (#237 )

2022-03-15 14:43:16 +01:00

run_inference.py

feat(Config): added start_date property (#245 )

2022-03-15 17:48:43 +01:00

run_model_dev.py

feat(HPO): added run_hpo script (#237 )

2022-03-15 14:43:16 +01:00

run_multi_asset_pipeline.py

feat(HPO): added run_hpo script (#237 )

2022-03-15 14:43:16 +01:00

run_pipeline.py

feat(Config): added start_date property (#245 )

2022-03-15 17:48:43 +01:00

run_portfolio_reporting.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.