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Mark Aron Szulyovszky b63d536db1 feature(Docs): added sphinx docs generation workfl;ow
2022-11-07 17:03:41 +01:00
.github/workflows
feature(Docs): added sphinx docs generation workfl;ow
2022-11-07 17:03:41 +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
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
conftest.py
feat(Tests): added basic unit tests for walk_forward_train_test() (#22)
2021-12-15 17:54:03 +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
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.

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Readme 11 MiB
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Python 85%
Jupyter Notebook 15%