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Mark Aron Szulyovszky f762ceed2a feat(FeatureExtraction): added fractionally differentiated returns to remove lagged returns (#95) 
* feat(FeatureExtraction): added fractionally differentiated returns to remove lagged returns

* fix(Sweep): config

* fix(Sweep): name

* fix(Sweep): grid

* feat(Config): separated sliding_window_size_level1 & sliding_window_size_level2

* feat(Dependencies): added ray, now using it to parallel process feature extraction

* fix(Dependencies): added pip explicitly

* fix(Dependencies): removed ray from root

* fix(Models): average model was probably not taking the right timestamp to average

* feat(Config): separated expanding_window_level1 & expanding_window_level2

* fix(Config): set n_features_to_select to the optimal 30
2021-12-28 22:50:09 +01:00
.github/workflows
refactor(Project): move out load_data to utils, rename fetch_data to run_fetch_data, got classifiers to work (#38)
2021-12-17 17:41:50 +01:00
.vscode
feat(Pipeline): save results, train on all assets
2021-12-14 22:59:44 +01:00
archive
refactor(Project): move out load_data to utils, rename fetch_data to run_fetch_data, got classifiers to work (#38)
2021-12-17 17:41:50 +01:00
data
refactor(Project): move out load_data to utils, rename fetch_data to run_fetch_data, got classifiers to work (#38)
2021-12-17 17:41:50 +01:00
feature_extractors
feat(FeatureExtraction): added fractionally differentiated returns to remove lagged returns (#95)
2021-12-28 22:50:09 +01:00
feature_selection
feat(FeatureExtraction): added fractionally differentiated returns to remove lagged returns (#95)
2021-12-28 22:50:09 +01:00
models
feat(FeatureExtraction): added fractionally differentiated returns to remove lagged returns (#95)
2021-12-28 22:50:09 +01:00
reporting
feat(Selection): added toggleable feature selection step into the pipeline (#83)
2021-12-27 21:59:22 +01:00
tests
feat(Data): add option to predict 3 classes (#79)
2021-12-23 13:24:56 +01:00
training
feat(Selection): added toggleable feature selection step into the pipeline (#83)
2021-12-27 21:59:22 +01:00
utils
feat(FeatureExtraction): added fractionally differentiated returns to remove lagged returns (#95)
2021-12-28 22:50:09 +01:00
.gitignore
feature: Added Weights and Biases configuration to the repo. (#48)
2021-12-20 14:01:14 +01:00
config.py
feat(FeatureExtraction): added fractionally differentiated returns to remove lagged returns (#95)
2021-12-28 22:50:09 +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(FeatureExtraction): added fractionally differentiated returns to remove lagged returns (#95)
2021-12-28 22:50:09 +01:00
exploration.ipynb
feat(FeatureExtraction): added fractionally differentiated returns to remove lagged returns (#95)
2021-12-28 22:50:09 +01:00
README.md
feat(Core): ensemble models, correct forward returns calculation, scaling, only train from when asset returns are available, major bug fixed in walk_forward_train_test (#35)
2021-12-17 14:32:17 +01:00
run_fetch_data.py
refactor(Project): move out load_data to utils, rename fetch_data to run_fetch_data, got classifiers to work (#38)
2021-12-17 17:41:50 +01:00
run_pipeline.py
feat(FeatureExtraction): added fractionally differentiated returns to remove lagged returns (#95)
2021-12-28 22:50:09 +01:00
run_sweep.py
Feature: Added sweep functionality (#65)
2021-12-21 17:28:36 +01:00
sweep_fracdiff.yaml
feat(FeatureExtraction): added fractionally differentiated returns to remove lagged returns (#95)
2021-12-28 22:50:09 +01:00
sweep_level_1.yaml
feat(FeatureExtraction): added fractionally differentiated returns to remove lagged returns (#95)
2021-12-28 22:50:09 +01:00
sweep_level_2.yaml
feat(FeatureExtraction): added fractionally differentiated returns to remove lagged returns (#95)
2021-12-28 22:50:09 +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 models

Installation

Use the conda environment file attached!:)

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