9 Commits

Author SHA1 Message Date
Mark Aron Szulyovszky 7deeb2de01 feat(Baseline): added one-split baseline, pure sklearn (#247)
* feat(Baseline): added sklearn_baseline

* feat(Baseline): completely working
2022-03-15 18:16:12 +01:00
Mark Aron Szulyovszky 5482e3fc95 feat(Config): added start_date property (#245)
* refactor(Training): remove non-expanding window option

* feat(Config): added `start_date` property

* fix(Inference): added start_date here as well

* fix(Linter): ran
2022-03-15 17:48:43 +01:00
Mark Aron Szulyovszky b656f790f5 feat(Data): resample exogenous/other datasource when their frequency is different (#234)
* feat(Data): resample exogenous/other datasource when their frequency is different

* fix(Linter): ran

* fix(Data): resampling done properly
2022-03-10 20:00:06 +01:00
Mark Aron Szulyovszky 8dd2d88740 chore(Linter): reformatted code with black (#211)
* chore(Linter): reformatted code with black

* Create black.yaml
2022-02-17 19:22:17 +01:00
Mark Aron Szulyovszky 42a1bc59cb feat(Events): added EventFilter, EventLabeller (#186) 2022-01-26 23:22:43 +01:00
Mark Aron Szulyovszky e6e2317fe0 refactor(Training): use date indexes instead of integers, need this to prepare for Events (#185) 2022-01-24 12:22:30 +01:00
Mark Aron Szulyovszky e80fffdb65 refactor(Config): use a Config object instead of dictionary of dictionaries! (#184)
* refactor(Config): use a Config object instead of dictionary of dictionaries!

* fix(Config): use default_ensemble_config

* fix(Portfolio): fixed portfolio construction
2022-01-23 18:37:43 +01:00
Mark Aron Szulyovszky 5c4a5b0cf1 refactor(Project): removed regression method (we can still use regression models, but we'll need map them to classification later) (#182)
* refactor(Project): removed regression method (we can still use regression models, but we'll need map them to classes later)

* fix(Training): removed mistakenly left in `method` parameter
2022-01-23 17:15:08 +01:00
Mark Aron Szulyovszky 1cd0119589 feat(DataLoader): caching MVP, added ability to use standard scaling for exogenous data, scaling is now also done before feature selection (#105)
* fix(FeatureExtractor): apply log to transform some series to normality

* feat(DataLoader): add ability of not returning returns when they're not needed (exogenous data), applied log to certain features

* feat(FeatureExtractors): added standard scaling for exogenous data

* feat(FeatureSelection): scale data with the passed in scaler before doing feature-selection

* fix(Config): sweep config

* feat(Models): output probability, store it

* feat(Core): added caching to select_features() and load_data()

* fix(Dependencies): added diskcache

* fix(Training): error when creating results DF

* feat(Models): added xgboost, fixed tests

* refactor(Cache): moved hashing to a separate function, created wrapper functions to separate business logic and caching

* fix(Tests): new syntax

* fix(Model): XGboost can't handle -1 class, so we'll use the deprecated label_encoder fornow

* fix(Model): XGBoost config

* feat(Cache): add run_clear_cache script

* fix(Pipeline) accidentally re-instatiating all_predictions for each asset
2022-01-04 11:44:35 +01:00