environment.yml

name: quant
channels:
  - johnsnowlabs
  - conda-forge
  - defaults
dependencies:
  - python=3.9
  - seaborn
  - scikit-learn-intelex=2021.4.0
  - ipython
  - ipykernel
  - scipy
  - scikit-learn
  - numba
  - pytorch
  - matplotlib
  - numpy
  - quantstats
  - pytorch-lightning
  - pytest
  - wandb
  - python-dotenv
  - tscv
  - tqdm
  - pip
  - pandas-ta
  - xgboost
  - pip:
    - fracdiff
    - ray
    - diskcache
prefix: /usr/local/anaconda3/envs/quant
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			`name: quant`
			`channels:`
			`- johnsnowlabs`
			`- conda-forge`
			`- defaults`
			`dependencies:`
			`- python=3.9`
			`- seaborn`
Feature: Added sweep functionality (#65 ) 2021-12-21 17:28:36 +01:00			`- scikit-learn-intelex=2021.4.0`
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			`- ipython`
feat(Models): added `debug_future_lookahead`, sped up LogisticRegression & DecisionTreeClassifier (#74 ) 2021-12-22 16:59:03 +01:00			`- ipykernel`
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			`- scipy`
			`- scikit-learn`
			`- numba`
			`- pytorch`
			`- matplotlib`
			`- numpy`
			`- quantstats`
			`- pytorch-lightning`
			`- pytest`
feature: Added Weights and Biases configuration to the repo. (#48 ) 2021-12-20 14:01:14 +01:00			`- wandb`
			`- python-dotenv`
feat(Selection): added toggleable feature selection step into the pipeline (#83 ) 2021-12-27 21:59:22 +01:00			`- tscv`
feat(Data): create DataSource, DataCollection, added hourly crypto data (#96 ) 2021-12-31 19:04:27 +01:00			`- tqdm`
feat(FeatureExtraction): added fractionally differentiated returns to remove lagged returns (#95 ) 2021-12-28 22:50:09 +01:00			`- pip`
feat(Data): added daily_glassnode DataCollection (#99 ) 2022-01-03 13:57:36 +01:00			`- pandas-ta`
feat(DataLoader): caching MVP, added ability to use standard scaling for exogenous data, scaling is now also done before feature selection (#105 ) 2022-01-04 11:44:35 +01:00			`- xgboost`
feat(FeatureExtraction): added fractionally differentiated returns to remove lagged returns (#95 ) 2021-12-28 22:50:09 +01:00			`- pip:`
			`- fracdiff`
			`- ray`
feat(DataLoader): caching MVP, added ability to use standard scaling for exogenous data, scaling is now also done before feature selection (#105 ) 2022-01-04 11:44:35 +01:00			`- diskcache`
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			`prefix: /usr/local/anaconda3/envs/quant`