environment.yml

name: quant
channels:
  - johnsnowlabs
  - conda-forge
  - defaults
  - ml4t
  - ranaroussi
dependencies:
  - python=3.9
  - scikit-learn-intelex=2021.4.0
  - pip:
    - skorch
    - fracdiff
    - ray
    - diskcache
    - seaborn
    - ipython
    - ipykernel
    - scipy
    - scikit-learn
    - numba
    - matplotlib
    - numpy
    - quantstats
    - pytest
    - wandb
    - python-dotenv
    - tscv
    - tqdm
    - pandas-ta
    - xgboost
    - lightgbm
    - alphalens-reloaded
    - vectorbt
    - pydantic
    - pandera[mypy]
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`
feat(Portfolio): reporting MVP (#108 ) 2022-01-08 00:05:36 +01:00			`- ml4t`
feat(Reporting): added backtesting with vectorbt (#148 ) 2022-01-11 09:34:08 +01:00			`- ranaroussi`
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			`dependencies:`
			`- python=3.9`
Feature: Added sweep functionality (#65 ) 2021-12-21 17:28:36 +01:00			`- scikit-learn-intelex=2021.4.0`
feat(FeatureExtraction): added fractionally differentiated returns to remove lagged returns (#95 ) 2021-12-28 22:50:09 +01:00			`- pip:`
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			`- skorch`
feat(FeatureExtraction): added fractionally differentiated returns to remove lagged returns (#95 ) 2021-12-28 22:50:09 +01:00			`- 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(Reporting): added backtesting with vectorbt (#148 ) 2022-01-11 09:34:08 +01:00			`- seaborn`
			`- ipython`
			`- ipykernel`
			`- scipy`
			`- scikit-learn`
			`- numba`
			`- matplotlib`
			`- numpy`
			`- quantstats`
			`- pytest`
			`- wandb`
			`- python-dotenv`
			`- tscv`
			`- tqdm`
			`- pandas-ta`
			`- xgboost`
			`- lightgbm`
			`- alphalens-reloaded`
			`- vectorbt`
refactor(Config): use a Config object instead of dictionary of dictionaries! (#184 ) 2022-01-23 18:37:43 +01:00			`- pydantic`
feat(Events): added EventFilter, EventLabeller (#186 ) 2022-01-26 23:22:43 +01:00			`- pandera[mypy]`
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`