environment.yml

name: quant
channels:
  - johnsnowlabs
  - conda-forge
  - defaults
  - ml4t
  - ranaroussi
dependencies:
  - python=3.9
  - numba=0.53.1
  - xgboost
  - scikit-learn
  - lightgbm
  - pydantic
  - seaborn
  - black
  - ipython
  - ipykernel
  - scipy
  - tqdm
  - numpy
  - pandas
  - matplotlib
  - quantstats
  - pytest
  - wandb
  - pandas-ta
  - optuna
  - pip
  - pip:
    - black
    - fracdiff
    - diskcache
    - python-dotenv
    - vectorbt
    - pandera[mypy]
    - binance_historical_data
    - alphalens-reloaded

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`
fix(Project): m1 support (#233 ) 2022-03-10 13:07:41 +01:00			`- numba=0.53.1`
			`- xgboost`
			`- scikit-learn`
			`- lightgbm`
			`- pydantic`
			`- seaborn`
			`- black`
			`- ipython`
			`- ipykernel`
			`- scipy`
feat(Models): added optional hpsklearn (#236 ) 2022-03-13 14:59:42 +01:00			`- tqdm`
			`- numpy`
			`- pandas`
			`- matplotlib`
			`- quantstats`
			`- pytest`
			`- wandb`
			`- pandas-ta`
feat(HPO): added `run_hpo` script (#237 ) 2022-03-15 14:43:16 +01:00			`- optuna`
fix(Project): m1 support (#233 ) 2022-03-10 13:07:41 +01:00			`- pip`
feat(FeatureExtraction): added fractionally differentiated returns to remove lagged returns (#95 ) 2021-12-28 22:50:09 +01:00			`- pip:`
chore(Linter): reformatted code with black (#211 ) 2022-02-17 19:22:17 +01:00			`- black`
feat(FeatureExtraction): added fractionally differentiated returns to remove lagged returns (#95 ) 2021-12-28 22:50:09 +01:00			`- fracdiff`
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			`- python-dotenv`
			`- vectorbt`
feat(Events): added EventFilter, EventLabeller (#186 ) 2022-01-26 23:22:43 +01:00			`- pandera[mypy]`
feat(Data): added script to download data from binance (#224 ) 2022-02-20 12:30:53 +01:00			`- binance_historical_data`
feat(Models): added optional hpsklearn (#236 ) 2022-03-13 14:59:42 +01:00			`- alphalens-reloaded`

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`