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feat(Portfolio): reporting MVP (#108)

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* feat(DataLoader): added load_only_returns() method

* feat(Portfolio): load predictions

* feat(Portfolio): normalize weights

* feat(Portfolio): started integrating with portfoliobt

* feat(Portfolio): include fees in the portfolio construction

* feat(Portfolio): demo of pyportfolioopt

* feat(Portfolio): get efficient frontier calculation to work

* feat(Portfolio): add a few strategies to create weights

* chore(Dependencies): remove pyportfolioopt for now

* fix(Dependencies): try to install all dependencies with pip

* fix(Dependencies): indentation

* fix(Dependencies): corrected pytorch module name

* fix(Dependencies): try to have as many modules installed by conda for the sake of sanity?

* fix(Dependencies): put fracdiff into pip modules

* fix(Dependencies): revert to using pip almost exclusively

* feat(Portfolio): added alphalens

* fix(Portfolio): got limited weights working

* feat(Portfolio): trying to get alphalens to work

* feat(Portfolio): alphalens working

* fix(Dependencies): removed vectorbt

* fix(Dependencies): use alphalens-reloaded

* fix(Dependencies): added conda source for alphalens-reloaded

* refactor(Portfolio): removed traces of vectorbt

* feat(Reporting): factor reporting done

* feat(Portfolio): added pyfolio reporting (fails bc alphalens is not working properly lol)
This commit is contained in:
Mark Aron Szulyovszky
2022-01-08 00:05:36 +01:00
committed by GitHub
parent f8af5641e8
commit 81c217a401
5 changed files with 105 additions and 89 deletions
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data_loader/load_data.py
+18 -19
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@@ -224,25 +224,24 @@ def datasource_to_file(data_source: DataSource) -> str:
return data_source[0] + '/' + data_source[1] + '.csv'
# These are needed for the portfolio feature, maybe we can do this in a more elegant way
# def load_crypto_only_returns(path: str, index_column: Literal['date', 'int'], returns: Literal['price', 'returns']) -> pd.DataFrame:
# files = [f for f in os.listdir(path) if os.path.isfile(os.path.join(path,f)) and 'USD' in f and not f.startswith('.')]
# dfs = [__load_df(
# path=os.path.join(path,f),
# prefix=f.split('.')[0],
# returns=returns,
# feature_extractors=[],
# narrow_format=False,
# ) for f in files]
# dfs = pd.concat(dfs, axis=1)
# dfs = dfs.applymap(lambda x: np.nan if x == 0 else x)
# dfs.index = pd.DatetimeIndex(dfs.index)
# dfs.columns = [column.split('_')[0] for column in dfs.columns]
# if index_column == 'int':
# dfs.reset_index(drop=True, inplace=True)
# return dfs
def load_only_returns(assets: DataCollection, index_column: Literal['date', 'int'], returns: Literal['price', 'returns']) -> pd.DataFrame:
# def load_crypto_assets_availability(path: str, index_column: Literal['date', 'int']) -> pd.DataFrame:
# return load_crypto_only_returns(path, index_column, 'returns').applymap(lambda x: 0 if x == 0.0 or x == 0 or np.isnan(x) else 1)
assets_future = [__load_df.remote(
data_source=data_source,
prefix=data_source[1],
returns=returns,
feature_extractors=[],
narrow_format=False,
) for data_source in assets]
target_asset_df = ray.get(assets_future)
dfs = [deduplicate_indexes(df) for df in target_asset_df]
dfs = pd.concat(dfs, axis=1)
# dfs = dfs.applymap(lambda x: np.nan if x == 0 else x)
dfs.index = pd.DatetimeIndex(dfs.index)
if index_column == 'int':
dfs.reset_index(drop=True, inplace=True)
return dfs