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refactor(Config): use a Config object instead of dictionary of dictionaries! (#184)

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* refactor(Config): use a Config object instead of dictionary of dictionaries!

* fix(Config): use default_ensemble_config

* fix(Portfolio): fixed portfolio construction
This commit is contained in:
Mark Aron Szulyovszky
2022-01-23 18:37:43 +01:00
committed by GitHub
parent 5c4a5b0cf1
commit e80fffdb65
19 changed files with 223 additions and 196 deletions
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data_loader/load_data.py
+3 -20
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@@ -31,7 +31,6 @@ def __load_data(assets: DataCollection,
exogenous_features: list[tuple[str, FeatureExtractor, list[int]]],
index_column: Literal['date', 'int'],
no_of_classes: Literal['two', 'three-balanced', 'three-imbalanced'],
narrow_format: bool = False
) -> tuple[pd.DataFrame, pd.Series, pd.Series]:
"""
Loads asset data from the specified path.
@@ -51,7 +50,6 @@ def __load_data(assets: DataCollection,
prefix=data_source[1],
returns='log_returns' if log_returns else 'returns',
feature_extractors=own_features,
narrow_format=narrow_format,
) for data_source in target_file]
target_asset_df = ray.get(target_asset_future)
@@ -60,7 +58,6 @@ def __load_data(assets: DataCollection,
prefix=target_file[0][1],
returns='returns',
feature_extractors=[],
narrow_format=narrow_format,
)
df_target_asset_only_returns = ray.get(target_asset_only_returns_future)
@@ -69,7 +66,6 @@ def __load_data(assets: DataCollection,
prefix=data_source[1],
returns='log_returns' if log_returns else 'returns',
feature_extractors=other_features,
narrow_format=narrow_format,
) for data_source in files]
asset_dfs = ray.get(asset_futures)
@@ -78,17 +74,13 @@ def __load_data(assets: DataCollection,
prefix=data_source[1],
returns='none',
feature_extractors=exogenous_features,
narrow_format=narrow_format,
) for data_source in exogenous_data]
exogenous_dfs = ray.get(exogenous_futures)
dfs = target_asset_df + asset_dfs + exogenous_dfs
dfs = [deduplicate_indexes(df) for df in dfs]
target_df = dfs[0]
if narrow_format:
dfs = pd.concat([df.sort_index().reindex(target_df.index) for df in dfs], axis=0).fillna(0.)
else:
dfs = pd.concat([df.sort_index().reindex(target_df.index) for df in dfs], axis=1).fillna(0.)
dfs = pd.concat([df.sort_index().reindex(target_df.index) for df in dfs], axis=1).fillna(0.)
dfs.index = pd.DatetimeIndex(dfs.index)
@@ -96,10 +88,6 @@ def __load_data(assets: DataCollection,
dfs.reset_index(drop=True, inplace=True)
df_target_asset_only_returns.reset_index(drop=True, inplace=True)
if narrow_format:
dfs = dfs.drop(index=dfs.index[0], axis=0)
df_target_asset_only_returns = df_target_asset_only_returns.drop(index=dfs.index[0], axis=0)
## Create target
target_col = 'target'
returns_col = target_asset[1] + '_returns'
@@ -119,8 +107,7 @@ def __load_data(assets: DataCollection,
def __load_df(data_source: DataSource,
prefix: str,
returns: Literal['none', 'price', 'returns', 'log_returns'],
feature_extractors: list[tuple[str, FeatureExtractor, list[int]]],
narrow_format: bool = False) -> pd.DataFrame:
feature_extractors: list[tuple[str, FeatureExtractor, list[int]]]) -> pd.DataFrame:
df = pd.read_csv(os.path.join(data_source[0], data_source[1] + '.csv'), header=0, index_col=0).fillna(0)
if returns == 'log_returns':
@@ -135,10 +122,7 @@ def __load_df(data_source: DataSource,
df = df.replace([np.inf, -np.inf], 0.)
df = drop_columns_if_exist(df, ['open', 'high', 'low', 'close', 'volume'])
if narrow_format:
df["ticker"] = np.repeat(prefix, df.shape[0])
else:
df.columns = [prefix + "_" + c if 'date' not in c else c for c in df.columns]
df.columns = [prefix + "_" + c if 'date' not in c else c for c in df.columns]
return df
@@ -239,7 +223,6 @@ def load_only_returns(assets: DataCollection, index_column: Literal['date', 'int
prefix=data_source[1],
returns=returns,
feature_extractors=[],
narrow_format=False,
) for data_source in assets]
target_asset_df = ray.get(assets_future)