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

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

* fix(Config): use default_ensemble_config

* fix(Portfolio): fixed portfolio construction

utils/helpers.py

@@ -4,6 +4,8 @@ import os
 import string
 import random
 from typing import Union
+from config.config import Config
+
 
 def get_files_from_dir(path: str) -> list[str]:
     return [f for f in os.listdir(path) if os.path.isfile(os.path.join(path,f)) and not f.startswith('.')]
@@ -17,10 +19,10 @@ def get_first_valid_return_index(series: pd.Series) -> int:
         return 0
     return nested_result[0]
 
-def has_enough_samples_to_train(X: pd.DataFrame, y: pd.Series, training_config: dict) -> bool:
+def has_enough_samples_to_train(X: pd.DataFrame, y: pd.Series, config: Config) -> bool:
     first_valid_index = get_first_valid_return_index(X.iloc[:,0])
     samples_to_train = len(y) - first_valid_index
-    return samples_to_train > training_config['sliding_window_size_primary'] + training_config['sliding_window_size_meta_labeling'] + 100
+    return samples_to_train > config.sliding_window_size_base + config.sliding_window_size_meta_labeling + 100
 
 def flatten(list_of_lists: list) -> list:
     return [item for sublist in list_of_lists for item in sublist]

utils/types.py

@@ -8,7 +8,7 @@ Name = str
 FeatureExtractorConfig = tuple[Name, FeatureExtractor, list[Period]]
 Path = str
 FileName = str
-DataSource = list[tuple[Path, FileName]]
+DataSource = tuple[Path, FileName]
 DataCollection = list[DataSource]
 
 ScalerTypes = Literal['normalize', 'minmax', 'standardize', 'none']