refactor(Naming): use new convention, added Ensemble model parameter back, support multiple Meta-Labeling models (#132)

* refactor(Naming): use `primary_models` & `meta_labeling_models`

* refactor(Naming): using primary * meta_labeling across config and in pipeline

* feat(Pipeline): added back Ensemble models

* fix(Pipeline): compiler error

* fix(Config): typo

* chore(Pipeline): removed unused averaging step

* revert the changes in discretizing

* chore(Pipeline): remove sharpe improvement logging

* fix(Pipeline): ensemble predictions should be a pd.Series instead of a DataFrame

* fix(Pipeline): discard unnecessary ensemble_probabilities

* fix(Pipeline): fixes regarding various meta-labeling ensemble bugs

* fix(Reporting): use the new naming convention

* fix(Reporting): use the right variable

* feat(Sweep): new sweep for ensemble models

* fix(Sweep): config reference

* fix(Config): simplified dev config

* fix(Models): use the faster LR model

* fix(Models): use LGBM in the meta-labeling model for speed

* fix(Selection): always use the first model for feature selection, commented out caching from select_features() as it's close to redundant in terms of speed

exploration.ipynb

@@ -25,13 +25,13 @@
    "source": [
     "import pandas as pd\n",
     "import pandas_ta as ta\n",
-    "from config.config import get_default_level_2_daily_config\n",
+    "from config.config import get_default_ensemble_config\n",
     "from config.preprocess import preprocess_config\n",
     "from data_loader.load_data import load_data\n",
     "import seaborn as sns\n",
     "import numpy as np\n",
     "\n",
-    "model_config, training_config, data_config = get_default_level_2_daily_config()\n",
+    "model_config, training_config, data_config = get_default_ensemble_config()\n",
     "model_config, training_config, data_config = preprocess_config(model_config, training_config, data_config)\n",
     "\n",
     "data_config['target_asset'] = data_config['assets'][0]\n",