feat(DataLoader): caching MVP, added ability to use standard scaling for exogenous data, scaling is now also done before feature selection (#105)

* fix(FeatureExtractor): apply log to transform some series to normality

* feat(DataLoader): add ability of not returning returns when they're not needed (exogenous data), applied log to certain features

* feat(FeatureExtractors): added standard scaling for exogenous data

* feat(FeatureSelection): scale data with the passed in scaler before doing feature-selection

* fix(Config): sweep config

* feat(Models): output probability, store it

* feat(Core): added caching to select_features() and load_data()

* fix(Dependencies): added diskcache

* fix(Training): error when creating results DF

* feat(Models): added xgboost, fixed tests

* refactor(Cache): moved hashing to a separate function, created wrapper functions to separate business logic and caching

* fix(Tests): new syntax

* fix(Model): XGboost can't handle -1 class, so we'll use the deprecated label_encoder fornow

* fix(Model): XGBoost config

* feat(Cache): add run_clear_cache script

* fix(Pipeline) accidentally re-instatiating all_predictions for each asset

sweep_exo_data.yaml

@@ -0,0 +1,60 @@
+program: run_sweep.py
+method: grid
+project: price-forecasting
+name: Exogenous data / data transformation
+metric:
+  goal: maximize
+  name: sharpe
+parameters:
+    assets:
+      value: ['daily_crypto']
+    other_assets:
+      value: ['daily_etf']
+    exogenous_data:
+      values: [['daily_glassnode'], []]
+      distribution: categorical
+    expanding_window_level1:
+      value: True
+    expanding_window_level2:
+      value: False
+    sliding_window_size_level1: 
+      value: 380
+    sliding_window_size_level2: 
+      value: 1
+    feature_selection:
+      value: True
+    n_features_to_select:
+      value: 30
+    dimensionality_reduction:
+      value: True
+    retrain_every: 
+      value: 20
+    scaler:
+      value: 'minmax'
+    include_original_data_in_ensemble:
+      value: False
+    method: 
+      value: 'classification'
+    no_of_classes:
+      value: 'three-balanced'
+    forecasting_horizon: 
+      value: 1
+    load_non_target_asset: 
+      value: True
+    log_returns: 
+      value: True
+    index_column: 
+      value: 'int'
+    level_1_models: 
+      value: ["LR", "LDA", "KNN", "CART", "NB", "AB", "RF", "StaticMom"]
+    level_2_model: 
+      value: "Ensemble_Average"
+    own_features:
+      values: [['date_days', 'level_2', 'lags_up_to_5'], ['date_days', 'level_2', 'fracdiff']]
+      distribution: categorical
+    other_features:
+      values: [['level_2', 'lags_up_to_5'], ['level_2', 'fracdiff']]
+      distribution: categorical
+    exogenous_features:
+      values: [[], ['fracdiff'], ['standard_scaling']]
+      distribution: categorical