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

feature_extractors/feature_extractors.py

@@ -1,6 +1,8 @@
 import pandas as pd
 import numpy as np
 from feature_extractors.utils import get_close_low_high
+from feature_extractors.utils import apply_log_if_necessary_series
+from sklearn.preprocessing import StandardScaler
 
 def feature_debug_future_lookahead(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.Series:
     return df['returns'].shift(-period)
@@ -9,6 +11,10 @@ def feature_lag(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.Series
     assert period > 0
     return df['returns'].shift(period)
 
+def feature_standard_scaling(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.Series:
+    scaler = StandardScaler()
+    return pd.Series(scaler.fit_transform(df['close'].to_numpy().reshape(-1, 1)).squeeze(), index = df.index)
+
 def feature_day_of_week(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.DataFrame:
     return pd.get_dummies(pd.DatetimeIndex(df.index).dayofweek, drop_first=True, prefix="date_day_week").set_index(df.index)
 
@@ -31,7 +37,7 @@ def feature_STOK(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.Serie
     close, low, high = get_close_low_high(df)
 
     STOK = ((close - low.rolling(period).min()) / (high.rolling(period).max() - low.rolling(period).min())) * 100
-    return STOK
+    return apply_log_if_necessary_series(STOK, "stok")
 
 def feature_STOD(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.Series:
     stok = feature_STOK(df, period, is_log_return)
@@ -48,10 +54,11 @@ def feature_RSI(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.Series
     d[d.index[period-1]] = np.mean( d[:period] ) #first value is sum of avg losses d = d.drop(d.index[:(period-1)])
     rs = u.ewm(com=period-1, adjust=False).mean() / \
     d.ewm(com=period-1, adjust=False).mean()
-    return 100-100/(1+rs)
+    return apply_log_if_necessary_series(100-100/(1+rs), "rsi")
 
 def feature_ROC(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.Series:
     returns = df['returns']
     M = returns.diff(period - 1)
     N = returns.shift(period - 1)
-    return pd.Series(((M / N) * 100), name = 'ROC_' + str(period))
+    roc = pd.Series(((M / N) * 100), name = 'ROC_' + str(period))
+    return apply_log_if_necessary_series(roc, "roc")