feat(Data): added feature extractors, and feature extractor presets, removed a bunch of custom arguments from load_data (#42)

feature_extractors/feature_extractor_presets.py

@@ -0,0 +1,22 @@
+from feature_extractors.feature_extractors import feature_lag, feature_mom, feature_ROC, feature_RSI, feature_STOD, feature_STOK, feature_vol, feature_day_of_month, feature_day_of_week, feature_month
+
+lags = [('lag', feature_lag, [1,2,3,4,5,6,7,8,9])]
+
+only_mom = [('mom', feature_mom, [30])]
+
+date = [
+    ('day_of_week', feature_day_of_week, [0]),
+    ('day_of_month', feature_day_of_month, [0]),
+    ('month', feature_month, [0])]
+
+level1 = [
+    ('mom', feature_mom, [10, 20, 30, 60, 90]),
+    ('vol', feature_vol, [10, 20, 30, 60]),
+]
+
+level2 = level1 + [
+    ('roc', feature_ROC, [10, 30]),
+    ('rsi', feature_RSI, [10, 30, 100]),
+    ('stod', feature_STOD, [10, 30, 200]),
+    ('stok', feature_STOK, [10, 30, 200]),
+]

feature_extractors/feature_extractors.py

@@ -0,0 +1,62 @@
+import pandas as pd
+import numpy as np
+
+## Utility functions
+
+def __get_close_low_high(df: pd.DataFrame) -> tuple[pd.Series, pd.Series, pd.Series]:
+    close = df['close']
+    low = df['low']
+    high = df['high']
+    return close, low, high
+
+## Feature extractors
+
+def feature_lag(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.Series:
+    return df['returns'].shift(period)
+
+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)
+
+def feature_day_of_month(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.DataFrame:
+    return pd.get_dummies(pd.DatetimeIndex(df.index).day, drop_first=True, prefix="date_day_month").set_index(df.index)
+
+def feature_month(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.DataFrame:
+    return pd.get_dummies(pd.DatetimeIndex(df.index).month, drop_first=True, prefix="date_month").set_index(df.index)
+
+def feature_vol(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.Series:
+    return df['returns'].rolling(period).std() * (252**0.5)
+
+def feature_mom(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.Series:
+    if is_log_return:
+        return np.log(df['close']).diff(period)
+    else:
+        return df['close'].pct_change(period)
+
+def feature_STOK(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.Series:
+    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
+
+def feature_STOD(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.Series:
+    stok = feature_STOK(df, period, is_log_return)
+    return stok.rolling(3).mean()
+
+def feature_RSI(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.Series:
+    returns = df['returns']
+    delta = returns.diff().dropna()
+    u=delta*0
+    d = u.copy()
+    u[delta > 0] = delta[delta > 0]
+    d[delta < 0] = -delta[delta < 0]
+    u[u.index[period-1]] = np.mean( u[:period] ) #first value is sum of avg gains u = u.drop(u.index[:(period-1)])
+    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)
+
+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))