def _extract_features(self, df_data: pd.DataFrame):
"""
Computes the features we want and keep only what is necessary for the neural network
Features:
- Volume is adjusted in order to keep consistent value in case of stock splits
- Bid and ask prices are transformed as followed : BIDLO ->(PRC - BIDLO)/PRC and (ASKHI - PRC) / PRC
:param df_data: Example:
date TICKER COMNAM BIDLO ASKHI PRC VOL RET SHROUT sprtrn
PERMNO
36468 20100104 SHW SHERWIN WILLIAMS CO 61.17000 62.14000 61.67000 1337900.0 0.000324 113341.0 0.016043
36468 20100105 SHW SHERWIN WILLIAMS CO 59.55000 61.86000 60.21000 3081500.0 -0.023674 113341.0 0.003116
:return: dataframe with modified features and only self._features as columns
"""
df_data.BIDLO = (df_data.BIDLO - df_data.PRC) / df_data.PRC
df_data.ASKHI = (df_data.ASKHI - df_data.PRC) / df_data.PRC
df_data.VOL = df_data.VOL / df_data.SHROUT # to compensate for stock splits
df_data.RET = df_data.RET + 1.
columns_to_get = self._features
return df_data[columns_to_get]
