feature_extractors/fractional_differentiation.py

from fracdiff.sklearn import FracdiffStat
import pandas as pd
import numpy as np


def feature_fractional_differentiation(df: pd.DataFrame, period: int) -> pd.Series:
    frac_diff = FracdiffStat(window=period)
    input_series = df["close"].to_numpy().reshape(-1, 1)
    result = frac_diff.fit_transform(input_series)
    return pd.Series(result.squeeze(), index=df.index)
feat(FeatureExtraction): added fractionally differentiated returns to remove lagged returns (#95 ) 2021-12-28 22:50:09 +01:00			`from fracdiff.sklearn import FracdiffStat`
			`import pandas as pd`
			`import numpy as np`

chore(Linter): reformatted code with black (#211 ) 2022-02-17 19:22:17 +01:00
feat(Events): added EventFilter, EventLabeller (#186 ) 2022-01-26 23:22:43 +01:00			`def feature_fractional_differentiation(df: pd.DataFrame, period: int) -> pd.Series:`
chore(Linter): reformatted code with black (#211 ) 2022-02-17 19:22:17 +01:00			`frac_diff = FracdiffStat(window=period)`
feat(FeatureExtraction): added fractionally differentiated returns to remove lagged returns (#95 ) 2021-12-28 22:50:09 +01:00			`input_series = df["close"].to_numpy().reshape(-1, 1)`
feat(Data): added daily_glassnode DataCollection (#99 ) 2022-01-03 13:57:36 +01:00			`result = frac_diff.fit_transform(input_series)`
chore(Linter): reformatted code with black (#211 ) 2022-02-17 19:22:17 +01:00			`return pd.Series(result.squeeze(), index=df.index)`