mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-08 00:27:50 +00:00
Mark Aron Szulyovszky
88 lines
2.8 KiB
Python
88 lines
2.8 KiB
Python
import pandas as pd
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import numpy as np
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from feature_extractors.utils import get_close_low_high
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from feature_extractors.utils import apply_log_if_necessary_series
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def feature_debug_future_lookahead(df: pd.DataFrame, period: int) -> pd.Series:
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return df["returns"].shift(-period)
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def feature_lag(df: pd.DataFrame, period: int) -> pd.Series:
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assert period > 0
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return df["returns"].shift(period)
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def feature_expanding_zscore(df: pd.DataFrame, period: int) -> pd.Series:
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close = df["close"]
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return (close - close.expanding(period).mean()) / close.expanding(period).std()
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def feature_day_of_week(df: pd.DataFrame, period: int) -> pd.DataFrame:
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return pd.get_dummies(
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pd.DatetimeIndex(df.index).dayofweek, drop_first=True, prefix="date_day_week"
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).set_index(df.index)
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def feature_day_of_month(df: pd.DataFrame, period: int) -> pd.DataFrame:
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return pd.get_dummies(
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pd.DatetimeIndex(df.index).day, drop_first=True, prefix="date_day_month"
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).set_index(df.index)
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def feature_month(df: pd.DataFrame, period: int) -> pd.DataFrame:
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return pd.get_dummies(
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pd.DatetimeIndex(df.index).month, drop_first=True, prefix="date_month"
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).set_index(df.index)
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def feature_vol(df: pd.DataFrame, period: int) -> pd.Series:
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return df["returns"].rolling(period).std() * (252**0.5)
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def feature_mom(df: pd.DataFrame, period: int) -> pd.Series:
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return np.log(df["close"]).diff(period)
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def feature_STOK(df: pd.DataFrame, period: int) -> pd.Series:
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close, low, high = get_close_low_high(df)
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STOK = (
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(close - low.rolling(period).min())
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/ (high.rolling(period).max() - low.rolling(period).min())
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) * 100
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return apply_log_if_necessary_series(STOK, "stok")
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def feature_STOD(df: pd.DataFrame, period: int) -> pd.Series:
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stok = feature_STOK(df, period)
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return stok.rolling(3).mean()
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def feature_RSI(df: pd.DataFrame, period: int) -> pd.Series:
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returns = df["returns"]
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delta = returns.diff().dropna()
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u = delta * 0
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d = u.copy()
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u[delta > 0] = delta[delta > 0]
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d[delta < 0] = -delta[delta < 0]
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u[u.index[period - 1]] = np.mean(
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u[:period]
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) # first value is sum of avg gains u = u.drop(u.index[:(period-1)])
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d[d.index[period - 1]] = np.mean(
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d[:period]
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) # first value is sum of avg losses d = d.drop(d.index[:(period-1)])
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rs = (
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u.ewm(com=period - 1, adjust=False).mean()
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/ d.ewm(com=period - 1, adjust=False).mean()
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)
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return apply_log_if_necessary_series(100 - 100 / (1 + rs), "rsi")
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def feature_ROC(df: pd.DataFrame, period: int) -> pd.Series:
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returns = df["returns"]
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M = returns.diff(period - 1)
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N = returns.shift(period - 1)
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roc = pd.Series(((M / N) * 100), name="ROC_" + str(period))
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return apply_log_if_necessary_series(roc, "roc")
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