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chore(Linter): reformatted code with black (#211)

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* chore(Linter): reformatted code with black

* Create black.yaml
This commit is contained in:
Mark Aron Szulyovszky
2022-02-17 19:22:17 +01:00
committed by GitHub
parent f3fee4a4e1
commit 8dd2d88740
101 changed files with 2595 additions and 2319 deletions
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feature_extractors/feature_extractors.py
+46 -19
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@@ -3,58 +3,85 @@ import numpy as np
from feature_extractors.utils import get_close_low_high
from feature_extractors.utils import apply_log_if_necessary_series
def feature_debug_future_lookahead(df: pd.DataFrame, period: int) -> pd.Series:
return df['returns'].shift(-period)
return df["returns"].shift(-period)
def feature_lag(df: pd.DataFrame, period: int) -> pd.Series:
assert period > 0
return df['returns'].shift(period)
return df["returns"].shift(period)
def feature_expanding_zscore(df: pd.DataFrame, period: int) -> pd.Series:
close = df['close']
close = df["close"]
return (close - close.expanding(period).mean()) / close.expanding(period).std()
def feature_day_of_week(df: pd.DataFrame, period: int) -> pd.DataFrame:
return pd.get_dummies(pd.DatetimeIndex(df.index).dayofweek, drop_first=True, prefix="date_day_week").set_index(df.index)
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) -> pd.DataFrame:
return pd.get_dummies(pd.DatetimeIndex(df.index).day, drop_first=True, prefix="date_day_month").set_index(df.index)
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) -> pd.DataFrame:
return pd.get_dummies(pd.DatetimeIndex(df.index).month, drop_first=True, prefix="date_month").set_index(df.index)
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) -> pd.Series:
return df['returns'].rolling(period).std() * (252**0.5)
return df["returns"].rolling(period).std() * (252**0.5)
def feature_mom(df: pd.DataFrame, period: int) -> pd.Series:
return np.log(df['close']).diff(period)
return np.log(df["close"]).diff(period)
def feature_STOK(df: pd.DataFrame, period: int) -> 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
STOK = (
(close - low.rolling(period).min())
/ (high.rolling(period).max() - low.rolling(period).min())
) * 100
return apply_log_if_necessary_series(STOK, "stok")
def feature_STOD(df: pd.DataFrame, period: int) -> pd.Series:
stok = feature_STOK(df, period)
return stok.rolling(3).mean()
def feature_RSI(df: pd.DataFrame, period: int) -> pd.Series:
returns = df['returns']
returns = df["returns"]
delta = returns.diff().dropna()
u=delta*0
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 apply_log_if_necessary_series(100-100/(1+rs), "rsi")
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 apply_log_if_necessary_series(100 - 100 / (1 + rs), "rsi")
def feature_ROC(df: pd.DataFrame, period: int) -> pd.Series:
returns = df['returns']
returns = df["returns"]
M = returns.diff(period - 1)
N = returns.shift(period - 1)
roc = pd.Series(((M / N) * 100), name = 'ROC_' + str(period))
return apply_log_if_necessary_series(roc, "roc")
roc = pd.Series(((M / N) * 100), name="ROC_" + str(period))
return apply_log_if_necessary_series(roc, "roc")