mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-04 22:57:46 +00:00
Mark Aron Szulyovszky
6ae8acf70e
* feat(Models): added `debug_future_lookahead`, sped up LogisticRegression & DecisionTreeClassifier * feat(Training): added ability to train on expanding_window * feat(Models): tuned some hyperparameters, added expanding_window to sweep config, fixed tests * feat(Models): tune parameters of ensemble models * fix(Config): use window size that works with ensembling
66 lines
2.8 KiB
Python
66 lines
2.8 KiB
Python
import pandas as pd
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import numpy as np
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## Utility functions
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def __get_close_low_high(df: pd.DataFrame) -> tuple[pd.Series, pd.Series, pd.Series]:
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close = df['close']
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low = df['low']
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high = df['high']
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return close, low, high
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## Feature extractors
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def feature_debug_future_lookahead(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.Series:
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return df['returns'].shift(-period)
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def feature_lag(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.Series:
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assert period > 0
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return df['returns'].shift(period)
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def feature_day_of_week(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.DataFrame:
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return pd.get_dummies(pd.DatetimeIndex(df.index).dayofweek, drop_first=True, prefix="date_day_week").set_index(df.index)
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def feature_day_of_month(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.DataFrame:
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return pd.get_dummies(pd.DatetimeIndex(df.index).day, drop_first=True, prefix="date_day_month").set_index(df.index)
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def feature_month(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.DataFrame:
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return pd.get_dummies(pd.DatetimeIndex(df.index).month, drop_first=True, prefix="date_month").set_index(df.index)
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def feature_vol(df: pd.DataFrame, period: int, is_log_return: bool) -> 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, is_log_return: bool) -> pd.Series:
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if is_log_return:
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return np.log(df['close']).diff(period)
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else:
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return df['close'].pct_change(period)
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def feature_STOK(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.Series:
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close, low, high = __get_close_low_high(df)
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STOK = ((close - low.rolling(period).min()) / (high.rolling(period).max() - low.rolling(period).min())) * 100
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return STOK
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def feature_STOD(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.Series:
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stok = feature_STOK(df, period, is_log_return)
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return stok.rolling(3).mean()
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def feature_RSI(df: pd.DataFrame, period: int, is_log_return: bool) -> 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( u[:period] ) #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( d[:period] ) #first value is sum of avg losses d = d.drop(d.index[:(period-1)])
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rs = u.ewm(com=period-1, adjust=False).mean() / \
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d.ewm(com=period-1, adjust=False).mean()
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return 100-100/(1+rs)
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def feature_ROC(df: pd.DataFrame, period: int, is_log_return: bool) -> 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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return pd.Series(((M / N) * 100), name = 'ROC_' + str(period)) |