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drift/feature_extractors/feature_extractor_presets.py
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Mark Aron SzulyovszkyandGitHub 442915f847 feat(Data): create DataSource, DataCollection, added hourly crypto data (#96)
* feat(Data): create DataSource, DataCollection, added hourly crypto data

* fix(Data): hourly data format, loading & config
2021-12-31 19:04:27 +01:00

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1.9 KiB
Python

from feature_extractors.feature_extractors import feature_lag, feature_mom, feature_ROC, feature_RSI, feature_STOD, feature_STOK, feature_vol, feature_day_of_month, feature_day_of_week, feature_month, feature_debug_future_lookahead
from utils.types import FeatureExtractorConfig
from utils.helpers import flatten
from feature_extractors.fractional_differentiation import feature_fractional_differentiation
__presets = dict(
debug_future_lookahead = [('debug_future', feature_debug_future_lookahead, [1])],
single_mom = [('mom', feature_mom, [30])],
single_vol = [('vol', feature_vol, [30])],
mom = [('mom', feature_mom, [10, 20, 30, 60, 90])],
vol = [('vol', feature_vol, [10, 20, 30, 60])],
lags_up_to_5 = [('lag', feature_lag, [1,2,3,4,5])],
lags_up_to_10 = [('lag', feature_lag, [1,2,3,4,5,6,7,8,9,10])],
date_all = [
('day_of_week', feature_day_of_week, [0]),
('day_of_month', feature_day_of_month, [0]),
('month', feature_month, [0])],
date_days = [
('day_of_week', feature_day_of_week, [0]),
('day_of_month', feature_day_of_month, [0]),
],
roc = [('roc', feature_ROC, [10, 30])],
rsi = [('rsi', feature_ROC, [10, 30, 100])],
stod = [('stod', feature_STOD, [10, 30, 200])],
stok = [('stok', feature_STOK, [10, 30, 200])],
fracdiff = [('fracdiff', feature_fractional_differentiation, [10, 30])],
)
presets = __presets | dict(
level_1 = __presets["mom"] + __presets["vol"],
level_2 = __presets["mom"] + __presets["vol"] + __presets["roc"] + __presets["rsi"] + __presets["stod"] + __presets["stok"],
)
def preprocess_feature_extractors_config(data_dict: dict) -> dict:
data_dict = data_dict.copy()
keys = ['own_features', 'other_features']
for key in keys:
preset_names = data_dict[key]
data_dict[key] = flatten([presets[preset_name] for preset_name in preset_names])
return data_dict