A module framework for constructing a network of features supporting each other, yet each feature is calculated only once.
pip install feature-space
basic creation of features and datasets with multiple dependencies.
import pandas as pd
import numpy as np
df = pd.DataFrame(
{
key: np.random.random(100)
for key in ('Open', 'High', 'Low', 'Close', 'Volume')
}
)
print(df)output
Open High Low Close Volume
0 0.306962 0.090669 0.957007 0.382841 0.331181
1 0.668492 0.233647 0.601794 0.533531 0.761473
2 0.582980 0.765049 0.453987 0.989116 0.439396
3 0.053769 0.512395 0.763573 0.589263 0.886496
4 0.690432 0.372401 0.960555 0.202977 0.133927
.. ... ... ... ... ...
95 0.469604 0.591768 0.590435 0.138835 0.217345
96 0.304976 0.521499 0.006687 0.545035 0.974107
97 0.816594 0.639280 0.702651 0.942868 0.681855
98 0.387333 0.232820 0.563151 0.123126 0.051621
99 0.930279 0.657109 0.620474 0.794123 0.134324
creating the indicators with their relationships.
from feature_space import Column, RSI, Change, Momentum
high = Column('High')
low = Column('Low')
close = Column('Close')
close_change = Change(close)
close_rsi_14 = RSI(close_change, 14)
close_momentum = Momentum(close_change, 35)creating a dataset to contain and control the features. using the dataset object is simple, but everything it does can be done with individual interactions with each feature.
from feature_space import Dataset
change_indicators = Dataset(
name='Change_Features',
features=[close_change, close_rsi_14, close_momentum]
)
change_indicators.calculate(df)
df.dropna(inplace=True)
print(df)output - all features are present in the dataframe, and each one was calculated only once, even though some features are required by more than one feature.
output
Open High Low Close Volume Close_Change Close_RSI_14 Close_Momentum_35
13 0.762020 0.053808 0.079920 0.061354 0.169120 -0.514332 45.932592 -0.321487
14 0.683689 0.948868 0.291903 0.461534 0.557272 0.400181 50.904070 0.078693
15 0.729113 0.352819 0.267228 0.923362 0.331447 0.461828 54.179768 0.540522
16 0.633024 0.931491 0.092854 0.910211 0.164508 -0.013152 49.065301 0.527370
17 0.321494 0.662967 0.253199 0.643929 0.810552 -0.266282 50.668724 0.261088
.. ... ... ... ... ... ... ... ...
95 0.469604 0.591768 0.590435 0.138835 0.217345 -0.635993 43.876190 -0.447753
96 0.304976 0.521499 0.006687 0.545035 0.974107 0.406200 51.201515 -0.293790
97 0.816594 0.639280 0.702651 0.942868 0.681855 0.397833 57.625151 0.194520
98 0.387333 0.232820 0.563151 0.123126 0.051621 -0.819742 45.336996 0.061992
99 0.930279 0.657109 0.620474 0.794123 0.134324 0.670997 48.895303 -0.160722
to recalculate each feature one again, maby after a change in the original source data, simply call the .clear() method on a Feature or a Dataset object. This will not remove any data from the dataframe, just clear the cached referenses to the series in the features.
if you wish to override existing data, you can specify.
change_indicators.clear()
change_indicators.calculate(df, override=True)Save and load a whole dataset with its inter-dependency of features:
change_indicators.save('dataset.pkl')
change_indicators = Dataset.load('dataset.pkl')
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