MinMaxNormalizer¶
A transformer for normalizing values within a feature pipeline by the column-wise extrema.
Class Parameters¶
columns
- A list of column names to normalize.
feature_min
- The minimum value in the range to scale to.
feature_max
- The maximum value in the range to scale to.
inplace
- If
False
, a new column will be added to the output for each input column.
- If
Properties and Setters¶
None
Functions¶
Below are the functions that the MinMaxNormalizer
uses to effectively operate.
Private¶
None
Public¶
transform
- Apply the pipeline of feature transformations to an observation frame.
Use Cases:¶
from tensortrade.features import FeaturePipeline
from tensortrade.features.scalers import MinMaxNormalizer
from tensortrade.features.stationarity import FractionalDifference
from tensortrade.features.indicators import SimpleMovingAverage
price_columns = ["open", "high", "low", "close"]
normalize_price = MinMaxNormalizer(price_columns)
moving_averages = SimpleMovingAverage(price_columns)
difference_all = FractionalDifference(difference_order=0.6)
feature_pipeline = FeaturePipeline(steps=[normalize_price,
moving_averages,
difference_all])
exchange.feature_pipeline = feature_pipeline