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MinMaxNormalizer.md

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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.

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