A transformer for normalizing values within a feature pipeline by removing the mean and scaling to unit variance.

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


Below are the functions that the StandardNormalizer uses to effectively operate.




  • transform
    • Apply the pipeline of feature transformations to an observation frame.
  • reset
    • Resets the history of the standard scaler.

Use Cases:

Use Case #1: Different Input Spaces

This StandardNormalizer operates differently depending on if we pretransform the observation to an ndarray or keep it as a pandas dataframe.

from tensortrade.features import FeaturePipeline
from tensortrade.features.scalers import StandardNormalizer
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,
exchange.feature_pipeline = feature_pipeline