StandardNormalizer¶
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.
- If
Properties and Setters¶
- None
Functions¶
Below are the functions that the StandardNormalizer
uses to effectively operate.
Private¶
None
Public¶
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,
moving_averages,
difference_all])
exchange.feature_pipeline = feature_pipeline