pysatl_tsp.implementations.processor.wma_handler

Module Contents

Classes

WMAHandler

Weighted Moving Average (WMA) handler.

API

class pysatl_tsp.implementations.processor.wma_handler.WMAHandler(length: int = 10, asc: bool = True, source: pysatl_tsp.core.Handler[Any, float | None] | None = None)[source]

Bases: pysatl_tsp.core.processor.inductive.weighted_moving_average_handler.WeightedMovingAverageHandler

Weighted Moving Average (WMA) handler.

Calculates a moving average where each data point is multiplied by a weight before being included in the average. The weights increase or decrease linearly, giving more importance to recent data points by default.

This implementation matches the functionality of pandas_ta.wma, using a linear weighting scheme where the weight of each value is proportional to its position in the window.

Inherits parameters from WeightedMovingAverageHandler: :param length: The period for the calculation, defaults to 10 :param asc: Whether weights should be applied in ascending order, defaults to False

When False, most recent values get higher weights

Parameters:

source – Input data source, defaults to None

Example:
# Create a data source with numeric values
data_source = SimpleDataProvider([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0])

# Create a WMA handler with length of 4
wma_handler = WMAHandler(length=4)
wma_handler.set_source(data_source)

# Process the data
for value in wma_handler:
    print(value)

# First 3 values will be None (not enough data points)
# For length=4, weights would be [0.1, 0.2, 0.3, 0.4] (with asc=False)
# So the 4th value would be: (1.0*0.1 + 2.0*0.2 + 3.0*0.3 + 4.0*0.4) = 3.0
# Similarly, the 5th value: (2.0*0.1 + 3.0*0.2 + 4.0*0.3 + 5.0*0.4) = 4.0

Initialization

Initialize weighted moving average handler with specified parameters.

_calculate_weights(length: int, asc: bool) → list[float][source]

Calculate linear weights for WMA.

Generates a sequence of linearly increasing weights that sum to 1.0. The weights increase from 1 to length, and are then normalized by dividing by their sum. The order can be reversed based on the asc parameter.

Parameters:

  • length – The number of weights to generate

  • asc – Whether weights should be in ascending order When False (default), higher weights are assigned to more recent values

Returns:

A list of normalized weights summing to 1.0