qfeval_functions.functions.bollinger_band

bollinger_band(x, window=20, sigma=2.0, dim=-1)[source]

Compute Bollinger Bands for a tensor.

Bollinger Bands are a technical analysis tool consisting of a middle band (moving average) and two outer bands (upper and lower). The outer bands are placed at a distance of sigma standard deviations from the middle band. This indicator is commonly used in financial analysis to identify potential support and resistance levels.

Parameters:

  • x (Tensor) – The input tensor containing time series data.

  • window (int) – The size of the moving window for calculating the moving average and standard deviation. Default is 20.

  • sigma (float) – The number of standard deviations for the upper and lower bands. Default is 2.0.

  • dim (int) – The dimension along which to compute the Bollinger Bands. Default is -1 (the last dimension).

Returns:

A tuple containing three tensors of the same shape as the input:

  • Upper band: middle band + (sigma * standard deviation)

  • Middle band: moving average

  • Lower band: middle band - (sigma * standard deviation)

Return type:

Tuple[Tensor, Tensor, Tensor]

Example

>>> x = torch.tensor([[100., 102., 101., 103., 105.],
...                   [50., 48., 49., 51., 52.]])
>>> upper, middle, lower = QF.bollinger_band(x, window=3, sigma=1.0, dim=1)
>>> upper
tensor([[     nan,      nan, 101.8165, 102.8165, 104.6330],
        [     nan,      nan,  49.8165,  50.5805,  51.9139]])
>>> middle
tensor([[     nan,      nan, 101.0000, 102.0000, 103.0000],
        [     nan,      nan,  49.0000,  49.3333,  50.6667]])
>>> lower
tensor([[     nan,      nan, 100.1835, 101.1835, 101.3670],
        [     nan,      nan,  48.1835,  48.0861,  49.4195]])