gs_quant.timeseries.technicals.bollinger_bands

bollinger_bands(x, w=<gs_quant.timeseries.helper.Window object>, k=2)[source]

Bollinger bands with given window and width

Parameters:

  • x (Series) – time series of prices

  • w (Union[Window, int, str]) – Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size and 10 the ramp up value. If w is a string, it should be a relative date like ‘1m’, ‘1d’, etc. Window size defaults to length of series.

  • k (float) – band width in standard deviations (default: 2)

Return type:

DataFrame

Returns:

date-based time series of return

Usage

Standard deviation bands around the moving average of asset price level. Bollinger bands can be used to determine a range around the price level which responds to local volatility changes. Returns two series, upper, \(u_t\) and lower, \(l_t\)

\(u_t = \bar{X_t} + k\sigma_t\)

\(l_t = \bar{X_t} - k\sigma_t\)

where \(\bar{X_t}\) is the moving average over specified window, and \(\sigma_t\) is the rolling standard deviation over the specified window

See Bollinger Bands for more information

Examples

Compute bollinger bands around \(20\) day moving average at \(2\) standard deviations:

>>> prices = generate_series(100)
>>> bollinger_bands(prices, 20, 2)

See also

moving_average() std()