Bollinger bands

easyQuantacceptance —~10 min

Implement bollinger(values, window, k) — the Bollinger bands at the last bar: the simple moving average of the trailing window values, and k population standard deviations either side of it.

The bands are the most-drawn overlay on the /pro chart and the envelope the workbench's Bollinger bot trades. Return [lower, middle, upper] rounded to 4 decimal places.

example 1

input

values=[100.0, 102.0, 101.0, 103.0, 104.0, 102.5], window=5, k=2.0

output

[100.5, 102.5, 104.5]

example 2

input

values=[100.0, 102.0, 101.0, 103.0, 104.0, 102.5], window=5, k=1.5

output

[101, 102.5, 104]

constraints

2 ≤ window ≤ len(values)
0.5 ≤ k ≤ 4

teacher · 2 hints · the first is free

↗ read the theory

the chapter this problem is cut from

▶ open in the workbench

run the same math on a real tape

●solution.py· utf-8 · spaces:4

from statistics import mean, pstdev


def bollinger(values: list, window: int, k: float) -> list:
    """[lower, middle, upper] at the last bar, rounded to 4 dp."""
    w = values[-window:]
    m = mean(w)
    s = pstdev(w)
    # TODO: the band width is k standard deviations — k is a parameter, not always 2
    return [round(m - 2 * s, 4), round(m, 4), round(m + 2 * s, 4)]

ln 1, col 1tab indent · esc leave editor

python 3.13 · pyodide

+ 6 hidden on submit

input · keyword arguments to bollinger()

values=[100.0, 102.0, 101.0, 103.0, 104.0, 102.5], window=5, k=2.0

expected

[100.5, 102.5, 104.5]

the judge calls bollinger() with exactly these inputs · outputs are compared to 6 decimal places

● standing bypython 3.13 in your browser · pyodide 0.28.3 · judged on the server to 6 dp⌘↵ run · ⌘⇧↵ submit

paper only · educational · not advice