Cross-sectional rank of 34 tickers

mediumMLacceptance —~25 min

Implement cs_rank(values) — the cross-sectional percentile rank of each value among its peers: rank the values ascending from 0 to n - 1, give TIED values the average of the ranks they would occupy, then divide by n - 1 so the result lies in [0, 1].

Ranking forward returns across the 34 names in the basket strips out whatever the whole market did that day — the cross-sectional model in ai quants predicts this rank, not the return. Return the ranks rounded to 4 decimal places.

example 1

input

values=[0.02, -0.01, 0.05, 0.0]

output

[0.6667, 0, 1, 0.3333]

example 2

input

values=[0.02, 0.02, 0.05, -0.01]

output

[0.5, 0.5, 1, 0]

note

Two names tied at 2% share rank 1.5.

constraints

2 ≤ len(values) ≤ 500

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

def cs_rank(values: list) -> list:
    """Percentile rank of each value among the others, in [0, 1], 4 dp."""
    n = len(values)
    order = sorted(values)
    out = []
    for v in values:
        # TODO: ties should share the average of their ranks — this takes the first position only
        out.append(round(order.index(v) / (n - 1), 4))
    return out

ln 1, col 1tab indent · esc leave editor

python 3.13 · pyodide

+ 6 hidden on submit

input · keyword arguments to cs_rank()

values=[0.02, -0.01, 0.05, 0.0]

expected

[0.6667, 0, 1, 0.3333]

the judge calls cs_rank() 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