Actually in Quantopian it's only possible to rank with the absolute order.
Why not add a percentage rank like in pandas?
pandas.DataFrame.rank ->
pct : boolean, default False
Computes percentage rank of data This is especially useful with combined scores, where the factors have a different size.
I've just finished my own implementation:
import numpy as np
from scipy.stats import rankdata
def pct_rank(data, ascending=True):
not_finite = ~np.isfinite(data)
ranks = rankdata(data, method='dense')
ranks[not_finite] = np.nan
max = np.nanmax(ranks)
if not ascending:
ranks = (max + 1) - ranks
return ranks / max
class PS_pct_rank(CustomFactor):
inputs = [morningstar.valuation_ratios.ps_ratio]
window_length = 1
def compute(self, today, assets, out, ps_ratio):
data = ps_ratio[-1]
out[:] = pct_rank(data) Anyway it would be great, if you could extend the method rank to provide this feature!
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