python - 在 pandas 中计算阿隆指标的有效方法

Question

我必须计算 Aroon indicator关于存储在数据框中的数据:

import pandas as pd
import numpy as np


N = 100000
np.random.seed(42)


df = pd.DataFrame()
df['Time'] = np.arange(1, N + 1, 1)
df['High'] = 10 + np.sin(2*np.pi/(N/2)*df['Time']) + 0.5*np.random.randn(N)
df['Low'] = df['High'] - (0.1*np.random.randn(N) + 1)**2

   Time       High       Low
0     1  10.248483  9.031743
1     2   9.931119  9.148842
2     3  10.324221  9.205823
3     4  10.762018  9.882031
4     5   9.883552  8.947960
5     6   9.883686  8.874142
6     7  10.790486  9.814241
7     8  10.384723  9.691851
8     9   9.766394  8.470937
9    10  10.272537  9.032786

已关注 this answer ，我可以使用:

n = 25
df['Aroon Up'] = 100*df['High'].rolling(n + 1).apply(lambda x: x.argmax())/n
df['Aroon Down'] = 100*df['Low'].rolling(n + 1).apply(lambda x: x.argmin())/n

这很好，但是在我必须操作的数据帧上速度非常慢，超过 500.000 行。
如何加快阿隆指标计算速度？

Answer 1

您可以使用sliding_window_view作为滚动的替代:

aroon_up = 100 * sliding_window_view(df['High'], n+1).argmax(1) / n
aroon_down = 100 * sliding_window_view(df['Low'], n+1).argmin(1) / n

# The original dimensions are trimmedas required by the size of the sliding window
df['Aroon Up'] = np.hstack([[np.nan]*n, aroon_up])
df['Aroon Down'] = np.hstack([[np.nan]*n, aroon_down])

对于 500K 条记录:

%timeit 100 * sliding_window_view(df['High'], n+1).argmax(1) / n
31.8 ms ± 482 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

%timeit 100*df['High'].rolling(n + 1).apply(lambda x: x.argmax())/n
30.7 s ± 412 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)