我必須計算存盤在資料框中的資料的Aroon 指標：
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
按照這個答案，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
這很好，但是在我必須操作的資料幀上非常慢，超過 500.000 行。
如何加快 Aroon 指標的計算？
uj5u.com熱心網友回復：
您可以sliding_window_view用作替代品rolling：
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])
對于 500K 記錄：
%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)
uj5u.com熱心網友回復：
這是一個numba版本
import numpy as np
from numba import jit
@jit(nopython=True)
def aroon(data, period):
size = len(data)
out_up = np.array([np.nan] * size)
out_down = np.array([np.nan] * size)
for i in range(period - 1, size):
window = np.flip(data[i 1 - period:i 1])
out_up[i] = ((period - window.argmax()) / period) * 100
out_down[i] = ((period - window.argmin()) / period) * 100
return out_up, out_down
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