時系列データ(株価データ)のある値(終値)の移動平均を計算するのに、どのやり方が早いのかやってみた。

pandas
numpy
bottleneck(中身はnumpyが使われているらしい）

PCスペック ryzen3600

結論. bottleneck

import pandas as pd
import numpy as np
import bottleneck as bn

# 読み込み
path = 'データ.csv'
df = pd.read_csv(path)

# 終値
close = df['Close']

# pandas
%time ma_close = close.rolling(25).mean()

# numpy
def moving_average(a, n) :
    ret = np.cumsum(a, dtype=float)
    ret[n:] = ret[n:] - ret[:-n]
    return ret[n - 1:] / n
%time ma_close = moving_average(close.to_numpy(), 25)

# bottleneck
def rollavg_bottlneck(a,n):
    return bn.move_mean(a, window=n,min_count = None)
%time ma_close = rollavg_bottlneck(close.to_numpy(), 25)

%time

CPU times: user 332 µs, sys: 0 ns, total: 332 µs Wall time: 280 µs
CPU times: user 130 µs, sys: 0 ns, total: 130 µs Wall time: 100 µs
CPU times: user 42 µs, sys: 0 ns, total: 42 µs Wall time: 45.5 µs