pandas的性能问题和对datetime列的过滤

Question

我在其中一列上有一个带有datetime64对象的pandas数据帧.

    time    volume  complete    closeBid    closeAsk    openBid openAsk highBid highAsk lowBid  lowAsk  closeMid
0   2016-08-07 21:00:00+00:00   9   True    0.84734 0.84842 0.84706 0.84814 0.84734 0.84842 0.84706 0.84814 0.84788
1   2016-08-07 21:05:00+00:00   10  True    0.84735 0.84841 0.84752 0.84832 0.84752 0.84846 0.84712 0.8482  0.84788
2   2016-08-07 21:10:00+00:00   10  True    0.84742 0.84817 0.84739 0.84828 0.84757 0.84831 0.84735 0.84817 0.847795
3   2016-08-07 21:15:00+00:00   18  True    0.84732 0.84811 0.84737 0.84813 0.84737 0.84813 0.84721 0.8479  0.847715
4   2016-08-07 21:20:00+00:00   4   True    0.84755 0.84822 0.84739 0.84812 0.84755 0.84822 0.84739 0.84812 0.847885
5   2016-08-07 21:25:00+00:00   4   True    0.84769 0.84843 0.84758 0.84827 0.84769 0.84843 0.84758 0.84827 0.84806
6   2016-08-07 21:30:00+00:00   5   True    0.84764 0.84851 0.84768 0.84852 0.8478  0.84857 0.84764 0.84851 0.848075
7   2016-08-07 21:35:00+00:00   4   True    0.84755 0.84825 0.84762 0.84844 0.84765 0.84844 0.84755 0.84824 0.8479
8   2016-08-07 21:40:00+00:00   1   True    0.84759 0.84812 0.84759 0.84812 0.84759 0.84812 0.84759 0.84812 0.847855
9   2016-08-07 21:45:00+00:00   3   True    0.84727 0.84817 0.84743 0.8482  0.84743 0.84822 0.84727 0.84817 0.84772

我的应用程序遵循以下(简化)结构:

class Runner():
    def execute_tick(self, clock_tick, previous_tick):
        candles = self.broker.get_new_candles(clock_tick, previous_tick)
        if candles:
            run_calculations(candles)

class Broker():
    def get_new_candles(clock_tick, previous_tick)
        start = previous_tick - timedelta(minutes=1)
        end = clock_tick - timedelta(minutes=3)
        return df[(df.time > start) & (df.time <= end)]

我注意到在分析应用程序时,调用df[(df.time > start) & (df.time <= end)]导致最高性能问题的原因,我想知道是否有办法加快这些调用？

编辑:我在这里添加了一些关于用例的更多信息(另外,源代码可在以下网址获得:https://github.com/jmelett/pyFxTrader)

• 该应用程序将接受一个工具列表(例如EUR_USD,USD_JPY,GBP_CHF),然后为它们中的每一个及其时间范围(例如,5分钟,30分钟,1小时等)预取刻度/ 蜡烛.初始化数据基本上是一个dict仪器,每个包含另一个dict具有M5,M30,H1时间帧的蜡烛数据.
• 每个"时间帧"都是一个像顶部所示的熊猫数据帧
• 一个时钟模拟器则用于查询个人蜡烛的具体时间(例如,在15:30:00,给我最后x"5分钟的烛光")为EUR_USD
• 这段数据然后用于" 模拟 "特定的市场条件(例如,过去1小时的平均价格增加了10%,购买市场位置)

Answer 1

如果效率是你的目标，我会使用 numpy 来完成几乎所有事情

我重写get_new_candles为get_new_candles2

def get_new_candles2(clock_tick, previous_tick):
    start = previous_tick - timedelta(minutes=1)
    end = clock_tick - timedelta(minutes=3)
    ge_start = df.time.values >= start.to_datetime64()
    le_end = df.time.values <= end.to_datetime64()
    return pd.DataFrame(df.values[ge_start & le_end], df.index[mask], df.columns)

数据设定

from StringIO import StringIO
import pandas as pd

text = """time,volume,complete,closeBid,closeAsk,openBid,openAsk,highBid,highAsk,lowBid,lowAsk,closeMid
2016-08-07 21:00:00+00:00,9,True,0.84734,0.84842,0.84706,0.84814,0.84734,0.84842,0.84706,0.84814,0.84788
2016-08-07 21:05:00+00:00,10,True,0.84735,0.84841,0.84752,0.84832,0.84752,0.84846,0.84712,0.8482,0.84788
2016-08-07 21:10:00+00:00,10,True,0.84742,0.84817,0.84739,0.84828,0.84757,0.84831,0.84735,0.84817,0.847795
2016-08-07 21:15:00+00:00,18,True,0.84732,0.84811,0.84737,0.84813,0.84737,0.84813,0.84721,0.8479,0.847715
2016-08-07 21:20:00+00:00,4,True,0.84755,0.84822,0.84739,0.84812,0.84755,0.84822,0.84739,0.84812,0.847885
2016-08-07 21:25:00+00:00,4,True,0.84769,0.84843,0.84758,0.84827,0.84769,0.84843,0.84758,0.84827,0.84806
2016-08-07 21:30:00+00:00,5,True,0.84764,0.84851,0.84768,0.84852,0.8478,0.84857,0.84764,0.84851,0.848075
2016-08-07 21:35:00+00:00,4,True,0.84755,0.84825,0.84762,0.84844,0.84765,0.84844,0.84755,0.84824,0.8479
2016-08-07 21:40:00+00:00,1,True,0.84759,0.84812,0.84759,0.84812,0.84759,0.84812,0.84759,0.84812,0.847855
2016-08-07 21:45:00+00:00,3,True,0.84727,0.84817,0.84743,0.8482,0.84743,0.84822,0.84727,0.84817,0.84772
"""

df = pd.read_csv(StringIO(text), parse_dates=[0])

测试输入变量

previous_tick = pd.to_datetime('2016-08-07 21:10:00')
clock_tick = pd.to_datetime('2016-08-07 21:45:00')

get_new_candles2(clock_tick, previous_tick)

定时