我正在嘗試在不使用for回圈（使用 OHLC 股票資料）的情況下執行新的列計算，該回圈表示基于True另一列中的值的滾動最高點。我已經將“交易的第一小時”時間視窗表示為 9:30 和 10:30 之間。在此期間，當我們遍歷行時，如果"first hour of trading" == True，并且當前最高價 >= 前一個最高價，則使用新的最高價。如果我們不在“交易的第一小時”內，只需將之前的值提前。
因此，使用這個可重現的資料集：
import numpy as np
import pandas as pd
from datetime import datetime
# Create a reproducible, static dataframe.
# 1 minute SPY data. Skip to the bottom...
df = pd.DataFrame([
{
"time": "2021-10-26 9:30",
"open": "457.2",
"high": "457.29",
"low": "456.78",
"close": "456.9383",
"volume": "594142"
},
{
"time": "2021-10-26 9:31",
"open": "456.94",
"high": "457.07",
"low": "456.8",
"close": "456.995",
"volume": "194061"
},
{
"time": "2021-10-26 9:32",
"open": "456.99",
"high": "457.22",
"low": "456.84",
"close": "457.21",
"volume": "186114"
},
{
"time": "2021-10-26 9:33",
"open": "457.22",
"high": "457.45",
"low": "457.2011",
"close": "457.308",
"volume": "294158"
},
{
"time": "2021-10-26 9:34",
"open": "457.31",
"high": "457.4",
"low": "457.25",
"close": "457.32",
"volume": "172574"
},
{
"time": "2021-10-26 9:35",
"open": "457.31",
"high": "457.48",
"low": "457.18",
"close": "457.44",
"volume": "396668"
},
{
"time": "2021-10-26 9:36",
"open": "457.48",
"high": "457.6511",
"low": "457.44",
"close": "457.57",
"volume": "186777"
},
{
"time": "2021-10-26 9:37",
"open": "457.5699",
"high": "457.73",
"low": "457.5699",
"close": "457.69",
"volume": "187596"
},
{
"time": "2021-10-26 9:38",
"open": "457.7",
"high": "457.73",
"low": "457.54",
"close": "457.63",
"volume": "185570"
},
{
"time": "2021-10-26 9:39",
"open": "457.63",
"high": "457.64",
"low": "457.31",
"close": "457.59",
"volume": "164707"
},
{
"time": "2021-10-26 9:40",
"open": "457.59",
"high": "457.72",
"low": "457.46",
"close": "457.7199",
"volume": "167438"
},
{
"time": "2021-10-26 9:41",
"open": "457.72",
"high": "457.8",
"low": "457.68",
"close": "457.72",
"volume": "199951"
},
{
"time": "2021-10-26 9:42",
"open": "457.73",
"high": "457.74",
"low": "457.6",
"close": "457.62",
"volume": "152134"
},
{
"time": "2021-10-26 9:43",
"open": "457.6",
"high": "457.65",
"low": "457.45",
"close": "457.5077",
"volume": "142530"
},
{
"time": "2021-10-26 9:44",
"open": "457.51",
"high": "457.64",
"low": "457.4001",
"close": "457.61",
"volume": "122575"
},
{
"time": "2021-10-26 9:45",
"open": "457.61",
"high": "457.76",
"low": "457.58",
"close": "457.75",
"volume": "119886"
},
{
"time": "2021-10-26 9:46",
"open": "457.74",
"high": "457.75",
"low": "457.37",
"close": "457.38",
"volume": "183157"
},
{
"time": "2021-10-26 9:47",
"open": "457.42",
"high": "457.49",
"low": "457.37",
"close": "457.44",
"volume": "128542"
},
{
"time": "2021-10-26 9:48",
"open": "457.43",
"high": "457.49",
"low": "457.33",
"close": "457.44",
"volume": "154181"
},
{
"time": "2021-10-26 9:49",
"open": "457.43",
"high": "457.5898",
"low": "457.42",
"close": "457.47",
"volume": "163063"
},
{
"time": "2021-10-26 9:50",
"open": "457.45",
"high": "457.59",
"low": "457.44",
"close": "457.555",
"volume": "96229"
},
{
"time": "2021-10-26 9:51",
"open": "457.56",
"high": "457.61",
"low": "457.31",
"close": "457.4217",
"volume": "110380"
},
{
"time": "2021-10-26 9:52",
"open": "457.42",
"high": "457.56",
"low": "457.42",
"close": "457.47",
"volume": "107518"
},
{
"time": "2021-10-26 9:53",
"open": "457.475",
"high": "457.51",
"low": "457.4",
"close": "457.48",
"volume": "78062"
},
{
"time": "2021-10-26 9:54",
"open": "457.49",
"high": "457.57",
"low": "457.42",
"close": "457.46",
"volume": "133883"
},
{
"time": "2021-10-26 9:55",
"open": "457.47",
"high": "457.56",
"low": "457.45",
"close": "457.51",
"volume": "98998"
},
{
"time": "2021-10-26 9:56",
"open": "457.51",
"high": "457.54",
"low": "457.43",
"close": "457.43",
"volume": "110237"
},
{
"time": "2021-10-26 9:57",
"open": "457.43",
"high": "457.65",
"low": "457.375",
"close": "457.65",
"volume": "98794"
},
{
"time": "2021-10-26 9:58",
"open": "457.66",
"high": "457.69",
"low": "457.35",
"close": "457.45",
"volume": "262154"
},
{
"time": "2021-10-26 9:59",
"open": "457.45",
"high": "457.47",
"low": "457.33",
"close": "457.4",
"volume": "74685"
},
{
"time": "2021-10-26 10:00",
"open": "457.41",
"high": "457.48",
"low": "457.18",
"close": "457.38",
"volume": "166617"
},
{
"time": "2021-10-26 10:01",
"open": "457.39",
"high": "457.7",
"low": "457.39",
"close": "457.5",
"volume": "265649"
},
{
"time": "2021-10-26 10:02",
"open": "457.51",
"high": "457.57",
"low": "457.39",
"close": "457.53",
"volume": "131947"
},
{
"time": "2021-10-26 10:03",
"open": "457.53",
"high": "457.54",
"low": "457.4",
"close": "457.51",
"volume": "80111"
},
{
"time": "2021-10-26 10:04",
"open": "457.51",
"high": "457.62",
"low": "457.5",
"close": "457.6101",
"volume": "117174"
},
{
"time": "2021-10-26 10:05",
"open": "457.621",
"high": "457.64",
"low": "457.51",
"close": "457.58",
"volume": "168758"
},
{
"time": "2021-10-26 10:06",
"open": "457.58",
"high": "457.64",
"low": "457.46",
"close": "457.61",
"volume": "84076"
},
{
"time": "2021-10-26 10:07",
"open": "457.62",
"high": "457.7401",
"low": "457.62",
"close": "457.66",
"volume": "125156"
},
{
"time": "2021-10-26 10:08",
"open": "457.665",
"high": "457.69",
"low": "457.5",
"close": "457.67",
"volume": "116919"
},
{
"time": "2021-10-26 10:09",
"open": "457.69",
"high": "457.72",
"low": "457.5",
"close": "457.57",
"volume": "102551"
},
{
"time": "2021-10-26 10:10",
"open": "457.56",
"high": "457.75",
"low": "457.56",
"close": "457.7",
"volume": "109165"
},
{
"time": "2021-10-26 10:11",
"open": "457.7",
"high": "457.725",
"low": "457.63",
"close": "457.66",
"volume": "146209"
},
{
"time": "2021-10-26 10:12",
"open": "457.665",
"high": "457.88",
"low": "457.64",
"close": "457.86",
"volume": "210620"
},
{
"time": "2021-10-26 10:13",
"open": "457.855",
"high": "457.96",
"low": "457.83",
"close": "457.95",
"volume": "159975"
},
{
"time": "2021-10-26 10:14",
"open": "457.95",
"high": "458.02",
"low": "457.93",
"close": "457.95",
"volume": "152042"
},
{
"time": "2021-10-26 10:15",
"open": "457.96",
"high": "458.15",
"low": "457.96",
"close": "458.08",
"volume": "146047"
},
{
"time": "2021-10-26 10:16",
"open": "458.085",
"high": "458.17",
"low": "457.99",
"close": "458.15",
"volume": "100732"
},
{
"time": "2021-10-26 10:17",
"open": "458.17",
"high": "458.33",
"low": "458.155",
"close": "458.245",
"volume": "235072"
},
{
"time": "2021-10-26 10:18",
"open": "458.25",
"high": "458.29",
"low": "458.14",
"close": "458.16",
"volume": "422002"
},
{
"time": "2021-10-26 10:19",
"open": "458.17",
"high": "458.2801",
"low": "458.1699",
"close": "458.28",
"volume": "114611"
},
{
"time": "2021-10-26 10:20",
"open": "458.29",
"high": "458.39",
"low": "458.24",
"close": "458.37",
"volume": "241797"
},
{
"time": "2021-10-26 10:21",
"open": "458.37",
"high": "458.42",
"low": "458.31",
"close": "458.345",
"volume": "124824"
},
{
"time": "2021-10-26 10:22",
"open": "458.33",
"high": "458.49",
"low": "458.33",
"close": "458.47",
"volume": "132125"
}
])
...以及可以低于上述df的代碼：
# Convert df to numeric and time to datetime re: the .csv to .json
# converter tool I used online...
df[['open','high','low','close','volume']] = df[['open','high','low','close','volume']].apply(pd.to_numeric)
df['time'] = pd.to_datetime(df['time'])
# When will the first hour of trading be?
startOfFirstHourTrading = datetime.strptime("0930", "%H%M").time()
endOfFirstHourTrading = datetime.strptime("1030", "%H%M").time()
# ...then using those times, define a boolean column denoting whether
# we're currently in the first hour of trading or not
df['isFirstHourTrading'] = np.where((df['time'].dt.time >= startOfFirstHourTrading) & (df['time'].dt.time < endOfFirstHourTrading), True, False)
df['FirstHourHigh'] = 0
# WORKING EXAMPLE
# Iterate through the df
for i in range(len(df)):
# If we're not currently within the first hour of trading, just
# bring forward the last value
if df['isFirstHourTrading'].iloc[i] == False:
df['FirstHourHigh'].iloc[i] = df['FirstHourHigh'].iloc[i-1]
continue
# ... otherwise if we ARE in the first hour of trading, keep track
# of the rolling highest high during the first hour
df['FirstHourHigh'].iloc[i] = max(df['high'].iloc[i], df['high'].iloc[i-1])
# Export the correct answer dataset to compare the next function
# to below
df.to_csv("correct_answers.csv", index=False)
# NON-WORKING EXAMPLE
# What I'd like to do is NOT use a for loop to do the above. I envision
# we can use np.where() and maybe a groupby() here? But I don't know how yet.
# Psuedo might look something like:
df['FirstHourHigh'] = df['high'].rolling(window=DYNAMIC_START_OF_FIRST_HOUR_TRADING_ROW?).max()
# ... but obviously doesn't work, and doesn't take into account if if df['isFirstHourTrading'] == True which it should
# Cross check for matches with correct_answers.csv
df.to_csv("correct_answers2.csv", index=False)
...你會看到我已經使用for回圈實作了我想要的東西，然后比較我正在尋找的東西。我希望實作與for回圈相同的功能，但不使用 for 回圈，僅使用 1 行列計算。有任何想法嗎？謝謝！
uj5u.com熱心網友回復：
首先將時間作為時間，然后在第一個交易小時屏蔽值，獲得它們的累積最大值，并將最高值向前移動：
>>> df['time'] = pd.to_datetime(df['time'])
>>> first_hour = (df['time'].dt.hour * 60 df['time'].dt.minute).between(9.5 * 60, 10.5 * 60)
>>> df['high'].where(first_hour).cummax().ffill()
這是它在一個較小的例子中所做的，它實際上也有第一個訂單之外的幾天和日期：
>>> df
time high
0 2021-10-26 09:30:00 525
1 2021-10-26 10:00:00 504
2 2021-10-26 10:30:00 550
3 2021-10-26 11:00:00 567
4 2021-10-27 09:30:00 520
5 2021-10-27 10:00:00 576
6 2021-10-27 10:30:00 508
7 2021-10-27 11:00:00 532
>>> df['high'].where((df['time'].dt.hour * 60 df['time'].dt.minute).between(9.5 * 60, 10.5 * 60)).cummax().ffill()
0 525.0
1 525.0
2 550.0
3 550.0
4 550.0
5 576.0
6 576.0
7 576.0
uj5u.com熱心網友回復：
首先，您可能有很多不符合 標準的資料"first hour of trading" == True，對吧？如果是這種情況，我們可以先過濾掉這些資料，這樣我們就不需要處理其他情況了。
firstHourTrading = df[df["isFirstHourTrading"] == True]
如果這不是一個選項，那也沒關系。我們可以通過使用 apply 并使用與您在 for 回圈中使用的演算法相同的前一行來立即提高回圈的性能。
df.apply(lambda row: your_business_logic(row, row.shift()))
或者cummax，如果滿足您的需要，您可以嘗試使用pandas 的函式：https : //pandas.pydata.org/docs/reference/api/pandas.DataFrame.cummax.html
uj5u.com熱心網友回復：
pandas cummax 函式可用于 isFirstHourTrading 高列，如下所示：
df["FirstHourHigh"] = (df["isFirstHourTrading"]*df["high"]).cummax()
轉載請註明出處，本文鏈接：https://www.uj5u.com/caozuo/367973.html
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