按時間范圍對數據幀中的值進行分組 Python Pandas

Question

嘗試自己解決這個問題，但我無法解決這個問題。 我有許多由更多交易組成的交易。 我試圖以 10 秒的間隔將它們組合在一起。

這就是我所在的地方。 我已經奠定了我認為的基礎，但最近的嘗試（最后一行）返回“AttributeError: 'Int64Index' object has no attribute 'to_period'”。

import pandas as pd
from datetime import timedelta

"""
read csv file
clean date column
convert date str to datetime
sort for equity options
replace date str column with datetime column
"""
trade_reader = pd.read_csv('TastyTrades.csv')
trade_reader['Date'] = trade_reader['Date'].replace({'T': ' ', '-0500': ''}, regex=True)
date_converter = pd.to_datetime(trade_reader['Date'], format="%Y-%m-%d %H:%M:%S")
options_frame = trade_reader.loc[(trade_reader['Instrument Type'] == 'Equity Option')]
clean_frame = options_frame.replace(to_replace=['Date'], value='date_converter')

# Separate opening transaction from closing transactions, combine frames
opens = clean_frame[clean_frame['Action'].isin(['BUY_TO_OPEN', 'SELL_TO_OPEN'])]
closes = clean_frame[clean_frame['Action'].isin(['BUY_TO_CLOSE', 'SELL_TO_CLOSE'])]
open_close_set = set(opens['Symbol']) & set(closes['Symbol'])
open_close_frame = clean_frame[clean_frame['Symbol'].isin(open_close_set)]

# convert Value to float, sort, write
ocf_float = open_close_frame['Value'].astype(float)
ocf_sorted = open_close_frame.sort_values(by=['Symbol', 'Call or Put', 'Date'], ascending=True)
ocf_sorted.to_csv('Sorted.csv')

BTO_frame = opens[opens['Action'].isin(['BUY_TO_OPEN'])]
STO_frame = opens[opens['Action'].isin(['SELL_TO_OPEN'])]
debit_single = []
vertical = []
iron_condor = []
delta = timedelta(seconds=10)

temp_list = BTO_frame.groupby(BTO_frame['Date'].index.to_period(second=10))

我正在使用的示例：

361,2020-01-15 15:27:18,Trade,BUY_TO_OPEN,QQQ   200221P00218000,Equity Option,Bought 1 QQQ 02/21/20 Put 218.00 @ 3.44,-344.00,1.0,-344.00,-1.0,-0.14,100.0,QQQ,2/21/20,218.0,PUT
356,2020-01-17 10:10:27,Trade,SELL_TO_CLOSE,QQQ   200221P00218000,Equity Option,Sold 1 QQQ 02/21/20 Put 218.00 @ 2.26,226.00,1.0,226.00,0.0,-0.15,100.0,QQQ,2/21/20,218.0,PUT
360,2020-01-15 15:27:18,Trade,SELL_TO_OPEN,QQQ   200221P00219000,Equity Option,Sold 1 QQQ 02/21/20 Put 219.00 @ 3.77,377.00,1.0,377.00,-1.0,-0.15,100.0,QQQ,2/21/20,219.0,PUT
357,2020-01-17 10:10:27,Trade,BUY_TO_CLOSE,QQQ   200221P00219000,Equity Option,Bought 1 QQQ 02/21/20 Put 219.00 @ 2.49,-249.00,1.0,-249.00,0.0,-0.14,100.0,QQQ,2/21/20,219.0,PUT
347,2020-01-24 12:28:19,Trade,BUY_TO_OPEN,QQQ   200221P00223000,Equity Option,Bought 1 QQQ 02/21/20 Put 223.00 @ 3.95,-395.00,1.0,-395.00,-1.0,-0.14,100.0,QQQ,2/21/20,223.0,PUT
299,2020-01-30 16:02:56,Trade,SELL_TO_CLOSE,QQQ   200221P00223000,Equity Option,Sold 1 QQQ 02/21/20 Put 223.00 @ 2.91,291.00,1.0,291.00,0.0,-0.15,100.0,QQQ,2/21/20,223.0,PUT
346,2020-01-24 12:28:19,Trade,SELL_TO_OPEN,QQQ   200221P00224000,Equity Option,Sold 1 QQQ 02/21/20 Put 224.00 @ 4.34,434.00,1.0,434.00,-1.0,-0.15,100.0,QQQ,2/21/20,224.0,PUT
300,2020-01-30 16:02:55,Trade,BUY_TO_CLOSE,QQQ   200221P00224000,Equity Option,Bought 1 QQQ 02/21/20 Put 224.00 @ 3.26,-326.00,1.0,-326.00,0.0,-0.14,100.0,QQQ,2/21/20,224.0,PUT
339,2020-01-27 09:56:51,Trade,SELL_TO_OPEN,QQQ   200320C00219000,Equity Option,Sold 1 QQQ 03/20/20 Call 219.00 @ 6.24,624.00,1.0,624.00,-1.0,-0.16,100.0,QQQ,3/20/20,219.0,CALL
15,2020-02-27 15:59:01,Trade,BUY_TO_CLOSE,QQQ   200320C00219000,Equity Option,Bought 1 QQQ 03/20/20 Call 219.00 @ 2.31,-231.00,1.0,-231.00,0.0,-0.14,100.0,QQQ,3/20/20,219.0,CALL
340,2020-01-27 09:56:51,Trade,BUY_TO_OPEN,QQQ   200320C00220000,Equity Option,Bought 1 QQQ 03/20/20 Call 220.00 @ 5.66,-566.00,1.0,-566.00,-1.0,-0.14,100.0,QQQ,3/20/20,220.0,CALL
14,2020-02-27 15:59:01,Trade,SELL_TO_CLOSE,QQQ   200320C00220000,Equity Option,Sold 1 QQQ 03/20/20 Call 220.00 @ 2.01,201.00,1.0,201.00,0.0,-0.15,100.0,QQQ,3/20/20,220.0,CALL

最終結果會將這 12 筆交易作為 3 筆交易，當從每個底層代碼的開盤方找到日期時，按 10 秒的時間范圍組合在一起。

編輯：

原始數據集的示例：

Date,Type,Action,Symbol,Instrument Type,Description,Value,Quantity,Average Price,Commissions,Fees,Multiplier,Underlying Symbol,Expiration Date,Strike Price,Call or Put
2020-02-29T10:09:05-0500,Money Movement,,,,Regulatory fee adjustment,-0.28,0.0,,,0.00,,,,,
2020-02-28T16:00:00-0500,Receive Deliver,,M     200228C00019500,Equity Option,Removal of 3 M 02/28/20 Call 19.50 due to expiration.,0.00,3.0,0.00,,0.00,100,M,2/28/20,19.5,CALL
2020-02-28T15:36:34-0500,Trade,BUY_TO_OPEN,SVXY  200619C00085000,Equity Option,Bought 1 SVXY 06/19/20 Call 85.00 @ 0.06,-6.00,1.0,-6.00,-1.00,-0.14,100,SVXY,6/19/20,85.0,CALL
2020-02-28T15:33:32-0500,Trade,BUY_TO_OPEN,SVXY  200320C00069000,Equity Option,Bought 1 SVXY 03/20/20 Call 69.00 @ 0.15,-15.00,1.0,-15.00,-1.00,-0.14,100,SVXY,3/20/20,69.0,CALL
2020-02-28T12:06:13-0500,Trade,BUY_TO_OPEN,GME   200417C00010000,Equity Option,Bought 10 GME 04/17/20 Call 10.00 @ 0.01,-10.00,10.0,-1.00,-10.00,-1.39,100,GME,4/17/20,10.0,CALL
2020-02-28T12:05:54-0500,Trade,BUY_TO_OPEN,GME   200417C00004500,Equity Option,Bought 1 GME 04/17/20 Call 4.50 @ 0.23,-23.00,1.0,-23.00,-1.00,-0.14,100,GME,4/17/20,4.5,CALL
2020-02-28T10:23:57-0500,Trade,SELL_TO_OPEN,VXX   200417C00025000,Equity Option,Sold 1 VXX 04/17/20 Call 25.00 @ 3.39,339.00,1.0,339.00,-1.00,-0.15,100,VXX,4/17/20,25.0,CALL
2020-02-28T10:23:57-0500,Trade,BUY_TO_OPEN,VXX   200417C00026000,Equity Option,Bought 1 VXX 04/17/20 Call 26.00 @ 3.02,-302.00,1.0,-302.00,-1.00,-0.14,100,VXX,4/17/20,26.0,CALL

Answer 1

請嘗試看看我是否理解你的意思。 我已將您的數據保存為 csv 並將數據框加載為opens 。 新數據框具有以下列；

['Date', 'Type', 'Action', 'Symbol', 'Instrument Type', 'Description',
       'Value', 'Quantity', 'Average Price', 'Commissions', 'Fees',
       'Multiplier', 'Underlying Symbol', 'Expiration Date', 'Strike Price',
       'Call or Put']

我將日期列轉換為日期時間

 opens['Date']=pd.to_datetime(opens['Date'])

我將日期列設置為數據框索引

opens.set_index('Date', inplace=True)

我按動作和符號對數據框進行分組，同時以 10 秒的間隔對索引進行分類。同時，我計算間隔內每組中的類型

opens.groupby(["Action","Symbol"]).resample("10S"). apply(lambda x: x['Type'].count())

或者你想要？

opens.groupby(["Action","Symbol"]).resample("10S"). apply(lambda x: x['Type'].count()).unstack()

推到垂直是什么意思？從你的代碼來看，垂直是一本字典。 它的鍵是什么，值應該是什么？