按时间范围对数据帧中的值进行分组 Python Pandas

Question

Tried solving this on my own and I just can't figure this one out. I have a number of transactions which are made up of more transactions. I am trying to group them together by 10 second intervals.

This is where I'm at. I've laid what I believe is the foundation but the most recent attempt ( the very last line ) comes back with "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))

A sample of what I am working with:

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

The end result would put these 12 transactions as 3 trades, grouped together by a 10 second time range when a date is found from the opening side per underlying symbol.

Edit:

A sample of the raw dataset:

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

Please try and see if I understood you right. I have saved your data as csv and loaded the dataframe as opens . The new dataframe has the following columns;

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

I convert Date column to a datetime

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

I set the Date Column as the dataframe index

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

I group the dataframe by Action and Symbol, while categorizing the index in a 10 second interval.Simultaneously, I count the Types in each group within the the interval

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

Or did you want?

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

What does pushing to vertical mean?From your code, vertical is a dictionary. What are its keys and what should be the values?