Data Retrieval#
OneTick is a time series database meaning that each record has a timestamp and timestamps of consecutive records are non-decreasing. Multiple time series are stored in OneTick. An individual time series is identified by the symbol (aka ticker, financial instrument, security), tick type (i.e., the type of data such as trades or quotes), and the name of the database where the time series is stored.
import onetick.py as otp
# we can retrieve the list of databases available
dbs = otp.databases()
list(dbs)[:5]
['ACTIV_ALPHA', 'ACTIV_CANADA', 'ACTIV_CSE', 'ACTIV_CSE2', 'ACTIV_CX2']
# the list of dates with data for the db
dbs['ACTIV_CANADA'].dates()[-5:]
[datetime.date(2023, 5, 22),
datetime.date(2023, 5, 23),
datetime.date(2023, 5, 24),
datetime.date(2023, 5, 25),
datetime.date(2023, 5, 26)]
# or just the last day with data
dbs['ACTIV_CANADA'].last_date
datetime.date(2023, 5, 26)
# and the list of tick types
dbs['ACTIV_CANADA'].tick_types()
['DAY', 'QTE', 'STAT', 'TRD']
We can now retrieve symbols traded in a given time range. (In many financial markets, there are properties that remain constant throughout the trading day. Examples include the name of a financial instrument and the set of instruments traded).
symbols = otp.Symbols('ACTIV_CANADA')
otp.run(symbols, start=otp.dt(2023, 4, 11), end=otp.dt(2023, 4, 12))
| Time | SYMBOL_NAME | |
|---|---|---|
| 0 | 2023-04-11 | A.H |
| 1 | 2023-04-11 | AAA.P |
| 2 | 2023-04-11 | AAAJ.P |
| 3 | 2023-04-11 | AAB |
| 4 | 2023-04-11 | AAC.P |
| ... | ... | ... |
| 5737 | 2023-04-11 | ZYZ.SB.A |
| 5738 | 2023-04-11 | ZYZ.UN.U |
| 5739 | 2023-04-11 | ZZE.H |
| 5740 | 2023-04-11 | ZZZ |
| 5741 | 2023-04-11 | ZZZD |
5742 rows × 2 columns
We used the otp.run method above, which executed a query that retrieved the list of symbols.
The start and end timestamps were specified with onetick-py’s datetime class otp.dt.
Now that we have database names, tick types, and symbols, we are ready to query a time series.
q = otp.DataSource('NYSE_TAQ', tick_type='TRD')
otp.run(q, start=otp.dt(2023, 3, 29, 9, 30), end=otp.dt(2023, 3, 29, 10), symbols=['SPY'])
| Time | EXCHANGE | COND | STOP_STOCK | SOURCE | TRF | TTE | TICKER | PRICE | DELETED_TIME | TICK_STATUS | SIZE | CORR | SEQ_NUM | TRADE_ID | PARTICIPANT_TIME | TRF_TIME | OMDSEQ | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2023-03-29 09:30:00.000877568 | P | T | N | C | 0 | SPY | 399.9200 | 1969-12-31 19:00:00 | 0 | 400 | 0 | 82274 | 52983525290315 | 2023-03-29 09:30:00.000854016 | 1969-12-31 19:00:00.000000000 | 0 | |
| 1 | 2023-03-29 09:30:00.001151232 | T | T | N | C | 0 | SPY | 399.9200 | 1969-12-31 19:00:00 | 0 | 1000 | 0 | 82280 | 62879132897848 | 2023-03-29 09:30:00.000801064 | 1969-12-31 19:00:00.000000000 | 0 | |
| 2 | 2023-03-29 09:30:00.001154304 | T | T | N | C | 0 | SPY | 399.9200 | 1969-12-31 19:00:00 | 0 | 1000 | 0 | 82281 | 62879132897849 | 2023-03-29 09:30:00.000801064 | 1969-12-31 19:00:00.000000000 | 1 | |
| 3 | 2023-03-29 09:30:00.001921280 | T | T | N | C | 0 | SPY | 399.9300 | 1969-12-31 19:00:00 | 0 | 657 | 0 | 82282 | 62879132897891 | 2023-03-29 09:30:00.001574348 | 1969-12-31 19:00:00.000000000 | 2 | |
| 4 | 2023-03-29 09:30:00.010831360 | Z | F | N | C | 1 | SPY | 399.9250 | 1969-12-31 19:00:00 | 0 | 100 | 0 | 82401 | 52983525039159 | 2023-03-29 09:30:00.010560000 | 1969-12-31 19:00:00.000000000 | 0 | |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 73249 | 2023-03-29 09:59:59.690966784 | D | I | N | C | T | 0 | SPY | 399.8582 | 1969-12-31 19:00:00 | 0 | 3 | 0 | 301129 | 71675710421041 | 2023-03-29 09:59:59.690309443 | 2023-03-29 09:59:59.690612916 | 0 |
| 73250 | 2023-03-29 09:59:59.697699840 | D | I | N | C | N | 0 | SPY | 399.8424 | 1969-12-31 19:00:00 | 0 | 5 | 0 | 301131 | 79371887987030 | 2023-03-29 09:59:59.696245391 | 2023-03-29 09:59:59.697677652 | 0 |
| 73251 | 2023-03-29 09:59:59.707425024 | D | I | N | C | T | 0 | SPY | 399.8600 | 1969-12-31 19:00:00 | 0 | 1 | 0 | 301132 | 71675710421059 | 2023-03-29 09:59:59.633000000 | 2023-03-29 09:59:59.707071825 | 0 |
| 73252 | 2023-03-29 09:59:59.928770304 | D | I | N | C | T | 0 | SPY | 399.8600 | 1969-12-31 19:00:00 | 0 | 1 | 0 | 301136 | 71675710423605 | 2023-03-29 09:59:59.897000000 | 2023-03-29 09:59:59.928417034 | 0 |
| 73253 | 2023-03-29 09:59:59.949504768 | D | I | N | C | T | 0 | SPY | 399.8500 | 1969-12-31 19:00:00 | 0 | 35 | 0 | 301138 | 71675710423626 | 2023-03-29 09:59:59.948898628 | 2023-03-29 09:59:59.949147444 | 0 |
73254 rows × 18 columns
Note that we specified the start and end of the time series to retrieve the corresponding interval.
Let’s just keep the columns we’re interested in to make it more digestible.
q = otp.DataSource('NYSE_TAQ', tick_type='TRD')
q = q[['PRICE', 'SIZE', 'COND', 'EXCHANGE']]
otp.run(q, start=otp.dt(2023, 3, 29, 9, 30), end=otp.dt(2023, 3, 29, 10), symbols=['SPY'])
| Time | PRICE | SIZE | COND | EXCHANGE | |
|---|---|---|---|---|---|
| 0 | 2023-03-29 09:30:00.000877568 | 399.9200 | 400 | T | P |
| 1 | 2023-03-29 09:30:00.001151232 | 399.9200 | 1000 | T | T |
| 2 | 2023-03-29 09:30:00.001154304 | 399.9200 | 1000 | T | T |
| 3 | 2023-03-29 09:30:00.001921280 | 399.9300 | 657 | T | T |
| 4 | 2023-03-29 09:30:00.010831360 | 399.9250 | 100 | F | Z |
| ... | ... | ... | ... | ... | ... |
| 73249 | 2023-03-29 09:59:59.690966784 | 399.8582 | 3 | I | D |
| 73250 | 2023-03-29 09:59:59.697699840 | 399.8424 | 5 | I | D |
| 73251 | 2023-03-29 09:59:59.707425024 | 399.8600 | 1 | I | D |
| 73252 | 2023-03-29 09:59:59.928770304 | 399.8600 | 1 | I | D |
| 73253 | 2023-03-29 09:59:59.949504768 | 399.8500 | 35 | I | D |
73254 rows × 5 columns
We can retrieve multiple time series.
q = otp.DataSource('NYSE_TAQ', tick_type='TRD')
q = q[['PRICE', 'SIZE', 'COND', 'EXCHANGE']]
mult = otp.run(q, start=otp.dt(2023, 3, 29, 9, 30), end=otp.dt(2023, 3, 29, 16), symbols=['SPY', 'QQQ'])
mult
{'SPY': Time PRICE SIZE COND EXCHANGE
0 2023-03-29 09:30:00.000877568 399.920 400 T P
1 2023-03-29 09:30:00.001151232 399.920 1000 T T
2 2023-03-29 09:30:00.001154304 399.920 1000 T T
3 2023-03-29 09:30:00.001921280 399.930 657 T T
4 2023-03-29 09:30:00.010831360 399.925 100 F Z
... ... ... ... ... ...
537609 2023-03-29 15:59:59.994555136 401.350 643 F P
537610 2023-03-29 15:59:59.995045376 401.350 900 F P
537611 2023-03-29 15:59:59.997313024 401.340 100 Z
537612 2023-03-29 15:59:59.997354752 401.340 498 N
537613 2023-03-29 15:59:59.997406208 401.340 200 T
[537614 rows x 5 columns],
'QQQ': Time PRICE SIZE COND EXCHANGE
0 2023-03-29 09:30:00.004574216 310.880 484 @F Z
1 2023-03-29 09:30:00.041854238 310.880 100 @ P
2 2023-03-29 09:30:00.041854358 310.880 100 @ Q P
3 2023-03-29 09:30:00.079583114 310.900 50 @ I J
4 2023-03-29 09:30:00.079611884 310.900 50 @ I Y
... ... ... ... ... ...
409494 2023-03-29 15:59:59.974708442 312.810 38 @ I Q
409495 2023-03-29 15:59:59.988868197 312.810 32 @F I X
409496 2023-03-29 15:59:59.988872215 312.805 200 @F Q
409497 2023-03-29 15:59:59.988874072 312.810 49 @F I Q
409498 2023-03-29 15:59:59.989201372 312.810 600 @F K
[409499 rows x 5 columns]}
Each time series is returned as the value of a dict keyed by the corresponding symbol.
mult['QQQ']
| Time | PRICE | SIZE | COND | EXCHANGE | |
|---|---|---|---|---|---|
| 0 | 2023-03-29 09:30:00.004574216 | 310.880 | 484 | @F | Z |
| 1 | 2023-03-29 09:30:00.041854238 | 310.880 | 100 | @ | P |
| 2 | 2023-03-29 09:30:00.041854358 | 310.880 | 100 | @ Q | P |
| 3 | 2023-03-29 09:30:00.079583114 | 310.900 | 50 | @ I | J |
| 4 | 2023-03-29 09:30:00.079611884 | 310.900 | 50 | @ I | Y |
| ... | ... | ... | ... | ... | ... |
| 409494 | 2023-03-29 15:59:59.974708442 | 312.810 | 38 | @ I | Q |
| 409495 | 2023-03-29 15:59:59.988868197 | 312.810 | 32 | @F I | X |
| 409496 | 2023-03-29 15:59:59.988872215 | 312.805 | 200 | @F | Q |
| 409497 | 2023-03-29 15:59:59.988874072 | 312.810 | 49 | @F I | Q |
| 409498 | 2023-03-29 15:59:59.989201372 | 312.810 | 600 | @F | K |
409499 rows × 5 columns
We can also retrieve all of the symbols from the database or all of the symbols matching a pattern.
q = otp.DataSource('NYSE_TAQ', tick_type='TRD')
q = q[['PRICE', 'SIZE', 'COND', 'EXCHANGE']]
mult = otp.run(q, start=otp.dt(2023, 3, 29, 9, 30), end=otp.dt(2023, 3, 29, 16), symbols=otp.Symbols('NYSE_TAQ', pattern='AAA%'))
mult
{'AAAU': Time PRICE SIZE COND EXCHANGE
0 2023-03-29 09:30:00.019952128 19.480 8892 O X Z
1 2023-03-29 09:30:00.236440064 19.475 4 I Z
2 2023-03-29 09:30:00.287164928 19.475 1 I D
3 2023-03-29 09:30:00.331036416 19.480 1 I D
4 2023-03-29 09:30:01.001156864 19.480 1 I D
... ... ... ... ... ...
1370 2023-03-29 15:59:57.926450432 19.470 7600 F Y
1371 2023-03-29 15:59:57.926453504 19.470 6200 F Y
1372 2023-03-29 15:59:57.926459648 19.470 10400 F Y
1373 2023-03-29 15:59:57.927043072 19.470 27709 F N
1374 2023-03-29 15:59:57.929156864 19.470 908 F A
[1375 rows x 5 columns],
'AAA': Time PRICE SIZE COND EXCHANGE
0 2023-03-29 10:13:42.861570816 24.5699 1 I D
1 2023-03-29 11:58:56.726636288 24.5100 39 I V
2 2023-03-29 12:30:32.157703680 24.5350 48 I V}
We can merge all of the time series by time.
q = otp.DataSource('NYSE_TAQ', tick_type='TRD')
q = q[['PRICE', 'SIZE', 'COND', 'EXCHANGE']]
q = otp.merge([q], symbols=['SPY', 'QQQ'], identify_input_ts=True)
single = otp.run(q, start=otp.dt(2023, 3, 29, 9, 30), end=otp.dt(2023, 3, 29, 16))
single
| Time | PRICE | SIZE | COND | EXCHANGE | SYMBOL_NAME | TICK_TYPE | |
|---|---|---|---|---|---|---|---|
| 0 | 2023-03-29 09:30:00.000877568 | 399.92 | 400 | T | P | SPY | TRD |
| 1 | 2023-03-29 09:30:00.001151232 | 399.92 | 1000 | T | T | SPY | TRD |
| 2 | 2023-03-29 09:30:00.001154304 | 399.92 | 1000 | T | T | SPY | TRD |
| 3 | 2023-03-29 09:30:00.001921280 | 399.93 | 657 | T | T | SPY | TRD |
| 4 | 2023-03-29 09:30:00.004574216 | 310.88 | 484 | @F | Z | QQQ | TRD |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 947108 | 2023-03-29 15:59:59.994555136 | 401.35 | 643 | F | P | SPY | TRD |
| 947109 | 2023-03-29 15:59:59.995045376 | 401.35 | 900 | F | P | SPY | TRD |
| 947110 | 2023-03-29 15:59:59.997313024 | 401.34 | 100 | Z | SPY | TRD | |
| 947111 | 2023-03-29 15:59:59.997354752 | 401.34 | 498 | N | SPY | TRD | |
| 947112 | 2023-03-29 15:59:59.997406208 | 401.34 | 200 | T | SPY | TRD |
947113 rows × 7 columns
The time range and symbols can be specified directly on the data source. This way we can have different times ranges for difference sources that we can later merge.
q = otp.DataSource('NYSE_TAQ', tick_type='TRD', start=otp.dt(2023, 3, 29, 9, 30), end=otp.dt(2023, 3, 29, 10), symbols=['SPY'])
otp.run(q)
| Time | EXCHANGE | COND | STOP_STOCK | SOURCE | TRF | TTE | TICKER | PRICE | DELETED_TIME | TICK_STATUS | SIZE | CORR | SEQ_NUM | TRADE_ID | PARTICIPANT_TIME | TRF_TIME | OMDSEQ | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2023-03-29 09:30:00.000877568 | P | T | N | C | 0 | SPY | 399.9200 | 1969-12-31 19:00:00 | 0 | 400 | 0 | 82274 | 52983525290315 | 2023-03-29 09:30:00.000854016 | 1969-12-31 19:00:00.000000000 | 0 | |
| 1 | 2023-03-29 09:30:00.001151232 | T | T | N | C | 0 | SPY | 399.9200 | 1969-12-31 19:00:00 | 0 | 1000 | 0 | 82280 | 62879132897848 | 2023-03-29 09:30:00.000801064 | 1969-12-31 19:00:00.000000000 | 0 | |
| 2 | 2023-03-29 09:30:00.001154304 | T | T | N | C | 0 | SPY | 399.9200 | 1969-12-31 19:00:00 | 0 | 1000 | 0 | 82281 | 62879132897849 | 2023-03-29 09:30:00.000801064 | 1969-12-31 19:00:00.000000000 | 1 | |
| 3 | 2023-03-29 09:30:00.001921280 | T | T | N | C | 0 | SPY | 399.9300 | 1969-12-31 19:00:00 | 0 | 657 | 0 | 82282 | 62879132897891 | 2023-03-29 09:30:00.001574348 | 1969-12-31 19:00:00.000000000 | 2 | |
| 4 | 2023-03-29 09:30:00.010831360 | Z | F | N | C | 1 | SPY | 399.9250 | 1969-12-31 19:00:00 | 0 | 100 | 0 | 82401 | 52983525039159 | 2023-03-29 09:30:00.010560000 | 1969-12-31 19:00:00.000000000 | 0 | |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 73249 | 2023-03-29 09:59:59.690966784 | D | I | N | C | T | 0 | SPY | 399.8582 | 1969-12-31 19:00:00 | 0 | 3 | 0 | 301129 | 71675710421041 | 2023-03-29 09:59:59.690309443 | 2023-03-29 09:59:59.690612916 | 0 |
| 73250 | 2023-03-29 09:59:59.697699840 | D | I | N | C | N | 0 | SPY | 399.8424 | 1969-12-31 19:00:00 | 0 | 5 | 0 | 301131 | 79371887987030 | 2023-03-29 09:59:59.696245391 | 2023-03-29 09:59:59.697677652 | 0 |
| 73251 | 2023-03-29 09:59:59.707425024 | D | I | N | C | T | 0 | SPY | 399.8600 | 1969-12-31 19:00:00 | 0 | 1 | 0 | 301132 | 71675710421059 | 2023-03-29 09:59:59.633000000 | 2023-03-29 09:59:59.707071825 | 0 |
| 73252 | 2023-03-29 09:59:59.928770304 | D | I | N | C | T | 0 | SPY | 399.8600 | 1969-12-31 19:00:00 | 0 | 1 | 0 | 301136 | 71675710423605 | 2023-03-29 09:59:59.897000000 | 2023-03-29 09:59:59.928417034 | 0 |
| 73253 | 2023-03-29 09:59:59.949504768 | D | I | N | C | T | 0 | SPY | 399.8500 | 1969-12-31 19:00:00 | 0 | 35 | 0 | 301138 | 71675710423626 | 2023-03-29 09:59:59.948898628 | 2023-03-29 09:59:59.949147444 | 0 |
73254 rows × 18 columns
For example, we can get the data from March 29 for SPY and from April 20 for QQQ
spy = otp.DataSource('NYSE_TAQ', tick_type='TRD', start=otp.dt(2023, 3, 29, 9, 30), end=otp.dt(2023, 3, 29, 10), symbols=['SPY'])
qqq = otp.DataSource('NYSE_TAQ', tick_type='TRD', start=otp.dt(2023, 4, 20, 9, 30), end=otp.dt(2023, 4, 20, 10), symbols=['QQQ'])
merged = otp.merge([spy, qqq])
otp.run(merged)
| Time | EXCHANGE | COND | STOP_STOCK | SOURCE | TRF | TTE | TICKER | PRICE | DELETED_TIME | TICK_STATUS | SIZE | CORR | SEQ_NUM | TRADE_ID | PARTICIPANT_TIME | TRF_TIME | OMDSEQ | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2023-03-29 09:30:00.000877568 | P | T | N | C | 0 | SPY | 399.920 | 1969-12-31 19:00:00 | 0 | 400 | 0 | 82274 | 52983525290315 | 2023-03-29 09:30:00.000854016 | 1969-12-31 19:00:00.000000000 | 0 | |
| 1 | 2023-03-29 09:30:00.001151232 | T | T | N | C | 0 | SPY | 399.920 | 1969-12-31 19:00:00 | 0 | 1000 | 0 | 82280 | 62879132897848 | 2023-03-29 09:30:00.000801064 | 1969-12-31 19:00:00.000000000 | 0 | |
| 2 | 2023-03-29 09:30:00.001154304 | T | T | N | C | 0 | SPY | 399.920 | 1969-12-31 19:00:00 | 0 | 1000 | 0 | 82281 | 62879132897849 | 2023-03-29 09:30:00.000801064 | 1969-12-31 19:00:00.000000000 | 1 | |
| 3 | 2023-03-29 09:30:00.001921280 | T | T | N | C | 0 | SPY | 399.930 | 1969-12-31 19:00:00 | 0 | 657 | 0 | 82282 | 62879132897891 | 2023-03-29 09:30:00.001574348 | 1969-12-31 19:00:00.000000000 | 2 | |
| 4 | 2023-03-29 09:30:00.010831360 | Z | F | N | C | 1 | SPY | 399.925 | 1969-12-31 19:00:00 | 0 | 100 | 0 | 82401 | 52983525039159 | 2023-03-29 09:30:00.010560000 | 1969-12-31 19:00:00.000000000 | 0 | |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 131129 | 2023-04-20 09:59:58.357733261 | D | @ I | N | Q | 0 | QQQ | 317.280 | 1969-12-31 19:00:00 | 0 | 1 | 0 | 580852 | 9942 | 2023-04-20 09:59:58.297000000 | 2023-04-20 09:59:58.357708864 | 0 | |
| 131130 | 2023-04-20 09:59:58.357810445 | D | @ I | N | Q | 0 | QQQ | 317.290 | 1969-12-31 19:00:00 | 0 | 1 | 0 | 580853 | 9943 | 2023-04-20 09:59:58.303000000 | 2023-04-20 09:59:58.357788025 | 1 | |
| 131131 | 2023-04-20 09:59:58.387578437 | V | @ | N | 0 | QQQ | 317.280 | 1969-12-31 19:00:00 | 0 | 100 | 0 | 580856 | 1322 | 2023-04-20 09:59:58.387322788 | 1969-12-31 19:00:00.000000000 | 0 | ||
| 131132 | 2023-04-20 09:59:58.706337586 | D | @ I | N | Q | 0 | QQQ | 317.285 | 1969-12-31 19:00:00 | 0 | 1 | 0 | 580951 | 9944 | 2023-04-20 09:59:58.455882000 | 2023-04-20 09:59:58.706311763 | 0 | |
| 131133 | 2023-04-20 09:59:59.676775261 | D | @ | N | N | 0 | QQQ | 317.260 | 1969-12-31 19:00:00 | 0 | 125 | 0 | 581035 | 3221 | 2023-04-20 09:59:59.674000000 | 2023-04-20 09:59:59.676437009 | 0 |
131134 rows × 18 columns
We can also specify multiple symbols in the data source in which case they will be merged by time.
q = otp.DataSource('NYSE_TAQ', tick_type='TRD', start=otp.dt(2023, 3, 29, 9, 30), end=otp.dt(2023, 3, 29, 10), symbols=['SPY', 'QQQ'])
otp.run(q)
| Time | EXCHANGE | COND | STOP_STOCK | SOURCE | TRF | TTE | TICKER | PRICE | DELETED_TIME | TICK_STATUS | SIZE | CORR | SEQ_NUM | TRADE_ID | PARTICIPANT_TIME | TRF_TIME | OMDSEQ | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2023-03-29 09:30:00.000877568 | P | T | N | C | 0 | SPY | 399.92 | 1969-12-31 19:00:00 | 0 | 400 | 0 | 82274 | 52983525290315 | 2023-03-29 09:30:00.000854016 | 1969-12-31 19:00:00.000000000 | 0 | |
| 1 | 2023-03-29 09:30:00.001151232 | T | T | N | C | 0 | SPY | 399.92 | 1969-12-31 19:00:00 | 0 | 1000 | 0 | 82280 | 62879132897848 | 2023-03-29 09:30:00.000801064 | 1969-12-31 19:00:00.000000000 | 0 | |
| 2 | 2023-03-29 09:30:00.001154304 | T | T | N | C | 0 | SPY | 399.92 | 1969-12-31 19:00:00 | 0 | 1000 | 0 | 82281 | 62879132897849 | 2023-03-29 09:30:00.000801064 | 1969-12-31 19:00:00.000000000 | 1 | |
| 3 | 2023-03-29 09:30:00.001921280 | T | T | N | C | 0 | SPY | 399.93 | 1969-12-31 19:00:00 | 0 | 657 | 0 | 82282 | 62879132897891 | 2023-03-29 09:30:00.001574348 | 1969-12-31 19:00:00.000000000 | 2 | |
| 4 | 2023-03-29 09:30:00.004574216 | Z | @F | N | 1 | QQQ | 310.88 | 1969-12-31 19:00:00 | 0 | 484 | 0 | 55679 | 519 | 2023-03-29 09:30:00.004371000 | 1969-12-31 19:00:00.000000000 | 0 | ||
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 129336 | 2023-03-29 09:59:59.909260429 | D | @ I | N | Q | 0 | QQQ | 311.52 | 1969-12-31 19:00:00 | 0 | 50 | 0 | 543084 | 11773 | 2023-03-29 09:59:59.907000000 | 2023-03-29 09:59:59.909233951 | 0 | |
| 129337 | 2023-03-29 09:59:59.924218351 | Q | @F | N | 1 | QQQ | 311.52 | 1969-12-31 19:00:00 | 0 | 100 | 0 | 543086 | 17305 | 2023-03-29 09:59:59.924202552 | 1969-12-31 19:00:00.000000000 | 0 | ||
| 129338 | 2023-03-29 09:59:59.928770304 | D | I | N | C | T | 0 | SPY | 399.86 | 1969-12-31 19:00:00 | 0 | 1 | 0 | 301136 | 71675710423605 | 2023-03-29 09:59:59.897000000 | 2023-03-29 09:59:59.928417034 | 0 |
| 129339 | 2023-03-29 09:59:59.949504768 | D | I | N | C | T | 0 | SPY | 399.85 | 1969-12-31 19:00:00 | 0 | 35 | 0 | 301138 | 71675710423626 | 2023-03-29 09:59:59.948898628 | 2023-03-29 09:59:59.949147444 | 0 |
| 129340 | 2023-03-29 09:59:59.955895840 | D | @ I | N | Q | 0 | QQQ | 311.53 | 1969-12-31 19:00:00 | 0 | 1 | 0 | 543093 | 11774 | 2023-03-29 09:59:59.921000000 | 2023-03-29 09:59:59.955870042 | 0 |
129341 rows × 18 columns