# Symbol Selection
This section contains 9 examples for Symbol Selection using the `onetick-py`.
\\\\
Each example is a self-contained script that can be run against the OneTick Cloud sample databases.
```default
# onetick-py WebAPI configuration for OneTick Cloud
import os
os.environ['OTP_WEBAPI'] = '1'
os.environ['OTP_HTTP_ADDRESS'] = 'https://rest.cloud.onetick.com'
os.environ['OTP_ACCESS_TOKEN_URL'] = 'https://cloud-auth.parent.onetick.com/realms/OMD/protocol/openid-connect/token'
os.environ['OTP_CLIENT_ID'] = '__FILL_IN__'
os.environ['OTP_CLIENT_SECRET'] = '__FILL_IN__'
```
## Data Retrieval with Single Symbol
Retrieve Data for a single symbol, by passing a single symbol string into [`otp.run`](https://docs.pip.distribution.sol.onetick.com/api/run.html.md#onetick.py.run).
```ipython3
import onetick.py as otp
data = otp.DataSource(db='US_COMP_SAMPLE', tick_type='TRD')
# Return first 100 Rows
data = data.limit(100)
result = otp.run(data,
start=otp.dt(2024, 1, 3, 9, 30),
end=otp.dt(2024, 1, 3, 9, 40),
timezone='America/New_York',
symbols='CSCO')
result
```
```myst-ansi
Time EXCHANGE COND STOP_STOCK SOURCE TRF TTE \
0 2024-01-03 09:30:00.065443591 Z @ I N 0
1 2024-01-03 09:30:00.111130049 Z @ I N 0
2 2024-01-03 09:30:00.127459523 V @ N 0
3 2024-01-03 09:30:00.128498068 Z @F I N 1
4 2024-01-03 09:30:00.135190071 Q @FTI N 1
.. ... ... ... ... ... .. ..
95 2024-01-03 09:30:00.897416820 Q @F I N 1
96 2024-01-03 09:30:00.897444128 Q @F I N 1
97 2024-01-03 09:30:00.897474806 Q @F I N 1
98 2024-01-03 09:30:00.897476583 Q @F I N 1
99 2024-01-03 09:30:00.897670983 D @ N Q 0
TICKER PRICE DELETED_TIME TICK_STATUS SIZE CORR SEQ_NUM \
0 CSCO 50.0200 1969-12-31 19:00:00 0 2 0 169103
1 CSCO 50.1600 1969-12-31 19:00:00 0 3 0 169148
2 CSCO 50.1700 1969-12-31 19:00:00 0 100 0 169181
3 CSCO 50.1700 1969-12-31 19:00:00 0 5 0 169182
4 CSCO 50.1300 1969-12-31 19:00:00 0 46 0 169193
.. ... ... ... ... ... ... ...
95 CSCO 50.0800 1969-12-31 19:00:00 0 51 0 171857
96 CSCO 50.0800 1969-12-31 19:00:00 0 29 0 171858
97 CSCO 50.0800 1969-12-31 19:00:00 0 12 0 171859
98 CSCO 50.0800 1969-12-31 19:00:00 0 2 0 171860
99 CSCO 50.0797 1969-12-31 19:00:00 0 100 0 171862
TRADE_ID PARTICIPANT_TIME TRF_TIME \
0 42 2024-01-03 09:30:00.065250000 1969-12-31 19:00:00.000000000
1 43 2024-01-03 09:30:00.110938000 1969-12-31 19:00:00.000000000
2 13 2024-01-03 09:30:00.065044730 1969-12-31 19:00:00.000000000
3 44 2024-01-03 09:30:00.128306000 1969-12-31 19:00:00.000000000
4 349 2024-01-03 09:30:00.135173836 1969-12-31 19:00:00.000000000
.. ... ... ...
95 365 2024-01-03 09:30:00.897398456 1969-12-31 19:00:00.000000000
96 366 2024-01-03 09:30:00.897428791 1969-12-31 19:00:00.000000000
97 367 2024-01-03 09:30:00.897456284 1969-12-31 19:00:00.000000000
98 368 2024-01-03 09:30:00.897456284 1969-12-31 19:00:00.000000000
99 100 2024-01-03 09:30:00.896374950 2024-01-03 09:30:00.897637536
OMDSEQ
0 0
1 0
2 0
3 0
4 0
.. ...
95 3
96 4
97 5
98 6
99 7
[100 rows x 18 columns]
```
## Data Retrieval with List of Symbols
Retrieve Data for a set of symbols, by passing a List of Symbols in [`otp.run`](https://docs.pip.distribution.sol.onetick.com/api/run.html.md#onetick.py.run).
```ipython3
import onetick.py as otp
# Define the DataSource
data = otp.DataSource(db='US_COMP_SAMPLE', tick_type='TRD')
# Limit to 1000 rows
data = data.limit(1000)
# Specify the Symbol List and Time Range
result = otp.run(data,
start=otp.dt(2024, 1, 3, 9, 30),
end=otp.dt(2024, 1, 3, 9, 40),
timezone='America/New_York',
symbols=['CSCO', 'MSFT'])
result
```
```myst-ansi
{'CSCO': Time EXCHANGE COND STOP_STOCK SOURCE TRF TTE \
0 2024-01-03 09:30:00.065443591 Z @ I N 0
1 2024-01-03 09:30:00.111130049 Z @ I N 0
2 2024-01-03 09:30:00.127459523 V @ N 0
3 2024-01-03 09:30:00.128498068 Z @F I N 1
4 2024-01-03 09:30:00.135190071 Q @FTI N 1
.. ... ... ... ... ... .. ..
995 2024-01-03 09:30:07.378682043 Q @ I N 0
996 2024-01-03 09:30:07.378683771 Q @ I N 0
997 2024-01-03 09:30:07.379088757 Z @F N 1
998 2024-01-03 09:30:07.390014965 P @ N 0
999 2024-01-03 09:30:07.396341167 D @4 I N Q 1
TICKER PRICE DELETED_TIME TICK_STATUS SIZE CORR SEQ_NUM \
0 CSCO 50.02 1969-12-31 19:00:00 0 2 0 169103
1 CSCO 50.16 1969-12-31 19:00:00 0 3 0 169148
2 CSCO 50.17 1969-12-31 19:00:00 0 100 0 169181
3 CSCO 50.17 1969-12-31 19:00:00 0 5 0 169182
4 CSCO 50.13 1969-12-31 19:00:00 0 46 0 169193
.. ... ... ... ... ... ... ...
995 CSCO 50.12 1969-12-31 19:00:00 0 2 0 192393
996 CSCO 50.12 1969-12-31 19:00:00 0 50 0 192394
997 CSCO 50.12 1969-12-31 19:00:00 0 100 0 192396
998 CSCO 50.12 1969-12-31 19:00:00 0 100 0 192406
999 CSCO 50.09 1969-12-31 19:00:00 0 1 0 192414
TRADE_ID PARTICIPANT_TIME TRF_TIME \
0 42 2024-01-03 09:30:00.065250000 1969-12-31 19:00:00.000000000
1 43 2024-01-03 09:30:00.110938000 1969-12-31 19:00:00.000000000
2 13 2024-01-03 09:30:00.065044730 1969-12-31 19:00:00.000000000
3 44 2024-01-03 09:30:00.128306000 1969-12-31 19:00:00.000000000
4 349 2024-01-03 09:30:00.135173836 1969-12-31 19:00:00.000000000
.. ... ... ...
995 636 2024-01-03 09:30:07.378665797 1969-12-31 19:00:00.000000000
996 637 2024-01-03 09:30:07.378665797 1969-12-31 19:00:00.000000000
997 138 2024-01-03 09:30:07.378898000 1969-12-31 19:00:00.000000000
998 337 2024-01-03 09:30:07.389672607 1969-12-31 19:00:00.000000000
999 453 2024-01-03 09:30:02.589000000 2024-01-03 09:30:07.396318790
OMDSEQ
0 0
1 0
2 0
3 0
4 0
.. ...
995 2
996 3
997 0
998 0
999 0
[1000 rows x 18 columns],
'MSFT': Time EXCHANGE COND STOP_STOCK SOURCE TRF TTE \
0 2024-01-03 09:30:00.001141139 Q @FTI N 1
1 2024-01-03 09:30:00.001232095 Q @FTI N 1
2 2024-01-03 09:30:00.001348140 Q @ TI N 0
3 2024-01-03 09:30:00.001835253 Z @ I N 0
4 2024-01-03 09:30:00.002218671 P @ T N 0
.. ... ... ... ... ... .. ..
995 2024-01-03 09:30:01.980424744 Z @F I N 1
996 2024-01-03 09:30:01.980430260 Z @F I N 1
997 2024-01-03 09:30:01.980438693 Z @F I N 1
998 2024-01-03 09:30:01.980454705 Z @F I N 1
999 2024-01-03 09:30:01.980463011 Z @F I N 1
TICKER PRICE DELETED_TIME TICK_STATUS SIZE CORR SEQ_NUM \
0 MSFT 368.990 1969-12-31 19:00:00 0 9 0 251795
1 MSFT 369.000 1969-12-31 19:00:00 0 10 0 251798
2 MSFT 369.000 1969-12-31 19:00:00 0 90 0 251800
3 MSFT 368.995 1969-12-31 19:00:00 0 1 0 251803
4 MSFT 368.990 1969-12-31 19:00:00 0 100 0 251808
.. ... ... ... ... ... ... ...
995 MSFT 369.100 1969-12-31 19:00:00 0 1 0 262423
996 MSFT 369.100 1969-12-31 19:00:00 0 1 0 262424
997 MSFT 369.100 1969-12-31 19:00:00 0 1 0 262425
998 MSFT 369.100 1969-12-31 19:00:00 0 1 0 262426
999 MSFT 369.100 1969-12-31 19:00:00 0 1 0 262427
TRADE_ID PARTICIPANT_TIME TRF_TIME OMDSEQ
0 2626 2024-01-03 09:30:00.001115161 1969-12-31 19:00:00 0
1 2627 2024-01-03 09:30:00.001215210 1969-12-31 19:00:00 1
2 2628 2024-01-03 09:30:00.001330517 1969-12-31 19:00:00 2
3 340 2024-01-03 09:30:00.001612000 1969-12-31 19:00:00 3
4 3095 2024-01-03 09:30:00.001875282 1969-12-31 19:00:00 0
.. ... ... ... ...
995 501 2024-01-03 09:30:01.980246000 1969-12-31 19:00:00 36
996 502 2024-01-03 09:30:01.980252000 1969-12-31 19:00:00 37
997 503 2024-01-03 09:30:01.980258000 1969-12-31 19:00:00 38
998 504 2024-01-03 09:30:01.980269000 1969-12-31 19:00:00 39
999 505 2024-01-03 09:30:01.980279000 1969-12-31 19:00:00 40
[1000 rows x 18 columns]}
```
## Data Retrieval with Symbol Mask
Retrieve Data for symbols matched by pattern.
```ipython3
import onetick.py as otp
# Define your symbol mask, e.g., all symbols starting with 'AA'
symbol_mask = 'AA%'
# Get all symbols matching the mask
symbols = otp.Symbols(db='US_COMP_SAMPLE_DAILY', pattern=symbol_mask)
# Define Data Source, in this case for DAY records
data = otp.DataSource(db='US_COMP_SAMPLE_DAILY', tick_type='DAY')
# Merge data into a single result across symbols
merged_data = otp.merge(data, symbols=symbols, identify_input_ts=True)
# Run query, with defined time range and time zone
result = otp.run(merged_data,
start=otp.dt(2024, 1, 2),
end=otp.dt(2024, 1, 3),
timezone='America/New_York')
result
```
```myst-ansi
Time CLOSE EXCHANGE HIGH LOW OMDSEQ OPEN \
0 2024-01-02 20:15:00 33.280 A 33.89 33.0800 18 33.890
1 2024-01-02 20:15:00 33.290 B 33.94 33.0500 19 33.420
2 2024-01-02 20:15:00 33.300 C 33.94 33.0500 20 33.455
3 2024-01-02 20:15:00 33.285 D 33.97 33.0428 21 33.435
4 2024-01-02 20:15:00 33.310 H 33.95 33.0600 22 33.620
.. ... ... ... ... ... ... ...
362 2024-01-02 20:15:00 65.470 V 65.75 65.3300 380 65.610
363 2024-01-02 20:15:00 65.435 X 65.62 65.3600 381 65.620
364 2024-01-02 20:15:00 65.465 Y 65.73 65.3200 382 65.615
365 2024-01-02 20:15:00 65.440 Z 65.73 65.3100 383 65.620
366 2024-01-02 20:15:00 65.440 65.76 65.3050 384 65.470
PRICE_CLOSING_AUCTION PRICE_OPENING_AUCTION VOLUME ... \
0 NaN NaN 3744 ...
1 NaN NaN 16712 ...
2 NaN NaN 12495 ...
3 NaN NaN 1503127 ...
4 NaN NaN 49660 ...
.. ... ... ... ...
362 NaN NaN 14151 ...
363 NaN NaN 12857 ...
364 NaN NaN 15640 ...
365 NaN NaN 48594 ...
366 NaN NaN 641995 ...
VOLUME_MAIN_SESSION VOLUME_ODD_LOT VOLUME_OFF_EXCHANGE \
0 3744 920 0
1 16712 5119 0
2 12495 5195 0
3 1361152 123934 1503127
4 49660 11096 0
.. ... ... ...
362 14151 1021 0
363 12857 172 0
364 15640 610 0
365 48594 4091 0
366 622992 36082 159440
VOLUME_OPENING_AUCTION VOLUME_POST_MARKET VOLUME_PRE_MARKET \
0 0 0 0
1 0 0 0
2 0 0 0
3 0 138988 2987
4 0 0 0
.. ... ... ...
362 0 0 0
363 0 0 0
364 0 0 0
365 0 0 0
366 2733 0 40
VOLUME_ROUND_LOT VWAP SYMBOL_NAME TICK_TYPE
0 2824 33.226834 AA DAY
1 11593 33.411275 AA DAY
2 7300 33.217808 AA DAY
3 1379193 33.474980 AA DAY
4 38564 33.526722 AA DAY
.. ... ... ... ...
362 13130 65.445476 AAXJ DAY
363 12685 65.493074 AAXJ DAY
364 15030 65.519747 AAXJ DAY
365 44503 65.490934 AAXJ DAY
366 605913 65.487922 AAXJ DAY
[367 rows x 21 columns]
```
## Data Retrieval Across Databases
Retrieve Trades for Symbols across Databases for the specified time range.
\\\\
The initial [`otp.DataSource`](https://docs.pip.distribution.sol.onetick.com/api/sources/data_source.html.md#onetick.py.DataSource) is defined without specifying the Database or symbol.
\\\\
The schema of the Data Source is specified manually.
\\\\
Symbols are specified including the Database name, with format `[Database]::[Symbol]` e.g. `LSE::VOD`.
```ipython3
import onetick.py as otp
# Define the Symbol List
sym_list = ['LSE::VOD', 'EURONEXT::AF', 'XETRA::DBK', 'LSE::TSCO',
'LSE::SHEL', 'EURONEXT::AF', 'LSE::VOD', 'XETRA::DBK']
# Define Data Source, in this case without specifying the Database or symbol name.
# As the schema is not yet known, set the schema policy to manual
trd = otp.DataSource(tick_type='TRD', schema_policy='manual')
# Define the output schema
trd.schema.set(
PRICE=float,
SIZE=int,
TRADE_VENUE=str,
BOOK_TYPE=str,
TRADE_PERIOD=str
)
# Specify Output Fields
trd = trd[['PRICE', 'SIZE', 'TRADE_VENUE', 'BOOK_TYPE', 'TRADE_PERIOD']]
# Filter on Lit Order Book
trd = trd.where(trd['BOOK_TYPE'] == '0')
# Filter on Continuous Trading
trd = trd.where(trd['TRADE_PERIOD'] == '-')
# Create a single output, merging all the inputs into a single resultset.
merged = otp.merge([trd], symbols=sym_list, identify_input_ts=True, separate_db_name=True)
# Return first 1000 Rows
merged = merged.limit(1000)
# Run the query returning the data in the selected timezone
result = otp.run(merged,
start=otp.datetime(2024, 1, 3, 8),
end=otp.datetime(2024, 1, 4, 16),
timezone='Europe/London')
result
```
```myst-ansi
Time PRICE SIZE TRADE_VENUE BOOK_TYPE \
0 2024-01-03 08:00:03.055 2574.500 147 XLON 0
1 2024-01-03 08:00:03.056 2574.500 46 XLON 0
2 2024-01-03 08:00:03.056 2574.500 44 XLON 0
3 2024-01-03 08:00:04.494 2574.500 207 XLON 0
4 2024-01-03 08:00:05.880 2575.000 24 XLON 0
.. ... ... ... ... ...
995 2024-01-03 08:06:57.076 13.302 23 XPAR 0
996 2024-01-03 08:06:57.151 295.200 2200 XLON 0
997 2024-01-03 08:06:57.151 295.200 22742 XLON 0
998 2024-01-03 08:06:57.151 295.200 760 XLON 0
999 2024-01-03 08:06:57.151 295.300 992 XLON 0
TRADE_PERIOD SYMBOL_NAME DB_NAME TICK_TYPE
0 - SHEL LSE TRD
1 - SHEL LSE TRD
2 - SHEL LSE TRD
3 - SHEL LSE TRD
4 - SHEL LSE TRD
.. ... ... ... ...
995 - AF EURONEXT TRD
996 - TSCO LSE TRD
997 - TSCO LSE TRD
998 - TSCO LSE TRD
999 - TSCO LSE TRD
[1000 rows x 9 columns]
```
## Data Retrieval across Symbol Changes
Ceridian HCM Holding rebranded as Dayforce, Inc on 1st Feb 2024, changing its symbol from `CDAY` to `DAY`.
Full History can be retrieved by querying for both symbols.
```ipython3
import onetick.py as otp
data = otp.DataSource(db='US_COMP_SAMPLE_DAILY', tick_type='DAY')
data = data[['EXCHANGE', 'OPEN', 'HIGH', 'LOW', 'CLOSE']]
result = otp.run(data,
start=otp.dt(2024, 1, 1),
end=otp.dt(2024, 4, 1),
timezone='America/New_York',
symbols=['CDAY', 'DAY'])
result
```
```myst-ansi
{'CDAY': Time EXCHANGE OPEN HIGH LOW CLOSE
0 2024-01-02 20:15:00 A 65.80 66.16 65.74 66.04
1 2024-01-02 20:15:00 B 65.74 66.13 65.74 66.12
2 2024-01-02 20:15:00 C 66.18 66.18 65.63 66.12
3 2024-01-02 20:15:00 D 66.10 66.80 65.25 66.02
4 2024-01-02 20:15:00 H 66.69 66.69 65.71 66.08
.. ... ... ... ... ... ...
373 2024-01-31 20:15:00 V 69.17 70.57 69.13 69.51
374 2024-01-31 20:15:00 X 69.34 70.58 69.19 69.53
375 2024-01-31 20:15:00 Y 69.44 70.10 69.13 69.51
376 2024-01-31 20:15:00 Z 69.10 70.54 69.10 69.54
377 2024-01-31 20:15:00 69.10 70.67 69.10 69.52
[378 rows x 6 columns],
'DAY': Time EXCHANGE OPEN HIGH LOW CLOSE
0 2024-02-01 20:15:00 A 70.990 70.990 68.840 69.960
1 2024-02-01 20:15:00 B 69.400 70.575 69.400 69.930
2 2024-02-01 20:15:00 C 69.720 70.310 68.750 69.910
3 2024-02-01 20:15:00 D 68.755 71.760 68.065 69.990
4 2024-02-01 20:15:00 H 68.940 70.235 68.280 70.235
.. ... ... ... ... ... ...
710 2024-03-28 20:15:00 V 66.150 66.375 65.695 66.235
711 2024-03-28 20:15:00 X 66.070 66.165 65.710 66.165
712 2024-03-28 20:15:00 Y 66.100 66.330 66.010 66.330
713 2024-03-28 20:15:00 Z 66.310 66.375 65.690 66.360
714 2024-03-28 20:15:00 66.450 66.450 65.650 66.210
[715 rows x 6 columns]}
```
## Data Retrieval with New Symbol
Ceridian HCM Holding rerbanded as Dayforce, Inc on 1st Feb 2024, changing its symbol from `CDAY` to `DAY`.
\\\\
Full History can be retrieved by specifying a single symbol and selecting the `SYMBOL_DATE` to when it is active.
\\\\
For example `DAY` after Feb 2024.
```ipython3
import onetick.py as otp
data = otp.DataSource(db='US_COMP_SAMPLE_DAILY', tick_type='DAY')
result = otp.run(data,
start=otp.dt(2024, 1, 1),
end=otp.dt(2024, 4, 1),
timezone='America/New_York',
symbols='DAY',
symbol_date=otp.dt(2024, 4, 1))
result
```
```myst-ansi
Time EXCHANGE OPEN HIGH LOW CLOSE VOLUME \
0 2024-01-02 20:15:00 A 65.80 66.160 65.740 66.040 1963
1 2024-01-02 20:15:00 B 65.74 66.130 65.740 66.120 3272
2 2024-01-02 20:15:00 C 66.18 66.180 65.630 66.120 3161
3 2024-01-02 20:15:00 D 66.10 66.800 65.250 66.020 366173
4 2024-01-02 20:15:00 H 66.69 66.690 65.710 66.080 4597
... ... ... ... ... ... ... ...
1088 2024-03-28 20:15:00 V 66.15 66.375 65.695 66.235 78488
1089 2024-03-28 20:15:00 X 66.07 66.165 65.710 66.165 992
1090 2024-03-28 20:15:00 Y 66.10 66.330 66.010 66.330 2127
1091 2024-03-28 20:15:00 Z 66.31 66.375 65.690 66.360 42920
1092 2024-03-28 20:15:00 66.45 66.450 65.650 66.210 1617713
VWAP PRICE_OPENING_AUCTION VOLUME_OPENING_AUCTION \
0 65.988380 NaN 0
1 66.040385 NaN 0
2 65.969379 NaN 0
3 65.943110 NaN 0
4 66.119785 NaN 0
... ... ... ...
1088 66.125359 NaN 0
1089 65.991250 NaN 0
1090 66.174067 NaN 0
1091 66.149626 NaN 0
1092 66.163421 NaN 6550
PRICE_CLOSING_AUCTION VOLUME_CLOSING_AUCTION VOLUME_MAIN_SESSION \
0 NaN 0 1963
1 NaN 0 3272
2 NaN 0 3160
3 NaN 0 313057
4 NaN 0 4597
... ... ... ...
1088 NaN 0 78488
1089 NaN 0 992
1090 NaN 0 2127
1091 NaN 0 42920
1092 NaN 491722 1028853
VOLUME_PRE_MARKET VOLUME_POST_MARKET VOLUME_ODD_LOT VOLUME_ROUND_LOT \
0 0 0 827 1136
1 0 0 2440 832
2 0 1 1695 1466
3 97 53019 119016 247157
4 0 0 3156 1441
... ... ... ... ...
1088 0 0 11964 66524
1089 0 0 592 400
1090 0 0 701 1426
1091 0 0 20470 22450
1092 107 90481 222184 1395529
VOLUME_OFF_EXCHANGE OMDSEQ
0 0 22931
1 0 22932
2 0 22933
3 366173 22934
4 0 22935
... ... ...
1088 0 32708
1089 0 32709
1090 0 32710
1091 0 32711
1092 570844 32712
[1093 rows x 19 columns]
```
## Data Retrieval with Old Symbol
Ceridian HCM Holding rebanded as Dayforce, Inc on 1st Feb 2024, changing its symbol from `CDAY` to `DAY`.
\\\\
Full History can be retrieved by specifying a single symbol and selecting the `SYMBOL_DATE` to when it is active.
\\\\
For example `CDAY` before Feb 2024.
```ipython3
import onetick.py as otp
data = otp.DataSource(db='US_COMP_SAMPLE_DAILY', tick_type='DAY')
result = otp.run(data,
start=otp.dt(2024, 1, 1),
end=otp.dt(2024, 4, 1),
timezone='America/New_York',
symbols='CDAY',
symbol_date=otp.dt(2024, 1, 1))
result
```
```myst-ansi
Time EXCHANGE OPEN HIGH LOW CLOSE VOLUME \
0 2024-01-02 20:15:00 A 65.80 66.160 65.740 66.040 1963
1 2024-01-02 20:15:00 B 65.74 66.130 65.740 66.120 3272
2 2024-01-02 20:15:00 C 66.18 66.180 65.630 66.120 3161
3 2024-01-02 20:15:00 D 66.10 66.800 65.250 66.020 366173
4 2024-01-02 20:15:00 H 66.69 66.690 65.710 66.080 4597
... ... ... ... ... ... ... ...
1088 2024-03-28 20:15:00 V 66.15 66.375 65.695 66.235 78488
1089 2024-03-28 20:15:00 X 66.07 66.165 65.710 66.165 992
1090 2024-03-28 20:15:00 Y 66.10 66.330 66.010 66.330 2127
1091 2024-03-28 20:15:00 Z 66.31 66.375 65.690 66.360 42920
1092 2024-03-28 20:15:00 66.45 66.450 65.650 66.210 1617713
VWAP PRICE_OPENING_AUCTION VOLUME_OPENING_AUCTION \
0 65.988380 NaN 0
1 66.040385 NaN 0
2 65.969379 NaN 0
3 65.943110 NaN 0
4 66.119785 NaN 0
... ... ... ...
1088 66.125359 NaN 0
1089 65.991250 NaN 0
1090 66.174067 NaN 0
1091 66.149626 NaN 0
1092 66.163421 NaN 6550
PRICE_CLOSING_AUCTION VOLUME_CLOSING_AUCTION VOLUME_MAIN_SESSION \
0 NaN 0 1963
1 NaN 0 3272
2 NaN 0 3160
3 NaN 0 313057
4 NaN 0 4597
... ... ... ...
1088 NaN 0 78488
1089 NaN 0 992
1090 NaN 0 2127
1091 NaN 0 42920
1092 NaN 491722 1028853
VOLUME_PRE_MARKET VOLUME_POST_MARKET VOLUME_ODD_LOT VOLUME_ROUND_LOT \
0 0 0 827 1136
1 0 0 2440 832
2 0 1 1695 1466
3 97 53019 119016 247157
4 0 0 3156 1441
... ... ... ... ...
1088 0 0 11964 66524
1089 0 0 592 400
1090 0 0 701 1426
1091 0 0 20470 22450
1092 107 90481 222184 1395529
VOLUME_OFF_EXCHANGE OMDSEQ
0 0 22931
1 0 22932
2 0 22933
3 366173 22934
4 0 22935
... ... ...
1088 0 32708
1089 0 32709
1090 0 32710
1091 0 32711
1092 570844 32712
[1093 rows x 19 columns]
```