LSEG Data Python Cookbook

Fetching stock prices

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import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)

import lseg.data as ld

ld.open_session()

<lseg.data.session.Definition object at 0x7fd52071b8c0 {name='rdp'}>

You can use the LSEG Data Library for Python to retrieve the latest stock prices for a single company by passing its Refinitiv Instrument Code to the get_data function.

ld.get_data("TRI.TO")

The get_data query requires that you account have access to real-time trading data, which is not available to all users. If you don’t, you can request the latest "1min" intervals from the get_history method.

ld.get_history(
    "TRI.TO",
    interval="1min",
).tail(1)
TRI.TO HIGH_1 LOW_1 OPEN_PRC TRDPRC_1 NUM_MOVES ACVOL_UNS HIGH_YLD LOW_YLD OPEN_YLD YIELD ... BID_NUMMOV ASK_HIGH_1 ASK_LOW_1 OPEN_ASK ASK ASK_NUMMOV MID_HIGH MID_LOW MID_OPEN MID_PRICE
Timestamp
2026-08-28 08:23:00 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> ... 1 149.87 149.87 149.87 149.87 1 <NA> <NA> <NA> <NA>

1 rows × 27 columns

Historical data

You can retrieve historical stock prices by passing a Refinitiv Instrument Code to the get_history function. By default it returns the closing price for the last 30 days.

ld.get_history('TRI.N')
TRI.N TRDPRC_1 HIGH_1 LOW_1 ACVOL_UNS OPEN_PRC BID ASK TRNOVR_UNS VWAP BLKCOUNT BLKVOLUM NUM_MOVES TRD_STATUS SALTIM VWAP_VOL
Date
2026-07-31 98.26 98.69 96.5 28715 96.57 98.04 98.33 2806630 97.7409 <NA> <NA> 873 1 71949 28715
2026-08-03 101.56 102.93 100.305 28993 101.215 101.36 101.76 2948966 101.713 <NA> <NA> 645 1 71995 28993
2026-08-04 109.0 109.36 101.255 88028 101.255 108.23 109.29 9389641 106.6665 <NA> <NA> 1603 1 71999 88028
2026-08-05 98.6 112.91 97.89 99849 110.51 98.46 98.95 10109799 101.2509 <NA> <NA> 1790 1 71996 99849
2026-08-06 100.14 101.48 96.405 70698 101.42 100.07 100.16 6979123 98.7174 <NA> <NA> 1380 1 71987 70698
2026-08-07 101.805 103.91 99.09 20045 99.62 101.79 101.84 2049169 102.2284 <NA> <NA> 403 1 71995 20045
2026-08-10 104.425 104.425 101.005 31843 101.005 104.18 104.44 3306961 103.8521 <NA> <NA> 605 1 71995 31843
2026-08-11 104.89 105.8 102.475 20829 103.59 104.69 105.0 2186434 104.9707 <NA> <NA> 355 1 71938 20829
2026-08-12 102.49 103.95 101.12 18365 103.53 102.42 102.74 1868755 101.7563 <NA> <NA> 457 1 71958 18365
2026-08-13 106.0 106.37 102.23 18785 104.0 105.97 106.16 1964364 104.5709 <NA> <NA> 422 1 71995 18785
2026-08-14 103.56 106.27 102.74 11157 106.27 103.53 103.62 1154574 103.4842 <NA> <NA> 362 1 71967 11157
2026-08-17 99.09 102.4 98.84 20578 102.39 98.98 99.08 2056080 99.9164 <NA> <NA> 411 1 71978 20578
2026-08-18 100.91 102.6 100.9 25970 101.305 100.9 101.09 2639534 101.6378 <NA> <NA> 520 1 71999 25970
2026-08-19 104.955 106.81 103.31 18005 103.31 104.86 105.68 1902241 105.6507 <NA> <NA> 428 1 71931 18005
2026-08-20 105.91 106.56 104.19 24685 104.89 105.79 106.05 2613515 105.8746 <NA> <NA> 536 1 71995 24685
2026-08-21 105.54 106.81 105.01 33407 106.1 105.38 105.66 3540136 105.9699 <NA> <NA> 542 1 71957 33407
2026-08-24 108.63 109.26 105.77 23161 105.77 108.5 108.77 2507374 108.2585 <NA> <NA> 474 1 71994 23161
2026-08-25 104.385 107.39 104.26 19580 105.0 104.3 104.52 2061719 105.2972 <NA> <NA> 425 1 71991 19580
2026-08-26 102.48 105.21 102.095 31530 104.57 102.36 102.65 3244182 102.8919 <NA> <NA> 607 1 71956 31530
2026-08-27 104.76 107.62 103.39 35716 103.39 104.62 104.89 3765602 105.4318 <NA> <NA> 619 1 71999 35716

Multiple instruments

You can retrieve data for multiple instruments by passing a list of Refinitiv Instrument Codes to the get_data and get_history functions.

ld.get_history(['TRI.N', 'LSEG.L'])
TRI.N ... LSEG.L
TRDPRC_1 HIGH_1 LOW_1 ACVOL_UNS OPEN_PRC BID ASK TRNOVR_UNS VWAP BLKCOUNT ... INT_AUC INT_AUCVOL EX_VOL_UNS ALL_C_MOVE ELG_NUMMOV NAVALUE TURN_ORDB ELG_ACVOL ORDBK_TRD OB_NUMMOV
Date
2026-07-31 98.26 98.69 96.5 28715 96.57 98.04 98.33 2806630 97.7409 <NA> ... <NA> <NA> 1637783 21721 19723 <NA> 11876242366 1493049 8398 19372
2026-08-03 101.56 102.93 100.305 28993 101.215 101.36 101.76 2948966 101.713 <NA> ... <NA> <NA> 2020272 16183 13567 <NA> 8527559936 1166872 8490 13271
2026-08-04 109.0 109.36 101.255 88028 101.255 108.23 109.29 9389641 106.6665 <NA> ... <NA> <NA> 1198588 14453 13117 <NA> 9387926752 1118088 8500 12946
2026-08-05 98.6 112.91 97.89 99849 110.51 98.46 98.95 10109799 101.2509 <NA> ... <NA> <NA> 1784703 19134 17448 <NA> 13896871624 1663350 8628 17262
2026-08-06 100.14 101.48 96.405 70698 101.42 100.07 100.16 6979123 98.7174 <NA> ... <NA> <NA> 1623624 14652 13544 <NA> 11876383598 1376307 8750 13379
2026-08-07 101.805 103.91 99.09 20045 99.62 101.79 101.84 2049169 102.2284 <NA> ... <NA> <NA> 2165442 12240 10616 <NA> 6990646368 822262 8920 10361
2026-08-10 104.425 104.425 101.005 31843 101.005 104.18 104.44 3306961 103.8521 <NA> ... <NA> <NA> 563325 9770 8512 <NA> 4497562624 529604 8838 8362
2026-08-11 104.89 105.8 102.475 20829 103.59 104.69 105.0 2186434 104.9707 <NA> ... <NA> <NA> 760873 7989 6869 <NA> 6084512380 703532 8806 6742
2026-08-12 102.49 103.95 101.12 18365 103.53 102.42 102.74 1868755 101.7563 <NA> ... <NA> <NA> 838784 9007 8037 <NA> 5832736978 747321 8752 7908
2026-08-13 106.0 106.37 102.23 18785 104.0 105.97 106.16 1964364 104.5709 <NA> ... <NA> <NA> 937809 8549 7388 <NA> 6139178510 823788 8556 7164
2026-08-14 103.56 106.27 102.74 11157 106.27 103.53 103.62 1154574 103.4842 <NA> ... <NA> <NA> 1612806 10658 8947 <NA> 6570264302 936631 8536 8815
2026-08-17 99.09 102.4 98.84 20578 102.39 98.98 99.08 2056080 99.9164 <NA> ... <NA> <NA> 691813 8711 7321 <NA> 4803659392 627396 8478 7169
2026-08-18 100.91 102.6 100.9 25970 101.305 100.9 101.09 2639534 101.6378 <NA> ... <NA> <NA> 1491463 11838 10739 <NA> 9868813577 1348346 8506 10587
2026-08-19 104.955 106.81 103.31 18005 103.31 104.86 105.68 1902241 105.6507 <NA> ... <NA> <NA> 1688575 10893 9414 <NA> 11437784413 1375378 8518 9237
2026-08-20 105.91 106.56 104.19 24685 104.89 105.79 106.05 2613515 105.8746 <NA> ... <NA> <NA> 1303894 6998 6234 <NA> 7325484462 869595 8542 6129
2026-08-21 105.54 106.81 105.01 33407 106.1 105.38 105.66 3540136 105.9699 <NA> ... 8504 30635 783581 7919 6954 <NA> 5745988662 688049 8602 6814
2026-08-24 108.63 109.26 105.77 23161 105.77 108.5 108.77 2507374 108.2585 <NA> ... <NA> <NA> 1868739 7799 6798 <NA> 4748977234 572145 8840 6630
2026-08-25 104.385 107.39 104.26 19580 105.0 104.3 104.52 2061719 105.2972 <NA> ... <NA> <NA> 1843684 10955 9310 <NA> 7902747682 922965 8798 9138
2026-08-26 102.48 105.21 102.095 31530 104.57 102.36 102.65 3244182 102.8919 <NA> ... <NA> <NA> 1684435 10519 9513 <NA> 6441144580 901290 8688 9382
2026-08-27 104.76 107.62 103.39 35716 103.39 104.62 104.89 3765602 105.4318 <NA> ... <NA> <NA> 1440562 10177 8766 <NA> 7085672762 841406 8980 8475

20 rows × 51 columns

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ld.close_session()