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 0x7fc0785878c0 {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
2026-07-20 08:33:00 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> ... 1 136.0 136.0 136.0 136.0 1 <NA> <NA> <NA> <NA>

1 rows × 25 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
2026-06-18 78.7 79.32 77.43 29867 78.53 78.42 79.2 2345512 78.5319 <NA> <NA> 652 1 71997 29867
2026-06-22 76.48 79.04 76.43 31985 77.65 76.43 76.66 2466724 77.1213 <NA> <NA> 649 1 71992 31985
2026-06-23 80.77 80.86 78.56 28367 78.82 80.59 80.8 2257979 79.5988 <NA> <NA> 623 1 71961 28367
2026-06-24 80.98 82.14 79.47 15275 79.85 80.9 81.11 1243674 81.4189 <NA> <NA> 418 1 71985 15275
2026-06-25 80.91 82.79 79.59 21718 79.98 80.8 81.52 1753233 80.7272 <NA> <NA> 591 1 71996 21718
2026-06-26 83.895 84.42 82.83 35218 83.08 83.65 83.93 2943581 83.5817 <NA> <NA> 655 1 71942 35218
2026-06-29 82.6 83.845 80.28 21490 83.28 82.59 82.68 1765800 82.1684 <NA> <NA> 521 1 71999 21490
2026-06-30 81.64 81.89 80.16 17677 81.35 81.64 81.67 1435548 81.21 <NA> <NA> 477 1 71990 17677
2026-07-01 84.94 86.82 83.49 19621 84.5 84.87 85.13 1675695 85.4031 <NA> <NA> 489 1 71993 19621
2026-07-02 89.15 89.35 84.455 33958 84.99 89.05 89.9 2969881 87.4575 <NA> <NA> 807 1 71999 33958
2026-07-06 87.855 89.0 87.075 20908 88.64 87.84 87.98 1839993 88.0043 <NA> <NA> 465 1 71989 20908
2026-07-07 90.77 93.18 90.68 33206 90.86 90.63 90.91 3049254 91.8284 <NA> <NA> 765 1 71999 33206
2026-07-08 88.88 90.77 88.43 27306 90.77 88.77 88.9 2431132 89.0329 <NA> <NA> 572 1 71979 27306
2026-07-09 88.76 88.79 87.23 18000 87.545 88.63 88.93 1589133 88.2852 <NA> <NA> 503 1 71958 18000
2026-07-10 89.615 91.76 89.47 20404 89.94 89.61 89.74 1845654 90.4555 <NA> <NA> 495 1 71987 20404
2026-07-13 94.31 94.33 91.28 58553 91.28 94.27 94.37 5467186 93.3716 <NA> <NA> 1035 1 71995 58553
2026-07-14 91.71 93.565 89.835 50216 89.835 91.05 91.94 4631278 92.2271 <NA> <NA> 759 1 71999 50216
2026-07-15 95.53 98.62 93.86 68791 93.86 95.5 95.68 6605609 96.0243 <NA> <NA> 1199 1 71999 68791
2026-07-16 98.83 101.02 97.17 47989 98.58 98.77 99.01 4760683 99.2036 <NA> <NA> 802 1 71999 47989
2026-07-17 96.27 100.32 95.8 35058 100.03 96.14 96.24 3404827 97.1198 <NA> <NA> 753 1 71981 35058

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
2026-06-18 78.7 79.32 77.43 29867 78.53 78.42 79.2 2345512 78.5319 <NA> ... <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
2026-06-22 76.48 79.04 76.43 31985 77.65 76.43 76.66 2466724 77.1213 <NA> ... <NA> <NA> 4904629 7530 6109 <NA> 5146603885 644932 8278 5918
2026-06-23 80.77 80.86 78.56 28367 78.82 80.59 80.8 2257979 79.5988 <NA> ... <NA> <NA> 2646609 11945 10902 <NA> 6588536952 935423 8370 10774
2026-06-24 80.98 82.14 79.47 15275 79.85 80.9 81.11 1243674 81.4189 <NA> ... <NA> <NA> 1359721 10548 9324 <NA> 8351924835 1054938 8280 9077
2026-06-25 80.91 82.79 79.59 21718 79.98 80.8 81.52 1753233 80.7272 <NA> ... <NA> <NA> 2515817 13389 11517 <NA> 12178200538 1647096 8028 11232
2026-06-26 83.895 84.42 82.83 35218 83.08 83.65 83.93 2943581 83.5817 <NA> ... 7820 8910 1396424 13309 11185 <NA> 8395982136 1165202 8062 10648
2026-06-29 82.6 83.845 80.28 21490 83.28 82.59 82.68 1765800 82.1684 <NA> ... <NA> <NA> 1243914 6413 4888 <NA> 5624374518 716479 8076 4745
2026-06-30 81.64 81.89 80.16 17677 81.35 81.64 81.67 1435548 81.21 <NA> ... <NA> <NA> 1881553 8639 6866 <NA> 8526566663 1378181 8162 6723
2026-07-01 84.94 86.82 83.49 19621 84.5 84.87 85.13 1675695 85.4031 <NA> ... <NA> <NA> 1761389 15720 14269 <NA> 8507668440 1089874 8352 14043
2026-07-02 89.15 89.35 84.455 33958 84.99 89.05 89.9 2969881 87.4575 <NA> ... <NA> <NA> 992274 10202 8917 <NA> 6733309745 814549 8608 8746
2026-07-03 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> ... <NA> <NA> 416191 4779 3911 <NA> 3294167659 396514 8670 3763
2026-07-06 87.855 89.0 87.075 20908 88.64 87.84 87.98 1839993 88.0043 <NA> ... <NA> <NA> 1703717 8155 6855 <NA> 5983550628 703081 8852 6661
2026-07-07 90.77 93.18 90.68 33206 90.86 90.63 90.91 3049254 91.8284 <NA> ... <NA> <NA> 1222428 8773 5975 <NA> 5940360903 688785 9034 5739
2026-07-08 88.88 90.77 88.43 27306 90.77 88.77 88.9 2431132 89.0329 <NA> ... <NA> <NA> 905493 8792 7707 <NA> 4324538148 499513 8862 7587
2026-07-09 88.76 88.79 87.23 18000 87.545 88.63 88.93 1589133 88.2852 <NA> ... <NA> <NA> 1729381 8118 7106 <NA> 6021108242 693294 8768 7024
2026-07-10 89.615 91.76 89.47 20404 89.94 89.61 89.74 1845654 90.4555 <NA> ... <NA> <NA> 726677 6257 5286 <NA> 5084371393 583761 8908 5188
2026-07-13 94.31 94.33 91.28 58553 91.28 94.27 94.37 5467186 93.3716 <NA> ... <NA> <NA> 794785 7834 6758 <NA> 5071704946 572489 9010 6656
2026-07-14 91.71 93.565 89.835 50216 89.835 91.05 91.94 4631278 92.2271 <NA> ... <NA> <NA> 573591 8174 7140 <NA> 4520524665 518330 8974 7016
2026-07-15 95.53 98.62 93.86 68791 93.86 95.5 95.68 6605609 96.0243 <NA> ... <NA> <NA> 1121280 8362 7501 <NA> 7002638723 788561 9076 7388
2026-07-16 98.83 101.02 97.17 47989 98.58 98.77 99.01 4760683 99.2036 <NA> ... <NA> <NA> 715216 6481 5713 <NA> 5792834847 648079 9094 5617
2026-07-17 96.27 100.32 95.8 35058 100.03 96.14 96.24 3404827 97.1198 <NA> ... 9050 56171 1886840 10502 9294 <NA> 7636281422 865255 8978 9176

21 rows × 51 columns

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ld.close_session()