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 0x7fd39c84a3c0 {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
2025-10-16 13:30:00 225.41 224.96 224.96 225.41 23 2800 <NA> <NA> <NA> <NA> ... 126 226.98 224.49 224.49 225.76 126 <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
2025-09-18 160.56 169.32 160.52 64044 169.32 159.87 160.82 10493940 163.8552 <NA> <NA> 1601 <NA> 71993 64044
2025-09-19 162.52 162.79 160.28 38953 161.855 162.23 163.62 6295321 161.6132 <NA> <NA> 1013 <NA> 71997 38953
2025-09-22 163.03 163.09 161.12 27899 161.51 162.85 163.21 4517233 161.9138 <NA> <NA> 777 <NA> 71998 27899
2025-09-23 160.79 163.02 160.44 15740 162.52 160.68 161.02 2542013 161.5002 <NA> <NA> 474 <NA> 71998 15740
2025-09-24 157.71 160.83 157.6 25138 160.83 157.58 157.92 3997779 159.0333 <NA> <NA> 599 <NA> 71985 25138
2025-09-25 158.01 159.22 157.56 13687 159.21 157.89 158.25 2165748 158.234 <NA> <NA> 452 <NA> 71997 13684
2025-09-26 156.96 157.9 156.65 17707 157.1 156.83 157.15 2783522 157.199 <NA> <NA> 460 <NA> 71991 17707
2025-09-29 156.16 157.53 155.83 16320 157.53 156.01 156.35 2554292 156.513 <NA> <NA> 443 <NA> 71985 16320
2025-09-30 155.29 157.3 155.11 27077 155.97 155.16 155.5 4220310 155.8633 <NA> <NA> 548 <NA> 71993 27077
2025-10-01 152.61 155.27 152.61 33684 154.89 152.47 152.78 5169990 153.485 <NA> <NA> 595 <NA> 71961 33684
2025-10-02 151.46 152.21 149.93 62914 152.21 151.29 151.66 9495920 150.9349 <NA> <NA> 807 <NA> 71998 62914
2025-10-03 153.0 153.57 150.77 30037 151.04 152.76 153.16 4571567 152.1978 <NA> <NA> 656 <NA> 71997 30037
2025-10-06 151.96 152.48 151.115 20566 151.515 151.86 152.17 3121929 151.8005 <NA> <NA> 525 <NA> 71943 20566
2025-10-07 151.53 152.21 150.58 15948 151.52 151.39 152.12 2414595 151.4043 <NA> <NA> 416 <NA> 71999 15948
2025-10-08 151.64 151.92 150.97 16321 151.21 151.54 151.89 2473209 151.5354 <NA> <NA> 423 <NA> 71996 16321
2025-10-09 150.08 150.94 149.655 13269 150.94 149.94 150.2 1992302 150.1471 <NA> <NA> 437 <NA> 71995 13269
2025-10-10 151.325 152.07 149.55 30173 150.79 151.27 151.62 4559474 151.1111 <NA> <NA> 624 <NA> 71990 30173
2025-10-13 152.66 154.11 152.66 15623 152.9 152.4 152.75 2392604 153.1463 <NA> <NA> 456 <NA> 71887 15623
2025-10-14 154.68 155.6 153.27 35461 153.52 154.51 154.81 5483428 154.6327 <NA> <NA> 646 <NA> 71996 35461
2025-10-15 159.34 160.61 157.845 33729 157.845 159.19 159.44 5376247 159.3954 <NA> <NA> 732 <NA> 71986 33729

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
2025-09-18 160.56 169.32 160.52 64044 169.32 159.87 160.82 10493940 163.8552 <NA> ... <NA> <NA> 1573065 9579 8562 <NA> 8666290886 1050148 8636 8305
2025-09-19 162.52 162.79 160.28 38953 161.855 162.23 163.62 6295321 161.6132 <NA> ... 8292 360557 3069755 17746 14838 <NA> 22703916016 2859088 8138 13950
2025-09-22 163.03 163.09 161.12 27899 161.51 162.85 163.21 4517233 161.9138 <NA> ... <NA> <NA> 910389 8293 6666 <NA> 5441239354 704105 8166 6286
2025-09-23 160.79 163.02 160.44 15740 162.52 160.68 161.02 2542013 161.5002 <NA> ... <NA> <NA> 1038248 7598 6631 <NA> 6629011800 839140 8182 6337
2025-09-24 157.71 160.83 157.6 25138 160.83 157.58 157.92 3997779 159.0333 <NA> ... <NA> <NA> 911907 7245 6249 <NA> 6221761150 790752 8256 5991
2025-09-25 158.01 159.22 157.56 13687 159.21 157.89 158.25 2165748 158.234 <NA> ... <NA> <NA> 3307902 7558 6481 <NA> 5209013622 663078 8234 6209
2025-09-26 156.96 157.9 156.65 17707 157.1 156.83 157.15 2783522 157.199 <NA> ... <NA> <NA> 2373923 7596 6587 <NA> 7331324468 951718 8300 6324
2025-09-29 156.16 157.53 155.83 16320 157.53 156.01 156.35 2554292 156.513 <NA> ... <NA> <NA> 1375895 11409 8669 <NA> 8350496934 1042741 8400 8384
2025-09-30 155.29 157.3 155.11 27077 155.97 155.16 155.5 4220310 155.8633 <NA> ... <NA> <NA> 2057617 13122 11293 <NA> 12319472106 1478392 8516 11030
2025-10-01 152.61 155.27 152.61 33684 154.89 152.47 152.78 5169990 153.485 <NA> ... <NA> <NA> 5143013 17110 15294 <NA> 14711046958 1846364 8636 14960
2025-10-02 151.46 152.21 149.93 62914 152.21 151.29 151.66 9495920 150.9349 <NA> ... <NA> <NA> 2018432 12701 10537 <NA> 10157224236 1196384 8610 10241
2025-10-03 153.0 153.57 150.77 30037 151.04 152.76 153.16 4571567 152.1978 <NA> ... <NA> <NA> 2574018 10482 9362 <NA> 8952745024 1071467 8606 9138
2025-10-06 151.96 152.48 151.115 20566 151.515 151.86 152.17 3121929 151.8005 <NA> ... <NA> <NA> 1015612 9900 7940 <NA> 7602451558 950417 8576 7699
2025-10-07 151.53 152.21 150.58 15948 151.52 151.39 152.12 2414595 151.4043 <NA> ... <NA> <NA> 1142788 8916 7923 <NA> 7797092590 926399 8570 7679
2025-10-08 151.64 151.92 150.97 16321 151.21 151.54 151.89 2473209 151.5354 <NA> ... <NA> <NA> 954279 8099 6406 <NA> 6724566672 827756 8618 6180
2025-10-09 150.08 150.94 149.655 13269 150.94 149.94 150.2 1992302 150.1471 <NA> ... <NA> <NA> 1374761 9489 7695 <NA> 8783813002 1038371 8744 7497
2025-10-10 151.325 152.07 149.55 30173 150.79 151.27 151.62 4559474 151.1111 <NA> ... <NA> <NA> 1620754 13021 11746 <NA> 10363382780 1382945 8780 11413
2025-10-13 152.66 154.11 152.66 15623 152.9 152.4 152.75 2392604 153.1463 <NA> ... <NA> <NA> 2598176 12903 11537 <NA> 9836214274 1217511 8884 11326
2025-10-14 154.68 155.6 153.27 35461 153.52 154.51 154.81 5483428 154.6327 <NA> ... <NA> <NA> 941585 9736 7532 <NA> 7586988594 884951 8822 7393
2025-10-15 159.34 160.61 157.845 33729 157.845 159.19 159.44 5376247 159.3954 <NA> ... <NA> <NA> 1310691 10419 9537 <NA> 10284240220 1269193 8790 9333

20 rows × 51 columns

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ld.close_session()