Fetching custom time ranges¶
You can use the LSEG Data Library for Python to retrieve the stock prices for custom time ranges by passing a start
and end
date to the get_history
function.
The inputs should be datetime.timedelta
objects. The start
argument is how many days before today to start the range, and the end
argument is how many days before today to end the range.
This example retrieves the closing price for the Thomson Reuters stock for the last 365 calendar days:
from datetime import timedelta
ld.get_history(
"TRI.TO",
# Note that this number is negative because it's in the past
start=timedelta(days=-365),
# `end` is set to zero to draw the latest numbers
end=timedelta(days=0),
)
TRI.TO | TRDPRC_1 | HIGH_1 | LOW_1 | ACVOL_UNS | OPEN_PRC | BID | ASK | VWAP | BLKCOUNT | BLKVOLUM | NUM_MOVES | TRD_STATUS | SALTIM | TRNOVR_UNS | NAVALUE | ALT_CLOSE |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Date | ||||||||||||||||
2024-10-17 | 232.45 | 233.34 | 231.75 | 235351 | 231.75 | 231.0 | 235.0 | 232.51805 | 1 | 58400 | 1212 | 1 | 72000 | 54723356.08 | <NA> | 232.45 |
2024-10-18 | 232.76 | 233.58 | 231.85 | 270498 | 232.36 | 232.27 | 233.28 | 232.67492 | 2 | 52100 | 1368 | 1 | 73107 | 62938100.86 | <NA> | 232.76 |
2024-10-21 | 232.18 | 233.86 | 230.63 | 170396 | 231.83 | 228.25 | 233.15 | 231.99079 | <NA> | <NA> | 1382 | 1 | 72000 | 39530302.12 | <NA> | 232.18 |
2024-10-22 | 230.44 | 231.68 | 229.36 | 207597 | 230.8 | 230.21 | 234.0 | 230.39046 | 2 | 40000 | 1277 | 1 | 72000 | 47828369.14 | <NA> | 230.44 |
2024-10-23 | 231.3 | 232.43 | 229.8 | 142508 | 230.43 | 230.0 | 234.0 | 231.37439 | <NA> | <NA> | 1203 | 1 | 72000 | 32972701.62 | <NA> | 231.3 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
2025-10-08 | 211.75 | 212.11 | 210.25 | 634701 | 211.41 | 210.01 | 214.9 | 211.64048 | 6 | 206700 | 2736 | 1 | 73410 | 134328426.24 | <NA> | 211.75 |
2025-10-09 | 210.48 | 211.59 | 209.7 | 292006 | 211.31 | 210.0 | 214.9 | 210.38067 | 1 | 11400 | 2163 | 1 | 72000 | 61432418.13 | <NA> | 210.48 |
2025-10-10 | 212.03 | 213.02 | 209.23 | 474377 | 211.39 | 212.03 | 214.9 | 211.7942 | <NA> | <NA> | 3399 | 1 | 73357 | 100470295.76 | <NA> | 212.03 |
2025-10-14 | 217.14 | 218.44 | 212.78 | 679248 | 213.63 | 216.88 | 218.5 | 217.07122 | 3 | 158100 | 4199 | 1 | 72000 | 147445189.16 | <NA> | 217.14 |
2025-10-15 | 223.78 | 225.5 | 221.49 | 581433 | 224.37 | 223.35 | 224.25 | 223.74637 | <NA> | <NA> | 4221 | 1 | 72000 | 130093524.5 | <NA> | 223.78 |
250 rows × 16 columns