Columns and types

VARCHAR

ticker

TIMESTAMP

executionDate

DOUBLE

splitFrom
splitTo

Current logical schema for lake.stock_splits

Inspect the complete lake.stock_splits schema

Column	Type
ticker	VARCHAR
executionDate	TIMESTAMP
splitFrom	DOUBLE
splitTo	DOUBLE

Current logical schema for lake.stock_splits

Query lake.stock_splits using the column names and types below. The query validator and engine use this logical catalog; a field absent from an older shard returns typed NULL.

What this table contains

Split events per ticker: execution date and the from/to ratio.

Inspect recorded split event ratios

Query example, not executed here. The dates and filters define the proposed research scope; returned rows and current coverage must be checked in your workspace.

The ratio records a split event; it does not instruct you to adjust an already adjusted closing series again. Use unadjustedClose when a calculation specifically needs the pre-adjustment price basis.

SQL

SELECT ticker, executionDate, splitFrom, splitTo
FROM lake.stock_splits
WHERE ticker = 'AAPL'
  AND executionDate >= TIMESTAMP '2020-01-01'
  AND executionDate < TIMESTAMP '2021-01-01'
ORDER BY executionDate
LIMIT 20;

Python SQL workflow ↗TypeScript SQL workflow ↗

Submit the stock_splits example and read back its parts

Run the exact SQL through the Python SDK

Save this page's SQL as query.sql and the runner below as query.py. Use a registered key with lake scope from Developers. The runner submits the displayed stock_splits query once, saves its operation, waits on that same ID, saves the returned manifest and downloads its Parquet parts.

This submission has not been executed for the reference page. Inspect the SQL, dates and resource limits before running it. Reuse the request key only for this identical SQL and limits; use a new key when changing them.

Shell

python3 -m pip install nexustrade==1.42.0
export NEXUSTRADE_API_KEY="YOUR_LAKE_SCOPED_KEY"
python3 query.py

Python

import json
from pathlib import Path
from nexustrade import NexusTradeClient

# Save the SQL displayed on this page as query.sql.
sql = Path("query.sql").read_text()
client = NexusTradeClient.from_environment()
operation = client.create_lake_query(
    {"query": sql, "limits": {"timeoutSeconds": 30, "maxRows": 50,
                              "maxResultBytes": 1048576}},
    idempotency_key="reference-stock_splits-20261012-v1",
)
Path("query-operation.json").write_text(json.dumps(operation, indent=2))
finished = client.wait_for_lake_query(operation["id"])
if finished["status"] != "completed":
    raise RuntimeError(finished)
manifest = client.get_lake_query_manifest(operation["id"])["result"]
Path("query-manifest.json").write_text(json.dumps(manifest, indent=2))
print(json.dumps(manifest, indent=2))
for part in manifest["parts"]:
    Path(f"query-{part['part']}.parquet").write_bytes(
        client.download_lake_query_part(operation["id"], part["part"], max_bytes=1048576)
    )

Create a lake-scoped key ↗Read Parquet results with Python ↗

Coverage and timing

Inspect source coverage and timing before using the rows

Split events per ticker: execution date and the from/to ratio.

Do NOT re-apply these onto `sec_daily_ohlc.closingPrice` — that series is already split-adjusted, so applying the ratio again double-counts. Use this table to reason about split events themselves, or alongside `unadjustedClose`.

Discussion

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lake.stock_splits dataset schema