Columns and types

VARCHAR

ticker
symbol
country
name

TIMESTAMP

date

DOUBLE

Current logical schema for lake.international_stock

Inspect the complete lake.international_stock schema

Column	Type
ticker	VARCHAR
symbol	VARCHAR
country	VARCHAR
name	VARCHAR
date	TIMESTAMP
shares	DOUBLE

Current logical schema for lake.international_stock

Query lake.international_stock 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

Non-US listing metadata: country, shares outstanding, company name. No prices.

Discover covered non-US listing metadata

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.

Inspect the returned country and listing identity before selecting a ticker. The table supplies metadata and shares, not prices. The bounded discovery query avoids assuming that AAPL is a covered non-US listing.

SQL

SELECT ticker, symbol, country, name, date, shares
FROM lake.international_stock
WHERE date >= TIMESTAMP '2026-09-01'
  AND date < TIMESTAMP '2026-09-19'
ORDER BY country, ticker, date
LIMIT 20;

Python SQL workflow ↗TypeScript SQL workflow ↗

Submit the international_stock 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 international_stock 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-international_stock-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

Non-US listing metadata: country, shares outstanding, company name. No prices.

Inspect the returned source dates and nullable fields. For an event-based strategy, use its public availability date to place the observation on the research timeline.

Discussion

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lake.international_stock dataset schema