Columns and types

VARCHAR

ticker

TIMESTAMP

timestamp

DOUBLE

open
high
low
close
volume
vwap

BIGINT

transactions

Current logical schema for lake.intraday_stock

Inspect the complete lake.intraday_stock schema

Filter rows

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ticker	VARCHAR
timestamp	TIMESTAMP
open	DOUBLE
high	DOUBLE
low	DOUBLE
close	DOUBLE
volume	DOUBLE
transactions	BIGINT
vwap	DOUBLE

Current logical schema for lake.intraday_stock

Query lake.intraday_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

Minute-level equity bars. Requires an explicit ticker list and a literal `timestamp` range. One object per day, so the range decides what is read and an unbounded query is rejected: e.g. `timestamp >= TIMESTAMP '2026-09-01' AND timestamp < TIMESTAMP '2026-09-19'`. For coverage questions (first/last date on file), bound a window or use lake.sec_daily_ohlc (lake.index_prices for an index).

Bounded intraday stock preview

Query example, not executed here. Inspect the actual returned rows and dates before treating it as evidence.

Read each row at its recorded timestamp. ticker identifies the equity; the displayed literal timestamp window bounds the minute history.

SQL

SELECT "ticker", "timestamp", "open", "high", "low"
FROM lake.intraday_stock
WHERE "ticker" IN ('AAPL')
  AND "timestamp" >= TIMESTAMP '2026-09-01'
  AND "timestamp" < TIMESTAMP '2026-09-19'
ORDER BY "timestamp", "ticker", "open", "high", "low"
LIMIT 20;

Python SQL workflow ↗TypeScript SQL workflow ↗

Submit the intraday_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 intraday_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-intraday_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

Minute bars for equities; `ticker` is the bare symbol (`AAPL`, `SPY`). Always give an explicit `ticker IN (...)` and a bounded `timestamp` range — an unbounded scan reads every shard.

Discussion

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lake.intraday_stock dataset schema