Columns and types
VARCHAR
- ticker
- symbol
TIMESTAMP
- date
DOUBLE
- openingPrice
- highestPrice
- lowestPrice
- closingPrice
- volume
- marketCap
- peRatioTTM
- psRatioTTM
- pbRatioTTM
Showing 12 of 14 recorded rows. The complete table is below.
Current logical schema for lake.daily_ohlc
Inspect the complete lake.daily_ohlc schema
Scroll sideways to compare all columns. Select a heading to sort.
| ticker | VARCHAR |
| symbol | VARCHAR |
| date | TIMESTAMP |
| openingPrice | DOUBLE |
| highestPrice | DOUBLE |
| lowestPrice | DOUBLE |
| closingPrice | DOUBLE |
| volume | DOUBLE |
| marketCap | DOUBLE |
| peRatioTTM | DOUBLE |
| psRatioTTM | DOUBLE |
| pbRatioTTM | DOUBLE |
| enterpriseValue | DOUBLE |
| dividendYield | DOUBLE |
No matching rows. Clear the filter to see all records.
Current logical schema for lake.daily_ohlc
Query lake.daily_ohlc 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
Legacy/vendor daily OHLC. Prefer sec_daily_ohlc.
Keep this table for compatibility with an existing query. Start a new task with the linked preferred schema.
Bounded daily ohlc preview
Query example, not executed here. Inspect the actual returned rows and dates before treating it as evidence.
This compatibility preview uses the declared fields. Prefer the linked current reference for a new research task.
SELECT "ticker", "symbol", "date", "openingPrice", "highestPrice"
FROM lake.daily_ohlc
WHERE "ticker" IN ('AAPL')
ORDER BY "ticker", "symbol", "date", "openingPrice", "highestPrice"
LIMIT 20;Submit the daily_ohlc 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 daily_ohlc 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.
python3 -m pip install nexustrade==1.42.0
export NEXUSTRADE_API_KEY="YOUR_LAKE_SCOPED_KEY"
python3 query.py
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-daily_ohlc-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)
)
Coverage and timing
Inspect source coverage and timing before using the rows
Legacy/vendor daily OHLC. Prefer sec_daily_ohlc.
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.
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