Columns and types
VARCHAR
- ticker
- fiscalPeriod
- model
TIMESTAMP
- date
DOUBLE
- rating
BIGINT
- fiscalYear
Current logical schema for lake.reports
Inspect the complete lake.reports schema
| Column | Type |
|---|---|
| ticker | VARCHAR |
| date | TIMESTAMP |
| rating | DOUBLE |
| fiscalYear | BIGINT |
| fiscalPeriod | VARCHAR |
| model | VARCHAR |
No matching rows. Clear the filter to see all records.
Current logical schema for lake.reports
Query lake.reports 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
Model-generated analyst ratings with a saved date, fiscal year, fiscal period and model. Query the supported logical table lake.reports.
Inspect a dated model rating
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.
Read rating alongside its model, saved date and fiscal period. This is a model assessment, not a verified company fact or a guaranteed performance filter.
SELECT ticker, date, rating, fiscalYear, fiscalPeriod, model
FROM lake.reports
WHERE ticker = 'AAPL'
AND date >= TIMESTAMP '2026-01-01'
AND date < TIMESTAMP '2026-09-19'
ORDER BY date DESC
LIMIT 20;Submit the reports 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 reports 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-reports-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
Model-generated analyst ratings with a saved date, fiscal year, fiscal period and model. Query the supported logical table lake.reports.
Fiscal labels also exist on the current SEC statement schemas; older shards may have NULL labels. Inspect the saved rating contract before interpreting its numeric scale. Higher values express a stronger model assessment.
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