Columns and types

VARCHAR

ticker
symbol

TIMESTAMP

date

DOUBLE

epsActual
epsEstimate
epsDifference
surprisePercent

Current logical schema for lake.quarterly_earnings

Inspect the complete lake.quarterly_earnings schema

Column	Type
ticker	VARCHAR
symbol	VARCHAR
date	TIMESTAMP
epsActual	DOUBLE
epsEstimate	DOUBLE
epsDifference	DOUBLE
surprisePercent	DOUBLE

Current logical schema for lake.quarterly_earnings

Query lake.quarterly_earnings 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 quarterly earnings. Prefer sec_quarterly_earnings, which is SEC-sourced.

Keep this table for compatibility with an existing query. Start a new task with the linked preferred schema.

Use the sec_quarterly_earnings reference ↗

Bounded quarterly earnings 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.

SQL

SELECT "ticker", "symbol", "date", "epsActual", "epsEstimate"
FROM lake.quarterly_earnings
WHERE "ticker" IN ('AAPL')
ORDER BY "ticker", "symbol", "date", "epsActual", "epsEstimate"
LIMIT 20;

Python SQL workflow ↗TypeScript SQL workflow ↗

Submit the quarterly_earnings 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 quarterly_earnings 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-quarterly_earnings-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

Legacy/vendor quarterly earnings. Prefer sec_quarterly_earnings, which is SEC-sourced.

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.quarterly_earnings dataset schema