Columns and types

VARCHAR

ticker
symbol
form
timing
source
extractionMethod

TIMESTAMP

date
periodEnd

DOUBLE

epsActual
epsEstimate
epsDifference
surprisePercent

Showing 12 of 31 recorded rows. The complete table is below.

Current logical schema for lake.sec_quarterly_earnings

Inspect the complete lake.sec_quarterly_earnings schema

Filter rows

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ticker	VARCHAR
symbol	VARCHAR
date	TIMESTAMP
epsActual	DOUBLE
epsEstimate	DOUBLE
epsDifference	DOUBLE
surprisePercent	DOUBLE
periodEnd	TIMESTAMP
form	VARCHAR
timing	VARCHAR
source	VARCHAR
extractionMethod	VARCHAR
originalFileAccepted	BOOLEAN
confidence	DOUBLE
evidence	VARCHAR
statementAccession	VARCHAR
statementSourceFilingUrl	VARCHAR
filedValueCorrections	VARCHAR
rawDocumentManifestKey	VARCHAR
promptId	VARCHAR
promptVersion	BIGINT
promptHash	VARCHAR
schemaId	VARCHAR
schemaVersion	BIGINT
schemaHash	VARCHAR
extractedAt	TIMESTAMP
cik	BIGINT
accession	VARCHAR
rawArchiveKey	VARCHAR
rawSha256	VARCHAR
sourceFilingUrl	VARCHAR

Current logical schema for lake.sec_quarterly_earnings

Query lake.sec_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

SEC-sourced quarterly EPS: actual, estimate, difference, surprise percent.

Inspect a deduplicated earnings observation

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.

The documented latest extraction rule retains one row per ticker and saved calendar date. Read actual and estimated EPS for the same period; NULL estimates cannot establish an earnings beat.

SQL

SELECT ticker, date, periodEnd, epsActual, epsEstimate, surprisePercent, accession
FROM lake.sec_quarterly_earnings
WHERE ticker = 'AAPL'
  AND date >= TIMESTAMP '2025-01-01'
  AND date < TIMESTAMP '2026-09-19'
QUALIFY ROW_NUMBER() OVER (PARTITION BY ticker, CAST(date AS DATE)
  ORDER BY extractedAt DESC NULLS LAST, accession DESC) = 1
ORDER BY date DESC
LIMIT 20;

Python SQL workflow ↗TypeScript SQL workflow ↗

Submit the sec_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 sec_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-sec_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

SEC-sourced quarterly EPS: actual, estimate, difference, surprise percent.

SEC earnings / EPS rows. The natural key includes `accession`, so dedup before aggregating: `QUALIFY ROW_NUMBER() OVER (PARTITION BY ticker, cast(date as date) ORDER BY extractedAt DESC NULLS LAST, accession DESC) = 1`.

Discussion

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lake.sec_quarterly_earnings dataset schema