Columns and types

VARCHAR

symbol
asset_class
sector
industry
exchange

VARCHAR[]

industry_tags

BOOLEAN

is_active

DATE

listing_date
delisted_date

Current logical schema for lake.backtest_tickers

Inspect the complete lake.backtest_tickers schema

Filter rows

Scroll sideways to compare all columns. Select a heading to sort.


symbol	VARCHAR
asset_class	VARCHAR
sector	VARCHAR
industry	VARCHAR
industry_tags	VARCHAR[]
exchange	VARCHAR
is_active	BOOLEAN
listing_date	DATE
delisted_date	DATE

Current logical schema for lake.backtest_tickers

Query lake.backtest_tickers 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

Backtest corpus: metadata for each symbol a corpus run traded. Join `symbol` against the `lake.backtest_runs.tickers` list. `industry_tags` is VARCHAR[].

Inspect the symbols traded by recorded runs

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.

Array membership joins a symbol to the run universe. A run can yield several rows, so these are run-symbol observations rather than independent experiment counts.

SQL

WITH eligible_runs AS (
  SELECT *
  FROM lake.backtest_runs
  WHERE author_type <> 'optimizer'
  QUALIFY ROW_NUMBER() OVER (
    PARTITION BY experiment_id
    ORDER BY created_at DESC, backtest_uuid
  ) = 1
)
SELECT r.backtest_uuid, r.start_date, r.end_date, t.symbol,
       t.asset_class, t.sector, t.industry
FROM eligible_runs r
JOIN lake.backtest_tickers t ON list_contains(r.tickers, t.symbol)
ORDER BY r.created_at DESC, r.backtest_uuid, t.symbol
LIMIT 20;

Python SQL workflow ↗TypeScript SQL workflow ↗

Submit the backtest_tickers 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 backtest_tickers 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-backtest_tickers-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

The logical view is backed by canonical manifest-resolved data. Query this name rather than guessing a physical database collection or object-storage path.