Blue Ocean ATS now available

We're excited to share that real-time and historical data from Blue Ocean ATS (BOATS) is now available on Databento.

With this launch, Databento's US Equities service now provides 24/5 coverage across 19 datasets sourced directly from Nasdaq, NYSE, Cboe, and other venues.

Blue Ocean ATS is a leading venue for after-hours US equities trading, supporting 24-hour access to US equity markets. The Blue Ocean Session runs from 8:00 PM to 4:00 AM ET, Sunday through Thursday, overlapping with Asian markets and the early European session. For market participants managing risk related to global news, macro events, earnings, or geopolitical developments, overnight price discovery enables faster reactions before US markets open.

Captured directly from the Blue Ocean ATS MEMOIR Depth feed, this dataset offers full order book coverage, including every quote and order at each price level. Users can access the data in L1, L2, or L3 granularity, along with trades, OHLCV, market statistics, and reference data formats. A complete list of supported schemas is available in our catalog.

Blue Ocean data can be requested using its dataset ID: OCEA.MEMOIR.

Historical coverage begins on August 24, 2025. Data is available for usage-based rates starting at $0.40/GB or included with any subscription.

The example below demonstrates how to request one day of trades data using the Python client library.

import databento as db

client = db.Historical("YOUR_API_KEY")

data = client.timeseries.get_range(
    dataset="OCEA.MEMOIR",
    schema="trades",
    symbols=["TQQQ", "SQQQ"],
    start="2026-03-30T00:00:00",
    end="2026-03-30T08:00:00",
)

print(data.to_df(tz="US/Eastern"))

Live data is available with a Plus or Unlimited subscription through a Databento US Equities plan. Visit our pricing page for more details or to upgrade your plan.

The example below demonstrates how to stream MBO (L3) data using the Python client library.

import databento as db

client = db.Live("YOUR_API_KEY")

client.subscribe(
    dataset="OCEA.MEMOIR",
    schema="mbo",
    symbols=["TQQQ", "SQQQ"],
)

client.add_callback(print)
client.start()
client.block_for_close(timeout=10)