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Databento

PoseiTrader provides an adapter for integrating with the Databento API and Databento Binary Encoding (DBN) format data. As Databento is purely a market data provider, there is no execution client provided - although a sandbox environment with simulated execution could still be set up. It's also possible to match Databento data with Interactive Brokers execution, or to calculate traditional asset class signals for crypto trading.

The capabilities of this adapter include:

  • Loading historical data from DBN files and decoding into Posei objects for backtesting or writing to the data catalog.
  • Requesting historical data which is decoded to Posei objects to support live trading and backtesting.
  • Subscribing to real-time data feeds which are decoded to Posei objects to support live trading and sandbox environments.
tip

Databento currently offers 125 USD in free data credits (historical data only) for new account sign-ups.

With careful requests, this is more than enough for testing and evaluation purposes. We recommend you make use of the /metadata.get_cost endpoint.

Overview​

The adapter implementation takes the databento-rs crate as a dependency, which is the official Rust client library provided by Databento.

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There is no need for an optional extra installation of databento, as the core components of the adapter are compiled as static libraries and linked automatically during the build process.

The following adapter classes are available:

  • DatabentoDataLoader: Loads Databento Binary Encoding (DBN) data from files.
  • DatabentoInstrumentProvider: Integrates with the Databento API (HTTP) to provide latest or historical instrument definitions.
  • DatabentoHistoricalClient: Integrates with the Databento API (HTTP) for historical market data requests.
  • DatabentoLiveClient: Integrates with the Databento API (raw TCP) for subscribing to real-time data feeds.
  • DatabentoDataClient: Provides a LiveMarketDataClient implementation for running a trading node in real time.
info

As with the other integration adapters, most users will simply define a configuration for a live trading node (covered below), and won't need to necessarily work with these lower level components directly.

Examples​

You can find live example scripts here.

Databento documentation​

Databento provides extensive documentation for new users which can be found in the Databento new users guide. We recommend also referring to the Databento documentation in conjunction with this PoseiTrader integration guide.

Databento Binary Encoding (DBN)​

Databento Binary Encoding (DBN) is an extremely fast message encoding and storage format for normalized market data. The DBN specification includes a simple, self-describing metadata header and a fixed set of struct definitions, which enforce a standardized way to normalize market data.

The integration provides a decoder which can convert DBN format data to Posei objects.

The same Rust implemented Posei decoder is used for:

  • Loading and decoding DBN files from disk
  • Decoding historical and live data in real time

Supported schemas​

The following Databento schemas are supported by PoseiTrader:

Databento schema Posei data type
MBO OrderBookDelta
MBP_1 (QuoteTick, Option<TradeTick>)
MBP_10 OrderBookDepth10
BBO_1S QuoteTick
BBO_1M QuoteTick
TBBO (QuoteTick, TradeTick)
TRADES TradeTick
OHLCV_1S Bar
OHLCV_1M Bar
OHLCV_1H Bar
OHLCV_1D Bar
DEFINITION Instrument (various types)
IMBALANCE DatabentoImbalance
STATISTICS DatabentoStatistics
STATUS InstrumentStatus
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See also the Databento Schemas and data formats guide.

Instrument IDs and symbology​

Databento market data includes an instrument_id field which is an integer assigned by either the original source venue, or internally by Databento during normalization.

It's important to realize that this is different to the Posei InstrumentId which is a string made up of a symbol + venue with a period separator i.e. "{symbol}.{venue}".

The Posei decoder will use the Databento raw_symbol for the Posei symbol and an ISO 10383 MIC (Market Identifier Code) from the Databento instrument definition message for the Posei venue.

Databento datasets are identified with a dataset code which is not the same as a venue identifier. You can read more about Databento dataset naming conventions here.

Of particular note is for CME Globex MDP 3.0 data (GLBX.MDP3 dataset code), the following exchanges are all grouped under the GLBX venue. These mappings can be determined from the instruments exchange field:

  • CBCM: XCME-XCBT inter-exchange spread
  • NYUM: XNYM-DUMX inter-exchange spread
  • XCBT: Chicago Board of Trade (CBOT)
  • XCEC: Commodities Exchange Center (COMEX)
  • XCME: Chicago Mercantile Exchange (CME)
  • XFXS: CME FX Link spread
  • XNYM: New York Mercantile Exchange (NYMEX)
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Other venue MICs can be found in the venue field of responses from the metadata.list_publishers endpoint.

Timestamps​

Databento data includes various timestamp fields including (but not limited to):

  • ts_event: The matching-engine-received timestamp expressed as the number of nanoseconds since the UNIX epoch.
  • ts_in_delta: The matching-engine-sending timestamp expressed as the number of nanoseconds before ts_recv.
  • ts_recv: The capture-server-received timestamp expressed as the number of nanoseconds since the UNIX epoch.
  • ts_out: The Databento sending timestamp.

Posei data includes at least two timestamps (required by the Data contract):

  • ts_event: UNIX timestamp (nanoseconds) when the data event occurred
  • ts_init: UNIX timestamp (nanoseconds) when the data object was initialized

When decoding and normalizing Databento to Posei we generally assign the Databento ts_recv value to the Posei ts_event field, as this timestamp is much more reliable and consistent, and is guaranteed to be monotonically increasing per instrument. The exception to this are the DatabentoImbalance and DatabentoStatistics data types, which have fields for all timestamps as these types are defined specifically for the adapter.

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See the following Databento docs for further information:

Data types​

The following section discusses Databento schema -> Posei data type equivalence and considerations.

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See Databento schemas and data formats.

Instrument definitions​

Databento provides a single schema to cover all instrument classes, these are decoded to the appropriate Posei Instrument types.

The following Databento instrument classes are supported by PoseiTrader:

Databento instrument class Code Posei instrument type
Stock K Equity
Future F FuturesContract
Call C OptionContract
Put P OptionContract
Future spread S FuturesSpread
Option spread T OptionSpread
Mixed spread M OptionSpread
FX spot X CurrencyPair
Bond B Not yet available

MBO (market by order)​

This schema is the highest granularity data offered by Databento, and represents full order book depth. Some messages also provide trade information, and so when decoding MBO messages Posei will produce an OrderBookDelta and optionally a TradeTick.

The Posei live data client will buffer MBO messages until an F_LAST flag is seen. A discrete OrderBookDeltas container object will then be passed to the registered handler.

Order book snapshots are also buffered into a discrete OrderBookDeltas container object, which occurs during the replay startup sequence.

MBP-1 (market by price, top-of-book)​

This schema represents the top-of-book only (quotes and trades). Like with MBO messages, some messages carry trade information, and so when decoding MBP-1 messages Posei will produce a QuoteTick and also a TradeTick if the message is a trade.

OHLCV (bar aggregates)​

The Databento bar aggregation messages are timestamped at the open of the bar interval. The Posei decoder will normalize the ts_event timestamps to the close of the bar (original ts_event + bar interval).

Imbalance & Statistics​

The Databento imbalance and statistics schemas cannot be represented as a built-in Posei data types, and so they have specific types defined in Rust DatabentoImbalance and DatabentoStatistics. Python bindings are provided via pyo3 (Rust) so the types behave a little differently to a built-in Posei data types, where all attributes are pyo3 provided objects and not directly compatible with certain methods which may expect a Cython provided type. There are pyo3 -> legacy Cython object conversion methods available, which can be found in the API reference.

Here is a general pattern for converting a pyo3 Price to a Cython Price: 

price = Price.from_raw(pyo3_price.raw, pyo3_price.precision)

Additionally requesting for and subscribing to these data types requires the use of the lower level generic methods for custom data types. The following example subscribes to the imbalance schema for the AAPL.XNAS instrument (Apple Inc trading on the Nasdaq exchange): 

from posei_trader.adapters.databento import DATABENTO_CLIENT_ID
from posei_trader.adapters.databento import DatabentoImbalance
from posei_trader.model import DataType

instrument_id = InstrumentId.from_str("AAPL.XNAS")
self.subscribe_data(
    data_type=DataType(DatabentoImbalance, metadata={"instrument_id": instrument_id}),
    client_id=DATABENTO_CLIENT_ID,
)

Or requesting the previous days statistics schema for the ES.FUT parent symbol (all active E-mini S&P 500 futures contracts on the CME Globex exchange): 

from posei_trader.adapters.databento import DATABENTO_CLIENT_ID
from posei_trader.adapters.databento import DatabentoStatistics
from posei_trader.model import DataType

instrument_id = InstrumentId.from_str("ES.FUT.GLBX")
metadata = {
    "instrument_id": instrument_id,
    "start": "2024-03-06",
}
self.request_data(
    data_type=DataType(DatabentoStatistics, metadata=metadata),
    client_id=DATABENTO_CLIENT_ID,
)

Performance considerations​

When backtesting with Databento DBN data, there are two options:

  • Store the data in DBN (.dbn.zst) format files and decode to Posei objects on every run
  • Convert the DBN files to Posei objects and then write to the data catalog once (stored as Posei Parquet format on disk)

Whilst the DBN -> Posei decoder is implemented in Rust and has been optimized, the best performance for backtesting will be achieved by writing the Posei objects to the data catalog, which performs the decoding step once.

DataFusion provides a query engine backend to efficiently load and stream the Posei Parquet data from disk, which achieves extremely high through-put (at least an order of magnitude faster than converting DBN -> Posei on the fly for every backtest run).

note

Performance benchmarks are currently under development.

Loading DBN data​

You can load DBN files and convert the records to Posei objects using the DatabentoDataLoader class. There are two main purposes for doing so:

  • Pass the converted data to BacktestEngine.add_data directly for backtesting.
  • Pass the converted data to ParquetDataCatalog.write_data for later streaming use with a BacktestNode.

DBN data to a BacktestEngine​

This code snippet demonstrates how to load DBN data and pass to a BacktestEngine. Since the BacktestEngine needs an instrument added, we'll use a test instrument provided by the TestInstrumentProvider (you could also pass an instrument object which was parsed from a DBN file too). The data is a month of TSLA (Tesla Inc) trades on the Nasdaq exchange: 

# Add instrument
TSLA_NASDAQ = TestInstrumentProvider.equity(symbol="TSLA")
engine.add_instrument(TSLA_NASDAQ)

# Decode data to legacy Cython objects
loader = DatabentoDataLoader()
trades = loader.from_dbn_file(
    path=TEST_DATA_DIR / "databento" / "temp" / "tsla-xnas-20240107-20240206.trades.dbn.zst",
    instrument_id=TSLA_NASDAQ.id,
)

# Add data
engine.add_data(trades)

DBN data to a ParquetDataCatalog​

This code snippet demonstrates how to load DBN data and write to a ParquetDataCatalog. We pass a value of false for the as_legacy_cython flag, which will ensure the DBN records are decoded as pyo3 (Rust) objects. It's worth noting that legacy Cython objects can also be passed to write_data, but these need to be converted back to pyo3 objects under the hood (so passing pyo3 objects is an optimization). 

# Initialize the catalog interface
# (will use the `NAUTILUS_PATH` env var as the path)
catalog = ParquetDataCatalog.from_env()

instrument_id = InstrumentId.from_str("TSLA.XNAS")

# Decode data to pyo3 objects
loader = DatabentoDataLoader()
trades = loader.from_dbn_file(
    path=TEST_DATA_DIR / "databento" / "temp" / "tsla-xnas-20240107-20240206.trades.dbn.zst",
    instrument_id=instrument_id,
    as_legacy_cython=False,  # This is an optimization for writing to the catalog
)

# Write data
catalog.write_data(trades)
info

See also the Data concepts guide.

Real-time client architecture​

The DatabentoDataClient is a Python class which contains other Databento adapter classes. There are two DatabentoLiveClients per Databento dataset:

  • One for MBO (order book deltas) real-time feeds
  • One for all other real-time feeds
warning

There is currently a limitation that all MBO (order book deltas) subscriptions for a dataset have to be made at node startup, to then be able to replay data from the beginning of the session. If subsequent subscriptions arrive after start, then an error will be logged (and the subscription ignored).

There is no such limitation for any of the other Databento schemas.

A single DatabentoHistoricalClient instance is reused between the DatabentoInstrumentProvider and DatabentoDataClient, which makes historical instrument definitions and data requests.

Configuration​

The most common use case is to configure a live TradingNode to include a Databento data client. To achieve this, add a DATABENTO section to your client configuration(s): 

from posei_trader.adapters.databento import DATABENTO
from posei_trader.live.node import TradingNode

config = TradingNodeConfig(
    ...,  # Omitted
    data_clients={
        DATABENTO: {
            "api_key": None,  # 'DATABENTO_API_KEY' env var
            "http_gateway": None,  # Override for the default HTTP historical gateway
            "live_gateway": None,  # Override for the default raw TCP real-time gateway
            "instrument_provider": InstrumentProviderConfig(load_all=True),
            "instrument_ids": None,  # Posei instrument IDs to load on start
            "parent_symbols": None,  # Databento parent symbols to load on start
        },
    },
    ..., # Omitted
)

Then, create a TradingNode and add the client factory: 

from posei_trader.adapters.databento.factories import DatabentoLiveDataClientFactory
from posei_trader.live.node import TradingNode

# Instantiate the live trading node with a configuration
node = TradingNode(config=config)

# Register the client factory with the node
node.add_data_client_factory(DATABENTO, DatabentoLiveDataClientFactory)

# Finally build the node
node.build()

Configuration parameters​

  • api_key: The Databento API secret key. If None then will source the DATABENTO_API_KEY environment variable.
  • http_gateway: The historical HTTP client gateway override (useful for testing and typically not needed by most users).
  • live_gateway: The raw TCP real-time client gateway override (useful for testing and typically not needed by most users).
  • parent_symbols: The Databento parent symbols to subscribe to instrument definitions for on start. This is a map of Databento dataset keys -> to a sequence of the parent symbols, e.g. {'GLBX.MDP3', ['ES.FUT', 'ES.OPT']} (for all E-mini S&P 500 futures and options products).
  • instrument_ids: The instrument IDs to request instrument definitions for on start.
  • timeout_initial_load: The timeout (seconds) to wait for instruments to load (concurrently per dataset).
  • mbo_subscriptions_delay: The timeout (seconds) to wait for MBO/L3 subscriptions (concurrently per dataset). After the timeout the MBO order book feed will start and replay messages from the initial snapshot and then all deltas.
tip

We recommend using environment variables to manage your credentials.

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