Version: latest

Data

The NautilusTrader platform provides a set of built-in data types specifically designed to represent a trading domain. These data types include:

OrderBookDelta (L1/L2/L3): Represents the most granular order book updates.
OrderBookDeltas (L1/L2/L3): Batches multiple order book deltas for more efficient processing.
OrderBookDepth10: Aggregated order book snapshot (up to 10 levels per bid and ask side).
QuoteTick: Represents the best bid and ask prices along with their sizes at the top-of-book.
TradeTick: A single trade/match event between counterparties.
Bar: OHLCV (Open, High, Low, Close, Volume) bar/candle, aggregated using a specified aggregation method.
InstrumentStatus: An instrument-level status event.
InstrumentClose: The closing price of an instrument.

NautilusTrader is designed primarily to operate on granular order book data, providing the highest realism for execution simulations in backtesting. However, backtests can also be conducted on any of the supported market data types, depending on the desired simulation fidelity.

Order books

A high-performance order book implemented in Rust is available to maintain order book state based on provided data.

OrderBook instances are maintained per instrument for both backtesting and live trading, with the following book types available:

L3_MBO: Market by order (MBO) or L3 data, uses every order book event at every price level, keyed by order ID.
L2_MBP: Market by price (MBP) or L2 data, aggregates order book events by price level.
L1_MBP: Market by price (MBP) or L1 data, also known as best bid and offer (BBO), captures only top-level updates.

note

Top-of-book data, such as QuoteTick, TradeTick and Bar, can also be used for backtesting, with markets operating on L1_MBP book types.

Instruments

The following instrument definitions are available:

Betting: Represents an instrument in a betting market.
BinaryOption: Represents a generic binary option instrument.
Cfd: Represents a Contract for Difference (CFD) instrument.
Commodity: Represents a commodity instrument in a spot/cash market.
CryptoFuture: Represents a deliverable futures contract instrument, with crypto assets as underlying and for settlement.
CryptoPerpetual: Represents a crypto perpetual futures contract instrument (a.k.a. perpetual swap).
CurrencyPair: Represents a generic currency pair instrument in a spot/cash market.
Equity: Represents a generic equity instrument.
FuturesContract: Represents a generic deliverable futures contract instrument.
FuturesSpread: Represents a generic deliverable futures spread instrument.
Index: Represents a generic index instrument.
OptionContract: Represents a generic option contract instrument.
OptionSpread: Represents a generic option spread instrument.
Synthetic: Represents a synthetic instrument with prices derived from component instruments using a formula.

Bars and aggregation

A bar—also known as a candle, candlestick, or kline—is a data structure that represents price and volume information over a specific period, including the opening price, highest price, lowest price, closing price, and traded volume (or ticks as a volume proxy). These values are generated using an aggregation method, which groups data based on specific criteria to create the bar.

The implemented aggregation methods are:

Name	Description	Category
`TICK`	Aggregation of a number of ticks.	Threshold
`TICK_IMBALANCE`	Aggregation of the buy/sell imbalance of ticks.	Threshold
`TICK_RUNS`	Aggregation of sequential buy/sell runs of ticks.	Information
`VOLUME`	Aggregation of traded volume.	Threshold
`VOLUME_IMBALANCE`	Aggregation of the buy/sell imbalance of traded volume.	Threshold
`VOLUME_RUNS`	Aggregation of sequential runs of buy/sell traded volume.	Information
`VALUE`	Aggregation of the notional value of trades (also known as "Dollar bars").	Threshold
`VALUE_IMBALANCE`	Aggregation of the buy/sell imbalance of trading by notional value.	Information
`VALUE_RUNS`	Aggregation of sequential buy/sell runs of trading by notional value.	Threshold
`MILLISECOND`	Aggregation of time intervals with millisecond granularity.	Time
`SECOND`	Aggregation of time intervals with second granularity.	Time
`MINUTE`	Aggregation of time intervals with minute granularity.	Time
`HOUR`	Aggregation of time intervals with hour granularity.	Time
`DAY`	Aggregation of time intervals with day granularity.	Time
`WEEK`	Aggregation of time intervals with week granularity.	Time
`MONTH`	Aggregation of time intervals with month granularity.	Time

Bar types

NautilusTrader defines a unique bar type (BarType) based on the following components:

Instrument ID (InstrumentId): Specifies the particular instrument for the bar.
Bar Specification (BarSpecification):
- step: Defines the interval or frequency of each bar.
- aggregation: Specifies the method used for data aggregation (see the above table).
- price_type: Indicates the price basis of the bar (e.g., bid, ask, mid, last).
Aggregation Source (AggregationSource): Indicates whether the bar was aggregated internally (within Nautilus) or externally (by a trading venue or data provider).

Bar data aggregation can be either internal or external:

INTERNAL: The bar is aggregated inside the local Nautilus system boundary.
EXTERNAL: The bar is aggregated outside the local Nautilus system boundary (typically by a trading venue or data provider).

Bar types can also be classified as either standard or composite:

Standard: Generated from granular market data, such as quotes or trades.
Composite: Derived from a higher-granularity bar type through subsampling.

Defining standard bars

You can define bar types from strings using the following convention:

{instrument_id}-{step}-{aggregation}-{price_type}-{INTERNAL | EXTERNAL}

For example, to define a BarType for AAPL trades (last price) on Nasdaq (XNAS) using a 5-minute interval, aggregated from trades locally by Nautilus:

bar_type = BarType.from_str("AAPL.XNAS-5-MINUTE-LAST-INTERNAL")

Defining composite bars

Composite bars are derived by aggregating higher-granularity bars into the desired bar type. To define a composite bar, use a similar convention to standard bars:

{instrument_id}-{step}-{aggregation}-{price_type}-INTERNAL@{step}-{aggregation}-{INTERNAL | EXTERNAL}

Notes:

The derived bar type must use an INTERNAL aggregation source (since this is how the bar is aggregated).
The sampled bar type must have a higher granularity than the derived bar type.
The sampled instrument ID is inferred to match that of the derived bar type.
Composite bars can be aggregated from INTERNAL or EXTERNAL aggregation sources.

For example, to define a BarType for AAPL trades (last price) on Nasdaq (XNAS) using a 5-minute interval, aggregated locally by Nautilus, from 1-minute interval bars aggregated externally:

bar_type = BarType.from_str("AAPL.XNAS-5-MINUTE-LAST-INTERNAL@1-MINUTE-EXTERNAL")

Timestamps

The platform uses two fundamental timestamp fields that appear across many objects, including market data, orders, and events. These timestamps serve distinct purposes and help maintain precise timing information throughout the system:

ts_event: UNIX timestamp (nanoseconds) representing when an event actually occurred.
ts_init: UNIX timestamp (nanoseconds) representing when Nautilus created the internal object representing that event.

Examples

Event Type	`ts_event`	`ts_init`
`TradeTick`	Time when trade occurred at the exchange.	Time when Nautilus received the trade data.
`QuoteTick`	Time when quote was created at the exchange.	Time when Nautilus received the quote data.
`OrderBookDelta`	Time when order book update occurred at the exchange.	Time when Nautilus received the order book update.
`Bar`	Time of the bar's closing (exact minute/hour).	Time when Nautilus generated (for internal bars) or received the bar data (for external bars).
`OrderFilled`	Time when order was filled at the exchange.	Time when Nautilus received and processed the fill confirmation.
`OrderCanceled`	Time when cancellation was processed at the exchange.	Time when Nautilus received and processed the cancellation confirmation.
`NewsEvent`	Time when the news was published.	Time when the event object was created (if internal event) or received (if external event) in Nautilus.
Custom event	Time when event conditions actually occurred.	Time when the event object was created (if internal event) or received (if external event) in Nautilus.

note

The ts_init field represents a more general concept than just the "time of reception" for events. It denotes the timestamp when an object, such as a data point or command, was initialized within Nautilus. This distinction is important because ts_init is not exclusive to "received events" — it applies to any internal initialization process.

For example, the ts_init field is also used for commands, where the concept of reception does not apply. This broader definition ensures consistent handling of initialization timestamps across various object types in the system.

Latency analysis

The dual timestamp system enables latency analysis within the platform:

Latency can be calculated as ts_init - ts_event.
This difference represents total system latency, including network transmission time, processing overhead, and any queueing delays.
It's important to remember that the clocks producing these timestamps are likely not synchronized.

Environment specific behavior

Backtesting environment

Data is ordered by ts_init using a stable sort.
This behavior ensures deterministic processing order and simulates realistic system behavior, including latencies.

Live trading environment

Data is processed as it arrives, ensuring minimal latency and allowing for real-time decision-making.
- ts_init field records the exact moment when data is received by Nautilus in real-time.
- ts_event reflects the time the event occurred externally, enabling accurate comparisons between external event timing and system reception.
We can use the difference between ts_init and ts_event to detect network or processing delays.

Other notes and considerations

For data from external sources, ts_init is always the same as or later than ts_event.
For data created within Nautilus, ts_init and ts_event can be the same because the object is initialized at the same time the event happens.
Not every type with a ts_init field necessarily has a ts_event field. This reflects cases where:
- The initialization of an object happens at the same time as the event itself.
- The concept of an external event time does not apply.

Persisted Data

The ts_init field indicates when the message was originally received.

Data flow

The platform ensures consistency by flowing data through the same pathways across all system environment contexts (e.g., backtest, sandbox, live). Data is primarily transported via the MessageBus to the DataEngine and then distributed to subscribed or registered handlers.

For users who need more flexibility, the platform also supports the creation of custom data types. For details on how to implement user-defined data types, refer to the advanced Custom data guide.

Loading data

NautilusTrader facilitates data loading and conversion for three main use cases:

Providing data for a BacktestEngine to run backtests.
Persisting the Nautilus-specific Parquet format for the data catalog via ParquetDataCatalog.write_data(...) to be later used with a BacktestNode.
For research purposes (to ensure data is consistent between research and backtesting).

Regardless of the destination, the process remains the same: converting diverse external data formats into Nautilus data structures.

To achieve this, two main components are necessary:

A type of DataLoader (normally specific per raw source/format) which can read the data and return a pd.DataFrame with the correct schema for the desired Nautilus object
A type of DataWrangler (specific per data type) which takes this pd.DataFrame and returns a list[Data] of Nautilus objects

Data loaders

Data loader components are typically specific for the raw source/format and per integration. For instance, Binance order book data is stored in its raw CSV file form with an entirely different format to Databento Binary Encoding (DBN) files.

Data wranglers

Data wranglers are implemented per specific Nautilus data type, and can be found in the nautilus_trader.persistence.wranglers module. Currently there exists:

OrderBookDeltaDataWrangler
QuoteTickDataWrangler
TradeTickDataWrangler
BarDataWrangler

warning

At the risk of causing confusion, there are also a growing number of DataWrangler v2 components, which will take a pd.DataFrame typically with a different fixed width Nautilus Arrow v2 schema, and output PyO3 Nautilus objects which are only compatible with the new version of the Nautilus core, currently in development.

These PyO3 provided data objects are not compatible where the legacy Cython objects are currently used (e.g., adding directly to a BacktestEngine).

Transformation pipeline

Process flow:

Raw data (e.g., CSV) is input into the pipeline.
DataLoader processes the raw data and converts it into a pd.DataFrame.
DataWrangler further processes the pd.DataFrame to generate a list of Nautilus objects.
The Nautilus list[Data] is the output of the data loading process.

The following diagram illustrates how raw data is transformed into Nautilus data structures:

  ┌──────────┐    ┌──────────────────────┐                  ┌──────────────────────┐
  │          │    │                      │                  │                      │
  │          │    │                      │                  │                      │
  │ Raw data │    │                      │  `pd.DataFrame`  │                      │
  │ (CSV)    ├───►│      DataLoader      ├─────────────────►│     DataWrangler     ├───► Nautilus `list[Data]`
  │          │    │                      │                  │                      │
  │          │    │                      │                  │                      │
  │          │    │                      │                  │                      │
  └──────────┘    └──────────────────────┘                  └──────────────────────┘

Conceretely, this would involve:

BinanceOrderBookDeltaDataLoader.load(...) which reads CSV files provided by Binance from disk, and returns a pd.DataFrame.
OrderBookDeltaDataWrangler.process(...) which takes the pd.DataFrame and returns list[OrderBookDelta].

The following example shows how to accomplish the above in Python:

from nautilus_trader import TEST_DATA_DIR
from nautilus_trader.adapters.binance.loaders import BinanceOrderBookDeltaDataLoader
from nautilus_trader.persistence.wranglers import OrderBookDeltaDataWrangler
from nautilus_trader.test_kit.providers import TestInstrumentProvider


# Load raw data
data_path = TEST_DATA_DIR / "binance" / "btcusdt-depth-snap.csv"
df = BinanceOrderBookDeltaDataLoader.load(data_path)

# Set up a wrangler
instrument = TestInstrumentProvider.btcusdt_binance()
wrangler = OrderBookDeltaDataWrangler(instrument)

# Process to a list `OrderBookDelta` Nautilus objects
deltas = wrangler.process(df)

Data catalog

The data catalog is a central store for Nautilus data, persisted in the Parquet file format.

We have chosen Parquet as the storage format for the following reasons:

It performs much better than CSV/JSON/HDF5/etc in terms of compression ratio (storage size) and read performance.
It does not require any separate running components (for example a database).
It is quick and simple to get up and running with.

The Arrow schemas used for the Parquet format are either single sourced in the core persistence Rust crate, or available from the /serialization/arrow/schema.py module.

note

2023-10-14: The current plan is to eventually phase out the Python schemas module, so that all schemas are single sourced in the Rust core.

Initializing

The data catalog can be initialized from a NAUTILUS_PATH environment variable, or by explicitly passing in a path like object.

The following example shows how to initialize a data catalog where there is pre-existing data already written to disk at the given path.

from pathlib import Path
from nautilus_trader.persistence.catalog import ParquetDataCatalog


CATALOG_PATH = Path.cwd() / "catalog"

# Create a new catalog instance
catalog = ParquetDataCatalog(CATALOG_PATH)

Writing data

New data can be stored in the catalog, which is effectively writing the given data to disk in the Nautilus-specific Parquet format. All Nautilus built-in Data objects are supported, and any data which inherits from Data can be written.

The following example shows the above list of Binance OrderBookDelta objects being written:

catalog.write_data(deltas)

Basename template

Nautilus makes no assumptions about how data may be partitioned between files for a particular data type and instrument ID.

The basename_template keyword argument is an additional optional naming component for the output files. The template should include placeholders that will be filled in with actual values at runtime. These values can be automatically derived from the data or provided as additional keyword arguments.

For example, using a basename template like "{date}" for AUD/USD.SIM quote tick data, and assuming "date" is a provided or derivable field, could result in a filename like "2023-01-01.parquet" under the "quote_tick/audusd.sim/" catalog directory. If not provided, a default naming scheme will be applied. This parameter should be specified as a keyword argument, like write_data(data, basename_template="{date}").

warning

Any data which already exists under a filename will be overwritten. If a basename_template is not provided, then its very likely existing data for the data type and instrument ID will be overwritten. To prevent data loss, ensure that the basename_template (or the default naming scheme) generates unique filenames for different data sets.

Rust Arrow schema implementations are available for the follow data types (enhanced performance):

OrderBookDelta
QuoteTick
TradeTick
Bar

Reading data

Any stored data can then be read back into memory:

from nautilus_trader.core.datetime import dt_to_unix_nanos
import pandas as pd


start = dt_to_unix_nanos(pd.Timestamp("2020-01-03", tz=pytz.utc))
end =  dt_to_unix_nanos(pd.Timestamp("2020-01-04", tz=pytz.utc))

deltas = catalog.order_book_deltas(instrument_ids=[instrument.id.value], start=start, end=end)

Streaming data

When running backtests in streaming mode with a BacktestNode, the data catalog can be used to stream the data in batches.

The following example shows how to achieve this by initializing a BacktestDataConfig configuration object:

from nautilus_trader.config import BacktestDataConfig
from nautilus_trader.model import OrderBookDelta


data_config = BacktestDataConfig(
    catalog_path=str(catalog.path),
    data_cls=OrderBookDelta,
    instrument_id=instrument.id,
    start_time=start,
    end_time=end,
)

This configuration object can then be passed into a BacktestRunConfig and then in turn passed into a BacktestNode as part of a run. See the Backtest (high-level API) tutorial for further details.

Data migrations

NautilusTrader defines an internal data format specified in the nautilus_model crate. These models are serialized into Arrow record batches and written to Parquet files. Nautilus backtesting is most efficient when using these Nautilus-format Parquet files.

However, migrating the data model between precision modes and schema changes can be challenging. This guide explains how to handle data migrations using our utility tools.

Migration tools

The nautilus_persistence crate provides two key utilities:

`to_json`

Converts Parquet files to JSON while preserving metadata:

Creates two files:
- <input>.json: Contains the deserialized data
- <input>.metadata.json: Contains schema metadata and row group configuration
Automatically detects data type from filename:
- OrderBookDelta (contains "deltas" or "order_book_delta")
- QuoteTick (contains "quotes" or "quote_tick")
- TradeTick (contains "trades" or "trade_tick")
- Bar (contains "bars")

`to_parquet`

Converts JSON back to Parquet format:

Reads both the data JSON and metadata JSON files
Preserves row group sizes from original metadata
Uses ZSTD compression
Creates <input>.parquet

Migration Process

The following migration examples both use trades data (you can also migrate the other data types in the same way). All commands should be run from the root of the nautilus_core/persistence/ crate directory.

Migrating from standard-precision (64-bit) to high-precision (128-bit)

This example describes a scenario where you want to migrate from standard-precision schema to high-precision schema.

note

If you're migrating from a catalog that used the Int64 and UInt64 Arrow data types for prices and sizes, be sure to check out commit e284162 before compiling the code that writes the initial JSON.

1. Convert from standard-precision Parquet to JSON:

cargo run --bin to_json trades.parquet

This will create trades.json and trades.metadata.json files.

2. Convert from JSON to high-precision Parquet:

Add the --features high-precision flag to write data as high-precision (128-bit) schema Parquet.

cargo run --features high-precision --bin to_parquet trades.json

This will create a trades.parquet file with high-precision schema data.

Migrating schema changes

This example describes a scenario where you want to migrate from one schema version to another.

1. Convert from old schema Parquet to JSON:

Add the --features high-precision flag if the source data uses a high-precision (128-bit) schema.

cargo run --bin to_json trades.parquet

This will create trades.json and trades.metadata.json files.

2. Switch to new schema version:

git checkout <new-version>

3. Convert from JSON back to new schema Parquet:

cargo run --features high-precision --bin to_parquet trades.json

This will create a trades.parquet file with the new schema.

Best Practices

Always test migrations with a small dataset first.
Maintain backups of original files.
Verify data integrity after migration.
Perform migrations in a staging environment before applying them to production data.

Order books​

Instruments​

Bars and aggregation​

Bar types​

Defining standard bars​

Defining composite bars​

Timestamps​

Examples​

Latency analysis​

Environment specific behavior​

Backtesting environment​

Live trading environment​

Other notes and considerations​

Persisted Data​

Data flow​

Loading data​

Data loaders​

Data wranglers​

Transformation pipeline​

Data catalog​

Initializing​

Writing data​

Basename template​

Reading data​

Streaming data​

Data migrations​

Migration tools​

to_json​

to_parquet​

Migration Process​

Migrating from standard-precision (64-bit) to high-precision (128-bit)​

Migrating schema changes​

Best Practices​

Order books

Instruments

Bars and aggregation

Bar types

Defining standard bars

Defining composite bars

Timestamps

Examples

Latency analysis

Environment specific behavior

Backtesting environment

Live trading environment

Other notes and considerations

Persisted Data

Data flow

Loading data

Data loaders

Data wranglers

Transformation pipeline

Data catalog

Initializing

Writing data

Basename template

Reading data

Streaming data

Data migrations

Migration tools

`to_json`

`to_parquet`

Migration Process

Migrating from standard-precision (64-bit) to high-precision (128-bit)

Migrating schema changes

Best Practices