Backtest (high-level API)

This tutorial walks through how to use a BacktestNode to backtest a simple EMA cross strategy on a simulated FX ECN venue using historical quote tick data.

The following points will be covered:

  • How to load raw data (external to Nautilus) into the data catalog

  • How to setup configuration objects for a BacktestNode

  • How to run backtests with a BacktestNode


We’ll start with all of our imports for the remainder of this tutorial:

import datetime
import shutil
from decimal import Decimal
from pathlib import Path

import fsspec
import pandas as pd

from nautilus_trader.backtest.node import BacktestNode, BacktestVenueConfig, BacktestDataConfig, BacktestRunConfig, BacktestEngineConfig
from nautilus_trader.core.datetime import dt_to_unix_nanos
from nautilus_trader.config import ImportableStrategyConfig
from import QuoteTick
from nautilus_trader.model.objects import Price, Quantity
from nautilus_trader.persistence.catalog import ParquetDataCatalog
from nautilus_trader.persistence.wranglers import QuoteTickDataWrangler
from nautilus_trader.test_kit.providers import CSVTickDataLoader
from nautilus_trader.test_kit.providers import TestInstrumentProvider

Getting raw data

As a once off before we start the notebook - we need to download some sample data for backtesting.

For this example we will use FX data from . Simply go to and select an FX pair, then select one or more months of data to download.

Once you have downloaded the data, set the variable DATA_DIR below to the directory containing the data. By default, it will use the users Downloads directory.

DATA_DIR = "~/Downloads/"

Then place the data archive into a /"HISTDATA" directory and run the cell below; you should see the files that you downloaded:

path = Path(DATA_DIR).expanduser() / "HISTDATA"
raw_files = list(path.iterdir())
assert raw_files, f"Unable to find any histdata files in directory {path}"

Loading data into the Data Catalog

The FX data from histdata is stored in CSV/text format, with fields timestamp, bid_price, ask_price . Firstly, we need to load this raw data into a pandas.DataFrame which has a compatible schema for Nautilus quote ticks.

Then we can create Nautilus QuoteTick objects by processing the DataFrame with a QuoteTickDataWrangler .

# Here we just take the first data file found and load into a pandas DataFrame
df = CSVTickDataLoader.load(raw_files[0], index_col=0, format="%Y%m%d %H%M%S%f")
df.columns = ["bid_price", "ask_price"]

# Process quote ticks using a wrangler
EURUSD = TestInstrumentProvider.default_fx_ccy("EUR/USD")
wrangler = QuoteTickDataWrangler(EURUSD)

ticks = wrangler.process(df)

See the Loading data guide for more details.

Next, we simply instantiate a ParquetDataCatalog (passing in a directory where to store the data, by default we will just use the current directory). We can then write the instrument and tick data to the catalog, it should only take a couple of minutes to load the data (depending on how many months).

CATALOG_PATH = Path.cwd() / "catalog"

# Clear if it already exists, then create fresh
if CATALOG_PATH.exists():

# Create a catalog instance
catalog = ParquetDataCatalog(CATALOG_PATH)
# Write instrument to the catalog

# Write ticks to catalog

Using the Data Catalog

Once data has been loaded into the catalog, the catalog instance can be used for loading data for backtests, or simply for research purposes. It contains various methods to pull data from the catalog, such as .instruments(...) and quote_ticks(...) (shown below).

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

catalog.quote_ticks(instrument_ids=[], start=start, end=end)

See the Data catalog guide for more details.

Configuring backtests

Nautilus uses a BacktestRunConfig object, which allows configuring a backtest in one place. It is a Partialable object (which means it can be configured in stages); the benefits of which are reduced boilerplate code when creating multiple backtest runs (for example when doing some sort of grid search over parameters).

Adding data and venues

We can now use configuration objects to build up our final run configuration:

instrument = catalog.instruments()[0]

venue_configs = [
        starting_balances=["1_000_000 USD"],

data_configs = [

strategies = [

config = BacktestRunConfig(

Run the backtest!

Now we can simply run the backtest node, which will simulate trading across the entire data stream:

node = BacktestNode(configs=[config])

results =