Backtest: Binance OrderBook data

Tutorial for NautilusTrader a high-performance algorithmic trading platform and event driven backtester.

View source on GitHub.

info

We are currently working on this tutorial.

Overview

This tutorial runs through how to set up the data catalog and a BacktestNode to backtest an OrderBookImbalance strategy or order book data. This example requires you bring your own Binance order book data.

Prerequisites

• Python 3.11+ installed
• JupyterLab or similar installed (pip install -U jupyterlab)
• NautilusTrader latest release installed (pip install -U nautilus_trader)

Imports

We'll start with all of our imports for the remainder of this guide:

import os
import shutil
from decimal import Decimal
from pathlib import Path

import pandas as pd

from nautilus_trader.adapters.binance.loaders import BinanceOrderBookDeltaDataLoader
from nautilus_trader.backtest.node import BacktestDataConfig
from nautilus_trader.backtest.node import BacktestEngineConfig
from nautilus_trader.backtest.node import BacktestNode
from nautilus_trader.backtest.node import BacktestRunConfig
from nautilus_trader.backtest.node import BacktestVenueConfig
from nautilus_trader.config import ImportableStrategyConfig
from nautilus_trader.config import LoggingConfig
from nautilus_trader.core.datetime import dt_to_unix_nanos
from nautilus_trader.model import OrderBookDelta
from nautilus_trader.persistence.catalog import ParquetDataCatalog
from nautilus_trader.persistence.wranglers import OrderBookDeltaDataWrangler
from nautilus_trader.test_kit.providers import TestInstrumentProvider

Loading data

# Path to your data directory, using user /Downloads as an example
DATA_DIR = "~/Downloads"

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

# First we'll load the initial order book snapshot
path_snap = data_path / "BTCUSDT_T_DEPTH_2022-11-01_depth_snap.csv"
df_snap = BinanceOrderBookDeltaDataLoader.load(path_snap)
df_snap.head()

# Then we'll load the order book updates, to save time here we're limiting to 1 million rows
path_update = data_path / "BTCUSDT_T_DEPTH_2022-11-01_depth_update.csv"
nrows = 1_000_000
df_update = BinanceOrderBookDeltaDataLoader.load(path_update, nrows=nrows)
df_update.head()

Process deltas using a wrangler

BTCUSDT_BINANCE = TestInstrumentProvider.btcusdt_binance()
wrangler = OrderBookDeltaDataWrangler(BTCUSDT_BINANCE)

deltas = wrangler.process(df_snap)
deltas += wrangler.process(df_update)
deltas.sort(key=lambda x: x.ts_init)  # Ensure data is non-decreasing by `ts_init`
deltas[:10]

Set up data catalog

CATALOG_PATH = os.getcwd() + "/catalog"

# Clear if it already exists, then create fresh
if os.path.exists(CATALOG_PATH):
    shutil.rmtree(CATALOG_PATH)
os.mkdir(CATALOG_PATH)

# Create a catalog instance
catalog = ParquetDataCatalog(CATALOG_PATH)

# Write instrument and ticks to catalog
catalog.write_data([BTCUSDT_BINANCE])
catalog.write_data(deltas)

# Confirm the instrument was written
catalog.instruments()

# Explore the available data in the catalog
start = dt_to_unix_nanos(pd.Timestamp("2022-11-01", tz="UTC"))
end =  dt_to_unix_nanos(pd.Timestamp("2022-11-04", tz="UTC"))

deltas = catalog.order_book_deltas(start=start, end=end)
print(len(deltas))
deltas[:10]

Configure backtest

instrument = catalog.instruments()[0]
book_type = "L2_MBP"  # Ensure data book type matches venue book type

data_configs = [BacktestDataConfig(
        catalog_path=CATALOG_PATH,
        data_cls=OrderBookDelta,
        instrument_id=instrument.id,
        # start_time=start,  # Run across all data
        # end_time=end,  # Run across all data
    )
]

venues_configs = [
    BacktestVenueConfig(
        name="BINANCE",
        oms_type="NETTING",
        account_type="CASH",
        base_currency=None,
        starting_balances=["20 BTC", "100000 USDT"],
        book_type=book_type,  # <-- Venues book type
    )
]

strategies = [
    ImportableStrategyConfig(
        strategy_path="nautilus_trader.examples.strategies.orderbook_imbalance:OrderBookImbalance",
        config_path="nautilus_trader.examples.strategies.orderbook_imbalance:OrderBookImbalanceConfig",
        config={
            "instrument_id": instrument.id,
            "book_type": book_type,
            "max_trade_size": Decimal("1.000"),
            "min_seconds_between_triggers": 1.0,
        },
    ),
]

# NautilusTrader currently exceeds the rate limit for Jupyter notebook logging (stdout output),
# this is why the `log_level` is set to "ERROR". If you lower this level to see
# more logging then the notebook will hang during cell execution. A fix is currently
# being investigated which involves either raising the configured rate limits for
# Jupyter, or throttling the log flushing from Nautilus.
# https://github.com/jupyterlab/jupyterlab/issues/12845
# https://github.com/deshaw/jupyterlab-limit-output
config = BacktestRunConfig(
    engine=BacktestEngineConfig(
        strategies=strategies,
        logging=LoggingConfig(log_level="ERROR"),
    ),
    data=data_configs,
    venues=venues_configs,
)

config

Run the backtest

node = BacktestNode(configs=[config])

result = node.run()

result

from nautilus_trader.backtest.engine import BacktestEngine
from nautilus_trader.model import Venue


engine: BacktestEngine = node.get_engine(config.id)

engine.trader.generate_order_fills_report()

engine.trader.generate_positions_report()

engine.trader.generate_account_report(Venue("BINANCE"))