Backtest (low-level API)

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

View source on GitHub.

Overview

This tutorial walks through how to use a BacktestEngine to backtest a simple EMA cross strategy with a TWAP execution algorithm on a simulated Binance Spot exchange using historical trade tick data.

The following points will be covered:

• Load raw data (external to Nautilus) using data loaders and wranglers.
• Add this data to a BacktestEngine.
• Add venues, strategies, and execution algorithms to a BacktestEngine.
• Run backtests with a BacktestEngine.
• Perform post-run analysis and repeated runs.

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 tutorial.

from decimal import Decimal

from nautilus_trader.backtest.config import BacktestEngineConfig
from nautilus_trader.backtest.engine import BacktestEngine
from nautilus_trader.examples.algorithms.twap import TWAPExecAlgorithm
from nautilus_trader.examples.strategies.ema_cross_twap import EMACrossTWAP
from nautilus_trader.examples.strategies.ema_cross_twap import EMACrossTWAPConfig
from nautilus_trader.model import BarType
from nautilus_trader.model import Money
from nautilus_trader.model import TraderId
from nautilus_trader.model import Venue
from nautilus_trader.model.currencies import ETH
from nautilus_trader.model.currencies import USDT
from nautilus_trader.model.enums import AccountType
from nautilus_trader.model.enums import OmsType
from nautilus_trader.persistence.wranglers import TradeTickDataWrangler
from nautilus_trader.test_kit.providers import TestDataProvider
from nautilus_trader.test_kit.providers import TestInstrumentProvider

Loading data

For this tutorial we use stub test data from the NautilusTrader repository (the automated test suite also uses this data to verify platform correctness).

First, instantiate a data provider to read raw CSV trade tick data into memory as a pd.DataFrame. Next, initialize the instrument that matches the data (in this case the ETHUSDT spot cryptocurrency pair for Binance) and reuse it for the remainder of the backtest run.

Then wrangle the data into a list of Nautilus TradeTick objects so you can add them to the BacktestEngine.

# Load stub test data
provider = TestDataProvider()
trades_df = provider.read_csv_ticks("binance/ethusdt-trades.csv")

# Initialize the instrument which matches the data
ETHUSDT_BINANCE = TestInstrumentProvider.ethusdt_binance()

# Process into Nautilus objects
wrangler = TradeTickDataWrangler(instrument=ETHUSDT_BINANCE)
ticks = wrangler.process(trades_df)

See the Loading External Data guide for a more detailed explanation of the typical data processing components and pipeline.

Initialize a backtest engine

Create a backtest engine. You can call BacktestEngine() to instantiate an engine with the default configuration.

We also initialize a BacktestEngineConfig (with only a custom trader_id specified) to illustrate the general configuration pattern.

See the Configuration API reference for details of all configuration options available.

# Configure backtest engine
config = BacktestEngineConfig(trader_id=TraderId("BACKTESTER-001"))

# Build the backtest engine
engine = BacktestEngine(config=config)

Add venues

Create a venue to trade on that matches the market data you add to the engine.

In this case we set up a simulated Binance Spot exchange.

# Add a trading venue (multiple venues possible)
BINANCE = Venue("BINANCE")
engine.add_venue(
    venue=BINANCE,
    oms_type=OmsType.NETTING,
    account_type=AccountType.CASH,  # Spot CASH account (not for perpetuals or futures)
    base_currency=None,  # Multi-currency account
    starting_balances=[Money(1_000_000.0, USDT), Money(10.0, ETH)],
)

Add data

Add data to the backtest engine. Start by adding the Instrument object we initialized earlier to match the data.

Then add the trades we wrangled earlier.

# Add instrument(s)
engine.add_instrument(ETHUSDT_BINANCE)

# Add data
engine.add_data(ticks)

note

Machine resources and your imagination limit the amount and variety of data types you can use (custom types are possible). You can also backtest across multiple venues, again constrained only by machine resources.

Add strategies

Add the trading strategies you plan to run as part of the system.

note

You can backtest multiple strategies and instruments; machine resources remain the only limit.

First, initialize a strategy configuration, then use it to initialize a strategy that you can add to the engine:

# Configure your strategy
strategy_config = EMACrossTWAPConfig(
    instrument_id=ETHUSDT_BINANCE.id,
    bar_type=BarType.from_str("ETHUSDT.BINANCE-250-TICK-LAST-INTERNAL"),
    trade_size=Decimal("0.10"),
    fast_ema_period=10,
    slow_ema_period=20,
    twap_horizon_secs=10.0,
    twap_interval_secs=2.5,
)

# Instantiate and add your strategy
strategy = EMACrossTWAP(config=strategy_config)
engine.add_strategy(strategy=strategy)

You may notice that this strategy config includes parameters related to a TWAP execution algorithm. We can flexibly use different parameters per order submit, but we still need to initialize and add the actual ExecAlgorithm component that executes the algorithm.

Add execution algorithms

NautilusTrader enables you to build complex systems of custom components. Here we highlight one built-in component: a TWAP execution algorithm. Configure it and add it to the engine using the same general pattern as for strategies.

note

You can backtest multiple execution algorithms; machine resources remain the only limit.

# Instantiate and add your execution algorithm
exec_algorithm = TWAPExecAlgorithm()  # Using defaults
engine.add_exec_algorithm(exec_algorithm)

Run backtest

After configuring the data, venues, and trading system, run a backtest. Call the .run(...) method to process all available data by default.

See the BacktestEngineConfig API reference for a complete description of all available methods and options.

# Run the engine (from start to end of data)
engine.run()

Post-run and analysis

After the backtest completes, the engine automatically logs a post-run tearsheet with default statistics (or custom statistics that you load; see the advanced Portfolio statistics guide).

The engine also keeps many data and execution objects in memory, which you can use to generate additional reports for performance analysis.

engine.trader.generate_account_report(BINANCE)

engine.trader.generate_order_fills_report()

engine.trader.generate_positions_report()

Repeated runs

You can reset the engine for repeated runs with different strategy and component configurations. Call the .reset(...) method to retain all loaded data and components while resetting other stateful values, as if you had a fresh BacktestEngine. This avoids loading the same data again.

# For repeated backtest runs make sure to reset the engine
engine.reset()

Remove and add individual components (actors, strategies, execution algorithms) as required.

See the Trader API reference for a description of all methods available to achieve this.

# Once done, good practice to dispose of the object if the script continues
engine.dispose()