Backtest: FX bar data

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

View source on GitHub.

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Overview

This tutorial runs through how to setup a BacktestEngine (low-level API) for a single 'one-shot' backtest run using FX bar data.

Prerequisites

• NautilusTrader latest release installed (pip install -U nautilus_trader)
• JupyterLab or similar installed (pip install -U jupyterlab)
• Python 3.10+ installed

Imports

We'll start with all of our imports for the remainder of this tutorial.

import time
from decimal import Decimal

import pandas as pd

from nautilus_trader.backtest.engine import BacktestEngine
from nautilus_trader.backtest.engine import BacktestEngineConfig
from nautilus_trader.backtest.models import FillModel
from nautilus_trader.backtest.modules import FXRolloverInterestConfig
from nautilus_trader.backtest.modules import FXRolloverInterestModule
from nautilus_trader.config import RiskEngineConfig
from nautilus_trader.config import LoggingConfig
from nautilus_trader.examples.strategies.ema_cross import EMACross
from nautilus_trader.examples.strategies.ema_cross import EMACrossConfig
from nautilus_trader.model.currencies import USD, JPY
from nautilus_trader.model.data import BarType
from nautilus_trader.model.enums import AccountType
from nautilus_trader.model.enums import OmsType
from nautilus_trader.model.identifiers import Venue
from nautilus_trader.model.objects import Money
from nautilus_trader.persistence.wranglers import QuoteTickDataWrangler
from nautilus_trader.test_kit.providers import TestDataProvider
from nautilus_trader.test_kit.providers import TestInstrumentProvider

Set up backtest engine

# Initialize a backtest configuration
config = BacktestEngineConfig(
    trader_id="BACKTESTER-001",
    logging=LoggingConfig(log_level="ERROR"),
    risk_engine=RiskEngineConfig(
        bypass=True,  # Example of bypassing pre-trade risk checks for backtests
    ),
)

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

Add simulation module

We can optionally plug in a module to simulate rollover interest. The data is available from pre-packaged test data.

provider = TestDataProvider()
interest_rate_data = provider.read_csv("short-term-interest.csv")
config = FXRolloverInterestConfig(interest_rate_data)
fx_rollover_interest = FXRolloverInterestModule(config=config)

Add fill model

For this backtest we'll use a simple probabilistic fill model.

fill_model = FillModel(
    prob_fill_on_limit=0.2,
    prob_fill_on_stop=0.95,
    prob_slippage=0.5,
    random_seed=42,
)

Add venue

For this backtest we just need a single trading venue which will be a similated FX ECN.

SIM = Venue("SIM")
engine.add_venue(
    venue=SIM,
    oms_type=OmsType.HEDGING,  # Venue will generate position IDs
    account_type=AccountType.MARGIN,
    base_currency=None,  # Multi-currency account
    starting_balances=[Money(1_000_000, USD), Money(10_000_000, JPY)],
    fill_model=fill_model,
    modules=[fx_rollover_interest],
)

Add instruments and data

Now we can add instruments and data. For this backtest we'll pre-process bid and ask side bar data into quote ticks using a QuoteTickDataWrangler.

# Add instruments
USDJPY_SIM = TestInstrumentProvider.default_fx_ccy("USD/JPY", SIM)
engine.add_instrument(USDJPY_SIM)

# Add data
wrangler = QuoteTickDataWrangler(instrument=USDJPY_SIM)
ticks = wrangler.process_bar_data(
    bid_data=provider.read_csv_bars("fxcm/usdjpy-m1-bid-2013.csv"),
    ask_data=provider.read_csv_bars("fxcm/usdjpy-m1-ask-2013.csv"),
)
engine.add_data(ticks)

Configure strategy

Next we'll configure and initialize a simple EMACross strategy we'll use for the backtest.

# Configure your strategy
config = EMACrossConfig(
    instrument_id=USDJPY_SIM.id,
    bar_type=BarType.from_str("USD/JPY.SIM-5-MINUTE-BID-INTERNAL"),
    fast_ema_period=10,
    slow_ema_period=20,
    trade_size=Decimal(1_000_000),
)

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

Run backtest

We now have everything required to run the backtest. Once the engine has completed running through all the data, a post-analysis report will be logged.

engine.run()

Generating reports

Additionally, we can produce various reports to further analyze the backtest result.

engine.trader.generate_account_report(SIM)

engine.trader.generate_order_fills_report()

engine.trader.generate_positions_report()