Your First FX Backtest

This tutorial runs a complete, reproducible FX backtest with TradeTide. It uses the bundled EUR/USD data, so no data-provider account or download is required. The strategy is deliberately small: Bollinger Bands identify potential mean-reversion entries, while fixed take-profit and stop-loss levels make the risk rules explicit.

The result is a compact four-panel report showing price and signals, equity, open positions, and drawdown. It is an example workflow—not investment advice or a claim about live-trading performance.

Imports

The public API keeps the essential pieces together: market data, an indicator-backed strategy, exit rules, capital management, and a backtester.

from TradeTide import Backtester, BacktestResult, Currency, Market, Strategy
from TradeTide import capital_management, exit_strategy
from TradeTide import BollingerBands
from TradeTide.times import days, minutes

Load bundled market data

time_span can be a timedelta (as used here) or a compact duration string such as "3d 6h". TradeTide ships samples for EUR/USD, GBP/USD, CHF/USD, JPY/USD, and CAD/USD.

market = Market()
market.load_from_database(
    currency_0=Currency.EUR,
    currency_1=Currency.USD,
    time_span=5 * days,
)

Define the signal rule

Bollinger Bands compare price with a rolling average and its volatility band. Adding the indicator to a strategy lets the backtester derive trade signals from it.

strategy = Strategy()
strategy.add_indicator(BollingerBands(window=30 * minutes, multiplier=2.0))

Set risk and sizing rules

Keep the example’s assumptions visible. Static exits each position at a four-pip stop loss or take profit; FixedLot caps both individual size and simultaneous exposure.

risk_rules = exit_strategy.Static(stop_loss=4, take_profit=4)
position_sizing = capital_management.FixedLot(
    capital=100_000,
    fixed_lot_size=10_000,
    max_capital_at_risk=10_000,
    max_concurrent_positions=1,
)

Run and inspect the backtest

run performs signal evaluation, position handling, and portfolio simulation. The chart is a useful first sanity check; evaluate strategies on appropriate out-of-sample data before making any trading decision.

backtester = Backtester(
    strategy=strategy,
    market=market,
    exit_strategy=risk_rules,
    capital_management=position_sizing,
)
backtester.run()
result = BacktestResult.from_portfolio(backtester.portfolio)
result.plot_equity_drawdown()

<Figure size 640x480 with 2 Axes>