Automated Backtesting with Bollinger Bands Strategy

This example demonstrates how to use TradeTide’s Backtester class for automated strategy evaluation. The Backtester streamlines the entire workflow by handling position management, portfolio simulation, and performance analysis automatically.

We’ll test a Bollinger Bands strategy on 100 days of CAD/USD data to evaluate its historical performance and risk characteristics.

Import Libraries and Setup

import matplotlib.pyplot as plt
from TradeTide import Backtester, Strategy, Market, Currency, days, hours, minutes
from TradeTide.indicators import BollingerBands
from TradeTide import capital_management, exit_strategy

# Configure plotting
plt.style.use("seaborn-v0_8-darkgrid")
plt.rcParams["figure.figsize"] = (12, 6)

Load Historical Market Data

Load 100 days of CAD/USD forex data for comprehensive backtesting.

market = Market()

market.load_from_database(
    currency_0=Currency.CAD,
    currency_1=Currency.USD,
    time_span=2 * days,
)
# market.plot()

print(f"Loaded {len(market.dates)} data points over {100} days")

Loaded 2857 data points over 100 days

Configure Trading Strategy

Set up a Bollinger Bands strategy with 2.0 standard deviation bands for more conservative signal generation.

indicator = BollingerBands(window=3 * minutes, multiplier=2.0)

indicator.run(market)

strategy = Strategy()
strategy.add_indicator(indicator)

Define Risk Management

Configure exit strategy and capital management parameters.

exit_strat = exit_strategy.Static(stop_loss=4, take_profit=4, save_price_data=True)

capital_mgmt = capital_management.FixedLot(
    capital=1_000,
    fixed_lot_size=10,
    max_capital_at_risk=100_000,
    max_concurrent_positions=10,
)

Run Backtest

The Backtester automatically handles the complete workflow: strategy execution, position management, and performance calculation.

backtester = Backtester(
    strategy=strategy,
    exit_strategy=exit_strat,
    market=market,
    capital_management=capital_mgmt,
)

backtester.run()

# backtester.plot_summary()

# print(backtester.portfolio)

Display Results

View comprehensive performance metrics and analysis.

backtester.print_performance()