Skip to main content

Event-Driven Backtesting

Event-driven backtesting simulates the market environment by processing each price update sequentially, just like live trading. It provides realistic order execution with full support for stop losses, take profits, and position sizing.

Looking for a high-level comparison of backtesting modes? See Backtesting. For batch-style high-throughput runs, see Vector Backtesting.

Quick Start​

from investing_algorithm_framework import create_app, BacktestDateRange
from datetime import datetime, timezone

app = create_app()
app.add_strategy(MyStrategy())
app.add_market(market="bitvavo", trading_symbol="EUR")

# Define the backtest period
backtest_range = BacktestDateRange(
    start_date=datetime(2023, 1, 1, tzinfo=timezone.utc),
    end_date=datetime(2024, 1, 1, tzinfo=timezone.utc)
)

# Run an event backtest
backtest = app.run_backtest(
    backtest_date_range=backtest_range,
    initial_amount=1000
)

Running an Event-Based Backtest​

backtest = app.run_backtest(
    backtest_date_range=backtest_range,
    initial_amount=1000,
    risk_free_rate=0.027,  # Optional: for metrics such as Sharpe ratio calculation
)

Multiple Date Ranges​

Test your strategy across different market conditions:

date_ranges = [
    BacktestDateRange(
        start_date=datetime(2022, 1, 1, tzinfo=timezone.utc),
        end_date=datetime(2022, 12, 31, tzinfo=timezone.utc),
        name="Bear Market 2022"
    ),
    BacktestDateRange(
        start_date=datetime(2023, 1, 1, tzinfo=timezone.utc),
        end_date=datetime(2023, 12, 31, tzinfo=timezone.utc),
        name="Recovery 2023"
    ),
]

backtests = app.run_backtests(
    backtest_date_ranges=date_ranges,
    ... # other params
)

Analyzing Results​

Backtest Report​

Generate a visual report of your backtest:

from investing_algorithm_framework import BacktestReport

# Single strategy
report = BacktestReport(backtest)
report.show(browser=True)  # Opens in your default browser

# Multi-strategy comparison
report = BacktestReport(backtests=[backtest_a, backtest_b])
report.show()

# Load from saved backtests on disk
report = BacktestReport.open(directory_path="./my_backtests")
report.show()

# Save as a self-contained HTML file
report.save("comparison_report.html")

See Backtest Reports for full documentation on the dashboard features, compare mode, and API reference.

Accessing Metrics​

# Get performance metrics
metrics = backtest.get_backtest_metrics()
print(f"Total Return: {metrics.total_return}%")
print(f"Sharpe Ratio: {metrics.sharpe_ratio}")
print(f"Max Drawdown: {metrics.max_drawdown}%")
print(f"Total Trades: {metrics.number_of_trades_closed}")

Accessing Trades​

for run in backtest.get_all_backtest_runs():
    trades = run.trades
    for trade in trades:
        print(f"Symbol: {trade.symbol}")
        print(f"Entry: {trade.entry_price}")
        print(f"Exit: {trade.exit_price}")
        print(f"Return: {trade.return_percentage}%")

Best Practices​

  1. Use representative date ranges: Include bull markets, bear markets, and sideways periods.
  2. Avoid overfitting: Don't optimize too heavily on historical data.
  3. Account for costs: Consider trading fees and slippage in your strategy.
  4. Start with event-based: Use event-based backtesting for realistic simulation, then scale out with vector backtesting for parameter sweeps.

Next Steps​