NautilusTrader

Use HyperOptimizer to run NautilusTrader backtests as managed optimization trials. Your container owns the strategy and engine setup; HyperOptimizer chooses parameter sets and collects metrics.

Why NautilusTrader fits HPO

NautilusTrader strategies are often sensitive to windows, thresholds, bar sizes, sizing rules, and risk parameters. HyperOptimizer lets you expose those values as CLI flags and evaluate them across many containerized backtests.

Repeatable backtests

Run the same Nautilus script repeatedly with different parameter values.

Managed search

Let HyperOptimizer choose candidates while you focus on strategy logic.

Risk-aware metrics

Emit PnL, drawdown, order count, runtime, or custom objective scores.

Integration architecture

1. Package: Build an image with your NautilusTrader environment and data access.
2. Publish: Push the image to your org Images registry.
3. Inject: Receive --hpo-* parameters in your backtest entrypoint.
4. Run: Configure your strategy and run one backtest.
5. Collect: Print metrics for HyperOptimizer to collect.

1. Parse HPO parameters

import argparse

def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--hpo-bar-size", type=str, default="5-MINUTE")
    parser.add_argument("--hpo-lookback-window", type=int, default=50)
    parser.add_argument("--hpo-risk-multiplier", type=float, default=1.0)
    return parser.parse_args()

args = parse_args()

2. Configure the backtest

Map CLI values into your strategy or config objects before running the engine.

strategy_config = StrategyConfig(
    bar_size=args.hpo_bar_size,
    lookback_window=args.hpo_lookback_window,
    risk_multiplier=args.hpo_risk_multiplier,
)

engine.add_strategy(MyStrategy(strategy_config))
engine.run()
backtest_result = engine.get_result()

Note

Each HyperOptimizer trial should call engine.run() once for one parameter set.

3. Emit results

import json

metrics = {
    "total_pnl": float(backtest_result.stats_pnls.get("PnL", 0)),
    "total_orders": backtest_result.total_orders,
    "elapsed_time": backtest_result.elapsed_time,
}

for key, value in metrics.items():
    print(f"hpo.metrics.{key}={json.dumps(value, default=str)}")

Recommended metrics

Metric	Role	Notes
total_pnl	objective	Useful for raw profitability, but pair it with risk guardrails.
max_drawdown	guardrail	Helps avoid unstable configurations.
total_orders	context	Helps spot overfit runs with too few or too many orders.
elapsed_time	runtime	Useful when you care about trial cost or operational latency.

After the wrapper works locally, publish the image to your org Images registry and create an experiment. For the full contract, read the Quickstart.