Any language, any framework
Use Python, Rust, C++, Go, or another language that can read trial arguments and print metrics. No SDK is required.
Bring your Docker workload.We run the trials.
Read a few arguments. Print a few metrics. HyperOptimizer runs your image with different parameter values and shows you how the results compare.
Why Docker?
The universal interface for optimization
Docker is the simplest contract: we give you CLI args, you give us stdout metrics. Everything else is yours.
Minimal integration effort
Two changes to your code: parse --hpo-* CLI arguments and print metrics to stdout. That's the entire contract between your code and our platform.
Managed trial containers
Your image carries your code and dependencies. Each trial runs in a container, with its logs and metrics available in the dashboard.
Parallel execution
Test several parameter combinations at once. Your plan sets the number of trial slots, and each trial uses your available compute.
How it works
Your role vs. ours
A clear split of responsibilities. You bring the container; we run the search.
You
HyperOptimizer
Build the Docker image
Run it for each trial with different parameter values
Configure parameters & ranges in the dashboard
Choose the next parameter set (Bayesian optimization)
Parse --hpo-* CLI args in your code
Inject those args at container start
Print metrics to stdout
Collect metrics, rank results, show the dashboard
Example
Two changes to your code
Parse hyperparameters from CLI args and print metrics to stdout. That's the entire integration surface.
1. Parse CLI args
args = parse_args()
lookback = args.hpo_lookback_window
multiplier = args.hpo_atr_multiplier
result = run_backtest(
lookback=lookback,
multiplier=multiplier,
)2. Print metrics
import json
metrics = {
"sharpe": result.sharpe,
"max_drawdown": result.max_dd,
}
for k, v in metrics.items():
print(f"hpo.metrics.{k}={json.dumps(v)}")More integrations
Framework-specific guides
HyperOptimizer works with any Docker container. These guides show how specific frameworks integrate.
NautilusTrader
High-performance algorithmic trading optimization with Nautilus backtests.
Freqtrade
Optimize your crypto trading bot hyperparameters with managed infrastructure.
Or explore all integrations on the product page.
Ready to optimize?
Follow the getting started guide for a full walkthrough with Dockerfile examples.