Beyond built-in Hyperopt
Freqtrade's built-in hyperopt runs locally. HyperOptimizer runs in managed infrastructure with Bayesian optimization, so you avoid long sessions tying up your machine.
Optimize your crypto botwithout tying up your machine
Build a Docker image of your Freqtrade backtest, configure parameter ranges in the dashboard, and let managed Bayesian optimization find the best settings.
Why HyperOptimizer for Freqtrade?
Managed optimization for crypto trading strategies
Parallel backtests at scale
Run multiple Freqtrade backtests in parallel on our infrastructure. Each trial is an isolated container; no local compute, no resource contention.
Strategy IP stays with you
Your strategy code, config, and trade data never leave the container. We only see the metric lines you print to stdout. Full isolation, zero access.
Visual optimization results
Compare profit, Sharpe, Sortino, and drawdown across all trials. Leaderboard, convergence plots, and Pareto frontiers, not just a log file.
Comparison
HyperOptimizer vs. built-in Hyperopt
Freqtrade Hyperopt
HyperOptimizer
Runs on
Your machine
Our managed infrastructure
Algorithm
Optuna (local)
Bayesian optimization (managed)
Parallelism
Limited by local CPU/RAM
5+ parallel containers by default
Results
Console output / JSON export
Dashboard with leaderboard & charts
Setup
Hyperopt spaces in strategy code
Parameter ranges in web dashboard
How it works
From strategy to optimized in three steps
1
Dockerize your backtest
Package Freqtrade, your strategy, data, and a small wrapper in a Docker image. The wrapper reads CLI args and runs one backtest per invocation.
2
Parse args & emit metrics
Read --hpo-* flags, run freqtrade backtesting, parse the result, and print metrics to stdout.
3
Read the leaderboard
We run parallel trials with Bayesian optimization, surface the best timeframes, stoploss values, and strategy parameters in the dashboard.
More integrations
Other ways to use HyperOptimizer
Optimize your Freqtrade strategy
Follow the Freqtrade integration guide for a step-by-step walkthrough with full code examples.