Trading Strategies
Optimize parameters for better returns and risk management.
experiment-3084112 / 240 trials
Beta access openDocker-native optimization
Your algorithm hasa better configuration.We find it.
Run hundreds of parallel trials for any Dockerized workload. HyperOptimizer tests parameter combinations, tracks your objective metric, and returns the best-performing configuration automatically.
See workflow
How it works
We handle the complexity.You get the best result.
HyperOptimizer runs thousands of trials in parallel to find the optimal configuration for your containerized workload.
Your Container
$ docker run strategy:latest \
--stop-loss=0.02 \
--take-profit=0.08 \
--lookback=120
Any Image. Any Workload.
HyperOptimizer Platform
QueuedRunningFinishedNew bestFailed
Parallel trials, smart search, and automatic metric tracking.
Best Configuration
{
"stop_loss": 0.020,
"take_profit": 0.080,
"lookback": 120,
"score": 1580.4,
}
Waiting for improved trial results
Better results, faster.
Compare every trial and get the most stable, highest performing configuration.
Built for any optimization problem
ML Models
Tune hyperparameters to improve accuracy and reduce training time.
Simulations
Find the best scenario settings for complex simulations.
Data Pipelines
Optimize performance, cost, and reliability of your pipelines.
Custom Algorithms
Bring your own code and search space. We handle the rest.
SECURITY
Your code and data stay under your control.
Optimization often involves sensitive models, proprietary algorithms, private datasets, or internal workflows. HyperOptimizer is designed around containerized execution, isolated trials, and clear boundaries between orchestration, metrics, and workload data.
Private images
Isolated trial execution
Controlled environments
PRODUCT
From giant search space to best configuration, in one managed run.
Define parameters, run scalable trials, inspect every metric, and keep the configurations that actually perform.
Experiment dashboardBest result
Search space
learning_rate[0.001..0.01]batch_size[32,64,128]max_depth[3,5,8]
Trial dashboard
trial-108 running
trial-109 running
trial-110 queued
trial-104 best score: 0.942
Metric comparison
#configscorelat
1lr=0.003 b=640.942182
2lr=0.005 b=320.928171
3lr=0.001 b=1280.914194
Trial logs
$ python train.py --learning-rate=0.003 --batch-size=64
hpo.metrics.score=0.942
hpo.metrics.latency_ms=182
artifact.model=checkpoint-v12
Experiment history
Compare past runs, inspect artifacts, and keep auditable records of which configurations shipped and why.
WORKFLOW
How HyperOptimizer works
1. Package your workload
Bring a Docker image or containerized job.
2. Define the experiment
Set parameters, ranges, objectives, constraints, and budgets.
3. Run scalable trials
HyperOptimizer schedules, monitors, retries, and captures results.
4. Compare outcomes
Rank configurations by metrics, logs, artifacts, cost, and custom scores.
FAQ
Frequently asked questions
Can't find the answer you're looking for? Reach out to oursupport team.
Is HyperOptimizer only for trading?
No. Trading strategies are one use case. HyperOptimizer is designed for any containerized workload with parameters, metrics, and an objective.
How is this different from Optuna, Ray Tune, or Katib?
Those are powerful optimization frameworks. HyperOptimizer provides the managed infrastructure, orchestration, trial execution, metric collection, dashboard, and workflow around the optimization process.
What do I need to use it?
A container: a containerized workload, a search space, and one or more metrics to optimize.
Can I use custom metrics?
Yes. HyperOptimizer is metric-agnostic: optimize for accuracy, return, drawdown, latency, throughput, token cost, runtime, or any custom score you report.
Does my workload need to be machine learning?
No. Workloads can be ML models, trading strategies, simulations, data pipelines, LLM workflows, or any custom algorithm that can run in a container and emit metrics.
Can I run private workloads?
HyperOptimizer is designed for controlled environments with containerized, isolated trial execution. It supports private images and keeps workload execution separated from orchestration and experiment metadata.
Do you support private Docker images?
Yes. You can run private container images as part of optimization workflows, with access and execution controlled by your environment configuration.
Can I control compute limits and budgets?
Yes. Experiments can be configured with resource limits, trial budgets, and runtime constraints so optimization stays within operational boundaries.
Do you support parallel optimization?
Yes. Multiple trials run in parallel, and scheduling adapts as results come in so experiments converge faster.
What happens if a trial fails or times out?
Each trial runs independently. Failed trials are surfaced in the dashboard with logs and status, and remaining trials continue based on your configured budget.
Stop guessing configurations. Start running better experiments.
Join the waitlist for managed optimization infrastructure built for models, strategies, simulations, pipelines, and custom algorithms. Try the platform, share your feedback, and help shape the future of optimization workflows. Want us to support your framework or stack?Let us know.