vaquum-limen 3.31.1

Manifest-driven Bitcoin research software for YAML-defined experiments, benchmark artifacts, backtest ledgers, and decoder cohorts.

Vaquum Limen turns Bitcoin market data into searchable signals, backtested outcomes, and decoder cohorts.

Limen • What Limen Is Not • Capabilities • First Experiment • Learn More

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Limen — Research engine

Manifest-driven Bitcoin alpha research engine that turns market data into searchable signals, backtested outcomes, and decoder cohorts.

Limen unifies parameter search across machine learning and rule-based strategies. Built-in analytics record benchmark, backtest, and cohort artifacts for inspection. The project evolves from Talos, a hyperparameter optimization framework for TensorFlow and Keras.

What Limen Is Not

Limen is not:

• a trade execution system
• a downstream trade decision engine
• a generic multi-asset research platform

In the wider Vaquum architecture, Origo sits upstream as the data layer. Nexus, Praxis, and Veritas sit downstream for decisioning, execution, and oversight.

Capabilities

• Manifest-driven experiment pipelines
• Search across models, rules, features, targets, and hyperparameters
• Built-in indicator and feature library for Bitcoin research
• Support for both machine learning and rule-based strategy research
• Bitcoin-native transforms, scaling, and target construction
• Split-first train, validation, and test workflows
• Built-in benchmark, backtest, and parameter diagnostics
• Decoder cohort construction with pluggable selection
• Reproducible runs with checkpointing, resumption, and retraining

First Experiment

The first runnable path is a YAML manifest executed through the limen CLI.

1. Install the package:

pip install vaquum-limen

2. Scaffold a starter manifest:

limen init logreg-first.yaml --template logreg_binary

3. Validate, profile, and dry-run the manifest:

limen validate logreg-first.yaml
limen profile logreg-first.yaml
limen run --dry-run logreg-first.yaml

4. Run it:

limen run logreg-first.yaml

5. Inspect the result directory printed by the CLI:

• copied YAML manifest
• metadata.json
• results.csv
• round_data.jsonl

That path runs the manifest-backed engine without Python orchestration code. The Python API remains available for custom SFDs, custom prep/model logic, and direct UEL integration.

Risk Boundary

Limen is research software. Benchmark and backtest outputs are not investment advice, trading advice, execution simulation, or a promise of future performance.

Learn more

• Start with the full docs hub at docs.vaquum.fi/limen
• Start with the YAML/CLI path in Command Line Interface and Experiment Manifest
• Use Universal Experiment Loop for the engine beneath CLI and direct Python integration
• Define extension research units in Single-File Decoder and Built-In SFDs
• Analyze results in Log, Benchmark, and Backtest
• Promote finished runs into reusable outputs with Trainer and Cohort
• Contribute through CONTRIBUTING.md and docs/Developer/README.md

Contributing

Contribution starts through CONTRIBUTING.md, docs changes, or open issues.

Before contributing, start with docs/Developer/README.md.

Support

Use SUPPORT.md for support routes and scope boundaries.

Vulnerabilities

Report vulnerabilities privately through GitHub Security Advisories. Do not report vulnerabilities through public issues.

Citations

Published work should cite:

Vaquum Limen [Computer software]. (2026). Retrieved from https://github.com/Vaquum/Limen.

Machine-readable citation metadata lives in CITATION.cff.

License

MIT License.