vaquum-limen 3.30.2

Limen unifies parameter search across machine learning and rule-based strategies, with built-in analytics that show not just what works, but why it works.

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 connect experiment outputs to benchmark, backtest, and cohort workflows. 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
• Leakage-safe train, validation, and test workflows
• Built-in backtesting, confusion analytics, and parameter diagnostics
• Decoder cohort construction with pluggable selection
• Reproducible runs with checkpointing, resumption, and retraining

First Experiment

The first runnable path is a small parameter sweep on the bundled BTC/USDT kline dataset with the built-in logistic-regression decoder.

1. Install the package:

pip install vaquum_limen

2. Load data and run a first experiment:

import limen

historical = limen.HistoricalData()
data = historical.get_spot_klines(kline_size=7200, row_count_limit=2000)

uel = limen.UniversalExperimentLoop(data=data, sfd=limen.sfd.logreg_binary)

uel.run(
    experiment_name="logreg-first",
    n_permutations=25,
    prep_each_round=True,
)

3. Inspect the core outputs:

• uel.experiment_log for the parameter sweep results
• uel.experiment_confusion_metrics for confusion analytics
• uel.experiment_backtest_results for backtest results

That path runs against public BTC/USDT data without relying on repo-local fixture files. The UEL documentation covers run directories, checkpoints, resumability, and stored round artefacts.

Learn more

• Start with the full docs hub in docs/README.md
• Define research units in docs/Single-File-Decoder.md, docs/Built-In-SFDs.md, and docs/Experiment-Manifest.md
• Run experiments in docs/Universal-Experiment-Loop.md and extend the artifact-backed path through docs/Advanced-Search.md and docs/Reducers-And-Feedback.md
• Analyze results in docs/Log.md, docs/Benchmark.md, and docs/Backtest.md
• Understand the model layer in docs/Reference-Architecture.md and the helper layer in docs/Utilities.md
• Promote finished runs into reusable outputs with docs/Trainer.md and docs/Cohort.md
• Contribute through docs/Developer/README.md

Contributing

Contribution starts through open discussions, docs changes, or open issues.

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

Vulnerabilities

Report vulnerabilities privately through GitHub Security Advisories.

Citations

Published work should cite:

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

License

MIT License.