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

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

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Limen

Limen is a manifest-driven Bitcoin alpha research engine. It turns market data into searchable experiments, benchmark-style analytics, backtest results, and decoder cohorts.

Limen brings data preparation, feature construction, target shaping, parameter search, and post-run analysis into one Python workflow. It supports both machine learning and rule-based research, but it stays focused on signal research rather than decisioning or execution.

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
• Extensive 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 and regime-diversified model pooling
• Reproducible runs with checkpointing, resumption, and retraining

First Experiment

The fastest first success 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 polars as pl
import limen

data = pl.read_csv(
    "https://raw.githubusercontent.com/Vaquum/Limen/refs/heads/main/datasets/klines_2h_2020_2025.csv",
    try_parse_dates=True,
)

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 is the simplest way to get a real Limen run on your machine. If you want richer run directories, checkpoints, resumability, and stored round artefacts, continue into the UEL documentation below.

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-rich 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/Regime-Diversified-Opinion-Pools.md
• Contribute through docs/Developer/README.md

Contributing

The simplest way to start contributing is by joining an open discussion, contributing to the docs, or by picking up an open issue.

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

Vulnerabilities

Report vulnerabilities privately through GitHub Security Advisories.

Citations

If you use Limen for published work, please cite:

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

License

MIT License.