The ASI Evolution Engine is how Treeova tunes itself. Instead of engineers hand-editing thresholds, a four-agent pipeline — Researcher, Engineer, Analyzer, Judge — proposes configuration changes for a named domain, evaluates them against frozen historical data, and only promotes changes that beat the incumbent on a contracted metric.
Platform
ASI Evolution Engine
The ASI Evolution Engine is how Treeova tunes itself. Instead of engineers hand-editing thresholds, a four-agent pipeline — Researcher, Engineer, Analyzer, Judge — proposes configuration changes for a named domain, evaluates them against frozen historical data, and only promotes changes that beat the incumbent on a contracted metric.
Quick definition
Treeova's self-improving configuration system: a four-agent pipeline (Researcher, Engineer, Analyzer, Judge) that proposes and evaluates configuration changes for named platform domains under hermetic evaluation contracts and a status-based mutex.
The four roles
Researcher gathers prior outcomes and recent failures for the domain under evolution. Engineer drafts candidate configurations. Analyzer runs each candidate through the domain's evaluation contract on a hermetic backtest window. Judge picks the winner — or rejects them all if no candidate beats the incumbent at statistical significance.
Evaluation contracts
Every evolvable domain ships with an evaluation contract: an input dataset, a scoring function, and a promotion threshold. This is what stops the engine from overfitting — a candidate that wins by 0.3% on noisy data will be rejected, while a robust 4% win on stable data gets promoted.
Mutex and audit
Only one evolution can run per domain at a time, enforced by a status-based mutex. Every promoted change is rollback-capable for 72 hours via the platform's audit bridge so a regression caught in production can be reverted without code deploy.
Go deeper
The full technical methodology behind ASI Evolution Engine is documented in the Treeova whitepaper series.
Glossary/ASI Evolution Engine PlatformASI Evolution EngineThe ASI Evolution Engine is how Treeova tunes itself. Instead of engineers hand-editing thresholds, a four-agent pipeline — Researcher, Engineer, Analyzer, Judge — proposes configuration changes for a named domain, evaluates them against frozen historical data, and only promotes changes that beat the incumbent on a contracted metric.Quick definitionTreeova's self-improving configuration system: a four-agent pipeline (Researcher, Engineer, Analyzer, Judge) that proposes and evaluates configuration changes for named platform domains under hermetic evaluation contracts and a status-based mutex.The four rolesResearcher gathers prior outcomes and recent failures for the domain under evolution. Engineer drafts candidate configurations. Analyzer runs each candidate through the domain's evaluation contract on a hermetic backtest window. Judge picks the winner — or rejects them all if no candidate beats the incumbent at statistical significance.Evaluation contractsEvery evolvable domain ships with an evaluation contract: an input dataset, a scoring function, and a promotion threshold. This is what stops the engine from overfitting — a candidate that wins by 0.3% on noisy data will be rejected, while a robust 4% win on stable data gets promoted.Mutex and auditOnly one evolution can run per domain at a time, enforced by a status-based mutex. Every promoted change is rollback-capable for 72 hours via the platform's audit bridge so a regression caught in production can be reverted without code deploy.Go deeperThe full technical methodology behind ASI Evolution Engine is documented in the Treeova whitepaper series.Read the ASI Evolution Engine whitepaper →Related termsReinforcement LearningA machine learning approach where Treeova's AI agents learn from the outcomes of past trades to improve future decision-making, adjusting conviction scores and strategy parameters over time.Arch-AGITreeova's multi-pass AI analysis engine that evaluates open options positions using conviction scoring, momentum analysis, scenario modeling, and reinforcement learning across up to 7 analytical passes.MetaChart EngineTreeova's charting layer where charts are first-class tools agents can invoke directly. Built on lightweight-charts + Three.js, with self-modulating indicators tuned by ASI Evolution, a vision pipeline, and a pattern decay tracker.← Back to the full glossary