Arch-AGI is the analytical brain Treeova runs on every open options position. Rather than producing a single recommendation, it executes up to seven sequential reasoning passes — each contributing a different lens (technical, fundamental, sentiment, scenario, reinforcement) — and folds the results into a single conviction score the rest of the platform can act on.
Platform
Arch-AGI
Arch-AGI is the analytical brain Treeova runs on every open options position. Rather than producing a single recommendation, it executes up to seven sequential reasoning passes — each contributing a different lens (technical, fundamental, sentiment, scenario, reinforcement) — and folds the results into a single conviction score the rest of the platform can act on.
Quick definition
Treeova'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.
How it works inside Treeova
When an agent triggers Arch-AGI, the engine pulls the position's contract metadata, the underlying's recent price and IV history, dark pool flow, and the agent's own past outcomes on similar setups. Each pass writes its intermediate verdict to the agent's Lossless Context Management ledger so later passes — and the user — can audit exactly which signal moved conviction up or down.
Why conviction scoring matters
A 0–100 conviction score isn't a price target. It's the platform's calibrated confidence that the position will close profitably under the agent's own exit rules. Treeova's Adaptive Risk Engine reads this score to decide whether to tighten a stop, scale out, or let a winner run, which is why Arch-AGI sits upstream of nearly every automated decision.
Reinforcement learning loop
Every Arch-AGI verdict is graded once the trade closes. Outcomes flow into the agent's RL posterior, which adjusts how heavily future passes weigh momentum vs. scenario modeling for that specific agent and instrument class. Over time agents specialize — an SPX iron-condor agent and a meme-stock momentum agent end up with very different conviction signatures.
Go deeper
The full technical methodology behind Arch-AGI is documented in the Treeova whitepaper series.
Glossary/Arch-AGI PlatformArch-AGIArch-AGI is the analytical brain Treeova runs on every open options position. Rather than producing a single recommendation, it executes up to seven sequential reasoning passes — each contributing a different lens (technical, fundamental, sentiment, scenario, reinforcement) — and folds the results into a single conviction score the rest of the platform can act on.Quick definitionTreeova'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.How it works inside TreeovaWhen an agent triggers Arch-AGI, the engine pulls the position's contract metadata, the underlying's recent price and IV history, dark pool flow, and the agent's own past outcomes on similar setups. Each pass writes its intermediate verdict to the agent's Lossless Context Management ledger so later passes — and the user — can audit exactly which signal moved conviction up or down.Why conviction scoring mattersA 0–100 conviction score isn't a price target. It's the platform's calibrated confidence that the position will close profitably under the agent's own exit rules. Treeova's Adaptive Risk Engine reads this score to decide whether to tighten a stop, scale out, or let a winner run, which is why Arch-AGI sits upstream of nearly every automated decision.Reinforcement learning loopEvery Arch-AGI verdict is graded once the trade closes. Outcomes flow into the agent's RL posterior, which adjusts how heavily future passes weigh momentum vs. scenario modeling for that specific agent and instrument class. Over time agents specialize — an SPX iron-condor agent and a meme-stock momentum agent end up with very different conviction signatures.Go deeperThe full technical methodology behind Arch-AGI is documented in the Treeova whitepaper series.Read the Arch-AGI whitepaper →Related termsConviction ScoringA numerical score (0-100) generated by Arch-AGI that represents the AI's confidence in a trade's expected outcome, based on technical, fundamental, and sentiment analysis.Adaptive Risk EngineTreeova's two-tier risk protection model combining deterministic Standard guardrails with a context-modulated Adaptive trailing tier. Agents pull levers; platform code performs all risk arithmetic.Reinforcement 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.Lossless Context Management (LCM)Treeova's three-layer agent memory system: append-only message ledger, RL-aware recursive summarization, and hybrid full-text + semantic retrieval. Lets long-running agents retain decision-grade signal across sessions without exceeding model context windows.← Back to the full glossary