Arch-AGI Conviction Analysis, Explained — A Plain-Language Companion
Treeova Research — Methodology TeamApril 18, 20264 min read
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Companion post. Plain-language summary of the Arch-AGI Conviction Methodology whitepaper. For the full methodology, limitations, and what's intentionally withheld, read the whitepaper at /whitepapers/arch-agi-conviction-methodology.
What is Arch-AGI?
Arch-AGI is Treeova's deep-intelligence engine for evaluating an open options position. Instead of producing a single number that says "buy" or "sell", it runs a structured multi-pass review that asks the questions a disciplined human trader would: what's the edge, what could go wrong, what does the regime tell us, and how have we been wrong before?
The output is a conviction score plus a written narrative — not a trade signal in isolation, and never a recommendation.
The seven passes (high level)
Edge analysis — what's the thesis, and what would invalidate it?
Scenario projection — best, base, and worst case outcomes.
Risk/Reward framing — breakeven, optimal exit, and stop-loss zones.
Regime context — does this position fit the current market regime?
Macro overlay — relevant tailwinds and headwinds.
RL calibration — how have similar conviction levels played out historically, segmented by regime?
Adversarial stress — a deliberate "challenge the thesis" pass that adjusts conviction down when the stress delta is large.
What this is not
Not a recommendation. Reports describe a position; they do not tell you to open, close, or size a trade.
Not a backtest. Conviction scores reflect current context, not historical replay.
Not infallible. The adversarial pass exists precisely because the earlier passes can be wrong.
Limitations
Past calibration does not guarantee future calibration.
Reports depend on the quality of the upstream intelligence feed.
The system is intentionally conservative when data is sparse for a given regime.
How Treeova's Arch-AGI conviction engine works in plain language: 7 structured passes, regime-aware calibration, and what it intentionally is not.Back to BlogArch-AGI Conviction Analysis, Explained — A Plain-Language CompanionTreeova Research — Methodology TeamApril 18, 20264 min readShare:Methodology ReferenceThis article summarizes a longer technical methodology — including limitations, scope, and what's intentionally withheld.Read the full whitepaperCompanion post. Plain-language summary of the Arch-AGI Conviction Methodology whitepaper. For the full methodology, limitations, and what's intentionally withheld, read the whitepaper at /whitepapers/arch-agi-conviction-methodology.What is Arch-AGI?Arch-AGI is Treeova's deep-intelligence engine for evaluating an open options position. Instead of producing a single number that says "buy" or "sell", it runs a structured multi-pass review that asks the questions a disciplined human trader would: what's the edge, what could go wrong, what does the regime tell us, and how have we been wrong before?The output is a conviction score plus a written narrative — not a trade signal in isolation, and never a recommendation.The seven passes (high level)Edge analysis — what's the thesis, and what would invalidate it?Scenario projection — best, base, and worst case outcomes.Risk/Reward framing — breakeven, optimal exit, and stop-loss zones.Regime context — does this position fit the current market regime?Macro overlay — relevant tailwinds and headwinds.RL calibration — how have similar conviction levels played out historically, segmented by regime?Adversarial stress — a deliberate "challenge the thesis" pass that adjusts conviction down when the stress delta is large.What this is notNot a recommendation. Reports describe a position; they do not tell you to open, close, or size a trade.Not a backtest. Conviction scores reflect current context, not historical replay.Not infallible. The adversarial pass exists precisely because the earlier passes can be wrong.LimitationsPast calibration does not guarantee future calibration.Reports depend on the quality of the upstream intelligence feed.The system is intentionally conservative when data is sparse for a given regime.Read the methodologyArch-AGI: Seven-Pass Conviction Methodology →Past performance does not guarantee future results. See /legal/risk-disclosure.Want the full methodology?This article summarizes a longer technical methodology — including limitations, scope, and what's intentionally withheld.Open the whitepaperRelated methodologyWP-02Adaptive Risk EngineWP-09Security & Data ArchitectureRelated readingArch-AGI full methodology