Investor Relations
Investor Relations
Otonomii builds perpetual machines — autonomous systems that discover, learn, and adapt in financial markets.
We are not a trading company.
We are a machine learning research organization that happens to operate in markets because markets provide the richest, most adversarial learning environment available.
Company
Profile
Founded on the principle that simplicity outlasts complexity, Otonomii is building a fully autonomous hedge fund where machines monitor machines. No humans in the operational loop. The dashboard is a viewer, not a control panel. The "operator" is another machine.
Our technology is organized into a four-layer architecture (Brain, Mind, Machine, Arena) with strict import boundaries enforced by automated AST scanning. This architecture is documented in three signed foundational documents (Genesis, Axioms, DNA) with a fourth (Charter) in active development.
The team operates under a governance-first model: four advisory boards, three overseers, a DevX quality team, and a multi-model AI execution team. Every decision has a signatory chain and a reversibility classification.
Key
Metrics
Strategy validation is conducted in two phases. Experiment 1 tested 990 strategies using standard pivot points — all failed when the volatility regime collapsed by 3x. This result, while commercially painful, validated our core thesis: regime-blind strategies are inherently fragile.
Experiment 2 introduced regime awareness and money zone detection across 888 strategies. 12 survived rigorous out-of-sample validation. The survival characteristics confirmed that selectivity IS the edge — high regime gate combined with high confluence produces the best validation survival
Our data infrastructure processes 11.2M candles across BTC (74K), ETH (74K), MES (46K), and MNQ (46K), with futures requiring contract stitching and crypto session definition still an open research question.
1,878
Strategies Tested
12
Validated Survivors
11.2M
Candles Processed
4
Asset Classes Active
Governance
Structure
Governance is not overhead — it is architecture. Every decision has a signatory chain, a reversibility classification, and a confidence level. This structure prevents both reckless action and analysis paralysis.
Causal Inference Board
Experimental design, counterfactual reasoning, methodology rigor.
Causal Ledger
Structural causal models methodology. The Causal Ledger is a formal record of every causal claim, evidence basis, and revision conditions. Ensures the system never confuses correlation with causation.
Compression Progress
Information-theoretic learning metric — learning measured as compression efficiency. The system is learning when it can represent the same information in fewer bits. Provides objective, learning measurement.
Reducibility Detector
Computational irreducibility framework. Estimates whether phenomena are tractable before investing resources. Raised the fundamental question of whether some market phenomena are provably unsolvable.
Representational Learning Board
Architecture, representational learning, self-supervised methods.
Self-Supervised World Models
JEPA (Joint Embedding Predictive Architecture) as the foundation for how our Mind layer encodes and predicts. Focus on learning representations without labeled data.
Affective Salience
Constructed emotion theory applied to resource allocation. The biological insight that emotional significance drives attention allocation, translated into computed salience for resource allocation in metacognition.
Neuromorphic Systems Board
Neuroscience-inspired computation, cortical columns, spatial encoding.
Market Grid Cells
Cortical column theory and spatial encoding of market states inspired by the brain's navigation system. Markets have a "location" in abstract state space that grid cells can encode and recognize.
Curiosity-Driven Learning
Curiosity as learning progress — the first derivative of prediction error. Systems should be curious about things where they are learning fastest, not things that are merely novel. Compression-based learning metric.
Applied Systems Board
Pragmatic implementation, scaling, testing methodology.
Shadow Mind
A parallel lightweight model for rapid hypothesis testing. Runs alongside the primary system to explore alternative strategies at low computational cost, feeding promising candidates for full evaluation.
Autonomous Intelligence For The Next Era of Finance
Otonomii Ltd
British Virgin Islands
Holding & Intellectual Property
Keep up with Otonomii
Get the latest news, insights directly to your inbox. *
By submitting, you agree to our Terms & Service.
* No spam, just lovely updates.
2026 © Otonomii LTD. All rights reserved.
TOP