PolyMind: Self-Learning, Multi-Model Paper-Trading Intelligence Platform
A research platform for comparing AI forecasts across four market domains using simulated trading. Its public examples focus on evidence quality, uncertainty, learning controls, and the difference between a transferable method and an earned result.
- Multi
- model comparison
- Paper
- simulated trading
- Gated
- learning changes
- 4
- market domains
- Scoped
- service boundaries
- Tested
- public examples
Architecture and synthetic examples, not private deployment inventory, current operating status, or investment performance.
Multi-model
Compare model forecasts with observed outcomes and explicit uncertainty.
How it works
The architecture separates model forecasts across prediction markets, crypto, options, and forex. Public examples demonstrate how evidence quality and uncertainty affect comparisons, without publishing private model rosters or trading records.
Learning controls
Require evaluation before a proposed method changes model behavior.
How it works
The design separates candidate methods, evaluation evidence, and promotion decisions. Proposed changes must preserve the distinction between a model's original reasoning and later additions.
Reset and transfer
Keep recovery policy separate from the measurements a model has earned.
How it works
Reset markers preserve history. A transferred method does not confer the donor's performance record, and borrowed calibration must remain labelled until independently evaluated.
Measurement states
Distinguish measured results, empty results, unavailable data, and failed measurements.
How it works
Public examples compare estimates with a declared baseline and report uncertainty. Placeholder records and failed reads require explicit handling; an unmeasured result is not a zero or evidence of skill.
Paper trading
Demonstrate research workflows with simulated positions and synthetic examples.
How it works
The public material describes paper-trading mechanisms. Worked rates and model-price differences are illustrative calculations, not live investment performance or realized returns.
Service architecture
Separate application services, storage, monitoring, and verification.
How it works
Containerized services and explicit health checks are architectural boundaries. The showcase describes those mechanisms without publishing private deployment inventories or claiming current service health.
Interface work
Group related views and make measurement status visible alongside each result.
How it works
The interface design uses grouped navigation, readable diagrams, and explicit data states. These are design principles rather than a claim that every private surface has passed an accessibility audit.
