Variance-Based Products & Integrations

Variance as a Protocol Primitive

Normalized variance scores represent structured information about how a market behaves internally while unresolved. These scores are independent of outcome direction and remain meaningful even when the aggregated market price appears stable.

Within the protocol, variance can be:

• queried

• ranked

• filtered

• referenced by on-chain or off-chain logic

• consumed by external systems

This makes variance a reusable primitive rather than a single-purpose signal.

Protocol-Native Products

The protocol can expose native products built directly on variance signals, including:

• variance-indexed instruments

• belief-divergence trackers

• regime-stability indicators

• disagreement persistence metrics

These products allow users to interact with markets by positioning around how beliefs evolve, not simply which outcome they expect.

All variance-based products remain anchored to the final outcome of the underlying prediction market for settlement, but their behavior is determined entirely by pre-resolution variance dynamics.

Strategy & Automation Layer

Variance signals are designed to be machine-consumable.

This enables:

• automated strategies reacting to changes in variance intensity

• regime-aware execution logic

• filters for identifying structurally unstable markets

• inputs for risk management, timing, or allocation decisions

Because variance scores are normalized and comparable, strategies can operate across multiple markets without per-market calibration.

External Integrations

Variance Markets exposes its outputs through integrations that can be consumed by external systems.

These integrations may include:

• dashboards and analytics platforms

• trading systems and bots

• research and monitoring tools

• downstream protocols using variance as an input

External consumers do not need to understand prediction market mechanics in depth. They receive standardized signals derived from markets such as Polymarket.

Separation From Outcome Exposure

A core design constraint is strict separation between:

• outcome exposure

• variance exposure

Users can:

• remain neutral on the outcome while interacting with variance

• agree on the outcome but disagree on belief structure

• express views on market disagreement without duplicating outcome risk

This separation is intentional and fundamental to the protocol’s design.

Outputs

This layer exposes:

• variance scores as consumable protocol outputs

• interfaces for human and machine interaction

• standardized signals usable across products and integrations

All outputs are resolved in the next section against the final outcome of the underlying prediction market.