Valuation frameworks for crypto assets
Execution and settlement networks evaluated through fee generation, app capital, stablecoin activity, staking, and ecosystem depth.
The model set is selected for the economic behavior of this asset class.
Static class view of valuation gap and evidence quality.
L1s & L2s Bubble Map
Static methodology preview. Live data pipeline not yet connected.
Each model states what it measures and where its confidence breaks down.
Capitalizes fee generation using explicit multiple assumptions.
Core inputs
Fees and tokenholder capture are separate questions.
Frames fees relative to network value as a yield-like metric.
Core inputs
Yield language can overstate cash-flow similarity.
Compares network value with capital held in protocols or applications.
Core inputs
TVL is not revenue and may move quickly across chains.
Uses stablecoin supply and transfer activity as a settlement demand proxy.
Core inputs
Stablecoin activity can be application-specific and low margin.
Maps the capital and fees of major applications back to the base network.
Core inputs
Attribution between apps and base network is subjective.
Links active users, developers, and capital depth to network value.
Core inputs
Adoption quality matters more than raw activity counts.
Adjusts model value for staking participation and effective float.
Core inputs
Staked supply can re-enter markets under stress.
Evaluates the asset as a settlement and collateral layer for ecosystem activity.
Core inputs
Requires distinguishing economic settlement from mechanical transfers.
Method confidence reflects evidence quality, source availability, and token-level capture clarity.
A local assumption shell for testing scenario sensitivity without live data.
Scenario sensitivity
Static methodology preview. Changes here recompute a local scenario number only.
Planned source families for turning this framework into a data-backed dashboard.
Assets currently mapped to this valuation class.