PrismLot

PrismLot explores tokenized stock positions as collectible baskets. A depositor assigns an ETH grade; that grade shapes how often a position is drawn and the standing bid available after a draw.

For grades g₁ … gₙ, each weight is 1/g. Divide a position’s weight by the sum of all weights to get its probability. The expected grade is the harmonic mean n / Σ(1/g). The model uses a 10% surcharge and excludes the separate randomness fee.

Every number in the model book comes from your base grade and spread inputs. These are mechanism calculations, not portfolio holdings or project trading records. One model unit represents one illustrative position; moving a control changes its grade and relative odds.

The intended choices follow the same mechanism: keep the basket, unwrap its underlying assets, take the standing bid in ETH or its reward-token equivalent, or relist at a new grade. The opening preview visualizes the sequence without acquiring a position. Acquisition is coming soon.

No. A rarity class describes a grade relative to the other grades in the book. A familiar company can occupy any class. The artwork identifies a card; it is not a valuation of the underlying business.

The largest grade is the standing crown. The next crown requires a grade 10% higher. The mechanism allocates a share of purchase fees to that depositor; buyer allowance depends on pool cooling. The grade lab displays parameters, not earned fees or issued rewards.

Connection is optional for exploring the model. Choose your installed MetaMask, Coinbase, OKX or Phantom extension, or select SKIP. The site reads the account and current network returned by that wallet. It does not request signatures, asset approvals, transactions or balances.

The card mechanism combines a permitted NFT, tokenized stock legs and a reward-token stake. The local Create flow lets you compose a basket and inspect its grade; it does not mint or deposit assets.