💡Solutions
MetaQuants' flagship products include NFT pricing and floor price oracles that serve as a backbone for protocols and platforms including our AI-driven NFT Terminal.
NFT Pricing Algorithm
MetaQuants provides users with an NFT Evaluation Solution which features three functionalities:
Wash Trading Filter - prevents deceitful sales from entering the model.
Point Estimate - a predicted price for the fair value of an asset.
Price Range - apart from a point estimate, a price range is computed, so different risk appetites can be accommodated.
Collection Floor Price
A Collection Floor Price API/Oracle is provided, a list of the supported projects can be found here. The solution encompasses:
Wash Trading Filter - prevents deceitful sales from entering the computation.
Anomaly Detection - a self-supervised machine learning model is employed to flag periods of anomalous activity.
Outlier Removal - sales over/under a predefined threshold are removed.
TWAP - time-weighted average of the floor price is returned by the API end-point.
MetaQuants offers a highly customizable framework through which the end user can define all variables for the Floor Price calculation. As an add-on, any collection can be included on-demand, but MetaQuants does not guarantee its legitimacy.
MQ NFT Terminal
We are developing an AI-powered terminal that is designed to address every facet of the NFT financialization narrative, and facilitate the generation of alpha for the end user.
General Metrics - NFT index, collection dominance, marketplaces volume, wash trading.
NFT News - aggregated information from leading NFT magazines and twitter profiles.
Collection Analytics - collection screener, market depth, floor price, top holders, sales.
Individual NFT Analysis - real-time appraisal, traits floor price, value drivers, cost basis.
NFT Finance Aggregator - NFT lending activities on P2Peer & P2Pool protocols.
Credit Risk Score - likelihood of liquidation for a loan on an address level.
Sentiment Analysis - correlation between events and floor data in a time-series manner.
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