NFT Market On-Chain Analytics Dashboard

ROLE: You are an NFT data analyst who uses on-chain metrics to evaluate collections, detect market manipulation, and identify undervalued projects. You go beyond marketplace-reported statistics to extract the real story from blockchain transaction data.

CONTEXT: NFT marketplace data is often misleading due to wash trading, artificial floor price manipulation, and inflated volume numbers. I need a framework for analyzing NFT collections using raw on-chain data to make informed buying and selling decisions. I focus on Ethereum and Solana NFT ecosystems.

TASK:
1. Wash Trading Detection for NFT Collections — Explain how to identify wash trading in NFT markets using on-chain data. Cover analyzing buyer-seller wallet relationships (same entity trading with itself through intermediary wallets), detecting circular transaction patterns where NFTs return to original holders, flagging sales where the buyer and seller share funding sources, comparing genuine vs inflated volume metrics for collections, and estimating the true organic volume after filtering wash trades.
2. Floor Price Integrity Analysis — Detail how to assess whether an NFT collection floor price is organic or manipulated. Cover tracking the number of unique listings at or near the floor, analyzing listing-to-sale ratios, identifying when a single entity holds multiple floor-priced NFTs, monitoring delisting patterns before floor sweeps, and building a floor price confidence score based on listing depth and holder diversity.
3. Collection Holder Health Metrics — Walk through evaluating the health of an NFT collection through holder analysis. Cover unique holder count trends (growing vs declining), diamond hand ratio (percentage holding longer than 90 days), listing rate as a percentage of total supply, average holding period by wallet segment, and profitability distribution (what percentage of holders are in profit vs loss at current floor).
4. Smart Money NFT Tracking — Describe how to track sophisticated NFT collectors and their moves. Cover identifying wallets with consistently profitable NFT trading histories, monitoring their new purchases as potential alpha signals, analyzing what traits and rarities smart money prefers, tracking when smart money exits collections as a warning signal, and building a smart money NFT portfolio overlap dashboard.
5. Rarity & Trait Premium Analysis — Explain how to use on-chain sales data to calculate real trait premiums. Cover scraping historical sales data and matching to metadata traits, calculating median sale price by trait combination, identifying underpriced rare items relative to historical trait premiums, analyzing how trait preferences shift over time, and building a rarity-adjusted fair value estimator for individual NFTs.
6. NFT Market Cycle Indicators — Design on-chain indicators for NFT market cycles. Cover tracking aggregate NFT volume across marketplaces (Blur, OpenSea, Magic Eden), monitoring new collection launch frequency and success rates, analyzing blue-chip NFT (CryptoPunks, BAYC) floor trends as market sentiment proxies, measuring NFT-to-DeFi capital rotation, and building a composite NFT market cycle indicator with bull/bear/accumulation phase classification.