/nft-trading-analysis

This project uncovers patterns in Azuki, Cryptopunks & Boyed Ape Yatch Club NFT trading.

Primary LanguageJupyter Notebook

Table of Contents

  1. Project Summary
  2. Hypothesis
  3. Data Collection & Cleanup
  4. Analysis
  5. Postmortem
  6. Discussion
  7. Contributors

🎒 1. Project Summary

  • Our project uncovers patterns in NFT trading for three NFT collections.
  • We'll examine relationships between types of:
    • Art and customers
    • Purchase prices and timestamps
    • Trends in purchases over time
    • Historical Volume
    • Purchase prices
    • Trends in sales
    • Transaction data ie. transaction fees paid for all collections
    • Other related questions as the data admits

🎩 2. Hypothesis

  • Should you invest in Azuki, BAYC or Crypto Punks?
    • What are people paying for NFT in USD value?
    • How much in fees are being paid per transaction?
    • Is the value appreciating or declining?
  • How can you tell which collection is performing well?
    • What is the daily transaction volume after Collection is released?

🧤 3. Data Collection & Cleanup

  • How do we collect NFT data?
    • Covalent APIs
    • Etherscan API
    • CSV data imports
    • Other Dependancies ie. Plotly Express APIs and Imports
  • Why cleanup data?
    • Prepare data for analysis
    • Isolate the types of data we are interested in from the rest
    • See what the customer data looks like
    • Evaluate performance

🦺 4. Analysis

  • What kind of data we like to work with and the field we're interested in
    • Market Caps
    • Transaction data
    • NFT art trading sales
    • Gas Prices prices
    • Price volatility
    • Collections
    • Token IDs
    • Contract addresses
    • NFT Owners
    • Global view of NFT Marketplace
    • Wrapped tokens
    • Major Exchanges that process NFT transactions
  • Part One: Bored Ape Yatch Club

    Daily Volume Bored Apes Ape Sales

  • Part Two : Azuki

    Daily Volume Azukis

    Azuki Sales

    Fees Comparison

    Comparing Collections performance

  • Part Three: Cryptopunks

    Wrapped Cryptopunks Cris Punks Chris Combined plot Chris Cryptopunks Sales Ether

⛑️ 5. Postmortem

  • Did we find everything we expected to find?
    • Difficulties
      • Setting proper scope on NFTs there is a lot of data that is available
      • Concating data from multiple data sources
      • Data cleaning
      • Choosing which data to compare and study
      • Selecting plots to display data
      • Using unfarmiliar libraries
      • Working in git as a group
      • Coordinating schedules with people remote
    • How did we deal with them
      • Googling
      • Stackoverflow
      • Reading official documentation
      • Consulting instructor
      • Asking tutors
    • Additional questions that came up that we would research next if we had more time
      • Global NFT market sales comparison
      • Burned tokens vs Active tokens
      • Correlate NFTs that were around pre 2020 to major crypto pricing trends
      • How can we separate authentic sales from suspicious NFT transactions?
      • Why would I buy an NFT vs just create my own?

🎤 6. Discussion

  • After we've analyzed our data to our satisfaction, we'll put together a presentation to show off our work, explain our process, and discuss our conclusions.
  • This presentation will be delivered as a slideshow, and it would give our classmates and instructional staff an overview of our work.

Summary

  • CryptoPunks, Azuki & BAYC
    • Would not recommend investment at this time unless you were a seasoned NFT collector, looking at long term royalty market in metaverse or were passionate about a particular punk.
    • I would recommend trying to get in on ground for new NFT releases and have a plan why you are getting into market.
    • Possibly reasons to invest:
      • Passion for NFTs
      • Flip for profit
      • Creator
      • Community love
    • CyrptoPunks activity and pricing stayed pretty level from inception 2017 through mid-2020 then the market exploded in parallel with adoption of Crypto currencies.

File: Analysis
File: Project Presentation

7. Contributors

  • @mmsaki
    • Organisation
    • Data Analysis
    • Powepoint Presentation
    • README.md
    • Azukis
  • @dockingbay24
    • Data Analysis
    • APIs selection
    • Reviewing Project
    • Suggesting Changes
    • Cryptopunks
  • @angel-estrada7
    • General Analysis
    • Assist w/ presentation
    • Bored Ape Yatch Club