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https://github.com/mmsaki/nft-trading-analysis

This project uncovers patterns in Azuki, Cryptopunks & Boyed Ape Yatch Club NFT trading.
https://github.com/mmsaki/nft-trading-analysis

azuki bored-ape-yacht-club covalent-api cryptocurrency cryptopunks erc721-tokens ethereum etherscan-api finance hvplot metaverse nfts plotly pyhton requests web3py

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

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README 

          
 

## Table of Contents



1. [Project Summary](#-1-project-summary)

2. [Hypothesis](#-2-hypothesis)

3. [Data Collection & Cleanup](#-3-data-collection--cleanup)

4. [Analysis](#-4-analysis)

5. [Postmortem](#%EF%B8%8F-5-postmortem)

6. [Discussion](#-6-discussion)

7. [Contributors](#7-contributors)



## 🎒 1. Project Summary

* Our project uncovers patterns in NFT trading for three NFT collections.

    * Part One : [*Boyed Ape Yatch Club*](https://boredapeyachtclub.com/)

    * Part Two : [*Azuki*](https://www.azuki.com)

    * Part Three : [*Cryptopunks*](http://larvalabs.com/cryptopunks)

* 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?

    - [x] Covalent APIs

    - [x] Etherscan API

    - [X] CSV data imports

    - [x] Other Dependancies ie. Plotly Express

    ![APIs and Imports](./exports/png/nft_analysis.png)

* 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



- [x] Part One: Bored Ape Yatch Club



    ![Daily Volume Bored Apes](./exports/BAYC_daily_volume.png)

    ![Ape Sales](./exports/BAYC_daily_sales.png)



- [x] Part Two : Azuki



    ![Daily Volume Azukis](./exports/azuki_daily_volume.png)



    ![Azuki Sales](./exports/azuki_1000_sales.png)



    ![Fees Comparison](./exports/fees_paid_comparison.png)



    ![Comparing Collections performance](./exports/Volume_sales_recent_1000.png)



- [x] Part Three: Cryptopunks



    ![Wrapped Cryptopunks](./images/wraped_punks.png)

    ![Cris Punks](./images/cryptopunks_bokeh_plot.png)

    ![Chris Combined plot](./images/punks_combined_wrapped_analysis.png)

    ![Chris Cryptopunks Sales Ether](./images/cryptopunks_sales_by_ether.png)



## ⛑️ 5. Postmortem

* Did we find everything we expected to find?

    - [x] 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

    - [x] How did we deal with them

        * Googling

        * Stackoverflow

        * Reading official documentation

        * Consulting instructor

        * Asking tutors

    - [x] 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](./project_analysis_2.0.ipynb) 


**File:** [Project Presentation](./group_presentation.pdf)



![](./exports/project_analysis_2.0.png)

## 7. Contributors 



- [@mmsaki](https://github.com/mmsaki)

    * Organisation

    * Data Analysis

    * Powepoint Presentation

    * README.md

    * Azukis

- [@dockingbay24](https://github.com/dockingbay24)

    * Data Analysis

    * APIs selection

    * Reviewing Project

    * Suggesting Changes

    * Cryptopunks

- [@angel-estrada7](https://github.com/angel-estrada7)

    * General Analysis

    * Assist w/ presentation

    * Bored Ape Yatch Club