As a public good for open research, I maintain a curated list of resources for learning and researching the quantitative aspects of Web3 and DeFi.
This list caters to researchers, analysts, enthusiasts, and students who are interested in these fields. Whether you're just starting your journey or looking to delve deeper, I trust you'll find value in the following sections.
A few things to note:
I order items in each section by difficulty level / knowledge prereq, from basic explanatory material to advanced, research-focused sources.
I only list 1. resources I've personally used and 2. resources that are free / open source (except a few textbooks)
I try to maintain a diverse set of formats (long and short text, video/audio, courses, threads, etc.)
Educational use only -- not product endorsement / financial advice
This page is work in progress. Feedbacks/suggestions are welcomed! (dm me on Twitter or Telegram @MingXDynasty) If I accept a suggestion I will add you to the contributor list.
Contributors: Mingxuan He
Last update: 07/23/2023
Coinbase Crypto Basics: Basic glossary for the absolute beginner
OdysseyDAO's Intro to Web3: Hands-on guides for setting up wallets, basic transactions, etc.
Whiteboard Crypto youtube channel: 10-minute illustrated videos covering basic crypto/blockchain concepts like "what is a blockchain", "what is DeFi", "what is Uniswap", "what is a DAO", etc.
Finematics youtube channel: Introductory videos to DeFi mechanisms and tokens
DeFi MOOC by Stanford & Berkeley RDI: Great overview course on DeFi
Messari research: Quarterly protocol reports (due-diligence type overview) - they might be paid by the protocol
@TheDeFiEdge, @DeFi_Cheetah: Twitter threadoors covering protocol deep-dives, news, etc. Good accounts to follow for those new to crypto twitter
Alex Nezlobin (@0x94305): Threadoor for more advanced / technical DeFi audience
Polygon DeFi research blog: Some deep dives into derivative protocols and ecosystem
Gauntlet research: Hard-core quantitative research on DeFi incentive & mechanism design, tokenomics, and risks
MOST IMPORTANTLY: READ THE WHITEPAPERS
For a survey and guide for on-chain data sources and tools check out this great guide by Xin Wan and Gordon Liao from Uniswap: On-chain data science guide, queries, and tools
CoinMarketCap: Aggregate stats for tokens, good for studying ecosystems like LSDs, gaming, etc.
Etherscan (or any other block scanner): Tracking specific transactions / wallets / contracts
DefiLlama: Aggregate data for DeFi projects / tokens. Best for: token usage / TVL, yields
Dune Analytics: Currently the best site for queryable databases and community dashboards, but other options like Flipside and Footprint are increasingly viable too. Some good Dune accounts to check out (fork queries from):
Messari API / CoinGecko API: Free APIs for (historical) crypto prices, market data metrics, on-chain metrics, and qualitative information
FRED: TradFi / Macroeconomic data including interest rates, inflation, real GDP growth, etc.
Subgraphs on The Graph: Indexing of unique data beyond event emits. See this tutorial here: GraphQL Walkthrough: How to Query Crypto
Blockchain-ETL Public Data: BigQuery-compatible, low-level ETL data for Ethereum and Polygon.
AWS Public Blockchain Data: low-level transactions data for Bitcoin and Ethereum.
Khan Academy's Intro to Statistics, Intro to Calculus, Intro to Linear Algebra: Beginner math foundations for data science / ML.
For a more challenging linear algebra class check out MIT's 18.06 by Gilbert Strang
W3Schools Python Tutorial: Beginner tutorials in Python, the standard language for ML / data science / quantitative research
UC Berkeley's Data 100 Course: Open course covering intro-level techniques for ML/DS in Python and some SQL. (update: they recently made a lot of recordings private but most slides and code are still publicly available)
Deeplearning.ai's Machine Learning Specialization and Deep Learning Specialization: Best online courses for ML, period. Andrew Ng is a legend. The entire sequence would require significant time commitment but worth it imo
The Elements of Statistical Learning: Stanford textbook covering low-level (math heavy) machine learning concepts. Good for advanced undergrad / grad students with solid math & stats foundation (intro-level real analysis / measure theory recommended)
Metrics DAO: DAO for Web3 data analysts. They offer a free Web3 Analytics 101 course using Dune/Flipside. Analysis bounties available
Token Engineering Commons: Community of token engineers and cryptoeconomic researchers. They offer a free course sequence on token engineering.
QuantEcon: Open source learning and development community for quantitative economics. Open courses available from advanced undergrad to PhD level. Code packages available in Python / Julia.
i.e. computational finance, financial engineering
Topics in Mathematics with Applications in Finance - MIT Open Courseware
Here're some high-impact papers related to Web3 & cryptocurrencies by subject. For a more complete list of published papers check out CBER's Blockchain Literature Catalog.
"Is Bitcoin a real currency? An Economic Appraisal" by Yermack
"Some Simple Bitcoin Economics" by Schilling and Uhlig
"The Economic Limits of Bitcoin and Anonymous, Decentralized Trust on the Blockchain" by Eric Budish
"Some Simple Economics of the Blockchain" by Catalini and Gans
"Blockchain without Waste: Proof-of-Stake" by Saleh
"Tokenomics: Dynamic Adoption and Valuation" by Cong, Li, and Wang
"Risks and Returns of Cryptocurrency" by Liu and Tsyvinski
"Trading and Arbitrage in Cryptocurrency Markets" by Makarov and Schoar
"Is Bitcoin really untethered?" by Griffin and Shams
"Sex, Drugs, and Bitcoin: How Much Illegal Activity Is Financed through Cryptocurrencies?" by Foley, Karlsen, and Putnins
"Initial Coin Offerings: Financing Growth with Cryptocurrency Token Sales" by Howell, Niessner, and Yermack
"Improved Price Oracles: Constant Function Market Makers" by Angeris and Chitra
"Automated Market Making and Loss-Versus-Rebalancing" by Milionis, Moallemi, Roughgarden, and Zhang