Chapter 34 Further Reading

Foundational Texts

On-Chain Analytics and Blockchain Data Science

  • Meiklejohn, S., et al. (2013). "A Fistful of Bitcoins: Characterizing Payments Among Men with No Names." Proceedings of the ACM Internet Measurement Conference. The seminal academic paper on Bitcoin address clustering and entity identification. Introduced the common-input-ownership heuristic and demonstrated that Bitcoin's pseudonymity is far weaker than anonymity. Available at: https://cseweb.ucsd.edu/~smeier/papers/bitcoin_imc13.pdf

  • Ron, D., & Shamir, A. (2013). "Quantitative Analysis of the Full Bitcoin Transaction Graph." Financial Cryptography and Data Security. Adi Shamir (the "S" in RSA) co-authored one of the earliest rigorous analyses of Bitcoin's transaction graph, revealing concentration patterns and network structure. A foundational work in on-chain analytics.

  • Harrigan, M., & Fretter, C. (2016). "The Unreasonable Effectiveness of Address Clustering." IEEE International Conference on Blockchain. Demonstrates how clustering heuristics can identify entities across the Bitcoin network, and quantifies the privacy loss that results.

Cryptocurrency Valuation and Metrics

  • Woo, W. (2017). "Is Bitcoin in a Bubble? Check the NVT Ratio." Woobull.com. The original blog post introducing the NVT ratio --- the on-chain equivalent of the P/E ratio. Willy Woo's metric has become one of the most widely cited on-chain indicators.

  • Puell, D. (2019). "Bitcoin's Market Value to Realized Value (MVRV)." A complementary valuation metric that compares current market cap to "realized cap" (each UTXO valued at the price when it last moved). Useful for identifying market cycle tops and bottoms.

Platform Documentation

Etherscan API

  • Etherscan API Documentation: https://docs.etherscan.io/ — Complete reference for all API modules, actions, parameters, and rate limits. Essential for building the Python tools in this chapter. Includes examples for account queries, contract verification, gas tracking, and event log retrieval.

Dune Analytics

  • Dune Documentation: https://docs.dune.com/ — Covers DuneSQL syntax, table schemas for each blockchain, the abstraction layers (decoded tables, spell tables, prices tables), and the API.

  • Dune Getting Started Guide: https://docs.dune.com/getting-started — A tutorial-style introduction covering table exploration, first queries, and dashboard creation. Recommended for readers who want to complement this chapter's Python focus with SQL-based analysis.

  • "Mastering Dune Analytics" community guide: https://dune.com/docs/guides — Community-contributed tutorials on specific analytical tasks: DEX volume analysis, NFT market tracking, token distribution, and governance metrics.

The Graph

  • The Graph Documentation: https://thegraph.com/docs/ — Covers subgraph development, GraphQL query syntax, and the decentralized indexing network. Essential for readers interested in protocol-specific analytics.

  • Subgraph Explorer: https://thegraph.com/explorer — Browse existing subgraphs for major protocols. Before writing custom indexing code, check whether a high-quality subgraph already exists.

Tools and Libraries

Python Libraries for Blockchain Analysis

  • web3.py Documentation: https://web3py.readthedocs.io/ — The primary Python library for Ethereum interaction. Used in this chapter for computing event topic hashes and can be used for direct node interaction.

  • pandas Documentation: https://pandas.pydata.org/docs/ — The foundation for data manipulation in all code examples. If you are unfamiliar with pandas, start with the "10 Minutes to Pandas" tutorial.

  • plotly Documentation: https://plotly.com/python/ — Used for interactive dashboard visualizations. The plotly Express module provides high-level charting, while plotly Graph Objects (used in this chapter) provides fine-grained control.

  • NetworkX Documentation: https://networkx.org/documentation/ — Graph analysis library used for wallet clustering and entity identification. The connected components algorithm is central to entity graph construction.

Professional Analytics Platforms

  • Nansen: https://www.nansen.ai/ — Entity labeling, wallet tracking, and enriched on-chain data. Free tier available with limited features. The platform whose analysis of FTX/Alameda fund flows is discussed in Case Study 34.1.

  • Glassnode: https://glassnode.com/ — Focus on Bitcoin on-chain metrics: NVT, MVRV, exchange flows, miner revenue, HODL waves. Offers both free and premium tiers. Publishes a weekly "The Week On-Chain" newsletter that demonstrates professional-grade on-chain analysis.

  • Chainalysis: https://www.chainalysis.com/ — The leading blockchain forensics firm. Their reports on cryptocurrency crime, ransomware, and sanctions evasion provide context for the forensics techniques discussed in this chapter. The annual "Crypto Crime Report" is freely available.

  • Arkham Intelligence: https://www.arkhamintelligence.com/ — Entity labeling platform with a unique "intelligence exchange" where users can buy and sell address labels. Provides freely accessible address lookups.

  • DeFiLlama: https://defillama.com/ — The most widely trusted open-source TVL aggregator. Methodology documentation explains how TVL is calculated and what adjustments are made to avoid double-counting.

Case Study Sources

FTX Collapse

  • Allison, I. (2022). "Divisions in Sam Bankman-Fried's Crypto Empire Blur on His Trading Titan Alameda's Balance Sheet." CoinDesk, November 2, 2022. The article that triggered the FTX collapse by revealing Alameda's FTT-heavy balance sheet.

  • Nansen Research. (2022). "FTX Situation Report." Multiple blog posts from November 2022 providing real-time on-chain analysis of the FTX collapse. Available at nansen.ai/research.

  • U.S. Department of Justice. (2022). "United States of America v. Samuel Bankman-Fried." Criminal complaint and subsequent indictment. Contains detailed blockchain evidence and demonstrates how on-chain analysis is used in federal prosecution.

  • Huang, V. (2023). "Going Infinite: The Rise and Fall of a New Tycoon." Michael Lewis. While not an on-chain analytics text, Lewis's account of FTX provides the narrative context for the on-chain evidence.

Dune Analytics

  • Haga, F. (2021). Various blog posts on the Dune Analytics blog describing the platform's architecture, ABI decoding process, and community model. Available at dune.com/blog.

  • Chapman, H. (2023). "The Rise of Dune Analytics." The Defiant. A history of Dune from founding to Series B ($69.4 million), covering the platform's growth, business model evolution, and competitive positioning.

Academic Research

Blockchain Network Analysis

  • Victor, F., & Lueders, B.K. (2019). "Measuring Ethereum-based ERC-20 Token Networks." Financial Cryptography and Data Security. Quantitative analysis of ERC-20 token distribution patterns, transfer networks, and concentration metrics across hundreds of tokens.

  • Chen, T., et al. (2020). "Understanding Ethereum via Graph Analysis." ACM Transactions on Internet Technology. Comprehensive graph analysis of Ethereum's transaction network, including degree distribution, clustering coefficients, and community structure.

  • Aspembitova, A., et al. (2021). "Fitness Model for the Bitcoin Network Transaction." Frontiers in Blockchain. Applies network science models to Bitcoin's transaction graph, providing statistical frameworks for anomaly detection.

DeFi Analytics

  • Xu, J., Paruch, K., Cousaert, S., & Feng, Y. (2023). "SoK: Decentralized Finance (DeFi)." IEEE Symposium on Security and Privacy. A systematic review of DeFi protocols including quantitative analysis of TVL, lending metrics, and DEX volumes using on-chain data.

  • Park, A. (2021). "The Conceptual Flaws of Decentralized Automated Market Making." SSRN Working Paper. Rigorous analysis of AMM mechanics using on-chain swap data, demonstrating impermanent loss and capital efficiency patterns.

Wash Trading and Manipulation Detection

  • Von Wachter, V., et al. (2022). "NFT Wash Trading: Quantifying suspicious behaviour in NFT markets." Financial Cryptography and Data Security. Systematic methodology for identifying and quantifying wash trading in NFT markets using on-chain transaction data.

  • Cong, L. W., et al. (2023). "Crypto Wash Trading." Management Science. Academic analysis of wash trading on centralized and decentralized exchanges, estimating that wash trading accounts for 70%+ of reported volume on some platforms.

Newsletters and Ongoing Analysis

  • Glassnode "The Week On-Chain": Weekly newsletter providing professional-quality on-chain analysis of Bitcoin and Ethereum markets. Excellent for seeing how experienced analysts interpret metrics and construct narratives from on-chain data.

  • Our Network: Weekly newsletter featuring data-driven analysis of specific protocols and sectors, written by community analysts. Each edition covers 4-5 protocols with original Dune queries and visualizations.

  • Dune "Spellbook": https://github.com/duneanalytics/spellbook — Open-source repository of curated, peer-reviewed SQL models (called "spells") that transform raw blockchain data into standardized analytical tables. Reading the spell code teaches both SQL best practices and blockchain data modeling.

For readers who want to deepen their on-chain analytics skills after completing this chapter:

  1. Week 1-2: Work through the Etherscan API documentation and build tools for tokens and protocols that interest you personally.
  2. Week 3-4: Create a Dune Analytics account and complete 10 community-posted SQL challenges.
  3. Week 5-6: Read the Meiklejohn (2013) and Victor (2019) papers for academic depth on clustering and network analysis.
  4. Week 7-8: Build a complete analytics dashboard for a specific protocol, combining Etherscan API data (Python) with Dune queries (SQL).
  5. Ongoing: Subscribe to "The Week On-Chain" and "Our Network" newsletters to see how professionals interpret on-chain data in real time.