Skip to main content

Onchain Data Analysis

How to analyse onchain data to research crypto assets.

Leveraging best available tools to develop best practice protocols to gain deeper insights into blockchain networks to make more informed decisions in the rapidly evolving crypto ecosystem.

Best Practices​

Best Practices for Blockchain Data Analysis

  1. Data Integration
    • Combine on-chain data with off-chain sources for a more comprehensive analysis.
    • Utilize APIs and data feeds from multiple sources to ensure data accuracy and completeness.
  2. Pattern Recognition
    • Employ machine learning algorithms to identify recurring patterns and anomalies in blockchain transactions.
    • Develop custom heuristics for detecting specific behaviors or entities on the blockchain.
  3. Visualization Techniques
    • Use advanced graph visualization tools to represent complex transaction networks.
    • Implement interactive dashboards for real-time monitoring of blockchain metrics.
  4. Privacy Considerations
    • Implement techniques like differential privacy to protect individual user data while still allowing for meaningful analysis.
    • Be aware of and comply with data protection regulations when handling blockchain data.
  5. Cross-Chain Analysis
    • Develop methodologies for analysing transactions and patterns across multiple blockchains.
    • Consider the impact of cross-chain bridges and interoperability protocols on data analysis.
  6. Smart Contract Analysis
    • Incorporate tools for analysing smart contract code and execution patterns.
    • Monitor interactions between different smart contracts to identify complex financial activities.

Products​

Essential On-Chain Data Analytics Platforms.

  1. Dune Analytics
    • Recently launched Dune Engine V2, improving query speed and data availability.
    • Introduced cross-chain analytics capabilities, allowing users to analyze data from multiple blockchains simultaneously.
  2. Glassnode
    • Expanded its on-chain metrics to include more DeFi protocols and Layer 2 solutions.
    • Launched Glassnode Studio, a customizable dashboard for advanced on-chain analysis.
  3. Chainalysis
    • Introduced real-time alert systems for suspicious transactions across multiple blockchains.
    • Expanded its coverage to include more privacy-focused cryptocurrencies.
  4. Nansen
    • Developed AI-powered analytics tools for predicting market trends based on on-chain data.
    • Launched Nansen Connect, a wallet-to-wallet messaging platform for crypto users.

Development​

Engineer your own on-chain data analytics tools.

GitHub​

GitHub repositories that can help you build your own on-chain data analysis solution:

  1. blockchain-etl/ethereum-etl https://github.com/blockchain-etl/ethereum-etl This repository contains ETL (Extract, Transform, Load) scripts for Ethereum blockchain data. It allows you to export Ethereum blockchain data to CSV or JSON files and load it into databases like PostgreSQL and Google BigQuery.
  2. blockchain-etl/bitcoin-etl https://github.com/blockchain-etl/bitcoin-etl Similar to the Ethereum ETL, this repo provides tools for exporting Bitcoin blockchain data to various formats and databases.
  3. WTFAcademy/WTF-Onchain-Analysis https://github.com/WTFAcademy/WTF-Onchain-Analysis This is a series of tutorials for blockchain analysis enthusiasts, helping new users learn blockchain data analysis from scratch. It includes code examples and explanations.
  4. duneanalytics/spellbook https://github.com/duneanalytics/spellbook Spellbook is an open-source repository of SQL queries for analysing blockchain data on Dune Analytics. It can serve as a great reference for building your own queries.
  5. paradigmxyz/artemis https://github.com/paradigmxyz/artemis Artemis is a Python framework for writing MEV bots and conducting blockchain data analysis. It provides a high-level API for interacting with blockchain data.
  6. tradingstrategy-ai/trading-strategy https://github.com/tradingstrategy-ai/trading-strategy This repository contains tools for algorithmic trading and blockchain data analysis, with a focus on decentralized exchanges.
  7. flashbots/mev-inspect-py https://github.com/flashbots/mev-inspect-py MEV-Inspect is a tool for analyzing Ethereum blocks for MEV (Maximal Extractable Value) opportunities. It can be useful for understanding complex on-chain interactions.
  8. blockchain-etl/ethereum-etl-airflow https://github.com/blockchain-etl/ethereum-etl-airflow This repo contains Airflow DAGs for exporting and loading Ethereum blockchain data, which can be helpful for setting up automated data pipelines.