This repository is dedicated to the analysis of trading volumes and liquidity across various decentralized exchanges (DEXs) on different blockchains, including Ethereum, Binance Smart Chain, Solana, Polygon, Arbitrum, and Optimism. The project utilizes historical trading data to derive insights into the dynamics of cryptocurrency trading on these platforms, aiming to identify trends, behaviors, and opportunities within the DeFi space.
README.md - The top-level README for developers and analysts using this project.
DEX Data Analysis Report Across Chains.py - Detailed analysis of DEX trading statistics with aggregated data of several blockchains with an interactive dashboard showcasing visualizations of dex statistics across chains.
Decentralized Exchange (DEX) Trading Volumes and Liquidity by Chains.py - Comparative analysis and an interactive dashboard for real-time data visualization by each chain respectively.
The project involves the following datasets, with each dataset containing fields such as daily, weekly, and monthly trading volumes, USD liquidity, and transaction counts:
- Ethereum (ETH) Trading Data
- Binance Smart Chain (BSC) Trading Data
- Solana (SOL) Trading Data
- Polygon (MATIC) Trading Data
- Arbitrum (ARB) Trading Data
- Optimism (OP) Trading Data
Data for this project is sourced from various blockchain APIs and third-party DEX analytics services. The raw data is processed and stored in CSV format for analysis.
Ensure Python 3.x is installed on your system. Install the required Python libraries in bash using:
pip install pandas matplotlib seaborn dash jupyter-dash
To run the analysis scripts:
python DEX Data Analysis Report by Chain.py python Decentralized Exchange (DEX) Trading Volumes and Liquidity Across Chains.py
The dashboards provide real-time insights into DEX trading volumes and liquidity. It features interactive charts and graphs that allow users to explore data across different time frames and blockchains.
Data cleaning involves standardizing date formats, handling missing values, and filtering irrelevant data points to ensure accuracy in analysis.
Various visualization techniques are employed to represent the data effectively:
- Line plots and bar charts to show trends over time.
- Heatmaps to display activity concentration.
- Comparative graphs to highlight differences between blockchains.
New features such as normalized volumes and liquidity ratios are derived to provide deeper insights into the market conditions.
The project employs statistical models to analyze trends and perform comparative assessments across different blockchains.
Concluding remarks provide strategic insights derived from the analysis, offering actionable recommendations for traders and investors.
This project is licensed under the MIT License - see the LICENSE.md file for details.
For inquiries or collaborations, please contact Mark Benhaim at [email protected].
Special thanks to Dune Analytics and the University of Michigan for academic support and resources. Additional gratitude to open-source communities and data providers in the cryptocurrency ecosystem.