DeFiPy 1.2.0

Python SDK for DeFi Analytics, Simulation, and Agents

DeFiPy: Python SDK for DeFi Analytics and Agents

DeFiPy is the first unified Python SDK for DeFi analytics, simulation, and autonomous agents. Built with modularity in mind, DeFiPy lets you isolate and extend your analytics by protocol using:

• UniswapPy
• BalancerPy
• StableSwapPy

For onchain event access and scripting, pair it with Web3Scout — a companion tool for decoding pool events and interfacing with Solidity contracts. Whether you’re building dashboards, simulations, or agent-based trading systems, DeFiPy + Web3Scout deliver a uniquely powerful toolset — unlike anything else in the ecosystem.

🔗 SPDX-Anchor: anchorregistry.ai/AR-2026-YdPXB5g

📝 Docs

Visit DeFiPy docs for full documentation

🔍 Install

DeFiPy requires Python 3.10 or later. Install via pip:

> pip install defipy

Book install (chapter 9 agents)

Chapter 9 of Hands-On AMMs with Python — Building Autonomous DeFi Agents — uses live chain integration via web3scout. To run those examples, install the [book] extra:

> pip install defipy[book]

This pulls in web3scout on top of the core install, enabling the chain event monitoring, ABI loading, and token-fetching utilities that chapter 9's agents require. Other chapters work with the core install alone.

Source install

To install from source:

> git clone https://github.com/defipy-devs/defipy
> cd defipy
> pip install .

System libraries for gmpy2

DeFiPy depends on gmpy2 for high-precision arithmetic in StableSwap math. On most platforms, pip will install gmpy2 from a prebuilt wheel and no further setup is needed. If the install fails, you may need the GMP, MPFR, and MPC system libraries installed before pip install:

macOS (Homebrew):

> brew install gmp mpfr libmpc

Linux (Debian / Ubuntu):

> sudo apt install libgmp-dev libmpfr-dev libmpc-dev

See the gmpy2 installation docs for other platforms.

🔍 Learning Resources

DeFiPy is accompanied by educational resources for developers and researchers interested in on-chain analytics and DeFi modeling.

📘 Textbook

DeFiPy: Python SDK for On-Chain Analytics

A comprehensive guide to DeFi analytics, AMM modeling, and simulation.

🔗 Buy on Amazon: https://www.amazon.com/dp/B0G3RV5QRB

🎓 Course

On-Chain Analytics Foundations

A practical course on transforming raw blockchain data into structured analytics pipelines using Python.

Topics include:

• retrieving blockchain data via Ethereum RPC
• decoding event logs
• analyzing AMM swap events
• building DeFi analytics pipelines

🔗 Course Page: https://defipy.thinkific.com/products/courses/foundations

🚀 Quick Example (Uniswap V3)

---

To setup a liquidity pool, you must first create the tokens in the pair using the ERC20 object. Next, create a liquidity pool (LP) factory using IFactory object. Once this is setup, an unlimited amount of LPs can be created; the procedures for such are as follows:

from defipy import *

# Step 1: Define tokens and parameters
eth = ERC20("ETH", "0x93")
tkn = ERC20("TKN", "0x111")
tick_spacing = 60
fee = 3000  # 0.3% fee tier

# Step 2: Set up exchange data for V3
exch_data = UniswapExchangeData(tkn0=eth, tkn1=tkn, symbol="LP", address="0x811", version='V3', tick_spacing=tick_spacing, fee=fee)

# Step 3: Initialize factory
factory = UniswapFactory("ETH pool factory", "0x2")

# Step 4: Deploy pool
lp = factory.deploy(exch_data)

# Step 5: Add initial liquidity within tick range
lwr_tick = UniV3Utils.getMinTick(tick_spacing)
upr_tick = UniV3Utils.getMaxTick(tick_spacing)
join = Join()
join.apply(lp, "user", 1000, 10000, lwr_tick, upr_tick)

# Step 6: Perform swap
swap = Swap()
out = swap.apply(lp, tkn, "user", 10)

# Check reserves and liquidity
lp.summary()

# OUTPUT:
Exchange ETH-TKN (LP)
Real Reserves:   ETH = 999.0039930189599, TKN = 10010.0
Gross Liquidity: 3162.277660168379  

License

Licensed under the Apache License, Version 2.0.
See LICENSE and NOTICE for details.
Portions of this project may include code from third-party projects under compatible open-source licenses.