This package is a python re-factor of the original Uniswap V2 pairing code and can be utilized for the purpose of analysing and modelling its behavior for DeFi

Visit docs for full documentation with walk-through tutorials

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

or

> pip install UniswapPy

• See test notebook for basic usage

from uniswappy.erc import ERC20
from uniswappy.cpt.factory import UniswapFactory
from uniswappy.utils.data import UniswapExchangeData

user_nm = 'user_intro'
eth_amount = 1000
dai_amount = 1000000

dai = ERC20("DAI", "0x111")
eth = ERC20("ETH", "0x09")
exchg_data = UniswapExchangeData(tkn0 = eth, tkn1 = dai, symbol="LP", address="0x011")

factory = UniswapFactory("ETH pool factory", "0x2")
lp = factory.deploy(exchg_data)
lp.add_liquidity("user0", eth_amount, dai_amount, eth_amount, dai_amount)
lp.summary()

Exchange ETH-DAI (LP)
Reserves: ETH = 1000, DAI = 1000000
Liquidity: 31622.776601683792

from uniswappy.process.swap import Swap

out = Swap().apply(lp, dai, user_nm, 1000)
lp.summary()

Exchange ETH-DAI (LP)
Reserves: ETH = 999.00399301896, DAI = 1001000
Liquidity: 31622.776601683792

• Abstracted Actions: Obfuscation is removed from standard Uniswap action events to help streamline analysis and lower line count; see article How to Handle Uniswap Withdrawals like an OG, and Setup your Uniswap Deposits like a Baller
• Indexing: Can calculate settlment LP token amounts given token amounts and vice versa; see article The Uniswap Indexing Problem
• Simulation: Can simulate trading using Geometric Brownian Motion (GBM) process, or feed in actual raw price data to analyze behavior; see article How to Simulate a Liquidity Pool for Decentralized Finance
• Randomized Events: Token amount and time delta models to simulate possible trading behavior
• Analytical Tools: Basic yeild calculators and risk tools to assist in analyzing outcomes