CFMMRouter.jl

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Convex optimization for fun and profit. (Now in Julia!)

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최종 버전 다운로드 (.zip)

- - CI.yml
  - CompatHelper.yml
  - TagBot.yml
- router_scaling.png
- scaling.jl
- - api.md
  - index.md
  - method.md
  - objective.md
- make.jl
- Project.toml
- arbitrage.jl
- liquidate.jl
- Univ3.jl
- CFMMRouter.jl
- cfmms.jl
- objectives.jl
- router.jl
- utils.jl
- arb.jl
- cfmms.jl
- objectives.jl
- runtests.jl
- swap.jl
.gitignore
LICENSE
Project.toml
README.md

// repository documentation

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# CFMMRouter

[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://bcc-research.github.io/CFMMRouter.jl/dev/)
[![Build Status](https://github.com/bcc-research/CFMMRouter.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/bcc-research/CFMMRouter.jl/actions/workflows/CI.yml?query=branch%3Amain)
[![codecov](https://codecov.io/gh/bcc-research/CFMMRouter.jl/branch/main/graph/badge.svg?token=TYizMgRYNE)](https://codecov.io/gh/bcc-research/CFMMRouter.jl)

## Overview
This package contains a fast solver for the [CFMM Routing problem](https://angeris.github.io/papers/cfmm-routing.pdf). 
We partially decompose the problem to enable fast solutions when the number
of CFMMs is large relative to the number of tokens. We describe our algorithm 
in detail in our paper, [An Efficient Algorithm for Optimal Routing Through Constant Function Market Makers](https://arxiv.org/abs/2302.04938).

For more information, also check out the [documentation](https://bcc-research.github.io/CFMMRouter.jl/dev/).

## Quick Start
First, add the package locally.
```julia 
using Pkg; Pkg.add("CFMMRouter")
```

Make some swap pools.
```julia
using LinearAlgebra
using CFMMRouter

equal_pool = ProductTwoCoin([1e6, 1e6], 1, [1, 2])
unequal_small_pool = ProductTwoCoin([1e3, 2e3], 1, [1, 2])
prices = ones(2)
```

Build a Router & route.
```julia
router = Router(
    LinearNonnegative(prices),
    [equal_pool, unequal_small_pool],
    2,
)
route!(router)
```

Check out the results.
```julia
Ψ = round.(Int, netflows(router))
println("Profit: $(dot(prices, Ψ))")
```

## Performance
This routing algorithm scales approximately linearly in the number of swap pools
for the [arbitrage problem](https://bcc-research.github.io/CFMMRouter.jl/dev/examples/arbitrage/).
These tests were run on a MacBook Pro with a 2.3GHz 8-Core Intel i9 processor.
Several performance improvements are possible.
![alt text](https://github.com/bcc-research/CFMMRouter.jl/blob/main/benchmark/router_scaling.png)

## Citing
```
@article{diamandis2023efficient,
  title={An Efficient Algorithm for Optimal Routing Through Constant Function Market Makers},
  author={Diamandis, Theo and Resnick, Max and Chitra, Tarun and Angeris, Guillermo},
  journal={arXiv preprint arXiv:2302.04938},
  year={2023}
}
```

## References
- T Diamandis, M Resnick, T Chitra, G Angeris. [An Efficient Algorithm for Optimal Routing Through Constant Function Market Makers](https://arxiv.org/abs/2302.04938)

- G Angeris, T Chitra, A Evans, and S Boyd. [Optimal Routing for Constant Function Market Makers](https://angeris.github.io/papers/cfmm-routing.pdf)

CFMMRouter.jl

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