https://github.com/primitivefinance/rmms-py

Python simulator to test implementation of the RMMS paper results.
https://github.com/primitivefinance/rmms-py

Last synced: 3 months ago
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Python simulator to test implementation of the RMMS paper results.

• Host: GitHub
• URL: https://github.com/primitivefinance/rmms-py
• Owner: primitivefinance
• Created: 2021-04-27T12:57:59.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2022-04-27T16:39:12.000Z (over 2 years ago)
• Last Synced: 2024-05-13T00:40:37.092Z (6 months ago)
• Language: Python
• Size: 829 KB
• Stars: 55
• Watchers: 10
• Forks: 13
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Citation: CITATION.cff

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• defi-derivatives - RMM Simulations

README

        
# RMMS simulations

This project is intended to investigate the replication of payoffs using custom Constant Function Market Makers (CFMMs) in the spirit of the 2021 paper from [Angeris, Evans and Chitra.](https://stanford.edu/~guillean/papers/rmms.pdf) For now it only focuses on the Covered Call replication. The project is organized as follows:

``modules`` contains all the simulation toolkit. In particular:

- ``modules/arb.py`` implements the optimal arbitrage logic.

- ``modules/cfmm.py`` implements the actual CFMM pool logic.

- ``modules/utils.py`` contains a number of utility functions (math, geometric brownian motion generation).

- ``modules/simulate.py`` is simply the function used to run an individual simulation.

- ``modules/optimize_fee.py`` contains the logic required to find the optimal fee given some market and pool parameters.

``simulation.py`` is a script used to run individual simulations whose parameters are specified in the ``config.ini`` file.

``optimal_fees_parallel.py`` is a script to run an actual fee optimization routine for a prescribed parameter space (to be specified within the script itself).

``optimal_fees_visualization.py`` is a script that generates a visual representation of the output of a fee optimization routine.

``error_distribution.py`` is a script to plot the distribution of errors given some market and pool parameters for different fee regimes.

All the different functions and design choices are documented in a separate document.

## Requirements

``pip install numpy, pip install scipy, pip install matplotlib, pip install joblib``