Managed pool (MP) controllers can manage liquidity across multiple sources. Managed pools (MPs) are a new type of liquidity pool introduced by Balancer. MP controllers extend the functionality of the MP through automated liquidity management. MP controllers follow a set of predefined rules and conditions and can be built both off-chain or on-chain. Controllers can also manage other sources of liquidity by taking inputs from those sources of liquidity such as a bonding curve. 

Topological data analysis (TDA) is a powerful data analytics technique that uses the underlying topological and geometric structures of data to create non-trivial, meaningful categories. The Mapper algorithm is a TDA tool that transforms data, which lives in a continuous Reeb space, to a simplicial complex, a topological combinatorial graph which lives in a discrete space. TDA started to turn heads when the Mapper algorithm was used to identify a highly treatable cluster of breast cancer patients from a highly dimensional dataset in 2011.

Maximal extractable value (MEV) bots perform atomic arbitrage transactions on DEX-based liquidity pools. Although accounting for less than 1% of total unique addresses,  these bots execute the majority of transactions in Olympus protocol owned liquidity (POL) both in transaction size and trading volume. This handful of bot addresses creates greater capital efficiency by increasing liquidity utilization and contributes most of the POL fees. Atomic arbitrage bots are also more motivated to execute higher valued swaps to allow OHM price to reflect the true market price. This behavior also extends to the OHM-BTRFLY liquidity pair, where MEV bots contribute the majority of trading volume (90%+) and liquidity utilization to the pool.

Recently I have been on a quest to understand AMM’s from a historical context (pre-DeFi). This 2013 paper by Othman et al titled “A Practical Liquidity-Sensitive Automated Market Maker” takes an axiomatic approach to unify AMM designs by characterizing AMMs by three properties - path independence, translation invariance, and liquidity sensitivity.

I read a paper from 2009 by Agrawal et al titled “A Unified Framework for Dynamic Pari-Mutuel Information Market Design” and these are my takeaways:

Decentralized exchanges (DEXs) have fueled immense growth and activity on the Ethereum blockchain, trading over 1 trillion USD in volume in 2021. Constant function market makers (CFMM) such as Uniswap are the most widely used DEX designs and offer permissionless ways for users to provide liquidity and trade tokens. CFMMs allow investors to permissionlessly construct short gamma portfolios and provide liquidity for a spot market for these portfolio positions.

This paper extends the results of DeFi Liquidity Management via Optimal Control (Tarun, Kshitij, Guillermo, Alex, Victor) by optimizing liquidity management and supply issuance via bonding and staking. The result is increased capital efficiency of bond supply issuance, decreased risk by minimizing price volatility, and sustainable long term supply emissions growth. By implementing this liquidity management model in practice, this will add an additional layer of risk management and capital efficiency to POL (protocol owned liquidity)  assets and increase decentralized exchange volume.

“DeFi Primitive Risk Methodology (DPRM) is an open source risk management library (currently in development) that lets users perform both quantitative and qualitative risk analysis on groups of DeFi primitives using stochastic methods to simulate first and second order effects from any combination of tokenomics designs.”