Compass Labs: algorithms for liquidity provisioning to decentralised exchanges

Name of Project:

Compass Labs

Proposal in one sentence:

Compass Labs ( is building a dynamic liquidity provisioning system for decentralised exchanges by optimising for the users’ risk-adjusted return, to enable all retail investors to participate in liquidity provisioning.

Description of the problem and what problem is it solving:

Currently, liquidity provisioning is complex and investors are not able to quantify risks such as impermanent loss, pool complexity and fee optimisation whilst maximising APY. As an example >50% of liquidity providers on uniswap is suffering negative returns. As a results, decentralised exchanges face significant liquidity risk because liquidity is concentrated to a few actors with in-house written strategies (e.g. on Balancer there are only 17k liquidity providers while the TVL of >1.3B$)

Compass aims to solve this by leveraging the open source infrastructure of blockchain systems through our decision making engine powered by statistics, agent-based simulation and reinforcement learning, to simplify the user experience and reduce barriers to entry for liquidity provisioning. With the product, Compass intends to protect the total value locked in decentralised exchanges from censorship and control by greatly increasing the number of liquidity providers.

At the core of the protocol are the on-chain asset vaults, which can be thought of as an intelligent optimization layer sitting between the liquidity provider and the corresponding liquidity pool on the decentralised exchange. Our liquidity provisioning research stack uses open-source on-chain data, data from centralized exchanges and sentiment data to train our machine learning model. Which predicts real-time price distributions. A constrained optimisation algorithm takes the predictions from the price volatility engine to solve for the portfolio that minimizes risk for a target APY. This enables non-uniform liquidity distribution strategies which are more capital efficient. The optimal position is then given as the vault strategy. Crucially, the results of each stage will be used as feedback to inform and update the model.

Grant Deliverables:

  • Grant Deliverable 1: Library for on-chain data extraction
  • Grant Deliverable 2: Library with auto-regressive (n) model to be fit onto any dataset & library for probabilistic learning through distribution of auto-regressive weighting.

Both deliverables can be published to the Ocean Marketplace, if the algorithm is kept private


  • Elisabeth Duijnstee (Co-founder): ( completed her Ph.D. in Physics at the University of Oxford a year early with multiple first-author papers in leading physics journals. She then joined a hedge fund before doing a Machine Learning fellowship at Faculty AI. She is also a partner at Founders and Funders at Oxford’s Business School.
  • Rohan Tangri (Co-founder): ( was a data scientist at JP Morgan London, who also sponsored his Ph.D. in Modern Statistics and Statistical Machine Learning at Imperial College London. He also was a lead scientist at Limbic Labs and helped them raise a successful £400K government funding. Rohan also did research with NASA to develop a noise detection algorithm for the InSight mission on Mars.
  • Peter Yatsyshin (Research Scientist): ( is a research associate at The Alan Turing Institute, focusing on scalable methods for statistical inference and machine Learning. He holds a Ph.D. from Imperial College London in computational statistical physics. Peter’s favourite tech stack is jax and numpyro and has successfully applied this to research for the NHS.
  • Theo Dale (Blockchain engineer): ( is a software engineer who graduated from the University of Bath with a Masters in Computer Science. Since then he helped multiple start-ups to deliver products ranging from building biodiversity analysis tools to decentralized NFT collateralized lending protocols.

Twitter: @labs_compass

Discord: Discord

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