Aquo DeFi Protocol
Based on DeFi Composition and Reinforcement Learning
Our Story
DeFi Composition and Optimization
A combination of dozens of conversations with DeFi professionals, feedback from investors, and finally doctoral research (part-time) led to the Aquo model.
The original ideas were to create derivatives on RWAs (real-world assets) to allow more markets to exist for RWAs. These ideas were shaped into DeFi Composition and then Reinforcement Learning (RL). As of today, we are testing RL systems.
The Aquo DeFi Protocol
The Aquo Journey
Aquo implements AI learning systems for optimization in an emerging DeFi sector which has over a 100 billion dollars in assets and revenues of billions of dollars.
About Us: Passion & Excellence
Who we are?
Our team is a collection of AI and blockchain specialists to deliver state-of-art solutions, using Reinforcement Learning, DeFi Composition, Optimization, Smart Contracts, and Wallet integrations.
What we do?
We work in the field of optimization by implement AI learning systems. These systems assign rewards to DeFi Protocols so we can use multiple DeFi Protocols in one transaction. This lowers slippage, improves returns and enable complex financial products to be built.
Core Values
We believe that DeFi solutions can revolutionize financial systems, improving access to financial services, providing better investment returns, more choice for users, and lower operating costs.
DeFi Protocols
Our Technologies
Reinforcement Learning
We used reinforcement and PyTorch to get the rewards and observations to optimize Aquo transaction.
DeFi Composition
DeFi Composition is used to call DeFi Protocols.
Optimization
We implement optimization strategies using RL.
Simulation
We simulate DeFi Protocols using python to build training datasets.
Aquo Pitchdeck & Whitepaper
Aquo is to revolutionize DeFi
Markets
DeFi Markets
DeFi markets are split into DEXs, Lending, Yield, Yield Aggregators, and more.
March 2023
Trevor conceived the notion of a DeFi Derivative for real-world assets (RWAs). He researched the subject, engaged with investors and produced some demos. By June 2023, investors embraced the idea but said tech uniqueness was needed.
June 2023
Trevor considered that DeFi Composition was viable to build complex financial products.
October 2023
Trevor started a part-time PhD in DeFi Composition.
April 2024
Trevor had some breakthroughs after considering convex optimization and determining that DeFi composition could be optimized with savings of 10 percent. He determined that Reinforcement Learning was suitable to solve these problems.
August 2024
Trevor projects that RL should be working for xy=k (bonding curve).
February 2025
Trevor projects that RL should be working derivatives.
Our Latest Case Studies
Using DeFi Composition and Optimization, Liquidity is aggregated.
Slippage can be reduced by leveraging multiple AMM bonding curves.
A full product life cycle can be integrated with DeFi Protocols
Liquidity Aggregation
Liquidity decreases slippage
By using DeFi Composition we access liquidity pools under optimal conditions.
Risk Mitigation
Optimize transactions on bonding curves
By splitting transactions across DeFi Protocols slippage is reduced.
Complex Financial Products
Building complete financial solutions
This allows a user to build a complete transaction to lend, borrow, buy or sell an option, exchange, and other actions within one transaction.
More Financial Products
Flexible Financial Products
More flexible and far reaching financial products can be created such as options.
More Efficient use of Capital
Capital Efficiency
Several DeFi Protocols can be integrated into one DeFi Composition Protocol (Aquo) allowing capital to be used across DeFi Protocols.
Single Point Contact
Accessibility
A user can access the Aquo platform and access all DeFi Protocols from a single point.
Developing Nations
Infrastructure
When users have limited access to financial systems and agent model can be used to top-up the Aquo app which then enables access to DeFi DAapps, e.g. for tokenization of RWAs.
Tokenization
RWAs
RWAs (real-world assets) can be tokenized via a standard SPV (special purpose model) and then additional products can be engineered via Aquo (e.g. lending).
Decentralized Energy
Energy
Decentralized Energy markets can exist via Aquo in which separate markets can be connected to one trading product. This is applicable for example to locally engineered energy via solar panels and the energy generated can then be traded.