diff --git a/README.md b/README.md index 1acaeed..95ad570 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # SOlush: A Push 3 Virtual Machine in Solidity -Welcome to **SOlush** (pronounced like *"polish"* without the "p"), a Solidity implementation of the [Push 3](http://faculty.hampshire.edu/lspector/push3-description.html) Virtual Machine designed to bring evolved genetic algorithms on-chain. By enabling the execution of Push 3 programs directly within Ethereum smart contracts, SOlush paves the way for decentralized, adaptive systems to address complex optimization problems. +Welcome to **SoLUSH 3** (pronounced like *"slush"* without the "o", composed from *S*o*L*idity and p*USH* 3), a Solidity implementation of the [Push 3](http://faculty.hampshire.edu/lspector/push3-description.html) stack machine, designed to bring evolved genetic algorithms on-chain. By enabling the execution of Push 3 programs directly within Ethereum smart contracts, SoLush 3 paves the way for decentralized, adaptive systems to address complex optimization problems. ## 🌟 Vision @@ -14,12 +14,12 @@ To understand the broader vision, check out the [Vision Document](vision.md). ## 🚀 Getting Started -SOlush is in active development. To contribute or test the functionality, follow these steps: +SoLUSH 3 is in active development. To contribute or test the functionality, follow these steps: 1. Clone the repository: ```bash - git clone https://github.com/your-username/SOlush.git - cd SOlush + git clone https://github.com/your-username/SoLUSH.git + cd SoLUSH ``` 2. Install dependencies (if applicable). 3. Review the examples and test cases to understand how Push 3 programs are executed on the VM. diff --git a/example.md b/example.md new file mode 100644 index 0000000..5e7d82a --- /dev/null +++ b/example.md @@ -0,0 +1,50 @@ +### Utilizing an AI Agent to Optimize Liquidity Provision in Uniswap V3 + +#### **Introduction** + +Providing liquidity on Uniswap V3 offers liquidity providers (LPs) the flexibility to concentrate their liquidity within specific price ranges. However, determining the optimal position for liquidity requires continuous monitoring of market conditions, price trends, and volatility. This process can be complex and resource-intensive for individual LPs. + +An AI agent can automate and optimize this process by analyzing pool data, running a pre-selected instance of a genetic algorithm from the latest evolved population on a stack-based virtual machine, and determining the optimal liquidity position. This ensures efficient execution without the need for evolving populations during runtime. This document outlines a real-world example of how such an agent could function. + +--- + +#### **Workflow of the Liquidity Optimization Agent** + +1. **Data Collection from the Uniswap V3 Pool** + + - The agent collects essential metrics from the Uniswap V3 pool, including: + - Current price of the asset pair. + - Historical price data to calculate volatility. + - Current liquidity distribution within the pool. + - Volume traded since the last invocation. + - Time elapsed since the last invocation. + - These metrics are normalized to ensure compatibility with the genetic algorithm and stack machine inputs. + +2. **Stack Machine Initialization** + + - The agent initializes the Push3 Virtual Machine (VM) and prepares the stack for computation: + - Places the collected data onto the stack as input parameters. + - Loads the genetic algorithm from its stored state onto the stack. + - The VM is then executed, running the genetic algorithm to process the input data. + +3. **Algorithm Execution** + + - While the specific behavior of the selected algorithm instance cannot be fully predicted due to the emergent nature of its evolutionary design, it is guided by the fitness function and optimized to provide a placing of liquidity optimized to: + - Maximize liquidity fees. + - Minimize risk of impermanent loss. + - Strategically consider liquidity concentration of competing LPs. + - The algorithm iterates through each instruction of its genome to arrive at a valid state of computation before a metered timeout of its execution complexity, similar to gas. + +4. **Output and Liquidity Adjustment** + + - The VM outputs the optimal position for liquidity placement, including: + - Price range: The lower and upper bounds for liquidity concentration. + - Liquidity depth: The amount of liquidity to allocate within the range. + - If no significant changes are needed, the algorithm may output a non-action signal. + - The outputs are forwarded to the LP or directly executed if integrated with an automated contract. + +--- + +#### **Conclusion** + +Through data collection, genetic algorithm execution, and actionable outputs, the agent empowers LPs to achieve higher returns with minimal effort. A single user can set up an instance and allocate funds to a management strategy that is automated, strategic, and constantly improving, as new trading data leads the population to constantly evolve. A small percentage of the profit generated by each agent is collected and used to pay rewards for the optimizer network. diff --git a/vision.md b/vision.md index 67ca003..655a703 100644 --- a/vision.md +++ b/vision.md @@ -1,4 +1,4 @@ -# AItherium: Evolving Intelligence On-Chain +# SoLUSH: Running Intelligence On-Chain In decentralized finance, systems often involve tasks such as determining optimal liquidity ranges for automated market makers, allocating staking rewards in a way that balances fairness and participation incentives, or dynamically adjusting collateralization ratios to minimize risks in lending protocols. These challenges are critical for ensuring the efficiency and stability of protocols, yet they are particularly difficult to address without centralization. Off-chain solutions often rely on external computation and centralized entities or oracles, introducing trust dependencies and undermining the decentralized ethos of blockchain systems. @@ -43,11 +43,11 @@ An agent is defined as an entity capable of perceiving its environment, processi ## **Theoretical Framework for Decentralized Algorithm Evolution** -A protocol providing agents as a service could leverage genetic algorithms to address complex problems within blockchain environments. Central to such a system would be the development of a Push 3 Virtual Machine (VM), enabling efficient execution of genetic algorithms. An "Optimizer Pool" of nodes could collaboratively train and evolve these algorithms. These nodes would compete to improve the fitness of the algorithms, with rewards distributed based on their contributions, similar to the incentivization model in proof-of-work systems. +A protocol providing agents as a service could leverage genetic algorithms to address complex problems within blockchain environments. Read through this [real-life usecase explaining the applicability of agents to DeFi](example.md). Central to such a system would be the development of a Push 3 Virtual Machine (VM), enabling efficient execution of genetic algorithms. An "Optimizer Pool", an network of nodes could collaboratively train and evolve these algorithms offchain. These nodes would compete to improve the fitness of the algorithms. Once an improvement, a measuruable better solution to the fitness function is found in off-chain computation, a node would submit it to be accepted into the population of solutions registered on chain, in exchange for a rewards payment by the protocol. The nodes of the optimizer pool would run under an incentivization model simira to proof-of-work systems. ![Components](/components.png) -To evaluate the effectiveness of distributed evolution, a "Council" mechanism could be introduced. This Council would consist of validators who execute the latest evolved algorithms and assess their performance against a predefined target fitness function. Validators would then cast votes on whether the new algorithms deliver measurable improvements. This transparent voting process ensures that only the most effective solutions are rewarded and integrated, fostering trust and collaboration across the Optimizer Pool. By aligning incentives with measurable progress, such a framework provides a reliable and decentralized method for advancing algorithmic evolution. +To evaluate the effectiveness of distributed evolution, a "Council" mechanism needs to be created. This Council would consist of validators who execute the latest evolved algorithms and assess their performance against a predefined target fitness function. Validators would then cast votes on whether the new algorithms deliver measurable improvements. This transparent voting process ensures that only the most effective solutions are rewarded and integrated, fostering trust and collaboration across the Optimizer Pool. By aligning incentives with measurable progress, such a framework provides a reliable and decentralized method for advancing algorithmic evolution. Ultimately, this system creates a foundation for scalable agent development, aligning with the broader concept of agents as a service, where users and protocols in DeFi can deploy and leverage optimized, evolving algorithms tailored to specific tasks.