DarwinFi introduces an evolutionary approach to yield optimization in decentralized finance by applying principles of natural selection to trading strategies. We've created a self-improving system that continuously adapts to market conditions without requiring manual intervention.
DarwinFi represents a fundamental shift with its Agent Evolution System, it is a framework where AI yield optimization strategies evolve through generations becoming more sophisticated and effective over time by using Injective's EVM capabilities, this way our intelligent agents can autonomously interact with DeFi protocols to maximize returns.
- AI strategic agents begin with fundamental yield farming approaches
- Machine learning models analyze market conditions
- Neural networks predict potential yield opportunities across multiple protocols
- Performance metrics determine which strategies survive and reproduce
- Successful strategies combine attributes to create improved strategies
- Failed approaches are systematically analyzed to prevent repeating ineffective methods
The platform creates a shared knowledge ecosystem where discoveries benefit all participants:
- Performance data from all agents feeds into a central analysis system
- Strategy components are modularized and recombined based on effectiveness
- Risk patterns are identified across multiple market conditions
- The system maintains strategic diversity to ensure resilience
DarwinFi's agents don't just find yield - they anticipate where opportunities will emerge:
- Agents adjust risk profiles based on market volatility indicators
- Liquidity is positioned ahead of projected yield opportunities
- Multi-chain deployment enables cross-chain arbitrage when beneficial
- Strategy distribution ensures capital efficiency while managing risk exposure
DarwinFi leverages cutting-edge AI technology to create intelligent agents that interact with Injective's blockchain infrastructure:
- Self-improving algorithms: Agents analyze their performance and adjust parameters to optimize returns
- Federated learning: Knowledge sharing between agents without compromising strategy uniqueness
- Reinforcement learning: Reward mechanisms incentivize profitable behavior and penalize poor performance
- Real-time sentiment analysis of crypto markets informs agent decision-making
- Pattern recognition identifies emerging trends before they become obvious
- Anomaly detection protects capital during unusual market conditions
- Smart contracts interface with AI agents through Injective's EVM compatibility
- Model execution happens both off-chain (for complex computations) and on-chain (for transparency)
- Agent decisions are verifiable and auditable through transaction history
DarwinFi leverages Injective's powerful EVM infrastructure for several critical advantages, creating an ideal environment for AI-driven finance:
- High-throughput trading capabilities enable rapid AI strategy adjustment
- Cross-chain interoperability expands the opportunity landscape for intelligent agents
- Advanced order types allow for sophisticated algorithmic entry and exit mechanisms
- Composable smart contracts facilitate AI strategy implementation with minimal gas costs
- Injective's orderly EVM execution provides predictable environments for AI decision-making
- Fast finality ensures AI agents can quickly react to changing market conditions
DarwinFi/
├── agents/ # Agent Evolution System implementation
├── contracts/ # Smart contracts for on-chain operations
│ ├── src/ # Contract source files
│ ├── scripts/ # Deployment and interaction scripts
│ └── test/ # Contract test suite
├── docs/ # Documentation and whitepapers
├── frontend/ # Web interface for monitoring and management
│ ├── public/ # Public assets
│ └── src/ # Frontend source code
├── LICENSE # Project license
└── README.md # This file
- Node.js (v16+)
- Python 3.9+
- Rust (for certain components)
- Injective CLI
- Clone the repository:
git clone https://github.com/ldmdldm/DarwinFi.git
cd DarwinFi- Install dependencies:
# For smart contracts
cd contracts
npm install
# For frontend
cd ../frontend
npm install
# For agent system
cd ../agents
pip install -r requirements.txt- Configure your environment:
cp .env.example .env
# Edit .env with your specific configuration- Run tests:
npm test- Compile contracts:
npx hardhat compile- Deploy to Injective EVM Testnet:
npx hardhat run scripts/deploy-injective-evm.ts --network injectiveEvmTestnetThis project is being developed as part of the Injective AI Agent Hackathon on DoraHacks, where the convergence of AI and blockchain technologies is creating unprecedented opportunities for innovation.
- Timeline: Opens January 21, 2025
Hackathon Start Date: February 4, 2025
Online Submission Deadline: March 17, 2025
Project Evaluation Deadline: March 31st, 2025
- Prize Pool: $100,000
- Track: AI Agent Innovation
- Team: DarwinFi Team
The hackathon challenges participants to build AI agents that can autonomously interact with the Injective blockchain. DarwinFi shows how AI agents can improve DeFi by creating adaptive strategies that evolve over time to maximize yield in changing market conditions!
For more information, please reach out to the team at [email protected]
DarwinFi employs a modular architecture
- Core smart contract deployment
- Basic strategy implementation
- Integration with Injective infrastructure
- Frontend dashboard for monitoring
- Advanced genetic algorithm implementation
- Expanded strategy library
- Multi-chain yield farming
- Risk management enhancements
- AI-assisted strategy development
- Predictive market analytics
- Community governance integration
- Protocol partnerships and integrations
- Node.js (v16+)
- Python 3.9+
- Rust (for certain components)
- Injective CLI (install with
npm install -g @injectivelabs/cli) - Docker & Docker Compose (for containerized deployment)
- Install dependencies:
cd contracts
npm install- Configure environment:
cp .env.example .env- Edit .env with your testnet configuration:
# Required for Injective Testnet
INJECTIVE_TESTNET_PRIVATE_KEY=your_private_key_here
INJECTIVE_TESTNET_RPC=https://testnet.sentry.tm.injective.network:443
INJECTIVE_TESTNET_CHAIN_ID=injective-888
- Deploy contracts to Injective Testnet:
npx hardhat run scripts/deploy.js --network injectiveTestnet- Verify contracts on Injective Explorer:
npx hardhat verify --network injectiveTestnet <CONTRACT_ADDRESS> <CONSTRUCTOR_ARGS>- Save the deployed contract addresses:
# Example output after deployment
# Strategy Manager deployed to: inj1...
# Strategy Registry deployed to: inj1...
# Strategy Factory deployed to: inj1...- Install dependencies:
cd backend
npm install- Configure environment for testnet:
cp .env.example .env- Edit .env with your testnet configuration and the contract addresses from the previous step:
# Node environment
NODE_ENV=production
# Server configuration
PORT=3001
API_PREFIX=/api
# Injective Testnet Configuration
INJECTIVE_NETWORK=testnet
INJECTIVE_ENDPOINT=https://testnet.sentry.tm.injective.network:443
INJECTIVE_CHAIN_ID=injective-888
INJECTIVE_PRIVATE_KEY=your_private_key_here
# Contract addresses (from previous deployment step)
STRATEGY_MANAGER_ADDRESS=inj1...
STRATEGY_REGISTRY_ADDRESS=inj1...
STRATEGY_FACTORY_ADDRESS=inj1...
- Build the TypeScript code:
npm run build- Start the backend server:
npm start- For production deployment, use PM2:
npm install -g pm2
pm2 start dist/server.js --name darwinfi-backend- Install dependencies:
cd agents
pip install -r requirements.txt- Configure the agent system:
cp config.example.json config.json
# Edit config.json with your specific settings and contract addresses- Start the agent system:
python main.py- Install dependencies:
cd frontend
npm install- Configure the frontend for testnet:
cp .env.example .env- Edit .env with your backend API endpoint and testnet contract addresses:
REACT_APP_API_URL=https://your-backend-url.com/api
REACT_APP_INJECTIVE_NETWORK=testnet
REACT_APP_STRATEGY_MANAGER_ADDRESS=inj1...
REACT_APP_STRATEGY_REGISTRY_ADDRESS=inj1...
REACT_APP_STRATEGY_FACTORY_ADDRESS=inj1...
- Build for production:
npm run build- Deploy the built frontend to a static hosting service (e.g., Netlify, Vercel, GitHub Pages):
# Example for Netlify
npx netlify-cli deploy --prod --dir buildFor a complete containerized deployment:
-
Configure all .env files as described above
-
Build and start all containers:
docker-compose up -d- Access the application:
- Frontend: http://localhost:3000
- Backend API: http://localhost:3001/api
Once deployed, you can interact with the DarwinFi platform on Injective Testnet:
- Visit your deployed frontend application
- Connect your Injective wallet (Metamask with Injective configuration or Keplr)
- Ensure you're connected to the Injective Testnet
- Navigate to the "Create Strategy" page
- Select a strategy type (Momentum, Mean Reversion, etc.)
- Configure strategy parameters
- Click "Create" to deploy the strategy to testnet
- Select a strategy from your dashboard
- Click "Backtest" and set the backtest parameters
- Review the backtesting results and performance metrics
- From the strategy details page, click "Deploy to Testnet"
- Confirm the transaction in your wallet
- Once confirmed, the strategy will be live on Injective Testnet
- View your active strategies in the dashboard
- Track performance metrics in real-time
- Make adjustments to parameters as needed
Visit the Injective Testnet Faucet to receive test INJ tokens.
Project Name: DarwinFi
Team: NeoLabs Team
Submission Date: March 3, 2025
DarwinFi is not like traditional yield aggregators because it has:
- Evolution: Strategies improve automatically through machine learning algorithms
- Adaptation: Neural networks enable quick response to changing market dynamics
- Learning: AI system analyzes unsuccessful strategies to avoid similar mistakes
- Diversification: Evolutionary algorithms preserve strategic diversity
- Operation: Agents can operate 24/7 without human intervention
- Verification: All AI decisions are transparent and verifiable on Injective's blockchain
- Implementation of on chain genetic algorithms and machine learning models for strategy evolution
- Novel AI risk scoring system for evaluating strategy fitness across various market conditions
- Efficient capital allocation mechanism that balances exploration and exploitation using reinforcement learning
- Integration with Injective's advanced trading features to enable complex AI-driven strategies
- Development of a secure oracle system for feeding external data to on-chain AI models
- Creation of a decentralized governance mechanism for AI parameter adjustment
Based on hackathon feedback we plan to:
- Bring the AI models with more sophisticated deep learning techniques
- Implement transformer models for better prediction of market trends
- Expand to additional blockchains beyond Injective through cross-chain bridges
- Develop an AI agent marketplace where users can subscribe to different strategies
- Integrate federated learning to enable collaborative improvement without data sharing
- Create API endpoints for third-party developers to build on top of our AI infrastructure
For more information, please reach out to the team at [email protected]