YieldSage is a project that implements advanced computational approaches to optimize yield farming strategies on Solana's DeFi ecosystem. The project demonstrates the application of graph theory, numerical optimization, and machine learning to solve complex multi-variable problems in decentralized finance.
For this hackathon, we prioritized implementing:
-
Mathematical Core:
- Portfolio optimization algorithm
- Risk correlation computation
- Return forecasting models
-
Machine Learning Components:
- LSTM prediction model for yield trends
- Risk classification system
- Anomaly detection for yield outliers
- Agent collaboration system with trust relationships
-
Protocol Integration:
- Adapter interfaces for Solana programs
- Data normalization pipeline
- Yield calculation engine
- Interactive visualization system
The project demonstrates the practical application of advanced computer science, mathematics, and machine learning techniques to solve real-world DeFi optimization problems within the constraints of a hackathon timeframe.
We model the Solana DeFi ecosystem as a weighted directed graph G = (V, E), where:
- V = set of protocols and liquidity pools as vertices
- E = set of possible fund movements between protocols as edges
- Each edge e ∈ E has associated properties:
- w(e): expected yield (weight)
- r(e): risk factor
- l(e): liquidity constraint
- t(e): transaction cost
The system performs traversal algorithms including:
- Modified Dijkstra's algorithm for finding optimal yield paths
- Topological sorting to identify dependency chains in complex strategies
- Strongly connected component analysis to identify circular arbitrage opportunities
The optimization engine employs a parallel processing model:
- Multi-threaded protocol data collection to minimize I/O bottlenecks
- MapReduce-inspired pattern for processing large datasets of historical yields
- Workload distribution using a task queue system for strategy computation
- Caching layer with LRU (Least Recently Used) invalidation policy
We've integrated a multi-agent AI system where specialized agents collaborate to make optimal yield strategy decisions:
- Each agent has a specific role in the DeFi optimization process (scanning, risk analysis, trading, coordination)
- Agents exchange insights and build trust relationships based on proven performance
- A collaboration protocol enables agents to reach consensus on complex decisions
- Visualization tools display agent relationships and collaborative decision-making
The agent collaboration system is implemented through:
// Core agent state management
export interface AgentState {
id: string;
name: string;
role: AgentRole;
status: AgentStatus;
lastActive: number;
actions: AgentAction[];
messages: AgentMessage[];
performance: {
successRate: number;
totalActions: number;
profitGenerated: number;
};
collaborationRelationships?: AgentCollaborationRelationship[];
}
// Insight sharing between agents
export interface SharedInsight {
id: string;
sourceAgentId: string;
type: "yield_opportunity" | "risk_assessment" | "market_trend";
data: any;
timestamp: number;
confidence: number;
verified: boolean;
trustScore: number;
}
// Collaborative decision-making
export interface CollaborativeDecision {
id: string;
type: "trade" | "risk_assessment" | "strategy";
participants: string[];
proposals: {
agentId: string;
proposal: any;
confidence: number;
weight: number;
}[];
finalDecision: any;
confidenceScore: number;
timestamp: number;
}Critical data structures implemented include:
- Priority queues for efficient strategy ranking
- Red-black trees for balanced protocol indexing
- Custom hash tables for O(1) protocol lookups
- Bloom filters for rapid opportunity filtering
- Force-directed graphs for agent collaboration visualization
The mathematical core implements Modern Portfolio Theory with adaptations for DeFi-specific constraints:
max_w [ w^T R - λ w^T Σ w ]
subject to:
sum(w) = 1
w ≥ 0
w^T Σ w ≤ σ²_max
Where:
- w: weight allocation vector
- R: expected return vector
- Σ: covariance matrix
- λ: risk aversion coefficient
Strategy optimization employs multiple numerical methods:
- Quadratic programming for constrained optimization problems
- Sequential least squares for handling non-linear constraints
- Gradient descent with momentum for rapid convergence
- Monte Carlo simulation for risk assessment and stress testing
The risk modeling system implements:
- GARCH(1,1) volatility models for yield fluctuation analysis
- Pearson correlation coefficients for inter-protocol relationships
- Value-at-Risk (VaR) estimation using historical bootstrap method
- Principal Component Analysis (PCA) for risk factor identification
- Trust scoring for agent relationship management
The yield prediction system employs a custom neural network architecture:
- Input layer: Time-series features from yield data and market indicators
- Hidden layers: LSTM cells with forget gates to capture temporal dependencies
- Attention mechanism: Self-attention for capturing contextual relationships
- Output layer: Multi-head prediction for different time horizons
class YieldPredictionLSTM(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim, n_layers):
super(YieldPredictionLSTM, self).__init__()
self.hidden_dim = hidden_dim
self.n_layers = n_layers
self.lstm = nn.LSTM(input_dim, hidden_dim, n_layers, batch_first=True)
self.attention = SelfAttention(hidden_dim)
self.fc = nn.Linear(hidden_dim, output_dim)
def forward(self, x):
lstm_out, _ = self.lstm(x)
attn_out = self.attention(lstm_out)
return self.fc(attn_out)The ML pipeline incorporates domain-specific feature engineering:
- Protocol health indicators derived from on-chain metrics
- Temporal features that capture cyclicality in yield rates
- Technical indicators adapted for yield movements (MA, RSI, Bollinger)
- Volatility-based features using exponential weighted moving averages
- Agent performance metrics for collaboration optimization
Training methodology includes:
- Time-series cross-validation with expanding window technique
- Bayesian hyperparameter tuning using expected improvement acquisition
- Early stopping with patience to prevent overfitting
- Learning rate scheduling with cosine annealing
The system implements a custom indexing layer for Solana data:
- Account state aggregation using custom deserializers for each protocol
- Transaction filtering using bloom filters for relevant instructions
- Event-based architecture for real-time data updates
- Custom RPC batching system for efficient data retrieval
Protocol interaction is facilitated through:
- Instruction composition framework for transaction building
- Transaction simulation layer for validation before submission
- Signature verification system for multi-wallet transactions
- Retry mechanism with exponential backoff for transaction reliability
We've implemented a comprehensive visualization layer to provide insights into:
- Agent collaboration networks using D3.js force-directed graphs
- Protocol relationship mapping for identifying optimal yield paths
- Performance metrics for each AI agent component
- Collaborative decision history with confidence scores
The visualization system helps users understand both the computational strategies and the AI agent collaboration that powers YieldSage's recommendations.
Future development would focus on:
- Implementing reinforcement learning for dynamic strategy adaptation
- Expanding the computational graph to include multi-hop yield strategies
- Enhancing the numerical optimization with interior-point methods
- Developing a distributed computation architecture for strategy simulation
- Extending the agent collaboration system with cryptographic verification of decisions
SOL