LSD AI Engine
Purpose
The LSD AI Engine is responsible for analyzing staking protocols and recommending SOL allocation to achieve optimal risk-adjusted returns, while strictly respecting strategy constraints defined by users and governance.
The AI does not execute transactions.
All execution and custody are handled by on-chain smart contracts.
The system is designed to:
Optimize sustainable yield rather than short-term spikes
Control downside risk through strict allocation limits
Adapt to changing protocol conditions on an epoch basis
Support transparent, explainable allocation decisions
Strategy Constraints (Important)
AI recommendations are always constrained by the active pool-wide strategy, which is determined through quadratic aggregation of user preferences:
Safe Strategy
→ 100% allocation to large, proven protocols (TVL > $1B)
Balanced Strategy
→ Up to 30% allocation to higher-yield, moderate-risk protocols
Max Returns Strategy
→ Up to 50% allocation to higher-yield, higher-risk protocols
The AI cannot exceed these limits, regardless of yield potential.
Data Inputs
The AI engine evaluates protocols using on-chain and market data, primarily sourced from DefiLlama and other public sources, including:
Total Value Locked (TVL) and TVL stability
Protocol revenue and fee generation
Base staking APY
Incentive programs (tokens, points, or additional rewards)
Historical yield performance
Liquidity and withdrawal constraints
Protocol maturity and operational track record
On-chain activity and usage trends
All data inputs are normalized to allow fair comparison across protocols.
Risk & Reward Analysis
Rather than optimizing for headline APY, LSD prioritizes risk-adjusted returns.
Each protocol is evaluated across two core dimensions:
Reward Factors
Base staking yield
Additional incentive value
Yield sustainability
Potential upside from incentive or token programs
Risk Factors
TVL size and concentration risk
Protocol maturity and history
Revenue consistency
Liquidity and exit latency
Smart contract complexity and integration risk
These inputs are combined into internal risk and reward scores, which are used to rank protocols.
Allocation Recommendation Logic
Based on the active strategy and scoring outputs, the AI:
Ranks protocols by risk-adjusted return
Recommends allocation percentages across multiple protocols
Avoids single-protocol concentration
Favors diversification within allowed risk bounds
The output is an allocation recommendation, not an instruction.
Rebalancing Cycle
AI analysis and allocation updates occur once per Solana epoch (~2–3 days)
Strategy preference is recalculated at each epoch
Allocation changes are applied only at epoch boundaries
This prevents excessive churn and ensures predictable behavior.
Performance Feedback Loop
After each epoch:
Realized returns are compared against AI projections
Deviations are analyzed
Persistently underperforming protocols are deprioritized
Risk and reward weights are adjusted conservatively over time
This creates a controlled feedback loop without autonomous execution.
Summary
DAO proposes protocols → AI analyzes risk & reward → strategy defines limits → AI recommends allocation → smart contracts execute → results feed back into analysis.
The LSD AI Engine acts as a decision-support layer, not a black box or autonomous trader.
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