Liquidity Provider Yield & Risk Intelligence System
About The Project
A research and data engineering initiative to analyze liquidity-providing (LP) yields across major decentralized exchanges and blockchains. The system focused on understanding real APY drivers, impermanent loss, gas costs, and reward structures to enable smarter capital allocation for liquidity-providing and staking strategies.
System Architecture
System Architecture diagram
Key Challenges
FRAGMENTED YIELD DATA
LP returns depend on multiple variables - fees, incentives, liquidity changes - spread across chains and protocols.
IMPERMANENT LOSS COMPLEXITY
Understanding when fee rewards offset impermanent loss required historical, block-level accuracy.
LACK OF RELIABLE HISTORICAL INDEXING
Most protocols do not expose long-term, structured LP performance data via simple APIs.
INFRASTRUCTURE COST VS ACCURACY TRADE-OFFS
Balancing precision (full indexing) with operational cost and scalability was non-trivial.
Our Solution
UNIFIED LIQUIDITY-PROVIDING YIELD FRAMEWORK
Designed a standardized methodology to break down APY into fees, incentives, and loss components.
PROTOCOL-AWARE AGGREGATION LOGIC
Implemented custom aggregation strategies for Uniswap-style AMMs and curve-based pools.
HYBRID DATA SOURCING ARCHITECTURE
Combined managed indexers, on-chain data providers, and custom indexing where needed.
SCALABLE RESEARCH ARCHITECTURE
Built a modular pipeline allowing future dexes, chains, and metrics to be added easily.
The Result
ACCURATE YIELD ATTRIBUTION
Clear separation of fee income, rewards, and impermanent loss drivers.
CAPITAL ALLOCATION INSIGHTS
Enabled smoother liquidity-providing portfolio construction with reduced volatility.
CROSS-CHAIN COMPARABILITY
Normalized liquidity-providing performance across Ethereum, BSC, Avalanche, and others.
FUTURE-READY FRAMEWORK
Reusable architecture for staking, correlation analysis, and strategy simulation.
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