Key Takeaways
We use a simulation to create a representative distribution of market participants interacting with the Liquity protocol in various roles. Each type of agent has predefined actions and utility functions that drive decision-making around protocol interactions.
Stress Testing
We stress test the Liquity protocol to evaluate the risks to users under a wide range of market conditions with varying ETH price volatility, Ethereum gas prices, and user reaction times.
Optimization
We provide optimal parameter values considering the risks of the protocol becoming insolvent or entering Recovery mode, the effects on capital efficiency, and the expected returns for LUSD stakers and liquidators under various market conditions.
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