Technical Documentation
  • 👋Getting Started
    • What is Amplifi
    • Architecture
  • 🔒Auth
    • Authentication
    • Authorisation
    • User Interaction
    • Flow Interaction
  • âž—Algorithmic Distribution
    • Increasing APYs
      • Defining Variables and inputs
        • Getting APY values from AAVE.
      • Objective Function
      • Monte Carlo Simulation
      • Optimisation
    • Redistribution
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  1. Algorithmic Distribution
  2. Increasing APYs

Monte Carlo Simulation

MCS is employed to simulate various scenarios of staking rewards over time:

  • Scenario Generation: Generate multiple scenarios of staking rewards based on historical data and probabilistic models.

  • Funds Allocation: For each scenario, allocate funds according to a predetermined strategy or randomly within specified constraints.

  • Evaluate Performance: Compute the APY for each scenario based on the funds allocation.

  • Aggregate Results: Aggregate the results of multiple simulations to derive statistical measures such as expected APY, variance and risk-adjusted returns.

  • Liquidity Pool Depth: Monitoring liquidity across all integrated pools, checking in and out flows, historical volume etc..

  • Historical Performance: Monitoring quarterly and annual performance to predict future performance.

MCS runs about 6000-10000 times before distribution/re-distribution of assets until an optimal distribution pattern is found.

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Last updated 3 months ago

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