Increasing APYs

We perform this via certain proprietary algorithms. Those can be standard input-output, or more complex ML techniques.

Most primitive approach of algorithmic distribution can be Monte Carlo Simulations That we heavily used during MVP testing phase. MCS is a broad class of powerful computational algorithms, it relies on repeated random sampling of data to generate useful output, in our case useful distribution patterns that can outperform other results.

Given a set of staking pools each offering different APYs and associated risks, the challenge is to allocate funds optimally across these pools to achieve the highest possible average APY over time, while considering the inherent uncertainties in staking rewards.