This strategy allows you to automatically rebalance the best risk/yield allocation

The risk-adjusted allocation strategy provides a way to earn the best rate at the lowest risk level. The risk-management algorithm takes account of the total assets within a pool, incorporates underlying protocol rate functions and levels of supply and demand, skimming protocols with a bad score/rate mix, and finally determining an allocation that achieves the highest risk-return score possible after the rebalance happens.

It has been developed in collaboration with DeFiScore, a framework for quantifying risk in permissionless lending pools. DeFiScore is a single, consistently comparable value for measuring protocol risk, based on factors including smart contract risk, collateralization, and liquidity. The model outputs a 0–10 score that represents the level of risk on a specific lending protocol (where 10 is the *upper bound = lowest risk*, and 0 is the *lower bound = highest risk*).

You can read more about the risk assessment model here.

With this strategy, we are trying to find the right balance between risk and returns. We are weighting score and apr based on `k`

parameter. This can be modeled as follows:

$max\ q(x) = \sum_{i=0}^{n} \frac{x_i}{tot} * (\frac{\frac{nextRate_i(x_i)}{maxNextRate} + k * \frac{nextScore_i(x_i)}{maxNextScore}}{k + 1})$

where `n`

is the number of lending protocols used, `x_i`

is the amount (in underlying) allocated in protocol `i`

, `nextRate(x_i)`

is a function which returns the new APR for protocol `i`

after supplying `x_i`

,`nextScore(x_i)`

is a function which returns the new Score for protocol `i`

after supplying `x_i`

amount of underlying, `maxNextRate`

is the highest rate of all implemented protocols after supplying `x_i`

amount, same for `maxNextScore`

with regard to the score, `tot`

is total amount to rebalance, finally `k`

is a coefficient for expressing weights of score and apr (k = 1 means equally weighted, currently k = 2 so score weights twice the APR).

$tot=\sum_{i=0}^{n} x_i$