# Risk and safety scores - final? maybe Overall grade, G_pool = max(G_asset) over all assets in the pool G_asset = max(h,c,v,l) G = 0 means top quality G = 1 means good quality G = 2 means average quality G = 3 or 4 means use at your own risk Max has been used to ensure that if even one criteria is failed, the pool risk score is lowered. #### Historical score (h) Use the simulation described in previous [model](link) (put link here) and backtest if safe for last 3 months, h = 0 else if safe for last 1 month, h = 1 else, h = 3 #### Crash score (c=0,1,2,3) To detect scam/hack/peg break Initalise c = 0 if no audit from *insert list of firms here*, c++ if MC < 3% * FDV, c++ if no discord/telegram or twitter, c++ DAO and team can override crash score if they feel a token is safe or unsafe for subjective reasons. #### Volatility score (v=0,1,2,3) We can assume that assets with low MC and high volatility have a higher propensity for that volatility to rise even further. if MC < \$100m, v1 = 2 else if MC < \$600m, v1 = 1 else v1 = 0 If volatility is high, let's see what happens if volatility doubles s = max price move in say 15 min if s > 10% and 2s < (1 - cf - li) and 2s < (li - slippage), v2 = 1 ? else v2 = 0 v = v1 + v2 volatility also depends on liquidity, but we will include that in a different score #### Liquidity score (l=0,1,2,3,4) We can assume lower the liquidity today, more likely it'll be even worse in the future if liquidable amount at allowed slippage < \$200k, l1 = 2 else if liquidable amount at allowed slippage < \$1m, l1 = 1 else l1 = 0 We can assume that liquidity incentives being pulled can be a risk. if significant liquidity incentives being pulled within a month, l2 = 1 else l2 = 0 We can assume that less number of humans providing liquidity increases chances of it being removed suddenly. if number of addresses holding LP tokens < ..., l3 = 1 else l3 = 0 l = l1 + l2 + l3