Technical Document

For geeks that care.

Demand Factor
In V1, we have chosen Price and TVL as the demand metrics. The chosen target levels are $0.18, and soon 500M, for Price and TVL respectively. The TVL metric will only be added in Q4 after launching, due to oracle-feed requirements.
DF=0.75 ⋅ PPbaseline+0.25 ⋅ TVLTVLbaselineDF = 0.75\ \cdot\ \frac{P}{P_{baseline}} + 0.25\ \cdot\ \frac{TVL}{TVL_{baseline}}DF=0.75 ⋅ Pbaseline​P​+0.25 ⋅ TVLbaseline​TVL​
And to make sure demand factor does not go out of control, we have introduced some limitations for demand factor.
DF≥0.10, and DF≤1.00DF \geq 0.10,\ and\ DF \leq 1.00DF≥0.10, and DF≤1.00
Distribution
Max Distribution
During the staking program duration PPP
 we can distribute total AmaxA_{max}Amax​
 amount of DAFI. This means each second, the maximum amount of DAFI we can distribute is: 
AmaxP\frac{A_{max}}{P}PAmax​​
However, we need to convert users' reward at the end using reward multiplication. This means at the most extreme case, reward will be multiplied by DFmaxDF_{max}DFmax​
 and divided byDFminDF_{min}DFmin​
.  We call this action Conversion Multiplier -  CMCM CM
, and it has some characteristics:
​DF∈R, and 0.1≤DF≤1DF \in R,\ and\ 0.1 \leq DF \leq 1DF∈R, and 0.1≤DF≤1
 
​⇒CM∈R,and 0.01≤CM≤10 ⇒ CMmax=10\Rightarrow CM\in R, and\ 0.01 \leq CM \leq10 \ \Rightarrow\ CM_{max} = 10⇒CM∈R,and 0.01≤CM≤10 ⇒ CMmax​=10
 
To make sure the total distributed DAFI will never become greater than Max DAFI, the maximum distribution per second needs to acknowledge the multiplier.
Dmax=DFminDFmax⋅AmaxPD_{max} = \frac{DF_{min}}{DF_{max}}\cdot\frac{A
_{max}}{P}Dmax​=DFmax​DFmin​​⋅PAmax​​
Actual Distribution
Actual distribution is the maximum distribution multiplied by demand factor.
D=Dmax⋅DFD = D_{max} \cdot DFD=Dmax​⋅DF
Fees
Fee Deposited
Every time users claim their rewards, 25% of their rewards will be deducted and distributed to other stakers.
Fu=25%∗RF_u = 25\% * RFu​=25%∗R
Fee Distribution
F=∑i=0tFiF = \sum\limits_{i=0}^t F_iF=i=0∑t​Fi​
Reward
Users' rewards are the accumulated amounts of reward every time demand factor changes. The actual implementation is similar to other staking programs like Sushi's MasterChef.
R=∑i=0tRiR = \sum\limits_{i=0}^t R_iR=i=0∑t​Ri​
Final Reward
Rfinal=R ⋅ DFlatestDFstaking+FR_{final} = R\ \cdot\ \frac{DF_{latest}}{DF_{staking}} + FRfinal​=R ⋅ DFstaking​DFlatest​​+F

High-level Document

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Super Staking V2

Last modified 1yr ago