Revenue Sharing Model

Revenue Sharing Model

Learn more about $KAGE's revenue share model

We are developing Kage with the goal of establishing it as a dominant trading platform on both Injective and potentially the larger Cosmos ecosystem. Achieving this ambitious objective will require a significant amount of time.

For Kage to prosper in the long term, it is crucial that the interests of our community, which includes Kage product users, $KAGE holders, and the broader ecosystem comprising DojoSwap and Injective, are all aligned and appropriately incentivized.

Hence, to achieve this alignment and incentivization, we have implemented a revenue-sharing model. Through this model, we aim to distribute the revenue generated by our platforms back to the community, ensuring that those who have contributed to KAGE's success are fairly rewarded.

Revenue Allocation Breakdown

  1. 30% - Liquidity provision for $KAGE LPs

  2. 25% - Operational Expenses and Development

  3. 20% - Buyback and burns of $KAGE

  4. 20% - Referral Fees

  5. 5% - Buyback and burns of $DOJO

Description of Revenue Allocation
  1. Operational Expenses and Development:
    Goes towards supporting ongoing operations, maintenance of bot, and continued R&D efforts to enhance the platform with new features.

  2. Buyback and burns of $KAGE:
    Reward $KAGE holders

  3. Buyback for Liquidity Provision on $KAGE Liquidity Pools on DojoSwap:
    Enhance liquidity to better protect the value of $KAGE. Rewards from LP will be channeled towards future rewards.

  4. Referral Fees:
    Each referrer will obtain a cut of the fees obtained from their down-line - ie. if someone they referred trades 1 INJ, they will get 0.2% as referral fees.

  5. Buyback and burns of $DOJO:
    Reward $DOJO holders

It is important to note that fee allocations will be adapted over time to align with changing market conditions, trading volumes. The Kage team will take into account the communities' interests when making such decision, but we require this flexibility in order to continually optimize.

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