Revamping Qredo Network’s Tokenomics: A Deep Dive
The world of decentralized finance (DeFi) is ever-evolving, and with it, the need for robust and sustainable tokenomics models. An example of this need for adaptation is that of the Qredo Network, a decentralized custody platform. They recently underwent significant tokenomics overhaul and, in this post, we’ll dive deep into the changes, the reasons behind them, and the technical methodologies employed.
Project Goals and Overview
The Qredo Network is a decentralized MPC-based custody platform designed to offer businesses and investors a secure and efficient way to manage digital assets. Qredo’s vision has always been to create a truly decentralized custody network, with its native token, QRDO, at its heart. Since the launch of QRDO in 2021, the team has been working tirelessly to enhance its usability and increase decentralization. In the medium term, the team wanted to improve on the following 3 axes:
- Decentralization: Transition to a Federated Proof-of-Stake consensus system and eventually to a Delegated Proof-of-Stake.
- Utility: Enhance the role of QRDO as Qredo’s utility token.
- Transparency: Foster community engagement through open governance.
To realize this vision, Qredo partnered with CryptoEconLab to redefine its tokenomics, ensuring the QRDO token adapts and thrives in this evolving landscape. In particular, we proposed a set of new tokenomic mechanisms, including two new Fee Models aimed at increasing token utility and a Staking Model to support Qredo’s introduction of a federated Proof-of-Stake consensus, and we created a bespoke model for the Qredo economy to test, analyze and tune the new mechanisms.
New Fee Models
One of the main changes we proposed for Qredo was the introduction of two new fee models. The idea was to have two different types of fees:
- Protocol fees — fees that need to be paid when submitting requests to the Qredo network. The goal is to avoid spamming attacks on the network and to support Validator operations.
- Service fees — fees computed in a pay-as-you-go model based on the total volume transacted in billable operations. The goal is to have a fairer distribution of network costs by requiring clients moving higher volumes through Qredo to pay more.
We looked into a set of fee models (a full table with all the models considered can be consulted here) and picked two different models for each type of fee.
For protocol fees, we focused on simplicity and proposed a fixed fee amount per transaction, which is denominated in QRDO. Once paid by users, this fee is burned by the protocol. We settled on this design for two main reasons:
- Burning is the most effective mechanism for preventing spam attacks. If validators received the protocol fees, they wouldn’t have costs for adding transactions. Instead, burning introduces a level-playing field for all users wanting to use Qredo’s protocol as the net cost of submitting a transaction is the same for everyone.
- Burning QRDO creates a token supply pressure that benefits all network participants that hold QRDO. In other words, the entire economy profits directly from the tokens being burned. In addition, as protocol usage increases, more tokens will be burned, thus tying adoption with token demand.
As for the Service fees, we have to consider how the team charges these fees to their clients. Currently, Qredo does an estimation of the volume in USD a certain client traded in the past 30 days and uses that volume to compute the fee that should be applied to the current request of that client. This involves using price feeds from Oracles to do the conversion to USD.
Once the fee is computed, clients can either pay in fiat or QRDO. Since a big portion of the clients are institutions, it was important to maintain the optionality to pay in fiat. Another important feature of Qredo’s business is that some institutional clients do not pay directly to the decentralized protocol. Instead, they signed a traditional B2B agreement with Qredo’s LLC, and the Qredo team is responsible for billing and collecting the fees. Thus, we had to consider these two types of clients — the ones that pay directly to the protocol and the ones that pay to the Qredo LLC.
So, we proposed a design where clients have two options — they either pay service fees in QRDO or fiat. If they pay in QRDO, these fees are directly pooled into the Fund controlled by the protocol (the Ecosystem Fund), which will be later distributed among network participants (i.e. Validators and Stakers). If they pay in fiat, these fees will be used to buy QRDO, and only then will they be deposited into the Ecosystem Fund. Since direct QRDO payments involve less work to the protocol, fees paid in QRDO receive a discount.
In addition, the fees collected by Qredo’s LLC are exposed to a tipping mechanism. As Qredo’s LLC is responsible for business development, client relations, and payment collection for these clients, they cannot pay the entirety of the fees back to the network. On the other hand, their clients are using the decentralized protocol and thus some value must be transferred.
Thus, from the total fees collected by the LLC, a percentage is “tipped” to the network, which means that is used to buy QRDO and deposited into the Ecosystem Fund. This fixed percentage is the tipping rate.
As we will see in the simulation results, this tipping mechanism plays two key roles in this new design. First, by using the fees in fiat to buy and lock QRDO tokens, it reduces the token’s circulating supply, thus capturing a part of the value being generated by the Qredo LLC into the protocol. Secondly, it will provide a sustainable and long-term flow of funding for the Staking Model.
New Staking Model
The other key outcome of this work was the design of their new Staking Model. The goal was to build a system to support the new federated model and allow new Validators to join while providing sustainable incentives for Stakers.
The model has two main components:
- Reward sourcing — the staking model is funded by two sources of tokens. Firstly, we have the Ecosystem Fund which is where the Service fees paid to the protocol are locked. Each day, a percentage of its balance is released and made available for distribution. This release rate is controlled by a function that depends on the number of Validators and the Total Value Locked (TVL) by Stakers. Thus, as the participation in the Staking Model grows, so do the rewards released from the Ecosystem Fund. Secondly, we have The Staking Model Support Fund. This fund vests according to an exponentially decaying function and provides a steady and predictable source of rewards. This guarantees that, independently of how the network performs, there are enough rewards to fund the staking program in the short to medium term.
- Reward distribution — From the pool of funds available for distribution, a fixed percentage is used to split this pool between Validators and Stakers. Once split, each participant receives a share that depends on their relative participation. For Validators, this is measured by their performance, while for Stakers, this is measured by their individual TVL share. This design means that there is no fixed Annual Rate for either Validators or Stakers. Instead, we let the participants compete for the available rewards and thus arrive at a fair market rate.
Setting Tokenomics Parameters with MechaQredo
In the first phase of our collaboration with Qredo focussed on designing the broad strokes of the various mechanisms. However, we still needed to find the optimal values for some tokenomic parameters (e.g. the tipping rate, the release rate function for the Ecosystem fund, the decay rate for the vesting of the Staking Model Support Fund, etc.). In addition, we wanted to verify that the mechanisms would behave as expected under different economic scenarios.
To conduct this analysis, we built a bespoke model we named MechaQredo. The model receives two types of inputs, the tokenomic parameters and the “market” inputs that encode user behavior, adoption, and the current status of the network. From these inputs, the model estimates a set of key metrics about the Qredo economy, such as circulating supply and profitability of the Staking Model.
Given a fixed set of inputs, MechaQredo always outputs the same estimates. In other words, MechaQredo does not introduce any source of randomness or uncertainty, leaving us with the flexibility to encode it in the “market” inputs.
With MechaQredo, we conducted two independent analyses:
- Sensitivity Analysis: This helped us understand how changes in specific parameters affected the overall economy. For instance, how does adjusting the tipping rate influence the circulating supply or inflation rate? Understanding these relationships is not straightforward as a 1% change in a particular input does not necessarily lead to a 1% difference in a given output (the impacts may range from negligible to very significant!). You can read more about the results and how we did it in this report.
- Monte-Carlo Simulation Analysis: We ran various Monte-Carlo simulations based on the MechaQredo model for different combinations of parameters, and we analyzed the tradeoffs of each parameter when considering key economic metrics such as circulating supply, supply inflation, and profitability of the Staking Model. We also designed different scenarios (optimistic, neutral, and pessimistic) and observed how the economic metrics evolved in each scenario and the differences between the various possible parameter values. You can read more about the results and how we did it in this report.
Let’s look at some of the results we obtained from the Monte-Carlo simulation we ran with the final parameters. Recall that, between the Fee Model and the Staking Model, we had three components that aimed to align the token’s circulating supply with the growth of the Qredo Network:
- Service fee model. A percentage of service fees collected by Qredo is converted from USD to QRDO and locked in the Ecosystem fund. This increases tokens locked, reducing circulating supply and providing more tokens for Validators and Stakers.
- Protocol fee model. Transactions in the Qredo network incur a fixed fee that is burned, reducing circulating supply. This mechanism has a minor impact compared to others.
- Release function for the Ecosystem fund. The rate at which tokens are released from the fund to pay Validators and Stakers depends on TVL and the number of validators. This ensures more tokens can be released as the participation in the Staking Model grows.
Looking at circulating supply and inflation, we see how these mechanisms work to soften the scenarios, creating a balancing effect on the economics of the network. In the pessimistic combined scenario (which considers at the same time low token prices, adoption, and staking), the daily inflation rates are slightly lower, and the circulating supply grows slower than in the other scenarios. After the legacy vesting schedules finish, the trend in circulating supply inverts, and it drops more sharply in the pessimistic scenario when compared with the other two scenarios.
We also see this effect in the inflows and outflows of the Ecosystem fund, with the pessimistic scenario leading at the same time with more tokens locked and fewer tokens released. This highlights how the tipping mechanism works as a balancing effect on token price and increases alignment between adoption and the token economy.
Finally, higher network usage and lower token prices increase APR for Stakers and QRDO-denominated rewards for Validators. This has two interesting effects:
- The participants of the Staking Model are aligned with the growth of the network since more adoption leads to more rewards for them.
- There is a buffer in case the token experiences economic pressures, with lower token prices leading to more QRDO-denominated rewards to Stakers and Validators.
Conclusion
As we have seen, the new models proposed introduce two interesting effects on the economics of the Qredo Network:
- As the economy experiences hard conditions due to low token prices, the mechanisms reduce circulating supply while, at the same time, increasing the long-term pool of tokens available for the Staking Model. This creates a buffer against hard conditions
- On the other hand, as the Network grows and we see more user adoption, the mechanisms work to also reduce the circulating supply and increase the long-term pool of tokens available for the Staking Model. This aligns all stakeholders with network growth and adoption.
The journey of refining tokenomics is a complex one, filled with tradeoffs and uncertainty. However, we aimed to design a set of mechanisms that look to the long-term health of the Qredo Network and aim to align all the participants with network growth. As Qredo continues its journey, we’re excited to see how these changes will shape its future!
If you want to read more about CryptoEconLab and our work, you can visit our website at cryptoeconlab.io or follow us on Twitter.
And if you are interested in creating a bespoke and robust tokenomics model for your project, we can help! Contact us at celadvisory@protocol.ai
