[RFC] reservegrowth.app - An RSR Valuation Simulator (Request for Feedback)

Ideas & suggestions
1

Hey Reserve community,

I’ve built a tool called reservegrowth.app to help us better understand the fundamental value of RSR based on Reserve Protocol’s actual economics and wanted you guys to try it out and ideally provide some feedback.

What the tool does

The model runs multiple different versions of how the future might unfold given the provided parameters. It models:

  • TVL growth in Index and Yield DTFs (with S-curve adoption and a realistic target TVL over the next 20 ~years)

  • Holders revenue from governance share (vote-locking), staking rewards and platform fees (mint fees, TVL fees, or in the case of ETH+ a yield DTF platform fee as % of collateral appreciation). This a fraction of total revenue, but the part that directly benefits RSR holders.

  • RSR buybacks & burns (protocol buys back at the calculated “fair value” and burns RSR using protocol fees)

  • Circulating supply reduction (or increase) over time

  • Different market regimes and growth assumptions (bear markets slow initial adoption speed, while bull markets accelerate it)

You can freely adjust key variables:

  • TVL growth rates & Total Addressable Market (as in max TVL for index and yield DTFs)
  • Fee structures
  • Unlock schedules
  • Starting “regime”: Does the model start in a bull / base / bear market (slightly changes the adoption speed)

The output shows a range of possible fair values for RSR based on projected cash flows. So all future cash flows are discounted down to today’s fair value.

Goal of the project

This is not ment to provide financial advice or price predictions (markets can be extremely irrational and unpredictable), but I see it as a tool to better understand how RSR holders can benefit from the protocols mechanics.

The goal: Give the community (governors, RSR holders, builders, and analysts) a shared, transparent framework to stress-test RSR’s valuation based on real protocol fundamentals.

Better collective understanding of the tokenomics can lead to more informed discussions around growth initiatives, revenue share, burns, emissions, and long-term alignment.

Request for feedback

I’d love the community’s input on:

  • Is the model directionally correct? Any major flaws or missing variables?

  • Which assumptions feel too optimistic / pessimistic?

  • What other scenarios or features would be useful? (e.g. different burn mechanisms, revenue share splits, integration with real on-chain data, etc.)

  • UI/UX feedback - is it easy to use and interpret?

You can play with it here: https://reservegrowth.app

Looking forward to your honest thoughts, positive or critical! This is very much a work-in-progress and meant to evolve with community input.

Thanks in advance!

0xd15c0

5 Likes
2

Lots of cool functionality here but wondering if could be simplified and organized according to the 3 questions you lay out in the header “how the protocol might grow, what revenue that generates, and what RSR’s fair value would be based on those fundamentals”, and limited to just 2 or 3 charts that tell that story?

re: right now noticing users may not know which way to go, where to start, and may not invest unless it pulls them in. For example when I see “odds” I start to think this is a prediction market or price prediction which I dont believe is the intention.

Regarding “fair value”, the market seems to increasingly like the P/S ratios tracked at both Token Terminal and DefiLlama although their methodologies differ so you will want to pick one and stick to it, and then its easy to compare against peers to see how the market defines “fair value.” Haven’t seen DCF used in startups (or crypto) as its more common on mature, slow moving companies.

Overall well done Disco. Hope others weigh in.

1 Like
3

Thanks for the feedback, James! Regarding your points:

This is something I’m definitely struggling with myself: What information is not needed or potentially confusing? I think the “odds table” might indeed be confusing to people. It shows the percentage of simulations ending up above a certain price range given the parameters, but as you said, it will likely be misinterpreted.

I guess one could remove the Index and Yield DTF contributions to holders’ revenue, or the Market Cap of RSR. These are not necessarily needed. I will definitely think about this.

I’ve actually used some of these ratios in early versions of the model, but I personally see these multiples as a simplified version of a DCF. How do you actually arrive at meaningful multiples, and how do they compress over time? At the end of the day, a protocol with a strong growth trajectory justifies higher multiples and vice versa. But how do you determine what growth should be expected, or how does it translate into actual cash flows that would justify paying a premium today?

I’m not saying multiples are wrong, but in my opinion, they just hide the underlying assumptions and make the result less interpretable, even though they’re quite easy to calculate. I have an “implied P/E ratio” graph on the page for those interested, since you can basically calculate a P/E from the DCF, but not the other way around.

I think the core question is: Who is the target audience? My (maybe very naive) interpretation is that investors with a long-term mindset might be more interested in how the protocol generates revenue and how it benefits RSR holders to arrive at a fair valuation. In contrast, the classic crypto crowd would more likely want to see actual market price predictions.

This model is definitely targeted more toward the first group and is not a tool for making price predictions. Whether that’s the right approach is obviously a different story.

Again, thank you for your input, it is highly appreciated!

4

This is really cool and good-looking. Played around with bear vs bull markets.
Thank you for creating this! Need to dig deeper into which scenarios I find realistic.

1 Like
5

Excellent work!

You’ve now shown: RSR value is downstream of TVL → revenue → buyback.

The open challenge isn’t the math, it’s the delivery, so the insights are spoon fed rather than a tool people have to drive on their own

That’s why I’d revive the idea we discussed a couple months back: a live or AMA where you walk through scenarios on the fly, what slower index adoption does to fair value, what a rapid adoption does.

Cause-and-effect in real time is the model’s real strength, and the hardest thing to get across in a standalone app.

Happy to help host or script a few tight scenario walkthroughs.

More broadly, this points to something structural.

Since fair value is coupled to TVL, More DeFi use cases for DTFs is what moves the variable everything else depends on.

1 Like
6

This is great - how did you write it? Like the charts - always better to get a point across than a number table. What metrics are you using for the data? It would be good to be able to plug a few numbers in - say TVL in DTFs and average fee structure, average burn - to be able to calculate remaining supply and fair value.

I used to post some static numbers on TVL and remaining supply to test pricing theory - if you could use a real world price to earnings index from a well known company e.g.

Traditional Banks

  • JPMorgan Chase: ≈ 16.0 ×
  • HSBC Holdings: ≈ 15.5 ×
  • Citigroup: ≈ 14.5 ×

Investment Banks

  • Goldman Sachs: ≈ 19.5 ×
  • Morgan Stanley: ≈ 17.5 ×

then also do the calculation of current money vs future money value

Step-by-Step Example

Imagine an investment costs $1,000 today. It will pay you $600 in Year 1 and $600 in Year 2. Your target return rate is 10% (0.10).

Step 1: Calculate Year 1 Present Value
\(PV_{1} = \frac{\$600}{(1 + 0.10)^1} = \frac{\$600}{1.10} = \$545.45\)

Step 2: Calculate Year 2 Present Value
\(PV_{2} = \frac{\$600}{(1 + 0.10)^2} = \frac{\$600}{1.21} = \$495.87\)

Step 3: Add them up and subtract the initial investment
NPV = $545.45 + $495.87 - $1,000 = $41.32

Because your result is positive (+$41.32), the investment will make you money

Just thinking out loud - my 2c

1 Like
7

Big agree on real world P/E index, or at least P/S. Crypto asset valuations are increasingly being viewed through the TradFi lens comparing opportunity cost of option A vs option B.

Worth flagging the P/E and P/S definitions in crypto sometimes have variance due to the difference in protocol fees vs revenue downstream, see TokenTerminal’s vs DefiLlama’s definitions for example. Just something to be aware of.

1 Like
8

Looked at this back when you posted it but didn’t reply as I personally do not value these type of calculators. IMO its all up for discussion. Price predictions with these type of highly speculative fundamentals that you and I have no influence over whatsoever (Reserve team’s execution and spending). I prefer time on building out product and advertising that product.

Personally therefore do not think this will move the needle. I do think that by creating this you have showed some real skill! So don’t want to downgrade your effort.

1 Like
9

@Mallo The tech itself is very “vanilla”: go (the programming language) in the backend for server side rendering and chartjs for the charts, so nothing fancy regarding the tech stack.

What is more interesting probably is how I approached it and that might address your other questions/input: In a nutshell, the simulator runs 10k scenarios (it is a Monte Carlo simulation), all with different picks for uncertain parameters (e.g. it randomly picks a growth rate from a probability distribution per iteration, or a different collateral yield etc.). It does all that over a time horizon of 20 years (growing slower as it reaches its TVL ceiling - which can also be set by the user) and then discounts the “holder’s revenue” (staking rewards, index DTF gov. share, buybacks) back to today’s value (Discounted Cash Flow). It also burns tokens using the actual revenue generated that month, assuming a price that uses the “fair value” according to the DCF method as a median. You can change the discount rate in the app, I’ve used 30%, to reflect the relatively high uncertainty of the scenarios. Basically, the higher the uncertainty, the higher the discount rate should be. So this is the user’s choice. I’ve provided 3 “stories” how it all could play out that basically set the parameters accordingly, but you can change all of it yourself and see how things might play out.

I think that should also address your and @0xJMG 's question of why I don’t offer to set a specific P/E ratio. The P/E ratio here is implied by the DCF method, so it is the result of the simulation, not the input. The approach of picking industry specific P/E ratios is basically a shortcut for what the app does, with the downside that it arguably is less suited in situations were uncertainty is extremely high like it is here. It is better suited for more established companies where the growth trajectory is a bit more “tangible”. That’s why you see very high implied P/E ratios in the early stage and more realistic ones after that. It basically shows that given an implied growth trajectory (and therefore implied future revenue), one would have to value the token price accordingly. That markets are irrational is a fact, so a market price is not what I am trying to predict here.

@zeb I appreciate your feedback, and I think it shows one of the weaknesses of my approach. The app is frequently mistaken as a price predictor, when it actually is just a way to show how revenue (fees, rewards etc.) and burns would change over time given the parameters. Because of the DCF approach (see above) the price is a derived “fair value” given the revenue generated under these circumstances. How markets react is an entirely different story obviously and I am in no way trying to predict that.

10

I did see those parameters and revenue, burn parts. But same logic applies. IF we have X amount of usage, then this and this happens. Sure. But knowing that certain numbers go up or down is IMO not interesting if the real driver is the underlying product being further developed or not and on what timeframe.

11

Thank you for building this @0xd15co. Crypto tokens vary widely in how they connect to and accrue value from their underlying products, and being able to visualize the direct link between RSR’s value and DTFs is helpful to anyone learning about Reserve.

@zeb’s reaction raises a fair question: how do you get the core point of this tool across as simply as possible?

A few thoughts:

What if there were a tools tab that included a deployer simulator showing the fees accruing to a deployer based on a DTF’s TVL?

This would be useful to prospective deployers evaluating DTFs as a business. I could imagine the Open Stablecoin Index (OPEN), which operates through the SQUILL DAO, finding a tool like this useful both for planning and as an easy reference point inside governance proposals. The same goes for all future DAOs built on top of DTFs.

If you want to take this to the next level, the ecosystem could use a Reserve-specific terminal: a single place aggregating news, metrics, and activity.

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That you’ve built this tool of your own volition is commendable. If you’d like to develop it further, I’d love to help. And if you decide to pursue it, consider crafting a grant proposal to be compensated for your work. It would have my support, and the DRF can help you get it ready before you publish.

1 Like
12

Some good feedback have rolled in.

Noticing a few patterns:

  1. Can the story be simplified?
  2. Can it use broader market language, instead of feeling too esoteric?
  3. Can it move beyond “what is RSR worth?” toward “what drives RSR value, and what can each of us do to help create it?”

Want to reiterate (differently) the DCF metric is for mature, predictable companies. P/S are for growth companies (and mature ones). P/E moves up the maturity curve, but not much. For this same reason you wont find DCF on the largest analytics platforms for tokenized projects, not on Defillama not on TokenTerminal. Imo its just so far out in left field its irrelevant in this context.

“Meaningful multiples” as you mention is a great callout. But it is less a matter of what can be predicted, and more a product of comparative reality. Predicting the value of the next GPU maker is more a product of comparing of what the market has already valued, so naturally the comparison is NVDA, INTC and others. And while these are much more mature companies, and indeed the back office does have a DCF, you wont even find DCF on Yahoo Finance or whatever your favorite stock picker website until, if at all, many clicks later.

Also good question is meaningful to who? which is a great segue to your question of, who is the core audience?

  • Are they CFOs?
  • Whats their minimum financial metrics literacy?
  • Are they actively congregating on metrics?
  • Do they allocate based on social network reccos?
  • Are they active in their favorite project’s community?
  • What other traits define this audience?

Encourage you to define, name and “size” the user personas and journeys.
More here: User personas | Bitcoin Design

Once you’ve got defined personas and user journeys, then sanity check that with today’s current reality on the RSR project or any failing or successful project. This will help define what the goalposts of success look like and start to inform what to measure.

On quick skim, my hunch is best way to measure your app and whether its successful is with (a) monthly unique website visitors or (b) monthly # of organic social shares in telegrams, on X, on Reddit, etc. There might be even better ways to measure success and I encourage you to find them as the goalpost will guide, constrain and turbocharge how you design the user experience.

Fwiw, social sharing screenshots of Hyperliquid PnLs and Polymarket Bet odds were major drivers in their early days (you might remember your feed filling up with them). Those websites were designed around, and optimized for driving social sharing.

Encourage you to setup event measurement to see where people are clicking and using the website. You can do this especially well with the free tier of Mixpanel. You can also do it with google analytics which is also free, but lower fidelity and flexibility.

  • Mixpanel = BMW
  • Google analytics = Kia
  • Both accessible on free tiers (for low traffic sites).

Last thing to note, right now the app is a secret and has received no marketing attention. No one will use or even know about it, without some surround sound awareness.

The app might benefit from the following:

  • Its own X or Reddit account with 1 or 2 weekly shares, including to relevant telegram channels
  • DRF doing doing 1 or 2 weekly X shares, including to relevant Telegrams
  • Positioning the RSR growth app content in a responsible and highly useful way that Reserve official X, or YouTube will organically want to amplify

I saw a similar tool ambition recently and think there is a lot to learn from it.

TLDR: less is more.

  • fewer numbers
  • fewer buttons
  • linear story top to bottom

Lesson in there, me thinks.

Thanks for putting all this together Disco. The last 10% is the hardest!

1 Like
13

Thanks @0xJMG and @blue for your input. I think I can summarize our discussion like this:

  • The app, while educational and fun to use, feels too theoretical and academic: I fully agree here. The main idea back then was to get a better understanding of how the different “parameters” affect the value of RSR, and this is actually a lot harder to model than I initially thought (there is a lot going on). It was also targeted towards people with a deep interest in the mechanics, but this group of users is obviously rather small.
  • @blue’s idea: Thank you for the UI mockups, they look great. This (or something similar at least) is what I had in mind back then: A platform that mostly targets (potential) deployers. They have different needs and might benefit from a few simple tools showcasing how they can profit from their own DTFs by setting a few known parameters like the fee structure. I like your idea to combine this with an overview of current DTFs and their performance, maybe showing fees collected per DTF, so we can figure out which ones are more profitable etc. So why didn’t I build this? It simply is a lot more work, but I guess we could just start with whatever part we think is more valuable (DTF revenue performance?) and progress from there.

So my personal hypothesis is that deployers are underserved right now and that there’s a real pain point in not knowing how much money you can potentially make by deploying your own DTF. How would your DTF compare to the existing ones? How would it look like at e.g. 500M TVL etc.? I think some sort of tool like this would be a lot more practical to use. We need to carve out the use case and audience first so we can get a better understanding of how to define (and measure) success here. What do you guys think?

14

Agree “carve out the use case and audience.”

I’d suggest listing 4 to 7 possible use case/audience segments first. Starter list (to workshop):

  1. RSR believers
  2. DTF builders/deployers
  3. DTF governors
  4. Professional allocators
  5. Speculators
  6. What else?

Then rank them against the same criteria:

  • Can this audience take clear, impactful action?
  • How would we measure success and adjust?
  • What must happen for this to grow TVL, fees, and ecosystem value?
  • Will users share or discuss it publicly? How specifically?
  • Is there a clear next step toward RSR-related action?
  • Does this align with reserve’s current strategy and ethos?

The hard part is pressure-testing each criteria against reality.

The ranking only works if several audience/use case options are compared through the same lens.