You’ve learned so far about the basic mechanics of data markets. The MarketToken tracking ownership, the voting mechanism, the process for getting listings accepted into the market. You now understand that a maker submits a listing candidate on-chain (and sends the data off-chain to the datatrust). If accepted, the maker is rewarded listing_reward of MarketToken. You might now be asking yourself, why would anyone bother? This MarketToken is a new ERC-20 token that no one has heard of. Why does it have any monetary value?

The answer to this question is that the MarketToken for a particular data market is backed by the Reserve for that data market. You can think of the Reserve as the “bank account” tied to each data market. It holds the funds tied to that data market and serves as incentive for makers to contribute the market. These funds are denominated in EtherToken (which if you recall, is 1-1 equivalent with ETH). Holders of MarketToken are authorized to their fair share of these funds, which means that MarketToken is directly redeemable for ETH. Since ETH has real value (in $), it means that MarketToken has real value, so long as the Reserve isn’t empty.

You might now say, that’s all well and good, but why would anyone bother putting any money into the reserve? What’s in it for them? You got a clue in the last chapter; when buyers purchase data, a fraction of the payment is sent to the reserve. Think of this as the fee that the data market takes for facilitating the data transaction. This means the reserve holds a fraction of earnings from the data in the data market. As the data in a data market is used more and more, the Reserve builds up funds, just like a company whose product is widely purchased builds up funds in its bank account.

It’s also important to emphasize that data purchases aren’t the only way the Reserve can gain funds. You’ll learn more about patron support in the next section. The remainder of this chapter introduces the basics of the reserve for a data market. In addition to discussing patronage, it introduces the “algorithmic price curve,” the market making mechanism which allows stakeholders to buy or sell MarketToken at all time, and discusses the smart contract code backing the Reserve.


Who is a patron? Following the dictionary definition, a patron is an individual with funds who wishes to support a worthy cause. For example, a rich patron of the arts may pay for artists to follow their dreams and make beautiful works of art. Although we don’t usually think of them this way, a venture capitalist could also be viewed as a type of patron, supporting the growth of companies they believe in (while hoping to make a healthy return of course). A patron may be driven by different motives, altruistic or economic, but the mechanics are the same in either case. A patron is an entity who transfers funds to other individuals to support their work.

Thanks to the algorithmic capabilities of smart contract systems, we can formalize this transfer in code. In particular, a patron is an entity who transfers funds to the Reserve for a data market. Why would the patron do this? In our case, it’s because the patron receives an amount of newly minted MarketToken in return for their contribution. Rather than being overly descriptive, let’s just take a look at the function.

def support(offer: wei_value):
  @notice Allow the purchase MarketToken with EtherToken priced according to the "buy-curve"
  @param offer An amount of Ether Token in Wei
  price: wei_value = self.getSupportPrice()
  assert offer >= price # you cannot buy less than one billionth of a market token
  self.ether_token.transferFrom(msg.sender, self, offer)
  minted: uint256 = (offer / price) * 1000000000 # NOTE the ONE_GWEI multiplier here as well # TODO maybe implement `mintFor()`
  self.market_token.transfer(msg.sender, minted)
  log.Supported(msg.sender, offer, minted)

Here offer is the amount of funds the patron is offering to the data market. The function computes the number of MarketToken that should be minted for this offering by by consulting the “algorithmic price curve” to obtain the current exchange rate (more on this shortly). Note that offer is in units of EtherToken wei. The returned value will be in terms of MarketToken wei. offer is added to the data market reserve and the returned MarketToken is newly minted.

Let’s take a look at a diagram that illustrates the core idea:

Support Flow

You might ask, what about the reverse operation? What if I supported a data market as a patron, but something’s changed and I no longer have believe that this market is worth my support. Is there any way for me to recoup my funds? In the physical world, this might have to involve lawyers and lawsuits, but in our case, we can create algorithmic rules which allow for a clean withdrawal. Let’s check out the Reserve.withdraw() function:

def withdraw():
  @notice Allows a supporter to exit the market. Burning any market token owned and
  withdrawing their share of the reserve.
  @dev Supporter, if owning a challenge, may want to wait until that is over (in case they win)
  withdrawn: wei_value = self.getWithdrawalProceeds(msg.sender)
  assert withdrawn > 0
  # before any transfer, burn their market tokens...
  self.ether_token.transfer(msg.sender, withdrawn)
  log.Withdrawn(msg.sender, withdrawn)

Reserve.withdraw() burns all the MarketTokens associated with its caller and withdraws their share of the reserve (the percent of reserve withdrawn equals the percent of MarketToken this stakeholder owns).

More precisely, the fractional ownership this stakeholder has is num_tokens/total_num_tokens where num_tokens is the number of MarketToken the stakeholder owns, and total_num_tokens is the total number of MarketToken out there. For example, if num_tokens=5 and total_num_tokens=100, this would be 5% fractional ownership. Then num_tokens market tokens are burned. Then the fractional part of the reserve belonging to this stakeholder is transferred to them. In the case above, 5% of the reserve would be transferred to the stakeholder’s address.

Here’s a diagram that captures the core flow:

Withdrawal Flow

There’s a really important point to make here. There’s nothing in this function which checks that the caller was a patron! In particular, makers or any stakeholders in the market are permitted to call Reserve.withdraw(). This brings a really powerful feature to the data market. Any participant can choose to exit the data market at any given time. This means patrons can leave when they wish, and so can makers. In addition, when leaving, they are allowed to leave with their fair share of the Reserve, real EtherToken which can be converted to ETH and subsequently to dollars. This means a data market is a very liquid entity since participants can opt-out at any time.

Algorithmic Price Curve

In the discussion in the previous section, we briefly mentioned that patron calling has MarketToken minted according to the current exchange rate. In the code, we invoked the function Reserve.getSupportPrice() to obtain this price but didn’t say much about its details. This function implements what we call the “algorithmic price curve.” It’s a way to get an algorithmically defined exchange rate between MarketToken and EtherToken. This is an extremely powerful tool, since it means that a data market can bootstrap itself from 0. In systems without an algorithmic price curve, an external party such as an exchange has to dicate the conversion price. This creates greater barriers to getting a new system off the ground.

You might now be wondering how the algorithmic price curve actually works. We’ve already given a few hints. The Reserve.withdraw() function allows a stakeholder to withdraw their fractional share of the reserve. If they own something like 5% of all MarketToken for that market, they are entitled to 5% of the reserves EtherToken, which converts to ETH. This sets a direct conversion from MarketToken to ETH, which sets a minimum price for MarketToken, since it’s worth at least that basic amount of ETH. Should we then have the algorithmic price curve basically return this amount of ETH?

This isn’t too far off from what the algorithmic price curve does, but there’s a couple subtleties. First, there’s initial condition issues. If the Reserve is empty, say at the birth of the market, what is the price of MarketToken? If it’s 0, that makes no sense since it’s not clear how much patrons should be awarded for their contribution. For this reason, there is a price_floor parameter set by the Parameterizer, which sets the minimum exchange rate.

There’s also one additional factor to consider. There’s some danger in setting the price of MarketToken at precisely its value in ETH. In particular, it makes it easy for speculators to rapidly move in and out of the market, which could destabilize the market. For this reason, there’s a small “fee” which is added on. This is governed by the spread parameter set by the Parameterizer. This corresponds directly to the spread set by a traditional market maker. The spread is awarded back into the reserve. For example, if spread is 110, that means a 10% spread is enforced. This creates a reward for early entrants, since they gain a 10% discount for being early to the game. This encourages early participation in a market to help it bootstrap.

Ok, we’ve said a lot of words, so let’s now take a look at the actual code:

def getSupportPrice() -> wei_value:
  @notice Return the amount of Ether token (in wei) needed to purchase one billionth of a Market token
  price_floor: wei_value = self.parameterizer.getPriceFloor()
  spread: uint256 = self.parameterizer.getSpread()
  reserve: wei_value = self.ether_token.balanceOf(self)
  total: wei_value = self.market_token.totalSupply()
  if total < 1000000000000000000: # that is, is total supply less than one token in wei
    return price_floor + ((spread * reserve * 1000000000) / (100 * 1000000000000000000))
    return price_floor + ((spread * reserve * 1000000000) / (100 * total)) # NOTE the multiplier ONE_GWEI

This is the most complex function in the entire Computable smart contract system. Before we dig into what the code means, it might help to take a look at this diagram to gain some basic understanding of the intuition:

Exchange Curve

Now let’s return to the algorithm. The main reason for this complexity is that today’s smart contract systems don’t support floating point. This means thata the basic math gets complicated. At heart, what we’re trying to implement is a linear function. Think of this as

price_floor + spread * withdrawal_price

The actual equations abbove are considerably more complex. What gives? The first issue is units. Since we don’t have floats, we have to perform computations in wei (recall a wei is a billion-billionth, or 1/10**18). To make this work out, Reserve.getSupportPrice() reports the amount of EtherToken in wei needed to purchase a gwei (“giga wei”, one-billionth, or 1/10**9) of a MarketToken. Take a second and let your mind wrap around this.

The other complications here arise from the fact that we’re performing integer division. What is the “withdrawal price” in this case? Well, we’re purchasing a gwei of market token. How much from the reserve would that get us? Let’s pretend we had floating point:


Ok, not bad. There’s an issue though. What are the units of this expression? Well, reserve is in EtherToken wei, and so is MarketToken wei. These cancel. Our current expression is basically the amount of of EtherToken in gwei you could withdraw for one gwei of MarketToken. We need to multiply by 10**9 to get the amount in EtherToken wei. (Don’t worry if this was confusing; getting unit math right is really tricky. It took us a number of tries before we derived the correct equation ourselves.)

(10**9 * reserve)/(MarketToken.totalSupply())

Let’s add on that price_floor. It’s just an additional term we add on.

price_floor + (10**9 * reserve)/(MarketToken.totalSupply())

Getting a little closer. Let’s see if we can work that spread in. A spread is a percentage (think 110 or 150 for 110% or 150%). For a spread of 110, we want to multiply by 1.1. Or more generally, by (spread/100). To deal with the lack of floats, we do this:

price_floor + (spread * reserve * 10**9)/(100 * MarketToken.totalSupply())

Ok, this matches one of the equations in the code above. There’s one complication though. What if MarketToken.totalSupply() is 0? There would be a division by 0. We need some cutoff to prevent division by 0. For conceptual simplicity, we say that if MarketToken.totalSupply() is less than 1 whole MarketToken, we cap the size of the denominator to get the equation

price_floor + (spread * reserve * 10**9)/(100 * 10**18)

We’ve now succeeded in deriving the full form of the algorithmic price curve! You might’ve gotten lost in all this math. If so, don’t sweat it too much. The basic intution is that the price is designed to be slightly above the Reserve.withdraw() price at all times, with a small spread which is awarded to the reserve. The rest of this is technical detail.

The future of the Reserve

At present, the reserve is denominated in EtherToken. This has the benefit of simplicity, but there are some inconveniences. In particular, as the price of ETH fluctuates, the value of the data market will fluctuate. This isn’t necessarily sensible, since the underlying market valueof the data shouldn’t be closely tied to ETH market fluctuations. For this reason, future versions of the protocol will likely enable ways to construct markets whose reserves are denominated in stablecoins. However, this feature is not yet present in the current version of the Computable protocol.

Last Thoughts

You’ve now begun to see the heart of the economic engine that drives on-chain behavior in the datatrust. But we haven’t yet tackled the off-chain system for actually handling data. You’ll learn more about the datatrust in the next chapter.

Next Chapter