Prediction markets have a very interesting history. Not sure if this is the first that they were ever written about but Robin Hanson is generally seen as the father of prediction markets. He first wrote about them in Idea Futures in which he describes how policy makers should turn to betting markets rather than experts for decision making (later this became Futarchy).

Here is a list of many more Hanson publications.

A few years after Hanson’s initial Idea Futures post multiple big corporations picked up the idea. Companies like Microsoft, Eli Lilly, and Hewlett-Packard have run experiments in which employees trade around new drug candidates, approval of drugs, etc.

Google ran internal prediction markets to “forecast product launch dates, new office openings, and many other things of strategic importance to Google”. After 2 years of continually running their internal markets Using Prediction Markets to Track Information Flows was released, detailing many biases and outcomes. They have their application public on GitHub.

Even crazier in 2005 the Pentagon set up a prediction market website in which terrorist attacks and assassinations.

The Pentagon is setting up a stock-market style system in which investors would bet on terror attacks, assassinations and other events in the Middle East. Defense officials hope to gain intelligence and useful predictions while investors who guessed right would win profits.
- Betting on Acts of Terrorism

Prediction markets have gone through many iterations been tried in a number of applications from private corporations, open on blockchains, to the government.

Although, outside of U.S. election cycles and certain internal company markets they have never really caught much mainstream interest. Liquidity, Regulation, Duration of the predictions all play into this.

Sports betting on the other hand has always been popular and with recent regulation changes seen tremendous growth. Sports betting benefits from short duration bets and “always on” liquidity provided by the House.

Sports betting and prediction markets are one in the same, they are both Event Markets.

Forecasting Events

The idea of forecasting events has been very popular with the EA (effective altruism) community for a while and now more so with the e/acc (effective accelerationism) community. Basically the slate star codex / astral codex ten, scott alexander, LessWrong type. Ironically perfectly described in scott alexander’s Bay Area House Party Substack Series (Every Bay Area Party, Another Bay Area House Party, Even More Bay Area House Party)

While different than EA, e/acc seemingly only became popular and mainstream after SBF ruined the EA name in 2022. Notably SBF offered conditional markets on FTX and FTX’s Future Fund made donations to the popular prediction market manifold.markets.

The forecasting community is niche but very popular. In fact prediction market site manifold.markets recently held their first conference Manifest. Here is some coverage on it in the New York Times.

Superforecasting by Philip E. Tetlock and Dan Gardner details the government funded Good Judgment Project. The yearly tournament poses between 100 and 150 questions on geo-politics*.* You can join challenges at gjopen.com/challenges, some of them have close to 100k forecasters participating.

Far easier to follow is the Annual Forecasting Contest held by Astral Codex Ten. In this contest a variety of 50 questions were asked. Rather than yes/no the questions asked for a probability that an event happened. Here are the results from the 2023 competition. I look forward to these competitions each year.

Forecasting for the most part is pure reputation based and forecasters take pride in getting probabilities exactly right year after year.

Of course there are individuals that want to wager actual money on their bets, thus there are a variety of prediction markets (which of course bring traders, funds, etc.).

There is not a better overall prediction market FAQ than this one from astral codex astralcodexten.com/p/prediction-market-faq. It would be a helpful read before continuing, why they are accurate, clever use cases, and common objectives are covered in it.

Futarchy and Impact Markets

I first read about this ideal in Radical Markets (here’s a pretty good summary) which among other things (like Harberger Tax’s and Quadratic Voting) the concept of a Futarchy is presented.

Robin Hanson describes the ability to vote on your values, but to bet on your beliefs in his post on Futarchy. The idea is simple in a Futarchy government betting markets would say what policies are past and what we do. Hanson expands on this in the paper Shall we Vote on Values, But Bet on Beliefs.

In "futarchy," we would vote on values, but bet on beliefs. Elected representatives would formally define and manage an after-the-fact measurement of national welfare, while market speculators would say which policies they expect to raise national welfare. - Futarchy: Vote Values, But Bet Beliefs

Here’s a podcast transcript of Hanson detailing Futarchy in 2021. It is worth a read.

The two largest scale examples of a Futarchy are [GnosisDAO](https://medium.com/@gnosisPM/announcing-gnosisdao-595f75776eab#:~:text=GnosisDAO is the prediction market,the Gnosis ecosystem through futarchy.&text=GnosisDAO uses futarchy (prediction markets,by joining the Gnosis Forum.)) and MetaDAO (follow futarchy.guide if you are curious) both of which are blockchain based DAOs. Outside of onchain examples it’s quite hard to find physical real-life examples. In 2021 Vitalik wrote about a few here but none of them really seemed to take shape. I feel like Zuzalu (a “pop-up” city launched with help from Vitalik) or Praxis both have a good shot at being one of the first to do so. (65% chance if Praxis actually launches or in one of the next Zuzalu cohorts either experiment with governance like this)

Impact markets on the other hand have seen decent traction. Generally impact markets allow for a fund of money to be distributed across a set of projects / initiatives according to various voting types. Sometimes there is matching (see Gitcoin Quadratic Funding) other times it’s based just on individual contributions. If you want to explore impact markets, social impact bonds, and retroactive funding, I highly suggest diving into impartial-priorities.org/toward-impact-markets along with Impact Markets the Annoying Details.

There are a variety of funds like

• Impact Market

• Astral Codex Ten Impact Fund

• Manifund

and they have actually funded some pretty interesting and impactful things. For example in 2024 ACX was able to fund research on lead-acid battery recycling in Nigeria, the building of anti-mosquito drones, and the use of ultraviolet lightbulbs to kill airborne germs.

News driven by Markets

One of the most exciting areas of prediction markets to me is the idea to drive news based on actual market odds. I first saw this in the baseratetimes.com. With the slogan of News through prediction markets they aggregate odds across various markets, pair the event with news articles from popular sites and report the actual odds.

Given sites like Kalshi have predicted events like Fed rate cuts more accurate than CME Fed watch and better than most analysts, news driven by odds can give highly accurate news.

On prediction site manifold.markets’s public roadmap they are considering “newsifying” manifold along with the ability to view prediction markets through tweets (most of us get our news through twitter anyway).

It seems like there is still a gap in predictions into news in actually generating content, assigning probabilities and creating an entire news organization based on the public market.

Prediction Market Construction

Problems

Designing a prediction market is quite interesting especially relative to traditional markets. Prediction Markets are binary options they resolve to one or another outcome. Due to this they often struggle to garner decent liquidity. A good thought exercise is what would have happened DraftKings launched without being the house, I bet they wouldn’t have had as successful of a time with their sportsbook.

Once again Robin Hanson has a good take in his Issues in information market design

Sidenote: I feel like prediction markets have bad marketing, information market or event exchanges seems nicer

He details some problems are

• Markets need to reach a critical mass of traders to be “accurate” only a handful of traders won’t tell you much and is easily manipulated

• Event and price manipulation, insiders can easily manipulate an event their way (this is actually a good case for regulation)

• Settlement, for sports markets money-lines are easy, did the Mavericks win last night? For some events the settlement outcomes can get muddied and the rules need to be hyper specific

• It can be very hard to gain interest in trading both sides of a market, tough to attract market makers, and thus liquidity becomes a problem. Hanson details a solution in Combinatorial Information Market Design

Design

When considering constructing a prediction market there is a lot to consider

• Will you use real money? Tokens? What about regulation?

• How will traders interact with your market? Via an Automated Market Maker, an Orderbook, RFQ/OTC?

• What source will your outcomes settle against?

• How will you support new markets? Will you support single/one-off events? What about series events? At what frequency?

After re-launching an internal market during COVID-19 in 2020, Google details what it takes to operate a prediction market in creating a prediction market. They mention the primary goal of a prediction market is to “incentivize the right people to forecast accurately, thereby producing a consensus forecast that is more accurate than any individual”. In order to do so they make the case of good UX, incentives, and strong feedback loops. In my mind we have not reached good UX until its not uncommon to overhear something like “let me tail your interest rate bet for next month”. Prediction markets need to have a similar appeal to sports betting.

More tactically there have been many attempts at differentiated prediction market design

• Gnosis launched Omen and conditional tokens with the idea that a token has (2) components YES and NO, you can mint a token and sell your NO shares to exclusively have YES exposure to the event. These tokens can trade on any exchange as they are ERC-20. This is very much so the Augur design (which was shut down in 2020)

• Polymarket first launched with an AMM which somewhat worked. If you already dislike impermanent loss wait till you try liquidity providing something that has an expiration date (like a binary option). Polymarket now has an orderbook. (Sidenote: primitive.xyz has an interesting approach on AMMs for assets with time bounds, read RMM-01)

• Most other markets Kalshi implement a standard orderbook

The orderbook versus automated market maker debate is something I have been interested in for a while, see Ramblings on AMMs and Orderbooks and Ramblings on DeFi Derivatives so we will detail these designs more.

Orderbooks

A majority of prediction markets today use an orderbook. Makers post bids and asks which is filled by takers just like your traditional markets. Orderbooks are always the best option in large cap, very liquid markets. They facilitate the tightest spreads and give the most flexibility to traders.

Given how nascent many of these markets are the spreads are often enormous 10-15% isn’t unheard of. Prediction market exchanges often need to have their own market making group to make the markets decent to trade on. For example Kalshi has a Kalshi Trading arm. With more clarity on regulation (CFTC regulated markets really helps) more traders and more liquidity will help facilitate tighter spreads. In the future maybe we see niche market making firms exclusively focus on trading prediction markets.

Automated Market Makers

When a market is new, niche, small and low liquidity often utilizing an AMM curve can be very helpful. Liquidity providers generally provide passive liquidity to an AMM allowing traders to always have a price to sell into (Just in Time liquidity is also a thing).

For a new meme coin AMMs can make a lot of sense and a super basic constant product function (CFMM) likely fits the use case well. For market’s that have fixed bounds (0 / NO and 100 / yes) this doesn’t make as much sense. Here is good paper on impermanent loss in CFMM’s like Augur look like.

After the launch of Uniswap V3 and the introduction of concentrated liquidity prediction market teams (along with many other teams) began exploring what event trading would look like. This is detailed in the Maniswap V3 design document where they explore binning Uniswap’s ticks to match discrete percentage increments.

manifold.markets recently put out a fantastic overview comparing different liquidity solutions in it they compare orderbook, CFMM, and concentrated liquidity approaches with and without fees.

With the success of various vAMM based perpetual DEXs like drift.trade, perp.com, and up and coming overlay.market it feels like one might be able to launch a successful prediction market on the same model.

Something notable across all prediction markets is the lack of leverage available. If you want to make a $100 prediction on something 12mos in the future that $100 is locked up for 12mos. This is something interestingly noted in compound.vc’s thesis development sheet. The ability to have leverage in prediction markets (like most other markets’s) would lead to better capital efficiency for the end user and likely more longer term bets.

Yet another FTX aside, in September 2023 Bitmex launched prediction markets for recovery rate of FTX funds, SBF’s sentencing, and approval of the BTC ETF.

Comparison to Sports Betting

Sports betting has been popular for a long time (especially in Australia about $34b wagered annually) but with recent regulation shifts in the US betting is on pace to hit over $100b in wagers. It is a extremely large market.

The interesting thing with sports betting versus say a political prediction market like betfair.com is that sports betting markets have a House. This is the casino, book, or company that runs the sports betting market.

The house includes what a *Vig* or House Edge to every line that they set. This is so they can turn a slight profit no matter the outcome of the bet.

Example: Odds are 50:50 for a match so the American Odds should be +100 / -100. With the traditional vig you can expect to see these odds for a 50:50 match to be +110 / -110. That way books can guarantee a profit either way.

Market

This is a weird difference from traditional markets in which there are (2) participants makers and takers where market makers fight to earn the spread between bids and asks and thus provide a tighter spread making the market more efficient.

In sports betting the House sets the spread statically and usually at 10% (!!).

Novig recently announced their seed round to make sports betting markets function more similarly to traditional markets (only in Colorado for now). Rather having static house vig baked into the odds, Novig allows for any market participant to make or take orders on the exchange. The idea is that traditional market makers (Jane Street, Citadel, etc.) eventually will step in and make sports markets.

How odds are displayed between sports betting and prediction markets is also interesting. Sports betting generally follows American (+120), decimal (2.2), fraction (6/5) or implied (45.45%) odds. Anecdotally American odds seem to be the most popular among retail (especially as odds are now discussed by sports announcers and plastered over ESPN). Prediction markets on the other hand default to quoting in implied odds. Perhaps prediction markets could benefit from showing all (4) types of odds.

Regulation

Another stark difference between prediction markets and sports betting is regulation. Sports betting is highly regulated state to state (full list of SEC rules here, my favorite excerpt here is that riverboat casinos are also subject to US Coast Guard regulations). Prediction markets on the other hand have had a much harder go. Kalshi is the only prediction market so far to get CFTC approval for a variety of their markets (and they had to raise $30m to do so).

Even Kalshi is still in a regulatory battle, they are suing the CFTC for the blocking of various election markets. This is kinda crazy to me because look at this market for if the Boeing CEO is gone by the end of 2024 rules. How does that get approved but election markets don’t.

It seems like traditional sports betting platforms might have a better go than prediction markets in actually launching prediction markets. Some stats sports books allow for political betting already.

See what Hanson had to say

To get an Idea Futures market approved as a security in the US, you'd need CFTC approval. But they require expensive review, require you to set up a physical pit for trading there, and are sure that there is no point to markets where there is not substantial hedging demand. (Respected academics can sometimes get exceptions though.)
- Robin Hanson

Towards a Event Exchanges

I believe that either (1) someone will create an all-in-one event exchange or (2) a sports betting company will purchase/launch a prediction market and we will have a complete, fully featured market to trade the outcome of any event (70% chance either happens by 2026).

Eventually I see event exchange’s markets referenced by the news in a way that benchmark prices are referenced from things like CME benchmarks (95% at least one major news outlet will regularly report sourcing a prediction market).

Resources

Many more resources can be found in the following repository https://github.com/0xperp/awesome-prediction-markets.

Everything written in the post is outlined in the following repository github.com/0xperp/awesome-blockspace

The ability to transact, store data and perform compute on a blockchain is what comprises blockspace. Intuitively the transactions that are included in a block are the blockspace.

Increasingly in various articles, posts, and podcasts blockspace is referred to as the “best product to be selling” or the “most important commodity” to be selling in the 2020s. Understanding what blockspace is, how it is created and valued can be quite confusing and is constantly evolving.

All blockspace is not created equal and actually varies quite a bit. Blockspace is a class of commodities that can be easily graded on common characteristics like security, flexibility, and decentralization. This piece will detail each characteristic and provide some examples then explore the market participants for one of the most valuable digital commodities that exists.

First a quick aside on consensus mechanisms that secure blockspace

Proof of work (PoW) requires miners to solve complex mathematical problems in order to validate transactions and create new blocks. The first miner to solve the problem is then rewarded with newly minted tokens. The process of solving these problems requires a large amount of computational power, making it difficult for any one miner to control more than 50% of the network's computing power. If a miner were to control more than 50%, they would be able to launch a 51% attack. In a 51% attack, the miner with the majority computing power can manipulate transactions and even reverse them, potentially allowing them to double spend coins. Today almost all of PoW production occurs through a mining pool.

Proof of stake (PoS) is a newer consensus mechanism that requires users, known as validators, to put up a certain amount of cryptocurrency as collateral in order to validate transactions and create new blocks. Validators are chosen based on the amount of tokens they have staked, and are incentivized to act in the best interest of the network.

Qualities

Blockspace can be built and produced in many different ways along with being consumed for a multitude of different purposes. Every blockspace market has varying levels of security, decentralization, and guarantees along with different choices in the size, amount, and how the blockspace is verified. When deciding to be participant in the blockspace economy there are a few different qualities that you might want to evaluate.

Security

Perhaps the most important quality is the security of the blockchain. How hard and how many resources are needed to attack the chain. Commonly this is known as a "51% attack" but there are other types of consensus mechanisms that only require 33% of producers to agree.

A common metric for measuring a blockchain's security is by looking at the "cost to attack". In order to control 51% of the network how much does it cost to rent and/or purchase the hashpower/stakeweight. You can find (extremely) rough estimates on sites like crypto51.

Full blown take over of a blockchain is quite rare and has only happened a few times one being the Justin Sun’s takeover of the steem blockchain. Often I think about why blockchains such as Dash, BitcoinSV, or even more well known blockchains like Zcash have not seen more regular full takeovers yet, but if they have such poor security its not hard to reason that their other qualities are just as or even worse.

A far more common attack is a simple reorg of a blockchain. This is seen quite often in blockchains like Polygon which reorgs quite often. It is important to note that re-orgs on Polygon aren’t always malicious given Polygon’s probabilistic consensus however a reorg can also be an attack in which block producers reorganize transactions from prior blocks for their benefit.

To better understand the problems with reorganization consider the following example of a small business bidding on advertising space on a billboard on a popular highway. The business owner finishes a bidding war with their competitor paying 20% more than they initially wanted. Happy with the billboard they send their designs to the billboard company. A few weeks later the small business owner takes a drive to the highway to see his advertisement only to find that their competitor is on the billboard. This is similar to what happens in a reorg, transactions that you previously paid for are “rolled back” and re organized.

Security is likely the most important characteristic for a consumer of blockspace, they want to ensure transactions that they pay for are secure and relatively immutable. This directly impacts the value of the blockspace, the producer’s willingness to spend resources to produce the blockspace, and eventually the traders appetite to trade it.

Decentralization

A close runner-up to the security of the blockchain is the decentralization of it. Decentralization has a few equally as important parts

• Nakamoto Coefficient

• Distinct Operators

• Geographic Distribution

• Client Diversity

• Unique Hardware

github.com/theSamPadilla/blockchain-infra-decentralization-metrics is a really nice repository that classifies blockchain decentralization based on

• Stake distribution across infrastructure providers.

• Stake distribution across geographies.

• Validator distribution across infrastructure providers.

• Validator distribution across geographies.

The Nakamoto Coefficient is by far the most common metric for measuring a blockchains decentralization. It is a very simple formula # of validators or % of hashrate needed for attack and is helpful in understanding the number of validators that would have to collude together to successfully slow down or block any respective blockchain from functioning properly.

Equally as important as the number of actors is the number of distinct actors that are needed for collusion. For example Coinbase operates about 7% of validators on Ethereum. Lido, a liquid staking provider, currently makes up ~33% of the validators on the Ethereum Network. Lido does not run validators themselves, instead they work with trusted operators in the space like Coinbase. When counting their base share plus the number of validators that they operate on behalf of Lido, Coinbase actually makes up ~12% of the entire network.

Bitcoin is generally regarded as a leader in decentralization although only has a nakamoto coefficient of around 5. While it is true that there are many unique and district mining operations Bitcoin mining pools have full and complete control over the ordering of transactions in a block (until Stratrum V2 is fully implemented and used).

For the majority of Bitcoin's lifetime 1 or 2 pools control over 33% of all the hashrate and thus the ordering of the transactions in a given block.

Some market participants care greatly about how their transactions are ordered in a particular block and having the transactions entirely ordered by a single party greatly degrades the quality of the blockspace for them. Note that this is the same for rollups that have a single sequencer.

Having a single party order/build the majority of the blockspace intuitively is a poor for decentralization but also can have significant ramifications with regards to producers extracting additional value for themselves. Commonly known as maximum extractable value (MEV), producers can extract large amount of value. It is also important to note that MEV is helpful for certain applications to function well liquidations (remember “black thursday” when liquidators failed to show up?) , arbitraging to keep markets competitive, and even searchers being needed for squeeth rebalances are all good examples.

MEV is a very large and important concept (the MEV supply chain is now it’s own multi-billion dollar industry) to understand when discussing blockspace it likely warrants it’s own piece. Instead here are a few points to know

It is important to build a separate MEV supply chain as to not harm blockspace consumers. There are many protocols working on solving this

• Suave from Flashbots

• Jito for Solana

• Skip for Comsos

• Stratum V2 for Bitcoin

• (still waiting on Polkadot and Near based MEV solutions)

MEV exists in Bitcoin to and large mining pools have started to extract it

The amount of MEV that can be extracted is a direct correlation with how valuable the given blockspace is

Size, Amount, Validation

The actual specifications of the blockspace are also import factors to consider when judging the quality. These are very simple and well defined

• How large are the blocks? How many transactions can fit in a single block? How much data can you fit in a block?

• How often are blocks produced? How many blocks are produced in a day?

• How does the network come to consensus on the blocks?

Questions around the size and the amount of blockspace are generally qualities that consumers or traders need to consider. If a consumer is looking to complete a transaction in an hour how many total blocks are there to bid for a transactions space on and as a trader how scarce is the blockspace.

Evaluating how the network comes to consensus on a block is likely a quality that an institutional consumer would care about. This could be a fund or trading firm, but more likely an application built on top of the given blockspace like an exchange, custodian, or L2. An exchange would likely assess how a network forms its consensus because it can impact the execution for their users. Some examples of how various network actors form consensus

• Round Robin / Leader Election

  • In a round robin validators are periodically selected to build, propose, and include the entire block themselves.

  • Examples of this include Solana, Cosmos, and Polygon

• General Consensus

  • In a general consensus network a producers broadcasts their block to the rest of the network and if agreed on the block is included.

  • Examples of this include Bitcoin

• Single Sequencer

  • A majority of layer 2 networks implement a single sequencer who orders all transactions, forms them into blocks, and publishes them to the layer 1 or data availability layer

  • Examples of this include Arbitrum and Optimism

Note: that the building of the blocks is often abstracted into its own auction like mechanism or even eventually abstracted to its own chain like SAUVE. Read here for a really good look at block production

How consensus is come to effects the execution of the exchange in question a little differently.

The round robin leader election is often very fast but for networks that have not abstracted the block building layer, exchange participants that are also validators have the upper hand. For a more detailed example see the DyDx example in this AMMs and Orderbooks piece.

The general consensus is the most fair for the execution the exchanges user's but currently struggles with handling high throughput. As of 2023 a well functioning and fast orderbook has yet to really be seen on a blockchain that uses a general consensus. In fact DyDx highlighted this in their blog post when they moved from Ethereum mainnet to Starkware.

A single sequencer is very fast but also very centralized and an exchange building on a network that currently uses a centralized sequencer needs to be okay with the possibility that the sequencer operator is frontrunning or extracting some type of value from their user's orders.

A fourth option for the exchange would be to operate some parts of their infrastructure offchain, like their orderbook. This is again highlighted in AMMs and Orderbooks under the hybrid section.

Note: many protocol teams are now also exploring operating their own appchain, this has the benefits of speed, scalability, and the centralized sequencer being operated by the protocol team although suffers from the need of bootstrapping your own validator set and likely having far less security. More on this in the flexibility quality.

Availability

Ensuring that access to consuming, accessing, and producing blockspace is widely available and can always be consumed can be an extremely important quality.

Are you able to easily access the blockspace? Is it always available or are their chain halts or downtime? How easy is it to run your own RPC node or access publicly available ones? Are RPCs generally overloaded, do they have consistent uptime?

Uptime

As a consumer it is very important that you can you access blockspace market at any time. If a blockchain has frequent halts or outages you can not reliably use the blockchain.

Take the example that you are a new liquidity provider who want to provide liquidity to a concentrated liquidity AMM and you are choosing between the Solana and Ethereum blockchain. Solely evaluating the characteristic of uptime the choice is easy, Solana and seemingly every L2 has seen several day long outages in the past year in which the entire chain is unusable.

Chain State and Storage

As a producer (and maybe as a consumer) considering how accessible it is to download, verify, and store the full chain state can be an important consideration.

As a blockspace producer you want to ensure that you have extremely high uptime and little dependence on others in case they have an outage. Some blockchains like Solana or Near effectively require you to download chaindata snapshots from AWS S3, Google BigTable, or other validators with often little to no way to sync and store the chain data entirely yourself.

Chain state and storage is also an upmost consideration for applications or protocols that require historical data. On Bitcoin or Ethereum it is possible to fully sync a node in a few days and have a full set of historical data. On the other hand Solana's full chain state is primarily stored in a Google BigTable database and takes up petabytes of data, rendering it next to impossible for any average consumer to sync and store.

Note: there are some significant engineering challenges in storing historical solana data but plenty of great teams working on it. One such example is the Triton One team building Old Faithful

Costs and Fees

Blockspace is valuable and has various costs and fees associated with producing and consuming it.

As a producer there are more traditional costs such as the upfront purchase and/or ongoing costs of various hardware and software that allows you to operate on the network. More often than not there is also the initial capital outlay for tokens that need to be posted as collateral to validate (as is the case in Proof of Stake networks). In turn you generally receive block rewards from the network which could also include fees generated from consumers of the blockspace you produced.

As a consumer in order to utilize blockspace on a given network you need to pay a fee for it to the block producers (this does not count additional fees you might need to pay a protocol for your interaction).

Costs and fees vary depending on the blockchain's implementation and also how the fee market is designed. Generally a chain implements one of the following designs

• Priority Gas Auctions (PGA)

  • Consumers who what to utilize blockspace will submit their transaction with a given fee, the transaction sits in the mempool. Since the producer accrues the fees in the block they (generally) order the transactions in by highest fee.

  • This is a very simple fee market design and is the most common.

• EIP 1559

  • Consumers who want to utilize blockspace will pay a base fee (which is burned) and a priority fee (or tip) which the block producers. Introduced for better consistency in estimated fees needed to be paid by a consumer.

  • This has primarily been implemented on Ethereum but prior was implemented on Filecoin

The design of the fee market heavily influences the amount and potential willingness of a consumer to pay and how much the producer is expected to receive for their block production.

The research space for fee market design is quite interesting and extensive and I have complied an awesome list at github.com/0xperp/awesome-fee-markets.

Flexibility

Blockspace can be highly adaptable or very static. The majority of the time consumers likely prefer the space that they are consuming to be very predictable and static, but there are certain use cases in which a consumer (often a protocol) might want their space to be highly adaptable and flexible.

Flexible blockspace is the idea that blocks can be built differently according to various use cases. Maybe this is adding pre and post instructions at the validator level, allowing blocksize to fluctuate or being able to abstract block building.

Take the example that you are a lending and borrowing protocol that has started exploring launching their own blockchain. They have a few options to consider launching

• Cosmos based Appchain

• Rollup based on the Optimism or Arbitrum stack

• Parachain on Polkadot

Each one of these has different considerations in security, decentralization, and usage.

• A Cosmos Appchain requires you to bootstrap your own security by incentivizing a validator set but allows you to have extreme flexibility in your consensus, block building and execution of transactions (especially by adding custom logic using functions like BeginBlock or EndBlock as referenced here)

• Building a rollup with the Optimism stack (currently) limits you to being centralized being able to only operate a single sequencer, but allows you to have create extremely fast, EVM compatible blockspace

• A Polkadot parachain allows for you to use the Polkadot shared security model but requires you to bid quite a lot of DOT in the auction in order to be included.

In brief

• A Cosmos Appchain has the ultimate flexibility in blockspace creation, control, and security

• A Rollup provides flexible blockspace creation, but control and security is limited to a single and centralized sequencer currently

• A Parachain bootstraps security from the Polkadot mainchain… except you have to bid for it

Note: joncharbonneau.substack.com/p/rollups-arent-real is a really good read dissecting the current state of rollups

Bitcoin, Ethereum, Polkadot, etc. all produce generalized blockspace. Osmosis, Aevo, Lyra, Sentential are use customized and specific blockspace to improve their products.

In the recent months it has become even easier to launch a OpStack, Arbitrum, or other rollup / appchain with products like Caldera and Conduit.

Market Participants

The blockspace market is extremely complex but can be broadly broken into producers and consumers.

Producers

A Blockspace producer is a network participant that takes a set of transactions waiting to be be included and actually builds them into a block by ordering them. Often this is one of the roles of a validator, miner, or mining pool on a given chain. With the rise of MEV protocols this block building has largely been outsourced to separate actors known as builders. The “MEV supply chain” is now quite complex and involves many distinct actors as illustrated below

On the proof of work side mining pools have full autonomy over the ordering of transactions in blocks that are mined by miners in their pool. With the release and adoption of stratum v2 this will change and allow individual miners to express transaction ordering preferences to mining pools.

Producers want to produce blockspace that is highly valuable or has some expectation to be in the future. Below is a list of some companies that are large producers across a variety of blockchains

• Coinbase Cloud (via Bison Trails acquisition)

• Chorus One

• Jump Crypto (via Certus One acquisition)

• Figment

• Marathon

• Galaxy Digital

• Riot

• Foundry

Consumers

A consumer is any entity that uses blockspace that is being produced. The actual usage can be for a large variety of things like transfers, swaps/trades, other financial transactions, etc.

However the largest consumers can often be overlooked. Often asset issuers, exchanges, and protocols built on top of the blockchain are some of the largest consumers. Below is a list of some protocols / companies that are large consumers

• Circle, Tether, Paxos other asset issuers

• Coinbase, Binance, other centralized exchanges

• Arbitrum, Optimism, other L2s (increasingly in a environment in which many app teams are deploying their own rollup)

• and of course power users / traders

Valuing

Blockspace can vary extremely in terms of its demand. Data Always had a really good overview of 2022 in blockspace demand. Below is a chart of blockspace fees across leading protocols

It is worth noting that a large percentage of the fees paid at native protocol block subsidies. Something valuable to watch in terms of real demand and growth is the percentage that user transactions fees make of the total block reward. For example on Bitcoin this averages around 2-4% but over on Ethereum it is similar in times of low activity but can spike to 60%+. When tracking a protocol with a developed MEV supply chain, tips should also be included.

A few chains have spent time developing unique fee markets one being Ethereum with EIP1559. This upgrade had a few goals such as lower fee volatility but also had an important long term goal of preventing instability of a blockchain in a world were there is no continued native issuance. 1559 lead to base fee burning and priority fees going to validators working towards placing less emphasis/importance on the role of the block subsidy in the overall block reward.

In order to keep up with its promise of cheap fees Solana created localized fee markets in which the transaction fee is localized to each distinct contract interaction. If there is a high demand NFT mint on Magic Eden transaction fees will not increase on Jupiter. This change is very consumer focused as it actually reduces the fees that a validator could expect to earn. However given Solana has a built out MEV supply chain, localized fees might bring in more users and thus more fees via MEV tips.

Valuing blockspace is still in it’s extremely early stages and with a majority of a block reward on a given chain is via the predictable block subsidy there is not much variability in the rewards and corresponding valuation. However as transaction fees/tips become a increasingly larger percentage of the overall block reward via halvings, increased demand, creation of MEV supply chains, etc. valuations become more unpredictable and introduce unique trading opportunities.

Trading

Blockspace is notoriously mispriced. In many cases the blockchain misprices how it pays its producers, as they continue to print more inflation for terrible quality blockspace. A majority of blockspace is cheap and abundant with very few being valuable.

This is were I look forward to newer implementations of block rewards and fee markets. Currently most blockchains put out a whitepaper with static block subsidies that are maybe on some disinflationary schedule. How can they be sure they are pricing their blockspace accurately before their blockchain is even live? I am looking forward to new implementations in determining block subsidies.

Given how new these markets are you likely have only ever been a producer or consumer and have not even considered that you could trade blockspace. Right now these trades are usually expressed through swap, forwards, and futures but there have also been more unique instruments like royalties, block inclusion reservation, and gas tokens.

Some of the primary places to trade blockspace currently are

• Luxor where you can trade non-deliverable forwards on Bitcoin “Hashprice”

• Alkimiya a much more flexible swap marketplace where you are able to trade swaps on Bitcoin and Ethereum blockspace along with potentially gas swaps

A few projects with a lot of potential

• Overlay a perpetual future platform that allows you to trade native data feeds and specifically mentions various blockspace components in their documentation

• Oiler which has a variety of products but one being Pitchlake in which you can trade base fees on Ethereum

• Volmex a volatility trading platform that I could see eventually launching something like fee or validator reward volatility indexes

A product that is interesting to reason about is forming an index on blockspace quality based on the characteristics above and forming a blockspace swap market across blockchains something like a swap between Bitcoin and Ethereum’s blockspace demand

Outside of speculation markets that Luxor, Alkimiya, and others are building are extremely important in creating markets for risk transfer between blockspace producers and those that are willing to take on the exposure. As public companies and energy companies enter the Bitcoin production space they will want to decrease volatility in their cashflow. As L2s become more sophisticated they will want to hedge their transaction expense on L1s. As exchanges and asset issuers look to operate more efficiently hedging their variable transaction cost becomes important.

Overall the introduction of more robust capital markets around blockspace consumption and production is something I am extremely excited for and interested to see how it develops.

This post is meant to provide an overview of the Automated Market Maker (AMM) space. First by reviewing some of the history and various designs. Following will be a walkthrough of using AMM pricing functions for other applications than spot trading leading to interesting dynamics and the evolving relationship between onchain orderbooks and AMMs.

Everything written in the post is outlined in the following repository https://github.com/0xperp/awesome-amm

A Little History

While the existence of AMMs truly took off with Uniswap there is quite a history to it.

Sep 2, 2015: Martin Köppelmann: Gnosis Market Maker Orderbook

• Describes the idea of including a market maker into an orderbook

Sep 26, 2016: Nick Johnson: Euler

• Using a single token Euler as a pair for all other tokens to always trade against

Oct 3, 2016: Vitalik: Let's run on-chain decentralized exchanges the way we run prediction markets

• Summarizing the posts above from Martin and Nick, Vitalik mentions that AMMs should function like existing on-chain prediction markets (such as Augur)

Mar 6, 2017: Alan Lu: Building a Decentralized Exchange in Ethereum

• Describes various potential invariants for an AMM

Mar 2, 2018: Vitalik: x*y=k market makers

• Vitalik recaps his post from two years prior and mentions Martin Köppelmann’s suggestion of the x*y=k automated market maker with further simulations and functions

• Uniswap V1 Contract

• Uniswap received a grant from the Ethereum Foundation in August

Various AMM Launches

Interestingly on the same day that Hayden posted his comment on the Uniswap V1 implementation, Bancor released their initial whitepaper. Bancor’s primary innovation was their Smart Tokens (can be thought of as a very early router).

• Kyber released their whitepaper about a year later in 2019 with aggregated liquidity pools and an institutional focus.

• September 2019 Balancer

• November 2019 Curve released the stable swap whitepaper meant for trading stablecoins and pegged assets

• March 2020 Uniswap released Uniswap V2

• August 2020 Mooniswap Whitepaper, Front Running Resistance

• March 2021 Uniswap V3, Concentrated Liquidity

If there is a notable design not listed please comment or send a DM my way

Common Designs

Below is a list of common AMM designs and their pros and cons

Constant Product

The constant product market maker is the most common AMM invariant that exists. It can be simply modeled as x * y = k . X and Y are the reserves for each asset, as assets are traded through this function X and Y increase or decrease in their reserves in a way that keeps a constant K (not counting fees charged).

Constant Product Market Maker

The constant product formula has generally become the most forked AMM invariant, primarily due to its simplicity and ease of use from a liquidity provider's perspective.

Stableswap

Curve launched an AMM with the stableswap invariant primarily to cater to stablecoins and pegged or like-priced assets.

The Curve team realized that like-priced tokens can trade on a constant sum, x + y = C for the majority of the time. However, when a pool does become imbalanced Curve adjusts to the constant product invariant making swaps more expensive and allowing arbitragers to bring the reserves of each asset back to equal

Here is a very good post on understanding the Stableswap invariant

Constant Mean

Balancer proposed and launched a generalization of the constant product market maker, the constant mean invariant. This allowed more than two assets to be provided as liquidity and for weights outside of 50/50.

Given this Balancer and other constant mean market makers can be used as a portfolio manager

Constant Mean Market Maker

For a very good understanding of Balancer refer here

Concentrated Liquidity

Uniswap V3 was launched as the first concentrated liquidity AMM. This allows for liquidity that is allocated within a custom price range. In earlier versions, liquidity was distributed uniformly along the price curve between 0 and infinity. Allowing custom liquidity distributions allows for much higher capital efficiency resulting in more flexibility for a liquidity provider regarding their exposure and payoff along with a deeper and tighter market for a trader.

Concentrated Liquidity relies on the concept of virtual liquidity which can be read and derived in their whitepaper

Virtual Automated Market Makers

A virtual automated market maker (vAMM) uses formulas, such as constant product, but only as a price discovery mechanism.

Generally, a vAMM product is designed with a "clearing house" or "controller" contract in which all of the collateral deposits are held.

vAMMs are primarily used for leverage on crypto perpetual futures contracts in which the collateral in the clearing house backs virtual assets that users can trade. However, they can be used to trade just about any price feed given sufficient liquidity and traders. For example overlay.market allows you to trade various data streams or nftperp.xyz which allows a trader to long or short floors of various NFT projects.

Following the release of Perpetual Protocol's first vAMM there have been many iterations since, particularly with the price discovery mechanism.

Quick aside: Even though this was based on the lightning network and did not explicitly mention a virtual automated market maker it seems like the Rainbow Network was one of the first written ideas of using collateral in one asset to trade virtual price feeds

vAMMs and their Pricing

There have been many iterations of vAMMs trying multiple AMM invariants for their pricing to drive higher liquidity, more consistent funding rates, and trying to attract more traders.

A few notable examples are

• Perpetual V1 and their constant product vAMM based on Uniswap V2

• Perpetual V2 and their concentrated liquidity pricing based on Uniswap V3

• Drift V1 and their dynamic constant product vAMM

• Hubble and their CurveCrypto invariant

To date a large majority of vAMMs are “player v. player” there has been some pretty good research from the rage.trade team on this in The Perpetual PvP Ponzi. With advancements like using newer AMM invariants (eg. concentrated liquidity) or adding in various market layers (like Drift V2 and JIT), vAMMs have the potential to become a preferred market for trading any asset (or data feed!).

A vAMM simply uses traditional AMM formulas for its pricing which allows for an almost infinite number of assets and product types to trade along with a lot of flexibility and experimentation on the execution and order-flow layer.

Onchain Orderbooks

High-performance chains like Solana and Near along with the upcoming variety of Diem forks (Aptos, Sui, OL, etc.) have allowed for the creation of on-chain orderbooks. Some of them are listed below

• dydx

• Serum

• Tonic

• Spin

• Orderly

• Econia

Orderbooks have the benefit over traditional AMMs as markets can be quoted very tight given traditional market makers will (generally) help to provide liquidity, trades can execute quickly, and this often leads to better pricing.

While orderbooks excel in providing optimal pricing and execution the catch is: There needs to be enough liquidity and active market makers constantly updating their quotes. This is especially important when you consider the pure number of assets traded onchain.

AMMs excel in this case as they support

• Permissionless asset listing and market creation

• Passive liquidity (anyone can be a maker!)

Order Matching and Liquidity Provisioning

When designing an orderbook or an AMM order flow and the provisioning of liquidity are very important. The flow of orders into the exchange along with how they are matched is important to ensure the best possible execution for a trader. Similarly, the provisioning of the liquidity to be used for matching orders is equally as important as it helps determine the best prices for traders.

Orderflow and matching are important to an exchange, particularly in assets that trade in large volumes, as poor execution can result in a bad experience for traders.

The provisioning of liquidity is important for longer tail assets as they are generally less liquid. This means that the liquidity that is provisioned needs to be provided in the most capital-efficient way possible to ensure the best execution for a trader.

Below are overviews of how various orderbooks and AMMs orders currently flow and match along with how liquidity is provisioned on them with a discussion on what various hybrid implementations look like.

Orderflow and Matching

There are currently a few ways in which orders are matched and/or routed into a trading protocol. A centralized orderbook runs its own internal matching engine, a decentralized orderbook operates its matching via a network of validators and/or keepers and generally, AMMs are at the mercy of the block producers.

AMM Matching and Flow

As mentioned above generally AMMs order flow and its matching are at the mercy of the block producers.

When a user initiates a swap on an AMM they submit their order which is propagated throughout the mempool waiting to be included in a block (executed). The rise of searchers extracting value from the mempool (MEV) leads to far different dynamics for AMMs flow and matching.

Operating as a block producer (or bribing one) grants priority access to flow, how it is ordered and matched.

Sometimes this results in better execution for traders such as in the case of Just in Time Liquidity (JIT). This is when a searcher sees a order in the mempool and re-orders the transactions into:

• Searcher provides liquidity

• Users trade

• Searcher removes liquidity and collects fees

This allows the searcher to only provide liquidity for a specific trade and collect a majority of fees for it, this results in better execution for the trader.

JIT is an anomaly, frontruns, backruns, and sandwiches all result in worse execution for the trader.

The struggle with a majority of AMMs is that all of their flow and matching can be directed and re-ordered by the block producers.

Orderbook Matching and Flow

Serum refers to the process of matching as crank tuning. Anyone is allowed to run a cranker and be rewarded with a portion of transaction fees for it. This is an example of an onchain orderbook, all orders are posted on chain and crankers ensure that they are being matched and executed properly.

In dydx V4 the dydx chain will have an offchain orderbook in which validators will each host a copy of the orderbook and match accordingly as they are selected to propose a block.

In dYdX V4, each validator will run an in-memory orderbook that is never committed to consensus (i.e., off-chain). Orders placed and cancellations will be propagated through the network similar to normal blockchain transactions, ensuring that orders placed and cancellations will always make their way through the network. The orderbook that each validator stores is eventually consistent with one another. On a real-time basis, orders will be matched together by the network. The resulting trades are then committed on-chain each block. - dydx.exchange/blog/dydx-chain

Creating an offchain orderbook in memory of the validators (or keepers) of your network can be extremely effective as it allows you to have a decentralized orderbook.

A primary difference between offchain and onchain orderbooks (dydx and serum) is how and where orders flow.

An onchain orderbook like Serum all orders are posted and matched onchain meaning that all orders (filled or canceled) are visible to everyone onchain and all keepers (the matching engine) see and match the exact same orders.

An offchain orderbook like dYdX v4 differs in its flow and (potentially matching). Orders are submitted to validators and thus need to be fully propagated in order for all validators to have the exact same set of orders (see The orderbook that each validator stores is eventually consistent with one another in the dydx chain excerpt above).

When orders are not fully posted on chain and are submitted to some type of validator or keeper network, the flow can be controlled, bribed, and prioritized directly at the protocol level.

With the rise of proprietary block building (Flashbots on Ethereum, Jito on Solana, Skip on Cosmos) controlling, bribing, and prioritizing flow is also possible for onchain orderbooks but outsourced to external block builders.

A good example of how reflexive it can be for a protocol to be able to incentivize and direct flow could be seen in something like this. A large trader on dYdX is already incentivized to acquire $DYDX for fee discounts but is also incentivized to acquire $DYDX so that they can run a validator node. When a large trader operates their own validator they can control the local flow of orders. Meaning when they are a block proposer they could prioritize their own orders and trades allowing the trader to ensure the best execution possible.

A Hybrid Approach

In an onchain model generally orders flow directly onchain where they are matched and executed by an external keeper network. In an offchain model, orders flow directly into the validator or keeper network in which they are matched and executed.

A primary example using both on and off-chain matching and execution can be found in CowSwap which uses batch auctions and order routing to fill either off or onchain.

CowSwap allows for optimal execution for traders by batching orders to find any that could be matched together and if not settling onchain on an AMM.

Through batching offchain and settling onchain if needed, CowSwap first directs the flow of orders offchain for optimal execution and if not matches it onchain (executing the trade as best as possible).

Another example can be found in Drift V2. Orders will flow and be matched through various forms of liquidity. Launching as the Liquidity Trifecta, Drift orders will flow through an auction, offchain order book, and AMM resulting in optimal execution for traders.

Orders flow as follows

1. Taker orders are first placed in a short-lived auction in which Makers can bid on filling the order

   1. This results in the most optimal execution for the trader

2. A limit or conditional order is routed through an offchain keeper network in which all orders initially are attempted to be matched against each other (if not routed to the auction first and then vAMM)

3. If no Maker bids in the auction (or a Maker does not fill the entire order size) orders flow into the AMM which serves as a constant source of guaranteed liquidity for orders to be filled against

   1. This is the final backstop and ensures that orders are always filled

By allowing orders to flow through multiple exchange venues a trader can see the best of both worlds. They can have optimal execution and ensure that they will always be filled.

Liquidity Provisioning

If you have been paying attention to the on-chain trading space over the last few years you will have noticed that AMMs (spot or virtualized) and orderbooks have started to feel the same.

Both AMMs and Orderbooks can now trade just about any asset long/short and with leverage: spot, expiring options or futures, perpetuals, everlasting options, or power perpetuals, etc. In fact, the primary difference between them is how their liquidity is provisioned and the management of it.

Just in Time Liquidity (JIT) and the ability to provide passive liquidity onto an orderbook have begun to blur the lines between an AMM and an orderbook.

Active Liquidity Provisioning

Just in Time Liquidity

In a concentrated liquidity AMM rather than providing passive liquidity or managing it by re-quoting your ranges, JIT is the concept of waiting for a trade, immediately provisioning liquidity explicitly at the price quoted, and then removing your liquidity.

As you might be able to see this type of order execution starts to look like a janky RFQ orderbook, traders receive a quote, queue their trade, and are filled (often at a better price) prior to the trade even hitting the passive liquidity in the pool.

JIT allows for active liquidity to be provisioned for any trade on an AMM.

Active Liquidity Managers

Various products like gamma, arrakis, or charm recognized that in a concentrated liquidity AMM and the ability to quote your own upper and lower tick ranges there needs to be a product that can actively manage all of the passive liquidity currently provided to an AMM.

Through a network of keepers, managers operate various strategies based on the total liquidity and volatility of a given pool.

Active liquidity managers allow for passive liquidity on an AMM to be actively managed.

Passive Liquidity Provisioning

Orderbooks often struggle to list new assets as fast as they can be provisioned on an AMM. This is due to insufficient market maker interest and thus liquidity that can actively quote prices.

Some projects have recognized that in order to create more depth in a given market passive liquidity might be able to help.

Taking passive liquidity batching it and layering it on top of the orderbook so that it follows the curve of a given AMM invariant can greatly help to provide more consistent pricing and quotes in an emerging market.

Passive liquidity provisioning allows for liquidity to be added to an orderbook according to an AMM invariant

In Practice

First realized by MEV searchers and implemented on Uniswap V3, JIT helps to provide better and more active price quoting on an AMM.

The vAMM Drift has implemented JIT liquidity into its V2 design. A given trade will first be routed to market makers who have a short amount of time in which they can choose to take the other side of the order. If the order is not or partially filled the remaining quantity will be routed to the passive liquidity providers. This allows traders to always receive the best execution possible.

The AMMs Raydium and Atrix have started to take passive liquidity and add it as a various set of orders on top of the Serum orderbook, providing further fees for liquidity providers and deeper markets for Serum.

The orderbook 01 took a similar approach and in other to provide deeper liquidity to their orderbook allowed users to provide passive liquidity which would then be batched and provided according to the constant product invariant on the 01 orderbook.

As hybrid implementations for order-flow and liquidity provisioning continue to be iterated on we are moving closer to the final design for on-chain trading. One that can support practically any product, permissionless markets, with great execution, depth, and liquidity.

If you find the AMM/Orderbook space interesting, are working on a new idea, just kicking around some new research, or have comments / thoughts on this article feel free to reach out on Twitter @0xperp

in the last year options protocols have grown from around $85m to over $1b in tvl, similarly perpetual swap trading platforms have seen quite the growth in trading volume from far less than $1b to coming close to clearing $10b in volume in a single day. value locked and volumes traded will only increase in 2022 with new platforms and products being released practically weekly (as im writing this squeeth, zeta, invariant, and 01protocol all released or are releasing in next few days).

https://twitter.com/0xperp/status/1481719561107947521?s=20

there have been several good writeups on defi derivatives and their outlook for 2022 here is a particularly good one from jump trading.

in the post shanav highlights that if you take crypto options volume (this includes centralized exchanges as well) as a % of spot volume, options only make up 2% of total volume traded. compare this to equity markets where options volume trades at a 35x multiple to spot volume. crypto derivative markets have a massive growth opportunity in front of them and the growth is particularly in defi.

in the rest of the post I will detail types of derivatives and their implementation and design in DeFi. along with innovations and what I am looking at next.

types

perpetuals

a product initially developed by bitmex perp swaps have no expiry, and function similar to a spot margin account allowing for highly leveraged long/short trading.

since perps have no expiry the concept of a funding rate is introduced to tether the price of the perp to the current spot price.

• mark price refers to the current price of the perp

• index price refers to the current spot price to tether to

• negative funding rate shorts pay longs

• positive funding rate longs pay shorts

• funding rate is calculated by funding_period * (mark - index) / index

paradigms cartoon guide to perps also gives a very good explanation

perps quickly have become the most liquid and favorite way to trade on centralized exchanges and are a hot topic to be built out in DeFi.

so far there have been about three different perp designs

• orderbook + margin account

• vAMM

• embedded funding rates

using an orderbook and margin account mirrors a centralized exchange experience and this is what protocols like mango work. trading on an orderbook is nice you can easily set limit orders, you trade directly with other counterparties, and for the protocol trading is very capital efficient (each trade is settled once it can be matched with a counterparty).

a vAMM based model can be extremely advantageous, for a trader there is always guaranteed liquidity since you are trading peer to pool, vAMMs can very easily be cross-margined with just about any asset since all of the trading is virtual.

the third design (which I have only seen opyn and squeeth use) is embedding the funding rate into a parameter of asset. known as in-kind funding squeeth uses a normalization factor to settle funding between longs and shorts without ever having to manage a cash payment. using in-kind funding is something that I think will become much more common management of funding was the largest limitation for perp composability. since funding is handled in-kind squeeth can be easily traded as an ERC20, used to LP, used as collateral on other defi platforms, and the list goes on.

a quick aside on vAMM design

this does not entirely relate to perps (its about interest rate swaps actually), but Voltz has increased the benefits of using a vAMM through a concept known as LP Collateral Recycling.

in voltz example there are three users

• fixed interest rate taker

• variable interest rate taker

• liquidity provider

if a fixed taker takes a trade that uses the LPs liquidity the LP is locked into the given swap at the rate the fixed taker took. but if a variable taker comes along and uses that same liquidity (the same rate) the positions can be netted out and the LP’s collateral can be freed and reused. in turn making the protocol much more capital efficient (there is more free liquidity to trade through).

even cooler this concept can be applied to most vAMMs. imagining recycling within a perp vAMM is even easier, if traders have opposite positions on the same asset you could settle their trades “off the curve”. additionally this might be an interesting way to integrate limit orders. a trader sets their limit price and if there is another trader that has an open position on the other side it settles. if not the limit is open until it can be filled by the vAMM.

in a way this makes a vAMM function like a pseudo orderbook with pricing based off of a curve.

these are all very raw thoughts, iterate, be wrong, and discuss

options

as referenced in the background of this options are only 2% of spot volume in crypto (note the majority of this 2% is centralized exchange volume not DeFi options), where in equity markets options volume trades at a 35x multiple to spot.

this might not be the best comparison as crypto options trade much more like commodities than equities, but either way eye opening

either DeFi and overall crypto options haven’t fully been figured out yet or no one wants to trade them.

I’ll take the former all day, right now it seems like DeFi options haven’t entirely taken off due to fragmentation (more on this in the liquidity section), the design, and getting large funds and market makers to trade on various chains.

the leading option protocol is opyn along with pool based protocols like hegic, pods, lyra, and premia. there was almost zero use of these protocols until option vaults landed early this year (there is an entire section dedicated to this below).

we have seen over a 10x increase in usage of onchain option protocols. I expect this growth to continue, especially as products become more cleanly packaged, capital efficient, and composable.

volatility

there have also been a decent amount of products in the market that package risk or volatility in a simple index. volmex has seen the most traction as a way to trade an assets volatility index. trading on volmex is similar to trading on a prediction market like polymarket or augur, you supply collateral and receive two tokens the IV index and its inverse. from here you can trade the volatility directionally how you choose.

I can see assets that volmex offers to be interesting and attractive to traders, however I think they struggle from a nascent market.

in their methodology section of the docs, its described that the volatility index is taken from averages across call and put options sourced offchain from deribit. while this solution works its not very decentralized or native to DeFi.

although this is not the fault of volmex. if there was a highly liquid onchain options market when they where designing their protocol they could have used that as a source rather than deribit.

power perpetuals can be used as a volatility oracle, also check out oracle-free derivatives from primitive and antimatter

as derivatives markets are built out further, I will be watching for volatility products.

innovations

liquidity and composability

liquidity is by far the most important part to design for when creating a derivatives project, there are just so many ways it can be fragmented.

• over chains

• over protocols

• over strikes

• over expires

A good thread detailing fragmentation in the options market

when option markets initially started to be designed on ethereum, the orderbook model was out of the question (still is on ethereum, but products like zeta on solana use it), and since AMMs worked so well for spot tokens many protocols began to develop an AMM or pool based model for trading options. Below are a few of the designs

• Hegic V1

  • hegic works by having option writers function as liquidity providers to a pool in which they are locked for the options expiration

  • hegic was the first to do this, and they have seen considerable first mover advantage

• Hegic v888

  • the v2 of hegic, which introduced auto-exercising options, pools for both calls and puts, zero-loss pools (when you provide liquidity you can optionally hedge your liquidity)

• Pods

  • pods released a great pool based options model in which the black scholes pricing model built into the AMM in which all that needs to be inputted is the IV

  • right now each pool is a separate option series, but the pods team is looking at how multiple option series could be in a single pool (see section 7 future work)

• Premia

  • premia has taken a lot of the options pool concepts to the next level, buyers can select your expiration date and strike price at a granular level and sellers (liquidity providers) can choose which markets they want to underwrite.

• Lyra

  • lrya functions with two pools, the collateral (writes the options) and the delta pool (hedges option writers delta). lyra also uses market driven IV which is then inputted to the black-scholes model to determine the option price

all of the research that has gone into designing these protocols is very impressive but due to poor capital efficiency and often much higher prices compared to say deribit most of these protocols have not seen significant volume compared to centralized counterparts.

looking at pods and premia’s total volume they have cleared less all time than squeeth has in 2 weeks. nothing against either team (I actually really enjoy reading their docs) but squeeth has cleared more in volume in a fraction of the time because squeeth has done away with 3/4 of the liquidity fragmentations. there is no expiry, no need to select a strike price, and due to built in funding rate (thanks to the normalization factor) squeeth can be cross protocol (see a list of integrations here).

more on squeeth and power perpetuals later, but its important to know that cleanly packaging a payoff, in a perpetual, and composable fashion will always win out.

in addition to composable perpetuals like squeeth and option based AMM formulas there has been significant research in using concentrated liquidity to form option payoffs.

if you are interested in this I suggest you read all ten articles from @guil_lambert starting with Uniswap V3 LP Tokens as Perpetual Put and Call Options, followed by 2, 3, 4, 5, 6, 7, 8, 9, 10. following that go and play around with info.yewbow.org observe the volatility of each pool and visualize the payoff of your LPs

the top tier team at primitive took this a step further and developed an entirely new protocol RMM-01 which focuses on being a spot and derivative exchange through concentrated liquidity. if you are curious on how you can create a replicating portfolio from a constant product market I suggest you read the following

• Replicating Market Makers

• The Replicating Portfolio of a Constant Product Market

• The Replicating Portfolio of a Constant Product Market with Bounded Liquidity

• Replicating Monotonic Payoffs Without Oracles

having a spot and derivative exchange bundled in one unlocks tons of capital efficiency, allows for granular strike selection, automatically rolling expires when the AMM rebalances (heads up for searchers), and freedom to select your own quote and base asset.

but… there are always tradeoffs replicating an option with concentrated liquidity only allows you to replicate selling options and the premium yield relies on swap fees from those using the spot exchange or arbitrageurs. yield being derived only from swap fees has two consequences, (1) no swaps or arbs occur in which you would not earn yield and (2) since yield is from swaps your premium is paid over time of the expiry and not given upfront.

there are a few solutions to these: if you want to have a long call payoff you could borrow a primitive position, if you want to have a somewhat guaranteed premium you could lend a primitive position

the vault structure

a somewhat common and basic options strategy to generate additional yield on an asset by forgoing potential upside is known as a covered call strategy. there are plenty of good guides on how this works and the risk associated. the basics are you sell a far out of the money option on a regular cadence taking home the premium as yield and praying (or hopefully hedging) that prices do not rise enough for your calls to be excised.

yield farming to earn 4 digit APYs is extremely lucrative, but not very sustainable by selling risk through options you will have a much lower but sustainable yield. these concepts are summed up nicely in where does yield come from by ribbon co-founder @juliankoh.

the ribbon team (and now a growing list of others) saw strategies like covered calls and yield vaults like yearn as a perfect fit, and thus Option Vaults were formed.

option vaults are simple, user deposits ETH and each week the ETH is used to collateralize 1w expiry and 0.1d options via oTokens using opyns gamma protocol which are then sold to market makers via telegram and airswap or through open access gnosis auctions.

these vaults are somewhat of a win-win for all parties. retail has a clean UX, simple, and easy way to generate constant yield. sophisticated market makers can take advantage of the not very competitive option pricing and arb the prices across other option exchanges.

there has been a decent backlash to option vaults on twitter see

https://twitter.com/saanglee/status/1478075681560936449?s=20

or the quote tweets from here.

all I’ll say on this is option vaults are much more transparent in their onchain activities and structure its just really about communicating that risk to the retail investors using the platform. if you are marketing the option premiums as risk free yield then ya backlash is deserved.

across all option vault protocols there is about $500m of short dated options being sold each week by DOVs, so much that they are starting to affect the skew

some smart traders new this was coming, but I assume most did not

the deribit insights thread aeto was looking for can now be found here

if you take away anything from this post, know that your yield will always be compressed even if it is “sustainable”

https://twitter.com/0xperp/status/1483234558464172035?s=20

so far we have seen 40% interest rate on stablecoins, 4-5 figure APYs on the newest yield farm, very high double digit yields on option vaults, and (likely) will see large swings in negative funding via algorithmic stablecoin mints. and this is were things start getting cool… DeFi products affecting the overall crypto market structure (who would have thought back in 2019 when the entire space was sub $1m TVL).

but high tvl vault projects decimating your yield isn’t the point of this post… so back to derivatives.

another vault structure that is becoming more common is the basis trading vault. uxd and lemma are leading the way. the basis trade is pretty simple especially when you are using perps.

• notice that funding on your favorite perp protocol is positive (longs pay shorts)

• spot long an asset

• short the asset on the perp protocol

• earn the funding rate while being delta neutral

of course you’ll need to continually hedge your positions to keep your delta neutral

uxd and lemma run strategies on decentralized perp markets (mango and perpetual) in a vault structure allowing anyone to invest. additionally they mint a stablecoin against the position. in uxd example you deposit SOL and the vault shorts SOL perps on mango.

algorithmic stablecoins are another interesting DeFi concept, maybe will detail in a later post but for now this piece from dragonfly and and FRAX’s docs on seniorage shares should get you started

as mentioned above a large influx in delta neutral backed stablecoins could push perp markets funding rates negative.

understanding how these stables pay the negative funding is important (and directly affects your coins stability), generally this is paid through an insurance fund and if thats not enough governance tokens may be auctioned off (similar to maker debt auctions).

however historically the basis trade has been really profitable

Following a 2x long ETH-USDC on Perp and 2x short ETH-PERP in FTX would have yielded over 100% APR since the market was opened

https://insights.deribit.com/market-research/the-quest-for-perp-amms/

hidden in this article you will also find some interesting finds relating to the open interest and price on perpetual protocol v1

a quick aside on stablecoins and perps and what they can learn from one another

if your timeline over the past few weeks has been 75% about squeeth like mine, then tweets around “everything being a perp” might have surfaced. here are a few of them

• from georgios

• from dan

• reference to rai, from charlie

note: all paradigm

quick overview for MakerDAO and DAI is that DAI is an ETH margined USD perp

• DAI holders are long DAI

• Vault depositors are short DAI

• DAI price is the mark

• $1 is the index

• the dai savings rate and stability fee act like a funding rate

on the RAI and reflexer side the team has called themselves a stablecoin/perp, if you read closely on the rai use cases you can see how RAIs redemption rate is similar to a funding rate.

another side note A Money God Rises, DAI purple paper, Rico are all good reads

you can read more about perps as stablecoins from a more technical side, written by opyn, here

and a final exercise to the reader from the above post

This is a collateralized zero coupon bond -- the original stablecoin

okay after that quick overview on how everything is a perp, what can perps learn from algorithmic stablecoins (this specifically applies to those built with an AMM for pricing)?

designing a perp market has the benefit of using an AMM for pricing, most vAMM designs use the constant product formula xy=k which has worked quite well.

there have been plenty of iterations and new designs for AMMs for similarly priced assets and if you think of the mark and index of a perp as two separate like priced assets you can start to see how implementing a stableswap-like curve might allow for a perp to maintain a tighter peg.

additionally almost all algorithmic stablecoins trade at a current value (the mark) and peg/target a rate of $1 (the index).

the FEI stablecoin initially had the concept of direct incentives, essentially as FEI deviated from its peg of $1 a reward/penalty would take place for a mint/burn. if FEI is trading at $0.98 minters earn 2% and burners are hit with a 4% penalty and vice versa. the trick here is that the farther FEI deviates from its peg the exponentially higher penalty. here is a (brutal) graph of what FEIs price movement from peg looks like when adding in direct incentives.

note: that this did not work well for FEI and is since removed from the protocol

as much as direct incentives did not really work for FEI, it could be interesting to try in a perp context. as mark deviates from the index you quadratically scale up/down the funding rate. doing so would hopefully attract for arbitrage opportunities and thus maintain a tighter mark/index peg.

closing out this aside, I have a feeling that we will see algo stablecoin designs implemented in perps, and protocols described in the context of perps in the future.

power perpetuals

power perpetuals are perhaps one of the most interesting research topics and products to come out of defi derivatives. the core concept is simple, a power perp (for example ETH^2) tracks the price of ETH squared. if ETHs price rises by 200% ETH^2 rises by 400%, if ETH price goes down you loss less than you would through 2x constant leverage

sidenote you should read: How to lose 99.9% and still score a 500x

squeeth or any power perpetual (01protocol will be launching sol^2 soon) can be used for a lot of different strategies, here is an initial list

• Longing Squeeth means you are long gamma, and is similar to holding a perpetual at the money call options

• Shorting Squeeth means you are short gamma, and is similar to selling a perpetual at the money straddle

• Hedging Options

• Hedging LP positions

• 1x Long ETH Exposure with a oSQTH:USDC LP

• 1.5x Long ETH Exposure with a oSQTH:ETH LP

• Usage as a Volatility oracle

you can see a full list of use cases and articles on squeeth here

I am extremely excited to see where power perps take us in 2022 simply because I know that the list above is just scratching the surface.

if you want to think about use cases I suggest reviewing squeeth mental models and iterating from there.

it can also be helpful to know that holding squeeth provides a similar payoff to holding an always at the money call (shorting is like an at the money straddle). however power perps are not limited to just thinking in the option space, it can also be compared to a perp swap and constant leverage (something like FLI).

whats next

this has already been a somewhat long post, so to end here is my shortlist of innovations I’ll be looking at in 2022.

• iterations on power perps

opyn laid it out nicely

• A 0-perp is a stablecoin

• A 1-perp is a future

• Any p-perp that is not 0 or 1 is a volatility oracle

• Power perps can be traded against fixed-expiry power futures

• A 2-perp (squeeth) is an excellent hedge for options and constant function market makers such as uniswap and curve

• A 0.5 perp (sqrth) is a perfect hedge for a uniswap LP position and it's coming next!

• an increased focus on composability

  • I feel like many derivative designs have been optimizing for liquidity and the next step will be composability

  • I am looking at usage in other protocols, cross margined, (maybe) cross chain margin

• vaults for everyone

  • continue to create vault products that are accessible (but transparent) to all investors and allow them to participate in high payoff niche trading strategies

I have made hopefully comprehensive list of the DeFi derivatives landscape here

https://github.com/0xperp/defi-derivatives

if you are working on a derivatives project, or think I missed any don’t hesitate to reach out and DM me at @0xperp