Today marks a monumental milestone for decentralized AI: the Upshot and Modulus Labs teams have teamed up to create the first ever zero-knowledge price oracle. This pioneering innovation __brings verifiable machine learning on-chain__, ushering in a new era of trustless modeling for DeFi.

Upshot's mission is to expand the horizons of finance by enabling the creation of liquid markets for everything. To that end, our advanced machine learning models enable the creation of active markets for traditionally inactive, long-tail assets.

However, as Upshot CEO [Nick Emmons](https://twitter.com/nick_emmons) explains, "As more of the financial system becomes backed by ‘black box’ AI models, investors expose themselves to risks from unverified AI models."

That's why we've joined forces with Modulus, the leader in specialized zkML infrastructure tailored for blockchains. According to Modulus Labs CEO [Daniel Shorr](https://twitter.com/realDanielShorr), "By combining the magic of ZK proofs and the superpowers of AI, we're building the biggest UX upgrade blockchain has seen."

The zkPredictor is a first-of-its-kind integration of Upshot's ML models into Modulus's optimized zero-knowledge circuits. This technology will verify Upshot's asset valuations inside tamper-proof ZK proofs, without exposing our proprietary model IP.

This tool unlocks new markets by providing trusted prices on-chain for the first time ever.
## Why ZK Proofs Matter
The ZK proof serves an important function in Upshot's oracle network by enabling efficient and low-cost verification of the machine learning models that generate our asset pricing data. 

Specifically, the nodes in the network share new price information to each other, then report these prices on-chain on a regular basis. To avoid the high cost of running an explicit proof on-chain every time, the system uses an optimistic verification approach––only running ZK proofs for transaction-relevant, hotly watched, and disputed prices.

This allows for a highly scalable system, where the ZK proof acts primarily as a deterrent against misbehavior by nodes. If a node is challenged and the ZK proof shows they reported incorrect data, they face penalties to their stake and reputation.

So while the ZK proof technology serves as a security backbone, its presence enables greater trust and accountability across the network. The ZK proof is thus a critical component that enables Upshot to bring verified, manipulation-resistant machine learning pricing models on-chain in a practical and cost-effective manner.
## How You Can Use the zkPredictor
The world's first zkPredictor enables a new generation of financial products powered by verifiable machine intelligence:

- __AMMs__ can leverage [Upshot's Machine Intelligence Network](https://upshot.xyz/post/announcing-upshot-machine-intelligence-network) to create deep liquidity pools for long-tail assets like in-game items, RWAs, or other obscure commodities. Our AI-powered pricing facilitates efficient exposure and price discovery in new market niches.

- __Lending protocols__ can integrate Upshot's verified valuations to securely accept unique collateral like music royalties, luxury vehicles, or virtual real estate. This unlocks undercollateralized loans against previously inaccessible digitally native assets.

- __On-chain index funds__ can develop ML-driven crypto index funds with on-chain cryptographic proof that their models are impartial, consistent, and manipulation-resistant––engendering trust in automated yet explainable investing strategies.

- __Prediction market platforms__ can build markets around niche topics with high confidence in crowd-powered pricing signals verified through Upshot. Obscure topics can now develop into active markets through trusted alpha.

And these are just the beginning. By incentivizing honesty and accuracy, the Upshot zkPredictor unlocks reliable machine intelligence for the exploding DeFi ecosystem––ushering in a new era of trusted modeling and vastly expanding what decentralized finance makes possible.

__About our partner, Modulus Labs:__

[Modulus](http://modulus.xyz) is the accountable magic company. They use bleeding-edge cryptography to verify that AI results weren’t secretly manipulated. This means that smart contracts can now access AI features without abandoning the trustless creed. The team is based in Stanford, California.


Our collaboration with Modulus Labs introduces the world's first zero-knowledge price predictor, integrating verifiable machine learning on-chain for unprecedented trust and innovation in DeFi.

Upshot x Modulus Introduce zkPredictor: The World’s First zkML Price Predictor

## Introduction
The world of investing has been transformed by the rise of digital assets, and none more so than NFTs. These unique digital assets have captured the imagination of investors around the world, and there is now a growing demand for indexes that can track their performance.

[__Upshot Indexes__](https://app.upshot.xyz/indexes) offer a new approach, providing index methodologies and API endpoints for developers to create index products that track the performance of a basket of NFTs. This allows projects to offer exposure to a broad range of NFTs or specific segments of the market. For example, developers might use Upshot to build an NFT index that tracks the performance of blue-chip NFTs or Yuga Labs NFTs.

## The Basics of NFT Indexes and ETFs
NFT indexes allow investors to track and gain diversified exposure to the world of non-fungible tokens without the need to purchase individual NFTs. These indexes function similarly to traditional indexes by tracking a basket of assets – in this case, NFTs – that meet specific criteria such as rarity, popularity, or historical value. Key benefits of NFT index funds include ease of entry, diversification, reduced time and knowledge requirements, and improved liquidity.

To learn more about the intricacies of NFT index funds and how they compare to traditional index funds, read our in-depth blog post [__here__](https://upshot.xyz/post/introduction-to-nft-index-funds).

### Challenges of Building NFT Indexes
Creating NFT indices presents a unique set of challenges such as limited scope when tracking floor NFTs, pricing complexities, and liquidity and capacity concerns. To learn more about these challenges, please refer to our [__previous blog post__](https://upshot.xyz/post/introduction-to-nft-index-funds) on NFT indexes more generally.

#### Limited Scope When Tracking Floor NFTs
Presently, NFT index funds predominantly focus on tracking the floor prices of NFT collections. However, this approach fails to capture a complete picture of the range of outputs that are in a collection, as some collections contain highly-valuable rare NFTs that are worth significantly more than floor NFTs. Consequently, this limited scope results in an inaccurate representation of the true diversity and value potential of a collection.

#### Pricing Complexities
NFT indices typically weight-adjust their construction based on the floor price the various collections within the index. This does not take into account the collection as a whole, which would require pricing algorithms to better understand the value of the entire collection. Developing sophisticated pricing models for NFTs can be challenging due to the unique characteristics of each asset and the lack of historical data compared to traditional financial assets.

#### Liquidity and Capacity Concerns
NFTs are non-fungible, low-velocity assets. This characteristic poses challenges for NFT indexes in that oftentimes there is not sufficient capacity in the open market to rebalance the portfolio. Moreover, the acquisition and disposal of NFTs can be time-consuming and expensive, potentially hampering the fund's ability to manage its portfolio efficiently and effectively.

## Introducing the Upshot Indexes: Empowering Projects to Create More Expressive NFT Financial Infrastructure
Upshot has constructed a set of NFT index methodologies to enable other projects to build more expressive financial primitives for NFTs with our API. By providing the framework and tools necessary for designing and maintaining NFT indexes, Upshot aims to enhance the accessibility of the NFT space and open up opportunities for more meaningful capital allocations as well as more interesting financial innovations.

Leveraging our quant team’s extensive research and experience in constructing indexes for less liquid financial assets, Upshot's initial set of indexes track core sectors of the NFT space. This initial set of indexes include the Upshot Yuga Index, Upshot Art Blocks Curated Index, and Upshot PFP Index. These methodologies can be used to track the performance of specific segments of the NFT market or the broader market as a whole, with updates available on an hourly cadence.

By equipping index creators with robust and accurate pricing methodologies, Upshot empowers them to overcome common challenges like liquidity constraints, capacity issues, and the accurate valuation of assets within a collection. Through this approach, Upshot facilitates the growth of the NFT index ecosystem, making it more diverse and catering to a wide range of investor preferences and risk appetites.

### Presenting Upshot's Premier NFT Index Methodologies
Upshot is launching three initial index methodologies that cater to various NFT market segments: the Upshot Yuga Index, Upshot Art Blocks Curated Index, and Upshot PFP Index. 

#### Upshot Yuga Index
The methodology behind the Upshot Yuga Index monitors the performance of collections under the Yuga Labs umbrella, weighted by appraisal-based market cap. This methodology captures some of the most liquid NFT collections, enabling developers to offer exposure to verticals such as PFPs, metaverse, generative art, and gaming in a single index. The Upshot Yuga Index includes collections such as Bored Ape Yacht Club, Otherdeed for Otherside, Mutant Ape Yacht Club, CryptoPunks, Meebits and Bored Ape Kennel Club. 

To view more details around the methodology of the Upshot Yuga Index, visit [__here__](https://app.upshot.xyz/indexes/yuga). 

#### Upshot Art Blocks Curated Index 
The methodology behind the Upshot Art Blocks Curated Index tracks the top 20 Art Blocks Curated NFT collections, enabling developers and projects the ability to offer exposure to cutting-edge generative art. This index construction is composed of the following collections: Chromie Squiggle by Snowfro, Fidenza by Tyler Hobbs, Ringers by Dmitri Cherniak, Gazers by Matt Kane, and Archetype by Kjetil Golid among others. 

For more information on the methodology of the Upshot Art Blocks Curated Index, click [__here__](https://app.upshot.xyz/indexes/artblocks-curated).

#### Upshot PFP Index
The methodology behind the Upshot PFP Index tracks the top 20 PFP (profile picture) collections, weighted by appraisal-based market cap. This index represents a significant portion of the NFT space, with many PFP projects driving social identity, community engagement, and network value within the digital realm. The Upshot PFP Index includes collections such as CryptoPunks, Bored Ape Yacht Club, Azuki, Clone X, Pudgy Penguins, and Doodles. 

For more information on the construction of the Upshot PFP Index, click [__here__](https://app.upshot.xyz/indexes/pfp).

## Ways to Use Upshot’s NFT Index Methodologies  
#### Utilizing Upshot's NFT Index API as a Price Oracle for Perpetuals
Upshot's NFT Index API endpoint can be utilized as a powerful price oracle for the creation of NFT perpetuals. Perpetuals are a type of financial derivative that allows traders to take long or short positions on an underlying asset without an expiry date. In the context of NFTs, perpetuals can enable traders to speculate on the future price movements of NFT collections or the overall NFT market without actually owning the underlying NFTs.

The Upshot NFT Index API endpoint provides access to novel index constructions and price feeds that are crucial for calculating the index value and ensuring that the perpetual futures accurately track the performance of the underlying NFT collections. This includes information such as weighting based on appraisal-determined market caps of various NFT collections included in the index. Additionally, the API endpoint allows users to retrieve historical index prices, which can be used to analyze price trends, develop trading strategies, and assess the risk and return characteristics of the NFT market.

By leveraging the Upshot NFT Index API endpoint as a price oracle, creators of NFT perpetual futures can benefit from a robust, reliable, and up-to-date source of pricing information. This not only ensures that the perpetual futures are accurately tracking the performance of the NFT market, but also helps to establish trust and credibility among traders and investors participating in the NFT perpetuals market. 

#### Unlocking Holistic NFT Market Insights for Analysts and Researchers 
Upshot's NFT Index API is a powerful tool that enables analysts, researchers, and media organizations to gain a comprehensive understanding of the NFT market. NFT indexes offer a novel method for analysts to understand the NFT market, as they provide a holistic perspective on the rapidly evolving landscape, moving beyond the limitations of individual collections and enabling a more comprehensive analysis.

By breaking down the NFT market into different segments, such as art, gaming, and PFPs, analysts can identify trends and patterns within each category. Tracking the performance of NFT indexes over time enables the identification of market movements, emerging trends, and potential investment opportunities. Utilizing Upshot’s NFT API, analysts can generate valuable insights by creating in-depth reports, research papers, or news articles, which in turn assist investors, enthusiasts, and the general public in making informed decisions and staying up-to-date on the latest developments in the NFT market.

## Conclusion
The launch of Upshot Indexes marks an exciting new chapter in the rapidly evolving intersection of DeFi and NFTs. As the NFT market continues to mature, we can expect to see more projects leveraging these NFT indexes as a way to offer exposure to this exciting new asset class. By providing developers with robust methodologies and an easy-to-use API endpoint to build with these NFT indexes, Upshot is paving the way for a future where the financialization of NFTs unlocks new investment opportunities and enhances our understanding of this dynamic market. 

To learn more about the technical details of Upshot's offering, we invite you to explore our [__developer API documentation__](https://docs.upshot.xyz/reference). If you're ready to start building your own NFT index products, you can sign up for access to [__our developer portal__](https://developer.upshot.xyz/signin). As NFTs continue to reshape the digital asset landscape, we believe that innovative financial infrastructure, like Upshot Indexes, will play a vital role in driving the development of novel financial primitives. 

Introduction article to Upshot Indexes: A New Era of NFT Exposure.

Introducing Upshot Indexes: A New Era of NFT Exposure

Index funds are an indirect way to get exposure to an entire market without having to put in the work to pick the underlying assets individually. Today, various types of index funds exist, including equity indexes, bond indexes,  commodity indexes–and NFT index funds–that cater to diverse investment needs and preferences.

An NFT index fund is a vehicle that tracks an index of NFTs, typically from similar collections (e.g. [Yuga Labs](https://www.yuga.com/)), traits (e.g. hoodie CryptoPunks), or prestige (e.g. Art Blocks Curated). This post will explore how NFT indexes work, what they enable, the challenges uniquely faced by NFT indexes, and how those challenges can be overcome 

## What is an NFT Index?

An NFT index contains a list of hand-picked NFTs whose overall value is determined using capitalization-weighted, equal-weighted, or price-weighted methods.

An NFT index’s basic objective is to provide investors with insight into an NFT’s performance in the [DeFi](https://www.coindesk.com/learn/what-is-defi/) market. It does this by acting as a benchmark or reference point for investors to track the movement of NFTs in the market and make trading decisions using their deductions.

As typical within the volatile crypto and [NFT markets](https://upshot.xyz/post/nft-markets-are-maturing), NFT indexes are regularly adjusted to match NFTs’ supply. So while these indexes may offer investors an opportunity to make better trading decisions, they can be volatile as their composition changes to reflect the adjustments in the market.

## What is an NFT Index Fund?

NFTs represent a new frontier of digital assets, art, and internet culture. Composed of unique digital items that cannot be replicated or replaced, NFT index funds provide a novel way for investors to gain diversified exposure to the new asset class of non-fungible tokens without having to research and pick the individual NFTs themselves.

An NFT index fund works similarly to traditional index funds in that it tracks a basket of assets–in this case, NFTs. To do this, the index fund manager will select NFT collections that qualify for the index based on specific criteria, such as [rarity](https://upshot.xyz/post/nft-rarity-tools), popularity, or historical value. 

For example, the NFT index fund manager may track a basket of Yuga Labs NFTs, which include Bored Ape Yacht Club, Mutant Ape Yacht Club, Bored Ape Kennel Club, and [Otherside NFTs](https://www.coindesk.com/layer2/2022/05/02/otherside-and-the-future-of-nft-consolidation/). They will also consistently monitor the collections for various risks, as set forth by the index guidelines.

The index fund manager will also determine how often each index is rebalanced, generally monthly, quarterly, or bi-annually. This rebalancing process involves adjusting the weight of each collection within the index, typically based on factors like their floor price, appraised value, or overall market capitalization.

### NFT Index Funds vs Traditional Index Funds

Like NFT index funds, traditional index funds are investment vehicles that offer profit-making opportunities through investing in assets. However, their operational strategy differs greatly. One major difference between both is that NFT index funds invest in NFTs while traditional index funds invest in [traditional financial assets](https://www.mdpi.com/1099-4300/24/11/1565) such as stocks, bonds, or commodities.

An NFT index fund allows investors to gain exposure to a diversified portfolio of NFTs. It tracks and invests in a basket of equities that align with a specific index.

On the other hand, a traditional index fund is a type of mutual fund or exchange-traded fund (ETF) where a portfolio is constructed to match or track the components of a financial market index. Essentially, traditional index funds aim to replicate the performance of a specific market index.

Additionally, traditional funds are typically considered a more passive type of fund management. Where NFT index funds feature fund managers who consistently monitor the collections for various risks and trade using the information discovered, index fund managers curate portfolios that match and mirror the movement of securities of a particular index. 

## How Traditional index funds work

Index funds are typically considered a more passive type of fund management. As opposed to hedge fund managers who actively try to stock pick and time the market with buys and sells, index fund managers build up a portfolio of holdings to mirror the securities of a particular index. The idea is that the investment profile of the index matches the performance of a whole, broad segment of the market. 

For example, index fund investors can buy funds that focus on companies with small, medium or large capital values, or they can buy funds that focus on sectors such as technology or energy. While these more specific index funds may be less diversified than the broadest market index, they are still more diversified than if someone were to buy individual stocks within a sector. 

### Traditional Index Fund Example

There are numerous traditional index funds designed to track various market indexes. Some popularly known examples include:

#### S&P 500
In the United States, the [S&P 500 Index](https://www.spglobal.com/spdji/en/indices/equity/sp-500/#overview) is one of the most popular indices. The S&P 500 Index, or Standard & Poor's 500 Index, is a market-capitalization-weighted index of 500 leading publicly traded companies in the U.S. It is not an exact list of the top 500 U.S. companies by market cap because there are other criteria that the index includes such as liquidity, financial stability, and a history of profitability. 

As a result, the S&P 500 Index offers a comprehensive representation of the U.S. stock market's health. The S&P 500 index is regarded as one of the best gauges of prominent American equities' performance.

#### Dow Jones Industrial Average (DJIA)
Also known as the Dow 30, the [Dow Jones Industrial Average](https://www.dowjones.com) (DJIA) is a stock market index that tracks 30 large, publicly-owned blue-chip companies trading on the New York Stock Exchange (NYSE) and Nasdaq. The Dow includes companies like Nike, Salesforce, Goldman Sachs, Visa, Coca-Cola, and IBM.

The DJIA is also designed to serve as a proxy for the health of the broader U.S. economy. Often referred to simply as the Dow, the DJIA is one of the most-watched stock market indexes in the world. Although the Dow encompasses a variety of businesses, each can be characterized as a blue-chip company that consistently generates stable earnings.

## NFT Index Funds to Know

NFT index funds are rapidly revolutionizing the NFT market and challenging the traditional index structure as a whole. By offering investors the exposure and insight they need in the NFT market, these asset classes provide the possibility of making a profit off NFTs without necessarily having to research and invest in individual NFTs.

Although NFT index funds are an innovation, they are becoming a staple in the NFT market and now play a crucial role in managing NFT investment complexities. While there may be a limited option of NFT index funds available in the market today, this will change in the coming years as NFT index funds become more implemented and embraced. However, some important NFT index funds every investor should know include:

### 1. Upshot Index
![upshot logo2](//images.ctfassets.net/lhkcghnr8ql4/3SrLPrMZWqZRw9lvxVxLWb/52cd65ee1b8819c01dbf51e7bebccfe6/upshot_logo2.png)

The [Upshot Index](https://upshot.xyz/post/introduction-upshot-indexes) enables developers to build financial infrastructures that allow investors to track the performance of the broader NFT market. It offers a new approach to index methodologies by providing investors with the framework and tools they need to create and maintain NFT indexes that will be used to track the performance of a basket of NFTs. 

For example, the Upshot Index can be used to create NFT indexes that track the performance of blue-chip NFTs. Developers can create expressive financial infrastructures that challenge the complexities that plague NFT indexes such as liquidity problems and capacity limitations. 

While Upshot will not be issuing these indexes as investable products directly, the company is actively exploring partnerships with financial institutions and developers to create ETFs, perpetuals, and other investment products based on the Upshot Indexes. As the NFT market continues to grow and evolve, Upshot plans to expand its index offerings to capture emerging trends and opportunities in the space.

For more information about Upshot and Upshot Indexes, please visit [https://upshot.xyz/](https://upshot.xyz/)

### 2. Bitwise Blue-Chip NFT Index Fund

![Bitwise](//images.ctfassets.net/lhkcghnr8ql4/4Mns5A5iwhvv5MKYJyIKl8/c93ddd9ce6e376507cfa420574e2b26d/Bitwise.png)

The [Bitwise Blue-Chip NFT Collections Index](https://bitwiseinvestments.com/crypto-funds/nft) was developed to [track blue-chip NFT collections](https://static.bitwiseinvestments.com/FundDocs/nftFund/Bitwise-Blue-Chip-NFT-Index-Fund-Investment-Case.pdf?_ga=2.248036487.1099504939.1648648633-923459484.1648479033) in the Arts and Collectibles sector. It screens and selects collections using criteria such as the minimum number of assets in its collection, liquidity, and category, and ranks the selected collections based on floor market capitalization. 

Some of the most popular index constituents in the Bitwise Blue-Chip NFT Index Fund include Crypto Punks, Bored Ape Yacht Club, Mutant Ape Yacht Club, Azuki, [Chromie Squiggles](https://upshot.xyz/post/buying-guide-chromie-squiggles), and Clone X. The assets are rebalanced quarterly.

__Where to Buy:__ The Bitwise Blue-Chip NFT Index Fund is not a publicly traded fund. It’s available to accredited investors for private placement subscription with a $25,000 minimum investment and will provide monthly redemptions after an initial lock-up period of 6 months.

### 3. Bitwise Metaverse NFT Index
![Bitwise](//images.ctfassets.net/lhkcghnr8ql4/4Mns5A5iwhvv5MKYJyIKl8/c93ddd9ce6e376507cfa420574e2b26d/Bitwise.png)

The [Bitwise Metaverse NFT index](https://bitwiseinvestments.com/crypto-funds/ball-multicoin-bitwise-metaverse-index-fund) is a new asset class launched by Bitwise and  Matthew Ball, CEO of Epyllion and the former global head of strategy for Amazon Studios. This index will include up to 40 metaverse-related crypto assets, their weighting, and a corresponding fund tracking the performance of the index. 

The crypto assets will be chosen using factors such as custody, liquidity, technological, and regulatory risk. 

__Where to Buy:__ The index fund is offered through Bitwise.

## Benefits of NFT Index Funds
![NFT Index Funds](//images.ctfassets.net/lhkcghnr8ql4/2eMvae9vKisAAqEP5a0S10/ffac846ed9c9162bd36164f151714668/NFT_Index_Funds.png)

### Ease of Use

With the rapid growth of the NFT market, NFT index funds offer a familiar and easy way for investors to gain exposure to the burgeoning market of digital assets in a manner that is very similar to how TradFi investors already operate. By leveraging the expertise of index fund managers, potential investors can enter the complex world of NFTs with greater confidence and reduced barriers to entry.

### Portfolio Diversification

The average investor does not have the capital to buy a breadth of NFTs to represent all of the various blue-chip NFT collections out there. With floor prices of blue-chip NFTs rapidly climbing, even one NFT purchase can eat up the majority of dry powder for an individual investor. NFT indices are a great way to gain broad exposure with a smaller amount of capital. 

Investors can enjoy the benefits of owning a diverse portfolio of NFTs, reducing the risk associated with concentrating investments in just a few assets. 

### Lower Time and Knowledge Requirements 

The [NFT market](https://upshot.xyz/post/nft-markets-are-maturing) moves lighting fast—collections can take off or dump within a weekend of news. It is exhausting for the average NFT participant to keep up with the major sources of news, especially when much of the information lives on Twitter or private Discord servers. 

Similar to how average TradFi investors are not listening to every public company earnings call, NFT indices allow people to participate in a broad manner without having to understand every detail. This not only saves time and energy, but also helps mitigate the risk of making financial decisions based on incomplete or inaccurate information.

### Liquidity 

One of the challenges NFT investors face is the lack of liquidity in the market, as buying and selling individual NFTs can sometimes be difficult due to their unique nature and varying demand. NFT index funds can help address this issue by providing investors with an investment vehicle that is easier to trade in and out of compared to individual NFTs. 

The pooled nature of index funds typically allows for greater liquidity, making it more convenient for investors to adjust their positions as needed.

## Complexities of Building NFT Indices 
![NFT Indices](//images.ctfassets.net/lhkcghnr8ql4/1hxz7gNXFerwXZWIAbaE6N/987f3a0480c210384df5bef1ac55d1d0/NFT_Indices.png)

### Some Only Track Floor NFTs

Today, the NFT index funds that do exist typically only track floor prices. [Floor NFTs](https://chain.link/education-hub/what-is-an-nft-floor-price) are not indicative of the entire collection, especially when some collections have grails (or rare NFTs) that are worth several orders of magnitude more than the floor NFT. The limited scope of floor NFTs does not accurately reflect the true diversity and potential value within a collection, which can lead to suboptimal investment performance.

### Pricing 

NFT indices typically weight-adjust their portfolios based on the floor price of a collection. This does not take into account the collection as a whole, which would require pricing algorithms to better understand the value of the entire collection. Developing sophisticated [pricing models for NFTs](https://upshot.xyz/post/how-upshots-ml-models-work) can be challenging due to the unique characteristics of each asset and the lack of historical data compared to traditional financial assets.

### Liquidity and Capacity Issues 

NFTs are non-fungible assets that often have low velocity. Given that each asset is unique, they change hands much less frequently than traditional [ERC-20 tokens](https://ethereum.org/en/developers/docs/standards/tokens/erc-20/) or stocks. It is difficult for an NFT fund to curate an index and have the right capacity on the open market to rebalance it as needed. 

Additionally, the process of acquiring and disposing of NFTs can be time-consuming and costly, which may limit the fund's ability to efficiently and effectively manage its portfolio.

## Possible Solutions to Building NFT Indices 

If you’re looking to build out an NFT index, work through these challenges with a few innovative solutions. 

### NFT Appraisals 

[NFT appraisals](https://upshot.xyz/post/how-upshots-ml-models-work) allow NFT indices to place a better basket of assets that more accurately reflect the broader collection, beyond simply adding floor assets. They can better index the higher end of the NFT market by taking into account rarity, metatraits, and historical trading patterns, among other factors. 

Developing comprehensive appraisal methodologies and leveraging advanced [ML models](https://upshot.xyz/post/how-upshots-ml-models-work) can help index fund managers more accurately assess the value of each NFT within a collection, leading to a more representative index.

### Capacity

NFT index funds may face capacity constraints due to the illiquid nature of the assets they hold. To address capacity issues, NFT index funds can implement strategies such as limiting the size of the fund, focusing on more liquid NFT collections, or utilizing derivatives and other financial instruments to gain exposure to the underlying assets without directly owning them. 

This approach can help improve the efficiency and flexibility of portfolio management while minimizing the impact of liquidity constraints. 

In traditional finance, the high-yield bond market, also known as the "junk bond" market, faces similar capacity constraints due to the illiquidity of these assets. To manage these constraints, some high-yield bond index funds employ strategies like sampling, wherein the fund invests in a representative sample of the bonds in the index, rather than holding each bond in the index. 

This sampling approach allows the fund to maintain a more manageable portfolio while still aiming to achieve a similar risk and return profile as the overall index. By adopting similar strategies, NFT index funds can address capacity constraints and provide investors with diversified exposure to the NFT market, even in the face of illiquidity challenges.

### Synthetics & Perpetuals

A synthetic asset is an asset that tracks the value of another asset, essentially mirroring its price movements without the need for direct ownership of the underlying asset. One common form of synthetic asset is a [perpetual contract, or perp](https://milkroad.com/funding/perpetual-contracts#:~:text=A%20perpetual%20contract%20is%20a,from%20the%20underlying%20crypto%20asset.), which is a type of derivative that does not have an expiration date. 

To create a synthetic asset, a pool of capital and a reliable price feed that consistently updates are required. A well-known example of a synthetic asset is DAI, which tracks the value of the US Dollar. Utilizing price feeds for an NFT index can provide investors with synthetic exposure to the index through instruments like perpetual contracts. 

This approach may help mitigate some of the capacity issues and potentially simplify the design of an investable NFT index product, making it more accessible to a wider range of investors.

## The Future of NFT Index Funds with Upshot

For several decades, traditional financial markets have offered investors the opportunity to get broad exposure to various asset classes by simply buying into a basket of goods that serve as a proxy for the market. As NFTs emerge as a novel class of digital assets, investors are increasingly eager to have vehicles that give exposure to the asset class without having to put in the time and effort to pick various jpegs individually. 

As the [NFT market continues to mature](https://upshot.xyz/post/nft-markets-are-maturing), innovative solutions to these problems will play a crucial role in addressing the complexities and challenges associated with building and managing NFT indices. By providing robust and accurate pricing methodologies, Upshot can empower index issuers to easily overcome obstacles such as liquidity constraints, capacity issues, and accurate valuation of assets within a collection. 

As a result, the development and adoption of NFT index funds will become more seamless and efficient, ultimately making the world of NFT investing more accessible and attractive to a broader range of investors. 

Ultimately, NFT indices will be an increasingly important frontier in allowing individuals to interact with the new digital renaissance. 

To leverage sophisticated tools like the Upshot Index and make more informed decisions in the NFT market, [start exploring with Upshot today](http://Upshot.xyz).


Introduction To NFT Index Funds

Automated market makers (AMMs) are decentralized exchanges that use algorithms and liquidity pools to provide liquidity to NFT traders on the blockchain. AMMs are tasked with eliminating the need for intermediaries in the NFT trading process, thereby promoting seamless token liquidity.

Although they were first introduced as a protocol for fungible tokens, AMMs quickly made their way into the NFT space. Automated market makers offer numerous benefits and have proven to drive financial innovation in the [maturing NFT markets](https://upshot.xyz/post/nft-markets-are-maturing).

This article will review automated market makers and what they mean for the [DeFi ecosystem.](https://www.techtarget.com/whatis/definition/decentralized-finance-DeFi)

## What is an automated market maker (AMM)?

AMMs are known as “automated” market makers because they don’t depend on third-party market makers to set prices for traded digital assets. “Market makers” are individuals or entities that work on behalf of a financial institution in an exchange system and provide trading services for investors to keep the financial market liquid. They do this by setting the “bid” and “ask” prices on their systems and displaying them publicly on their quote screens.

AMM is a blockchain protocol that powers [decentralized exchanges (DEXs)](https://worldcoin.org/articles/what-is-a-dex) by providing liquidity for traders that buy and sell cryptocurrencies or digital assets. This protocol functions by pooling liquidity into smart contracts and so users can complete automatic transactions in a permissionlessly and without an intermediary. 

The easiest way to truly understand automated market makers is to compare them with the currency or asset exchange process in traditional financial institutions. 

Transactions on centralized or traditional exchange platforms usually feature a financial institution (market maker) who acts as a liquidity provider. The market maker oversees the exchange and provides liquidity by using the order book to connect buyer and seller at an overlapping price point. 

Essentially, if a trader wants to buy an asset for $500, the financial institution creates multiple bid-ask orders to match the trader’s price order. In this case, the financial institution is an intermediary to facilitate liquidity in an otherwise illiquid trading pair.

Automated market makers function like market makers in centralized systems but eliminate the need for centralized market-related trading techniques or intermediary intervention. They replace order books and human market makers with smart contracts, liquidity pools, and mathematical algorithms that can determine the price of digital assets based on their supply and demand.

## What are liquidity pools and liquidity providers?

For an AMM to be functional, there has to be a liquidity pool built into the blockchain’s smart contract and backed by liquidity providers. The liquidity providers deposit equal amounts of a trading pair (such as ETH/USDT) into the pool for traders to trade against. 

Liquidity pools are a collection of crowdsourced tokens locked in a [smart contract](https://www.simplilearn.com/tutorials/blockchain-tutorial/what-is-smart-contract) where they are used to facilitate decentralized trading. These pools can be compared to a large pile of digital assets deposited to let users trade with them. The investors who deposit digital assets into these pools are known as liquidity providers or LPs.

After the pool is created, the AMM is charged with using its algorithm to ensure the ratio of the assets in the pool remains balanced. As trades are made, the algorithm continuously adjusts the price of the tokens to match the market’s supply and demand and retain equilibrium. This ensures high liquidity and low slippage for all transactions on the blockchain.

Liquidity in a DeFi system refers to how easily one digital asset can be converted into another without affecting its market price. Without an adequate liquidity pool to draw from, the DEX could suffer significant price impacts that may lead to capital inefficiency and loss after a transaction. Hence, the more liquidity an AMM has, the more stability the DEX enjoys.

To ensure adequate liquidity within a DEX ecosystem, AMMs incentivize users to collectively deposit assets into liquidity pools to enable other traders to execute transactions on the platform. In exchange for supplying the pools with assets for users to trade against, AMMs reward liquidity providers with a fraction of the fees generated during every exchange, which is known as [yield farming.](https://worldcoin.org/articles/crypto-yield-farming#:~:text=Yield%20farming%20is%20a%20investment,trading%2C%20lending%2C%20or%20borrowing.) This idea of a passive income from trading fees encourages investors to deposit assets in secure liquidity pools.

The rewards are usually distributed according to the LP’s contribution to the pool. Therefore, if you contributed 5% of the total assets in the liquidity pool, you will be rewarded 5% of the accumulated transaction fees of that pool. However, the sharing of rewards in a liquidity pool is often dictated by the protocol.

In addition to the percentage rewards that AMMs offer, many DEX platforms also offer liquidity providers bonus rewards like governance tokens. These tokens enable liquidity providers to have voting rights on subjects concerning the development of the AMM protocol. Therefore, if there’s a proposed change in the protocol, liquidity providers can have a say by casting votes.

## AMMs in the NFT space

Although automated market makers first became significant with fungible tokens (also known as ERC20 tokens), they are quickly becoming a staple tool in the NFT or non-fungible token (ERC721 tokens) ecosystem. Many NFT markets have adopted AMMs in their ecosystem to unlock the liquidity-enhancing abilities of this unique protocol.

Like any other token, NFTs are digital assets secured by smart contracts on a blockchain. While the market enjoyed significant growth, it was faced with limitations including its liquidity. Unlike many other cryptocurrencies, such as stablecoins, [NFTs are relatively illiquid](https://mirror.xyz/origins-research.eth/5QnDGHw0KLtmOBn-W_69baheg45jV3rwbtvD6F2aCdY). They’re not easy to sell at their estimated market value which means many creators and NFT holders find themselves cutting the floor price to liquidate their asset and make it more appealing and affordable to buyers.

NFT AMMs solve this challenge by enabling users to trade or swap NFTs instantly via liquidity pools. With this solution, NFT holders don’t have to rely on a price agreement from a buyer or undercut their NFTs to render them liquid. Essentially, NFT AMMs eliminate the waiting phase in the NFT trading process, making it easier to liquidate NFTs.

While the option of instantly selling an NFT is exciting, the NFT holder does not dictate the price of the NFT when trading on an AMM. Instead, the AMM uses an algorithm known as a [bonding curve](https://phemex.com/academy/what-is-bonding-curve#:~:text=Bonding%20curves%20are%20a%20type,shape%20and%20the%20token%20supply.) to appraise the NFT based on the market’s supply and demand. This means there’s a chance that the algorithm may [value your NFT](https://upshot.xyz/post/determining-what-matters-NFT-valuation) below its intended floor price. While this may lead to a loss,  AMMs allow you to sell the NFT immediately–and potentially at a higher price than what a buyer might offer on a marketplace if you need liquidity quickly. 

Generally, it’s better to use an AMM if you’re in a hurry to sell an NFT.  While you won’t have the opportunity to set the price like you would in the  marketplace, AMMs offer instant liquidation without having to undercut your asset’s floor price.

### How to Interact with NFT AMMs as a Trader

NFT AMMs allow traders to interact with the NFT space in innovative ways and enjoy increased liquidity for their assets. Traders can unlock promising NFT trading potentials and make more lucrative trading decisions on the blockchain. They can potentially profit from price fluctuations as the value of an NFT is not determined by the buyer or seller but by the level of supply and demand in the market.

To buy an NFT on an AMM, a trader must first connect their wallet to the DEX being used. This can be done through a  web3-enabled browser that allows the user to interact with decentralized applications (dApps), such as MetaMask, that let traders interact with the Ethereum blockchain. The wallet chosen must have enough funds to purchase the NFT and pay gas fees.

After connecting a wallet, the trader can browse through the catalog of NFTs available for sale on the AMM. The AMM’s algorithm determines the price using supply, demand, and liquidity available.

Afterward, the trader approves the transaction by paying the NFT’s price and the gas fee associated with the transaction. Once the transaction is approved, the trader's order is submitted and the AMM automatically executes the trade by transferring funds out of and the NFT into the trader’s wallet.

The selling process follows a similar pattern. The trader must connect their wallet to the DEX, select the NFT they want to sell, wait for the algorithm to appraise the NFT, and accept the price. The NFT is then listed for sale until a buyer places a purchase order, at which point the smart contract will automatically execute the trade.

In addition to buying and selling, a trader can choose to join an AMM liquidity pool or create their own. In this case, the trader will invest funds (NFTs and/or ERC20 tokens) to liquidate the AMM in exchange for a percentage of the accrued trading fees and other incentives dictated by the pool.

In summary, a trader can buy an NFT, sell an NFT, or provide liquidity on an NFT AMM. To interact with an NFT AMM, a trader must:

- Choose a reputable NFT platform
- Connect the wallet supported by the platform
- Browse the available NFTs and select the NFT they would like to sell
- Wait for the AMM to determine the price of the NFT
- Place a buy/sell order
- Monitor the trade to determine if it’s successful.

## Popular NFT AMMs to Know

There are numerous NFT AMMs in the DeFi space. Some of the most popular options include: 

### 1. Upshot: Coming Soon 
![upshot logo](//images.ctfassets.net/lhkcghnr8ql4/mCKEljS1B7kiBH3xawZaN/b5ffac3ba652d7b95639fa8eeec84674/upshot_logo_.png)

With novel tools ranging from [advanced machine learning models to gmi calculators](https://upshot.xyz/post/how-upshots-ml-models-work), Upshot opens the door to new DeFi and DEX possibilities. Currently, NFT AMMs like Sudoswap (which we’ll review next) use a minimum floor price to enable NFT trading. This makes it less desirable for someone to deposit NFTs that are above floor into an AMM. 

Using Upshot appraisals, developers could support the trading of more than just floor prices by providing quotes and automated liquidity. Upshot’s appraisal platform uses trained models to continuously value NFTs in the market, providing accurate values based on changes in the market.  It currently provides near real-time appraisals for 74M+ NFTs from over 67K collections. 

Upshot’s appraisals could also help market maker bots automatically provide liquidity to NFT markets across AMMs and other marketplaces. 

__Features & Tools:__

- __Real-time portfolio tracking__: Track the value of your NFT portfolio powered by near real-time appraisals.
- __Build smarter trading strategies__: Upshot's API can help developers build smarter trading strategies informed by appraisals, liquidity analysis, and other advanced stats.
- __Dealfinding__: Upshot's appraisals are integrated into the core of our NFT aggregator to make it as easy as possible for collectors to find the best deals in the NFT market.
- __Identify and track smart collectors__: gmi is a novel grading index for NFT wallets that you can use to track the smartest collectors in the space to help inform your investing decisions.

Upshot’s ground-breaking tools and novel features make it possible to create secure AMM protocols where traders can easily access NFT liquidity. To enjoy instant NFT trades and other exciting NFT market interactions, [join Upshot](https://upshot.xyz/) and unlock a world of possibilities in the NFT market.

### 2. Sudoswap
![sudoswap logo](//images.ctfassets.net/lhkcghnr8ql4/6RfwfSdAOZCLpvEcZUA3yI/80333cea8102da247205bae4d71f53b1/sudoswap_logo.png)

[Sudoswap](https://sudoswap.xyz/#/) is a DEX protocol that enables traders to exchange NFTs in a trustless process on the Ethereum blockchain. It facilitates NFT-to-token (and vice versa) swaps using customizable bonding curves that determine the price of the NFT being traded. Sudoswap charges users a minimal transaction fee of 0.5% and [does not pay royalties](https://www.cryptotimes.io/sudoswap-amm-nft-marketplace-sparks-creator-royalties-debate/) to the NFT collection’s creator(s).

__Features and Tools:__

- User-friendly interface
- High-speed transactions
- Minimal trading fees
- Presence of liquidity pools
- No intermediaries
- Supports a wide range of tokens

__Pricing__: Sudoswap is free to use but charges users 0.5% to process the transaction

### 3. Caviar.sh
![caviar logo](//images.ctfassets.net/lhkcghnr8ql4/31E7sFQ0zf40pZrNtWIhA8/a750ca3709bc3b2fe01a66299595887c/caviar_logo.png)

[Caviar.sh](https://www.caviar.sh/) is an on-chain AMM protocol that facilitates the swapping of NFTs and ERC20 tokens in whole or fractional amounts. Caviar.sh uses a combination of liquidity pools and desirability classifiers to enable traders to unlock easy NFT liquidation.

__Features and Tools:__

- Liquidity provision through liquidity providers (LP)
- Yield farming to enable LPs to earn rewards 
- Staking 
- NFT lending
- User-friendly interface.

__Pricing:__ Caviar.sh is free to use but charges 1% on every transaction. This fee goes towards liquidity providers. 

## Create Capital-Efficient NFT AMMs with Upshot

AMMs are revolutionizing the NFT space and the DeFi ecosystem as we know it. By unleashing instant liquidity options, AMMs have officially set the stage for futuristic NFT trading possibilities. 

Upshot offers a solution to create capital-efficient AMMs on NFT platforms. With innovative features such as NFT appraisals and instant sell options, Upshot offers better trading options and more efficient ways of interacting with the NFT market.

[Trade smarter with Upshot.](https://upshot.xyz/)


Learn about automated market makers (AMMs), their ability to make the DeFi ecosystem more efficient, and why they are revolutionary in the NFT space. 

What are Automated Market Makers? Ultimate Guide to NFT AMMs

## Intro

Upshot is entering the NFT lending space. By leveraging our appraisal models and new infrastructure built specifically for lending, Upshot will be launching managed NFT lending strategies. Our lending strategies use different methodologies that can appeal to a range of liquidity providers with different risk profiles. In this post, we’ll outline how our appraisals enable us to construct efficient portfolios of NFT loans, details into the new lending-specific models that we’ve built over the past couple of months, and how we’ll be bringing these lending models to market in the coming weeks.

## What Goes Into a Successful Lending Strategy

When constructing terms for NFT loans, it’s vital to understand the value of the NFTs you are lending against. The amount you loan against an NFT relative to its value is known as the “loan-to-value” (LTV). As a lender, if your LTV is too high, you are exposing yourself and your backers to undue principal risk. On the other hand, if your LTV is too low, you are leaving money on the table and will likely lose borrower interest to competing lenders.

This is where appraisals fit in. NFT appraisals give us a reliable in-the-moment estimate of the value of an NFT, allowing us to set loan terms that are competitive while mitigating our risk.

### Appraisals

Upshot's appraisals are among the most accurate and robust models for NFTs on the market. Every hour, over 74 Million NFTs from more than 67,000 collections are appraised by our ML models with industry leading accuracy (MAPE of 3-10% for our best-performing collections). It is these appraisals that are powering our lending strategies.

While appraisals are at the core of our lending strategies, we have built several other components that are essential to being a successful lender in the NFT space. These other pieces include forward-looking estimates of asset value changes (recovery rates), default probability estimates, and our risk management framework. A quick description of each follows:

#### Recovery Rates

Our recovery rate model calculates forward-looking estimates of NFTs’ value, which takes into account the current market situation and helps the lending strategy to predict the future collateralization of an NFT-backed loan. Without recovery rate estimates vs the current fair value (our appraisal) a strategist cannot estimate how much money they stand to lose when a borrower defaults.

#### Default Probability Model

Like TradFi credit providers, our models factor in the probability of a borrower defaulting on their loan. This model helps us to estimate the possible frequency and likelihood that the borrower misses an interest payment and assess the chances that we will have to liquidate the assets that we are left in possession of.

#### Risk Management Framework

Our risk management framework is where our concentration analyses occur and where we manage the number of collections we are writing loans against. This framework also takes into account the loan concentration per collection on other major lending platforms, ownership concentration per collection, our own loan concentration per collection, and collection concentration per wallet. By not only acting at an individual NFT level, but also at a holistic collection and market level, we can protect both liquidity providers and ourselves from any potential downsides.

The number of collections we lend for is also not fixed. When market conditions improve we are able to provide terms for more collections, but when there is volatility in the market our framework automatically scales down the number of collections we lend for.

## Striving for Breadth

Because of Upshot’s appraisal coverage, we are capable of offering relatively safe and competitive terms for dozens of collections. This is achieved by adjusting the collections we publish terms for based on our fair value estimates at the horizons we lend for. To decide which collections are suitable, we analyze the volume, liquidity, wash trading as a percentage of total trading, ownership and transaction wallet concentration.

The criteria that go into selecting collections are volume, liquidity, wash trading as a percentage of total trading, ownership and transaction wallet concentration, over multiple months, so that we lend for more stable but also diverse collections that do not suffer from and are less prone to manipulation.

## What’s Ahead

With this lending infrastructure and strategies in place, we will start to bring them online in the coming weeks. We will be underwriting loans directly on leading NFT lending platforms, but we will also be one of the initial strategists on Astaria when they launch. As an Astaria strategist, it will be trivial for anyone to get exposure to our strategies’ performance through the Astaria protocol. We will be releasing some more information about how our strategies work, the types of loan we’ll be underwriting, and more in the next few weeks.

Upshot has entered the NFT Lending space. Our lending strategies, which leverage our appraisal models and new infrastructure built specifically for lending, appeals to a range of liquidity providers with different risk profiles.

Introducing: Upshot Lending Strategies


At Upshot, we believe a robust interpretation layer for NFT data is key to the success and growth of the NFT industry. Keeping this in mind, we built collection recommendations: A feature that enables users to better discover NFT collections based on their current interests and holdings.

This article will cover why collection recommendations are important for the NFT space as a whole, how we used AI to put personalized curation front and center for our users, and of course, how you can get started using them today.  

[Try it: Collection Recommendations is live on our aggregator](https://app.upshot.xyz "Upshot App")
![Upshot Web App with Collection Recommendations feature](//images.ctfassets.net/lhkcghnr8ql4/0ZW23Boca6kirS9hdq9W4/030b3ebbf9f4fee19b55506cbfbe6aa7/Screen_Shot_2023-02-28_at_6.53.57_AM.png)

## Why Recommend

The NFT space is exploding. Amongst many things, this has led to an ever-increasing number of collections in various categories such as PFPs, gaming, music, generative art, etc.

With more than 160K collections (at the time of writing) available through various marketplaces, it has become increasingly more challenging for individuals to decide which collection to focus on. 

Analogous to the evolution of the e-commerce industry, the NFT space will soon realize the importance of content curation and personalization for individual wallets. To create this will need to develop data-driven techniques that reduce the search space for wallets and help individuals navigate the sheer volume of content produced in the NFT space.

### For the User
A recommendation system uses algorithms that take a large set of items as input to determine what to display to a user next based on those inputs. Recommendations are part of our everyday life - think of Facebook News Feed, YouTube's homepage, and Netflix's "Recommended for you" navigation page.  At Upshot, our recommendation algorithm can infer “what the user may like to own” based on the collections they currently hold in their wallet. 

### For Developers
Using our collection recommendations algorithm (which is bundled into the [Upshot API](https://docs.upshot.xyz/docs)) developers can create a better, more personalized experience for their end users. 

This personalization can lead to new products, enhanced user stickiness and transaction volume, and an overall better experience in the NFT space - one that cuts through the noise and presents the relevant information on a specific per-user basis.

## Our Methodology

We based our initial approach on inferring the wallet's preferences for collections based on what the wallet already owns. 

For baseline recommendations, collaborative filtering is the first step. This step doesn't require too much information about collections except for which collections specific wallets have traded, owned, viewed, etc. 

Collaborative filtering analyzes the relationships between wallets and interdependencies among collections to identify new wallet-collection associations. The technique recommends collections to wallets based on the preferences of other wallets by identifying similar wallets or collections. 

In our case, we use NFT ownership as a wallet's preference. A critical step in the recommendation process for a given wallet is the identification of similar wallets (known as user-based) or similar collections (known as item-based).

At Upshot, our recommendation technique is item-based where similarities between collections are leveraged to determine the associations between wallets and collections. This item-based technique is explained using Figure 1 which shows wallets ownerships of collections.

![collaborative filtering figure](//images.ctfassets.net/lhkcghnr8ql4/7zDQO0delNgh4CI49G11Qi/c79f69078e745358f228d44b0b5b2bdb/collaborative-filtering.png)
Figure 1: Item-based Collaborative Filtering

To recommend more collections to wallet 1, we first find the collections owned by wallet 1 which are collection 1 and collection 2. Now, the next step is to find similar collections to collection 1 and collection 2. As collection 2 is also owned by wallet 3 who also owns collection 3 and it is not owned by wallet 1, the process will present collection 3 to wallet 1 as one of the recommendations.

From a technical perspective, the similarity between collections is determined using Cosine Similarity, which calculates the similarity between two vectors by calculating the cosine angle between them. The advantage of using this method is that it is invariant to vector size as in our dataset, the number of collections each wallet interacts with varies a lot. This difference in magnitude does not affect the similarity measure since it only considers the angle between vectors instead of their sizes. 

Mathematically, cosine similarity is calculated as the dot-product of collection pairs divided by the product of normalized vectors. i.e.

$$
cos(\theta) = \frac{\sum_iA_iB_i}{\sqrt{\sum_iA_i^2} \sqrt{\sum_iB_i^2}}
$$

Once a collection-collection similarity matrix is available, the next step is to calculate a score between the wallet and every collection not currently owned by the wallet. The association between a wallet and a collection is the weighted average of a set of collections that are similar to collections owned by the wallet. Notice we use weighted average where weights are the similarity score between collections. The weighted average reduces the bias in situations where similar collections are not equally similar to the owned collection.

The final step in the recommendation process is ranking the wallet-collection associations in the descending order and determining the top 5 recommendations per wallet. Here, we apply a recency factor based on seven day volume in order to generate recommendations that are not just personalized to the wallet but also are relevant in the current period.
Our complete process of generating recommendations can be visually represented as a sequential pipeline of multiple stages as:
![Recommendation build flow](//images.ctfassets.net/lhkcghnr8ql4/5EGiB0BcHIcPmROPmiEysA/cf0f039bab9c87dc46e4a878190ffce6/image2.png)

## Conclusion
In conclusion, a reliable recommendation engine is important for the growth of the NFT industry and the marketplaces that operate within it. As more content producers enter the NFT space, it is becoming increasingly challenging for users to identify relevant collections that match their interests. This makes it hard for marketplaces to retain the attention and transactions of their users. 

Finally, at Upshot, we’re constantly iterating. This recommendation system will continue to improve with time. For example, we plan to add auxiliary information about wallets and collections, such as collection category, number of assets in the collection, asset traits, trait rarity, etc., to greatly enhance the recommendations. Furthermore, from the wallet’s perspective, we’re looking into ways to handle the  “cold-start problem” where we can still make accurate recommendations to a wallet that has not owned anything yet. 

As the NFT space continues to evolve, it is essential to have a tool that can keep up with the industry's changing landscape. Upshot's collection recommendations are an excellent step towards achieving this goal.


A reliable recommendation engine is important for the growth of the NFT industry and the marketplaces that operate within it. So, we built one. 

Introducing Collection Recommendations


Upshot’s mission is, and always has been, to enable the creation of efficient financial markets for *anything*.

To realize this, we’re proud to announce the Upshot Machine Intelligence Network, a network designed to crowdsource financial alpha produced by machine learning models and powered by our new “Proof of Alpha” reward mechanism.

The Upshot Machine Intelligence Network and Proof of Alpha usher in a new era for DeFi. Now, more sophisticated DeFi protocols can be built leveraging decentralized AI.

In this post, we dive deeper into:

- The problems we sought to solve with our network
- What the Upshot Machine Intelligence Network is
- How it works
- The role zkML plays in it
- The applications being built on top of it, such as Ditto, the first yield protocol with AI-powered strategies
- And what all of this means for the next era of DeFi

## How We Got Here
Our machine intelligence network (or MIN) is the culmination of years spent evolving and fine-tuning our approach as a company.

Two and a half years ago, we launched a product called UpshotOne. With so many disparate assets in crypto, there was no way a single entity could accurately price and create markets for it all. So UpshotOne was an attempt to leverage the wisdom of crowds to incorporate that information. To that end, we created peer prediction models to price assets based on subjective opinions from “experts” in the space (e.g. whales, collectors, traders).

![Hot or Not for NFTs](//images.ctfassets.net/lhkcghnr8ql4/4dRCxzA11dfl6ZaEkypn1c/eb269721c71879bf88db9ee15c564504/Hot_or_Not_for_NFTs.jpg)

UpshotOne showed promise, but human crowdsourcing alone wasn’t scalable or accurate enough to be a sustainable solution for the long-tail pricing problem. Moreover, we learned that we needed a solution to reward people based on the efficacy of their contributions.

So we explored other options.

We realized that using AI would be far more powerful than crowdsourcing alone. Our models would allow us to unlock more accurate and scalable price predictions for assets, which could power new innovations in lending, analytics, and trading strategies. Initially, we focused solely on developing AI models for NFT price predictions. This allowed us to build:

- Lending strategies
- Market making strategies
- Appraisals
- Indices
- Analytical tools
- And more

![upshot appraisals](//images.ctfassets.net/lhkcghnr8ql4/6C3DSLNljKYhYj1b0J1xYb/918f05aabf6ea8d52f75f969315bf071/upshot_apprasails.jpg)

As our models proved useful across crypto, not just NFTs, we expanded our infrastructure to encapsulate more markets (such as RWAs, gaming, and DeFi more broadly).
But as a single entity, we ran the risk of centralization. One model creator isn’t sustainable enough to support the entire universe of potential markets or AI-influenced primitives. And there was no way, even with AI, we could possibly coagulate all the financial insights in the world without passing through the crowd at some point.
This led us to build the Upshot Machine Intelligence Network––a distillation of all our past learnings building novel crowdsourcing infrastructure and industry-leading machine learning models.

By combining these two disciplines, we’re creating a scalable and ever-learning network of collective intelligence. It takes the best of both previous approaches––

- The efficiency of AI
- And the wisdom of the collective

––to realize our mission of enabling the creation of efficient markets for anything.

The Upshot Machine Intelligence Network is the culmination of our successes, mistakes, lessons, and experiments over the past three years.
## The Benefits of Decentralized AI
To address the challenges of rewarding crowdsourced intelligence, as well as the challenges of bringing together model creators in a decentralized way, we’ve created a novel solution that aligns incentives for all participants in a decentralized AI network.

Decentralized AI can yield multiple benefits:

1. __Collective intelligence__: A networked AI ecosystem can harness the collective intelligence of various participants, including traders, data providers, and model creators. This collective wisdom can lead to better market predictions and informed decision-making.

2. __Reducing information asymmetry__: Entities holding specialized data, such as a Rolex marketplace, may lack the necessary analytical skills to derive insights from their data. Conversely, those with data science expertise may lack access to niche data sources or the capital required to invest in such specialized markets. When participants are incentivized within a network to share insights and predictions, it becomes harder for a select few to monopolize critical information, leading to more accurate predictions.

__3. Liquidity and market accessibility__: AI-driven algorithms can enhance liquidity in markets by providing continuous, automated trading and market-making activities. This can make markets more accessible to a wider range of participants, increasing trading volume and efficiency.

__4. Market innovation__: In a networked AI environment, market innovations and improvements can be developed collaboratively, leading to more efficient trading mechanisms, fairer market structures, and new kinds of financial products.

To realize these benefits of decentralized AI, we developed a mechanism that not only incentivizes contributions to the network, but also ensures that rewards are distributed based on the accuracy and usefulness of each contribution.

That mechanism is called Proof of Alpha.
## What Is Proof of Alpha?
Proof of Alpha is the core reward mechanism powering the Upshot Machine Intelligence Network.

Upshot MIN participants contribute alpha in the form of proprietary data, predictive features, machine learning models, and other elements aimed at enhancing the network’s ability to optimize specific objective functions. The participants who contribute the most useful alpha will earn the most rewards.

![proof-of-alpha](//images.ctfassets.net/lhkcghnr8ql4/5DFNBjvpBKexbzt8Niqfpk/0510e4d7a9e52bfc4a8fd25807434f98/proof_of_alpha_imagery.jpg)

The result is a system somewhat akin to a perpetual prediction market, with regular rewards distributed based on contribution quality and accuracy. Proof of Alpha ensures the most effective models for each subnetwork are compensated accordingly.

Historically, profiting from unique insights required extensive business infrastructure:

- Attracting investors
- Hiring a team
- Customer outreach
- And more

This precluded most people from financially benefiting from their alpha.

Now, because of Proof of Alpha, the Upshot MIN enables anyone to contribute specialized data/insights and receive rewards based on that alpha’s measured accuracy. Almost no other overhead required.
## How Does the Upshot Machine Intelligence Network Work?
A MIN is a new kind of decentralized network that brings together different ML models and incentivizes them to optimize some objective together, such as:

- Minimizing mean absolute direction loss in predicting the future price of an asset
- Maximizing Sharpe ratio with a proposed portfolio of assets
- Forecasting volatility of an index of assets of a specified time period

The power of this networked approach is amplified through the deflationary capabilities of machine learning and AI. These technologies use vast computational power and data-driven algorithms to uncover insights, make predictions, and drive optimizations that are far beyond the scope of human cognition or what can currently be expressed on-chain.

![deflationary-ai](//images.ctfassets.net/lhkcghnr8ql4/2edAjvAFCOXfRUOUd6ttSs/40f19dbba2521b30eabb6dbf1c4b4b4b/deflationary_AI.png)

This abstraction provides the bedrock for an abundance of use cases; any application that could benefit from ML can be improved by sourcing its insights from a network of collaborative machine intelligence.

Here’s a more detailed breakdown of how it works:
### 1. Create or Find the Best Subnetwork for Your Alpha
Each subnetwork in the Upshot MIN is defined by a specific objective function for participants to optimize around. For example, subnetworks might focus on the following objectives:

- Predicting the future price of an asset, such as the price of ETH/USD in 1 hour or the median sales price of CryptoPunks over the course of one week; e.g. minimizing the mean absolute directional loss of those predictions.
- Managing a portfolio of crypto assets; e.g. maximizing the Sharpe ratio of that portfolio.

Once you create or find the subnetwork best suited to your alpha, you’ll stake in it and start contributing alpha to earn rewards.
### 2. Start Mining Alpha
After joining your subnetwork of choice, you’ll provide your alpha in the form of:

- Relevant data
- Predictive model features
- ML models

The quality of your alpha will be represented by its “stake,” or the amount of money placed behind it, signaling its worth. You may place a stake in your own expertise, and others may delegate their stake to you, adding to your total stake. Your total stake represents how influential you are in your chosen subnetwork, and will fluctuate based on the realized usefulness of your alpha.

You, along with other alpha miners in your subnetwork, will regularly learn from each other, boosting your respective models based on the results of your peers. You’ll also score your own peers as well based on how useful their contributions were. Usefulness is measured by how well the subnetwork’s objective function was optimized.

![proof of alpha peer scores](//images.ctfassets.net/lhkcghnr8ql4/1xiBqDmEligmSuznJFHaIS/74b28ab87bd3c8ad68d0d23187311fc8/proof_of_alpha_peer_scores.jpg)
### 3. The Proof of Alpha Mechanism Aggregates Scores and Updates Alpha Miners’ Stakes
This process by which the network aggregates scores and updates participants’ stakes is called “Proof of Alpha.” It’s used to reach consensus on how useful each participant's alpha is to the subnetwork’s objective. Specifically, a miner’s Proof of Alpha score determines how many rewards they receive as incentive, or how many are deducted as penalties.

Whenever participant scores are updated, the network distributes (or slashes) rewards related to contributors' scores and stakes; participants with higher stakes earn or lose more rewards relative to participants with smaller stakes, and participants earn or lose depending on the direction of their score. This means that more insightful participants “rise to the top,” meaning that the network becomes smarter over time.

Earnings are paid for by network consumers–those who request predictions from the network. If they’re interested in querying for, say, the price of some NFT, they’ll pay a fee to the subnetwork devoted to pricing NFTs. Given finite computational resources, as demand for such predictions grows, so do the fees. In other words, more adoption begets more rewards for those supplying the most useful alpha.
### 4. A Novel zkPredictor Verifies Model Outputs
If we stopped with the network as described so far, we’d have a mechanism that generates increasingly accurate predictions of future events. However, for these predictions to be maximally useful, they need to be delivered on-chain, which raises a problem:

__How can an on-chain application verify that predictions received actually came from models of a particular set of miners?__

Imagine a DeFi platform that uses AI for high-frequency trading to profit from arbitrage. You see it 100x your friend’s portfolio, so you want to invest too. But how can you verify the platform is using the same profitable models to manage your capital?

Zero-knowledge (zk) proofs would prove pivotal in this situation and many similar situations in DeFi. With zk proofs, miners can verify that predictions came from their proprietary model without revealing their model and therefore revealing their IP and means of recurring revenue.

Fortunately, recent advances in zkML technology allows us to do just that. Hence, we’ve partnered with [Modulus Labs](https://www.modulus.xyz/) to create the first zkPredictor, which leverages zkSNARKS to verify the output of certain machine learning models. Our first version of the zkPredictor uses a novel proof system called Remainder, built by Modulus Labs, to cryptographically verify the outputs of tree-based machine learning models (a class of models that [is most adept](https://arxiv.org/pdf/2207.08815.pdf) at tackling ML problems with tabular datasets, which describe many financial applications of ML).

We plan to seed our network with a zkPredictor utilizing a model of our own, a verified version of our flagship long-tail asset valuation model, which has been exhibiting industry-leading performance in predicting NFT prices for over 2 years.

This zkPredictor showcases a substantial leap forward in the practical use of zkML. With this new zkPredictor, the number of verifiable outputs may approach 10,000 per hour (previously, the record was nearly 12 outputs per day), a throughput suitable for early crypto x AI applications. As the network expands, participants can apply a zkPredictor of the same capacity to their own models.
### 5. DeFi Protocols Benefit from Curated ML Models
The Proof of Alpha reward mechanism allows the network to curate the best alpha and ML models into a “meta-model” that represents the collective intelligence of a given subnetwork. The results of this meta-model can then be provably linked to reputable models that have already demonstrated their efficacy.

At this point, model results are ready to be consumed by applications, especially DeFi protocols, which can now remain decentralized while benefiting from the efficiencies of ML. This is especially important for protocols that manage larger amounts of capital, such as:

- Perpetual platforms
- Decentralized insurance platforms
- Investment DAOs using AI strategies to help manage their treasuries

What’s more, our novel foundation for serving decentralized ML can power a variety of novel use cases, such as:

- Asset managers can create manipulation-resistant index funds for niche markets.
- Lending platforms can offer more unique collateral types and advanced term logic with lending terms created by machine intelligence.
- Tokenized vaults can use advanced AI-powered strategies for potentially higher yield.
- And much more.
### Ditto: The First App on the Upshot MIN
The first application built on the Upshot Machine Intelligence Network will be [Ditto](https://dittohq.xyz/), a decentralized yield protocol that gives users passive access to verifiable, AI-powered strategies.

![ditto header](//images.ctfassets.net/lhkcghnr8ql4/pj81piByKGFI1ZyJu8KjK/299bfb65328461b1c5f75dd0af8cf17c/ditto_header.jpg)

DeFi has shown us how decentralization, composability, and verifiable computation can enable us to significantly speed up the rate of innovation in our financial system. However, existing DeFi primitives are currently limited by what can be expressed on-chain, thus restricting strategy sophistication and therefore limiting yield.

In Ditto, users deposit capital into on-chain vaults, each managed by machine-powered strategies from the Upshot Machine Intelligence Network. The goal of each strategy is to use the capital in the corresponding vault to generate yield for its depositors. These strategies are proven to have been made by these models using our pioneering zkPredictor.

![ditto how it works](//images.ctfassets.net/lhkcghnr8ql4/788nLQCv0tyCoD5Sd3es0j/a59dee0e6d690affd7bce41fba1e52a9/ditto_how_it_works.jpg)

Ditto combines passive on-chain capital provision with advanced AI-powered strategies and zkML technology, bringing more powerful, higher-yield strategies to DeFi and NFTs.
## Monetize Your Alpha
At first glance, the idea of a decentralized machine intelligence network may seem abstract. Yet its roots stem from our mission of enabling the creation of efficient financial markets for anything.

The Upshot Machine Intelligence Network incentivizes the collaboration and aggregation of machine intelligence to generate financial alpha for on-chain applications. It provides a means for people to earn from their unique insights, thanks to the Proof of Alpha reward mechanism.

At the same time, it enables the development of advanced protocols like Ditto, which leverage the wisdom of decentralized AI networks. As more developers plug into the network, the financial markets the network supports will grow more sophisticated.

But with that growing sophistication comes a need for security, transparency, and fairness. That is why innovations like the zkPredictor are so critical: they ensure decentralized AI is provably trustworthy.

With the combined powers of decentralized AI, Proof of Alpha, and the zkPredictor, we believe the Upshot Machine Intelligence Network will create new opportunities to build sophisticated, inclusive, and trustworthy financial systems.

![dev graph](//images.ctfassets.net/lhkcghnr8ql4/7Cwe8aI6Q5zD0tgRk1Yx29/7237c17c4c01231e91203da653d28a4f/dev_graph.jpg)
### How to Get Involved
If you’re interested in joining the Upshot Machine Intelligence Network, you can choose one of the following roles based on your skills and preferences:

- __Data providers__: If you have access to proprietary or valuable data that can be used to train machine learning models, you can become a data provider and earn rewards for sharing your data with our network. [Learn more here](https://docs.upshot.xyz/docs/data-provisioning).

- __Alpha miners__: If you have expertise in machine learning and can create or improve models that generate useful alpha, you can become an alpha miner and earn rewards for contributing your models to our network. [Learn more here](https://docs.upshot.xyz/docs/alpha-mining-1).

- __Developers__: If you need financial insights for your on-chain applications or DeFi protocols, you can become a developer and get access to the aggregated insights from our network. [Learn more here](https://docs.upshot.xyz/docs/protocol-overview).

- __Join the community__: If you’d like to contribute to the research and continued development of the Upshot Machine Intelligence Network, you can join our community Discord. [Join our Discord here](https://discord.gg/upshot).

Our new Machine Intelligence Network leverages decentralized AI and Proof of Alpha to enable advanced DeFi protocols and opportunities for anyone to contribute and monetize their financial insights.

Announcing the Upshot Machine Intelligence Network

Let’s face it: NFT interest and trading volumes have declined significantly in 2023. However, lending against NFTs remains an important option for investors seeking liquidity.

To make the most out of the opportunities available to us in a bear market, it’s important to determine appropriate lending terms. This requires carefully calculating recovery rates–i.e., the percentage of collateral value recovered in default. This is essential for assessing your risks, even if fewer deals are being written.

In this blog post, we’ll dive into the calculation, usage, and adjustment of recovery rates for NFT lending.

Understanding this process provides key insights, regardless of market conditions.

## The Complex Calculations Behind NFT Recovery Rates

Determining appropriate NFT lending terms requires estimating recovery rates. But how exactly are these rates calculated for different NFT collections?

We use some robust calculations. To start:

![recovery rate formula](//images.ctfassets.net/lhkcghnr8ql4/6QgOJuwa1bveSxX05fbhhT/8a1981885ff88c14aa7b91f3da98a94e/recovery_rate_formula.png)

In this calculation, recovery rate R serves as a forecast of the change in an NFT's appraised value over a fixed time horizon. This rate is expressed as a relative change, where R < 1 signifies a decline in asset value, while R > 1 indicates an increase.

Recovery rates are inherently uncertain, and 
therefore, we treat R as a random variable following a skew-normal distribution.

To estimate the parameters of this distribution, we utilize the daily relative changes in floor prices based on Upshot appraisals, considering them as realizations of R.

It's important to note that when using floor prices, the resulting distribution applies to an entire collection, assuming that recovery rates remain consistent across assets within the same collection.

For point forecasting purposes, the mean of each skew-normal distribution serves as an estimate of the expected recovery rate for a given collection and time horizon.

Additionally, the default probability of a loan for a specific time horizon is determined by the probability that an asset's value depreciates below its appraised value at the loan's inception.

This probability can be derived from the cumulative distribution function (CDF) of R, specifically:

![recovery rate formula 2](//images.ctfassets.net/lhkcghnr8ql4/3I5eRhdRUz3u4W2q6sA6qD/fbe7a62a783cf32bed27601120346fda/recovery_rate_formula_2.png)

While these calculations provide valuable insights, it's crucial to acknowledge that they assume rational investor behavior.

It can be challenging to model and account for irrational behavioral patterns, which may impact the accuracy of these estimates.

To address this uncertainty, lenders must validate and adjust recovery rate forecasts to account for real-world irrationalities.

One approach is to compare projected returns to actual market performance over time. The gaps between projections and reality can then be used to refine your recovery rate estimates.

## Adjusting Recovery Rates for Forecasting Uncertainty

To assess forecast performance, we compare forward-looking returns based on floor prices with the forecast distribution means over rolling windows of 60 days. By calculating the mean absolute percentage error for each time horizon, we can adjust the recovery rate downward to account for forecasting uncertainty.

This adjustment, known as the "uncertainty margin," is calculated as the uncertainty-adjusted recovery rate equals the recovery rate multiplied by (1 - uncertainty margin) for each collection and time horizon.

With rigorously validated recovery rate estimates, lenders can incorporate these projections into competitive lending terms.

## Utilizing Recovery Rates in the Lending Process

The recovery rate model's output serves as a critical component in determining two key aspects of the lending terms: the loan amount and the interest rate.

### Loan Amount Determination

The uncertainty-adjusted recovery rate plays a pivotal role in determining the loan amount. For each collection, the loan amount is calculated as the maximum of the uncertainty-adjusted recovery rate multiplied by the appraised value of the asset.

The maximum uncertainty-adjusted recovery rate corresponds to the time horizon selected as the loan duration.

However, to prevent concentration risk and ensure a diversified lending strategy, the loan amount is capped at twice the collection-level loan median.

### Interest Rate Calculation

The interest rate for each asset is calculated at the collection level using the formula:

![Interest rate formula](//images.ctfassets.net/lhkcghnr8ql4/5txevxMsgxShLHHhNbNBUl/2963beaaf68539c981312403bb135ecc/Interest_rate_formula.png)

The risk-free rate is set at 4.3%, reflecting the long-term average of the 10-year US Government bond.

The premium, set at 0%, 2.5% and 5% depending on each NFT’s value bucket, is added to compensate for the additional risk associated with NFT lending.

The default probability utilized in this calculation corresponds to the default probability associated with the best recovery rate. The term (1 - default probability) increases the overall interest rate as the default probability rises, effectively compensating for default risk in the loan portfolio.

Our methodology offers various levers that can be adjusted to tailor the lending terms and enhance competitiveness.

### Interest Rates

The premium contributes to the interest rate formula and can be adjusted to make the rates offered more competitive.

### Loan Duration

Currently, loan durations range from 14 to 30 days. Depending on demand and risk considerations, this range can be extended to accommodate different lending preferences.

### Loan Amount

The loan amount is based on the adjusted recovery rate at a specific time horizon. The adjustment is determined using the mean absolute percentage error. The adjustment can be increased or decreased to align with market conditions, allowing for flexibility in loan amounts.

### Range of Collections

Currently, we generate lending terms for a range of liquid blue chip NFT collections. This selection of collections provides a competitive advantage as many other NFT lenders cover a smaller number of projects.

To further enhance our offerings, we have the option to selectively add more collections to the terms provided, catering to a wider range of investor preferences.

## Building Sustainable NFT Lending with Recovery Rates

Recovery rates are indispensable for structuring competitive, risk-aware NFT lending. Thoroughly understanding rate calculations lets lenders estimate collateral value retention across time horizons. Validating projections and adjusting for uncertainty creates actionable forecasts to inform loan terms.

Ultimately, recovery rates provide the foundation for sustainable NFT lending. Rather than reacting irrationally, lenders can rely on data-driven forecasts to confidently support investors long-term.

Careful rate analysis transforms lending from speculation to discipline. As NFT markets progress, recovery-focused strategies separate resilient programs from short-lived trends.


Learn how carefully calculating, validating, and incorporating NFT recovery rates into lending terms enables sustainable programs that withstand bear markets.