PyTAMI 0.0.1

Time-Adjusted Market Index Package for Python

Python Time-Adjusted Market Index (PyTAMI)

This is a Python adaptation for the TAMI package that exists for Javascript. TAMI is a universal mechanism for calculating the estimated value of a collection of assets. This project is based on the Card Ladder Value algorithm and the Index Mathmatics Methodology published by S&P Dow Jones Indices, a division of S&P Global.

Usage

from pytami import tami

mocktransaction_history = [
  Transaction(price=612, item_id='Mars', timestamp=six_weeks_ago), 
  Transaction(price=500, item_id="Lavender", timestamp=three_days_ago),
  Transaction(price=700, item_id="Hyacinth", timestamp=one_month_ago),
  Transaction(price=1200, item_id="Mars", timestamp=two_days_ago)
]
tami(mocktransaction_history)

Also WIP

Motivation

Prior to the development of this tool, there was no standardized way to transparently value a collection of NFTs. Various data providers such as ArtCentral, UpShot, bitsCrunch, NFTGo, Gallop, Banksea, and others have developed their own proprietary market cap methodology. This fragmentation serves some users, like traders seeking alpha, but it does not serve the best interests of all users. For example, opaque AI solutions are not sufficient for users that wish to rely upon a trustless reference price for NFTfi lending and derivative instruments. Moreover, market caps calculated using the floor price of a collection are too easily manipulated. This is especially dangerous when considered in conjunction with derivative protocols for NFT collections.

Speical Note: While this tool was created with NFT collections in mind, it will work for any generic basket of non-fungible assets such as trading cards, houses, music licenses, etc.

How it Works

Part 1. Calculate the Index Price

Methodology

We first calculate an Index Price for an NFT collection, calculated as follows:

Index Price = S / (N * D)

Where:

• S = Sum of the Last Sold Value of Every Non-Excluded NFT in Collection X
• N = Number of Non-Excluded NFTs in Collection X with a Secondary Market Sale
• D = Divisor_old * (Index Price_new / Index Price_old)

When the Index Price is first calculated, we begin with a Divisor value of 1. When the second NFT in the collection records its first secondary market sale, the Divisor is adjusted. The same happens when the third NFT in the Index Price records its first secondary market sale, and the fourth, and so on.

Exclusions

Each NFT must have at least 2 sales in the last year, and at least one in the last 6 months, in order to be included in the Index Price calculation.

Example

Pretend we want to calculate the value of the Crypto Coven NFT project. For simplicity's sake, we'll say the collection only has three NFTs:

• Lavender
• Hyacinth
• Mars

We'll also ignore the exclusion rules to focus on the calculation itself. Assume the entire secondary market purchase history of the Crypto Coven project looks like this:

#	name	price
1	Lavender	$500
2	Hyacinth	$700
3	Hyacinth	$400
4	Mars	$612
5	Mars	$1200

Given this information, let's see how each sale affects the Index Value:

Transaction 1

• Name: Lavender
• Price: $500
• Unique NFTs sold: 1
• Divisor_old: 1 (initial value)
• Index Price_old: n/a
• Index Price_new: $500 / (1 * 1) = $500
• Divisor_new: 1 (we keep 1 as the new Divisor value after the initial transaction)

Transaction 2

• Name: Hyacinth (we have a first secondary market sale 🧙‍♀️)
• Price: $700
• Unique NFTs sold: 2
• Divisor_old: 1
• Index Price_old: $500
• Index Price_new: ($500 + $700) / (2 * 1) = $600
• Divisor_new: 1 * ($600 / $500) = 1.2
• Divisor Adjusted Index Price: ($500 + $700) / (2 * 1.2) = $500
  • Note that a first secondary market sale does not affect the index price due to the divisor

Transaction 3

• Name: Hyacinth
• Price: $400
• Unique NFTs sold: 2
• Divisor_old: 1.2
• Index Price_old: $500
  • Unused, since we only use this to adjust the divisor, and we only adjust the divisor when there is a first secondary market sale
• Index Price_new: ($500 + $400) / (2 * 1.2) = $375
• Divisor_new: 1.2

Transaction 4

• Name: Mars (first secondary market sale 🎉)
• Price: $612
• Unique NFTs sold: 3
• Divisor_old: 1.2
• Index Price_old: $375
• Index Price_new: ($500 + $400 + $612) / (3 * 1.2) = $420
• Divisor_new: 1.2 * ($420 / $375) = 1.344
• Divisor Adjusted Index Price: ($500 + $400 + $612) / (3 * 1.344) = $375

Transaction 5

• Name: Mars
• Price: $1200
• Unique NFTs sold: 3
• Divisor_old: 1.344
• Index Price_old: $375
• Index Price_new: ($500 + $400 + $1200) / (3 * 1.344) = $520.83
• Divisor_new: 1.344

Result: The Index Price of this collection is $520.83

The index price of the collection at each transaction looks like this:

#	name	price	index price
1	Lavender	$500	$500
2	Hyacinth	$700	$500
3	Hyacinth	$400	$375
4	Mars	$612	$375
5	Mars	$1200	$520.83

Part 2. Calcualte the Index Ratios

Next we must calculate an "index ratio" for each NFT in the collection, calculated as follows:

Index Ratio = V / IP

Where:

• V = NFT Value on Date of Last Sale
• IP = Index Price on Date of Last Sale

Example

name	price	index price	index ratio
Lavender	$500	$500	1
Hyacinth	$400	$375	1.066...
Mars	$1200	$520.83	2.304

Part 3. Calculate the Time-Adjusted Market Index

The second to last step is to determine the time-adjusted values of every non-excluded NFT in the collection, calculated as follows:

TAV = IR * IP

Where:

• IR = Each NFT's Index Ratio
• IV = The Index Price of the Collection

Example

Collection Index Price: $520.83

name	index ratio	time-adjusted values
Lavender	1	$520.83 * 1 = $520.83
Hyacinth	1.066...	$520.83 * 1.066... = $555.56
Mars	2.304	$520.83 * 2.304 = $1199.99

And then finally we must sum all the TAVs to calculate the Time-Adjusted Market Index:

TAMI = TAV₁ + TAV₂ + TAV₃ + etc.

I.e. the Time Adjusted Market Index in this example is: $2276.38

Advantages to this Methodology

Time-Adjusted Market Indexes offer an analytical lens through which to appraise NFT collections. Unlike an opaque and closed-source AI model, this open-source methodology is fully transparent and reproducible by any market participant. This methodology is also quite resistant to price manipulation, unlike a calculation that is based on floor prices.

Pitfalls and Shortcomings

The limitations of this approach are worth noting.

From a practical point of view, TAMIs are limited in scope by the number of NFTs that comprise them. Some indexes may have thousands of NFTs while others will have a few dozen.

Second, by their nature, TAMIs create generalizations about a market that run the risk of overlooking unique trends that might be happening in different segments of that market. For example, this approach does not consider individual NFT attributes such as which hat a Bored Ape may be wearing.

Third, TAMIs are calculated based on the "last sold" value of every NFT in the index, subject to the above-mentioned limitations that if an NFT has not sold at least twice in the last year, as well as at least once in the last 6 months, then it is temporarily excluded until it meets those criteria. It is conceivable that a NFT collections’s market cap can shift during a period in which their NFTs are not transacted frequently, which means the index may not track the true market trend.

These three plausible shortcomings of TAMIs are not exhaustive; it is wise to contemplate all conceivable shortcomings when utilizing a TAMI to perform analysis.

List of Web3 Supporters in Favor of this Methodology

The aim of this project is to acheive industry-wide adoption of a standardized and transparent methodology for appraising the value of an NFT collection. Please edit this README and create a pull request on behalf of your organization if you'd like to be included in the list below.

• Mimicry Protocol