AI-Based Price Estimation of CryptoPunks and Beyond

Alper Kaplan
Ginoa.io
Published in
13 min readJan 9, 2023

--

Introduction

CryptoPunks are a series of digital collectibles released on the Ethereum blockchain in 2017 by the company Larva Labs. Each CryptoPunk is a unique non-fungible token (NFT) representing a pixel art character with various attributes and characteristics. These NFTs have recently gained popularity and have become a significant digital art and collectibles market.

One of the primary challenges in estimating the price of a CryptoPunk NFT is the lack of a central marketplace or exchange where costs can be easily obtained. Prices for CryptoPunks have been recorded on various online marketplaces and sales, but these prices can vary significantly depending on the platform and the specific NFT being sold.

To accurately estimate the price of a CryptoPunk NFT, it is essential to consider a range of factors and features. Some of the most relevant features for price estimation include the NFT's attributes and characteristics, such as its rarity, uniqueness, and aesthetic appeal. Other crucial elements include the NFT's historical sale price and the change in price over time, as well as various descriptive statistics based on sale data, such as the mean, median, and standard deviation of prices.

Image: https://cryptopunks.app/public/images/product/cryptopunks/punk-variety-2x.png

In addition to these features, it is essential to consider the image features of the NFT, such as its visual appeal and the quality of the pixel art. Marketplace data, such as the prices of ETH and BTC, can also be relevant for price estimation, as these cryptocurrencies are often used to buy and sell NFTs.

To eliminate the effects of inorganic transactions, such as wash trades, a wash trade detection function in the price estimation model must be incorporated. This function can identify and exclude transactions not representative of the true market demand for the NFT.

Overall, developing a machine learning model for CryptoPunk (or any NFT collection for this matter) and NFT price estimation requires the consideration of a wide range of features and factors. By carefully selecting and analyzing these features, it is possible to build a model that accurately estimates the price of a CryptoPunk NFT based on its attributes, historical sale data, image features, and marketplace data.

Ginoa Model Performance for CryptoPunks — comparison with Competitor 1 and Competitor 2

According to the data provided by Competitor 1 and Competitor 2

  • Competitor 1's tool has a success rate of 88% for estimating the price of CryptoPunks,
  • Competitor 2's tool has a slightly higher success rate of 90%.
  • However, the most powerful tool belongs to Ginoa, as its model has the highest success rate of 93%!

Also, the below graph shows the average weekly accuracies of the Ginoa price estimation model for the last quarter of 2022. For almost all weeks, the model operates with more than a 90% success rate.

The last chart reveals the robustness of the model as the comparison between the actual sold prices vs. Ginoa estimations is very close to the linear line.

Follow-Up NFT Sales

Upon further comparison of the follow-up sales of an NFT and Ginoa price estimates, a high error rate does not necessarily mean that Ginoa's model needs to be corrected. For example, look at the three sample cases for punks #7567, #3054, and #5659.

On Nov 11, 2022, punk #7567 was sold for 55 ETH, and the Ginoa model estimated its price as 84.13 ETH. This may look like a high error, but for the next sale, this punk was sold for 67 ETH, a 12 ETH net profit.

This shows that the Ginoa model can accurately estimate an NFT's true potential and give users a good idea of whether to invest in an NFT.

CryptoPunks from left to right: #7567, #3054, and #5659

The same scenario occurs for punks #3054 and #5659. On Dec 13, 2022, punk #3054 was sold for 74 ETH, and the Ginoa model estimated its price as 96.07 ETH then. But this punk's next sale was for 85 ETH, an 11 ETH net profit. Similarly, On Nov 11, 2022, punk #5659 was sold for 77 ETH, with the Ginoa estimated price of 106.67. Its follow-up sale was for 101 ETH, a net 24 ETH profit. Below is the table which shows the above-referenced cases and other NFT sales and their follow-up sales.

Punk Sales, Ginoa Estimations, Next Sales and Profits for the Last Quarter of 2022 (Oct-Nov-Dec)

Although in some cases, the error rate of the model may seem high at first, it is clear that there is a strong possibility to make a profit because the model correctly predicts the direction of the price of an NFT.

Additional Factors

To further improve the accuracy of the price estimation model, it is helpful to incorporate additional features and data sources. For example, the model could consider the historical performance of similar NFTs or the activity of specific marketplaces or collectors.

It is essential to carefully select and preprocess the data used to train the model. This may involve cleaning and filtering the data to remove errors or outliers and normalizing or scaling the features to ensure they are on the same scale.

One potential approach for building the price estimation model is to use a supervised learning algorithm. These algorithms can learn to predict the price of a CryptoPunk NFT based on the input features and historical data.

Regardless of the specific approach taken, a careful evaluation of the model's performance using a robust testing and validation process must be undertaken. This may involve splitting the data into training and test sets and using various metrics, such as mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), or median absolute percentage error (MdAPE), to measure the accuracy of the model's predictions.

The error metrics mentioned above are standard loss functions used in regression problems.

  • MSE measures the average of the squared differences between the predicted and actual values.
  • MAE measures the average absolute differences between the predicted and actual values.
  • MAPE measures the average absolute percentage error between the predicted and actual values.
  • And lastly, MdAPE measures the middle error value of all the absolute percentage errors.

Developing a machine learning model for CryptoPunk NFT price estimation requires the consideration of a wide range of features and factors, as well as careful data selection and preprocessing. By using a supervised learning algorithm and evaluating the model's performance through robust testing and validation, it is possible to build a model that accurately estimates the price of a CryptoPunk NFT.

Detecting Hype

One potential mechanism that could be useful to gauge the hype over a specific CryptoPunk NFT is to analyze social media (Twitter, Reddit) and online conversations (Discord) about the NFT. This could involve collecting and analyzing data from social media platforms, online forums, and other online sources to identify trends and patterns in the discussions about the NFT.

There are several ways that this data could be used to detect hype. For example, the model could analyze the frequency and intensity of discussions about the NFT or use natural language processing techniques to identify positive or negative sentiments in the conversations. The model could also consider the influence and reach of the individuals or communities discussing the NFT, such as the number of followers or the visibility of their posts.

In addition to analyzing social media and online conversations, the model could also consider other hype indicators, such as the presence of media coverage or the involvement of high-profile collectors or artists.

Including features in detecting hype can help to provide a more comprehensive view of the market for CryptoPunk NFTs and identify potential trends or patterns that may not be immediately apparent from the raw data. By analyzing social media and online conversations and considering other indicators of hype, it is possible to build a model more attuned to the dynamic and subjective nature of the market.

Model Bias

Another point to note is that model bias, which includes the limitations and potential biases of the price estimation model, must be considered. For example, the model may be limited by the quality and quantity of the data used to train it, or it may be influenced by biases or assumptions in the data or the algorithms used. One way to address these limitations is to continually update and improve the model by incorporating new data and features as they become available. This can help to reduce the impact of any biases or assumptions in the model and improve its accuracy over time.

Another approach is to use ensemble methods, combining multiple models' predictions to create a more accurate and robust prediction. For example, the price estimation model could be combined with other models considering different features or data sources, such as social media sentiment or expert opinions.

It is crucial to consider the price estimation model's ethical implications and any potential effects it might have on the market for CryptoPunk NFTs. For example, the model may be used to inform buying and selling decisions, or it may be used to manipulate prices. It is important to ensure that the model is transparent and unbiased and that it is not used to exploit or manipulate the market.

Developing a machine learning model for CryptoPunk NFT price estimation requires the consideration of a wide range of features and factors, as well as careful data selection and preprocessing. By using a supervised algorithm and evaluating the model's performance through robust testing and validation, it is possible to build a model that accurately estimates the price of a CryptoPunk NFT.

Long-term Viability

The price estimation model's long-term viability must also be considered because the market for CryptoPunk NFTs is dynamic and influenced by various outside factors. For instance, variations in the values of ETH and BTC, as well as changes in consumer patterns or tastes, may impact the market. Regular updates and refining is needed to reflect these changes. This may involve incorporating new data or features or adjusting the algorithms or parameters.

In addition to updating the model, it may also be helpful to consider alternative approaches or technologies for price estimation. For example, the model could be augmented with additional data sources, such as social media sentiment or expert opinions. It could be integrated with other blockchain or market data sources to provide a more comprehensive market view.

It is important to consider the potential risks and uncertainties associated with the market for CryptoPunk NFTs and to build safeguards or contingency plans to mitigate these risks. For example, the model could be designed to be more resilient to extreme market events or sudden price fluctuations, or it could include risk management features to help protect against losses.

Overall, developing and maintaining a machine learning model for CryptoPunk NFT price estimation requires a long-term approach that is mindful of the constantly changing market and the potential risks and uncertainties associated with it. By regularly updating and refining the model and considering alternative approaches and technologies, it is possible to build a model that is accurate, reliable, and long-lasting.

Legal and Regulatory Issues

In addition to the technical challenges and considerations of developing a machine learning model for CryptoPunk NFT price estimation, there are also legal and regulatory issues to consider. For example, depending on the location and jurisdiction in which the model is used, it may be subject to laws and regulations related to financial services, consumer protection, and data privacy. It is critical to confirm that the model and any associated activities comply with these rules and regulations and seek legal counsel as necessary.

Another legal consideration is intellectual property. The CryptoPunk NFTs and the underlying pixel art characters are protected by copyright and other intellectual property laws. It is of utmost importance to respect these rights and to obtain any necessary licenses or permissions before using the NFTs or the pixel art in the price estimation model.

There may be legal issues related to the use and ownership of the NFTs themselves. For example, there may be disputes over the ownership of an NFT or its rights. To ensure that the model and any associated activities conform with the pertinent laws and regulations, it is crucial to be aware of these concerns and get legal advice as necessary.

In summary, developing and maintaining a machine learning model for CryptoPunk NFT price estimation requires careful consideration of a range of technical, legal, and regulatory issues. By staying informed about these issues and seeking guidance as needed, it is possible to build a model that is accurate, reliable, and compliant with the relevant laws and regulations.

The Role of the Human Expert

Another important consideration when developing a machine learning model for CryptoPunk NFT price estimation is the role of the human expert. While machine learning algorithms can analyze large amounts of data and identify patterns and relationships that may not be immediately apparent to humans, they cannot fully capture the complexity and nuance of the market for CryptoPunk NFTs.

In particular, the market for CryptoPunk NFTs is highly subjective, with prices often influenced by aesthetics, rarity, and cultural significance that may be difficult for a machine learning model to fully understand. As a result, the insights and expertise of human experts can be valuable in complementing the predictions of the model and providing a more comprehensive view of the market.

One way to incorporate the insights of human experts into the model is to use a hybrid approach that combines machine learning algorithms with human input. For example, the model could incorporate the opinions of expert collectors or art critics as additional features, or it could allow for human oversight and decision-making in certain situations.

Human professionals' contribution to the model's creation and upkeep must also be taken into account. Human specialists can be utilized, for instance, to choose and prepare the data required to train the model or to assess the model's performance and make any improvements.

Incorporating human expert insights and expertise can be valuable in complementing the predictions of a machine learning model for CryptoPunk NFT price estimation and in providing a more comprehensive view of the market. By using a hybrid approach and involving human experts in the development and maintenance of the model, it is possible to build a more accurate and reliable model.

Ethical Considerations

In addition to the technical, legal, and regulatory challenges and considerations of developing a machine learning model for CryptoPunk NFT price estimation, there are also ethical considerations to be aware of. One ethical concern is the potential for the model to manipulate or exploit the market for CryptoPunk NFTs. For example, the model could be used to artificially inflate or deflate prices or to engage in insider trading or other unethical practices. It is key to ensure that the model is transparent, unbiased, and not used to exploit or manipulate the market.

Another ethical concern is the potential impact of the model on the market for CryptoPunk NFTs and the broader art market. For example, the model could inform buying and selling decisions, or it could influence the prices of specific NFTs or collections. To avoid harming or disadvantaging collectors or artists, it is key to take into account the possible effects of the model's projections.

To address these ethical concerns, it is useful to consider the use of oversight or governance mechanisms, such as independent oversight boards or ethical review committees, to ensure that the model is being used ethically and responsibly. It is also helpful to engage with collectors, artists, and other stakeholders to better understand the potential impact of the model and to identify ways to mitigate any negative consequences.

Developing and using a machine learning model for CryptoPunk NFT price estimation require careful consideration of a range of ethical issues. By being transparent and responsible, and by engaging with stakeholders, and using oversight mechanisms as needed, it is possible to build and operate an ethical and accountable model.

Social and Cultural Considerations

In addition to the technical, legal, regulatory, and ethical challenges and considerations of developing a machine learning model for CryptoPunk NFT price estimation, there are also social and cultural considerations to be aware of. One social and cultural concern is the potential impact of the model on the market for CryptoPunk NFTs and the broader art market. For example, the model could inform buying and selling decisions or influence the prices of specific NFTs or collections. One thing to consider is the potential consequences of the model’s predictions and to ensure that it is not used to harm or disadvantage collectors or artists.

Another social and cultural concern is the potential impact of the model on the accessibility and inclusivity of the market for CryptoPunk NFTs. For example, the model could exclude certain collectors or artists based on biases or assumptions in the data or algorithms used.

To address these social and cultural concerns, it is useful to consider the use of diversity, equity, and inclusion (DEI) principles in the development and use of the model. This may involve engaging with a diverse group of collectors, artists, and other stakeholders to ensure that their perspectives and needs are taken into account and to identify ways to promote accessibility and inclusivity.

Overall, developing and using a machine learning model for CryptoPunk NFT price estimation requires careful consideration of a range of social and cultural issues. By being mindful of these issues and using DEI principles as needed, building and operating an inclusive and respectful model of diverse perspectives and needs is possible.

--

--

Alper Kaplan
Ginoa.io

Sr. Machine Learning Engineer | Computer Engineer, Cognitive Scientist | Amateur Triathlete