NFT Market Analysis Using Artificial Intelligence — Services and Solutions

The trends within today's NFT marketplace are continuously evolving and becoming increasingly difficult to navigate. From 2022 to 2024 it is estimated that approximately 87% of NFT collections lost approximately 100% of their value, as well as an overall reduction in trading volume by 94% versus 2021. According to a Chainalysis report released in 2024, approximately 78% of new collections marketed as NFT were predicted to lose 90% of their value within the first 3 months. Therefore, if I were to guess on the next steps within the NFT marketplace, my answer would be that if an in-depth analysis of NFT collections is not conducted using on-chain data as well as a combination of social media sentiment analysis, the probability of success attempting to predict the now-marketplace will be nearly impossible to achieve. Older methodologies such as Google or manual parsing of information do not cut the mustard; they are either too slow to yield sophisticated analysis or do not provide the user with essential detail.

A Brief Overview of the NFT Market

The following section is intended to provide an overview of the basic terminology associated with non-fungible tokens; as well as provide clarification on each term. In addition, I wish to outline the basic premise on how tokens serve to confirm the ownership of a specific defined digital item (e.g.; Art, Music, Game item.) NFT Tokens are digital assets that appear on Blockchains as Non-Fungible. Unlike its counterparts such as Bitcoin, every NFT Token is classified as unique to itself. Listed below are several items for thought when determining your collectible investment choices: 1) Collectible – These artifacts could be examples like Crypto Punk or Bored Ape; their value is completely contingent upon rarity and community acceptance; 2) Game Items – Weapons/Skins/Plots of land within the world of Web3 gaming; 3) Virtual Real Estate – This is a term used when referring to plots of land within Decentraland or The Sandbox; it's value is reliant on their location and traffic.

What are indicators of whether or not you are spending your time wisely looking at a particular collection? There are several items that indicate how viable a collection may be for investment:

• Floor Price: The lowest price for a collection, which shows the overall demand for tokens.
• 24-Hour and Weekly Trading Volume: This indicates how actively people are buying and selling tokens within a collection.
• Unique Owners: If the majority of tokens are owned by a select number of people, then those projects are potentially uninvestable.
• Wash Trading Rate: The percentage of false trades to inflate a demand. This may range from 40% - 60%.
• Average Holding Time: If a collection has an average holding time of a few days, this means that most holders are speculators.

The NFT marketplace can be a wild ride with massive volatility. The number of mentions on Twitter can propel a price upwards in a moment's notice, making it nearly impossible to track hundreds of different projects at once.

How Artificial Intelligence Can Help with NFT Analysis

Specialized Web3 level AIs fill the void of traditional intelligence models (like ChatGPT) that are not capable of processing the millions of transactions, messages, and news updates that really matter regarding NFTs. Examples of AI technologies include:

Machine Learning: Looks at past blockchain data to identify manipulative behaviors and whale movements that are useful for predicting liquidity.

Big Data: Gathers data from marketplace platforms (OpenSea, Blur, LooksRare), social networks (Twitter, Discord, Telegram) as well as Ethereum, Solana and Polygon nodes.

Sentiment Analysis: Uses NLP (Natural Language Processing) to get an impression of how people are speaking in regards to their emotions (FOMO = Fear of Missing Out, FUD = Fear, Uncertainty and Doubt).

Time-Series Forecasting: ARIMA and Long Short Term Memory (LSTM) models provide short-term predictions on floor prices and trading volume.

Analyzing Traditional vs AI

Example: An Investment Fund based in Dubai with a $4.2 Million Portfolio utilized an AI module to filter out NFT collections prior to listing on marketplace. The AI filter helped the fund identify 23 of 30 projects with 55% wash trading rates on those projects within 8 weeks. The remaining three of those 30 projects provided a 140% to 210% increase with no wash activity and saved analysts a total of 120 hours/month.

Ways to Use AI for NFT Trend Analysis

Our process/methodology consists of 4 distinct steps, each with individual responsibilities from source setup to final reporting/recommendations.

Step 1: Establishing Data Sources (0–2 hrs)

We select the necessary blockchains (ETH, SOL, POLYGON) and marketplaces (OpenSea, Blur, MagicEden), social media (Twitter API, Discord, Telegram), and news feeds, connecting to our own nodes in order to load on-chain data as quickly and completely as possible.

Step 2: Collection/Normalizing Available Data (10–30 seconds)

We automatically collect fresh data (transaction records, ownership changes, token metadata, total trading volume, wallet activity) by pulling in all data from source in ETH/USD, standardizing all currency values to ETH/USD, and syncing time.

Step 3: AI Processing & Signal Detection (10–60 seconds)

We use machine learning to analyze social media posts in three ways: on-chain signals, sentiment analysis, and comparative analysis.

On-Chain Signals include: the concentration of top 10 buyers, minting & burning events, ratio of offers to buyers, and average amount of transactions.

Sentiment Analysis is where we look at the tone of posts on Twitter and Discord, look for evidence of bots and coordinating campaigns, and how changes in the tone of posts affect the behavior of buyers.

In our Comparative Analysis, we look at how well a project performs compared to other projects in the same genre and market cap range.

The result is a risk rating (between 0 and 100), a prediction of the floor price for 1, 2 and 4 weeks, and flags for potentially suspicious activities such as wash trading and mass withdrawals of liquidity.

Step 4: Report and Recommendation (Instant)

We provide our clients with a detailed report containing key findings, graphical charts and a checklist of recommended actions.

Example: Collection X: Risk 78/100, 62% of transactions were sold by wash traders, 54% of transactions were controlled by the top 5 holders, and there has been a decline in social activity for the fourth consecutive week.

Technology Stack

We operate using our own Ethereum and Solana nodes for fast indexing, BigQuery and Apache Kafka for scalable data processing, Python & libraries such as Pandas, NumPy & Scikit-learn as well as Hugging Face Transformers for sentiment analysis. We use TradingView API and Dune for additional verification of our reports. We also use Telegram and Discord APIs to send our clients instant notifications.

Example: A trader with a $180k USD portfolio on Solana who received daily reports made profits of 34%, 19% and 41% over two months. The AI identified two separate floor price drops of 70% three days before they occurred, allowing the trader to take advantage of the opportunities to sell at higher prices.

Reporting Options

1. Quick Summary

• Basic Information: name of the collection, blockchain, date of creation, current floor price
• Key Metrics: # of holders, token distribution, average time held, risk level coded with colors
• Verdict Example: stable or low-risk product vs. high-level wash-trading product

2. Full Report (10–15 pages PDF / Interactive Dashboard)

• Tokenomics: total supply, how and when tokens were minted, how and when tokens will be burnt, roadmap
• On-chain reports (wallet activity, big trades)
• Sentiment: tone charts, important news, etc.
• Comparison of products with other like products
• Projected scenarios: positives, negatives, expected
• Recommendations (when to buy, when to sell, stop-loss orders, position size)

The above report will have an interactive dashboard that will allow users to filter/search by live data.

3. Individual Strategies (on-demand request)

• Team due diligence
• Legal/compliance analysis of smart contracts
• Macroeconomic analysis: links to ETH, BTC and the rest of the crypto market
• Personalized advice about trading and hedging

Risk Levels Explained

• 0–30 = Low Risk, Long-Term Potential
• 31–60 = Medium Risk; Short positions
• 61–100 = High Risk; contains Fraud and/or Liquidity Dump; Highly Advisable to stay away

For example, 58% wash trading indicates that approximately 50% of the transactions are not real and therefore the demand is artificially inflated.

Success Stories: AI Helped Generate and Protect Investment

Case 1: Rapid earnings while the market crashed (October 2024)

On Oct 11, the market crashed 22% and recovered in a few hours due to the announcement of sanctions against crypto exchanges. A network of AI is capable of observing the floor price of assets of different types on various exchanges. We found that the floor price for some tokens on Exchange A dropped anywhere from 40%–50%, while other exchanges dropped only 15%–20%. When one of our clients received a notification (within 8 minutes) that there was an opportunity for arbitrage, he completed three arbitrage transactions and profited $8,700, or 34% on each trade.

Case 2: A large portfolio saved by AI (December 2024)

A client who had a total investment value of $4.2 million received a signal from ASCN.AI that large holders were dumping tokens on the market 36 hours before the news of a delay in the Falcon Finance Project came out. As a result of the AI alert, he was able to sell 60% of his positions at $1.20. Two days later the price dropped 85%. He avoided a loss of $1.8 million because he sold prior to the price drop.

Testimonials from Users

During my first three months working with ASCN.AI, I was able to avoid catastrophic losses in four projects. Rather than spending 4–5 hours a day analyzing projects to find problems, I can now spend 15 minutes a day on alerts.

We were able to successfully implement the ASCN.AI API into our marketplace, which allowed us to eliminate 18 fraudulent collections in the past 6 months. This has led to increased trust and a 23% decrease in user churn.

Benefits of Choosing ASCN.AI

Data that regular AI does not have: ASCN.AI is 100% Web3 trained. Therefore, it has access to the largest amount of information from its nodes, private Telegram and Discord communities, and via the hundreds of marketplaces it collects from. Blur updates every 10 seconds, whereas regular systems take 1–2 days to update.

Fast decision-making capabilities: Using ASCN.AI, you can create a report within 30 seconds, which is extremely important when the market is moving quickly. In comparison, creating reports using manual collection/analysis typically takes several hours.

Money and time savings: When using individual tools for analysis (like Nansen, LunarCrush and Dune) it can cost you over $2000 per month. With our all-in-one solution starting at $29/month, you save money and time.

Beginner-Friendly: Our platform does not require the use of a complicated database or SQL codes; you simply ask your question or enter your criteria, and our AI will give you clear, straightforward answers via the chat feature.

Fraud Protection: The algorithms we use for analysis help determine the validity of NFT projects by identifying fake users, wash trade, and sudden price drops.

Objective/Emotionless: While humans can become emotional and/or panic in the face of extreme price fluctuations (30% drop, for example), our AI is not emotionally tied to a project. Our AI will tell you to continue holding a position if it finds no evidence that the price has suffered a "dump."

API and White Label Solutions: We can provide all of the analytics to your platform or fund under your own brand, of course.

Frequently Asked Questions (FAQs)

1. What are NFTs, what do they do and how do they work?

NFTs are digital certificates of ownership of an exclusive item on the blockchain. An NFT is typically an ERC-721 or SPL token, and cannot be exchanged for another NFT. NFTs allow individuals to trade digital property while preserving its uniqueness.

2. How does AI help with the NFT market?

Our AI determines trade opportunities (arbitrage) between NFT platforms, makes price predictions based on whale trading activity and user sentiment, and identifies manipulation and bots. Using the trade history of other purchasers, our AI generates tailored recommendations based on your unique risk profile. For example, if a project shows a surge in social media activity and has little to no actual demand for the tokens, our AI would most likely recommend not purchasing those tokens.

3. How long does it take to complete the analysis?

Quick summaries take 10 to 30 seconds to generate; detailed reports can take from 5 to 15 minutes; strategic reports can take as long as 72 hours due to team and legal checks. Pro-level users will receive notifications to their devices via Telegram or Discord as soon as they have access to report summaries.

How much does it cost?

1. Basic Plan: An AI Assistant for generating quick summaries of any content you provide, allowing for up to 50 requests per month; $29 per month.
2. Advanced Plan: More features; allows for the generation of in-depth strategic reports; API access; allows for up to 200 requests per month; $79 per month.
3. Individual Reports: Strategy guidance, due diligence, and reports on demand; starting at $500.
4. Business API: Integrations for automation and white label solutions; starting price is $1200 per month.

Technical Description & Analysis Methods

System Architecture

Layer 1: Data Collection — We have nodes in three blockchain networks that collect transactions (Ethereum - Geth, Solana - Validator, Polygon). Additionally, our marketplace APIs include OpenSea, Blur, and others; we parse Twitter, Discord, & Telegram; and we aggregate news sources like CoinDesk & The Block. The data collected from these sources is processed using Apache Kafka to provide reliability.

Layer 2: Processing — Apache Flink processes data streams and normalizes them; TimescaleDB stores time-series data; Redis caches requests to allow for rapid response times.

Layer 3: AI Analysis — We have built machine learning (ML) models that we train with machine learning algorithms. We have a Random Forest model (RF), an XGBoost model, and a Long Short Term Memory model (LSTM). The data we use to train our ML models is gathered through the use of our web crawlers and data visualization tools, which will allow for more accurate and timely analyses. Sentiment analysis is conducted using the BERT NLP framework and has been shown to provide around 84% accuracy. Anomaly detection is accomplished by running our Isolation Forest algorithm on transaction data. Using graph analysis techniques like those developed by our Robotic Process Automation framework, we can identify instances of wash trading and Sybil attacks.

Core Algorithms & Metrics

1. Wash Trade Detection: We identify wash trades by searching for transaction cycles between wallets that frequently send and receive the same tokens. When the percentage of wash trades exceeds 40%, it triggers a wash trade alert in our system.
2. Smart Money Tracking: Smart Money is an industry term that describes people who have a high rate of return on investment (ROI) and who can afford to take large risks; we will track wallets that have at least a 300% ROI over the last year and are actively buying; when 5 or more Smart Money wallets buy from the same collection within 48 hours, this signals growth.
3. Momentum Sentiment Index: We create a Momentum Index that totally tracks the growth of positive sentiment by analyzing the data that is related to how fast people are spreading that positive sentiment. If the Momentum Index is greater than +2, there is a lot of hype surrounding the product; if the Momentum Index is less than -2, the product has poor sentiment.
4. Holder Concentration Index: We calculate the percentage of the total supply held by the top 10 holders of a product. If the total concentration of a product is greater than 40% held by its top 10 holders, there will be a significantly higher risk that it may be subject to manipulation.

Summary

The NFT market does not wait for slow-moving decisions. Modern-day NFT traders cannot compete with what can be done by artificial intelligence (AI) algorithms powered by Web3 data. ASCN.AI delivers accurate data, quickly exposes market manipulation, and stops the emotional decision-making process. In addition, ASCN.AI provides anyone interested in NFTs with an all-in-one solution for 24 hours/day, seven days/week for all users from novice to professional. In the last two years, our customers have avoided $8,300,000 in loss through our timely arbitrage and trading signals and made $2,100,000 in profits.

So if you find yourself still searching for answers regarding NFTs online, remember that your competitors have access to AI technology that can provide them with thousands of times more information than you can. Get onboard now!