Important takeouts:
ChatGpt can integrate social media and news sentiment to uncover early stories and reveal market topics around new tokens.
By supplying technical indicators and Onchain transaction data to ChatGPT, traders can track “smart money” movements and identify accumulation or distribution pattern.
Exploring multiple GPTs in workflows enables traders to secure cross-reference metrics, emotions and contracts for more informed decisions.
Automate high potential token discovery by building a data-driven scanner with embedding, clustering, anomaly detection and toconomique metrics.
Finding a coin with high potential before they take off is often mistaken for pure luck, but savvy investors realize that finding them requires diligence rather than luck. ChatGpt and other AI-powered tools allow you to sort thousands of tokens and identify their actual value.
This guide explains the process of using CHATGPT as a research tool for cryptocurrency analysis.
Explore market emotions and stories with ChatGpt
Coins have a big fundamental, but if no one is talking about it, that possibility remains unrealized.
Hidden gems are often gems that are just beginning to generate positive topics. You can integrate public opinion photos by getting ChatGpt and providing IT information from a variety of sources.
For example, you can copy and paste recent headlines from major crypto news outlets or snippets on popular social media platforms such as X and Reddit.
Try using a prompt similar to the following:
“We analyze news headlines and social media comments below (coin name). We integrate sentiments across the market, identify new stories, and flag any potential dangers or major concerns that the community is discussing.”
AI can use the data provided to summarise whether emotions are neutral, bullish, or negative, and show which particular story points are gaining traction. This method can help you determine the overall emotional state of the market.
Additionally, CHATGPT can be asked to look for signs of growth of the project’s ecosystem. You can send snapshots from platforms like Defillama, but you cannot provide real-time data.
For example, you can use the following prompt:
“Based on the following data points regarding the total value locked to the protocol within the (coinname) ecosystem, we will identify which sectors have the most momentum and which protocols have grown the fastest in the last 30 days.”
In this way, ChatGpt can emphasize outliers. The protocol draws in liquidity and makes users faster than others. These standouts tend to be technically more than just a sound. They build traction that attracts market attention and often drives sharp price movements.
Did you know? According to a 2025 MEXC survey, 67% of Gen Z Crypto traders have been energizing trading bots or strategies with at least one AI in the last 90 days, indicating a major generational change towards automated AI-assisted trading.
A data-driven approach to using chatgpt
For advanced traders, delving into technical and on-chain metrics can bring outstanding opportunities to surface. Here we move from researchers to analysts, actively collecting the appropriate data and feeding AI for deeper insights.
For more technical indicator interpretations, the charting platform can supply raw technical data for ChatGpt. For example, you can give a specific coin’s relative strength index (RSI), moving average convergence layer (MACD), and different moving average values ​​over a specific period.
Here is a quick example that can be useful:
“We analyze the following technical indicator data for (coin name) over the last 90 days. Based on the provided RSI, MACD and 50/200 day moving average crossover, what can we infer about current market trends and potential price movements in the future?
With Onchain data analysis, you can uncover the truth behind your project’s activities. You can copy and paste raw data from Block Explorer or from the analysis tool.
for example:
“Here is a list of recent transactions and wallet activities in (coin name). We analyze this data to identify the movement of “smart money,” a massive transaction from wallets that have been working well historically. Can you detect accumulation or distribution patterns based on this? ”
This method will help you track the movement of major players, ideally finding early signs of potential price movements before being visible to the rest of the market.
ChatGpt Advanced GPTS
In Crypto, exploring GPTS, a custom version of CHATGPT tailored to a particular use case, creates the true power of ChATGPT. Many GPTs are built to extend the capabilities of ChatGPT, including analyzing smart contracts, summarizing blockchain research, and pulling structured market data. For example, you might use GPT designed for token safety analysis, another for Onchain wallet tracking, or an optimized one for analysis of Crypto research reports.
Here is a step-by-step guide on how to access GPT for crypto transactions:
Step 1: Get a ChatGPT subscription
To get started with GPTS, you will need a ChatGpt Plus account ($20 per month).
Step 2: Explore GPTS
In the left menu, click Explore GPTS. Use the search bar to find cryptographic-related GPTs. Select the GPT you want to use and start it.
A workflow allows multiple GPTs to be run simultaneously. For example, it is combined with GPTs that summarize toconomies to check the safety of your contract. Still, it’s important to remember. These tools need to speed up your own research rather than completely replacing them.
How to build a data-driven scanner using chatgpt
You can move past one-time prompts by creating a portion of ChatGPT for your automated discovery pipeline.
Start by creating an embed from Project White Papers, social media posts, and GitHub commits. We combine these vectors into surface outliers worthy of human review. Weigh circulating supply, unlock schedules, unlock cliffs, and weigh liquidity depth metrics built from order snapshots and spreads in distributed exchange (DEX) pools.
It also allows for large transfer and contract interactions to overlap anomaly detection and flag anomalous activity in real time.
To run this system, collect data from Github, Coingecko and Etherscan via APIs. Process in Python (or another language) to generate numeric metrics and embeddings. Apply clustering and anomaly detection to highlight anomaly projects and push results into a dashboard or alert system to allow you to act quickly.
Finally, backtest the signal by replaying past on-chain events and transaction flows. This transforms scattered data points into a structured process that generates repeated, high signal trade ideas.
This article does not include investment advice or recommendations. All investment and trading movements include risk and readers must do their own research when making decisions.
