Analyze A Wallet With AI
Turn a raw transaction history into a readable profile: strategy, holding periods, risk behaviour and realised performance.
/ quick answer
Export and normalise the history, let AI classify and summarise behaviour, then verify the conclusions that would change your decisions. Turn a raw transaction history into a readable profile: strategy, holding periods, risk behaviour and realised performance.
- 01Export the address history per chain via an explorer or analytics API, including token transfers and swaps.
- 02Normalise into one table: timestamp, action, token, size, counterparty, USD value at the time.
- 03Filter out spam airdrops, dust and transfers between the wallet's own addresses.
- 04Have AI classify behaviour: trader, holder, farmer, market maker, bot — with the evidence for the label.
- 05Ask for realised and unrealised performance estimates and demand the calculation, not just the number.
- 06Verify decisive numbers directly in the explorer before using them.
- 07Save the profile and re-run periodically to see whether the behaviour changed.
What does the Analyze A Wallet With AI workflow do?
Export and normalise the history, let AI classify and summarise behaviour, then verify the conclusions that would change your decisions.
What problem does Analyze A Wallet With AI solve?
A wallet's history is thousands of undifferentiated transactions. Reading it manually is slow and it is easy to mistake noise for strategy.
How many steps does Analyze A Wallet With AI take?
7 steps. It starts with export the address history per chain via an explorer or analytics api, including token transfers and swaps. and ends with save the profile and re-run periodically to see whether the behaviour changed..
Which tools does Analyze A Wallet With AI need?
It uses onchain-research-stack, ai-crypto-research-stack — each linked below with its own node.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →On-chain Data
On-chain data is the public record of every transaction, balance and contract call — the raw material for wallet tracking and market research.
- →Wallet Tracking
Wallet tracking is the practice of monitoring specific addresses and getting notified when they trade, transfer or interact with contracts.
- →Smart Money
Smart money is a label for wallets with a documented history of profitable, early positioning — a research filter, not a signal to copy blindly.
- →Multichain
Multichain means operating across several networks at once — more opportunity, more surfaces to secure and more addresses to monitor.
Related workflows
Turn this into a repeatable process.
- →AI Crypto Research Workflow
A repeatable research loop: turn a question into market data, on-chain evidence and a written risk view before any position is considered.
- →AI Token Research Workflow
Screen a token in under 30 minutes: contract facts, liquidity structure, holder concentration and a written risk verdict.
- →Turn Deep Research Into a Weekly Executive Brief
Use an AI Deep Research agent every Monday to produce a cited market brief in 20 minutes.
- →Build a Research Automation Pipeline
Question in, sourced structured brief out — on a schedule.
Related tool stacks
The tools that run it in production.
- →On-chain Research Stack
Explorer, indexer and analytics layers combined so wallet and token questions get answered with verifiable data.
- →AI Crypto Research Stack
A read-only research stack combining an AI assistant, web search, market data and on-chain analytics to screen assets quickly.
Related prompts
Reusable prompts for this job.
- →Wallet Analysis Prompt
Turns a transaction export into a behavioural profile: strategy type, timeframes, risk pattern and whether the wallet is worth watching.
- →Transaction Analysis Prompt
Explains what a specific transaction did, what it authorised, and what risk it left behind.
- →Token Research Prompt
Structures a full token due-diligence pass: mechanics, liquidity, concentration, bear case and unverifiable claims flagged explicitly.
- →Smart Money Analysis Prompt
Tests whether a group of 'smart money' wallets is genuinely informative or a survivorship-biased label.
Related use cases
How people apply it, and what came out.
- →Find Smart Money Activity
A research group verified 40 leaderboard wallets and kept only 11, turning a noisy signal source into a usable research queue.
- →Monitor Wallet Transactions
Alerting on the user's own wallet activity caught an unauthorised approval attempt and forced an approval cleanup that removed 14 standing allowances.
- →Build An AI Crypto Research Agent
A read-only research agent produced daily briefings on a 30-token watchlist, cutting a 90-minute manual routine to a 10-minute review.
- →Analyze Wallets With AI
AI profiling of 60 candidate wallets cut a week of manual review to an afternoon and identified 4 worth monitoring.
Comparisons & alternatives
Pick between the options.
- →AI Research vs Traditional Research
AI research compresses breadth and drafting; traditional research supplies verification and accountability for the claims you act on.
- →Hot Wallet vs Cold Wallet
Hot wallets trade convenience for exposure; cold wallets trade friction for a signing key that never touches an internet-connected device.
- →On-chain Data vs Exchange Data
On-chain data shows verifiable wallet-level behaviour; exchange data shows aggregate price discovery. Serious research needs both.