Wallet Analysis Prompt
Turns a transaction export into a behavioural profile: strategy type, timeframes, risk pattern and whether the wallet is worth watching.
/ quick answer
Paste a normalised transaction table for one address. Works best with 100–1000 rows including USD values at transaction time. Turns a transaction export into a behavioural profile: strategy type, timeframes, risk pattern and whether the wallet is worth watching.
Analyse this wallet's transaction history and profile the operator. Use only the rows provided. ADDRESS: [ADDRESS] CHAIN: [CHAIN] TRANSACTIONS (timestamp, action, token, size, USD value, counterparty): [TABLE] Produce: 1. Operator type (trader / holder / farmer / bot / market maker / treasury) with the evidence. 2. Typical position size, hold time and number of concurrent positions. 3. Entry behaviour: early buyer, momentum follower, or reactive. 4. Risk behaviour: does it cut losses, average down, or hold to zero? Cite transactions. 5. Realised performance estimate with the calculation shown, plus its limitations. 6. Whether this wallet is worth adding to a watchlist, and what filters to apply to its activity. 7. UNKNOWN: what the on-chain data cannot tell us (CEX activity, hedges, other addresses). Never assert identity. Never claim the wallet's future behaviour.
Operator type: discretionary trader — 43 swaps, no LP positions, no automation cadence. Median size $18k, median hold 9 days, 3–5 concurrent positions. Entry: momentum follower; 31 of 43 entries came 2–6 days after a token's first volume spike. Risk: cuts losses, 9 exits under -20%. Estimated realised PnL +$212k across the period (sum of paired swaps; excludes any CEX legs). Watchlist: yes, filtered to buys over $15k. UNKNOWN: linked addresses, off-chain hedges.
What does the Wallet Analysis Prompt prompt do?
Paste a normalised transaction table for one address. Works best with 100–1000 rows including USD values at transaction time.
Which AI models work with this prompt?
It is model-agnostic: it works with any capable general model. Replace the bracketed variables with your own context before running it.
What output should I expect?
Operator type: discretionary trader — 43 swaps, no LP positions, no automation cadence. Median size $18k, median hold 9 days, 3–5 concurrent positions. Entry: momentum follower; 31 of 43 entries came 2–6 days after a token's first volume spike. Risk: cuts losses, 9 exits under -20%. Estimated realis.
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Related concepts
The vocabulary this page depends on.
- →Whale Wallet
A whale wallet holds a position large enough that its trades move price or signal intent — which makes it worth watching and easy to misread.
- →Wallet Tracking
Wallet tracking is the practice of monitoring specific addresses and getting notified when they trade, transfer or interact with contracts.
- →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.
Related workflows
Turn this into a repeatable process.
- →Analyze A Wallet With AI
Turn a raw transaction history into a readable profile: strategy, holding periods, risk behaviour and realised performance.
- →Smart Money Tracking Workflow
Build a curated wallet watchlist, monitor it for meaningful trades, and use alerts as research triggers rather than buy signals.
- →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.
- →Track A Whale Wallet
Monitor a single large address correctly: separate real position changes from custody moves before drawing any conclusion.
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.
- →Smart Money Stack
Wallet tracker, on-chain analytics, alert delivery and an AI layer that turns raw wallet events into researchable signals.
Related prompts
Reusable prompts for this job.
- →Smart Money Analysis Prompt
Tests whether a group of 'smart money' wallets is genuinely informative or a survivorship-biased label.
- →Deep Research Prompt Template
Reusable Deep Research prompt that produces cited, structured reports every time.
- →Sourced Research Brief Prompt
Produces a structured brief where every claim carries a citation.
- →Token Research Prompt
Structures a full token due-diligence pass: mechanics, liquidity, concentration, bear case and unverifiable claims flagged explicitly.
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.
- →Track A Whale Wallet
A trader replaced noisy whale alerts with a labelled watchlist and counterparty classification, cutting alerts by 94% while keeping the useful ones.
- →Research A Token With AI
A structured AI research pass cut token screening from three hours to 35 minutes and produced documented passes instead of impulse entries.
- →Create An On-chain Alert System
A purpose-built alert pipeline with enrichment and deduplication achieved a 30% action rate, versus near-zero for off-the-shelf feeds.
Comparisons & alternatives
Pick between the options.
- →Smart Money vs Technical Analysis
Smart money tracking reads who is positioning; technical analysis reads how price behaves. They answer different questions and fail differently.
- →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.
- →AI Research vs Traditional Research
AI research compresses breadth and drafting; traditional research supplies verification and accountability for the claims you act on.