Smart Money Analysis Prompt
Tests whether a group of 'smart money' wallets is genuinely informative or a survivorship-biased label.
/ quick answer
Use before building a watchlist or copying any wallet cohort. Tests whether a group of 'smart money' wallets is genuinely informative or a survivorship-biased label.
Evaluate whether this set of wallets is genuinely informative. Assume the "smart money" label is unproven. WALLETS (address, claimed PnL, period, position count, notable trades): [LIST] Assess: 1. For each wallet: is performance broad-based or concentrated in one or two positions? Show the concentration. 2. Survivorship and selection bias in how these wallets were identified. 3. Signs of non-replicable edge: insider timing, wash activity, MEV, launch access. 4. Whether their entries were realistically copyable at the time (liquidity, timing, gas). 5. Which wallets, if any, deserve monitoring — and the filters to apply. 6. What would falsify the "informative" conclusion within 60 days. Be sceptical by default. Do not recommend copying without filters.
Wallet A: 84% of PnL from one position — not evidence of skill. Wallet C: profitable across 27 positions and two market phases; broad-based. Selection bias: all wallets were chosen from a post-hoc profit leaderboard, so failures are invisible. Non-replicable edge: Wallet B bought within 40 seconds of pool creation twice — likely launch access. Monitor: C only, filtered to buys over $20k in pools over $2M. Falsified if C's next 10 tracked entries underperform a majors benchmark.
/ frequently asked
What does the Smart Money Analysis Prompt prompt do?
Use before building a watchlist or copying any wallet cohort.
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.
How do I use this prompt?
1) Copy the prompt with the copy button. 2) Replace every bracketed variable with your real details. 3) Paste it into your AI assistant. 4) Compare the answer with the example output and ask for changes if needed.
What output should I expect?
Wallet A: 84% of PnL from one position — not evidence of skill. Wallet C: profitable across 27 positions and two market phases; broad-based. Selection bias: all wallets were chosen from a post-hoc profit leaderboard, so failures are invisible. Non-replicable edge: Wallet B bought within 40 seconds o.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →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.
- →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.
- →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.
- →AI Token Research Workflow
Screen a token in under 30 minutes: contract facts, liquidity structure, holder concentration and a written risk verdict.
- →Build An On-chain Alert System
Assemble a monitoring pipeline that watches addresses, tokens and contracts and delivers deduplicated, contextual alerts.
Related tool stacks
The tools that run it in production.
- →Smart Money Stack
Wallet tracker, on-chain analytics, alert delivery and an AI layer that turns raw wallet events into researchable signals.
- →On-chain Research Stack
Explorer, indexer and analytics layers combined so wallet and token questions get answered with verifiable data.
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.
- →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.
- →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.
- →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.