According to a public write-up, whether you trade actively or hold for the medium to long term, AI has become one of the higher-leverage tools in crypto and finance research: it can wire sentiment, on-chain data, filings, and charts into one workflow — not just answer casual questions. Below in three parts: build a daily research toolkit, then custom pipelines you can own, then trading execution and prediction markets. English prompts are kept paste-ready; after each prompt, a short note explains the use case and how to fill the bracketed placeholders.
1. Essential Research Toolkit
You do not need every app on day one. First lock who digs deep, who watches sentiment, and who handles retrieval and source packs. Claude is often treated as the main brain: a stronger model for deep analysis, research mode for multi-path retrieval, connectors for live data, Claude Code for automation and dashboards, Design for visuals; Projects store holdings, time-frame preferences, and research rules so you are not re-briefing from zero every session. You can paste the block below into a dedicated “crypto research” Project as a lasting persona.
You are my crypto research analyst.
MY PROFILE
- Timeframe: [e.g. 6 to 24 month holds]
- Risk tolerance: [low / medium / high]
- Current holdings: [list]
- Sectors I focus on: [e.g. AI, DeFi, infrastructure]
RULES
- Always pull live data from connected tools. Never quote prices, market caps, TVL, or unlock figures from memory.
- Cite the source for every number.
- If a number can't be verified, say so clearly.
- Always include the bear case, even if I don't ask for it.
- Never tell me whether to buy or sell. Give me the evidence and the risks.
OUTPUT FORMAT
- Start with a 3-line summary
- Then the detailed analysis
- End with key risks and what to monitorHow to use this prompt: This is a Project-level “research analyst” persona. Replace [e.g. 6 to 24 month holds], [low / medium / high], [list], and [e.g. AI, DeFi, infrastructure] with your hold period, risk tolerance, current holdings, and focus sectors. The rules deliberately ban quoting prices, market caps, TVL, and unlock figures from memory, require a bear case, and forbid direct buy/sell advice — a solid default research guardrail.
On the sentiment side, Grok can search crypto talk (CT) on X (formerly Twitter) directly, so it fits mood scans, long-post breakdowns, and catching new narratives. A common prompt:
Search X for posts about [token / sector] from the last 7 days.
1. Summarise the main narratives being discussed.
2. Which accounts are driving the conversation? Separate builders, researchers, and traders from anonymous or promotional accounts.
3. How has sentiment changed compared to the previous week?
4. List any concerns, criticisms, or red flags being raised.
5. What's being said that isn't reflected in the price yet?
Link the most important posts so I can read them myself.How to use this prompt: Swap [token / sector] for a concrete token or sector name (e.g. a specific L2 or DeFi niche). Output splits narratives, account types, week-over-week sentiment, red flags, and “discussion not yet in the price,” and asks for original post links so you can verify instead of trusting the summary alone.
Perplexity (including Finance) fits custom watchlists, filings, and announcements, plus Computer for fuller automated retrieval. NotebookLM with Gemini can turn finance YouTube into a queryable source library and mind map. In a more advanced terminal, agent harnesses such as Codex and Claude Code run long workflows; tools like Grok Bot, Muse, and Dots lean toward social search, spreadsheet cleanup, and general standby respectively.
One layer down is MCP (Model Context Protocol): connect the model to live data sources instead of training memory. Common links include CoinGecko (markets), DefiLlama (TVL / fees / DEX volume), Tokenomist (unlocks and supply), Dune (custom on-chain queries), LunarCrush (social sentiment), and TradingView MCP (charts). On the research-product side, Projects, Markdown context, research mode, web search, Artifacts, Skills, scheduled tasks, and in-browser browsing (when MCP is not required) all upgrade one-off Q&A into repeatable flows. Crypto-native AI tools can run in parallel — e.g. Kaito Pro, Messari Copilot, Nansen, ChainGPT — focused on datasets and on-chain smart-money views that complement general models.
Treat the stack more like a “research OS”: Projects hold long-lived context; Markdown is versionable notes and a strategy brain; research mode and web search cover breadth; Artifacts turn tables and charts into reusable surfaces; Skills hard-code flows such as a “token deep dive”; scheduled tasks fit weekly sentiment scans or unlock calendars. What you are missing is not another chat window — it is a repeatable, handoff-friendly research rhythm.
2. Build Your Own Custom Tools
Plug-and-play is easy, but the ceiling is fixed. According to the same public write-up, Claude Code plus MCPs such as TradingView can support a homemade backtest engine: pull strategy source from TradingView, Quantpedia, and similar, inject it into the model, backtest on live-history market data, then build a dashboard to spot outliers. Another path is agent trading: paste the Robinhood agents trading MCP (https://agent.robinhood.com/mcp/trading) into connectors so an agent can help with portfolio and orders inside compliance bounds.
On the chart side, encode strategy rules as a Pine Script indicator: write entry, exit, stop, timeframe, and filters clearly, generate v6 code with the prompt below, paste into TradingView’s Pine Editor, and iterate by pasting errors back. You can also study the open-source Garch Method example for how a quant frame lands on a chart.
Write a Pine Script v6 indicator for TradingView based on my strategy:
MY RULES
- Entry: [e.g. price closes above the 200 EMA and RSI crosses above 50]
- Exit: [e.g. RSI crosses below 40 or price closes below the 50 EMA]
- Stop loss: [e.g. 2x ATR below entry]
- Timeframe: [e.g. 4H]
- Filters: [e.g. only take longs when BTC is above its 200 EMA]
REQUIREMENTS
- Plot clear buy and sell signals on the chart
- Show stop loss levels
- Add alerts for every signal
- Make every parameter adjustable in the settings
- No repainting: signals must only trigger on confirmed candle closes
- Add comments explaining each section of the codeHow to use this prompt: Replace the bracketed Entry / Exit / Stop loss / Timeframe / Filters with your own rules and examples. Requirements include buy/sell signals, stop lines, alerts, tunable parameters, no repainting (confirmed close only), and section comments. Fits the stage where you have a verbal strategy but no indicator yet.
For an asset view, a low-friction pipeline works: export CSVs from exchanges, wallets, and brokers, upload them into a research Project, connect CoinGecko live prices, and generate a “personal CFO” dashboard with the prompt below — then save it as a reusable Artifact.
Act as my personal CFO. I've uploaded exports of my holdings from [list exchanges, wallets, brokerages].
Build me an interactive dashboard that shows:
1. NET WORTH
Total value across all accounts using live prices.
2. ALLOCATION
Breakdown by asset class (crypto, stocks, cash) and by sector.
3. PERFORMANCE
P&L per holding and overall. Compare to BTC and the S&P 500 over 30 days, 90 days, and 1 year.
4. RISK FLAGS
- Any single holding above [e.g. 20%] of my portfolio
- Any sector above [e.g. 40%]
- Any token I hold with an unlock in the next 60 days
5. MONTHLY SUMMARY
What changed this month, what's performing best and worst, and three things I should review.
Keep it clean and easy to scan. Flag any data you couldn't verify.How to use this prompt: Put data sources in [list exchanges, wallets, brokerages]; change [e.g. 20%] and [e.g. 40%] to your concentration thresholds. The dashboard covers net worth, allocation, performance vs BTC and the S&P, risk flags (including unlocks within 60 days), and three monthly review points. When data does not reconcile, the model should say so — avoid false precision.
Backtests and auto-generated indicators can look “professional” while still overfitting or repainting. Tunable parameters, signals only on confirmed closes, and fees/slippage in the evaluation matter more than extra fancy lines. The same for a CFO dashboard: CSV export conventions often disagree — unify quote currency and timestamps before you talk allocation and risk flags.
Go further with an “agent swarm”: multiple roles in parallel (price alerts, CT scans, unlock watches, and so on). Other build directions include a trade journal, DCA plans, watchlist pushes to Telegram/WhatsApp, and airdrop tracking. Principle: do not spend hundreds of dollars rebuilding what a twenty-dollar subscription already covers.
3. Advanced: Execution Layer and Prediction Markets
If you want AI in the execution path, three common routes: exchange-native bots (fast to start, low ceiling, higher fees), no-code platforms (grid / DCA / portfolios — you hit a wall quickly), and self-built (more control). Self-build can compress to three steps: brain (strategy model) + hands (exchange MCP/API); turn a strategy brain-dump into Markdown rules; then a terminal dashboard that can place orders. Equities can use broker APIs with paper trading; crypto uses exchange keys. Two starter lines:
I want to automate my trading with the [exchange] MCP. Connect my API key and build a dashboard to manage my trading.How to use this prompt: Replace [exchange] with a concrete exchange or broker brand, and prepare API keys with clearly split read vs trade permissions. Goal: connect first, then a management dashboard — not full live automation on day one.
Create a markdown file of my entire trading strategy based on [brain dump].How to use this prompt: Put your messy verbal strategy in [brain dump] (entry logic, filters, sizing, hard bans). The model should organize a clean Markdown “strategy brain” so backtests and bots share one rule source.
On multi-model architecture, public write-ups suggest splitting “design” from “decision”: e.g. a stronger general model for backtests and strategy design, then a decision model that takes state inputs (such as TypeSafe’s Jev) to read live tape and return probabilities, while the strategy layer still owns stops, takes, sizing, and hard limits, and the exchange only executes. A decision engine is not the whole trading system — state, gates, and risk controls still have to be yours.
The easiest trap on the execution layer is treating “API connected” as “safe to leave unattended.” Split key permissions; validate signal density and reject handling on paper or tiny notional first. In multi-model chains, decision timeouts, feed drops, and exchange rejects need a degrade ladder — not a default of keeping opening risk.
On prediction markets, edge often comes from “is the market-implied probability wrong?” A workable loop: pull current odds via MCP/API → deep-research news, data, and historical base rates → produce an independent probability → compare the gap to the market. Prompt:
Here are prediction markets I'm watching, with current odds:
[market question + current Yes price for each]
For each market:
1. RESEARCH
Search for the latest news, data, and expert views. What are the historical base rates for events like this?
2. YOUR ESTIMATE
Give your own probability that it resolves Yes, with your reasoning. Be specific about what drives the number.
3. THE GAP
Compare your estimate to the market price. Flag any market where the gap is more than [10] percentage points.
4. WHAT COULD MAKE YOU WRONG
What does the market know or price in that you might be missing?
5. FINE PRINT
Summarise the resolution rules, liquidity, and time until resolution. Flag anything ambiguous.
Rank the markets by size of the gap. Do not tell me what to bet. Show me the evidence.How to use this prompt: Fill [market question + current Yes price for each] as a list; [10] is your gap threshold in percentage points. Output ranks by gap size, requires evidence and fine print (resolution rules, liquidity, expiry), and explicitly says “do not tell me what to bet” — for research screening, not auto-following.
Other advanced directions include smart-money wallet discovery and copy flows, portfolio stress tests, and hedging real-world risk. A safer start is locking 1–2 daily workflows first (e.g. Project research persona + weekly X sentiment scan, or a CFO dashboard), then adding backtest, agents, or prediction-market modules once those run smoothly.
This article is for reference only and is not investment, trading, or financial advice. Prompt and product capabilities change; when API keys, automated orders, and live capital are involved, isolate permissions, paper-validate, control risk yourself, and check the latest official docs.
