30+ Crypto Trading AI Prompts (Copy-Paste Library)

Scenario: upload a chart screenshot and let AI describe the structure for you.

You are a neutral technical analysis assistant. I will upload a cryptocurrency
candlestick chart (with volume). Describe it using this structure
(do NOT give buy/sell advice):

1. Inferred timeframe (daily / 4h / 1h)
2. Current trend (uptrend / downtrend / sideways)
3. Key support and resistance levels (specific prices)
4. Classic patterns visible, if any (head & shoulders, double bottom,
   triangle consolidation, etc.)
5. Volume confirmation
6. Three technical signals worth watching

Describe what is on the chart only. Do not predict direction. End with a
reminder that TA has limits and must be combined with fundamentals and
risk management.

Note: AI vision is limited and will misread patterns. Use the output as a second opinion, never as the only one.

Scenario: check whether multiple timeframes agree on direction.

I am tracking [BTC]. Analyze the multi-timeframe confluence using the
data below:

- Weekly:  [trend + key levels]
- Daily:   [trend + key levels]
- 4-hour:  [trend + key levels]
- 1-hour:  [trend + key levels]

Answer:
1. Do all three timeframes point the same way?
2. When short and long timeframes disagree, which should I weight more?
3. Confluence strength rating (strong / medium / weak)
4. What risk signals are visible from technicals alone?

Do not predict direction. Describe the current structure only.

Scenario: combine RSI / MACD / Bollinger Bands and read them together.

Current technical indicator readings for [asset]:
- RSI(14): [value]
- MACD:    [DIF / DEA / histogram]
- Bollinger Bands: [price vs upper / middle / lower band]
- Volume:  [multiple of 20-day average]
- ATR(14): [current volatility]

Read them together:
1. State of each indicator (overbought / oversold / neutral)
2. Are the indicators aligned or diverging?
3. How has this combination historically tended to evolve?
4. The 1-2 most important risk points right now

Describe what the indicators mean. No trade calls.

Scenario: get a clean list of key price zones to watch.

List the key technical price levels for [asset]:
- Highs and lows of the last 30 days
- Fibonacci retracements (0.382 / 0.5 / 0.618 / 0.786)
- The last three obvious reversal prices
- Psychological round numbers (e.g. BTC 60K / 70K)
- Long-term moving average (weekly MA200) current position

Output as a table. Tag each level with its type and strength
(strong / medium / weak). Do not predict whether levels will break —
just list the reference zones.

Scenario: pull historical analogues for the current pattern (as a learning tool, not a prediction).

Over the last 60 days [asset] formed this pattern: [describe it].

Search your training data for historically similar patterns. Provide:
1. 3-5 high-similarity historical cases (date, asset, what happened next)
2. Statistical tendency of this pattern (mostly bullish / bearish / chop)
3. How the macro backdrop influenced each outcome
4. The single biggest difference between now and then
   (macro / regulation / on-chain structure)

Stress: history does not repeat. "Similar" is not "the same".
This is for learning, not prediction.

I will paste (or attach) a crypto project whitepaper. Break it down with
this framework:

1. The problem it solves (one sentence)
2. Core technical approach (consensus / novel design)
3. Tokenomics: total supply, emission curve, initial allocation,
   team / investor share
4. Team background (founders' prior projects, public identity?)
5. Investors / partners
6. Top 3 competitors
7. Top 5 risks (technical / regulatory / team / market / liquidity)
8. Five specific items I should verify myself

If any data point is uncertain, flag it explicitly as
"needs verification" — do not invent numbers.

Evaluate the tokenomics health of [token]:

Inputs:
- Total supply / current circulating supply
- Allocation breakdown (team / investors / community)
- Emission curve (next 12 / 24 / 36 months)
- Current FDV (fully diluted valuation)
- Current MCap (circulating market cap)

Analyze:
1. Inflation pressure (new supply in the next 6 / 12 months as a %
   of current float)
2. Unlock concentration (when are the big unlocks?)
3. FDV / MCap ratio — is there a hidden "delayed inflation" overhang?
4. Is the team / investor share excessive?
5. Five tokenomics red flags, if any

Describe the structure. Do not predict price.

Pull together public information on [project] team and investors:

List:
- Founder names, LinkedIn / Twitter, prior projects (including failed ones)
- Key team members
- Major investors (round + amount if public)
- Any known controversies or negative coverage

Flag clearly:
- Which items come from your training data (and may be stale)
- Which items I must verify on Crunchbase / LinkedIn / RootData myself

Do NOT invent investors or partners. When unsure, say
"needs verification".

Compare [Project A] and [Project B] (direct competitors):

Produce a comparison table covering:
- How each one solves the same problem differently
- Core technical architecture differences
- Tokenomics health
- User base / TVL (with date of the data point)
- Major backers
- Each one's core risks

End with: 3 objective differences + 2 unknowns that could
swing the verdict. Do NOT say which is "better".
Describe differences only.

Read the following on-chain metrics for [project]:

Inputs (from Glassnode / Dune / Etherscan):
- Active addresses, 7d and 30d trend
- Large transfers ($100K+) count trend
- Exchange net inflow / outflow
- Whale wallet balance change
- Smart-contract TVL change (if applicable)

Describe:
1. The current state of each metric
2. Whether they agree or contradict
3. What market phase this on-chain structure has historically
   corresponded to
4. Metrics easily misread (e.g. wash-trading addresses)

Do not predict price. Read structure only.

My current crypto allocation:
- BTC: 40%
- ETH: 30%
- SOL: 15%
- Altcoin A: 8%
- Altcoin B: 5%
- Stablecoins: 2%

Total notional = [amount], which is [%] of my household
investable assets.
Max drawdown I can stomach = [%]

Review:
1. Concentration risk (am I over-weighted in any one category?)
2. Correlation risk (do these all crash together in a bear?)
3. Liquidity risk (can I exit altcoins in extreme conditions?)
4. Is the position size sensible vs my total assets?
5. Three specific improvements

Point out the risks. Do not tell me what to trade.

I am planning this leveraged trade:
- Asset: [BTC / ETH / ...]
- Direction: [long / short]
- Leverage: [N]x
- Margin: [amount]
- Current price: [price]

Calculate and explain:
1. Approximate liquidation price (the exchange has the final word)
2. Loss if price moves 5% / 10% / 20% against me
3. Largest 24h move [asset] has shown historically at this leverage
4. Is this leverage level sane for [asset]?
5. Funding rate cost (cumulative over 24h / 7d / 30d)

Tell me directly: with my margin, what happens if [asset]
prints its worst historical day?
Do not encourage me to trade. Show me the consequences.

Run the following black-swan scenarios against my portfolio:

My portfolio: [list it]

For each scenario, estimate damage + what I can do +
which actions need preparation in advance:
1. BTC down 25% in a single day (size of March 2020, June 2022)
2. A top-tier exchange implodes (FTX size)
3. USDT briefly de-pegs to 0.95
4. Sudden regulatory crackdown (US or my jurisdiction)
5. The L1 chain I rely on suffers a major attack

Be specific, not abstract.

I am considering depositing into [protocol]:

Evaluate:
1. Protocol type (lending / DEX / LP farm / derivatives)
2. Audit status (who audited, when, how many firms?)
3. Historical exploit record
4. Is the protocol upgradable? Any admin keys / backdoors?
5. TVL and days online (is this a new protocol?)
6. Five items I should verify myself on DeFi Llama and audit reports
7. If hacked, what recovery exists (insurance? bug bounty?)

Do not tell me to use it or skip it. List the risk facts.
When unsure, say so.

Give me a complete crypto wallet security self-audit checklist,
ordered by importance:

Cover:
- How to store seed phrases
- Hardware wallet best practices
- Phishing defense (fake sites, fake support, fake airdrops)
- Token approval cleanup
- When to use multisig / timelock
- Cold vs hot wallet separation for large balances

Skip vague advice like "watch out for phishing".
Give specific, executable steps.

Analyze [project / asset] sentiment on Twitter (X) over the last 24 hours:

1. Mention volume vs 7-day average
2. Sentiment split (positive / negative / neutral)
3. Take of major accounts (>50K followers)
4. Any coordinated KOL push? (look at timing clustering)
5. Latest from the project's official handle
6. Five specific conversation threads worth following

Tag every piece of information with its timestamp. Do not
predict price. Describe the structure of the conversation.

The current Crypto Fear & Greed Index reads [value] ([Fear / Greed]):

Deep-read:
1. Historical percentile of this reading (top X%)
2. What did the market do last time we were here?
3. What goes into this index (volume / volatility / BTC dominance, etc.)?
4. Index limitations (it does not guarantee a reversal)
5. Two or three complementary indicators worth pairing with it

No trade calls.

Assess the potential impact of this news on [asset]
(do not forecast % moves):

News: [paste the article / summary]

Analyze:
1. Direct impact: which on-chain / off-chain metrics react?
2. Indirect impact: what chain reactions are plausible?
3. 2-3 historical precedents for similar events (and what followed)
4. The point the market is most likely to misread
5. What data should I monitor over the next 3-7 days /
   1 month to confirm or invalidate?

Lead with uncertainty. No directional calls.

A KOL just said: [paste the full quote]

Play a neutral analyst and run the rebuttal:
1. What are the core assumptions of this argument?
2. Which assumptions don't hold up?
3. Five counter-arguments or counter-examples
4. If the argument is wrong, what's the earliest signal that would
   reveal it? (which metric?)
5. Does the KOL have a conflict of interest?
   (position holder? project insider?)

The point of the rebuttal is not to be contrarian — it's to
see both sides clearly before deciding.

Here is everything crypto-related I read today:

[paste 10-20 items]

Sort them:
1. High-signal (worth following up) — 3 to 5 items
2. Noise (safe to skip) — list them
3. Watch out (classic exit-liquidity pitches) — list them
4. Information gap (key facts missing) — list them

Rank by actual impact on my decisions.

Explain whether cross-exchange arbitrage is actually viable:

1. Typical spread on majors (BTC / ETH) across top exchanges, in bps
2. Why altcoin spreads are wider
3. True cost structure: fees + withdrawal cost + on-chain gas + slippage
4. Withdrawal time risk (price moves while you wait)
5. When manual arb works vs when you need a bot
6. Hidden costs at size (large fills)

Conclude: 90% of cross-exchange arbitrage is no longer profitable
for retail. Explain why.

Current perpetual funding rate on [BTC / ETH] is [value]
(annualised [%]).

Evaluate cash-and-carry funding arbitrage
(spot long + perpetual short):

1. Net annualised yield after fees
2. Main risks: settlement timing / liquidation / liquidity
3. Where this funding rate sits in its historical distribution
4. Probability of funding flipping suddenly
   (given current market structure)
5. What capital size works / doesn't work

State plainly: this is the classic "looks risk-free, hides
real risk" trade.

I spotted a new project [name], launched [date], TVL [amount].

Run this framework. Refuse to give a "buy / don't buy" verdict.

Bullish signals (if present):
- Public-identity team with credentials
- Tier-1 VC leading the round
- Multi-firm audit complete
- Real user growth + on-chain activity
- Product is live with paying users

Bearish signals (if present):
- Anonymous team
- TVL spike in days (likely incentive bubble)
- Hard marketing ("guaranteed pump" language)
- High token concentration
- Similar projects have already imploded

Output: the signal checklist + 5 specific items
I must verify myself.

Assess airdrop-farming ROI for [project]:

1. Expected airdrop value range (based on comparable projects)
2. Time cost (how many actions / how long to qualify?)
3. Capital cost (gas + tied-up principal)
4. Risks: sybil detection, rule changes, token going to zero
5. Has the golden window already closed?
6. Opportunity cost vs simply holding spot

If ROI < 50%, say "not worth it" directly.

Project [name] is launching its IDO / IEO on [platform]:

Analyze:
1. Fundamentals (run the 2.1 framework quickly)
2. Valuation: FDV vs comparable competitors
3. Unlock curve — early-round investor cost vs retail cost
4. Quality of the listing exchange
5. Historical first-day / 7-day / 30-day performance of
   similar IDOs
6. Exit feasibility (liquidity depth + slippage)

State plainly: 90% of IDOs trade below launch price within a
month. Explain the structural reasons.

I just closed a [winning / losing] trade:

Trade record:
- Asset:     [BTC / ETH / ...]
- Direction: [long / short]
- Entry:     [price + time]
- Exit:      [price + time]
- Return:    [%]
- Size:      [%] of total portfolio
- Reasoning at entry: [brief]

Debrief:
1. Was the entry logic sound, independent of outcome?
2. Was the position size appropriate?
3. Exit: discipline or emotion?
4. If the outcome had been the opposite, would the same
   decision still be approved? (outcome-bias test)
5. The single biggest learning from this trade

Don't say "good job" or "bad trade". Describe the facts.

Here are my trades over the last 30 days ([N] trades):
[paste trade history]

Analyze:
1. Win rate, average win/loss, rough Sharpe
2. Common traits of my 3 best trades + 3 worst trades
3. My high-frequency mistakes
   (e.g. soft stops, exiting winners too early, chasing tops)
4. Position-sizing score (out of 10)
5. Are any "wins" actually luck (small-sample noise)?
6. The single most important thing to fix next month

Give me objective analysis. No comfort, no scolding.

I missed a [%] move in [asset]. At the time I: [paste
the decision and reasoning]

Analyze:
1. Was my decision sound at the time, independent of outcome?
2. With the same information, what should the decision have been?
3. Will FOMO bleed into my next trade?
4. Symmetry of "miss" vs "loss" psychology — a miss is not a loss
5. One concrete rule for how to handle this situation next time

No comfort, no encouragement. Help me see clearly.

I held [asset] through a [%] drawdown and eventually closed
with a [%] profit.

Review the mindset side:
1. Emotional peak points (which day was the most anxious?)
2. Did I do anything I shouldn't have?
   (averaging down / cutting / adding leverage)
3. My stated max-drawdown tolerance vs what I actually felt
4. How can I pre-simulate extreme volatility next time?
5. Was the position too large for my emotional bandwidth?

Evaluate the process, not the outcome.

My long-term crypto strategy: [paste it — DCA / value /
trend following / etc.]

Actual execution over 90 days: [paste log]

Evaluate:
1. Strategy drift (how many trades violated the playbook?)
2. Typical drift contexts (chasing rallies / panic exits /
   emotional triggers)
3. Counterfactual: what would strict execution have produced?
4. Does the strategy itself need adjusting, or does my
   execution need adjusting?
5. The single most concrete discipline fix

When strategy and execution diverge, which side deserves
more attention?