Crypto Analysis with AI (Part 1)

Using AI in trading has grown rapidly in recent years, showing clear results in practice. From building automated trading bots to aggregating market data, using AI in trading has proven to save time, reduce stress, and improve efficiency for traders.

That’s why I decided to create a series called Crypto Analysis with AI. The goal is to make trading insights more accessible, whether you’re a beginner just entering the market or an experienced trader looking for sharper tools.

Okay, Let’s go!

👨‍💻 Fundamental Analysis Made Simple

Every strong investment starts with fundamentals. In crypto analysis, I always begin with the basics before moving into detailed prompts. Below are the key criteria I use to evaluate any token:

• Overview information: Understand the project’s fundamentals to see if the token is actually building or just running on hype.

• Team & Backers: Check the credibility of founders, advisors, and investors.

• Tokenomics: Study supply, allocation, vesting, and inflation/deflation mechanisms.

• Ecosystem Growth: Look at integrations, partnerships, developer activity, and real adoption.

• Community Strength: Analyze traction on social media, engagement, and regional presence.

• Liquidity & Market Depth: Review trading volume, exchange listings, and stability of liquidity pools

• Macro/ Narratives: Consider broader market narratives and sector trends driving demand.

• News: Track recent announcements, updates, and events.

• Entry Points and Target Price: Identify potential buy zones and reasonable targets.

I generate all of the above insights with a single detailed AI prompt. Here’s the full version:

Perform a comprehensive fundamental analysis of [Crypto Name] listed on [Exchanges Name], using the latest available on-chain data, market insights, and valuation metrics. Provide structured details under the following sections:
1. Project Overview
- What is the project? Core vision and mission.
- Key features and unique selling points (USP).
- Target market, user base, and real use cases.
- Competitor comparison (which projects solve similar problems?).
2. Fundamental Analysis (FA)
- Team & Backers: Founders, advisors, strategic investors, and notable partnerships (include credibility from LinkedIn or past projects).
- Tokenomics:
	Total/max supply, circulating supply, vesting schedules, lockups, inflation/deflation model.
	Allocation breakdown (team, investors, treasury, community).
	Staking, burning, or yield models.
- Ecosystem Growth:
	Exchange listings (CEX/DEX).
	Developer activity (GitHub commits, open-source traction).
	Integrations and partnerships.
- Community Strength:
	Social media metrics (Telegram, Discord, X followers, engagement).
	Regional traction (strong in which countries/markets?).
- Liquidity & Market Depth:
	Trading volume across exchanges.
	Market makers, liquidity pools, slippage analysis.
- Macro/Narratives:
	Current crypto narratives the project fits into (e.g., privacy, RWA, AI, L2s).
3. Recent News & Catalysts
- Partnerships, funding rounds, or new investors.
- Major events (upgrades, token unlocks, new listings, regulatory updates).
- Ecosystem expansion (new dApps, collaborations).
4. Recommendation for Buyers
- Potential entry points and Target Price.
- Risk management strategies (short-term traders vs long-term holders).
5. Outlook
- Bullish case: What factors could drive significant growth?
- Bearish case: What risks could limit adoption or crash the price?]

📈 Automating Technical Analysis

Many people assume that using AI in trading chart analysis is not comprehensive. But I’ll show you how the results can actually be far superior.

Step 1: Capture the Chart

Go to TradingView and take a screenshot of the token’s chart at the trading timeframe you want.

Step 2: Use the AI Prompt

Paste your chart screenshot into ChatGPT (or any AI LLM) with the following prompt template.

TASK: Perform a technical analysis for <ASSET / PAIR> using both:
1) The attached chart screenshot (primary visual reference).
2) Latest market data (secondary confirmation).

Instructions
1. Carefully analyze the provided chart screenshot. Identify:
   - Trend structure (higher highs/lows, lower highs/lows).
   - Any visible chart pattern (triangle, flag, H&S, double bottom, wedge, etc.).
   - Key support/resistance zones visible on the screenshot.
   - Breakouts, retests, or failed breakouts if present.

2. Cross-check with the latest market data (web sources) to validate:
   - Price levels, volume, funding rate, and open interest trend.
   - RSI(14), MACD, moving averages (20/50/200 EMA or SMA).
   - Volatility (ATR) if available.

3. Provide both **chart-based observations** (from the screenshot) and **data-based confirmation** (from web).

4. List potential scenarios:
   - Bullish case (what must happen to confirm).
   - Bearish case (what invalidates the setup).

- **Checklist:** What to watch next (3–6 bullets).
- Sources cited (with links).
- Last updated: <Asia/Bangkok time>.

Then, simply wait for the results.

👉 Trust me, using AI in trading will generate a highly detailed crypto analysis covering multiple metrics, such as RSI, MACD, Moving Averages (MA), Resistance and Support levels,…

Step 3: Compare with Indicators

For the best outcome, I recommend turning on the same indicators directly on the TradingView chart. This lets you compare and validate the AI’s output with real-time market data.

When you combine both, you’ll get a much clearer and more reliable trading perspective.

💬 Sentiment Analysis Across Markets

Sentiment is the emotional pulse of the market. It’s the collective mood of traders, investors, and institutions, shaped by headlines, social chatter, and on-chain signals. In crypto analysis, this mood often shifts faster than in traditional markets.

Tracking sentiment is not about guessing feelings. It’s about measuring them: monitoring news flow, analyzing trading volumes... Together, these signals reveal if the market leans bullish, bearish, or cautious.

Prompt:

TASK: News Sentiment for <ASSET or PROJECT NAME>

Instructions
- Use the fetched news. Classify overall sentiment: Positive / Negative / Neutral.
- Give 3–6 concrete reasons tied to specific headlines (cite links).
- Note any market-moving catalysts (token unlocks, listings, exploits, partnerships, regulation).
- State confidence (low/med/high) and what could change it.

Output
- Summary (≤80 words) with sentiment label.
- Table: date | headline | impact (+/–/0) | why it matters | link
- Section: “Reasons for Sentiment” (bullets).
- Section: “What could flip the bias” (bullets).
- Sources + Last updated.

Outcome:

With these prompts, AI doesn’t collect headlines, it turns fragmented news into actionable sentiment signals. This helps you stay ahead of the narratives moving the market and have informative crypto analysis.

📰 Crypto News and Summaries

A rate decision from the Fed or a major exchange listing can shift billions in market cap within minutes. That’s why keeping your eyes on the market is essential for any serious crypto analysis.

Instead of scrolling endlessly, using AI in trading support you to filter out noise and highlight only the highest-signal events: macro shifts, large-cap movements, policy changes, and ecosystem updates. This way, you focus on facts that actually move markets, not hype or speculation.

Prompt:

TASK: Latest Crypto Market News (Bullets)

Scope
- Macro (Fed, rates, ETF flows), major chains, top L1/L2, large caps, security incidents, big listings/partnerships, policy/regulation.

Instructions
- Select 8–12 high-signal items from the last 24–72 hours.
- Each bullet: 1 sentence, plain English, include the date and a source link.
- Avoid hype words. If uncertain, mark “unconfirmed”.

Output
- Summary (≤60 words).
- Bullets:
  - [YYYY-MM-DD] Short fact. <link>
  - ...
- “What to watch next” (3–5 bullets).
- Last updated: <Asia/Bangkok time>.

Outcome:

By running this prompt, using AI in trading can help you turn hundreds of fragmented headlines into a concise market digest.

🔍 Token Filters for Hidden Opportunities

In the past, I would manually scroll through listing boards and study each token one by one. Only after finding a project that matched my criteria would I decide to invest. That process worked, but it took a lot of time. That’s why I created a dedicated prompt to simplify the workflow and make it easier for using AI in trading.

Prompt:

TASK: Undervalued Crypto Screener

Goal
- Surface 5–15 candidates that look undervalued on fundamentals and traction.
- Include links to investor discussions (Reddit, X/Twitter, StockTwits) where retail analysis exists.

Filters:
- Market Cap: $5M–$1B
- 24h Volume / MC: > 3%
- Liquidity: top exchanges listed; no obvious wash-trading
- Tokenomics: circulating supply %, unlock schedule sane (no massive near-term cliffs)
- Traction: active users/dev activity uptrend if available
- Valuation: pick at least 2 ratios:
  - FDV/TVL (for DeFi)
  - MC/TVL
  - NVT (or similar activity/valuation proxy)
  - Revenue/MC or Fees/MC
  - Active Addresses trend (7d/30d)
  - GitHub commits or dev contributors trend
- Exclusions: tokens with unresolved exploits, thin liquidity, or unclear disclosures.

Instructions
1) Pull data from CoinGecko + CoinMarketCap. Cross-check with Messari/TokenTerminal/Artemis if available.
2) For each candidate, compute selected metrics and add one-line thesis.
3) Find 1–3 recent investor discussion links per asset:
   - Reddit (subreddits like r/CryptoCurrency, r/<project>)
   - X/Twitter posts or threads with real analysis (not shills)
   - StockTwits for sentiment snapshots
   Include direct URLs; skip spam/airdrop threads.
4) Be explicit about limitations: stale data, conflicting figures, or missing TVL.

Output
- Summary (≤120 words).
- Table (Markdown) with these columns:
  | Ticker | Name | MC | FDV | 24h Vol | Top Exchanges | Circulating % | Unlocks (30–90d)| Discussion Links |
- Notes on data gaps (bullets).
- Sources (links) and Last updated.

Instead of manual screening, you can now filter projects and run crypto analysis automatically by adjusting the criteria under the Filter section. It’s flexible and fully up to you how deep or narrow you want the search to be.

Outcome:

See? It’s much faster and makes research a lot easier, doesn’t it?