Historical Trend Analysis

Historical Trend Analysis

When to use this skill

Use when the user asks about:

• Has this happened before in crypto? What happened next?
• How did the market react to [type of event] historically?
• What did BTC do after the last halving?
• Show me the narrative timeline for [coin/sector]
• Is this cycle different from previous ones?
• What were people saying about [coin] in [year]?
• When was the last time sentiment was this low/high?

Data Sources

All endpoints free, no authentication required.

Archive structure covers 2021–present with articles, market snapshots, on-chain data, social metrics, and narrative indexes.

Additional parameters:

• from / to — ISO date range filtering
• limit — number of results (up to 100 per call)
• category — filter by event category

Analysis Framework

1. Define the Historical Question

Before querying, clarify what the user is actually asking:

• Pattern match: "Did X type of event ever happen before, and what followed?"
• Narrative evolution: "How did coverage of [coin/sector] change over time?"
• Cycle positioning: "Where are we in the current cycle vs historical cycles?"
• Sentiment comparison: "Was market sentiment ever this [fearful/greedy] before, and what happened?"
• Event impact: "How did the market react to [regulation type / hack / ETF / halving]?"

2. Historical Data Retrieval Strategy

Match the query type to the right archive call:

For broad cycle analysis, batch calls by year:

1. Call /api/archive?year=2021 — bull peak and crash
2. Call /api/archive?year=2022 — bear market
3. Call /api/archive?year=2023 — recovery and consolidation
4. Call /api/archive?year=2024 — new cycle emergence
5. Call /api/archive?year=2025 — current cycle data

3. Pattern Recognition

When analyzing historical data, look for:

Narrative Cycles:

• When did a narrative first appear in news coverage?
• How long did it take from first mention to mainstream peak coverage?
• What triggered the narrative to fade?
• What replaced it?

Event Templates (common patterns that repeat):

• Pre-halving accumulation → narrative builds 12 months before → price runs → post-halving sell-the-news
• Regulatory FUD → immediate sharp drop → gradual recovery if enforcement is limited
• Protocol exploit → immediate -20% to -80% → recovery depends on team response time and compensation
• ETF speculation → multi-month accumulation on rumor → volatility on approval/denial
• Airdrop season → usage spikes → mercenary capital departs → consolidation

Sentiment Extremes:

• Pull historical Fear & Greed data for comparable readings
• What was happening in the news when the index last hit this level?
• Did the market bottom/top within weeks of the extreme reading?

4. Cycle Positioning Analysis

Use price history + narrative timeline to identify cycle phase:

Compare today's narrative tone to these historical phases to estimate positioning.

5. Coin-Specific History

For a specific coin, build a timeline:

1. First significant news coverage — what was the original narrative?
2. First major price milestone — what drove it?
3. Major inflection points (exploit, upgrade, regulatory event) — what happened to price?
4. Narrative evolution — has the thesis changed, and is the new thesis stronger or weaker?
5. Community evolution — is the developer/holder community growing or shrinking over time?

6. Contrarian Historical Insight

The most valuable historical patterns are the ones the crowd ignores:

• Most people remember the peaks and crashes — focus on what happened between them
• "This time is different" is almost always wrong — identify which historical pattern the current setup most resembles
• Projects that survived a full bear cycle typically emerged stronger — look for those that kept building
• Narratives that failed once often succeed on the second or third attempt (DeFi, NFTs, L2s all had false starts)

7. Output Format

Historical Analysis: [Topic/Coin/Question]

Time Range Analyzed: [start] to [end]

Historical Parallels Found:

1. [Date range] — [Similar situation] — [What followed]: [price/narrative outcome]
2. [Date range] — [Similar situation] — [What followed]

Narrative Timeline (for coin or sector research):

• [Year-Month]: [Narrative emerged / peaked / faded]
• [Year-Month]: [Key event and its impact on coverage]

Pattern Match: [Current situation most resembles [historical period] based on [evidence]]

Key Differences: [What is different this time — this determines if the pattern holds]

Historical Precedent: [What happened after the most similar historical setup]

Confidence in Analogy: High / Medium / Low — [why]

Actionable Insight: [What the historical context suggests about current positioning or risk management]

Notes for Agent Use

• The archive is the deepest data source — use it for due diligence, not just news consumption
• Combine with coin-research skill for a complete fundamental + historical picture
• For macro questions, combine archive news with /api/market/history/{coinId} price data
• When comparing cycle phases, use BTC as the baseline reference — altcoins typically lag BTC by weeks
• Historical patterns are probabilistic, not deterministic — present ranges of outcomes, not single predictions