# Historical Trend Analysis

> Analyze historical cryptocurrency news patterns, market narratives, and past cycle behavior using the free-crypto-news archive to identify recurring patterns, validate current theses, and provide context for current market conditions. Use when the user wants to understand how crypto history might rhyme with today.

- Skill: `nirholas/historical-trend-analysis` (Agent Skill)
- Install (CLI): `npx skillmds@latest add nirholas/historical-trend-analysis`
- Raw SKILL.md: https://api.skillmd.com/api/skills/nirholas/historical-trend-analysis/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: MIT
- Author: nirholas (https://skillmd.com/u/nirholas)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/nirholas/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.

| Endpoint | Purpose |
|----------|---------|
| `GET https://cryptocurrency.cv/api/archive` | Full historical news archive |
| `GET https://cryptocurrency.cv/api/archive?year={YYYY}` | Archive filtered to a specific year |
| `GET https://cryptocurrency.cv/api/archive?coin={SYMBOL}` | All historical coverage of a coin |
| `GET https://cryptocurrency.cv/api/archive?q={topic}` | Full-text search across all historical articles |
| `GET https://cryptocurrency.cv/api/market/history/{coinId}` | Historical price/market data |

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:

| Question Type | Best Endpoint |
|---------------|---------------|
| Past events on a specific coin | `/api/archive?coin={SYMBOL}&from={start}&to={end}` |
| Regulatory history | `/api/archive?q=regulation+SEC+ban&from=2021-01-01` |
| Hack/exploit history | `/api/archive?q=exploit+hack+rug+loss` |
| Halving narrative | `/api/archive?q=halving&coin=BTC` |
| DeFi summer patterns | `/api/archive?q=yield+farming&from=2020-05-01&to=2021-01-01` |
| Narrative emergence timing | `/api/archive?q={narrative}` sorted by date ascending |

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:

| Phase | Narrative Pattern | News Tone | Price Action |
|-------|------------------|-----------|--------------|
| Accumulation | Mostly negative, "crypto is dead" | Bearish, disinterested | Flat to slowly rising |
| Early markup | First positive narratives re-emerge | Cautiously optimistic | Steady uptrend, low attention |
| Acceleration | Dominant narrative forms | Bullish, mainstream coverage grows | Parabolic starts |
| Distribution | Euphoric narratives, everyone is bullish | Extreme greed, price targets escalating | Choppy at highs |
| Markdown | Narratives collapse, blame game | Panic, capitulation language | Sharp decline |
| Deep bear | No narrative, disillusionment | Silent, abandoned, "it's over" | Flat at lows, low volume |

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