Add heuristic incident correlation, IRNA source, flatten news icon color

Economic Incident History: each spike now gets "likely factors" — plain
word-frequency across every headline in its window, stopwords dropped
(English + German), no AI/LLM involved (new backend/app/textutil.py, shared
with the existing cluster-topic labeling). Spikes across instruments within
an hour of each other now merge into one incident (new
GET /api/markets/incidents) rather than showing WTI/Brent-style correlated
moves as separate entries.

Also: IRNA (Iranian state news agency) added as a source; news cluster
icons flattened from a violet->pink gradient to a single bright magenta for
legibility against the globe's own violet tint (size still scales with
article count); lightweight column-migration mechanism in db.py so the new
MarketSpike field doesn't break existing deployments' SQLite files.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Um48tTvZDrEgDeweFyhPYC

README.md

@@ -24,7 +24,14 @@ composition diagram when Wikipedia has one.
 - **Top-left "i" button** — opens the **Economic Incident History** view: every
   detected market spike, full clock-hour window, with the complete list of
   articles published in it (not just the top few) — a comprehensive log,
-  distinct from the quick preview in the right rail.
+  distinct from the quick preview in the right rail. Spikes across
+  instruments within an hour of each other are merged into one incident
+  (e.g. WTI and Brent crude spiking together shows as one entry, not two —
+  a timestamp heuristic, not evidence the moves actually share a cause).
+  Each incident also shows its **likely factors**: the words that recur
+  most across every headline published in that window, stopwords filtered
+  out — plain word-frequency counting, no AI/LLM involved (`textutil.py`).
+  Read it as "worth checking these articles," not an explanation.
 - **Bottom drawer** — collapsible conflict/military-event log (ACLED-backed;
   see below).
 - **Flights toggle** — live global air traffic (OpenSky Network) as airplane
@@ -160,6 +167,10 @@ into the red dots.
   alone qualifies a story as a candidate. Click a ticker item for a quick
   preview, or the "i" button (top-left) for the full log. Heuristic
   correlation, not a verified causal link.
+- `GET /api/markets/incidents?symbol=&hours=` — the same spikes merged
+  across instruments within `INCIDENT_MERGE_HOURS` (1h, `markets.py`) of
+  each other, each with a `top_keywords` word-frequency list. Backs the
+  Economic Incident History view.
 - `GET /api/conflict-events?hours=168`
 - `GET /api/flights` — latest global OpenSky snapshot, with `is_military`
   per aircraft (see the heuristic caveat above)
@@ -173,8 +184,14 @@ into the red dots.
 - Add gazetteer locations: `backend/app/data/gazetteer.csv`.
 - Add/fix parliament page mappings: `backend/app/data/parliaments.yaml`.
 - Add/fix military ICAO24/callsign ranges: `backend/app/data/military_ranges.yaml`.
+- Extend stopwords for keyword extraction (cluster topics, incident "likely
+  factors"): `backend/app/textutil.py`.
 - Change poll intervals / clustering window: `.env`.
 
+New model fields need a matching entry in `_COLUMN_MIGRATIONS` in
+`backend/app/db.py` — `create_all()` only creates missing tables, not
+columns on tables that already exist in a deployed `data/newsatlas.db`.
+
 ## Deploying as a Proxmox LXC
 
 See `deploy/lxc/README.md` for a `pct`-based build script that provisions a

backend/app/clustering.py

@@ -1,31 +1,12 @@
 import datetime as dt
-import re
+from collections import defaultdict
-from collections import Counter, defaultdict
 
 from sqlalchemy import select
 from sqlalchemy.orm import Session
 
 from .config import settings
 from .models import Article
-
+from .textutil import topic_label as _topic_label
-_STOPWORDS = {
-    "the", "a", "an", "in", "on", "of", "to", "for", "and", "or", "is", "as",
-    "at", "by", "with", "from", "after", "over", "amid", "amid", "into",
-    "says", "say", "will", "has", "have", "had", "its", "it", "his", "her",
-    "new", "up", "out", "how", "why", "what", "who", "be", "are", "was",
-    "were", "this", "that", "than", "not", "no", "us", "u.s.",
-}
-_WORD_RE = re.compile(r"[A-Za-z][A-Za-z'-]{2,}")
-
-
-def _topic_label(titles: list[str]) -> str:
-    words = Counter()
-    for title in titles:
-        for word in _WORD_RE.findall(title.lower()):
-            if word not in _STOPWORDS:
-                words[word] += 1
-    top = [w for w, _ in words.most_common(3)]
-    return ", ".join(top) if top else ""
 
 
 def build_clusters(session: Session) -> list[dict]:

backend/app/db.py

@@ -1,6 +1,6 @@
 from pathlib import Path
 
-from sqlalchemy import create_engine
+from sqlalchemy import create_engine, text
 from sqlalchemy.orm import DeclarativeBase, sessionmaker
 
 from .config import settings
@@ -18,10 +18,30 @@ class Base(DeclarativeBase):
     pass
 
 
+# Columns added to existing tables after their first release. create_all()
+# only creates missing *tables*, so an upgrade on an existing SQLite file
+# needs these added by hand — a lightweight alternative to a full migration
+# framework, appropriate for this project's scale. Add a new (table, column,
+# ddl-type-and-default) tuple here whenever a model gains a field.
+_COLUMN_MIGRATIONS = [
+    ("market_spikes", "top_keywords_json", "TEXT DEFAULT '[]'"),
+]
+
+
+def _run_column_migrations() -> None:
+    with engine.connect() as conn:
+        for table, column, ddl in _COLUMN_MIGRATIONS:
+            existing = {row[1] for row in conn.execute(text(f"PRAGMA table_info({table})"))}
+            if column not in existing:
+                conn.execute(text(f"ALTER TABLE {table} ADD COLUMN {column} {ddl}"))
+        conn.commit()
+
+
 def init_db() -> None:
     from . import models  # noqa: F401  (registers tables on Base.metadata)
 
     Base.metadata.create_all(bind=engine)
+    _run_column_migrations()
 
 
 def get_session():

backend/app/main.py

@@ -15,7 +15,7 @@ from .config import settings
 from .db import get_session, init_db, SessionLocal
 from .flights import get_latest_flights
 from .ingest import fetch_all
-from .markets import poll_markets, INSTRUMENTS
+from .markets import poll_markets, merge_spikes_into_incidents, INSTRUMENTS
 from .conflict import enabled as conflict_enabled, poll_conflict_events
 from .models import Article, ConflictEvent, MarketPrice, MarketSpike
 from .scheduler import start_scheduler
@@ -212,6 +212,48 @@ def api_markets_spikes(symbol: str | None = None, hours: int = Query(168, le=24
                     "window_end": s.window_end.isoformat(),
                     "detected_at": s.detected_at.isoformat(),
                     "candidate_articles": articles,
+                    "top_keywords": json.loads(s.top_keywords_json or "[]"),
+                }
+            )
+        return out
+    finally:
+        session.close()
+
+
+@app.get("/api/markets/incidents")
+def api_markets_incidents(symbol: str | None = None, hours: int = Query(720, le=24 * 365)):
+    """Spikes merged across instruments when they land within
+    MERGE_INCIDENT_HOURS of each other (see markets.merge_spikes_into_incidents)
+    — the comprehensive view behind the Economic Incident History UI.
+    `symbol`, if given, filters to incidents that include that instrument,
+    but still returns every instrument in the merged incident, not just it."""
+    since = dt.datetime.utcnow() - dt.timedelta(hours=hours)
+    session = next(get_session())
+    try:
+        spikes = session.execute(select(MarketSpike).where(MarketSpike.detected_at >= since)).scalars().all()
+        incidents = merge_spikes_into_incidents(spikes)
+        if symbol:
+            incidents = [i for i in incidents if any(instr["symbol"] == symbol for instr in i["instruments"])]
+
+        out = []
+        for inc in incidents:
+            article_ids = inc["article_ids"]
+            articles = []
+            if article_ids:
+                rows = session.execute(select(Article).where(Article.id.in_(article_ids))).scalars().all()
+                by_id = {a.id: a for a in rows}
+                articles = [_article_dict(by_id[i]) for i in article_ids if i in by_id]
+            out.append(
+                {
+                    "instruments": [
+                        {**instr, "detected_at": instr["detected_at"].isoformat()} for instr in inc["instruments"]
+                    ],
+                    "window_start": inc["window_start"].isoformat(),
+                    "window_end": inc["window_end"].isoformat(),
+                    "detected_at": inc["detected_at"].isoformat(),
+                    "latest_detected_at": inc["latest_detected_at"].isoformat(),
+                    "candidate_articles": articles,
+                    "top_keywords": inc["top_keywords"],
                 }
             )
         return out

backend/app/markets.py

@@ -1,6 +1,7 @@
 import datetime as dt
 import json
 import logging
+from collections import Counter
 
 import httpx
 from sqlalchemy import select
@@ -8,6 +9,14 @@ from sqlalchemy.orm import Session
 
 from .config import settings
 from .models import Article, MarketPrice, MarketSpike
+from .textutil import extract_keywords
+
+# Two spikes within this many hours of each other are treated as one
+# "incident" — e.g. WTI and Brent crude almost always move together, and a
+# real shock often shows up across several indices within the same hour.
+# Chain-merged: A+B within range and B+C within range merges all three even
+# if A and C individually aren't, same declutter pattern used for the globe.
+INCIDENT_MERGE_HOURS = 1.0
 
 log = logging.getLogger("newsatlas.markets")
 
@@ -100,7 +109,9 @@ def _score_article(article: Article, keywords: list[str], country: str | None) -
     return score
 
 
-def _find_candidate_articles(session: Session, symbol: str, window_start: dt.datetime, window_end: dt.datetime, limit: int = 60) -> list[int]:
+def _find_candidate_articles(
+    session: Session, symbol: str, window_start: dt.datetime, window_end: dt.datetime, limit: int = 60
+) -> tuple[list[int], list[tuple[str, int]]]:
     hints = _SPIKE_HINTS.get(symbol, {"keywords": [], "country": None})
     rows = session.execute(
         select(Article)
@@ -115,7 +126,14 @@ def _find_candidate_articles(session: Session, symbol: str, window_start: dt.dat
     # first), it just no longer excludes zero-score articles.
     scored = [(_score_article(a, hints["keywords"], hints["country"]), a) for a in rows]
     scored.sort(key=lambda pair: (pair[0], pair[1].published_at), reverse=True)
-    return [a.id for _, a in scored[:limit]]
+
+    # "Most likely reasons," heuristically: just the words that recur across
+    # every headline in the window (stopwords dropped) — no AI/LLM involved,
+    # see textutil.py. Computed over the whole window, not just the
+    # `limit`-truncated list below, for a more robust signal.
+    keywords = extract_keywords([a.title for _, a in scored], top_n=8)
+
+    return [a.id for _, a in scored[:limit]], keywords
 
 
 def _floor_hour(t: dt.datetime) -> dt.datetime:
@@ -140,7 +158,7 @@ def _detect_and_record_spike(
     # "14:00-16:00"), covering at least the full hour the spike landed in.
     window_start = _floor_hour(prev.recorded_at - dt.timedelta(hours=settings.market_spike_lookback_hours))
     window_end = _ceil_hour(recorded_at)
-    article_ids = _find_candidate_articles(session, symbol, window_start, window_end)
+    article_ids, keywords = _find_candidate_articles(session, symbol, window_start, window_end)
 
     session.add(
         MarketSpike(
@@ -153,6 +171,7 @@ def _detect_and_record_spike(
             window_end=window_end,
             detected_at=recorded_at,
             article_ids_json=json.dumps(article_ids),
+            top_keywords_json=json.dumps(keywords),
         )
     )
     log.info("Spike detected: %s %.2f%% (%d candidate articles)", symbol, pct_change, len(article_ids))
@@ -194,3 +213,63 @@ def poll_markets(session: Session) -> int:
 
     session.commit()
     return added
+
+
+def merge_spikes_into_incidents(spikes: list[MarketSpike]) -> list[dict]:
+    """Chain-merge spikes within INCIDENT_MERGE_HOURS of each other into
+    "incidents" spanning possibly multiple instruments, on the assumption
+    that near-simultaneous moves are more likely to share a cause than
+    coincidence. Purely a timestamp heuristic — no correlation of the
+    actual price movements is attempted."""
+    ordered = sorted(spikes, key=lambda s: s.detected_at)
+    groups: list[list[MarketSpike]] = []
+    threshold = dt.timedelta(hours=INCIDENT_MERGE_HOURS)
+
+    for spike in ordered:
+        target = next(
+            (g for g in groups if any(abs(spike.detected_at - member.detected_at) <= threshold for member in g)),
+            None,
+        )
+        if target is not None:
+            target.append(spike)
+        else:
+            groups.append([spike])
+
+    incidents = []
+    for group in groups:
+        group.sort(key=lambda s: s.detected_at)
+        article_ids: list[int] = []
+        seen_ids: set[int] = set()
+        keyword_counts: Counter[str] = Counter()
+        for s in group:
+            for aid in json.loads(s.article_ids_json or "[]"):
+                if aid not in seen_ids:
+                    seen_ids.add(aid)
+                    article_ids.append(aid)
+            for word, count in json.loads(s.top_keywords_json or "[]"):
+                keyword_counts[word] += count
+
+        incidents.append(
+            {
+                "instruments": [
+                    {
+                        "symbol": s.symbol,
+                        "label": s.label,
+                        "from_price": s.from_price,
+                        "to_price": s.to_price,
+                        "pct_change": s.pct_change,
+                        "detected_at": s.detected_at,
+                    }
+                    for s in group
+                ],
+                "window_start": min(s.window_start for s in group),
+                "window_end": max(s.window_end for s in group),
+                "detected_at": group[0].detected_at,
+                "latest_detected_at": group[-1].detected_at,
+                "article_ids": article_ids[:60],
+                "top_keywords": keyword_counts.most_common(8),
+            }
+        )
+
+    incidents.sort(key=lambda i: i["latest_detected_at"], reverse=True)
+    return incidents

backend/app/models.py

@@ -58,6 +58,10 @@ class MarketSpike(Base):
 
     # JSON-encoded list of Article.id, ranked most-to-least likely relevant.
     article_ids_json: Mapped[str] = mapped_column(Text, default="[]")
+    # JSON-encoded [[word, count], ...] — plain word-frequency across every
+    # article in the window's titles, stopwords dropped. A heuristic hint at
+    # "likely factors," not an explanation; see textutil.py.
+    top_keywords_json: Mapped[str] = mapped_column(Text, default="[]")
 
 
 class ConflictEvent(Base):

backend/app/sources.yaml

@@ -61,3 +61,7 @@ sources:
   - name: Der Standard (International)
     url: https://www.derstandard.at/rss/international
     bias: austrian-mainstream
+
+  - name: IRNA (Islamic Republic News Agency)
+    url: https://en.irna.ir/rss
+    bias: iranian-state-run

backend/app/textutil.py

@@ -0,0 +1,56 @@
+"""Plain word-frequency text analysis — no ML/LLM involved.
+
+Shared by clustering.py (per-cluster topic labels) and markets.py (heuristic
+"likely factors" behind a detected market spike): tokenize, drop stopwords,
+count. That's the whole technique — it surfaces words that recur across a
+set of headlines, nothing more. Treat the result as a hint worth reading the
+linked articles over, not an explanation.
+"""
+
+import re
+from collections import Counter
+
+_WORD_RE = re.compile(r"[A-Za-zÀ-ÖØ-öø-ÿ][A-Za-zÀ-ÖØ-öø-ÿ'-]{2,}")
+
+# English + German (taz, Der Standard contribute German-language headlines)
+# function words. Deliberately does NOT include short, meaningful tokens
+# common in market/political news (us, uk, eu, fed, opec, ...) — those are
+# exactly the signal this is trying to surface, not noise to remove.
+_STOPWORDS = {
+    # English
+    "the", "a", "an", "in", "on", "of", "to", "for", "and", "or", "is", "as",
+    "at", "by", "with", "from", "after", "over", "amid", "into", "says",
+    "say", "said", "will", "has", "have", "had", "its", "it", "his", "her",
+    "new", "up", "out", "how", "why", "what", "who", "be", "are", "was",
+    "were", "this", "that", "than", "not", "no", "been", "but", "also",
+    "more", "their", "they", "you", "your", "we", "our", "can", "could",
+    "would", "should", "about", "against", "all", "any", "some", "such",
+    "one", "two", "first", "if", "so", "just", "most", "other", "which",
+    "when", "where", "while", "during", "before", "between", "under",
+    "again", "then", "there", "here", "still", "now", "only", "own", "same",
+    "too", "very", "did", "does", "do", "being", "them", "he", "she", "i",
+    "my", "me", "him", "off", "down", "because", "each", "few", "further",
+    "once", "both", "don",
+    # German
+    "der", "die", "das", "und", "zu", "den", "von", "mit", "auf", "für",
+    "ist", "im", "dem", "des", "ein", "eine", "einer", "eines", "auch",
+    "nach", "bei", "aus", "wie", "was", "wird", "werden", "sich", "sie",
+    "er", "es", "noch", "nur", "schon", "über", "um", "als", "aber", "oder",
+    "wenn", "wir", "ihr", "man", "sind", "hat", "haben", "kann", "können",
+    "wurde", "wurden", "einem", "einen", "diese", "dieser", "dieses", "vor",
+    "durch", "zum", "zur", "doch",
+}
+
+
+def extract_keywords(texts: list[str], top_n: int = 8) -> list[tuple[str, int]]:
+    """Word-frequency count across `texts`, stopwords dropped, most common first."""
+    counts: Counter[str] = Counter()
+    for text in texts:
+        for word in _WORD_RE.findall((text or "").lower()):
+            if word not in _STOPWORDS:
+                counts[word] += 1
+    return counts.most_common(top_n)
+
+
+def topic_label(titles: list[str], top_n: int = 3) -> str:
+    return ", ".join(w for w, _ in extract_keywords(titles, top_n))

frontend/css/style.css

@@ -273,10 +273,22 @@ html, body {
 }
 
 .econ-incident { padding: 14px 0; border-bottom: 1px solid #241533; }
-.econ-incident h3 { margin: 0 0 4px; font-size: 14px; color: #f2dede; }
+.econ-incident h3 { margin: 0 0 4px; font-size: 14px; color: #f2dede; display: flex; flex-wrap: wrap; gap: 8px 14px; font-weight: 600; }
+.instrument-chip { display: inline-flex; gap: 6px; align-items: baseline; }
 .econ-incident .window { font-size: 11px; color: var(--c-text-muted); margin-bottom: 8px; }
 .econ-incident .articles { margin-top: 8px; }
 
+.keyword-tags { margin: 6px 0 10px; font-size: 11px; display: flex; flex-wrap: wrap; align-items: center; gap: 5px; }
+.keyword-tag {
+  display: inline-block;
+  background: var(--c-tag-bg);
+  border: 1px solid var(--c-panel-border);
+  color: var(--c-dark-soft);
+  border-radius: 10px;
+  padding: 2px 8px;
+  font-size: 11px;
+}
+
 .panel-section { margin-bottom: 20px; }
 .panel-section h2 {
   font-size: 13px;

frontend/js/app.js

@@ -5,6 +5,12 @@ const API = "/api";
 // than a separate alert layer, per the flights-overlay color spec.
 const GLOBE_TINT_HEX = 0x6a3fd9;
 const MILITARY_FLIGHT_HEX = 0xff33ff;
+// News icons used to gradient violet->pink by corroborating-source count;
+// flattened to a single bright magenta so they stay legible against the
+// (also violet) globe tint — size still scales with source/article count.
+const NEWS_ICON_HEX = 0xff33ff;
+// Must match INCIDENT_MERGE_HOURS in backend/app/markets.py — display label only.
+const INCIDENT_MERGE_HOURS_LABEL = "1h";
 
 let config = { weather_enabled: false, conflict_enabled: false };
 let clusters = []; // raw, server-side ~11km-grid clusters
@@ -85,12 +91,6 @@ function resizeGlobe() {
 window.addEventListener("resize", resizeGlobe);
 setTimeout(resizeGlobe, 0);
 
-function colorForCluster(d) {
-  // violet (few corroborating sources) -> hot pink (many) — theme accent gradient
-  const n = Math.min(d.source_count, 6);
-  const palette = ["#5018DD", "#7018C4", "#9018AB", "#B01092", "#D00879", "#E40046"];
-  return palette[n - 1] || palette[0];
-}
 
 // ---- globe icon sprites (news clusters + conflict events) --------------
 //
@@ -164,7 +164,7 @@ function buildGlobeObject(d) {
   if (d.kind === "conflict") {
     return makeIconSprite(helmetIconTexture(), MILITARY_FLIGHT_HEX, HELMET_ICON_DEG * sceneUnitsPerDeg);
   }
-  return makeIconSprite(newspaperIconTexture(), colorForCluster(d), pointRadiusFor(d) * sceneUnitsPerDeg * 2.4);
+  return makeIconSprite(newspaperIconTexture(), NEWS_ICON_HEX, pointRadiusFor(d) * sceneUnitsPerDeg * 2.4);
 }
 
 function objectLabelFor(d) {
@@ -846,6 +846,16 @@ function mountSparkline(container, history) {
   });
 }
 
+// "Likely factors": the words that recur most across every headline
+// published in a spike's window (backend textutil.py — stopword-filtered
+// word frequency, no AI/LLM). A hint worth reading the linked articles
+// over, not an explanation, so it's labeled as such wherever it's shown.
+function keywordTagsHtml(keywords) {
+  if (!keywords || !keywords.length) return "";
+  const tags = keywords.map(([word, count]) => `<span class="keyword-tag">${escapeHtml(word)} · ${count}</span>`).join("");
+  return `<div class="keyword-tags"><span class="subtle">Likely factors (common words in this window):</span> ${tags}</div>`;
+}
+
 async function toggleMarketDetail(symbol, idx) {
   const el = document.getElementById(`market-detail-${idx}`);
   const wasHidden = el.classList.contains("hidden");
@@ -871,6 +881,7 @@ async function toggleMarketDetail(symbol, idx) {
           <p><span class="${dir}">${arrow} ${s.pct_change.toFixed(2)}%</span>
              at ${new Date(s.detected_at).toLocaleString()}
              (${s.from_price.toLocaleString()} → ${s.to_price.toLocaleString()})</p>
+          ${keywordTagsHtml(s.top_keywords)}
           ${articlesHtml}
         `;
       })
@@ -912,22 +923,23 @@ async function loadEconHistory() {
   listEl.innerHTML = `<p class="subtle">Loading…</p>`;
   try {
     const url = symbol
-      ? `${API}/markets/spikes?symbol=${encodeURIComponent(symbol)}&hours=8760`
+      ? `${API}/markets/incidents?symbol=${encodeURIComponent(symbol)}&hours=8760`
-      : `${API}/markets/spikes?hours=8760`;
+      : `${API}/markets/incidents?hours=8760`;
-    const spikes = await (await fetch(url)).json();
+    const incidents = await (await fetch(url)).json();
-    if (!spikes.length) {
+    if (!incidents.length) {
       listEl.innerHTML = `<p class="subtle">No moves ≥ the spike threshold recorded yet.</p>`;
       return;
     }
-    listEl.innerHTML = spikes.map(econIncidentHtml).join("");
+    listEl.innerHTML = incidents.map(econIncidentHtml).join("");
   } catch (e) {
     listEl.innerHTML = `<p class="subtle">Could not load incident history.</p>`;
   }
 }
 
-function econIncidentHtml(s) {
+// An "incident" is one or more spikes merged because they landed within an
-  const dir = s.pct_change > 0 ? "up" : "down";
+// hour of each other (backend: markets.merge_spikes_into_incidents) — e.g.
-  const arrow = s.pct_change > 0 ? "▲" : "▼";
+// WTI and Brent crude spiking together shows as one incident, not two.
+function econIncidentHtml(inc) {
   const fmtHour = (iso) =>
     new Date(iso).toLocaleString(undefined, {
       month: "short",
@@ -935,17 +947,25 @@ function econIncidentHtml(s) {
       hour: "2-digit",
       minute: "2-digit",
     });
-  const articlesHtml = s.candidate_articles.length
+  const instrumentsHtml = inc.instruments
-    ? s.candidate_articles.map(articleItemHtml).join("")
+    .map((i) => {
+      const dir = i.pct_change > 0 ? "up" : "down";
+      const arrow = i.pct_change > 0 ? "▲" : "▼";
+      return `<span class="instrument-chip">${escapeHtml(i.label)} <span class="${dir}">${arrow} ${i.pct_change.toFixed(2)}%</span></span>`;
+    })
+    .join("");
+  const articlesHtml = inc.candidate_articles.length
+    ? inc.candidate_articles.map(articleItemHtml).join("")
     : `<p class="subtle">No stories found published in that window.</p>`;
   return `
     <div class="econ-incident">
-      <h3>${escapeHtml(s.label)} <span class="${dir}">${arrow} ${s.pct_change.toFixed(2)}%</span></h3>
+      <h3>${instrumentsHtml}</h3>
       <div class="window">
-        ${fmtHour(s.window_start)} – ${fmtHour(s.window_end)}
+        ${fmtHour(inc.window_start)} – ${fmtHour(inc.window_end)}
-        · ${s.from_price.toLocaleString()} → ${s.to_price.toLocaleString()}
+        · ${inc.candidate_articles.length} stor${inc.candidate_articles.length === 1 ? "y" : "ies"} in window
-        · ${s.candidate_articles.length} stor${s.candidate_articles.length === 1 ? "y" : "ies"} in window
+        ${inc.instruments.length > 1 ? ` · ${inc.instruments.length} instruments moved within ${INCIDENT_MERGE_HOURS_LABEL} of each other` : ""}
       </div>
+      ${keywordTagsHtml(inc.top_keywords)}
       <div class="articles">${articlesHtml}</div>
     </div>
   `;