jobs: extract shared market-context helpers from ai_log_job

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

app/jobs/_market_context.py

@@ -0,0 +1,86 @@
+"""Shared market-context helpers consumed by LLM-driven jobs.
+
+Both ai_log_job and email_digest_job pull "the latest tape" the same
+way — most-recent quote per (group, symbol), last N hours of headlines
+bucketed by category, and the running month's LLM spend. Moved here so
+neither job depends on the other's internals.
+"""
+from __future__ import annotations
+
+from collections import defaultdict
+from datetime import timedelta
+
+from sqlalchemy import desc, func, select
+
+from app.db import utcnow
+from app.models import AICall, Headline, Quote
+from app.services.openrouter import month_start
+
+
+REFERENCE_LINE = (
+    "S&P 7,501 (ATH) · VIX 18.0 · US 10y 4.45% · HY OAS 279bps · "
+    "Brent $109/bbl · Gold $4,651/oz · CPI 3.8% YoY"
+)
+
+
+async def latest_quotes_by_group(session) -> dict[str, list[dict]]:
+    """Latest quote per (group, symbol). Skips error rows where price is null."""
+    sub = (
+        select(
+            Quote.group_name,
+            Quote.symbol,
+            func.max(Quote.fetched_at).label("mx"),
+        )
+        .group_by(Quote.group_name, Quote.symbol)
+        .subquery()
+    )
+    stmt = (
+        select(Quote)
+        .join(
+            sub,
+            (Quote.group_name == sub.c.group_name)
+            & (Quote.symbol == sub.c.symbol)
+            & (Quote.fetched_at == sub.c.mx),
+        )
+        .order_by(Quote.group_name, Quote.symbol)
+    )
+    rows = (await session.execute(stmt)).scalars().all()
+    by_group: dict[str, list[dict]] = defaultdict(list)
+    for q in rows:
+        by_group[q.group_name].append(dict(
+            symbol=q.symbol, source=q.source, label=q.label,
+            note="", price=q.price, currency=q.currency,
+            as_of=q.as_of, changes=q.changes,
+        ))
+    return by_group
+
+
+async def recent_headlines_by_bucket(session, hours: float = 24) -> dict[str, list[dict]]:
+    """Last N hours of headlines, bucketed by category. Hard cap per
+    bucket to keep the prompt under ~40KB."""
+    cutoff = utcnow() - timedelta(hours=hours)
+    stmt = (
+        select(Headline)
+        .where(Headline.published_at >= cutoff)
+        .order_by(desc(Headline.published_at))
+        .limit(400)
+    )
+    rows = (await session.execute(stmt)).scalars().all()
+    by_bucket: dict[str, list[dict]] = defaultdict(list)
+    for h in rows:
+        if len(by_bucket[h.category]) >= 40:
+            continue
+        by_bucket[h.category].append(dict(
+            when=h.published_at.isoformat(),
+            source=h.source, title=h.title,
+        ))
+    return by_bucket
+
+
+async def month_spend(session) -> float:
+    start = month_start()
+    total = (await session.execute(
+        select(func.coalesce(func.sum(AICall.cost_usd), 0.0))
+        .where(AICall.called_at >= start)
+    )).scalar()
+    return float(total or 0.0)