Giorgio Gilestro
b47c45e218
- indicator_summary_job.py imported its own copies of _month_spend and _latest_quotes_by_group; _market_context.py already exposes these. Switched to the canonical imports. Also fixed _market_context's latest_quotes_by_group to actually filter null prices (it claimed to in its docstring but lacked the WHERE clause). - api.py duplicated REFERENCE_LINE as CHAT_REFERENCE_LINE — same string, two sources of truth. Now imports REFERENCE_LINE. - Chat endpoint used the deprecated `call_openrouter` alias and passed an explicit `model=` that bypassed the provider chain. Switched to `call_llm` with default model selection, then removed the alias. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
87 lines
2.8 KiB
Python
87 lines
2.8 KiB
Python
"""Shared market-context helpers consumed by LLM-driven jobs.
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Both ai_log_job and email_digest_job pull "the latest tape" the same
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way — most-recent quote per (group, symbol), last N hours of headlines
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bucketed by category, and the running month's LLM spend. Moved here so
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neither job depends on the other's internals.
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"""
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from __future__ import annotations
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from collections import defaultdict
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from datetime import timedelta
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from sqlalchemy import desc, func, select
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from app.db import utcnow
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from app.models import AICall, Headline, Quote
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from app.services.openrouter import month_start
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REFERENCE_LINE = (
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"S&P 7,501 (ATH) · VIX 18.0 · US 10y 4.45% · HY OAS 279bps · "
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"Brent $109/bbl · Gold $4,651/oz · CPI 3.8% YoY"
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)
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async def latest_quotes_by_group(session) -> dict[str, list[dict]]:
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"""Latest quote per (group, symbol). Skips error rows where price is null."""
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sub = (
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select(
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Quote.group_name,
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Quote.symbol,
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func.max(Quote.fetched_at).label("mx"),
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)
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.group_by(Quote.group_name, Quote.symbol)
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.subquery()
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)
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stmt = (
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select(Quote)
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.join(
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sub,
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(Quote.group_name == sub.c.group_name)
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& (Quote.symbol == sub.c.symbol)
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& (Quote.fetched_at == sub.c.mx),
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)
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.where(Quote.price.is_not(None))
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.order_by(Quote.group_name, Quote.symbol)
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)
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rows = (await session.execute(stmt)).scalars().all()
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by_group: dict[str, list[dict]] = defaultdict(list)
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for q in rows:
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by_group[q.group_name].append(dict(
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symbol=q.symbol, source=q.source, label=q.label,
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note="", price=q.price, currency=q.currency,
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as_of=q.as_of, changes=q.changes,
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))
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return by_group
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async def recent_headlines_by_bucket(session, hours: float = 24) -> dict[str, list[dict]]:
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"""Last N hours of headlines, bucketed by category. Hard cap per
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bucket to keep the prompt under ~40KB."""
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cutoff = utcnow() - timedelta(hours=hours)
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stmt = (
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select(Headline)
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.where(Headline.published_at >= cutoff)
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.order_by(desc(Headline.published_at))
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.limit(400)
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)
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rows = (await session.execute(stmt)).scalars().all()
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by_bucket: dict[str, list[dict]] = defaultdict(list)
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for h in rows:
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if len(by_bucket[h.category]) >= 40:
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continue
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by_bucket[h.category].append(dict(
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when=h.published_at.isoformat(),
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source=h.source, title=h.title,
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))
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return by_bucket
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async def month_spend(session) -> float:
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start = month_start()
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total = (await session.execute(
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select(func.coalesce(func.sum(AICall.cost_usd), 0.0))
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.where(AICall.called_at >= start)
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)).scalar()
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return float(total or 0.0)
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