jobs: extract shared market-context helpers from ai_log_job
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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3 changed files with 104 additions and 84 deletions
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@ -4,8 +4,6 @@ and a row in the cost ledger."""
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from __future__ import annotations
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import asyncio
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from collections import defaultdict
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from datetime import timedelta
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import httpx
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from sqlalchemy import desc, func, select
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@ -13,7 +11,13 @@ from sqlalchemy import desc, func, select
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from app.config import get_settings
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from app.db import utcnow
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from app.jobs._helpers import job_lifecycle, log
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from app.models import AICall, Headline, JobRun, Quote, StrategicLog
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from app.jobs._market_context import (
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REFERENCE_LINE,
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latest_quotes_by_group,
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month_spend,
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recent_headlines_by_bucket,
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)
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from app.models import AICall, JobRun, StrategicLog
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from app.services.cadence import DEFAULT_POLICY
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from app.services.openrouter import (
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PROMPT_VERSION,
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@ -22,79 +26,9 @@ from app.services.openrouter import (
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build_user_prompt,
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call_llm,
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llm_configured,
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month_start,
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)
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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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.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 bucket
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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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async def run() -> None:
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async with job_lifecycle("ai_log_job") as (session, jr):
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if jr.status == "skipped":
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@ -119,7 +53,7 @@ async def run() -> None:
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jr.error = reason
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return
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spent = await _month_spend(session)
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spent = await month_spend(session)
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if spent >= s.OPENROUTER_MONTHLY_CAP_USD:
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log.warning("ai_log.cap_reached", spent=spent,
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cap=s.OPENROUTER_MONTHLY_CAP_USD)
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@ -127,8 +61,8 @@ async def run() -> None:
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jr.error = f"monthly cost cap reached (${spent:.2f})"
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return
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quotes = await _latest_quotes_by_group(session)
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news = await _recent_headlines_by_bucket(session)
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quotes = await latest_quotes_by_group(session)
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news = await recent_headlines_by_bucket(session)
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if not quotes and not news:
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log.warning("ai_log.no_data_yet")
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jr.status = "skipped"
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@ -169,7 +103,7 @@ async def run() -> None:
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for tone, analysis in variants:
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# Re-check cost cap between variants so a runaway run is
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# bounded.
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spent = await _month_spend(session)
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spent = await month_spend(session)
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if spent >= s.OPENROUTER_MONTHLY_CAP_USD:
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log.warning("ai_log.cap_reached_midrun",
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spent=spent, completed=written)
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