read.markets/app/jobs/ai_log_job.py

"""Hourly AI strategic-log generator. Pulls already-persisted market data and
headlines from the DB (no live fetches), calls OpenRouter, persists the log
and a row in the cost ledger."""
from __future__ import annotations

import asyncio
from collections import defaultdict
from datetime import timedelta

import httpx
from sqlalchemy import desc, func, select

from app.config import get_settings
from app.db import utcnow
from app.jobs._helpers import job_lifecycle, log
from app.models import AICall, Headline, JobRun, Quote, StrategicLog
from app.services.cadence import DEFAULT_POLICY
from app.services.openrouter import (
    PROMPT_VERSION,
    active_model,
    build_system_prompt,
    build_user_prompt,
    call_llm,
    llm_configured,
    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)


async def run() -> None:
    async with job_lifecycle("ai_log_job") as (session, jr):
        if jr.status == "skipped":
            return
        s = get_settings()
        if not llm_configured():
            log.warning("ai_log.skipped_no_key", provider=s.LLM_PROVIDER)
            jr.status = "skipped"
            return

        # Cadence: hourly during EU/US active hours; throttled off-hours.
        last_success = (await session.execute(
            select(func.max(JobRun.finished_at)).where(
                JobRun.name == "ai_log_job",
                JobRun.status == "success",
            )
        )).scalar()
        should_run, reason = DEFAULT_POLICY.should_run(last_success)
        if not should_run:
            log.info("ai_log.cadence_skip", reason=reason)
            jr.status = "skipped"
            jr.error = reason
            return

        spent = await _month_spend(session)
        if spent >= s.OPENROUTER_MONTHLY_CAP_USD:
            log.warning("ai_log.cap_reached", spent=spent,
                        cap=s.OPENROUTER_MONTHLY_CAP_USD)
            jr.status = "skipped"
            jr.error = f"monthly cost cap reached (${spent:.2f})"
            return

        quotes = await _latest_quotes_by_group(session)
        news = await _recent_headlines_by_bucket(session)
        if not quotes and not news:
            log.warning("ai_log.no_data_yet")
            jr.status = "skipped"
            return

        # Look up the most recent log generated today (UTC) so the model can
        # update it rather than start from scratch. This gives the model
        # temporal awareness — "since this morning's read, X has changed".
        today_start = utcnow().replace(hour=0, minute=0, second=0, microsecond=0)
        previous_log = (await session.execute(
            select(StrategicLog)
            .where(StrategicLog.generated_at >= today_start)
            .order_by(desc(StrategicLog.generated_at))
            .limit(1)
        )).scalar_one_or_none()

        anchor = s.CASSANDRA_ANCHOR_DATE or None
        user_prompt = build_user_prompt(
            today=utcnow(),
            anchor=anchor,
            quotes_by_group=quotes,
            headlines_by_bucket=news,
            reference_line=REFERENCE_LINE,
            previous_log=previous_log,
        )

        # Phase 2 voice pivot (PROMPT_VERSION 6): generate both tones per
        # run so the dashboard toggle is instant. Analysis stays on the
        # operator-configured default (DRY|SPECULATIVE is a system-wide
        # preference, not a per-user toggle). PRO was dropped.
        analysis = (s.CASSANDRA_ANALYSIS or "SPECULATIVE").upper()
        variants = [
            ("NOVICE", analysis),
            ("INTERMEDIATE", analysis),
        ]
        written = 0
        async with httpx.AsyncClient(follow_redirects=True) as client:
            for tone, analysis in variants:
                # Re-check cost cap between variants so a runaway run is
                # bounded.
                spent = await _month_spend(session)
                if spent >= s.OPENROUTER_MONTHLY_CAP_USD:
                    log.warning("ai_log.cap_reached_midrun",
                                spent=spent, completed=written)
                    break

                system_prompt = build_system_prompt(tone, analysis)
                try:
                    result = await call_llm(
                        client,
                        [{"role": "system", "content": system_prompt},
                         {"role": "user", "content": user_prompt}],
                    )
                except Exception as e:
                    session.add(AICall(
                        model=active_model(), status="error",
                        error=f"{tone}/{analysis}: {str(e)[:480]}",
                    ))
                    await session.commit()
                    log.error("ai_log.variant_failed",
                              tone=tone, analysis=analysis, error=str(e)[:200])
                    continue

                session.add(StrategicLog(
                    generated_at=utcnow(),
                    model=result.model,
                    anchor_date=anchor,
                    prompt_version=PROMPT_VERSION,
                    tone=tone,
                    analysis=analysis,
                    content=result.content,
                    prompt_tokens=result.prompt_tokens,
                    completion_tokens=result.completion_tokens,
                    cost_usd=result.cost_usd,
                ))
                session.add(AICall(
                    model=result.model,
                    prompt_tokens=result.prompt_tokens,
                    completion_tokens=result.completion_tokens,
                    cost_usd=result.cost_usd,
                    status="ok",
                ))
                await session.commit()
                written += 1
                log.info("ai_log.variant_done",
                         tone=tone, analysis=analysis,
                         prompt_tokens=result.prompt_tokens,
                         completion_tokens=result.completion_tokens)

        jr.items_written = written
        log.info("ai_log.done", variants=written, total=len(variants))


if __name__ == "__main__":
    asyncio.run(run())