trumpsignal-backend/app/api/posts.py

import logging
from typing import List, Optional

from fastapi import APIRouter, Depends, HTTPException, Query, Request
from fastapi.responses import Response
from slowapi import Limiter
from slowapi.util import get_remote_address

limiter = Limiter(key_func=get_remote_address)
from sqlalchemy import select
from sqlalchemy.ext.asyncio import AsyncSession

from app.database import get_db
from app.models import Post, iso_utc
from app.schemas import PriceImpact, TrumpPost

router = APIRouter()
logger = logging.getLogger(__name__)


def _direction_correct(signal: Optional[str], pct: Optional[float]) -> Optional[bool]:
    if pct is None or signal is None:
        return None
    if signal == "buy":
        return pct > 0
    if signal in ("short", "sell"):
        return pct < 0
    return None  # hold has no direction


def _post_to_schema(post: Post) -> TrumpPost:
    price_impact: Optional[PriceImpact] = None
    if post.price_impact_asset and post.price_at_post is not None:
        # Overlay live rolling peaks for windows that haven't closed yet
        from app.services.price_impact_monitor import get_live_impact
        live = get_live_impact(post.id) or {}

        m5  = live.get("price_impact_m5",  post.price_impact_m5)
        m15 = live.get("price_impact_m15", post.price_impact_m15)
        m1h = live.get("price_impact_m1h", post.price_impact_m1h)

        price_impact = PriceImpact(
            asset=post.price_impact_asset,
            m5=m5,
            m15=m15,
            m1h=m1h,
            price_at_post=post.price_at_post,
            correct_m5=_direction_correct(post.signal, m5),
            correct_m15=_direction_correct(post.signal, m15),
            correct_m1h=_direction_correct(post.signal, m1h),
        )
    return TrumpPost(
        id=post.id,
        text=post.text,
        source=post.source,
        published_at=iso_utc(post.published_at),
        sentiment=post.sentiment,
        signal=post.signal,
        ai_confidence=post.ai_confidence,
        ai_reasoning=post.ai_reasoning,
        prefilter_reason=post.prefilter_reason,
        analysis_version=post.analysis_version,
        relevant=post.relevant,
        price_impact=price_impact,
        # v5 routing fields — null for pre-v5 posts
        target_asset=post.target_asset,
        category=post.category,
        expected_move_pct=post.expected_move_pct,
        invalidation_price=post.invalidation_price,
    )


@router.get("/posts", response_model=List[TrumpPost])
@limiter.limit("60/minute")
async def get_posts(
    request: Request,
    limit: int = Query(default=20, ge=1, le=500),
    page: int = Query(default=1, ge=1),
    db: AsyncSession = Depends(get_db),
    response: Response = None,
):
    offset = (page - 1) * limit
    result = await db.execute(
        select(Post).order_by(Post.published_at.desc()).offset(offset).limit(limit)
    )
    posts = result.scalars().all()
    # Posts are scraped every 5s but rarely change once written — allow CDN/browser
    # to cache for 30s. stale-while-revalidate=60 means stale content is served
    # while a fresh fetch happens in the background (no loading flash).
    if response is not None:
        response.headers["Cache-Control"] = "public, max-age=30, stale-while-revalidate=60"
    return [_post_to_schema(p) for p in posts]


@router.get("/signals/accuracy")
async def signal_accuracy(db: AsyncSession = Depends(get_db)):
    """Aggregate accuracy of directional signals (buy/sell/short) against realised price moves."""
    result = await db.execute(
        select(Post).where(Post.signal.in_(["buy", "sell", "short"]))
    )
    posts = result.scalars().all()

    def bucket():
        return {"checked": 0, "correct": 0}

    stats = {"m5": bucket(), "m15": bucket(), "m1h": bucket()}
    by_signal: dict[str, dict] = {}

    for p in posts:
        sig = p.signal
        if sig not in by_signal:
            by_signal[sig] = {"m5": bucket(), "m15": bucket(), "m1h": bucket(), "count": 0}
        by_signal[sig]["count"] += 1
        for win, val in (("m5", p.price_impact_m5), ("m15", p.price_impact_m15), ("m1h", p.price_impact_m1h)):
            ok = _direction_correct(sig, val)
            if ok is None:
                continue
            stats[win]["checked"] += 1
            stats[win]["correct"] += int(ok)
            by_signal[sig][win]["checked"] += 1
            by_signal[sig][win]["correct"] += int(ok)

    def pct(b): return round(b["correct"] / b["checked"] * 100, 1) if b["checked"] else None

    return {
        "overall": {k: {**v, "accuracy_pct": pct(v)} for k, v in stats.items()},
        "by_signal": {
            s: {
                "count": d["count"],
                "m5": {**d["m5"], "accuracy_pct": pct(d["m5"])},
                "m15": {**d["m15"], "accuracy_pct": pct(d["m15"])},
                "m1h": {**d["m1h"], "accuracy_pct": pct(d["m1h"])},
            }
            for s, d in by_signal.items()
        },
        "total_directional_signals": len(posts),
    }


@router.get("/posts/{post_id}", response_model=TrumpPost)
async def get_post(post_id: int, db: AsyncSession = Depends(get_db)):
    result = await db.execute(select(Post).where(Post.id == post_id))
    post = result.scalar_one_or_none()
    if post is None:
        raise HTTPException(status_code=404, detail="Post not found")
    return _post_to_schema(post)