# -*- coding: utf-8 -*-
"""Stock story generation for explain view."""

from __future__ import annotations

from datetime import datetime, timedelta
from typing import Any

from backend.data.market_store import MarketStore


def build_stock_story(
    *,
    symbol: str,
    as_of_date: str,
    price_rows: list[dict[str, Any]],
    news_rows: list[dict[str, Any]],
) -> str:
    """Build a compact markdown story from enriched news and recent price action."""
    lines = [f"## {symbol} Story", f"As of `{as_of_date}`"]
    if not price_rows:
        lines.append("")
        lines.append("No OHLC data available for story generation.")
        return "\n".join(lines)

    open_price = float(price_rows[0].get("open") or price_rows[0].get("close") or 0.0)
    close_price = float(price_rows[-1].get("close") or 0.0)
    price_change = ((close_price - open_price) / open_price) * 100 if open_price else 0.0
    high_price = max(float(row.get("high") or row.get("close") or 0.0) for row in price_rows)
    low_price = min(float(row.get("low") or row.get("close") or 0.0) for row in price_rows)

    lines.append("")
    lines.append(
        f"The stock moved {'up' if price_change >= 0 else 'down'} "
        f"{abs(price_change):.2f}% over the recent window, trading between "
        f"${low_price:.2f} and ${high_price:.2f}."
    )

    positive = [row for row in news_rows if str(row.get("sentiment") or "").lower() == "positive"]
    negative = [row for row in news_rows if str(row.get("sentiment") or "").lower() == "negative"]
    lines.append("")
    lines.append(
        f"Recent coverage included {len(news_rows)} relevant articles "
        f"({len(positive)} positive / {len(negative)} negative)."
    )

    if news_rows:
        lines.append("")
        lines.append("### Key Moments")
        ranked_rows = sorted(
            news_rows,
            key=lambda row: (
                0 if str(row.get("relevance") or "").lower() in {"high", "relevant"} else 1,
                -abs(float(row.get("ret_t0") or 0.0)),
            ),
        )
        for row in ranked_rows[:5]:
            trade_date = row.get("trade_date") or str(row.get("date") or "")[:10]
            title = row.get("title") or "Untitled"
            key_discussion = row.get("key_discussion") or row.get("summary") or ""
            sentiment = str(row.get("sentiment") or "neutral").lower()
            lines.append(
                f"- `{trade_date}` [{sentiment}] {title}: {str(key_discussion).strip()[:220]}"
            )

    if positive:
        lines.append("")
        lines.append("### Bullish Threads")
        for row in positive[:3]:
            reason = row.get("reason_growth") or row.get("key_discussion") or row.get("summary") or row.get("title")
            lines.append(f"- {str(reason).strip()[:220]}")

    if negative:
        lines.append("")
        lines.append("### Bearish Threads")
        for row in negative[:3]:
            reason = row.get("reason_decrease") or row.get("key_discussion") or row.get("summary") or row.get("title")
            lines.append(f"- {str(reason).strip()[:220]}")

    return "\n".join(lines)


def get_or_create_stock_story(
    store: MarketStore,
    *,
    symbol: str,
    as_of_date: str,
) -> dict[str, Any]:
    """Return cached story or build a new one from recent market context."""
    cached = store.get_story_cache(symbol, as_of_date=as_of_date)
    if cached:
        return {
            "symbol": symbol,
            "as_of_date": as_of_date,
            "story": cached.get("content") or "",
            "source": cached.get("source") or "cache",
        }

    start_date = None
    if len(as_of_date) >= 10:
        target_date = datetime.strptime(as_of_date[:10], "%Y-%m-%d").date()
        start_date = (target_date - timedelta(days=29)).isoformat()

    price_rows = (
        store.get_ohlc(symbol, start_date, as_of_date)
        if start_date
        else []
    )
    news_rows = store.get_news_items_enriched(
        symbol,
        start_date=start_date,
        end_date=as_of_date,
        limit=40,
    )
    story = build_stock_story(
        symbol=symbol,
        as_of_date=as_of_date,
        price_rows=price_rows,
        news_rows=news_rows,
    )
    store.upsert_story_cache(symbol, as_of_date=as_of_date, content=story, source="local")
    return {
        "symbol": symbol,
        "as_of_date": as_of_date,
        "story": story,
        "source": "local",
    }