# -*- coding: utf-8 -*-
"""Local range explanation built from price and persisted news."""

from __future__ import annotations

from typing import Any, Dict

from backend.enrich.llm_enricher import analyze_range_with_llm
from backend.explain.category_engine import categorize_news_rows
from backend.tools.data_tools import get_prices


def _rank_event_score(row: Dict[str, Any]) -> float:
    relevance = str(row.get("relevance") or "").strip().lower()
    relevance_score = {"high": 3.0, "relevant": 3.0, "medium": 2.0, "low": 1.0}.get(
        relevance,
        0.5,
    )
    impact_score = abs(float(row.get("ret_t0") or 0.0)) * 100
    return relevance_score + impact_score


def summarize_bullish_factors(
    news_rows: list[Dict[str, Any]],
    *,
    limit: int = 5,
) -> list[str]:
    factors = []
    for row in news_rows:
        if str(row.get("sentiment") or "").strip().lower() != "positive":
            continue
        candidate = row.get("reason_growth") or row.get("key_discussion") or row.get("summary") or row.get("title")
        if candidate:
            factors.append(str(candidate).strip())
    seen = set()
    output = []
    for factor in factors:
        if factor in seen:
            continue
        seen.add(factor)
        output.append(factor[:200])
        if len(output) >= limit:
            break
    return output


def summarize_bearish_factors(
    news_rows: list[Dict[str, Any]],
    *,
    limit: int = 5,
) -> list[str]:
    factors = []
    for row in news_rows:
        if str(row.get("sentiment") or "").strip().lower() != "negative":
            continue
        candidate = row.get("reason_decrease") or row.get("key_discussion") or row.get("summary") or row.get("title")
        if candidate:
            factors.append(str(candidate).strip())
    seen = set()
    output = []
    for factor in factors:
        if factor in seen:
            continue
        seen.add(factor)
        output.append(factor[:200])
        if len(output) >= limit:
            break
    return output


def build_trend_analysis(prices: list[Any]) -> str:
    if len(prices) < 2:
        return "区间样本较短，暂不具备足够趋势信息。"
    if len(prices) < 3:
        open_price = float(prices[0].open)
        close_price = float(prices[-1].close)
        change = ((close_price - open_price) / open_price) * 100 if open_price else 0.0
        return f"短区间内价格变动 {change:+.2f}%，趋势信息有限。"

    mid = len(prices) // 2
    first_open = float(prices[0].open)
    first_close = float(prices[mid].close)
    second_open = float(prices[mid].open)
    second_close = float(prices[-1].close)
    first_half = ((first_close - first_open) / first_open) * 100 if first_open else 0.0
    second_half = ((second_close - second_open) / second_open) * 100 if second_open else 0.0
    return (
        f"前半段{'上涨' if first_half >= 0 else '下跌'} {abs(first_half):.2f}%，"
        f"后半段{'上涨' if second_half >= 0 else '下跌'} {abs(second_half):.2f}%，"
        "说明价格驱动在区间内部出现了阶段性切换。"
    )


def build_range_explanation(
    *,
    ticker: str,
    start_date: str,
    end_date: str,
    news_rows: list[Dict[str, Any]],
) -> Dict[str, Any]:
    """Explain a price range with local price and news heuristics."""
    prices = get_prices(ticker, start_date, end_date)
    if not prices:
        return {
            "symbol": ticker,
            "start_date": start_date,
            "end_date": end_date,
            "error": "No OHLC data for this range",
        }

    open_price = float(prices[0].open)
    close_price = float(prices[-1].close)
    high_price = max(float(price.high) for price in prices)
    low_price = min(float(price.low) for price in prices)
    total_volume = sum(int(price.volume) for price in prices)
    price_change_pct = (
        ((close_price - open_price) / open_price) * 100 if open_price else 0.0
    )

    categories = categorize_news_rows(news_rows)
    news_count = len(news_rows)
    dominant_categories = sorted(
        (
            {"category": key, "count": value["count"]}
            for key, value in categories.items()
            if value["count"] > 0
        ),
        key=lambda item: item["count"],
        reverse=True,
    )

    direction = "上涨" if price_change_pct > 0 else "下跌" if price_change_pct < 0 else "横盘"
    category_text = (
        f"主要主题集中在 {', '.join(item['category'] for item in dominant_categories[:3])}。"
        if dominant_categories
        else "区间内未识别出明显的主题聚类。"
    )
    summary = (
        f"{ticker} 在 {start_date} 至 {end_date} 区间内{direction} {abs(price_change_pct):.2f}%，"
        f"区间覆盖 {len(prices)} 个交易日，关联新闻 {news_count} 条。{category_text}"
    )

    bullish_factors = summarize_bullish_factors(news_rows)
    bearish_factors = summarize_bearish_factors(news_rows)
    trend_analysis = build_trend_analysis(prices)
    llm_source = "local"

    range_payload = {
        "ticker": ticker,
        "start_date": start_date,
        "end_date": end_date,
        "price_change_pct": round(price_change_pct, 2),
        "trading_days": len(prices),
        "news_count": news_count,
        "dominant_categories": dominant_categories[:5],
        "bullish_factors": bullish_factors[:3],
        "bearish_factors": bearish_factors[:3],
        "trend_analysis": trend_analysis,
        "top_news": [
            {
                "date": row.get("trade_date") or str(row.get("date") or "")[:10],
                "title": row.get("title") or "",
                "summary": row.get("summary") or "",
                "sentiment": row.get("sentiment") or "",
                "relevance": row.get("relevance") or "",
                "ret_t0": row.get("ret_t0"),
            }
            for row in sorted(news_rows, key=_rank_event_score, reverse=True)[:5]
        ],
    }
    llm_analysis = analyze_range_with_llm(range_payload)
    if isinstance(llm_analysis, dict):
        summary = llm_analysis.get("summary") or summary
        trend_analysis = llm_analysis.get("trend_analysis") or trend_analysis
        bullish_factors = llm_analysis.get("bullish_factors") or bullish_factors
        bearish_factors = llm_analysis.get("bearish_factors") or bearish_factors
        llm_source = "llm"

    key_events = [
        {
            "date": row.get("trade_date") or str(row.get("date") or "")[:10],
            "title": row.get("title") or "Untitled news",
            "summary": row.get("summary") or "",
            "category": row.get("category") or "",
            "id": row.get("id"),
            "sentiment": row.get("sentiment"),
            "ret_t0": row.get("ret_t0"),
        }
        for row in sorted(news_rows, key=_rank_event_score, reverse=True)[:8]
    ]

    return {
        "symbol": ticker,
        "start_date": start_date,
        "end_date": end_date,
        "price_change_pct": round(price_change_pct, 2),
        "open_price": open_price,
        "close_price": close_price,
        "high_price": high_price,
        "low_price": low_price,
        "total_volume": total_volume,
        "trading_days": len(prices),
        "news_count": news_count,
        "dominant_categories": dominant_categories[:5],
        "analysis": {
            "summary": summary,
            "key_events": key_events,
            "bullish_factors": bullish_factors,
            "bearish_factors": bearish_factors,
            "trend_analysis": trend_analysis,
            "analysis_source": llm_source,
            "analysis_model_label": llm_analysis.get("model_label") if isinstance(llm_analysis, dict) else None,
        },
    }