"""个股分析 API"""

from fastapi import APIRouter

from app.data.tushare_client import tushare_client
from app.data import tencent_client
from app.analysis.technical import add_all_indicators
from app.analysis.signals import generate_signals

router = APIRouter(prefix="/api/stocks", tags=["stocks"])


@router.get("/{ts_code}/quote")
async def get_quote(ts_code: str):
    """获取个股实时行情"""
    quote = await tencent_client.get_realtime_quote(ts_code)
    if not quote:
        return {"error": "获取行情失败"}
    return quote.model_dump()


@router.get("/{ts_code}/kline")
async def get_kline(ts_code: str, days: int = 120):
    """获取个股K线数据（含技术指标）"""
    df = tushare_client.get_stock_daily(ts_code, days=days)
    if df.empty:
        return []
    df = df.sort_values("trade_date").reset_index(drop=True)
    df = add_all_indicators(df)
    # 替换 NaN 为 None（JSON 兼容）
    import math
    records = df.to_dict(orient="records")
    for rec in records:
        for k, v in rec.items():
            if isinstance(v, float) and (math.isnan(v) or math.isinf(v)):
                rec[k] = None
    return records


@router.get("/{ts_code}/signals")
async def get_signals(ts_code: str):
    """获取个股技术面买卖信号"""
    signal = generate_signals(ts_code)
    return signal.model_dump()


@router.get("/{ts_code}/capital_flow")
async def get_capital_flow(ts_code: str, days: int = 10):
    """获取个股资金流向（含大/中/小单分拆）"""
    df = tushare_client.get_stock_moneyflow(ts_code, days=days)
    if df.empty:
        return []
    df = df.sort_values("trade_date")
    records = []
    for _, row in df.iterrows():
        main_net = (
            (row.get("buy_elg_amount", 0) or 0) - (row.get("sell_elg_amount", 0) or 0) +
            (row.get("buy_lg_amount", 0) or 0) - (row.get("sell_lg_amount", 0) or 0)
        )
        records.append({
            "trade_date": row["trade_date"],
            "main_net_inflow": round(main_net, 2),
            "net_mf_amount": round(float(row.get("net_mf_amount", 0) or 0), 2),
            "elg_net": round(
                (row.get("buy_elg_amount", 0) or 0) - (row.get("sell_elg_amount", 0) or 0), 2
            ),
            "lg_net": round(
                (row.get("buy_lg_amount", 0) or 0) - (row.get("sell_lg_amount", 0) or 0), 2
            ),
            "md_net": round(
                (row.get("buy_md_amount", 0) or 0) - (row.get("sell_md_amount", 0) or 0), 2
            ),
            "sm_net": round(
                (row.get("buy_sm_amount", 0) or 0) - (row.get("sell_sm_amount", 0) or 0), 2
            ),
        })
    return records


@router.get("/search")
async def search_stock(keyword: str):
    """搜索股票"""
    basic = tushare_client.get_stock_basic()
    if basic.empty:
        return []
    matches = basic[
        basic["name"].str.contains(keyword, na=False) |
        basic["ts_code"].str.contains(keyword, na=False) |
        basic["symbol"].str.contains(keyword, na=False)
    ].head(20)
    return matches[["ts_code", "name", "industry"]].to_dict(orient="records")


@router.post("/{ts_code}/diagnose")
async def diagnose_stock(ts_code: str):
    """AI 诊断个股"""
    from app.config import settings
    if not settings.deepseek_api_key:
        return {"status": "error", "message": "未配置 LLM API Key"}

    from app.llm.client import get_client

    # 收集数据
    quote = await tencent_client.get_realtime_quote(ts_code)
    signals = generate_signals(ts_code)

    df_daily = tushare_client.get_stock_daily(ts_code, days=30)
    df_flow = tushare_client.get_stock_moneyflow(ts_code, days=10)

    # 构建数据摘要
    quote_str = ""
    if quote:
        quote_str = (
            f"当前价: {quote.price}, 涨跌幅: {quote.pct_chg}%, "
            f"换手率: {quote.turnover_rate}%, 量比: {quote.volume_ratio}, "
            f"PE: {quote.pe}, PB: {quote.pb}, "
            f"总市值: {quote.total_mv}亿, 流通市值: {quote.circ_mv}亿"
        )

    signal_str = (
        f"技术评分: {signals.score}/100（基于7项技术信号触发计分，触发少不代表一定差，可能处于蓄势阶段）, "
        f"信号数: {signals.signal_count}/7, "
        f"均线多头: {signals.ma_bullish}, "
        f"放量突破: {signals.volume_breakout}, "
        f"MACD金叉: {signals.macd_golden}, "
        f"RSI健康: {signals.rsi_healthy}, "
        f"缩量回踩: {signals.pullback_support}, "
        f"放量长阳: {signals.big_yang}, "
        f"布林支撑: {signals.boll_support}, "
        f"支撑位: {signals.support_price}, "
        f"压力位: {signals.resist_price}, "
        f"止损位: {signals.stop_loss_price}"
    )

    position_str = (
        f"位置安全评分: {signals.position_score}/100（越高表示位置越低越安全，96分以上表示处于相对低位）, "
        f"近5日涨幅: {signals.rally_pct_5d}%, "
        f"近10日涨幅: {signals.rally_pct_10d}%, "
        f"距60日高点: {signals.distance_from_high}%"
    )

    trend_str = ""
    ma_info = ""
    if not df_daily.empty:
        df_daily = df_daily.sort_values("trade_date")
        latest = df_daily.iloc[-1]
        if len(df_daily) >= 5:
            pct_5d = (latest["close"] - df_daily.iloc[-5]["close"]) / df_daily.iloc[-5]["close"] * 100
            trend_str += f"5日涨幅: {pct_5d:.2f}%, "
        if len(df_daily) >= 20:
            pct_20d = (latest["close"] - df_daily.iloc[-20]["close"]) / df_daily.iloc[-20]["close"] * 100
            trend_str += f"20日涨幅: {pct_20d:.2f}%, "
        vol_avg_5 = df_daily.tail(5)["vol"].mean()
        vol_latest = latest["vol"]
        trend_str += f"量比(5日均): {vol_latest / vol_avg_5:.2f}" if vol_avg_5 > 0 else ""
        # MA 信息
        if "ma5" in latest and "ma20" in latest:
            ma5 = latest.get("ma5", 0)
            ma10 = latest.get("ma10", 0)
            ma20 = latest.get("ma20", 0)
            ma60 = latest.get("ma60", 0)
            price = latest["close"]
            ma_info = (
                f"价格与均线关系: 现价{price:.2f}, "
                f"MA5={ma5:.2f}, MA10={ma10:.2f}, MA20={ma20:.2f}, MA60={ma60:.2f}, "
                f"{'价格在MA5上方' if price > ma5 else '价格在MA5下方'}, "
                f"{'价格在MA20上方' if price > ma20 else '价格在MA20下方'}, "
                f"{'均线多头排列' if ma5 > ma10 > ma20 else '均线未多头排列'}"
            )

    flow_str = ""
    if not df_flow.empty:
        df_flow = df_flow.sort_values("trade_date")
        recent_3 = df_flow.tail(3)
        total_main = 0
        for _, r in recent_3.iterrows():
            main_net = (
                (r.get("buy_elg_amount", 0) or 0) - (r.get("sell_elg_amount", 0) or 0) +
                (r.get("buy_lg_amount", 0) or 0) - (r.get("sell_lg_amount", 0) or 0)
            )
            total_main += main_net
        flow_str = f"近3日主力净流入: {total_main:.0f}万"

    # 基本信息
    basic_info = ""
    basic_df = tushare_client.get_stock_basic()
    if not basic_df.empty:
        row = basic_df[basic_df["ts_code"] == ts_code]
        if not row.empty:
            r = row.iloc[0]
            basic_info = f"名称: {r['name']}, 行业: {r.get('industry', '未知')}"

    user_msg = f"""请对以下A股进行全面诊断分析：

股票: {ts_code} ({basic_info})
{quote_str}

技术面: {signal_str}

位置安全: {position_str}

趋势: {trend_str}
{ma_info}
资金面: {flow_str}

重要提示：技术评分基于7项信号触发计分，分数低不代表股票差，可能处于蓄势阶段。位置安全评分高(>80)表示股价处于相对低位。请综合技术评分和位置安全评分一起判断。

请从以下维度分析（Markdown格式，简洁专业）：
## 综合评级
（给出1-5星评级和一句话总结，综合技术面和位置安全评分）

## 技术面分析
（趋势方向、均线关系、支撑压力、量价配合，注意区分"技术信号未触发"和"技术面恶化"）

## 资金面分析
（主力资金态度、筹码集中度推测）

## 操作建议
（适合什么类型的投资者、入场时机、风险提示）"""

    try:
        client = get_client()
        response = await client.chat.completions.create(
            model=settings.deepseek_model,
            messages=[
                {"role": "system", "content": "你是一位专业的A股分析师，擅长技术面和资金面分析。回复使用Markdown格式，简洁专业，客观理性。"},
                {"role": "user", "content": user_msg},
            ],
            max_tokens=1500,
            temperature=0.5,
        )
        content = response.choices[0].message.content.strip()
        return {"status": "ok", "ts_code": ts_code, "diagnosis": content}
    except Exception as e:
        return {"status": "error", "message": str(e)}