#!/usr/bin/env python3
"""
股息登记日前一天提醒 — 纯 LongPort 版
用 LongPort 同时查价格和行情，美股走 Nasdaq API。
"""
import os, sys, json, re, time
from datetime import datetime, timedelta
import requests
import pandas as pd

# 关掉长桥走 SOCKS 代理 (历史 bug)
for k in ['http_proxy','https_proxy','HTTP_PROXY','HTTPS_PROXY']:
    os.environ.pop(k, None)
import requests

BJ_TZ = timedelta(hours=8)
HEADERS = {'User-Agent':'Mozilla/5.0'}

def bj_now():
    return datetime.utcnow() + BJ_TZ
def next_trading_day(d):
    while d.weekday() >= 5:
        d += timedelta(days=1)
    return d

# ─── LongPort 初始化 ───
# 2026-07-21 改用 longport_http module 替代 SDK (WSS 不稳定)
# batch_quote 直接调 longport_http.get_quotes, 不再依赖 SDK
_lp = True  # 占位兼容旧代码
def get_lp():
    """始终返回 True (longport_http module 替代 SDK)"""
    return True

# ─── A股 + 港股: AKShare 百度除权数据 ───
def fetch_ah(target_date):
    import akshare as ak
    ds = target_date.strftime('%Y%m%d')
    time.sleep(0.5)
    try:
        df = ak.news_trade_notify_dividend_baidu(date=ds)
        res = []
        for _, row in df.iterrows():
            code = str(row['股票代码']).strip()
            dr = str(row.get('分红','') or '').strip()
            xch = str(row.get('交易所','') or '').strip()
            name = str(row.get('股票简称','') or '').strip()
            val = 0.0
            if dr:
                dr = dr.replace(',','')
                nums = re.findall(r'[\d.]+', dr)
                if nums: val = float(nums[0])
            if val <= 0: continue
            mkt = 'A' if xch != 'HK' else 'HK'
            if xch == 'BJ': mkt = 'BJ'
            res.append({'code':code,'name':name,'market':mkt,'div':val})
        return res
    except Exception as e:
        return [{'error':str(e)}]

# ─── 美股除权: Nasdaq API ───
def fetch_us(target_date):
    ds = target_date.strftime('%Y-%m-%d')
    try:
        r = requests.get(f'https://api.nasdaq.com/api/calendar/dividends?date={ds}',
                         headers=HEADERS, timeout=15)
        rows = r.json().get('data',{}).get('calendar',{}).get('rows',[])
        res = []
        for row in rows:
            sym = row.get('symbol','').strip()
            rate = float(row.get('dividend_Rate',0) or 0)
            ann = float(row.get('indicated_Annual_Dividend',0) or 0)
            rec = row.get('record_Date', row.get('dividend_Ex_Date','')).strip()
            if rate <= 0: continue
            res.append({'code':sym,'market':'US','div':rate,'ann':ann,'rec':rec})
        return res
    except Exception as e:
        print(f"[WARN] fetch_us failed: {e}")
        return []

# ─── 价格查询（全走 LongPort） ───
def map_a(code):
    """603733 → 603733.SH"""
    n = int(code) if code.isdigit() else 0
    if 500000 <= n <= 689999: return f"{code}.SH"
    if 0 <= n <= 399999: return f"{code}.SZ"
    return f"{code}.BJ"

def map_hk(code):
    """01088 → 01088.HK"""
    try: return f"{int(code):05d}.HK"
    except: return f"{code}.HK"

def batch_quote(symbols):
    """Batch quote via longport_http (HTTP, 替代 longport SDK WSS)"""
    if not symbols:
        return {}
    try:
        # 2026-07-21 改用 longport_http module (避免 WSS 不稳定)
        import sys
        sys.path.insert(0, '/home/openclaw/.hermes/scripts')
        from longport_http import get_quotes
        quotes = get_quotes(symbols)
        # 返回 {symbol: price} 格式 (兼容旧代码)
        return {sym: q['price'] for sym, q in quotes.items()}
    except Exception:
        return {}

# ─── 分红稳定性评分 ───
_stability_cache = {}  # symbol -> dict (避免重复 akshare 调用)

def score_dividend_stability(symbol: str, market: str) -> dict:
    """
    评分: 派息年数 + 派息增长 + 波动率 → 0-100 分
    返回: {"score": 75, "level": 4, "years": 10, "cagr": 5.2, "volatility": 0.3, "label": "基本稳定"}
    """
    cache_key = f"{market}:{symbol}"
    if cache_key in _stability_cache:
        return _stability_cache[cache_key]
    try:
        if market != "CN":
            return None  # 暂时只支持 A 股 (akshare)

        import akshare as ak
        # 拿历史派息
        if market == "CN":
            code = symbol.replace(".SH", "").replace(".SZ", "").replace(".BJ", "")
            df = ak.stock_history_dividend_detail(symbol=code, indicator="分红")
        if df is None or len(df) < 3:
            _stability_cache[cache_key] = None
            return None

        # 只取"实施"过(排除预案/未实施)
        df = df[df["进度"] == "实施"].copy()
        df["派息"] = pd.to_numeric(df["派息"], errors="coerce")
        df = df.dropna(subset=["派息"])
        df = df[df["派息"] > 0]  # 排除 0 派息
        if len(df) < 3:
            return None

        # 排序(新→旧)
        df["年份"] = pd.to_datetime(df["公告日期"]).dt.year
        df = df.sort_values("年份", ascending=False).reset_index(drop=True)
        years_count = df["年份"].nunique()
        latest_div = df["派息"].iloc[0]
        oldest_div = df["派息"].iloc[-1]

        # 1. 派息年数 (30分)
        years_score = min(30, years_count * 2)  # 15 年封顶 30 分

        # 2. 派息 CAGR (20分) - 复合年增长
        if years_count >= 2 and oldest_div > 0:
            cagr = (latest_div / oldest_div) ** (1 / (years_count - 1)) - 1
            if cagr >= 0.10:
                cagr_score = 20
            elif cagr >= 0.05:
                cagr_score = 15
            elif cagr >= 0.02:
                cagr_score = 10
            elif cagr >= 0:
                cagr_score = 5
            else:
                cagr_score = 0
        else:
            cagr = 0
            cagr_score = 0

        # 3. 波动率 (25分) - 派息变异系数 (std/mean)
        if len(df) >= 3:
            mean_div = df["派息"].mean()
            std_div = df["派息"].std()
            cv = std_div / mean_div if mean_div > 0 else 1
            if cv <= 0.2:
                vol_score = 25
            elif cv <= 0.4:
                vol_score = 20
            elif cv <= 0.6:
                vol_score = 15
            elif cv <= 0.8:
                vol_score = 10
            else:
                vol_score = 5
        else:
            cv = 1
            vol_score = 5

        # 4. 最近派息正向 (15分) - 最新 ≥ 上一次
        if len(df) >= 2 and df["派息"].iloc[0] >= df["派息"].iloc[1]:
            recent_score = 15
        else:
            recent_score = 5

        # 5. 连续性 (10分) - 最近 3 年都派
        if years_count >= 3 and len(df["年份"].head(3).unique()) >= 3:
            consecutive_score = 10
        else:
            consecutive_score = 0

        total = years_score + cagr_score + vol_score + recent_score + consecutive_score
        # 等级
        if total >= 80:
            level, label = 5, "长期稳定"
        elif total >= 60:
            level, label = 4, "基本稳定"
        elif total >= 40:
            level, label = 3, "不稳定"
        elif total >= 20:
            level, label = 2, "风险大"
        else:
            level, label = 1, "不推荐"

        return {
            "score": total,
            "level": level,
            "label": label,
            "years": years_count,
            "cagr": cagr * 100,
            "cv": cv,
            "latest_div": latest_div,
        }
        _stability_cache[cache_key] = {
            "score": total,
            "level": level,
            "label": label,
            "years": years_count,
            "cagr": cagr * 100,
            "cv": cv,
            "latest_div": latest_div,
        }
        return _stability_cache[cache_key]
    except Exception as e:
        print(f"  [WARN] score_dividend_stability {symbol} failed: {e}", file=sys.stderr)
        _stability_cache[cache_key] = None
        return None


# ─── 格式化 ───
def fmt(a_h, hk_h, us_h, a_p, hk_p, us_p):
    now = bj_now()
    tomorrow = next_trading_day(now + timedelta(days=1))
    wd = ['周一','周二','周三','周四','周五','周六','周日'][tomorrow.weekday()]
    L = ['', '📢 明日除权·红利提醒', '━'*24,
         f'📅 今日 {now.strftime("%Y-%m-%d")} 推送',
         f'⏰ 明天 {tomorrow.strftime("%Y-%m-%d")} ({wd}) 除权除息',
         '💡 明天是登记日，今天买入仍享分红', '']
    has = False

    if a_h:
        has = True; L += ['─'*24, '🇨🇳 A股 明日除权 TOP', '']
        for r in a_h[:12]:
            d,c,n = r['div'],r['code'],r['name']
            star = '⭐' if d>=10 else '💎' if d>=5 else ''
            p = a_p.get(f"{c}.SH") or a_p.get(f"{c}.SZ") or a_p.get(f"{c}.BJ")
            y = f' | 股息率 {d/10/p*100:.2f}%' if p and p>0 else ''
            # 2026-07-22 加分红稳定性评分
            stab = score_dividend_stability(c, "CN")
            stab_tag = f' [{stab["level"]}★{stab["label"]} {stab["years"]}年CAGR{stab["cagr"]:+.0f}%]' if stab else ''
            L += [f'  {star}{c} {n}{stab_tag}', f'    💰每10股派{d:.2f}元 | 📊{p if p else "N/A"}{y}', '']

    if hk_h:
        has = True; L += ['─'*24, '🇭🇰 港股 明日除权 TOP', '']
        for r in hk_h[:8]:
            d,c,n = r['div'],r['code'],r['name']
            lp = f"{int(c):05d}.HK"
            p = hk_p.get(lp)
            y = f' | 股息率 {d/10/p*100:.2f}%' if p and p>0 else ''
            L += [f'  {c} {n}', f'    💰每10股派{d:.2f}港元 | 📊HKD {p if p else "N/A"}{y}', '']

    if us_h:
        has = True; L += ['─'*24, '🇺🇸 美股 明日除权 TOP', '']
        for r in us_h[:8]:
            d,c = r['div'],r['code']
            ann = r.get('ann',0)
            rec = r.get('rec','')
            p = us_p.get(f"{c}.US")
            y = f' | 股息率 {ann/p*100:.2f}%' if p and p>0 and ann>0 else ''
            L += [f'  {c}', f'    💰${d:.2f}/股 | 年化${ann:.2f} | {ann/d:.0f}次/年']
            if p: L += [f'    📊${p:.2f}{y}']
            L += [f'    📅登记日{rec}', '']

    if not has: L += ['✅ 明天没有高息股票除权，休息一天~', '']
    L += ['━'*24, '📌 操作提示',
          '• 今天买入 → 明天登记 → 拿分红',
          '• A股持股>1年免税，<1月20%税',
          '━'*24, '🤖 Hermes 每日红利雷达']
    return '\n'.join(L)

def main():
    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument('--market', default='all',
                        choices=['all', 'cn_hk', 'us'],
                        help='all = A+HK+US, cn_hk = A股+港股, us = 美股')
    args = parser.parse_args()

    now = bj_now()
    tomorrow = next_trading_day(now + timedelta(days=1))
    print(f'[Div] {now.strftime("%Y-%m-%d")} - ex-div {tomorrow.strftime("%Y-%m-%d")} market={args.market}')

    # 1. A+H除权数据
    a_r, hk_r = [], []
    if args.market in ('all', 'cn_hk'):
        ah = fetch_ah(tomorrow)
        a_r = [r for r in ah if r.get('market') in ('A','BJ')]
        hk_r = [r for r in ah if r.get('market')=='HK']
        print(f'  A股: {len(a_r)} 港股: {len(hk_r)}')

    # 2. 美股除权
    us_r = []
    if args.market in ('all', 'us'):
        us_r = fetch_us(tomorrow)
        print(f'  美股: {len(us_r)}')

    # 3. 临时按 div 排序 (占位), 后面 batch_quote 拿到价后会重新按股息率排序
    a_h = sorted(a_r, key=lambda x: -x['div'])
    hk_h = sorted(hk_r, key=lambda x: -x['div'])
    us_h = sorted(us_r, key=lambda x: -x['div'])

    # 4. 批量查价
    a_syms = [map_a(r['code']) for r in a_h[:15]] if a_h else []
    hk_syms = [map_hk(r['code']) for r in hk_h[:10]] if hk_h else []
    us_syms = [f"{r['code']}.US" for r in us_h[:10]] if us_h else []

    all_syms = a_syms + hk_syms + us_syms
    if not get_lp():
        print("[WARN] LongPort not available")
    
    p_all = batch_quote(all_syms) if get_lp() else {}
    # Split prices by market
    a_p = {k:v for k,v in p_all.items() if k.endswith(('.SH','.SZ','.BJ'))}
    hk_p = {k:v for k,v in p_all.items() if k.endswith('.HK')}
    us_p = {k:v for k,v in p_all.items() if k.endswith('.US')}

    # 4.5. 重排序 - 按股息率% 倒序 (派息 / 当前价 × 100), 越高越排前
    def _yr_a(r):
        price = (a_p.get(r['code'] + '.SH') or a_p.get(r['code'] + '.SZ') or a_p.get(r['code'] + '.BJ') or 0)
        if price <= 0: return 0
        return (r.get('div', 0) / 10) / price * 100
    def _yr_hk(r):
        price = (hk_p.get(r['code'] + '.HK') or 0)
        if price <= 0: return 0
        return (r.get('div', 0) / 10) / price * 100
    def _yr_us(r):
        price = (us_p.get(r['code'] + '.US') or 0)
        if price <= 0: return 0
        # fetch_us 字段名是 'ann' (不是 'ann_div'), 单次派息是 'div'
        ann = r.get('ann', 0) or r.get('div', 0)
        return ann / price * 100

    if a_p: a_h = sorted(a_h, key=lambda x: -_yr_a(x))
    if hk_p: hk_h = sorted(hk_h, key=lambda x: -_yr_hk(x))
    if us_p: us_h = sorted(us_h, key=lambda x: -_yr_us(x))

    # 5. 输出
    print('\n' + fmt(a_h, hk_h, us_h, a_p, hk_p, us_p))

if __name__ == '__main__':
    main()