#!/usr/bin/env python3
"""
divergence_detector.py — 跨市场背离监测器（no_agent）

每30分钟检测：
  1. 科创50 vs 恒指 → 科技股超买/超卖信号
  2. A/H 隐含溢价 → 内外资分歧度
  3. 上证50 vs 创业板 → 风格轮动信号
  4. 恒指 vs 国企指数 → 离岸市场情绪
  5. 指数连涨/连跌天数 → 趋势延续/衰竭

输出：
  - HIGH/medium divergence → 写入 signal_news (source=divergence_watch)
  - 状态文件 macro_divergence_state.json
  - no_agent: 有信号才出声
"""
import sys, json, re, datetime, os, urllib.request

from pathlib import Path

BASE = Path("/home/hmo/MoFin")
DATA = BASE / "data"
DB_PATH = DATA / "mofin.db"
STATE_PATH = DATA / "macro_divergence_state.json"

# ── 监测的指数 ──
INDEX_CODES = {
    "上证指数": "sh000001",
    "深证成指": "sz399001",
    "创业板指": "sz399006",
    "科创50":   "sh000688",
    "上证50":   "sh000016",
    "沪深300":  "sh000300",
    "恒生指数": "hkHSI",
    "国企指数": "hkHSCEI",
}

# ── 背离阈值 ──
DIVERGENCE_STRONG = 5.0   # >5% → strong信号
DIVERGENCE_MODERATE = 3.0 # >3% → moderate信号
STREAK_DAYS = 3           # 连涨/连跌3天 → 信号

def fetch_indices():
    """获取所有指数实时数据（指数无 DB 缓存，腾讯 API 是唯一源）"""
    symbols = list(INDEX_CODES.values())
    url = f"http://qt.gtimg.cn/q={','.join(symbols)}"
    try:
        r = urllib.request.urlopen(url, timeout=10)
        text = r.read().decode("gbk")
    except Exception as e:
        print(f"[DIVERGE] 采集失败: {e}", file=sys.stderr)
        return {}

    indices = {}
    for line in text.strip().split("\n"):
        line = line.strip()
        if not line or "=" not in line:
            continue
        try:
            sym = line.split("=", 1)[0].strip().lstrip("v_")
            raw = line.split("=", 1)[1].strip().strip('"').strip(";")
            fields = raw.split("~")
            if len(fields) < 35:
                continue
            name = fields[1]
            price = float(fields[3]) if fields[3].strip() else 0
            close = float(fields[4]) if fields[4].strip() else 0
            change_pct = ((price - close) / close * 100) if close else 0
            high = float(fields[33]) if fields[33].strip() else 0
            low = float(fields[34]) if fields[34].strip() else 0
            timestamp = fields[30] if len(fields) > 30 else ""
            indices[sym] = {
                "name": name,
                "price": price,
                "close": close,
                "change_pct": round(change_pct, 2),
                "high": high,
                "low": low,
                "timestamp": timestamp,
            }
        except Exception:
            continue
    return indices

def load_history():
    """从MACRO_CONTEXT_LOG加载前几天的指数数据"""
    try:
        import sqlite3
        conn = sqlite3.connect(str(DB_PATH))
        rows = conn.execute(
            "SELECT indices, created_at FROM macro_context_log "
            "WHERE has_valid_data=1 ORDER BY created_at DESC LIMIT 5"
        ).fetchall()
        conn.close()
        history = []
        for row in rows:
            idx_data = json.loads(row[0]) if row[0] else {}
            history.append({
                "indices": idx_data,
                "timestamp": row[1],
            })
        return history
    except Exception:
        return []

def detect_divergences(indices, history):
    """
    检测跨市场背离信号
    返回: list of signal dicts {type, level, desc, pairs}
    """
    signals = []
    
    def get(name):
        """按中文名找指数"""
        sym = INDEX_CODES.get(name)
        if sym and sym in indices:
            return indices[sym]
        # 模糊匹配
        for s, idx in indices.items():
            if name in idx.get("name", ""):
                return idx
        return None

    sh_comp = get("上证指数")
    sz_comp = get("深证成指")
    cyb = get("创业板指")
    kc = get("科创50")
    sz50 = get("上证50")
    hs300 = get("沪深300")
    hsi = get("恒生指数")
    hscei = get("国企指数")

    # ── 信号1: 科创50 vs 恒指（科技股vs国际资本）──
    if kc and hsi:
        kc_chg = kc["change_pct"]
        hsi_chg = hsi["change_pct"]
        divergence = abs(kc_chg - hsi_chg)
        if divergence > DIVERGENCE_STRONG:
            signals.append({
                "type": "a_h_tech_divergence",
                "level": "high",
                "desc": f"科创50({kc_chg:+.1f}%) vs 恒指({hsi_chg:+.1f}%) 背离{divergence:.1f}个百分点",
                "pairs": [kc, hsi],
                "direction": "risk" if kc_chg > hsi_chg else "opportunity",
            })
        elif divergence > DIVERGENCE_MODERATE:
            signals.append({
                "type": "a_h_tech_divergence",
                "level": "medium",
                "desc": f"科创50({kc_chg:+.1f}%) vs 恒指({hsi_chg:+.1f}%) 背离{divergence:.1f}个百分点",
                "pairs": [kc, hsi],
                "direction": "risk" if kc_chg > hsi_chg else "opportunity",
            })

    # ── 信号2: 上证50 vs 创业板（价值vs成长）──
    if sz50 and cyb:
        sz50_chg = sz50["change_pct"]
        cyb_chg = cyb["change_pct"]
        divergence = abs(sz50_chg - cyb_chg)
        if divergence > DIVERGENCE_STRONG:
            direction = "opportunity" if sz50_chg > cyb_chg else "risk"
            signals.append({
                "type": "value_growth_divergence",
                "level": "high",
                "desc": f"上证50({sz50_chg:+.1f}%) vs 创业板({cyb_chg:+.1f}%) 背离{divergence:.1f}个百分点",
                "pairs": [sz50, cyb],
                "direction": direction,
            })
        elif divergence > DIVERGENCE_MODERATE:
            direction = "opportunity" if sz50_chg > cyb_chg else "risk"
            signals.append({
                "type": "value_growth_divergence",
                "level": "medium",
                "desc": f"上证50({sz50_chg:+.1f}%) vs 创业板({cyb_chg:+.1f}%) 背离{divergence:.1f}个百分点",
                "pairs": [sz50, cyb],
                "direction": direction,
            })

    # ── 信号3: 恒指 vs 国企指数（国际资本流向）──
    if hsi and hscei:
        hsi_chg = hsi["change_pct"]
        hscei_chg = hscei["change_pct"]
        if hsi_chg < 0 and hscei_chg < hsi_chg:
            # 国企跌得比恒指多 → 外资恐慌性卖出H股
            signals.append({
                "type": "hk_panic_selling",
                "level": "high",
                "desc": f"国企指数({hscei_chg:+.1f}%)跌幅大于恒指({hsi_chg:+.1f}%)→外资恐慌抛售H股",
                "pairs": [hscei, hsi],
                "direction": "risk",
            })

    # ── 信号4: A/H 价格背离（用历史数据检测趋势延续）──
    if history and len(history) >= 2:
        latest = history[0]["indices"]
        prev = history[1]["indices"]
        
        # 科创50连涨检测
        if kc:
            kc_now = kc["change_pct"]
            kc_prev = prev.get("科创50", {}).get("change_pct", 0) if "科创50" in prev else 0
            kc_yest = latest.get("科创50", {}).get("change_pct", 0)
            
            # 检测连涨
            if isinstance(kc_yest, (int, float)) and isinstance(kc_prev, (int, float)):
                streak = 0
                if kc_yest > 0: streak += 1
                if kc_prev > 0: streak += 1
                if kc_now > 0: streak += 1
                if streak >= STREAK_DAYS and kc_now > 0:
                    signals.append({
                        "type": "tech_streak",
                        "level": "medium",
                        "desc": f"科创50连涨{streak}日({kc_prev:+.1f}%→{kc_yest:+.1f}%→{kc_now:+.1f}%)→超买风险",
                        "pairs": [kc],
                        "direction": "risk",
                    })

    # ── 信号5: 大盘宽度 + 季节效应 ──
    now = datetime.datetime.now()
    is_month_end = now.day >= 25  # 月末最后一周
    is_friday = now.weekday() == 4
    
    # 总体判断
    risk_count = sum(1 for s in signals if s["direction"] == "risk")
    opp_count = sum(1 for s in signals if s["direction"] == "opportunity")
    
    # 月末+周五叠加 → 脆弱性增强
    if is_month_end and is_friday and risk_count >= 2:
        signals.append({
            "type": "time_window_risk",
            "level": "high",
            "desc": f"月末({now.day}日)+周五效应+{risk_count}个风险信号叠加→市场脆弱性高",
            "pairs": [],
            "direction": "risk",
        })
    elif is_month_end and risk_count >= 1:
        signals.append({
            "type": "time_window_risk",
            "level": "medium",
            "desc": f"月末窗口({now.day}日)+{risk_count}个风险信号→注意控制仓位",
            "pairs": [],
            "direction": "risk",
        })

    return signals

def write_state(signals, indices):
    """写入状态文件，供监控 cron 消费"""
    levels = [s["level"] for s in signals]
    highest = "high" if "high" in levels else ("medium" if "medium" in levels else "none")
    directions = [s["direction"] for s in signals]
    bias = "risk" if directions.count("risk") > directions.count("opportunity") else "opportunity"
    
    state = {
        "level": highest,
        "bias": bias,
        "signal_count": len(signals),
        "signals": signals,
        "indices": {k: v["change_pct"] for k, v in indices.items()},
        "created_at": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
    }
    STATE_PATH.write_text(json.dumps(state, ensure_ascii=False, indent=2))

def write_to_signal_news(signals):
    """HIGH signal → signal_news"""
    if not signals:
        return
    import sqlite3
    conn = sqlite3.connect(str(DB_PATH))
    
    high_signals = [s for s in signals if s["level"] == "high"]
    if high_signals:
        for s in high_signals:
            conn.execute(
                "INSERT INTO signal_news (signal_id, sector, overall_sentiment, summary, key_articles, searched_stocks, source) VALUES (?, ?, ?, ?, ?, ?, ?)",
                (0, "跨市场", f"背离-{s['direction'].upper()}", s["desc"], json.dumps(s, ensure_ascii=False), "", "divergence_watch")
            )
        conn.commit()
    
    med_signals = [s for s in signals if s["level"] == "medium"]
    if med_signals:
        summary = "\n".join([s["desc"] for s in med_signals])
        conn.execute(
            "INSERT INTO signal_news (signal_id, sector, overall_sentiment, summary, key_articles, searched_stocks, source) VALUES (?, ?, ?, ?, ?, ?, ?)",
            (0, "跨市场", "背离-MEDIUM", summary, json.dumps(med_signals, ensure_ascii=False), "", "divergence_watch")
        )
        conn.commit()
    
    conn.close()

def print_report(signals, indices):
    """no_agent 输出"""
    if not signals:
        return  # SILENT

    # 有信号就输出
    high = [s for s in signals if s["level"] == "high"]
    med = [s for s in signals if s["level"] == "medium"]
    
    lines = []
    if high:
        for s in high:
            icon = "\u26a0\ufe0f" if s["direction"] == "risk" else "\u2b06\ufe0f"
            lines.append(f"[DIVERGE] {icon} {s['level'].upper()} {s['type']}: {s['desc']}")
    if med:
        for s in med[:3]:  # 最多3条
            icon = "\u26a0\ufe0f" if s["direction"] == "risk" else "\u2b06\ufe0f"
            lines.append(f"[DIVERGE] {icon} {s['level'].upper()} {s['type']}: {s['desc']}")

    # 输出指数全景
    idx_line = "  |  ".join([f"{n}: {indices.get(s, {}).get('change_pct', 0):+.1f}%" for n, s in INDEX_CODES.items() if s in indices])
    lines.append(f"[DIVERGE] 指数全景: {idx_line}")
    
    print("\n".join(lines))

def main():
    indices = fetch_indices()
    if not indices:
        return
    
    history = load_history()
    signals = detect_divergences(indices, history)
    
    # 写入 state + signal_news
    write_state(signals, indices)
    write_to_signal_news(signals)
    
    # no_agent 输出
    print_report(signals, indices)

if __name__ == "__main__":
    main()