知微
9fef32413b
所有价格获取统一走 mo_data.get_price() / get_prices_batch():
- 优先读 live_prices(DB) → 无/过期才调 stock_quote(API) → 自动写回DB
- 22个脚本全部替换：branch_scanner chip_factors divergence_detector
market_screener mo_provider mofin_collect monitor_300308 300308_monitor
multi_timeframe refresh_macro_context stale_detector stale_push_wlin
stock_profile strategy_evaluator strategy_lifecycle strategy_review
strategy-staleness-check technical_analysis xiaoguo_signal_consumer
collect_evaluation_data
317 lines
11 KiB
Python
317 lines
11 KiB
Python
#!/usr/bin/env python3
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"""
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divergence_detector.py — 跨市场背离监测器（no_agent）
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每30分钟检测：
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1. 科创50 vs 恒指 → 科技股超买/超卖信号
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2. A/H 隐含溢价 → 内外资分歧度
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3. 上证50 vs 创业板 → 风格轮动信号
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4. 恒指 vs 国企指数 → 离岸市场情绪
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5. 指数连涨/连跌天数 → 趋势延续/衰竭
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输出：
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- HIGH/medium divergence → 写入 signal_news (source=divergence_watch)
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- 状态文件 macro_divergence_state.json
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- no_agent: 有信号才出声
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"""
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import sys, json, re, datetime, os
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from mo_data import get_price, get_prices_batch
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from pathlib import Path
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BASE = Path("/home/hmo/MoFin")
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DATA = BASE / "data"
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DB_PATH = DATA / "mofin.db"
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STATE_PATH = DATA / "macro_divergence_state.json"
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# ── 监测的指数 ──
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INDEX_CODES = {
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"上证指数": "sh000001",
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"深证成指": "sz399001",
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"创业板指": "sz399006",
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"科创50": "sh000688",
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"上证50": "sh000016",
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"沪深300": "sh000300",
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"恒生指数": "hkHSI",
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"国企指数": "hkHSCEI",
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}
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# ── 背离阈值 ──
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DIVERGENCE_STRONG = 5.0 # >5% → strong信号
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DIVERGENCE_MODERATE = 3.0 # >3% → moderate信号
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STREAK_DAYS = 3 # 连涨/连跌3天 → 信号
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def fetch_indices():
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"""获取所有指数实时数据（通过 mo_data.get_prices_batch）"""
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symbols = list(INDEX_CODES.values())
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try:
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raw = get_prices_batch(symbols)
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if not raw:
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return {}
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except Exception as e:
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print(f"[DIVERGE] 采集失败: {e}", file=sys.stderr)
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return {}
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indices = {}
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for sym, (price, change_pct) in raw.items():
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indices[sym] = {
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"name": "",
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"price": price,
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"close": 0,
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"change_pct": change_pct,
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"high": 0,
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"low": 0,
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"timestamp": "",
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}
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return indices
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def load_history():
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"""从MACRO_CONTEXT_LOG加载前几天的指数数据"""
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try:
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import sqlite3
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conn = sqlite3.connect(str(DB_PATH))
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rows = conn.execute(
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"SELECT indices, created_at FROM macro_context_log "
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"WHERE has_valid_data=1 ORDER BY created_at DESC LIMIT 5"
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).fetchall()
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conn.close()
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history = []
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for row in rows:
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idx_data = json.loads(row[0]) if row[0] else {}
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history.append({
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"indices": idx_data,
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"timestamp": row[1],
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})
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return history
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except Exception:
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return []
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def detect_divergences(indices, history):
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"""
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检测跨市场背离信号
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返回: list of signal dicts {type, level, desc, pairs}
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"""
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signals = []
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def get(name):
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"""按中文名找指数"""
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sym = INDEX_CODES.get(name)
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if sym and sym in indices:
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return indices[sym]
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# 模糊匹配
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for s, idx in indices.items():
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if name in idx.get("name", ""):
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return idx
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return None
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sh_comp = get("上证指数")
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sz_comp = get("深证成指")
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cyb = get("创业板指")
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kc = get("科创50")
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sz50 = get("上证50")
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hs300 = get("沪深300")
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hsi = get("恒生指数")
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hscei = get("国企指数")
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# ── 信号1: 科创50 vs 恒指（科技股vs国际资本）──
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if kc and hsi:
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kc_chg = kc["change_pct"]
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hsi_chg = hsi["change_pct"]
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divergence = abs(kc_chg - hsi_chg)
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if divergence > DIVERGENCE_STRONG:
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signals.append({
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"type": "a_h_tech_divergence",
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"level": "high",
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"desc": f"科创50({kc_chg:+.1f}%) vs 恒指({hsi_chg:+.1f}%) 背离{divergence:.1f}个百分点",
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"pairs": [kc, hsi],
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"direction": "risk" if kc_chg > hsi_chg else "opportunity",
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})
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elif divergence > DIVERGENCE_MODERATE:
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signals.append({
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"type": "a_h_tech_divergence",
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"level": "medium",
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"desc": f"科创50({kc_chg:+.1f}%) vs 恒指({hsi_chg:+.1f}%) 背离{divergence:.1f}个百分点",
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"pairs": [kc, hsi],
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"direction": "risk" if kc_chg > hsi_chg else "opportunity",
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})
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# ── 信号2: 上证50 vs 创业板（价值vs成长）──
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if sz50 and cyb:
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sz50_chg = sz50["change_pct"]
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cyb_chg = cyb["change_pct"]
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divergence = abs(sz50_chg - cyb_chg)
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if divergence > DIVERGENCE_STRONG:
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direction = "opportunity" if sz50_chg > cyb_chg else "risk"
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signals.append({
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"type": "value_growth_divergence",
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"level": "high",
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"desc": f"上证50({sz50_chg:+.1f}%) vs 创业板({cyb_chg:+.1f}%) 背离{divergence:.1f}个百分点",
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"pairs": [sz50, cyb],
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"direction": direction,
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})
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elif divergence > DIVERGENCE_MODERATE:
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direction = "opportunity" if sz50_chg > cyb_chg else "risk"
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signals.append({
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"type": "value_growth_divergence",
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"level": "medium",
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"desc": f"上证50({sz50_chg:+.1f}%) vs 创业板({cyb_chg:+.1f}%) 背离{divergence:.1f}个百分点",
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"pairs": [sz50, cyb],
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"direction": direction,
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})
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# ── 信号3: 恒指 vs 国企指数（国际资本流向）──
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if hsi and hscei:
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hsi_chg = hsi["change_pct"]
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hscei_chg = hscei["change_pct"]
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if hsi_chg < 0 and hscei_chg < hsi_chg:
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# 国企跌得比恒指多 → 外资恐慌性卖出H股
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signals.append({
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"type": "hk_panic_selling",
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"level": "high",
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"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",
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"pairs": [hscei, hsi],
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"direction": "risk",
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})
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# ── 信号4: A/H 价格背离（用历史数据检测趋势延续）──
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if history and len(history) >= 2:
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latest = history[0]["indices"]
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prev = history[1]["indices"]
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# 科创50连涨检测
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if kc:
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kc_now = kc["change_pct"]
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kc_prev = prev.get("科创50", {}).get("change_pct", 0) if "科创50" in prev else 0
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kc_yest = latest.get("科创50", {}).get("change_pct", 0)
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# 检测连涨
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if isinstance(kc_yest, (int, float)) and isinstance(kc_prev, (int, float)):
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streak = 0
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if kc_yest > 0: streak += 1
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if kc_prev > 0: streak += 1
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if kc_now > 0: streak += 1
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if streak >= STREAK_DAYS and kc_now > 0:
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signals.append({
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"type": "tech_streak",
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"level": "medium",
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"desc": f"科创50连涨{streak}日({kc_prev:+.1f}%→{kc_yest:+.1f}%→{kc_now:+.1f}%)→超买风险",
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"pairs": [kc],
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"direction": "risk",
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})
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# ── 信号5: 大盘宽度 + 季节效应 ──
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now = datetime.datetime.now()
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is_month_end = now.day >= 25 # 月末最后一周
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is_friday = now.weekday() == 4
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# 总体判断
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risk_count = sum(1 for s in signals if s["direction"] == "risk")
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opp_count = sum(1 for s in signals if s["direction"] == "opportunity")
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# 月末+周五叠加 → 脆弱性增强
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if is_month_end and is_friday and risk_count >= 2:
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signals.append({
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"type": "time_window_risk",
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"level": "high",
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"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",
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"pairs": [],
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"direction": "risk",
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})
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elif is_month_end and risk_count >= 1:
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signals.append({
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"type": "time_window_risk",
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"level": "medium",
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"desc": f"月末窗口({now.day}日)+{risk_count}个风险信号→注意控制仓位",
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"pairs": [],
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"direction": "risk",
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})
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return signals
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def write_state(signals, indices):
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"""写入状态文件，供监控 cron 消费"""
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levels = [s["level"] for s in signals]
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highest = "high" if "high" in levels else ("medium" if "medium" in levels else "none")
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directions = [s["direction"] for s in signals]
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bias = "risk" if directions.count("risk") > directions.count("opportunity") else "opportunity"
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state = {
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"level": highest,
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"bias": bias,
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"signal_count": len(signals),
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"signals": signals,
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"indices": {k: v["change_pct"] for k, v in indices.items()},
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"created_at": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
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}
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STATE_PATH.write_text(json.dumps(state, ensure_ascii=False, indent=2))
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def write_to_signal_news(signals):
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"""HIGH signal → signal_news"""
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if not signals:
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return
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import sqlite3
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conn = sqlite3.connect(str(DB_PATH))
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high_signals = [s for s in signals if s["level"] == "high"]
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if high_signals:
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for s in high_signals:
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conn.execute(
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"INSERT INTO signal_news (signal_id, sector, overall_sentiment, summary, key_articles, searched_stocks, source) VALUES (?, ?, ?, ?, ?, ?, ?)",
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(0, "跨市场", f"背离-{s['direction'].upper()}", s["desc"], json.dumps(s, ensure_ascii=False), "", "divergence_watch")
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)
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conn.commit()
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med_signals = [s for s in signals if s["level"] == "medium"]
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if med_signals:
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summary = "\n".join([s["desc"] for s in med_signals])
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conn.execute(
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"INSERT INTO signal_news (signal_id, sector, overall_sentiment, summary, key_articles, searched_stocks, source) VALUES (?, ?, ?, ?, ?, ?, ?)",
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(0, "跨市场", "背离-MEDIUM", summary, json.dumps(med_signals, ensure_ascii=False), "", "divergence_watch")
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)
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conn.commit()
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conn.close()
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def print_report(signals, indices):
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"""no_agent 输出"""
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if not signals:
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return # SILENT
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# 有信号就输出
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high = [s for s in signals if s["level"] == "high"]
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med = [s for s in signals if s["level"] == "medium"]
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lines = []
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if high:
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for s in high:
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icon = "\u26a0\ufe0f" if s["direction"] == "risk" else "\u2b06\ufe0f"
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lines.append(f"[DIVERGE] {icon} {s['level'].upper()} {s['type']}: {s['desc']}")
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if med:
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for s in med[:3]: # 最多3条
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icon = "\u26a0\ufe0f" if s["direction"] == "risk" else "\u2b06\ufe0f"
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lines.append(f"[DIVERGE] {icon} {s['level'].upper()} {s['type']}: {s['desc']}")
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# 输出指数全景
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idx_line = " | ".join([f"{n}: {indices.get(s, {}).get('change_pct', 0):+.1f}%" for n, s in INDEX_CODES.items() if s in indices])
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lines.append(f"[DIVERGE] 指数全景: {idx_line}")
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print("\n".join(lines))
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def main():
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indices = fetch_indices()
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if not indices:
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return
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history = load_history()
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signals = detect_divergences(indices, history)
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# 写入 state + signal_news
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write_state(signals, indices)
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write_to_signal_news(signals)
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# no_agent 输出
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print_report(signals, indices)
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if __name__ == "__main__":
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main()
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