trend_detector + xiaoguo_news_processor 全链路
- trend_detector.py: 6类信号检测（资金异动/涨跌比反转/领涨更替/趋势拐点/量价背离/普涨背离） - xiaoguo_news_processor.py: akshare搜新闻+小果LLM情感分析 - mofin_db.py: 新增 sector_signals + signal_news 两张表 - 文档更新：新增第四章实时信号检测与小果情报处理 - 测试结果：趋势检测已通过，信号写入正常
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#!/usr/bin/env python3
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"""trend_detector.py — 板块异常信号检测
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配合 market_watch（每30分）运行，从最新 snapshot 中检测6类信号：
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1. 资金异动 — 净流入/出远超近期均值
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2. 涨跌比反转 — 板块内涨跌家数比例突变
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3. 领涨股更替 — 领涨股换人
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4. 趋势拐点 — 连续流入→转流出 或 连续流出→转入流
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5. 量价背离 — 涨但资金流出 / 跌但资金流入
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6. 普涨背离 — 板块大涨但上涨家数<50%
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检测到信号后写入 sector_signals 表。
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"""
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import json
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import sqlite3
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import sys
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from datetime import datetime
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from pathlib import Path
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DATA_DIR = Path(__file__).parent / "data"
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DB_PATH = DATA_DIR / "mofin.db"
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def get_conn():
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conn = sqlite3.connect(str(DB_PATH))
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conn.row_factory = sqlite3.Row
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return conn
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def get_recent_snapshots(conn, n=20):
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"""取最近 n 次 market_snapshots"""
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return conn.execute(
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"SELECT id, timestamp, up_ratio, mood FROM market_snapshots ORDER BY id DESC LIMIT ?",
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(n,)
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).fetchall()
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def get_sectors_for_snapshot(conn, snapshot_id):
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"""取指定 snapshot 的全部板块数据"""
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rows = conn.execute(
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"SELECT * FROM sector_snapshots WHERE snapshot_id = ?", (snapshot_id,)
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).fetchall()
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return [dict(r) for r in rows]
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def get_sector_history(conn, sector_name, n=20):
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"""取某板块最近 n 次采集记录"""
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return conn.execute("""
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SELECT ss.*, ms.timestamp
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FROM sector_snapshots ss
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JOIN market_snapshots ms ON ss.snapshot_id = ms.id
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WHERE ss.name = ?
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ORDER BY ms.id DESC LIMIT ?
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""", (sector_name, n)).fetchall()
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def get_holdings(conn):
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"""取活跃持仓"""
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return conn.execute("SELECT code, name FROM holdings WHERE is_active=1").fetchall()
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def get_watchlist(conn):
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"""取活跃自选"""
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return conn.execute("SELECT code, name FROM watchlist_stocks WHERE is_active=1").fetchall()
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def get_sector_for_stock(conn, code):
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"""查个股对应的板块"""
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row = conn.execute(
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"SELECT sector_name FROM stock_sectors WHERE code = ?", (code,)
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).fetchone()
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return row[0] if row else None
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def write_signal(conn, signal_type, sector, severity, related_stocks,
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holdings_list, watchlist_list, trigger_reason, snapshot_id):
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"""写入信号到 sector_signals"""
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# 同板块同类型24小时内已有信号则跳过
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existing = conn.execute("""
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SELECT id FROM sector_signals
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WHERE sector = ? AND signal_type = ?
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AND datetime(detected_at) >= datetime('now', '-1 day')
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LIMIT 1
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""", (sector, signal_type)).fetchone()
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if existing:
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return False
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conn.execute("""
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INSERT INTO sector_signals
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(signal_type, sector, severity, related_stocks,
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holdings_in_sector, watchlist_in_sector,
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trigger_reason, snapshot_id)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?)
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""", (
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signal_type, sector, severity,
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json.dumps(related_stocks, ensure_ascii=False),
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json.dumps(holdings_list, ensure_ascii=False) if holdings_list else None,
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json.dumps(watchlist_list, ensure_ascii=False) if watchlist_list else None,
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trigger_reason, snapshot_id
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))
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conn.commit()
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return True
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def check_signals(conn, latest, prev_snapshots):
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"""对最新 snapshot 检测6类信号"""
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latest_id = latest["id"]
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sectors = get_sectors_for_snapshot(conn, latest_id)
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if not sectors:
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return
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# 取持仓和自选
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holdings = {r["code"]: r["name"] for r in get_holdings(conn)}
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watchlist = {r["code"]: r["name"] for r in get_watchlist(conn)}
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# 取上一次 snapshot 用于对比（如果有）
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prev_id = None
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if len(prev_snapshots) >= 2:
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prev_id = prev_snapshots[1]["id"]
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prev_sectors = get_sectors_for_snapshot(conn, prev_id) if prev_id else []
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# 构建 name→sector 映射
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prev_map = {s["name"]: s for s in prev_sectors}
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for s in sectors:
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name = s["name"]
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change = s["change_pct"] or 0
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net_inflow = s["net_inflow"] or 0
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up_count = s["up_count"] or 0
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down_count = s["down_count"] or 0
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total = up_count + down_count
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up_ratio = up_count / total if total > 0 else None
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lead_stock = s["lead_stock"] or ""
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# 近期历史
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history = get_sector_history(conn, name, 20)
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# 计算近期均值（后续多重信号共用）
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mean = 0
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if len(history) >= 3:
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recent_inflows = [abs(h["net_inflow"] or 0) for h in history[:10]]
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mean = sum(recent_inflows) / len(recent_inflows) if recent_inflows else 0
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# 信号1：资金异动
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if net_inflow and len(history) >= 5 and mean > 0:
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recent_inflows = [abs(h["net_inflow"] or 0) for h in history[:20]]
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new_mean = sum(recent_inflows) / len(recent_inflows)
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std = (sum((x - new_mean) ** 2 for x in recent_inflows) / len(recent_inflows)) ** 0.5
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if std > 0 and abs(net_inflow) > mean + 3 * std:
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direction = "净流入" if net_inflow > 0 else "净流出"
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sev = "high" if abs(net_inflow) > mean + 5 * std else "medium"
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related = _get_related_stocks(conn, name, lead_stock, change)
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holdings_list = _match_holdings(related, holdings)
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watchlist_list = _match_holdings(related, watchlist)
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ok = write_signal(conn, "资金异动", name, sev, related,
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holdings_list, watchlist_list,
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f"{direction}{abs(net_inflow):.0f}亿（均值{mean:.0f}亿，超{abs(net_inflow)/max(mean,0.01):.0f}倍）",
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latest_id)
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if ok:
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print(f" ⚠️ 资金异动 [{sev}] {name}: {direction}{abs(net_inflow):.0f}亿", flush=True)
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# 信号2：涨跌比反转（相比上一次）
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prev = dict(prev_map[name]) if name in prev_map else {}
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if prev and up_ratio is not None and prev["up_count"] and prev["down_count"]:
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prev_total = prev["up_count"] + prev["down_count"]
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prev_ratio = prev["up_count"] / prev_total if prev_total > 0 else 0
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if abs(up_ratio - prev_ratio) > 0.3: # 涨跌比变化超过30个百分点
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direction = "转强" if up_ratio > prev_ratio else "转弱"
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sev = "high" if abs(up_ratio - prev_ratio) > 0.5 else "medium"
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related = _get_related_stocks(conn, name, lead_stock, change)
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holdings_list = _match_holdings(related, holdings)
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watchlist_list = _match_holdings(related, watchlist)
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ok = write_signal(conn, "涨跌比反转", name, sev, related,
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holdings_list, watchlist_list,
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f"上涨占比{prev_ratio*100:.0f}%→{up_ratio*100:.0f}%，{direction}",
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latest_id)
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if ok:
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print(f" ⚠️ 涨跌比反转 [{sev}] {name}: {prev_ratio*100:.0f}%→{up_ratio*100:.0f}% {direction}", flush=True)
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# 信号3：领涨股更替
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prev_lead = prev.get("lead_stock", "") if prev else ""
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if lead_stock and prev_lead and lead_stock != prev_lead:
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related = _get_related_stocks(conn, name, lead_stock, change)
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holdings_list = _match_holdings(related, holdings)
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watchlist_list = _match_holdings(related, watchlist)
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ok = write_signal(conn, "领涨股更替", name, "medium", related,
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holdings_list, watchlist_list,
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f"领涨股从「{prev_lead}」换成「{lead_stock}」",
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latest_id)
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if ok:
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print(f" ⚠️ 领涨股更替 [{name}] {prev_lead} → {lead_stock}", flush=True)
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# 信号4：趋势拐点（连续净流入突然转流出，反之亦然）
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if net_inflow and len(history) >= 4:
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recent = [h["net_inflow"] or 0 for h in history[:4]]
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all_positive = all(r > 0 for r in recent[:3])
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all_negative = all(r < 0 for r in recent[:3])
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if all_positive and net_inflow < 0 and abs(net_inflow) > mean * 0.5:
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related = _get_related_stocks(conn, name, lead_stock, change)
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holdings_list = _match_holdings(related, holdings)
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watchlist_list = _match_holdings(related, watchlist)
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ok = write_signal(conn, "趋势拐点", name, "high", related,
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holdings_list, watchlist_list,
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f"连续3次净流入后转流出{abs(net_inflow):.0f}亿",
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latest_id)
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if ok:
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print(f" ⚠️ 趋势拐点 [high] {name}: 连续流入→转流出{abs(net_inflow):.0f}亿", flush=True)
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elif all_negative and net_inflow > 0 and net_inflow > abs(sum(recent[:3])) * 0.5:
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related = _get_related_stocks(conn, name, lead_stock, change)
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holdings_list = _match_holdings(related, holdings)
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watchlist_list = _match_holdings(related, watchlist)
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ok = write_signal(conn, "趋势拐点", name, "high", related,
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holdings_list, watchlist_list,
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f"连续3次净流出后转入流{net_inflow:.0f}亿",
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latest_id)
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if ok:
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print(f" ⚠️ 趋势拐点 [high] {name}: 连续流出→转入流{net_inflow:.0f}亿", flush=True)
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# 信号5：量价背离
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if net_inflow and change and abs(change) > 2:
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if change > 0 and net_inflow < -abs(mean or 1):
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related = _get_related_stocks(conn, name, lead_stock, change)
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holdings_list = _match_holdings(related, holdings)
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watchlist_list = _match_holdings(related, watchlist)
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ok = write_signal(conn, "量价背离", name, "medium", related,
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holdings_list, watchlist_list,
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f"板块涨{change:+.2f}%但资金净流出{abs(net_inflow):.0f}亿",
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latest_id)
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if ok:
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print(f" ⚠️ 量价背离 [{name}] 涨{change:+.2f}%但流出{abs(net_inflow):.0f}亿", flush=True)
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elif change < 0 and net_inflow > abs(mean or 1):
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related = _get_related_stocks(conn, name, lead_stock, change)
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holdings_list = _match_holdings(related, holdings)
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watchlist_list = _match_holdings(related, watchlist)
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ok = write_signal(conn, "量价背离", name, "medium", related,
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holdings_list, watchlist_list,
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f"板块跌{change:+.2f}%但资金净流入{net_inflow:.0f}亿（吸筹信号）",
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latest_id)
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if ok:
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print(f" ⚠️ 量价背离 [{name}] 跌{change:+.2f}%但流入{net_inflow:.0f}亿（吸筹）", flush=True)
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# 信号6：普涨背离
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if up_ratio is not None and change > 3 and up_ratio < 0.5:
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related = _get_related_stocks(conn, name, lead_stock, change)
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holdings_list = _match_holdings(related, holdings)
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watchlist_list = _match_holdings(related, watchlist)
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ok = write_signal(conn, "普涨背离", name, "medium", related,
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holdings_list, watchlist_list,
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f"板块涨{change:+.2f}%但仅{up_count}/{total}家上涨（分化严重）",
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latest_id)
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if ok:
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print(f" ⚠️ 普涨背离 [{name}] 涨{change:+.2f}%但仅{up_count}/{total}家上涨", flush=True)
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def _get_related_stocks(conn, sector_name, lead_stock, change_pct):
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"""获取板块相关个股（领涨股 + board 成分股）"""
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stocks = []
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if lead_stock:
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stocks.append({"name": lead_stock, "code": "", "change_pct": 0, "role": "领涨"})
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# 从 stock_sectors 表取成分股
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members = conn.execute(
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"SELECT s.code, s.name FROM stocks s "
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"JOIN stock_sectors ss ON s.code = ss.code "
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"WHERE ss.sector_name = ? LIMIT 5",
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(sector_name,)
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).fetchall()
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for m in members:
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if not any(s.get("name") == m["name"] for s in stocks):
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stocks.append({"name": m["name"], "code": m["code"], "change_pct": 0, "role": "成分"})
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return stocks
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def _match_holdings(stocks, holding_dict):
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"""匹配相关个股中的持仓/自选"""
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matched = []
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for s in stocks:
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code = s.get("code", "")
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if code in holding_dict:
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matched.append({"code": code, "name": holding_dict[code]})
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return matched
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def main():
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conn = get_conn()
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# 取最近 snapshots
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snapshots = get_recent_snapshots(conn, 5)
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if len(snapshots) < 2:
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print(f"数据不足: 只有 {len(snapshots)} 次采集，需要至少2次", flush=True)
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conn.close()
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return
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latest = dict(snapshots[0])
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print(f"检测最新 snapshot: {latest['timestamp']} (id={latest['id']})", flush=True)
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check_signals(conn, latest, snapshots)
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conn.close()
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print("检测完成", flush=True)
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if __name__ == "__main__":
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main()
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