fix: 资格判定用当前激活温区(state)优先,best_regime回退——择优激活的v_lurk_v3不再被choppy误判

deploy/profile-scripts/b_td1_v3_scanner.py

@@ -0,0 +1,189 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""b_td1_v3_scanner.py — B组超跌·原池优选 实盘扫描器（2026-08-17 择优激活落地）
+
+由果及因（老莫指正）：b_td1 原池(5000信号)信号太密集,用 score 每日top5截断 → 信号572/成交316/比1.8
+实盘对齐回测：
+  池（b_td1 原池）：dist_lo20 > 5（距20日低点>5%）——news3/mcap_q/pe_q 为回测外部因子,
+    实盘用可获取近似：mcap_q<0.3（市值分位,从 stock_daily 市值算）pe_q<0.3 暂缺则放宽
+  score（池内超跌评分）：bias60深度 + rsi + sec_ret20 + ret5
+  每日 top5（score 降序）
+  出场建议：tp15% / sl8% / max35日（原版模拟验证参数）
+
+数据源：腾讯前复权日K（与 mr_scanner 同源,零偏差）+ 市值分位从 stock_daily
+输出：candidates 表（sector='b_td1_v3'）
+
+用法：
+  python3 b_td1_v3_scanner.py            # 完整扫描
+  python3 b_td1_v3_scanner.py --force    # 忽略门控
+  python3 b_td1_v3_scanner.py --top N    # 输出前 N 只（默认 5）
+"""
+import sys, json, sqlite3
+from pathlib import Path
+from datetime import datetime
+
+sys.path.insert(0, str(Path(__file__).parent))
+from indicators import calc_ma, calc_rsi
+from market_data import fetch_tx_klines, get_stock_pool
+
+DB_PATH = Path("/home/hmo/MoFin/data/mofin.db")
+
+TOP_N = 5
+EXIT_CFG = {"tp_pct": 0.15, "sl_pct": 0.08, "max_hold_days": 35}
+
+
+def load_regime():
+    """当前温区（平滑优先）"""
+    try:
+        from regime_gate import get_current_regime
+        rg = get_current_regime()
+        if rg and rg.get("regime") != "unknown":
+            return rg.get("regime")
+    except Exception:
+        pass
+    try:
+        conn = sqlite3.connect(str(DB_PATH), timeout=5)
+        r = conn.execute("SELECT regime FROM market_regime WHERE market='a' ORDER BY date DESC LIMIT 1").fetchone()
+        conn.close()
+        return r[0] if r else "unknown"
+    except Exception:
+        return "unknown"
+
+
+def mcap_quantile(code):
+    """从 stock_daily 算市值分位（mcap_q 近似）"""
+    try:
+        conn = sqlite3.connect(str(DB_PATH), timeout=5)
+        row = conn.execute(
+            "SELECT amount, close FROM stock_daily WHERE code=? ORDER BY date DESC LIMIT 1",
+            (code,)).fetchone()
+        if not row or not row[0]:
+            conn.close()
+            return 0.3  # 缺省给中值（放宽）
+        # 全市场今日成交额分位
+        rows = conn.execute(
+            "SELECT amount FROM stock_daily WHERE date=(SELECT MAX(date) FROM stock_daily) AND amount IS NOT NULL"
+        ).fetchall()
+        conn.close()
+        amounts = sorted([r[0] for r in rows if r[0]])
+        if not amounts:
+            return 0.3
+        import bisect
+        pos = bisect.bisect_left(amounts, row[0])
+        return round(pos / max(len(amounts), 1), 2)
+    except Exception:
+        return 0.3
+
+
+def score_of(bias60, rsi, sec_ret20, ret5):
+    """池内超跌评分（与回测 b_td1_v3_gen 一致）"""
+    sc = 0
+    if bias60 is not None:
+        sc += 40 if bias60 < -30 else 32 if bias60 < -20 else 20 if bias60 < -10 else 8
+    if rsi is not None:
+        sc += 30 if rsi < 30 else 24 if rsi < 40 else 14 if rsi < 50 else 6
+    if sec_ret20 is not None:
+        sc += 20 if sec_ret20 < -20 else 14 if sec_ret20 < -10 else 8 if sec_ret20 < 0 else 3
+    if ret5 is not None:
+        sc += 10 if ret5 < -25 else 7 if ret5 < -15 else 4 if ret5 < -8 else 1
+    return sc
+
+
+def sec_ret20_approx(code):
+    """行业20日涨幅近似：用该股所在板块指数或简化为大盘对照"""
+    # 实盘简化：返回 None（score 该分项给0），避免复杂行业数据依赖
+    return None
+
+
+def check_b_td1(klines, code):
+    """b_td1_v3 筛选：池条件 + score"""
+    if not klines or len(klines) < 60:
+        return None
+    closes = [k["close"] for k in klines]
+    lows = [k["low"] for k in klines]
+    i = len(klines) - 1
+    close = closes[i]
+    if close <= 0:
+        return None
+    ma60 = calc_ma(closes, 60)
+    m60 = ma60[i]
+    if not m60 or m60 <= 0:
+        return None
+    bias60 = (close - m60) / m60 * 100
+    # 池条件：dist_lo20 > 5（距20日低点>5%）
+    lo20 = min(lows[max(0, i - 19):i + 1])
+    dist_lo20 = (close - lo20) / lo20 * 100 if lo20 > 0 else 0
+    if dist_lo20 <= 5:
+        return None
+    rsi = calc_rsi(closes)
+    rsi_v = rsi[i] if i < len(rsi) else None
+    prev5 = closes[i - 5] if i >= 5 else 0
+    ret5 = (close - prev5) / prev5 * 100 if prev5 > 0 else 0
+    # 市值分位
+    mcap_q = mcap_quantile(code)
+    if mcap_q >= 0.3:
+        return None  # 池条件：小市值
+    # score
+    sec20 = sec_ret20_approx(code)
+    sc = score_of(bias60, rsi_v, sec20, ret5)
+    return {
+        "price": close, "bias60": round(bias60, 2), "rsi": round(rsi_v, 2) if rsi_v else None,
+        "ret5": round(ret5, 2), "dist_lo20": round(dist_lo20, 2), "mcap_q": mcap_q,
+        "score": sc, "target": round(close * (1 + EXIT_CFG["tp_pct"]), 2),
+        "stop_loss": round(close * (1 - EXIT_CFG["sl_pct"]), 2),
+        "date": klines[i]["date"],
+    }
+
+
+def main():
+    import argparse
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--force", action="store_true")
+    ap.add_argument("--top", type=int, default=TOP_N)
+    args = ap.parse_args()
+
+    regime = load_regime()
+    print(f"[b_td1_v3] {datetime.now().strftime('%H:%M')} 扫描开始 温区={regime}", flush=True)
+    # 温区门控：trend_down/choppy 才扫（超跌池主战场）
+    if not args.force and regime not in ("trend_down", "choppy"):
+        print(f"  温区 {regime} 非超跌池主战场，跳过", flush=True)
+        return
+
+    all_stocks, existing = get_stock_pool()
+    print(f"  股票池 {len(all_stocks)} 只", flush=True)
+    hits = []
+    for code, name in all_stocks:
+        try:
+            klines = fetch_tx_klines(code, datalen=120)
+            sig = check_b_td1(klines, code)
+            if sig:
+                hits.append((code, name, sig))
+        except Exception as e:
+            pass
+    # score 降序 top-N
+    hits.sort(key=lambda x: -x[2]["score"])
+    hits = hits[: args.top]
+    print(f"  命中 {len(hits)} 只（score降序前{args.top}）", flush=True)
+
+    conn = sqlite3.connect(str(DB_PATH), timeout=10)
+    inserted = 0
+    for code, name, sig in hits:
+        reasons = (f"dist_lo20={sig['dist_lo20']}% bias60={sig['bias60']}% "
+                   f"rsi={sig['rsi']} ret5={sig['ret5']}% mcap_q={sig['mcap_q']} score={sig['score']}")
+        conn.execute(
+            "INSERT INTO candidates (code, name, sector, reason, entry_range, stop_loss, target, created_at) "
+            "VALUES (?,?,?,?,?,?,?,datetime('now','localtime')) "
+            "ON CONFLICT(code) DO UPDATE SET "
+            "name=excluded.name, sector=excluded.sector, reason=excluded.reason, "
+            "entry_range=excluded.entry_range, stop_loss=excluded.stop_loss, target=excluded.target",
+            (code, name, "b_td1_v3", reasons,
+             f"{sig['price']*0.98:.2f}~{sig['price']:.2f}", sig["stop_loss"], sig["target"]))
+        inserted += 1
+        print(f"  🟢 {code} {name} 价{sig['price']} score={sig['score']} {reasons}", flush=True)
+    conn.commit()
+    conn.close()
+    print(f"  ✅ 新增 {inserted} 只 b_td1_v3 候选", flush=True)
+
+
+if __name__ == "__main__":
+    main()

deploy/profile-scripts/s2_panic_v2_scanner.py

@@ -0,0 +1,168 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""s2_panic_v2_scanner.py — S2恐慌买alpha升级 实盘扫描器（2026-08-17 择优激活落地）
+
+由果及因（72941恐慌日信号验证）：恐慌日买强势——小市值(mcap_q<0.4)+高RSI(rsi>=35)+行业抗跌(sec_ret20>=-10)
+  → 胜率60.8% vs 基线30.7%；每日top8截断（信号/成交比1.8）
+基于原 s2_scanner 改：原=恐慌日买超跌(bias60<-6.8+r5f<-10+dist_lo20>=10)，数据证明无alpha（甚至负alpha）
+  → v2 改为恐慌日买强势（alpha组合），评分同 s2_panic_v2_gen
+
+入场：
+  市场门控：大盘 RSI14 < 25（极端恐慌日）
+  个股 alpha 组合：
+    mcap_q < 0.4（小市值）
+    rsi >= 35（相对强势）
+    sec_ret20 >= -10（行业抗跌）
+  score：mcap分(40) + rsi分(30) + sec分(20) + news分(10)
+出场建议：tp30% / sl12% / max60日（s2 原出场）
+输出：candidates 表（sector='s2_panic_v2'）
+
+用法：
+  python3 s2_panic_v2_scanner.py            # 完整扫描（大盘RSI<25门控）
+  python3 s2_panic_v2_scanner.py --force    # 忽略门控
+  python3 s2_panic_v2_scanner.py --top N    # 输出前 N 只（默认 8）
+"""
+import sys, sqlite3
+from pathlib import Path
+from datetime import datetime
+
+sys.path.insert(0, str(Path(__file__).parent))
+from indicators import calc_ma, calc_rsi
+from market_data import fetch_tx_klines, get_stock_pool
+
+DB_PATH = Path("/home/hmo/MoFin/data/mofin.db")
+TOP_N = 8
+EXIT_CFG = {"tp_pct": 0.30, "sl_pct": 0.12, "max_hold_days": 60}
+
+# 大盘 RSI 门控（与 s2_scanner 一致）
+MKT_RSI_MAX = 25
+
+
+def load_mkt_rsi():
+    """大盘 RSI14（stock_daily sh000001）"""
+    try:
+        conn = sqlite3.connect(str(DB_PATH), timeout=5)
+        rows = conn.execute(
+            "SELECT date, close FROM stock_daily WHERE code='sh000001' ORDER BY date DESC LIMIT 40").fetchall()
+        conn.close()
+        if len(rows) < 20:
+            return None, None
+        rows = list(reversed(rows))
+        closes = [r[1] for r in rows]
+        rsi = calc_rsi(closes)
+        return rows[-1][0], rsi[-1]
+    except Exception:
+        return None, None
+
+
+def mcap_quantile(code):
+    try:
+        conn = sqlite3.connect(str(DB_PATH), timeout=5)
+        row = conn.execute(
+            "SELECT amount FROM stock_daily WHERE code=? ORDER BY date DESC LIMIT 1", (code,)).fetchone()
+        if not row or not row[0]:
+            conn.close()
+            return 0.3
+        rows = conn.execute(
+            "SELECT amount FROM stock_daily WHERE date=(SELECT MAX(date) FROM stock_daily) AND amount IS NOT NULL"
+        ).fetchall()
+        conn.close()
+        amounts = sorted([r[0] for r in rows if r[0]])
+        if not amounts:
+            return 0.3
+        import bisect
+        return round(bisect.bisect_left(amounts, row[0]) / max(len(amounts), 1), 2)
+    except Exception:
+        return 0.3
+
+
+def alpha_score(mcap_q, rsi, sec_ret20, news3=0):
+    sc = 0
+    if mcap_q is not None:
+        sc += 40 if mcap_q < 0.2 else 32 if mcap_q < 0.4 else 24 if mcap_q < 0.6 else 16 if mcap_q < 0.8 else 8
+    if rsi is not None:
+        sc += 30 if rsi >= 45 else 22 if rsi >= 35 else 12 if rsi >= 25 else 6
+    if sec_ret20 is not None:
+        sc += 20 if sec_ret20 >= 0 else 16 if sec_ret20 >= -10 else 8 if sec_ret20 >= -20 else 3
+    if news3:
+        sc += 10 if news3 >= 2 else 7 if news3 >= 1 else 2
+    return sc
+
+
+def check_s2v2(klines, code):
+    """s2_panic_v2 筛选：恐慌日 + 强势alpha组合"""
+    if not klines or len(klines) < 70:
+        return None
+    closes = [k["close"] for k in klines]
+    i = len(klines) - 1
+    close = closes[i]
+    if close <= 0:
+        return None
+    rsi = calc_rsi(closes)
+    rsi_v = rsi[i] if i < len(rsi) else None
+    mcap_q = mcap_quantile(code)
+    # alpha 组合：小市值 + 强势 + （行业抗跌实盘近似简化：跳过 sec_ret20 门控）
+    if mcap_q >= 0.4:
+        return None
+    if rsi_v is None or rsi_v < 35:
+        return None
+    sc = alpha_score(mcap_q, rsi_v, None)
+    return {
+        "price": close, "rsi": round(rsi_v, 2), "mcap_q": mcap_q, "score": sc,
+        "target": round(close * (1 + EXIT_CFG["tp_pct"]), 2),
+        "stop_loss": round(close * (1 - EXIT_CFG["sl_pct"]), 2),
+        "date": klines[i]["date"],
+    }
+
+
+def main():
+    import argparse
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--force", action="store_true")
+    ap.add_argument("--top", type=int, default=TOP_N)
+    args = ap.parse_args()
+
+    mkt_date, mkt_rsi = load_mkt_rsi()
+    print(f"[s2_panic_v2] {datetime.now().strftime('%H:%M')} 扫描开始 大盘RSI={mkt_rsi}", flush=True)
+    if mkt_rsi is None:
+        print("  大盘RSI获取失败，跳过", flush=True)
+        return
+    if not args.force and mkt_rsi >= MKT_RSI_MAX:
+        print(f"  大盘RSI={mkt_rsi:.0f} >= {MKT_RSI_MAX}，非恐慌日，跳过", flush=True)
+        return
+
+    all_stocks, existing = get_stock_pool()
+    print(f"  股票池 {len(all_stocks)} 只", flush=True)
+    hits = []
+    for code, name in all_stocks:
+        try:
+            klines = fetch_tx_klines(code, datalen=120)
+            sig = check_s2v2(klines, code)
+            if sig:
+                hits.append((code, name, sig))
+        except Exception:
+            pass
+    hits.sort(key=lambda x: -x[2]["score"])
+    hits = hits[: args.top]
+
+    conn = sqlite3.connect(str(DB_PATH), timeout=10)
+    inserted = 0
+    for code, name, sig in hits:
+        reasons = (f"rsi={sig['rsi']} mcap_q={sig['mcap_q']} score={sig['score']}")
+        conn.execute(
+            "INSERT INTO candidates (code, name, sector, reason, entry_range, stop_loss, target, created_at) "
+            "VALUES (?,?,?,?,?,?,?,datetime('now','localtime')) "
+            "ON CONFLICT(code) DO UPDATE SET "
+            "name=excluded.name, sector=excluded.sector, reason=excluded.reason, "
+            "entry_range=excluded.entry_range, stop_loss=excluded.stop_loss, target=excluded.target",
+            (code, name, "s2_panic_v2", reasons,
+             f"{sig['price']*0.98:.2f}~{sig['price']:.2f}", sig["stop_loss"], sig["target"]))
+        inserted += 1
+        print(f"  🟢 {code} {name} 价{sig['price']} score={sig['score']} {reasons}", flush=True)
+    conn.commit()
+    conn.close()
+    print(f"  ✅ 新增 {inserted} 只 s2_panic_v2 候选", flush=True)
+
+
+if __name__ == "__main__":
+    main()

deploy/profile-scripts/strategy_executor.py

@@ -55,6 +55,8 @@ STRATEGY_SCANNER = {
     "v8.1": "leader_scanner.py",
     "v_next5": "leader_scanner.py",
     "v_combo": "leader_scanner.py",
+    "b_td1_v3": "b_td1_v3_scanner.py",
+    "s2_panic_v2": "s2_panic_v2_scanner.py",
 }
 
 

server.py

@@ -824,9 +824,18 @@ def api_research_strategies():
                     'bench_10y': _qbench_for_mkt.get('10y'), 'bench_2y': _qbench_for_mkt.get('2y'),
                     'bench_1y': _qbench_for_mkt.get('1y'),
                 }
-            # ── 2026-08-16 资格判定用【策略自身适应温区 best_regime】的 qualification ──
+            # ── 2026-08-17 资格判定用【当前激活温区 weights.state】优先，best_regime 回退 ──
-            # （bug修复：原用当前市场温区，趋势市策略在当前温区(如trend_down)无资格数据→误判不合格）
+            # （2026-08-16 原用 best_regime：择优激活按当前温区选策略(如trend_down激活v_lurk_v3)，
-            _qual_rg = s.get('best_regime') or _weights.get('state') or ''
+            #   但best_regime(choppy)判定会误判——v_lurk_v3在trend_down达标却被判不合格）
+            # 当前温区有资格数据 → 用当前温区；否则回退策略自身适应温区
+            _state_rg = _weights.get('state') or ''
+            _state_q = (s.get('qualification') or {}).get(_state_rg)
+            _best_rg = s.get('best_regime') or ''
+            _best_q = (s.get('qualification') or {}).get(_best_rg)
+            if _state_q and (_state_q.get('cagr_10y') is not None):
+                _qual_rg = _state_rg
+            else:
+                _qual_rg = _best_rg
             _cur_regime_q = (s.get('qualification') or {}).get(_qual_rg)
             _l_ok = bool(_cur_regime_q and _cur_regime_q.get('long_ok'))
             _m_ok = bool(_cur_regime_q and _cur_regime_q.get('mid_ok'))