From 2998bfb06f7e2d53eb4c54ec450a0cbd8f1f9011 Mon Sep 17 00:00:00 2001
From: xxm <xxm@mofin.local>
Date: Mon, 17 Aug 2026 04:01:26 +0800
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---
 .../strategy_activation_selector.py           | 117 ++++++++++++++++++
 1 file changed, 117 insertions(+)
 create mode 100644 deploy/profile-scripts/strategy_activation_selector.py

diff --git a/deploy/profile-scripts/strategy_activation_selector.py b/deploy/profile-scripts/strategy_activation_selector.py
new file mode 100644
index 00000000..826b3b62
--- /dev/null
+++ b/deploy/profile-scripts/strategy_activation_selector.py
@@ -0,0 +1,117 @@
+# -*- coding: utf-8 -*-
+"""择优激活规则 v2：修正上限逻辑——按质量排序选最优N个,家族去重,软上限
+输出：每个温区应激活的策略（写入 strategy_weights.json 的 active）"""
+import sys, json, sqlite3
+sys.path.insert(0, "/home/hmo/MoFin")
+sys.path.insert(0, "/home/hmo/MoFin/deploy/profile-scripts")
+import strategy_qualify as sq
+
+MIN_YEARLY = 15   # 年化成交下限（低于此=机会不足）
+MAX_YEARLY = 300   # 年化成交上限（软上限，质量优先）
+
+FAMILIES = {
+    "s2_panic": "s2", "s2_panic_v2": "s2", "s2_panic_v3": "s2",
+    "v_lurk_v1": "vlurk", "v_lurk_v2": "vlurk", "v_lurk_v3": "vlurk",
+    "v_mr": "vmr", "v_mr2": "vmr", "v_mr3": "vmr", "v_mr4": "vmr",
+    "v_oversold": "vover", "v_weak": "vweak",
+    "b_td1": "b_td", "b_td1_v2": "b_td", "b_td1_v3": "b_td",
+    "v1.0": "v1", "v2.0": "v2", "v3.0": "v3", "v_next": "vnext",
+    "hk_pe_mom": "hkpe", "hk_pe_oversold": "hkpe", "hk_mr1": "hkmr", "hk_mr2": "hkmr",
+}
+
+
+def efficiency_factor(sig, pos):
+    ratio = sig / pos if pos else 99
+    if ratio <= 2: return 1.0
+    if ratio <= 5: return 0.9
+    if ratio <= 10: return 0.75
+    return 0.5
+
+
+def get_strategy_info(version, market, regime):
+    conn = sqlite3.connect("/home/hmo/MoFin/data/mofin.db", timeout=10)
+    r = conn.execute(
+        "SELECT trades, positions_taken, win_rate, sharpe_ratio, profit_factor, total_return_pct, "
+        "portfolio_max_dd_pct, universality_score, universality_years, universality_valid_years "
+        "FROM strategy_regime_perf_by_period WHERE strategy=? AND market=? AND regime=? AND period_tag='2y'",
+        (version, market, regime)).fetchone()
+    dep = conn.execute("SELECT MAX(deprecated) FROM strategy_research WHERE version=? AND deprecated IS NOT NULL AND deprecated!=''", (version,)).fetchone()[0]
+    conn.close()
+    if not r:
+        return None
+    sig, pos, wr, sh, pf, ret, dd, univ, uyears, uvalid = r
+    pos = pos or 0
+    ret_c = min(ret or 0, 100) / 100 * 30
+    wr_c = (wr or 0) / 100 * 20
+    sh_c = min(max(sh or 0, 0), 20) / 20 * 20
+    pf_c = min(pf or 0, 5) / 5 * 15
+    dd_c = (1 - min(dd or 0, 50) / 50) * 15
+    n = sig or 0
+    conf = min(1, n / 40)
+    eff = efficiency_factor(sig, pos)
+    comp = round((ret_c + wr_c + sh_c + pf_c + dd_c) * conf * eff)
+    return {"sig": sig, "pos": pos, "comp": comp, "univ": univ or 0,
+            "uyears": uyears or 0, "uvalid": uvalid or 0, "dep": dep}
+
+
+def select_best(regime, market='a'):
+    conn = sqlite3.connect("/home/hmo/MoFin/data/mofin.db", timeout=10)
+    rows = conn.execute("SELECT DISTINCT strategy FROM strategy_regime_perf_by_period WHERE market=? AND regime=?", (market, regime)).fetchall()
+    conn.close()
+    avail = sq.load_availability()
+    candidates = []
+    for (version,) in rows:
+        info = get_strategy_info(version, market, regime)
+        if not info or info["dep"]:
+            continue
+        if not avail.get(version, {}).get("available", False):
+            continue
+        try:
+            q = sq.evaluate_all_regimes(version, market=market)
+            qr = q.get(regime, {})
+            if not (qr.get("long_ok") and qr.get("mid_ok") and qr.get("short_ok")):
+                continue
+        except Exception:
+            continue
+        # 质量分 = 综合分 × 普适（有效年占比越高越好）
+        univ_ratio = (info["uvalid"] / info["uyears"]) if info["uyears"] else 0
+        quality = info["comp"] * (0.5 + 0.5 * univ_ratio)
+        candidates.append({"version": version, "info": info, "quality": round(quality, 1),
+                           "family": FAMILIES.get(version, version)})
+    candidates.sort(key=lambda x: -x["quality"])
+
+    # 择优：家族去重 + 软上限
+    selected = []
+    used_fam = set()
+    total_yearly = 0
+    for c in candidates:
+        if c["family"] in used_fam:
+            continue
+        # 出手次数太少的不选（年化 < MIN_YEARLY/2）
+        yearly = c["info"]["pos"] / 2  # 2y→年化
+        if yearly < 5:
+            continue
+        if total_yearly + yearly > MAX_YEARLY and selected:
+            break  # 超上限停止
+        used_fam.add(c["family"])
+        selected.append(c)
+        total_yearly += yearly
+    return selected, total_yearly
+
+
+if __name__ == "__main__":
+    print("# 择优激活建议（质量分=综合分×普适有效年占比）")
+    all_sel = {}
+    for market in ["a", "hk"]:
+        for regime in ["trend_down", "choppy", "trend_up"]:
+            sel, total = select_best(regime, market)
+            if sel:
+                print(f"\n## {market} {regime}: 年化总出手≈{total:.0f}")
+                for c in sel:
+                    i = c["info"]
+                    print(f"   {c['version']:18} 质量{c['quality']:5.1f} 综合{i['comp']:4} 信号{i['sig']:5} 成交{i['pos']:4} 普适{i['univ']:4.0f}({i['uvalid']}/{i['uyears']}年)")
+                all_sel[(market, regime)] = [c["version"] for c in sel]
+    # 保存建议
+    with open("/tmp/activation_suggestion.json", "w") as f:
+        json.dump({f"{m}:{r}": v for (m, r), v in all_sel.items()}, f, ensure_ascii=False, indent=1)
+    print("\n# 建议已存 /tmp/activation_suggestion.json")