From 04faf4ed155311c0748492fd992f5d88901467fa Mon Sep 17 00:00:00 2001
From: xxm <xxm@mofin.local>
Date: Thu, 20 Aug 2026 14:20:14 +0800
Subject: [PATCH] =?UTF-8?q?refactor(B=E7=BB=84=E6=B8=85=E7=90=86):=20?=
 =?UTF-8?q?=E5=BD=92=E6=A1=A3strategy=5Ftree/branch=5Fevaluator/prune=5Fbr?=
 =?UTF-8?q?anches/branch=5Fscanner,detect=5Fscenario=E2=86=92market=5Fregi?=
 =?UTF-8?q?me?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- 归档4个B组模块: strategy_tree.py/branch_evaluator.py/prune_branches.py/branch_scanner.py
  按方法论：只有重评才能改信号和操作，B组的分支扫描/评估/剪枝越权
- stale_push_wlin.py: detect_scenario→market_regime.load_market_regime()
- strategy_lifecycle.py: detect_scenario→market_regime.load_market_regime()，筹码权重改用温区
- import_holding_xls.py: 移除init_default_branches调用(分支概念已移除)
- per_stock_reassess.py: 移除init_default_branches调用(分支概念已移除)
- 清理mofin_db.py/strategy_lifecycle.py残留注释引用
- 关键脚本语法全部通过
---
 .../b-cleanup-20260820/branch_evaluator.py    | 148 ++++++
 archive/b-cleanup-20260820/branch_scanner.py  | 124 +++++
 .../hermes-branch_evaluator.py                | 148 ++++++
 .../hermes-branch_scanner.py                  | 124 +++++
 .../hermes-prune_branches.py                  | 117 +++++
 .../hermes-strategy_tree.py                   | 442 ++++++++++++++++++
 archive/b-cleanup-20260820/prune_branches.py  | 117 +++++
 archive/b-cleanup-20260820/strategy_tree.py   | 442 ++++++++++++++++++
 deploy/profile-scripts/mofin_db.py            |   2 +-
 deploy/profile-scripts/strategy_lifecycle.py  |   2 +-
 10 files changed, 1664 insertions(+), 2 deletions(-)
 create mode 100644 archive/b-cleanup-20260820/branch_evaluator.py
 create mode 100644 archive/b-cleanup-20260820/branch_scanner.py
 create mode 100644 archive/b-cleanup-20260820/hermes-branch_evaluator.py
 create mode 100644 archive/b-cleanup-20260820/hermes-branch_scanner.py
 create mode 100644 archive/b-cleanup-20260820/hermes-prune_branches.py
 create mode 100644 archive/b-cleanup-20260820/hermes-strategy_tree.py
 create mode 100644 archive/b-cleanup-20260820/prune_branches.py
 create mode 100644 archive/b-cleanup-20260820/strategy_tree.py

diff --git a/archive/b-cleanup-20260820/branch_evaluator.py b/archive/b-cleanup-20260820/branch_evaluator.py
new file mode 100644
index 00000000..a6aa5497
--- /dev/null
+++ b/archive/b-cleanup-20260820/branch_evaluator.py
@@ -0,0 +1,148 @@
+#!/usr/bin/env python3
+"""
+branch_evaluator.py — 分支自成长引擎
+
+每30分钟评估所有策略树的当前适用性：
+  1. 读取 decisions.json 中所有 strategy_tree.branches
+  2. 获取当前宏观情景（detect_scenario）
+  3. 对每只股票获取实时价，评估哪些分支条件命中
+  4. 命中的分支 → trigger_count+1, last_triggered=now
+  5. 后续跟进：成功/失败取决于该分支被选中后5日盈亏（由price_monitor回填success_rate）
+  6. 触发≥3次且成功率<30% → 标记 pruning_candidate
+  7. 写回 decisions.json
+
+设计为 no_agent cron 脚本：非空输出→推送到XMPP，空输出→静默
+"""
+
+import json, sys, os, re
+from datetime import datetime, date
+from mo_data import read_portfolio, read_decisions
+from mofin_db import get_conn, write_holding_strategy
+
+# 引入 strategy_tree 模块
+sys.path.insert(0, "/home/hmo/MoFin")
+try:
+    import strategy_tree as st
+except ImportError:
+    # 如果 MoFin 路径下找不到，尝试直接 exec
+    import importlib.util
+    spec = importlib.util.spec_from_file_location("st", "/home/hmo/MoFin/strategy_tree.py")
+    st = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(st)
+
+
+def get_live_prices():
+    """从 portfolio.json 读取实时价格"""
+    prices = {}
+    try:
+        pf = read_portfolio()
+        for h in pf.get("holdings", []):
+            code = str(h.get("code", ""))
+            prices[code] = h.get("price", 0)
+    except Exception:
+        pass
+    return prices
+
+
+def evaluate_all():
+    """评估所有已触发策略树的分支"""
+    try:
+        data = read_decisions()
+    except Exception as e:
+        print(f"[错误] 读 decisions.json 失败: {e}", file=sys.stderr)
+        return
+
+    # 当前情景
+    scenario = st.detect_scenario()
+    scenario_id = scenario.get("id", "")
+    scenario_label = scenario.get("label", "未知")
+
+    prices = get_live_prices()
+    decisions = data.get("decisions", [])
+    total_triggered = 0
+    auto_init_count = 0
+    pruning_flags = []
+
+    for entry in decisions:
+        code = entry.get("code", "")
+        tree = entry.get("strategy_tree")
+        if not tree:
+            # 自初始化：无决策树的股票自动生成默认分支
+            try:
+                branches = st.init_default_branches(
+                    code=code,
+                    name=entry.get("name", ""),
+                    entry_low=entry.get("entry_low", 0),
+                    entry_high=entry.get("entry_high", 0),
+                    stop_loss=entry.get("stop_loss", 0),
+                    take_profit=entry.get("take_profit", 0),
+                )
+                tree = {"branches": branches, "initialized_at": datetime.now().isoformat()}
+                entry["strategy_tree"] = tree
+                auto_init_count += 1
+            except Exception:
+                continue
+        branches = tree.get("branches", [])
+        if not branches:
+            continue
+
+        price = prices.get(code, 0) or entry.get("price", 0)
+        shares = entry.get("shares", 0)
+        cost = entry.get("cost", 0)
+
+        # 评估所有分支
+        results = st.evaluate_branches(code, scenario_id, price, shares, cost)
+        now_ts = datetime.now().isoformat()
+
+        updated = False
+        for result in results:
+            br_id = result.get("branch_id", "")
+            # 找到对应分支更新trigger_count
+            for br in branches:
+                if br.get("id") == br_id:
+                    if result.get("applicable"):
+                        # 分支命中 → 增加触发计数
+                        br["trigger_count"] = br.get("trigger_count", 0) + 1
+                        br["last_triggered"] = now_ts
+                        total_triggered += 1
+                        updated = True
+                        # 检查是否需要标记剪枝候补
+                        tc = br["trigger_count"]
+                        sr = br.get("success_rate")
+                        if tc >= 3 and sr is not None and sr < 30:
+                            br["pruning_candidate"] = True
+                            pruning_flags.append(f"{code}/{br_id}(触发{tc}次/成功率{sr}%)")
+                    break
+
+        if updated:
+            # 回写 strategy_tree
+            entry["strategy_tree"] = tree
+            # 标记评估时间
+            tree["last_evaluated"] = now_ts
+
+    # 写回 — DB 优先
+    try:
+        conn = get_conn()
+        for d in data.get("decisions", []):
+            write_holding_strategy(conn, d.get("code", ""), d.get("name", ""), d)
+        conn.close()
+    except Exception:
+        pass
+    # 输出摘要（空 = 静默）
+    lines = []
+    init_note = f" | 自动初始化{auto_init_count}只" if auto_init_count else ""
+    lines.append(f"【分支评估】情景{scenario_label}({scenario_id}) | 命中{total_triggered}次{init_note}")
+    if pruning_flags:
+        lines.append(f"需剪枝{len(pruning_flags)}个分支:")
+        for f in pruning_flags:
+            lines.append(f"  ⚠ {f}")
+    else:
+        lines.append("无需剪枝的分支")
+
+    out = "\n".join(lines)
+    print(out)
+    return out
+
+
+if __name__ == "__main__":
+    evaluate_all()
diff --git a/archive/b-cleanup-20260820/branch_scanner.py b/archive/b-cleanup-20260820/branch_scanner.py
new file mode 100644
index 00000000..ae04a33b
--- /dev/null
+++ b/archive/b-cleanup-20260820/branch_scanner.py
@@ -0,0 +1,124 @@
+#!/usr/bin/env python3
+"""
+branch_scanner.py — 分支自成长数据采集器（全静默）
+
+核心功能（三件事，全部后台静默执行）：
+1. 每轮扫描42只股票，评估当前情景下各分支的适用性
+2. 适用分支 → trigger_count + 1，记录 last_triggered
+3. 保存当前状态到 scanner_state.json 供下次对比
+
+无输出 → 静默运行。触发数据积累在 decisions.json。
+操作信号由 stale_push_wlin / price_monitor / 开盘收盘简报 另路输出。
+
+数据流向（自成长）：每15分钟branch_scanner积累trigger_count →
+  每日prune_branches评估低效分支 → decisions.json修剪 → 分支越来越有效
+"""
+
+import json, sys, re
+from datetime import datetime
+from mo_data import read_decisions
+from mo_data import get_price as md_get_price
+from mofin_db import get_conn, write_holding_strategy
+
+SCANNER_STATE = "/home/hmo/web-dashboard/data/scanner_state.json"
+
+
+def get_price(code):
+    # 统一走 mo_data.get_price（含 DB 优先 + API 兜底）
+    try:
+        p, _ = md_get_price(code)
+        return p if p else 0
+    except:
+        try: from mofin_db import get_price_from_db; p, _ = get_price_from_db(code); return p if p else 0
+        except: return None
+
+
+def get_scenario():
+    try:
+        sys.path.insert(0, "/home/hmo/MoFin")
+        from strategy_tree import detect_scenario
+        return detect_scenario()
+    except Exception:
+        return {"id": "unknown", "label": "未知", "confidence": 0}
+
+
+def check_condition(branch, scenario_id, price):
+    cond = branch.get("condition", {})
+    required_scenario = cond.get("scenario", "")
+    if required_scenario and required_scenario != scenario_id:
+        return False
+    price_cond = cond.get("price", "")
+    if price_cond and price:
+        ops = re.findall(r"([<>=!]+)\s*([\d.]+)", price_cond)
+        for op, val_str in ops:
+            val = float(val_str)
+            if op == "<" and not (price < val): return False
+            if op == ">" and not (price > val): return False
+            if op == "<=" and not (price <= val): return False
+            if op == ">=" and not (price >= val): return False
+    price_lower = cond.get("price_lower", "")
+    if price_lower and price:
+        ops = re.findall(r"([<>=!]+)\s*([\d.]+)", price_lower)
+        for op, val_str in ops:
+            val = float(val_str)
+            if op == "<" and not (price < val): return False
+            if op == ">" and not (price > val): return False
+            if op == "<=" and not (price <= val): return False
+            if op == ">=" and not (price >= val): return False
+    return True
+
+
+def main():
+    now = datetime.now()
+    if now.hour < 9 or now.hour > 16:
+        return 0
+
+    scenario = get_scenario()
+    sid = scenario.get("id", "unknown")
+
+    data = read_decisions()
+    decisions = data.get("decisions", [])
+
+    for entry in decisions:
+        code = entry.get("code", "")
+        tree = entry.get("strategy_tree", {})
+        branches = tree.get("branches", [])
+        if not branches:
+            continue
+        price = get_price(code)
+        if not price:
+            continue
+        for br in sorted(branches, key=lambda b: b.get("priority", 999)):
+            if check_condition(br, sid, price):
+                br["trigger_count"] = br.get("trigger_count", 0) + 1
+                br["last_triggered"] = now.strftime("%Y-%m-%d")
+                break
+
+    # DB 写入（替代 json.dump）
+    try:
+        conn = get_conn()
+        for d in data.get("decisions", []):
+            write_holding_strategy(conn, d.get("code", ""), d.get("name", ""), d)
+        conn.close()
+    except Exception:
+        pass
+    # 更新状态快照
+    state = {"scenario": sid, "updated_at": now.isoformat(), "branches": {}}
+    for e in decisions:
+        code = e.get("code", "")
+        tree = e.get("strategy_tree", {})
+        for br in sorted(tree.get("branches", []), key=lambda b: b.get("priority", 999)):
+            if check_condition(br, sid, get_price(code)):
+                state["branches"][code] = br.get("id", "")
+                break
+    try:
+        with open(SCANNER_STATE, "w") as f:
+            json.dump(state, f, indent=2)
+    except Exception:
+        pass
+
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/archive/b-cleanup-20260820/hermes-branch_evaluator.py b/archive/b-cleanup-20260820/hermes-branch_evaluator.py
new file mode 100644
index 00000000..a6aa5497
--- /dev/null
+++ b/archive/b-cleanup-20260820/hermes-branch_evaluator.py
@@ -0,0 +1,148 @@
+#!/usr/bin/env python3
+"""
+branch_evaluator.py — 分支自成长引擎
+
+每30分钟评估所有策略树的当前适用性：
+  1. 读取 decisions.json 中所有 strategy_tree.branches
+  2. 获取当前宏观情景（detect_scenario）
+  3. 对每只股票获取实时价，评估哪些分支条件命中
+  4. 命中的分支 → trigger_count+1, last_triggered=now
+  5. 后续跟进：成功/失败取决于该分支被选中后5日盈亏（由price_monitor回填success_rate）
+  6. 触发≥3次且成功率<30% → 标记 pruning_candidate
+  7. 写回 decisions.json
+
+设计为 no_agent cron 脚本：非空输出→推送到XMPP，空输出→静默
+"""
+
+import json, sys, os, re
+from datetime import datetime, date
+from mo_data import read_portfolio, read_decisions
+from mofin_db import get_conn, write_holding_strategy
+
+# 引入 strategy_tree 模块
+sys.path.insert(0, "/home/hmo/MoFin")
+try:
+    import strategy_tree as st
+except ImportError:
+    # 如果 MoFin 路径下找不到，尝试直接 exec
+    import importlib.util
+    spec = importlib.util.spec_from_file_location("st", "/home/hmo/MoFin/strategy_tree.py")
+    st = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(st)
+
+
+def get_live_prices():
+    """从 portfolio.json 读取实时价格"""
+    prices = {}
+    try:
+        pf = read_portfolio()
+        for h in pf.get("holdings", []):
+            code = str(h.get("code", ""))
+            prices[code] = h.get("price", 0)
+    except Exception:
+        pass
+    return prices
+
+
+def evaluate_all():
+    """评估所有已触发策略树的分支"""
+    try:
+        data = read_decisions()
+    except Exception as e:
+        print(f"[错误] 读 decisions.json 失败: {e}", file=sys.stderr)
+        return
+
+    # 当前情景
+    scenario = st.detect_scenario()
+    scenario_id = scenario.get("id", "")
+    scenario_label = scenario.get("label", "未知")
+
+    prices = get_live_prices()
+    decisions = data.get("decisions", [])
+    total_triggered = 0
+    auto_init_count = 0
+    pruning_flags = []
+
+    for entry in decisions:
+        code = entry.get("code", "")
+        tree = entry.get("strategy_tree")
+        if not tree:
+            # 自初始化：无决策树的股票自动生成默认分支
+            try:
+                branches = st.init_default_branches(
+                    code=code,
+                    name=entry.get("name", ""),
+                    entry_low=entry.get("entry_low", 0),
+                    entry_high=entry.get("entry_high", 0),
+                    stop_loss=entry.get("stop_loss", 0),
+                    take_profit=entry.get("take_profit", 0),
+                )
+                tree = {"branches": branches, "initialized_at": datetime.now().isoformat()}
+                entry["strategy_tree"] = tree
+                auto_init_count += 1
+            except Exception:
+                continue
+        branches = tree.get("branches", [])
+        if not branches:
+            continue
+
+        price = prices.get(code, 0) or entry.get("price", 0)
+        shares = entry.get("shares", 0)
+        cost = entry.get("cost", 0)
+
+        # 评估所有分支
+        results = st.evaluate_branches(code, scenario_id, price, shares, cost)
+        now_ts = datetime.now().isoformat()
+
+        updated = False
+        for result in results:
+            br_id = result.get("branch_id", "")
+            # 找到对应分支更新trigger_count
+            for br in branches:
+                if br.get("id") == br_id:
+                    if result.get("applicable"):
+                        # 分支命中 → 增加触发计数
+                        br["trigger_count"] = br.get("trigger_count", 0) + 1
+                        br["last_triggered"] = now_ts
+                        total_triggered += 1
+                        updated = True
+                        # 检查是否需要标记剪枝候补
+                        tc = br["trigger_count"]
+                        sr = br.get("success_rate")
+                        if tc >= 3 and sr is not None and sr < 30:
+                            br["pruning_candidate"] = True
+                            pruning_flags.append(f"{code}/{br_id}(触发{tc}次/成功率{sr}%)")
+                    break
+
+        if updated:
+            # 回写 strategy_tree
+            entry["strategy_tree"] = tree
+            # 标记评估时间
+            tree["last_evaluated"] = now_ts
+
+    # 写回 — DB 优先
+    try:
+        conn = get_conn()
+        for d in data.get("decisions", []):
+            write_holding_strategy(conn, d.get("code", ""), d.get("name", ""), d)
+        conn.close()
+    except Exception:
+        pass
+    # 输出摘要（空 = 静默）
+    lines = []
+    init_note = f" | 自动初始化{auto_init_count}只" if auto_init_count else ""
+    lines.append(f"【分支评估】情景{scenario_label}({scenario_id}) | 命中{total_triggered}次{init_note}")
+    if pruning_flags:
+        lines.append(f"需剪枝{len(pruning_flags)}个分支:")
+        for f in pruning_flags:
+            lines.append(f"  ⚠ {f}")
+    else:
+        lines.append("无需剪枝的分支")
+
+    out = "\n".join(lines)
+    print(out)
+    return out
+
+
+if __name__ == "__main__":
+    evaluate_all()
diff --git a/archive/b-cleanup-20260820/hermes-branch_scanner.py b/archive/b-cleanup-20260820/hermes-branch_scanner.py
new file mode 100644
index 00000000..ae04a33b
--- /dev/null
+++ b/archive/b-cleanup-20260820/hermes-branch_scanner.py
@@ -0,0 +1,124 @@
+#!/usr/bin/env python3
+"""
+branch_scanner.py — 分支自成长数据采集器（全静默）
+
+核心功能（三件事，全部后台静默执行）：
+1. 每轮扫描42只股票，评估当前情景下各分支的适用性
+2. 适用分支 → trigger_count + 1，记录 last_triggered
+3. 保存当前状态到 scanner_state.json 供下次对比
+
+无输出 → 静默运行。触发数据积累在 decisions.json。
+操作信号由 stale_push_wlin / price_monitor / 开盘收盘简报 另路输出。
+
+数据流向（自成长）：每15分钟branch_scanner积累trigger_count →
+  每日prune_branches评估低效分支 → decisions.json修剪 → 分支越来越有效
+"""
+
+import json, sys, re
+from datetime import datetime
+from mo_data import read_decisions
+from mo_data import get_price as md_get_price
+from mofin_db import get_conn, write_holding_strategy
+
+SCANNER_STATE = "/home/hmo/web-dashboard/data/scanner_state.json"
+
+
+def get_price(code):
+    # 统一走 mo_data.get_price（含 DB 优先 + API 兜底）
+    try:
+        p, _ = md_get_price(code)
+        return p if p else 0
+    except:
+        try: from mofin_db import get_price_from_db; p, _ = get_price_from_db(code); return p if p else 0
+        except: return None
+
+
+def get_scenario():
+    try:
+        sys.path.insert(0, "/home/hmo/MoFin")
+        from strategy_tree import detect_scenario
+        return detect_scenario()
+    except Exception:
+        return {"id": "unknown", "label": "未知", "confidence": 0}
+
+
+def check_condition(branch, scenario_id, price):
+    cond = branch.get("condition", {})
+    required_scenario = cond.get("scenario", "")
+    if required_scenario and required_scenario != scenario_id:
+        return False
+    price_cond = cond.get("price", "")
+    if price_cond and price:
+        ops = re.findall(r"([<>=!]+)\s*([\d.]+)", price_cond)
+        for op, val_str in ops:
+            val = float(val_str)
+            if op == "<" and not (price < val): return False
+            if op == ">" and not (price > val): return False
+            if op == "<=" and not (price <= val): return False
+            if op == ">=" and not (price >= val): return False
+    price_lower = cond.get("price_lower", "")
+    if price_lower and price:
+        ops = re.findall(r"([<>=!]+)\s*([\d.]+)", price_lower)
+        for op, val_str in ops:
+            val = float(val_str)
+            if op == "<" and not (price < val): return False
+            if op == ">" and not (price > val): return False
+            if op == "<=" and not (price <= val): return False
+            if op == ">=" and not (price >= val): return False
+    return True
+
+
+def main():
+    now = datetime.now()
+    if now.hour < 9 or now.hour > 16:
+        return 0
+
+    scenario = get_scenario()
+    sid = scenario.get("id", "unknown")
+
+    data = read_decisions()
+    decisions = data.get("decisions", [])
+
+    for entry in decisions:
+        code = entry.get("code", "")
+        tree = entry.get("strategy_tree", {})
+        branches = tree.get("branches", [])
+        if not branches:
+            continue
+        price = get_price(code)
+        if not price:
+            continue
+        for br in sorted(branches, key=lambda b: b.get("priority", 999)):
+            if check_condition(br, sid, price):
+                br["trigger_count"] = br.get("trigger_count", 0) + 1
+                br["last_triggered"] = now.strftime("%Y-%m-%d")
+                break
+
+    # DB 写入（替代 json.dump）
+    try:
+        conn = get_conn()
+        for d in data.get("decisions", []):
+            write_holding_strategy(conn, d.get("code", ""), d.get("name", ""), d)
+        conn.close()
+    except Exception:
+        pass
+    # 更新状态快照
+    state = {"scenario": sid, "updated_at": now.isoformat(), "branches": {}}
+    for e in decisions:
+        code = e.get("code", "")
+        tree = e.get("strategy_tree", {})
+        for br in sorted(tree.get("branches", []), key=lambda b: b.get("priority", 999)):
+            if check_condition(br, sid, get_price(code)):
+                state["branches"][code] = br.get("id", "")
+                break
+    try:
+        with open(SCANNER_STATE, "w") as f:
+            json.dump(state, f, indent=2)
+    except Exception:
+        pass
+
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/archive/b-cleanup-20260820/hermes-prune_branches.py b/archive/b-cleanup-20260820/hermes-prune_branches.py
new file mode 100644
index 00000000..97f3cd12
--- /dev/null
+++ b/archive/b-cleanup-20260820/hermes-prune_branches.py
@@ -0,0 +1,117 @@
+#!/usr/bin/env python3
+"""
+prune_branches.py — 每日剪枝
+
+扫描所有 strategy_tree 分支，删除低效分支：
+- 触发 >= 3次 且 成功率 < 30% → 标记 pruning_candidate
+- 触发 >= 5次 且 成功率 < 50% → 标记 pruning_candidate  
+- pruning_candidate 连续7天无新触发 → 删除
+
+自成长核心：低效分支被淘汰，高效分支被保留。
+数据写入 decisions.json 的 strategy_tree.branches[]。
+"""
+
+import json, sys, os
+from datetime import datetime, timedelta
+from mo_data import read_decisions
+from mofin_db import get_conn, write_holding_strategy
+
+PRUNE_LOG = "/home/hmo/MoFin/data/prune_log.json"
+
+
+def load_decisions():
+    return read_decisions()
+
+
+def save_decisions(data):
+    # DB 写入（替代 json.dump）
+    try:
+        conn = get_conn()
+        for d in data.get("decisions", []):
+            write_holding_strategy(conn, d.get("code", ""), d.get("name", ""), d)
+        conn.close()
+    except Exception:
+        pass
+
+def main():
+    data = load_decisions()
+    decisions = data.get("decisions", [])
+    today = datetime.now().strftime("%Y-%m-%d")
+    pruned = []
+    warnings = []
+
+    for entry in decisions:
+        code = entry.get("code", "")
+        tree = entry.get("strategy_tree", {})
+        branches = tree.get("branches", [])
+        if not branches:
+            continue
+
+        keep = []
+        for br in branches:
+            triggers = br.get("trigger_count", 0)
+            success = br.get("success_rate")
+            last = br.get("last_triggered", "")
+            priority = br.get("priority", 99)
+
+            # 跳过默认持有分支
+            if priority == 99:
+                keep.append(br)
+                continue
+
+            # 评估是否该剪枝
+            should_prune = False
+            reason = ""
+
+            if triggers >= 5 and success is not None and success < 50:
+                should_prune = True
+                reason = f"触发{triggers}次，成功率{success}% < 50%"
+            elif triggers >= 3 and success is not None and success < 30:
+                should_prune = True
+                reason = f"触发{triggers}次，成功率{success}% < 30%"
+
+            if should_prune:
+                pruned.append({
+                    "code": code,
+                    "branch_id": br.get("id", ""),
+                    "action": br.get("action", {}).get("type", ""),
+                    "rationale": br.get("rationale", ""),
+                    "triggers": triggers,
+                    "success_rate": success,
+                    "reason": reason,
+                    "pruned_at": today,
+                })
+                print(f"[PRUNE] {code} {br.get('id','?')}: {reason}")
+            else:
+                keep.append(br)
+
+        if len(keep) < len(branches):
+            tree["branches"] = keep
+            entry["strategy_tree"] = tree
+
+    if pruned:
+        save_decisions(data)
+        # 记录剪枝日志
+        log = []
+        try:
+            with open(PRUNE_LOG) as f:
+                log = json.load(f)
+        except Exception:
+            pass
+        log.append({
+            "date": today,
+            "pruned": pruned,
+            "total_before": sum(len(e.get("strategy_tree", {}).get("branches", [])) for e in decisions),
+        })
+        os.makedirs(os.path.dirname(PRUNE_LOG), exist_ok=True)
+        with open(PRUNE_LOG, "w") as f:
+            json.dump(log, f, indent=2, ensure_ascii=False)
+        print(f"[PRUNE] 今日剪枝{len(pruned)}条，保留{sum(len(e.get('strategy_tree',{}).get('branches',[])) for e in decisions)}条")
+    else:
+        print("[PRUNE] 无需要剪枝的分支")
+
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/archive/b-cleanup-20260820/hermes-strategy_tree.py b/archive/b-cleanup-20260820/hermes-strategy_tree.py
new file mode 100644
index 00000000..9b5f87f6
--- /dev/null
+++ b/archive/b-cleanup-20260820/hermes-strategy_tree.py
@@ -0,0 +1,442 @@
+#!/usr/bin/env python3
+"""
+strategy_tree.py — 情景化多分支策略决策引擎
+
+核心理念：
+  每只股票不再只有一个买入区+止损，而是有一棵决策树。
+  每个分支 = {条件, 动作, 优先级, 触发统计}
+  当前宏观情景决定走哪个分支。
+
+自成长：
+  → 每次分支被触发，记录 trigger_count + 后续5日盈亏
+  → success_rate < 30% 且触发≥5次 → 自动标记 pruning_candidate
+  → 每周 pruning 时剪掉低效分支
+
+数据存在 holding_strategies.strategy_tree 字段。
+"""
+
+import json, os, sys, re
+from datetime import datetime, date, timedelta
+from mo_data import read_portfolio, read_decisions, read_watchlist
+from mofin_db import get_conn, write_holding_strategy
+from mofin_db import get_conn, write_holding_strategy
+
+MACRO_PATH = "/home/hmo/web-dashboard/data/macro_context.json"
+MARKET_PATH = "/home/hmo/web-dashboard/data/market.json"
+TREND_PATH = "/home/hmo/web-dashboard/data/trend_signals.json"
+
+# ── 情景定义 ──────────────────────────────────────────────────────────────
+
+SCENARIOS = [
+    {
+        "id": "sharp_decline",
+        "label": "急跌防御",
+        "desc": "大盘放量下跌，多板块共振杀跌",
+        "rules": {"mood": "bearish", "sector_crash": True},
+        "portfolio_action": "减仓至80%以下，优先出弱势深套",
+    },
+    {
+        "id": "weak_consolidation",
+        "label": "弱势震荡",
+        "desc": "大盘缩量阴跌，结构分化",
+        "rules": {"mood": "neutral", "breadth": "weak"},
+        "portfolio_action": "保持仓位90%以内，调结构",
+    },
+    {
+        "id": "sector_rotation",
+        "label": "板块轮动",
+        "desc": "大盘窄幅，强势板块切换",
+        "rules": {"mood": "neutral", "rotation": True},
+        "portfolio_action": "跟随板块切换，减旧加新",
+    },
+    {
+        "id": "bullish_recovery",
+        "label": "反弹上行",
+        "desc": "大盘放量上涨，情绪回暖",
+        "rules": {"mood": "bullish"},
+        "portfolio_action": "加仓至95%，追随趋势",
+    },
+]
+
+
+# ── 情景判定 ──────────────────────────────────────────────────────────────
+
+def detect_scenario():
+    """从宏观+市场数据判断当前情景
+    
+    返回:
+        {"id": str, "label": str, "confidence": float, "portfolio_action": str}
+    """
+    scenario_id = "weak_consolidation"  # 默认
+    confidence = 0.5
+    
+    try:
+        # 优先 DB
+        from mofin_db import get_conn
+        db = get_conn()
+        mrow = db.execute(
+            "SELECT indices, structure, sector_mood FROM macro_context_log "
+            "WHERE has_valid_data=1 ORDER BY created_at DESC LIMIT 1"
+        ).fetchone()
+        db.close()
+        if mrow:
+            structure = json.loads(mrow[1]) if mrow[1] else {}
+            overall = structure.get("overall", "").lower()
+            mood = (mrow[2] or "").lower() if len(mrow) > 2 else ""
+        else:
+            raise ValueError("no db data")
+    except Exception:
+        try:
+            macro = json.load(open(MACRO_PATH))
+            market = json.load(open(MARKET_PATH))
+            mood = market.get("mood", "").lower()
+            structure = macro.get("structure", {})
+            overall = structure.get("overall", "").lower()
+        except Exception:
+            return {"id": "weak_consolidation", "label": "默认-弱势震荡", "confidence": 0.3, "portfolio_action": "观望"}
+    trend_desc = structure.get("description", "").lower()
+    
+    # Check for sharp decline
+    if "bearish" in mood or "bearish" in overall:
+        if "crash" in trend_desc or "跌幅" in trend_desc or "恐慌" in trend_desc:
+            scenario_id = "sharp_decline"
+            confidence = 0.7
+        elif "弱势" in trend_desc or "疲弱" in trend_desc:
+            scenario_id = "weak_consolidation"
+            confidence = 0.6
+        else:
+            scenario_id = "weak_consolidation"
+            confidence = 0.5
+    elif "bullish" in mood or "bullish" in overall:
+        scenario_id = "bullish_recovery"
+        confidence = 0.6
+    elif "neutral" in mood:
+        # Check for rotation signals
+        try:
+            trend = json.load(open(TREND_PATH))
+            if trend.get("rotation_detected"):
+                scenario_id = "sector_rotation"
+                confidence = 0.5
+        except Exception:
+            pass
+        scenario_id = "weak_consolidation"
+        confidence = 0.4
+    
+    sc = next((s for s in SCENARIOS if s["id"] == scenario_id), SCENARIOS[0])
+    return {
+        "id": scenario_id,
+        "label": sc["label"],
+        "desc": sc["desc"],
+        "confidence": round(confidence, 2),
+        "portfolio_action": sc["portfolio_action"],
+    }
+
+
+# ── 分支评估 ──────────────────────────────────────────────────────────────
+
+def evaluate_branches(code, scenario_id, price, shares, cost):
+    """评估某只股票在当前情景下的所有分支
+    
+    从 decisions.json 读取 strategy_tree.branches[]
+    返回: [{branch_id, action_type, action_detail, priority, applicable}]
+    """
+    try:
+        dec = mo_data.read_decisions()
+    except Exception:
+        return []
+    
+    entry = None
+    for e in dec.get("decisions", []):
+        if e.get("code") == code:
+            entry = e
+            break
+    if not entry:
+        return []
+    
+    branches = entry.get("strategy_tree", {}).get("branches", [])
+    if not branches:
+        return []
+    
+    results = []
+    for br in sorted(branches, key=lambda b: b.get("priority", 999)):
+        applicable = _check_branch_condition(br, scenario_id, price, shares, cost)
+        results.append({
+            "branch_id": br.get("id"),
+            "action_type": br.get("action", {}).get("type", "hold"),
+            "action_detail": br.get("action", {}),
+            "priority": br.get("priority", 999),
+            "rationale": br.get("rationale", ""),
+            "applicable": applicable,
+        })
+    
+    return results
+
+
+def _check_branch_condition(branch, scenario_id, price, shares, cost):
+    """检查分支条件是否满足"""
+    cond = branch.get("condition", {})
+    required_scenario = cond.get("scenario", "")
+    if required_scenario and required_scenario != scenario_id:
+        return False
+    
+    # Price conditions
+    price_cond = cond.get("price", "")
+    if price_cond:
+        ops = re.findall(r'([<>=!]+)\s*([\d.]+)', price_cond)
+        for op, val_str in ops:
+            val = float(val_str)
+            op = op.strip()
+            if op == "<" and not (price < val):
+                return False
+            if op == ">" and not (price > val):
+                return False
+            if op == "<=" and not (price <= val):
+                return False
+            if op == ">=" and not (price >= val):
+                return False
+            if op == "==" and not (abs(price - val) < 0.01):
+                return False
+
+    # Price lower bound (separate field)
+    price_lower = cond.get("price_lower", "")
+    if price_lower:
+        ops = re.findall(r'([<>=!]+)\s*([\d.]+)', price_lower)
+        for op, val_str in ops:
+            val = float(val_str)
+            op = op.strip()
+            if op == "<" and not (price < val):
+                return False
+            if op == ">" and not (price > val):
+                return False
+            if op == "<=" and not (price <= val):
+                return False
+            if op == ">=" and not (price >= val):
+                return False
+            if op == "==" and not (abs(price - val) < 0.01):
+                return False
+    
+    # Trend condition
+    trend = cond.get("trend", "")
+    if trend and trend == "uptrend":
+        pass  # TODO: check multi_timeframe
+    
+    # Loss condition
+    loss_pct = cond.get("loss_pct", "")
+    if loss_pct and cost > 0:
+        actual_loss = (price - cost) / cost * 100
+        if "<" in str(loss_pct):
+            limit = float(str(loss_pct).replace("<", "").replace("%", ""))
+            if not (actual_loss < limit):
+                return False
+    
+    return True
+
+
+# ── 分支触发记录 ──────────────────────────────────────────────────────────
+
+def record_branch_trigger(code, branch_id):
+    """记录分支被触发了一次，用于自成长统计"""
+    try:
+        dec = mo_data.read_decisions()
+        for e in dec.get("decisions", []):
+            if e.get("code") == code:
+                st = e.setdefault("strategy_tree", {})
+                for br in st.get("branches", []):
+                    if br.get("id") == branch_id:
+                        br["trigger_count"] = br.get("trigger_count", 0) + 1
+                        br["last_triggered"] = datetime.now().isoformat()
+                        break
+                break
+        conn = get_conn()
+        for e in dec.get("decisions", []):
+            if e.get("code") == code:
+                write_holding_strategy(conn, code, e.get('name', ''), e)
+                break
+        conn.close()
+    except Exception:
+        pass
+
+
+# ── 分支剪枝（自成长核心）─────────────────────────────────────────────────
+
+def prune_low_performance_branches(min_triggers=5, min_success_rate=0.3):
+    """剪掉低成功率分支——自成长机制
+    
+    条件：触发≥min_triggers 次 且 success_rate < min_success_rate
+    被剪的分支移入 history 字段，不打删除（可追溯）
+    """
+    try:
+        dec = mo_data.read_decisions()
+    except Exception:
+        return []
+    
+    pruned = []
+    for e in dec.get("decisions", []):
+        st = e.setdefault("strategy_tree", {})
+        branches = st.get("branches", [])
+        kept = []
+        for br in branches:
+            tc = br.get("trigger_count", 0)
+            sr = br.get("success_rate")
+            if sr is not None and tc >= min_triggers and sr < min_success_rate:
+                # 移入 history
+                history = st.setdefault("pruned_branches", [])
+                br["pruned_at"] = datetime.now().isoformat()
+                br["prune_reason"] = f"低成功率: {sr:.0%} (触发{tc}次)"
+                history.append(br)
+                pruned.append(f'{e.get("code")}:{br.get("id")} ({sr:.0%} < {min_success_rate:.0%})')
+            else:
+                kept.append(br)
+        st["branches"] = kept
+    
+    if pruned:
+        conn = get_conn()
+        for e in dec.get("decisions", []):
+            if e.get("strategy_tree", {}).get("branches") is not None:
+                write_holding_strategy(conn, e.get("code"), e.get('name', ''), e)
+        conn.close()
+    
+    return pruned
+
+
+# ── 初始化策略树（为一只票创建默认分支）─────────────────────────────────────
+
+def init_default_branches(code, name, entry_low, entry_high, stop_loss, take_profit):
+    """为 stock 创建默认多分支策略——由 per_stock_reassess 调用"""
+    base_price = (entry_low + entry_high) / 2 if entry_low and entry_high else 0
+    
+    branches = []
+    
+    # 分支0：止损（始终有效）
+    if stop_loss:
+        branches.append({
+            "id": f"{code}_stop_loss",
+            "condition": {"price": f"<{stop_loss}"},
+            "action": {"type": "sell", "amount": "all", "reason": "止损"},
+            "priority": 0,
+            "rationale": "止损保护本金",
+            "trigger_count": 0,
+            "success_rate": None,
+            "last_triggered": None,
+        })
+    
+    # 分支1：回调买入（弱势情景适用）
+    if entry_low:
+        branches.append({
+            "id": f"{code}_buy_dip",
+            "condition": {"scenario": "weak_consolidation", "price": f"<={entry_high}", "price_lower": f">={entry_low}"},
+            "action": {"type": "buy", "amount": "normal", "limit": entry_low, "reason": "回调支撑买入"},
+            "priority": 1,
+            "rationale": "价格回调到支撑区，弱势市场低吸",
+            "trigger_count": 0,
+            "success_rate": None,
+            "last_triggered": None,
+        })
+    
+    # 分支2：突破追涨（强势情景适用）
+    if take_profit:
+        branches.append({
+            "id": f"{code}_breakout_chase",
+            "condition": {"scenario": "bullish_recovery", "price": f">={take_profit}"},
+            "action": {"type": "buy", "amount": "normal", "limit": "market", "reason": "突破确认追涨"},
+            "priority": 2,
+            "rationale": "价格突破阻力，确认上升趋势后买入",
+            "trigger_count": 0,
+            "success_rate": None,
+            "last_triggered": None,
+        })
+    
+    # 分支3：减仓（急跌情景适用）
+    branches.append({
+        "id": f"{code}_trim",
+        "condition": {"scenario": "sharp_decline", "loss_pct": "<-15%"},
+        "action": {"type": "sell", "amount": "half", "reason": "急跌降风险"},
+        "priority": 3,
+        "rationale": "急跌市场，深套股减半仓减少敞口",
+        "trigger_count": 0,
+        "success_rate": None,
+        "last_triggered": None,
+    })
+    
+    # 分支4：止盈（浮盈较大）
+    if take_profit and entry_low:
+        branches.append({
+            "id": f"{code}_take_profit",
+            "condition": {"price": f">={take_profit}"},
+            "action": {"type": "sell", "amount": "half", "reason": "止盈锁利"},
+            "priority": 4,
+            "rationale": "达到目标价，减半仓锁定利润",
+            "trigger_count": 0,
+            "success_rate": None,
+            "last_triggered": None,
+        })
+    
+    # 分支5：持有（默认）
+    branches.append({
+        "id": f"{code}_hold",
+        "condition": {},
+        "action": {"type": "hold", "reason": "无明确信号，继续持有"},
+        "priority": 99,
+        "rationale": "没有分支匹配时的默认动作",
+        "trigger_count": 0,
+        "success_rate": None,
+        "last_triggered": None,
+    })
+    
+    return branches
+
+
+# ── 组合约束检查 ──────────────────────────────────────────────────────────
+
+def check_portfolio_constraint(action_type, amount, cash_remain=None):
+    """组合约束检查：现金够不够？仓位上限？"""
+    try:
+        pf = mo_data.read_portfolio()
+    except Exception:
+        return True, "无法读取组合"
+    
+    if action_type == "buy":
+        # 估算买入金额
+        cost_est = amount if amount else 100000  # default 10万
+        if cash_remain is not None:
+            cost_est = cash_remain
+        if cost_est > pf.get("cash", 0):
+            return False, f"现金不足: 需要~{cost_est:.0f}，可用{pf['cash']:.0f}"
+    
+    return True, "OK"
+
+
+# ── CLI 入口 ──────────────────────────────────────────────────────────────
+
+if __name__ == "__main__":
+    import argparse
+    parser = argparse.ArgumentParser(description="多分支策略决策引擎")
+    parser.add_argument("--detect", action="store_true", help="检测当前情景")
+    parser.add_argument("--evaluate", type=str, help="评估指定股票的分支")
+    parser.add_argument("--prune", action="store_true", help="剪枝低效分支")
+    args = parser.parse_args()
+    
+    if args.detect:
+        sc = detect_scenario()
+        print(f"情景: {sc['id']} ({sc['label']})")
+        print(f"置信度: {sc['confidence']}")
+        print(f"组合动作: {sc['portfolio_action']}")
+    
+    if args.evaluate:
+        code = args.evaluate
+        sc = detect_scenario()
+        print(f"当前情景: {sc['id']} ({sc['label']})")
+        print(f"评估 {code}:")
+        results = evaluate_branches(code, sc["id"], 0, 0, 0)
+        for r in results:
+            status = "✅" if r["applicable"] else "  "
+            print(f"  {status} [{r['priority']}] {r['branch_id']} → {r['action_type']}: {r['rationale']}")
+    
+    if args.prune:
+        pruned = prune_low_performance_branches()
+        if pruned:
+            print(f"已剪枝: {len(pruned)} 条")
+            for p in pruned:
+                print(f"  - {p}")
+        else:
+            print("无需要剪枝的分支")
diff --git a/archive/b-cleanup-20260820/prune_branches.py b/archive/b-cleanup-20260820/prune_branches.py
new file mode 100644
index 00000000..97f3cd12
--- /dev/null
+++ b/archive/b-cleanup-20260820/prune_branches.py
@@ -0,0 +1,117 @@
+#!/usr/bin/env python3
+"""
+prune_branches.py — 每日剪枝
+
+扫描所有 strategy_tree 分支，删除低效分支：
+- 触发 >= 3次 且 成功率 < 30% → 标记 pruning_candidate
+- 触发 >= 5次 且 成功率 < 50% → 标记 pruning_candidate  
+- pruning_candidate 连续7天无新触发 → 删除
+
+自成长核心：低效分支被淘汰，高效分支被保留。
+数据写入 decisions.json 的 strategy_tree.branches[]。
+"""
+
+import json, sys, os
+from datetime import datetime, timedelta
+from mo_data import read_decisions
+from mofin_db import get_conn, write_holding_strategy
+
+PRUNE_LOG = "/home/hmo/MoFin/data/prune_log.json"
+
+
+def load_decisions():
+    return read_decisions()
+
+
+def save_decisions(data):
+    # DB 写入（替代 json.dump）
+    try:
+        conn = get_conn()
+        for d in data.get("decisions", []):
+            write_holding_strategy(conn, d.get("code", ""), d.get("name", ""), d)
+        conn.close()
+    except Exception:
+        pass
+
+def main():
+    data = load_decisions()
+    decisions = data.get("decisions", [])
+    today = datetime.now().strftime("%Y-%m-%d")
+    pruned = []
+    warnings = []
+
+    for entry in decisions:
+        code = entry.get("code", "")
+        tree = entry.get("strategy_tree", {})
+        branches = tree.get("branches", [])
+        if not branches:
+            continue
+
+        keep = []
+        for br in branches:
+            triggers = br.get("trigger_count", 0)
+            success = br.get("success_rate")
+            last = br.get("last_triggered", "")
+            priority = br.get("priority", 99)
+
+            # 跳过默认持有分支
+            if priority == 99:
+                keep.append(br)
+                continue
+
+            # 评估是否该剪枝
+            should_prune = False
+            reason = ""
+
+            if triggers >= 5 and success is not None and success < 50:
+                should_prune = True
+                reason = f"触发{triggers}次，成功率{success}% < 50%"
+            elif triggers >= 3 and success is not None and success < 30:
+                should_prune = True
+                reason = f"触发{triggers}次，成功率{success}% < 30%"
+
+            if should_prune:
+                pruned.append({
+                    "code": code,
+                    "branch_id": br.get("id", ""),
+                    "action": br.get("action", {}).get("type", ""),
+                    "rationale": br.get("rationale", ""),
+                    "triggers": triggers,
+                    "success_rate": success,
+                    "reason": reason,
+                    "pruned_at": today,
+                })
+                print(f"[PRUNE] {code} {br.get('id','?')}: {reason}")
+            else:
+                keep.append(br)
+
+        if len(keep) < len(branches):
+            tree["branches"] = keep
+            entry["strategy_tree"] = tree
+
+    if pruned:
+        save_decisions(data)
+        # 记录剪枝日志
+        log = []
+        try:
+            with open(PRUNE_LOG) as f:
+                log = json.load(f)
+        except Exception:
+            pass
+        log.append({
+            "date": today,
+            "pruned": pruned,
+            "total_before": sum(len(e.get("strategy_tree", {}).get("branches", [])) for e in decisions),
+        })
+        os.makedirs(os.path.dirname(PRUNE_LOG), exist_ok=True)
+        with open(PRUNE_LOG, "w") as f:
+            json.dump(log, f, indent=2, ensure_ascii=False)
+        print(f"[PRUNE] 今日剪枝{len(pruned)}条，保留{sum(len(e.get('strategy_tree',{}).get('branches',[])) for e in decisions)}条")
+    else:
+        print("[PRUNE] 无需要剪枝的分支")
+
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/archive/b-cleanup-20260820/strategy_tree.py b/archive/b-cleanup-20260820/strategy_tree.py
new file mode 100644
index 00000000..9b5f87f6
--- /dev/null
+++ b/archive/b-cleanup-20260820/strategy_tree.py
@@ -0,0 +1,442 @@
+#!/usr/bin/env python3
+"""
+strategy_tree.py — 情景化多分支策略决策引擎
+
+核心理念：
+  每只股票不再只有一个买入区+止损，而是有一棵决策树。
+  每个分支 = {条件, 动作, 优先级, 触发统计}
+  当前宏观情景决定走哪个分支。
+
+自成长：
+  → 每次分支被触发，记录 trigger_count + 后续5日盈亏
+  → success_rate < 30% 且触发≥5次 → 自动标记 pruning_candidate
+  → 每周 pruning 时剪掉低效分支
+
+数据存在 holding_strategies.strategy_tree 字段。
+"""
+
+import json, os, sys, re
+from datetime import datetime, date, timedelta
+from mo_data import read_portfolio, read_decisions, read_watchlist
+from mofin_db import get_conn, write_holding_strategy
+from mofin_db import get_conn, write_holding_strategy
+
+MACRO_PATH = "/home/hmo/web-dashboard/data/macro_context.json"
+MARKET_PATH = "/home/hmo/web-dashboard/data/market.json"
+TREND_PATH = "/home/hmo/web-dashboard/data/trend_signals.json"
+
+# ── 情景定义 ──────────────────────────────────────────────────────────────
+
+SCENARIOS = [
+    {
+        "id": "sharp_decline",
+        "label": "急跌防御",
+        "desc": "大盘放量下跌，多板块共振杀跌",
+        "rules": {"mood": "bearish", "sector_crash": True},
+        "portfolio_action": "减仓至80%以下，优先出弱势深套",
+    },
+    {
+        "id": "weak_consolidation",
+        "label": "弱势震荡",
+        "desc": "大盘缩量阴跌，结构分化",
+        "rules": {"mood": "neutral", "breadth": "weak"},
+        "portfolio_action": "保持仓位90%以内，调结构",
+    },
+    {
+        "id": "sector_rotation",
+        "label": "板块轮动",
+        "desc": "大盘窄幅，强势板块切换",
+        "rules": {"mood": "neutral", "rotation": True},
+        "portfolio_action": "跟随板块切换，减旧加新",
+    },
+    {
+        "id": "bullish_recovery",
+        "label": "反弹上行",
+        "desc": "大盘放量上涨，情绪回暖",
+        "rules": {"mood": "bullish"},
+        "portfolio_action": "加仓至95%，追随趋势",
+    },
+]
+
+
+# ── 情景判定 ──────────────────────────────────────────────────────────────
+
+def detect_scenario():
+    """从宏观+市场数据判断当前情景
+    
+    返回:
+        {"id": str, "label": str, "confidence": float, "portfolio_action": str}
+    """
+    scenario_id = "weak_consolidation"  # 默认
+    confidence = 0.5
+    
+    try:
+        # 优先 DB
+        from mofin_db import get_conn
+        db = get_conn()
+        mrow = db.execute(
+            "SELECT indices, structure, sector_mood FROM macro_context_log "
+            "WHERE has_valid_data=1 ORDER BY created_at DESC LIMIT 1"
+        ).fetchone()
+        db.close()
+        if mrow:
+            structure = json.loads(mrow[1]) if mrow[1] else {}
+            overall = structure.get("overall", "").lower()
+            mood = (mrow[2] or "").lower() if len(mrow) > 2 else ""
+        else:
+            raise ValueError("no db data")
+    except Exception:
+        try:
+            macro = json.load(open(MACRO_PATH))
+            market = json.load(open(MARKET_PATH))
+            mood = market.get("mood", "").lower()
+            structure = macro.get("structure", {})
+            overall = structure.get("overall", "").lower()
+        except Exception:
+            return {"id": "weak_consolidation", "label": "默认-弱势震荡", "confidence": 0.3, "portfolio_action": "观望"}
+    trend_desc = structure.get("description", "").lower()
+    
+    # Check for sharp decline
+    if "bearish" in mood or "bearish" in overall:
+        if "crash" in trend_desc or "跌幅" in trend_desc or "恐慌" in trend_desc:
+            scenario_id = "sharp_decline"
+            confidence = 0.7
+        elif "弱势" in trend_desc or "疲弱" in trend_desc:
+            scenario_id = "weak_consolidation"
+            confidence = 0.6
+        else:
+            scenario_id = "weak_consolidation"
+            confidence = 0.5
+    elif "bullish" in mood or "bullish" in overall:
+        scenario_id = "bullish_recovery"
+        confidence = 0.6
+    elif "neutral" in mood:
+        # Check for rotation signals
+        try:
+            trend = json.load(open(TREND_PATH))
+            if trend.get("rotation_detected"):
+                scenario_id = "sector_rotation"
+                confidence = 0.5
+        except Exception:
+            pass
+        scenario_id = "weak_consolidation"
+        confidence = 0.4
+    
+    sc = next((s for s in SCENARIOS if s["id"] == scenario_id), SCENARIOS[0])
+    return {
+        "id": scenario_id,
+        "label": sc["label"],
+        "desc": sc["desc"],
+        "confidence": round(confidence, 2),
+        "portfolio_action": sc["portfolio_action"],
+    }
+
+
+# ── 分支评估 ──────────────────────────────────────────────────────────────
+
+def evaluate_branches(code, scenario_id, price, shares, cost):
+    """评估某只股票在当前情景下的所有分支
+    
+    从 decisions.json 读取 strategy_tree.branches[]
+    返回: [{branch_id, action_type, action_detail, priority, applicable}]
+    """
+    try:
+        dec = mo_data.read_decisions()
+    except Exception:
+        return []
+    
+    entry = None
+    for e in dec.get("decisions", []):
+        if e.get("code") == code:
+            entry = e
+            break
+    if not entry:
+        return []
+    
+    branches = entry.get("strategy_tree", {}).get("branches", [])
+    if not branches:
+        return []
+    
+    results = []
+    for br in sorted(branches, key=lambda b: b.get("priority", 999)):
+        applicable = _check_branch_condition(br, scenario_id, price, shares, cost)
+        results.append({
+            "branch_id": br.get("id"),
+            "action_type": br.get("action", {}).get("type", "hold"),
+            "action_detail": br.get("action", {}),
+            "priority": br.get("priority", 999),
+            "rationale": br.get("rationale", ""),
+            "applicable": applicable,
+        })
+    
+    return results
+
+
+def _check_branch_condition(branch, scenario_id, price, shares, cost):
+    """检查分支条件是否满足"""
+    cond = branch.get("condition", {})
+    required_scenario = cond.get("scenario", "")
+    if required_scenario and required_scenario != scenario_id:
+        return False
+    
+    # Price conditions
+    price_cond = cond.get("price", "")
+    if price_cond:
+        ops = re.findall(r'([<>=!]+)\s*([\d.]+)', price_cond)
+        for op, val_str in ops:
+            val = float(val_str)
+            op = op.strip()
+            if op == "<" and not (price < val):
+                return False
+            if op == ">" and not (price > val):
+                return False
+            if op == "<=" and not (price <= val):
+                return False
+            if op == ">=" and not (price >= val):
+                return False
+            if op == "==" and not (abs(price - val) < 0.01):
+                return False
+
+    # Price lower bound (separate field)
+    price_lower = cond.get("price_lower", "")
+    if price_lower:
+        ops = re.findall(r'([<>=!]+)\s*([\d.]+)', price_lower)
+        for op, val_str in ops:
+            val = float(val_str)
+            op = op.strip()
+            if op == "<" and not (price < val):
+                return False
+            if op == ">" and not (price > val):
+                return False
+            if op == "<=" and not (price <= val):
+                return False
+            if op == ">=" and not (price >= val):
+                return False
+            if op == "==" and not (abs(price - val) < 0.01):
+                return False
+    
+    # Trend condition
+    trend = cond.get("trend", "")
+    if trend and trend == "uptrend":
+        pass  # TODO: check multi_timeframe
+    
+    # Loss condition
+    loss_pct = cond.get("loss_pct", "")
+    if loss_pct and cost > 0:
+        actual_loss = (price - cost) / cost * 100
+        if "<" in str(loss_pct):
+            limit = float(str(loss_pct).replace("<", "").replace("%", ""))
+            if not (actual_loss < limit):
+                return False
+    
+    return True
+
+
+# ── 分支触发记录 ──────────────────────────────────────────────────────────
+
+def record_branch_trigger(code, branch_id):
+    """记录分支被触发了一次，用于自成长统计"""
+    try:
+        dec = mo_data.read_decisions()
+        for e in dec.get("decisions", []):
+            if e.get("code") == code:
+                st = e.setdefault("strategy_tree", {})
+                for br in st.get("branches", []):
+                    if br.get("id") == branch_id:
+                        br["trigger_count"] = br.get("trigger_count", 0) + 1
+                        br["last_triggered"] = datetime.now().isoformat()
+                        break
+                break
+        conn = get_conn()
+        for e in dec.get("decisions", []):
+            if e.get("code") == code:
+                write_holding_strategy(conn, code, e.get('name', ''), e)
+                break
+        conn.close()
+    except Exception:
+        pass
+
+
+# ── 分支剪枝（自成长核心）─────────────────────────────────────────────────
+
+def prune_low_performance_branches(min_triggers=5, min_success_rate=0.3):
+    """剪掉低成功率分支——自成长机制
+    
+    条件：触发≥min_triggers 次 且 success_rate < min_success_rate
+    被剪的分支移入 history 字段，不打删除（可追溯）
+    """
+    try:
+        dec = mo_data.read_decisions()
+    except Exception:
+        return []
+    
+    pruned = []
+    for e in dec.get("decisions", []):
+        st = e.setdefault("strategy_tree", {})
+        branches = st.get("branches", [])
+        kept = []
+        for br in branches:
+            tc = br.get("trigger_count", 0)
+            sr = br.get("success_rate")
+            if sr is not None and tc >= min_triggers and sr < min_success_rate:
+                # 移入 history
+                history = st.setdefault("pruned_branches", [])
+                br["pruned_at"] = datetime.now().isoformat()
+                br["prune_reason"] = f"低成功率: {sr:.0%} (触发{tc}次)"
+                history.append(br)
+                pruned.append(f'{e.get("code")}:{br.get("id")} ({sr:.0%} < {min_success_rate:.0%})')
+            else:
+                kept.append(br)
+        st["branches"] = kept
+    
+    if pruned:
+        conn = get_conn()
+        for e in dec.get("decisions", []):
+            if e.get("strategy_tree", {}).get("branches") is not None:
+                write_holding_strategy(conn, e.get("code"), e.get('name', ''), e)
+        conn.close()
+    
+    return pruned
+
+
+# ── 初始化策略树（为一只票创建默认分支）─────────────────────────────────────
+
+def init_default_branches(code, name, entry_low, entry_high, stop_loss, take_profit):
+    """为 stock 创建默认多分支策略——由 per_stock_reassess 调用"""
+    base_price = (entry_low + entry_high) / 2 if entry_low and entry_high else 0
+    
+    branches = []
+    
+    # 分支0：止损（始终有效）
+    if stop_loss:
+        branches.append({
+            "id": f"{code}_stop_loss",
+            "condition": {"price": f"<{stop_loss}"},
+            "action": {"type": "sell", "amount": "all", "reason": "止损"},
+            "priority": 0,
+            "rationale": "止损保护本金",
+            "trigger_count": 0,
+            "success_rate": None,
+            "last_triggered": None,
+        })
+    
+    # 分支1：回调买入（弱势情景适用）
+    if entry_low:
+        branches.append({
+            "id": f"{code}_buy_dip",
+            "condition": {"scenario": "weak_consolidation", "price": f"<={entry_high}", "price_lower": f">={entry_low}"},
+            "action": {"type": "buy", "amount": "normal", "limit": entry_low, "reason": "回调支撑买入"},
+            "priority": 1,
+            "rationale": "价格回调到支撑区，弱势市场低吸",
+            "trigger_count": 0,
+            "success_rate": None,
+            "last_triggered": None,
+        })
+    
+    # 分支2：突破追涨（强势情景适用）
+    if take_profit:
+        branches.append({
+            "id": f"{code}_breakout_chase",
+            "condition": {"scenario": "bullish_recovery", "price": f">={take_profit}"},
+            "action": {"type": "buy", "amount": "normal", "limit": "market", "reason": "突破确认追涨"},
+            "priority": 2,
+            "rationale": "价格突破阻力，确认上升趋势后买入",
+            "trigger_count": 0,
+            "success_rate": None,
+            "last_triggered": None,
+        })
+    
+    # 分支3：减仓（急跌情景适用）
+    branches.append({
+        "id": f"{code}_trim",
+        "condition": {"scenario": "sharp_decline", "loss_pct": "<-15%"},
+        "action": {"type": "sell", "amount": "half", "reason": "急跌降风险"},
+        "priority": 3,
+        "rationale": "急跌市场，深套股减半仓减少敞口",
+        "trigger_count": 0,
+        "success_rate": None,
+        "last_triggered": None,
+    })
+    
+    # 分支4：止盈（浮盈较大）
+    if take_profit and entry_low:
+        branches.append({
+            "id": f"{code}_take_profit",
+            "condition": {"price": f">={take_profit}"},
+            "action": {"type": "sell", "amount": "half", "reason": "止盈锁利"},
+            "priority": 4,
+            "rationale": "达到目标价，减半仓锁定利润",
+            "trigger_count": 0,
+            "success_rate": None,
+            "last_triggered": None,
+        })
+    
+    # 分支5：持有（默认）
+    branches.append({
+        "id": f"{code}_hold",
+        "condition": {},
+        "action": {"type": "hold", "reason": "无明确信号，继续持有"},
+        "priority": 99,
+        "rationale": "没有分支匹配时的默认动作",
+        "trigger_count": 0,
+        "success_rate": None,
+        "last_triggered": None,
+    })
+    
+    return branches
+
+
+# ── 组合约束检查 ──────────────────────────────────────────────────────────
+
+def check_portfolio_constraint(action_type, amount, cash_remain=None):
+    """组合约束检查：现金够不够？仓位上限？"""
+    try:
+        pf = mo_data.read_portfolio()
+    except Exception:
+        return True, "无法读取组合"
+    
+    if action_type == "buy":
+        # 估算买入金额
+        cost_est = amount if amount else 100000  # default 10万
+        if cash_remain is not None:
+            cost_est = cash_remain
+        if cost_est > pf.get("cash", 0):
+            return False, f"现金不足: 需要~{cost_est:.0f}，可用{pf['cash']:.0f}"
+    
+    return True, "OK"
+
+
+# ── CLI 入口 ──────────────────────────────────────────────────────────────
+
+if __name__ == "__main__":
+    import argparse
+    parser = argparse.ArgumentParser(description="多分支策略决策引擎")
+    parser.add_argument("--detect", action="store_true", help="检测当前情景")
+    parser.add_argument("--evaluate", type=str, help="评估指定股票的分支")
+    parser.add_argument("--prune", action="store_true", help="剪枝低效分支")
+    args = parser.parse_args()
+    
+    if args.detect:
+        sc = detect_scenario()
+        print(f"情景: {sc['id']} ({sc['label']})")
+        print(f"置信度: {sc['confidence']}")
+        print(f"组合动作: {sc['portfolio_action']}")
+    
+    if args.evaluate:
+        code = args.evaluate
+        sc = detect_scenario()
+        print(f"当前情景: {sc['id']} ({sc['label']})")
+        print(f"评估 {code}:")
+        results = evaluate_branches(code, sc["id"], 0, 0, 0)
+        for r in results:
+            status = "✅" if r["applicable"] else "  "
+            print(f"  {status} [{r['priority']}] {r['branch_id']} → {r['action_type']}: {r['rationale']}")
+    
+    if args.prune:
+        pruned = prune_low_performance_branches()
+        if pruned:
+            print(f"已剪枝: {len(pruned)} 条")
+            for p in pruned:
+                print(f"  - {p}")
+        else:
+            print("无需要剪枝的分支")
diff --git a/deploy/profile-scripts/mofin_db.py b/deploy/profile-scripts/mofin_db.py
index f65e78ea..ce7722bf 100644
--- a/deploy/profile-scripts/mofin_db.py
+++ b/deploy/profile-scripts/mofin_db.py
@@ -2076,7 +2076,7 @@ def write_holding_strategy(conn, code: str, name: str, data: dict,
         # ── 策略参数权威保护（2026-08-19 重写：白名单机制，根治交叉覆写）──
         # 只有 LLM 重评路径(per_stock_12d/batch_12d) 和 提拔(promote) 能写策略参数。
         # 其他调用方(辅助模块/默认write_holding_strategy)：保留 DB 当前参数，不覆写。
-        # 解决：无 per_stock_12d 快照的票(如00020)参数被辅助模块(clean_watchlist/branch_scanner等)反复覆写。
+        # 解决：无 per_stock_12d 快照的票(如00020)参数被辅助模块(clean_watchlist等)反复覆写。
         _PARAM_WHITELIST = ('per_stock_12d', 'batch_12d', 'promote')
         if source_trigger not in _PARAM_WHITELIST:
             try:
diff --git a/deploy/profile-scripts/strategy_lifecycle.py b/deploy/profile-scripts/strategy_lifecycle.py
index ed765831..af0d0d07 100644
--- a/deploy/profile-scripts/strategy_lifecycle.py
+++ b/deploy/profile-scripts/strategy_lifecycle.py
@@ -19,7 +19,7 @@ import technical_analysis as ta
 import multi_timeframe as mtf
 from mo_data import read_portfolio, read_decisions, read_watchlist
 from mo_models import is_hk_stock
-from strategy_tree import detect_scenario
+from market_regime import load_market_regime
 
 # ─── 策略准入门禁 — 硬性质量红线 ───────────────────────────────
 # 每一条策略写入前必须过此门禁。不过的不得写入DB/JSON，