自成长：分支评估+剪枝+报告接入

补齐「顺势而为 环境预判 策略多分枝」体系中缺失的组件：

branch_evaluator.py（新增）— 每30min评估所有策略树分支
  1. detect_scenario() 获取当前宏观情景
  2. 对42只股票评估哪个分支当前适用
  3. 适用分支 trigger_count+1, last_triggered=now
  4. 触发>=3次且成功率<30%→标记pruning_candidate
  5. 无决策树的股票自动初始化（init_default_branches）

prune_branches.py（新增）— 每日16:30收盘后剪枝
  阈值：触发>=3次且成功率<30%→裁掉并归档到pruned_branches
  Dad说「每周太低频」→改为每日

stale_push_wlin.py（修改）— 报告每只股增加分支行：
  【弱势震荡→buy_dip】价格回调到支撑区，弱势市场低吸

cron更新：
  分支扫描(b809fcabfa5b) → 指向branch_evaluator.py, 每30min
  剪枝(a3697c108c7b) → 指向prune_branches.py, 每日16:30

自成长核心：branch_evaluator 运行时自动发现并初始化无策略树股票,
252个分支已生成, trigger_count已开始累积, 反馈循环上线

scripts/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
+
+# 路径
+DECISIONS_PATH = "/home/hmo/web-dashboard/data/decisions.json"
+PORTFOLIO_PATH = "/home/hmo/web-dashboard/data/portfolio.json"
+
+# 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:
+        with open(PORTFOLIO_PATH) as f:
+            pf = json.load(f)
+        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:
+        with open(DECISIONS_PATH) as f:
+            data = json.load(f)
+    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
+
+    # 写回文件
+    with open(DECISIONS_PATH, "w") as f:
+        json.dump(data, f, indent=2, ensure_ascii=False)
+
+    # 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()