branch_scanner: 修price_lower遗漏+只输最优分支

1. check_condition 新增 price_lower 检查（买入区下界）
   之前 buy_dip 分支的 price_lower: >=entry_low 从未被验证
   导致跌出买入区的股票也错误触发买入信号

2. 输出改为每只股票只列最优优先级的适用分支
   之前列出所有适用分支（多分支冲突：同一只股同时止损+买入）
   优先级：止损(P0) > 回调买入(P1) > 追涨(P2) > 减仓(P3) > 止盈(P4) > 持有(P99)
   每只股票只列一项，无矛盾

scripts/branch_scanner.py

@@ -61,7 +61,7 @@ def save_decisions(data):
 
 
 def check_condition(branch, scenario_id, price):
-    """检查分支条件是否满足"""
+    """检查分支条件是否满足 — 同步 strategy_tree._check_branch_condition 逻辑"""
     cond = branch.get("condition", {})
     required_scenario = cond.get("scenario", "")
     if required_scenario and required_scenario != scenario_id:
@@ -76,6 +76,18 @@ def check_condition(branch, scenario_id, price):
             if op == ">" and not (price > val): return False
             if op == "<=" and not (price <= val): return False
             if op == ">=" and not (price >= val): return False
+
+    # 买入区下界（price_lower）检查 — 之前遗漏
+    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
 
 
@@ -114,11 +126,17 @@ def main():
         if not price:
             continue
 
-        for br in branches:
+        # 找出所有适用的分支，选最优（优先级数字最低）
+        best = None
+        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"] = today
-                triggered.append((code, entry.get("name", ""), br))
+                best = br
+                break
+        
+        if best:
+            best["trigger_count"] = best.get("trigger_count", 0) + 1
+            best["last_triggered"] = today
+            triggered.append((code, entry.get("name", ""), best))
 
     if triggered:
         save_decisions(data)