feat: LLM并发模式统一——batch_reassess/trade_capture加concurrent=True(盘前批量4worker不再全压同一key,router round-robin分key), parallel_batch worker 4→6(6 key用满)

deploy/profile-scripts/batch_reassess.py

@@ -998,7 +998,7 @@ def process_stock(code, force_today=False):
     prompt = build_prompt(data)
     
     # ── 使用共享 LLM 客户端（替代 curl subprocess）──
-    result = call_llm(prompt, model=REASSESS_MODEL, max_tokens=None)  # 文档: 推理模型不指定max_tokens
+    result = call_llm(prompt, model=REASSESS_MODEL, max_tokens=None, concurrent=True)  # 2026-08-24 并发模式: router round-robin分key(4worker不再全压同一key)
     
     if not result["ok"] or not (result.get("content") or "").strip():
         print(f"  \u274c LLM调用失败或空输出: {result.get('error') or 'empty content'}")
@@ -1012,7 +1012,7 @@ def process_stock(code, force_today=False):
     # ── 截断保护：输出过短且无信号 = 低质输出，升级 pro 重试一次 ──
     if not parsed.get("signal") and len(full_text) < 1500:
         print(f"  ⚠️ 输出截断({len(full_text)}字)且无信号，升级 {FALLBACK_MODEL} 重试...", flush=True)
-        result2 = call_llm(prompt, model=FALLBACK_MODEL, max_tokens=None)  # 文档: 推理模型不指定max_tokens
+        result2 = call_llm(prompt, model=FALLBACK_MODEL, max_tokens=None, concurrent=True)  # 2026-08-24 并发模式
         if result2["ok"] and len((result2.get("content") or "").strip()) > len(full_text):
             full_text = result2["content"]
             parsed = parse_response(full_text)