update

app/agents/departments.py

@@ -93,6 +93,38 @@ class DepartmentAgent:
         self._resolver = resolver
         self._broker = DataBroker()
         self._max_rounds = 3
+        self._tool_choice = "auto"
+
+    @property
+    def max_rounds(self) -> int:
+        return self._max_rounds
+
+    @max_rounds.setter
+    def max_rounds(self, value: Any) -> None:
+        try:
+            numeric = int(round(float(value)))
+        except (TypeError, ValueError):
+            raise ValueError("max_rounds must be numeric") from None
+        if numeric < 1:
+            numeric = 1
+        if numeric > 6:
+            numeric = 6
+        self._max_rounds = numeric
+
+    @property
+    def tool_choice(self) -> str:
+        return self._tool_choice
+
+    @tool_choice.setter
+    def tool_choice(self, value: Any) -> None:
+        if value is None:
+            self._tool_choice = "auto"
+            return
+        normalized = str(value).strip().lower()
+        allowed = {"auto", "none", "required"}
+        if normalized not in allowed:
+            raise ValueError(f"Unsupported tool choice: {value}")
+        self._tool_choice = normalized
 
     def _get_llm_config(self) -> LLMConfig:
         if self._resolver:
@@ -159,7 +191,7 @@ class DepartmentAgent:
                     primary_endpoint,
                     messages,
                     tools=tools,
-                    tool_choice="auto",
+                    tool_choice=self._tool_choice,
                 )
             except LLMError as exc:
                 LOGGER.warning(

app/backtest/decision_env.py

@@ -3,6 +3,7 @@ from __future__ import annotations
 
 import json
 import math
+import copy
 from dataclasses import dataclass, replace
 from typing import Any, Callable, Dict, Iterable, List, Mapping, Optional, Sequence, Tuple
 
@@ -18,17 +19,27 @@ LOG_EXTRA = {"stage": "decision_env"}
 
 @dataclass(frozen=True)
 class ParameterSpec:
-    """Defines how a scalar action dimension maps to strategy parameters."""
+    """Defines how an action dimension maps to strategy parameters or behaviors."""
 
     name: str
     target: str
     minimum: float = 0.0
     maximum: float = 1.0
+    values: Optional[Sequence[Any]] = None
 
     def clamp(self, value: float) -> float:
         clipped = max(0.0, min(1.0, float(value)))
         return self.minimum + clipped * (self.maximum - self.minimum)
 
+    def resolve(self, value: float) -> Any:
+        if self.values is not None:
+            if not self.values:
+                raise ValueError(f"ParameterSpec {self.name} configured with empty values list")
+            clipped = max(0.0, min(1.0, float(value)))
+            index = int(round(clipped * (len(self.values) - 1)))
+            return self.values[index]
+        return self.clamp(value)
+
 
 @dataclass
 class EpisodeMetrics:
@@ -68,6 +79,7 @@ class DecisionEnv:
         self._reward_fn = reward_fn or self._default_reward
         self._last_metrics: Optional[EpisodeMetrics] = None
         self._last_action: Optional[Tuple[float, ...]] = None
+        self._last_department_controls: Optional[Dict[str, Dict[str, Any]]] = None
         self._episode = 0
         self._disable_departments = bool(disable_departments)
 
@@ -75,10 +87,15 @@ class DecisionEnv:
     def action_dim(self) -> int:
         return len(self._specs)
 
+    @property
+    def last_department_controls(self) -> Optional[Dict[str, Dict[str, Any]]]:
+        return self._last_department_controls
+
     def reset(self) -> Dict[str, float]:
         self._episode += 1
         self._last_metrics = None
         self._last_action = None
+        self._last_department_controls = None
         return {
             "episode": float(self._episode),
             "baseline_return": 0.0,
@@ -90,14 +107,24 @@ class DecisionEnv:
         action_array = [float(val) for val in action]
         self._last_action = tuple(action_array)
 
-        weights = self._build_weights(action_array)
-        LOGGER.info("episode=%s action=%s weights=%s", self._episode, action_array, weights, extra=LOG_EXTRA)
+        weights, department_controls = self._prepare_actions(action_array)
+        LOGGER.info(
+            "episode=%s action=%s weights=%s controls=%s",
+            self._episode,
+            action_array,
+            weights,
+            department_controls,
+            extra=LOG_EXTRA,
+        )
 
         cfg = replace(self._template_cfg)
         engine = BacktestEngine(cfg)
         engine.weights = weight_map(weights)
         if self._disable_departments:
             engine.department_manager = None
+            applied_controls: Dict[str, Dict[str, Any]] = {}
+        else:
+            applied_controls = self._apply_department_controls(engine, department_controls)
 
         self._clear_portfolio_records()
 
@@ -135,19 +162,153 @@ class DecisionEnv:
             "risk_events": getattr(result, "risk_events", []),
             "portfolio_snapshots": snapshots,
             "portfolio_trades": trades_override,
+            "department_controls": applied_controls,
         }
+        self._last_department_controls = applied_controls
         return observation, reward, True, info
 
-    def _build_weights(self, action: Sequence[float]) -> Dict[str, float]:
+    def _prepare_actions(
+        self,
+        action: Sequence[float],
+    ) -> Tuple[Dict[str, float], Dict[str, Dict[str, Any]]]:
         weights = dict(self._baseline_weights)
+        department_controls: Dict[str, Dict[str, Any]] = {}
         for idx, spec in enumerate(self._specs):
-            value = spec.clamp(action[idx])
+            try:
+                resolved = spec.resolve(action[idx])
+            except ValueError as exc:
+                LOGGER.warning("参数 %s 解析失败：%s", spec.name, exc, extra=LOG_EXTRA)
+                continue
             if spec.target.startswith("agent_weights."):
                 agent_name = spec.target.split(".", 1)[1]
-                weights[agent_name] = value
+                try:
+                    weights[agent_name] = float(resolved)
+                except (TypeError, ValueError):
+                    LOGGER.debug(
+                        "spec %s produced non-numeric weight %s; skipping",
+                        spec.name,
+                        resolved,
+                        extra=LOG_EXTRA,
+                    )
+                continue
+            if spec.target.startswith("department."):
+                target_path = spec.target.split(".")[1:]
+                if len(target_path) < 2:
+                    LOGGER.debug("未识别的部门目标：%s", spec.target, extra=LOG_EXTRA)
+                    continue
+                dept_code = target_path[0]
+                field = ".".join(target_path[1:])
+                dept_controls = department_controls.setdefault(dept_code, {})
+                dept_controls[field] = resolved
+                continue
             else:
                 LOGGER.debug("暂未支持的参数目标：%s", spec.target, extra=LOG_EXTRA)
-        return weights
+        return weights, department_controls
+
+    def _apply_department_controls(
+        self,
+        engine: BacktestEngine,
+        controls: Mapping[str, Mapping[str, Any]],
+    ) -> Dict[str, Dict[str, Any]]:
+        manager = getattr(engine, "department_manager", None)
+        if not manager or not getattr(manager, "agents", None):
+            return {}
+
+        applied: Dict[str, Dict[str, Any]] = {}
+        for dept_code, payload in controls.items():
+            agent = manager.agents.get(dept_code)
+            if not agent or not isinstance(payload, Mapping):
+                continue
+
+            applied_fields: Dict[str, Any] = {}
+
+            # Ensure mutable settings clone to avoid global side-effects
+            try:
+                original_settings = agent.settings
+                cloned_settings = replace(original_settings)
+                cloned_settings.llm = copy.deepcopy(original_settings.llm)
+                agent.settings = cloned_settings
+            except Exception as exc:  # noqa: BLE001
+                LOGGER.warning(
+                    "复制部门 %s 配置失败：%s",
+                    dept_code,
+                    exc,
+                    extra=LOG_EXTRA,
+                )
+                continue
+
+            for raw_field, value in payload.items():
+                field = raw_field.lower()
+                if field == "function_policy":
+                    field = "tool_choice"
+                if field in {"prompt", "instruction"}:
+                    agent.settings.prompt = str(value)
+                    applied_fields[field] = agent.settings.prompt
+                    continue
+                if field == "description":
+                    agent.settings.description = str(value)
+                    applied_fields[field] = agent.settings.description
+                    continue
+                if field in {"prompt_template_id", "prompt_template"}:
+                    agent.settings.prompt_template_id = str(value)
+                    applied_fields["prompt_template_id"] = agent.settings.prompt_template_id
+                    continue
+                if field == "prompt_template_version":
+                    agent.settings.prompt_template_version = str(value)
+                    applied_fields["prompt_template_version"] = agent.settings.prompt_template_version
+                    continue
+                if field in {"temperature", "llm.temperature"}:
+                    try:
+                        temperature = max(0.0, min(2.0, float(value)))
+                        agent.settings.llm.primary.temperature = temperature
+                        applied_fields["temperature"] = temperature
+                    except (TypeError, ValueError):
+                        LOGGER.debug(
+                            "无效的温度值 %s for %s",
+                            value,
+                            dept_code,
+                            extra=LOG_EXTRA,
+                        )
+                    continue
+                if field in {"tool_choice", "tool_strategy"}:
+                    try:
+                        agent.tool_choice = value
+                        applied_fields["tool_choice"] = agent.tool_choice
+                    except ValueError:
+                        LOGGER.debug(
+                            "部门 %s 工具策略 %s 无效",
+                            dept_code,
+                            value,
+                            extra=LOG_EXTRA,
+                        )
+                    continue
+                if field == "max_rounds":
+                    try:
+                        agent.max_rounds = value
+                        applied_fields["max_rounds"] = agent.max_rounds
+                    except ValueError:
+                        LOGGER.debug(
+                            "部门 %s max_rounds %s 无效",
+                            dept_code,
+                            value,
+                            extra=LOG_EXTRA,
+                        )
+                    continue
+                if field == "prompt_template_override":
+                    agent.settings.prompt = str(value)
+                    applied_fields["prompt"] = agent.settings.prompt
+                    continue
+                LOGGER.debug(
+                    "部门 %s 未识别的控制项 %s",
+                    dept_code,
+                    raw_field,
+                    extra=LOG_EXTRA,
+                )
+
+            if applied_fields:
+                applied[dept_code] = applied_fields
+
+        return applied
 
     def _compute_metrics(
         self,

app/llm/templates.py

@@ -252,98 +252,352 @@ class TemplateRegistry:
 DEFAULT_TEMPLATES = {
     "department_base": {
         "name": "部门基础模板",
-        "description": "通用的部门分析提示模板",
+        "description": "所有部门通用的审慎分析提示词骨架",
         "template": """
-部门名称：{title}
+部门：{title}
 股票代码：{ts_code}
 交易日：{trade_date}
 
-角色说明：{description}
-职责指令：{instruction}
+【角色定位】
+- 角色说明：{description}
+- 行动守则：{instruction}
 
-【可用数据范围】
+【数据边界】
+- 可用字段：
 {data_scope}
-
-【核心特征】
+- 核心特征：
 {features}
-
-【市场背景】
+- 市场背景：
 {market_snapshot}
-
-【追加数据】
+- 追加数据：
 {supplements}
 
-请基于以上数据给出该部门对当前股票的操作建议。输出必须是 JSON，字段如下：
+【分析步骤】
+1. 判断信息是否充分，如不充分，请说明缺口并优先调用工具 `fetch_data`（仅限 `daily`、`daily_basic`）。
+2. 梳理 2-3 个关键支撑信号与潜在风险，确保基于提供的数据。
+3. 结合量化证据与限制条件，给出操作建议和信心来源，避免主观臆测。
+
+【输出要求】
+仅返回一个 JSON 对象，不要添加额外文本：
 {{
   "action": "BUY|BUY_S|BUY_M|BUY_L|SELL|HOLD",
-  "confidence": 0-1 之间的小数，表示信心,
-  "summary": "一句话概括理由",
-  "signals": ["详细要点", "..."],
-  "risks": ["风险点", "..."]
+  "confidence": 0-1 之间的小数，
+  "summary": "一句话结论",
+  "signals": ["关键支撑要点", "..."],
+  "risks": ["关键风险要点", "..."]
 }}
-
-如需额外数据，请调用工具 `fetch_data`，仅支持请求 `daily` 或 `daily_basic` 表。
-请严格返回单个 JSON 对象，不要添加额外文本。
+如需说明未完成的数据请求，请在 `risks` 或 `signals` 中明确。
 """,
         "variables": [
-            "title", "ts_code", "trade_date", "description", "instruction",
-            "data_scope", "features", "market_snapshot", "supplements"
+            "title",
+            "ts_code",
+            "trade_date",
+            "description",
+            "instruction",
+            "data_scope",
+            "features",
+            "market_snapshot",
+            "supplements",
         ],
         "required_context": [
-            "ts_code", "trade_date", "features", "market_snapshot"
+            "ts_code",
+            "trade_date",
+            "features",
+            "market_snapshot",
         ],
-        "validation_rules": [
-            "len(features) > 0",
-            "len(market_snapshot) > 0"
-        ]
+        "metadata": {
+            "category": "department",
+            "preset": "base",
+        },
     },
     "momentum_dept": {
-        "name": "动量研究部门",
-        "description": "专注于动量因子分析的部门模板",
+        "name": "动量研究部门模板",
+        "description": "围绕价格与量能动量的决策提示",
         "template": """
-部门名称：动量研究部门
+部门：动量研究部门
 股票代码：{ts_code}
 交易日：{trade_date}
 
-角色说明：专注于分析股票价格动量、成交量动量和技术指标动量
-职责指令：重点关注以下方面:
-1. 价格趋势强度和持续性
-2. 成交量配合度
-3. 技术指标背离
+【角色定位】
+- 专注价格动量、成交量共振与技术指标背离。
+- 保持纪律，识别趋势延续与反转风险。
 
-【可用数据范围】
+【研究重点】
+1. 多时间窗口动量是否同向？
+2. 成交量是否验证价格走势？
+3. 是否出现过热或背离信号？
+
+【数据边界】
+- 可用字段：
 {data_scope}
-
-【动量特征】
+- 动量特征：
 {features}
-
-【市场背景】
+- 市场背景：
 {market_snapshot}
-
-【追加数据】
+- 追加数据：
 {supplements}
 
-请基于以上数据进行动量分析并给出操作建议。输出必须是 JSON，字段如下：
-{{
-  "action": "BUY|BUY_S|BUY_M|BUY_L|SELL|HOLD",
-  "confidence": 0-1 之间的小数，表示信心,
-  "summary": "一句话概括动量分析结论",
-  "signals": ["动量信号要点", "..."],
-  "risks": ["动量风险点", "..."]
-}}
+请沿用【部门基础模板】的分析步骤与输出要求，重点量化趋势动能和量价配合度。
 """,
         "variables": [
-            "ts_code", "trade_date", "data_scope", 
-            "features", "market_snapshot", "supplements"
+            "ts_code",
+            "trade_date",
+            "data_scope",
+            "features",
+            "market_snapshot",
+            "supplements",
         ],
         "required_context": [
-            "ts_code", "trade_date", "features", "market_snapshot"
+            "ts_code",
+            "trade_date",
+            "features",
+            "market_snapshot",
         ],
-        "validation_rules": [
-            "len(features) > 0",
-            "'momentum' in ' '.join(features.keys()).lower()"
-        ]
-    }
+        "metadata": {
+            "category": "department",
+            "preset": "momentum",
+        },
+    },
+    "value_dept": {
+        "name": "价值评估部门模板",
+        "description": "衡量估值与盈利质量的提示词",
+        "template": """
+部门：价值评估部门
+股票代码：{ts_code}
+交易日：{trade_date}
+
+【角色定位】
+- 关注估值分位、盈利质量与安全边际。
+- 从中期配置角度评价当前价格的性价比。
+
+【研究重点】
+1. 历史及同业视角的估值位置。
+2. 盈利与分红的可持续性。
+3. 潜在的估值修复催化或压制因素。
+
+【数据边界】
+- 可用字段：
+{data_scope}
+- 估值与质量特征：
+{features}
+- 市场背景：
+{market_snapshot}
+- 追加数据：
+{supplements}
+
+请按照【部门基础模板】的分析步骤输出结论，并明确估值安全边际来源。
+""",
+        "variables": [
+            "ts_code",
+            "trade_date",
+            "data_scope",
+            "features",
+            "market_snapshot",
+            "supplements",
+        ],
+        "required_context": [
+            "ts_code",
+            "trade_date",
+            "features",
+            "market_snapshot",
+        ],
+        "metadata": {
+            "category": "department",
+            "preset": "value",
+        },
+    },
+    "news_dept": {
+        "name": "新闻情绪部门模板",
+        "description": "针对舆情热度与事件影响的提示词",
+        "template": """
+部门：新闻情绪部门
+股票代码：{ts_code}
+交易日：{trade_date}
+
+【角色定位】
+- 监控舆情热度、事件驱动与短期情绪。
+- 评估新闻对价格波动的正负面影响。
+
+【研究重点】
+1. 新闻情绪是否集中且持续？
+2. 主题与行情是否匹配？
+3. 情绪驱动的风险敞口。
+
+【数据边界】
+- 可用字段：
+{data_scope}
+- 舆情特征：
+{features}
+- 市场背景：
+{market_snapshot}
+- 追加数据：
+{supplements}
+
+请遵循【部门基础模板】的分析步骤，突出情绪驱动的力度与时效性。
+""",
+        "variables": [
+            "ts_code",
+            "trade_date",
+            "data_scope",
+            "features",
+            "market_snapshot",
+            "supplements",
+        ],
+        "required_context": [
+            "ts_code",
+            "trade_date",
+            "features",
+            "market_snapshot",
+        ],
+        "metadata": {
+            "category": "department",
+            "preset": "news",
+        },
+    },
+    "liquidity_dept": {
+        "name": "流动性评估部门模板",
+        "description": "衡量成交活跃度与执行成本的提示词",
+        "template": """
+部门：流动性评估部门
+股票代码：{ts_code}
+交易日：{trade_date}
+
+【角色定位】
+- 评估成交活跃度、交易成本与可执行性。
+- 提醒潜在的流动性风险与仓位限制。
+
+【研究重点】
+1. 当前成交量与历史均值的对比。
+2. 价量限制（涨跌停、停牌等）对执行的影响。
+3. 预估滑点与转手难度。
+
+【数据边界】
+- 可用字段：
+{data_scope}
+- 流动性特征：
+{features}
+- 市场背景：
+{market_snapshot}
+- 追加数据：
+{supplements}
+
+请遵循【部门基础模板】的分析步骤，重点描述执行可行性与仓位建议。
+""",
+        "variables": [
+            "ts_code",
+            "trade_date",
+            "data_scope",
+            "features",
+            "market_snapshot",
+            "supplements",
+        ],
+        "required_context": [
+            "ts_code",
+            "trade_date",
+            "features",
+            "market_snapshot",
+        ],
+        "metadata": {
+            "category": "department",
+            "preset": "liquidity",
+        },
+    },
+    "macro_dept": {
+        "name": "宏观研究部门模板",
+        "description": "宏观与行业景气度分析提示词",
+        "template": """
+部门：宏观研究部门
+股票代码：{ts_code}
+交易日：{trade_date}
+
+【角色定位】
+- 追踪宏观周期、行业景气与相对强弱。
+- 评估宏观事件对该标的的方向性影响。
+
+【研究重点】
+1. 行业相对大盘的表现与热点程度。
+2. 宏观/政策事件对行业或标的的指引。
+3. 需警惕的宏观风险与流动性环境。
+
+【数据边界】
+- 可用字段：
+{data_scope}
+- 宏观特征：
+{features}
+- 市场背景：
+{market_snapshot}
+- 追加数据：
+{supplements}
+
+请执行【部门基础模板】的分析步骤，并输出宏观驱动的信号与风险。
+""",
+        "variables": [
+            "ts_code",
+            "trade_date",
+            "data_scope",
+            "features",
+            "market_snapshot",
+            "supplements",
+        ],
+        "required_context": [
+            "ts_code",
+            "trade_date",
+            "features",
+            "market_snapshot",
+        ],
+        "metadata": {
+            "category": "department",
+            "preset": "macro",
+        },
+    },
+    "risk_dept": {
+        "name": "风险控制部门模板",
+        "description": "识别极端风险与限制条件的提示词",
+        "template": """
+部门：风险控制部门
+股票代码：{ts_code}
+交易日：{trade_date}
+
+【角色定位】
+- 防范停牌、涨跌停、仓位与合规限制。
+- 必要时对高风险决策行使否决权。
+
+【研究重点】
+1. 交易限制或异常波动情况。
+2. 仓位、集中度或风险指标是否触顶。
+3. 潜在的黑天鹅或执行障碍。
+
+【数据边界】
+- 可用字段：
+{data_scope}
+- 风险特征：
+{features}
+- 市场背景：
+{market_snapshot}
+- 追加数据：
+{supplements}
+
+请按照【部门基础模板】的分析步骤，必要时明确阻止交易的理由。
+""",
+        "variables": [
+            "ts_code",
+            "trade_date",
+            "data_scope",
+            "features",
+            "market_snapshot",
+            "supplements",
+        ],
+        "required_context": [
+            "ts_code",
+            "trade_date",
+            "features",
+            "market_snapshot",
+        ],
+        "metadata": {
+            "category": "department",
+            "preset": "risk",
+        },
+    },
 }
 
 

app/ui/views/backtest.py

@@ -331,6 +331,7 @@ def render_backtest_review() -> None:
                 )
 
                 specs: List[ParameterSpec] = []
+                spec_labels: List[str] = []
                 action_values: List[float] = []
                 range_valid = True
                 for idx, agent_name in enumerate(selected_agents):
@@ -374,15 +375,111 @@ def render_backtest_review() -> None:
                             maximum=max_val,
                         )
                     )
+                    spec_labels.append(f"agent:{agent_name}")
                     action_values.append(action_val)
 
+                controls_valid = True
+                with st.expander("部门 LLM 参数", expanded=False):
+                    dept_codes = sorted(app_cfg.departments.keys())
+                    if not dept_codes:
+                        st.caption("当前未配置部门。")
+                    else:
+                        selected_departments = st.multiselect(
+                            "选择需要调整的部门",
+                            dept_codes,
+                            default=[],
+                            key="decision_env_departments",
+                        )
+                        tool_policy_values = ["auto", "none", "required"]
+                        for dept_code in selected_departments:
+                            settings = app_cfg.departments.get(dept_code)
+                            if not settings:
+                                continue
+                            st.subheader(f"部门：{settings.title or dept_code}")
+                            base_temp = 0.2
+                            if settings.llm and settings.llm.primary and settings.llm.primary.temperature is not None:
+                                base_temp = float(settings.llm.primary.temperature)
+                            prefix = f"decision_env_dept_{dept_code}"
+                            col_tmin, col_tmax, col_tslider = st.columns([1, 1, 2])
+                            temp_min = col_tmin.number_input(
+                                "温度最小值",
+                                min_value=0.0,
+                                max_value=2.0,
+                                value=max(0.0, base_temp - 0.3),
+                                step=0.05,
+                                key=f"{prefix}_temp_min",
+                            )
+                            temp_max = col_tmax.number_input(
+                                "温度最大值",
+                                min_value=0.0,
+                                max_value=2.0,
+                                value=min(2.0, base_temp + 0.3),
+                                step=0.05,
+                                key=f"{prefix}_temp_max",
+                            )
+                            if temp_max <= temp_min:
+                                controls_valid = False
+                                st.warning("温度最大值必须大于最小值。")
+                                temp_max = min(2.0, temp_min + 0.01)
+                            span = temp_max - temp_min
+                            if span <= 0:
+                                ratio_default = 0.0
+                            else:
+                                clamped = min(max(base_temp, temp_min), temp_max)
+                                ratio_default = (clamped - temp_min) / span
+                            temp_action = col_tslider.slider(
+                                "动作值（映射至温度区间）",
+                                min_value=0.0,
+                                max_value=1.0,
+                                value=float(ratio_default),
+                                step=0.01,
+                                key=f"{prefix}_temp_action",
+                            )
+                            specs.append(
+                                ParameterSpec(
+                                    name=f"dept_temperature_{dept_code}",
+                                    target=f"department.{dept_code}.temperature",
+                                    minimum=temp_min,
+                                    maximum=temp_max,
+                                )
+                            )
+                            spec_labels.append(f"department:{dept_code}:temperature")
+                            action_values.append(temp_action)
+
+                            col_tool, col_hint = st.columns([1, 2])
+                            tool_choice = col_tool.selectbox(
+                                "函数调用策略",
+                                tool_policy_values,
+                                index=tool_policy_values.index("auto"),
+                                key=f"{prefix}_tool_choice",
+                            )
+                            col_hint.caption("映射提示：0→auto，0.5→none，1→required。")
+                            if len(tool_policy_values) > 1:
+                                tool_value = tool_policy_values.index(tool_choice) / (len(tool_policy_values) - 1)
+                            else:
+                                tool_value = 0.0
+                            specs.append(
+                                ParameterSpec(
+                                    name=f"dept_tool_{dept_code}",
+                                    target=f"department.{dept_code}.function_policy",
+                                    values=tool_policy_values,
+                                )
+                            )
+                            spec_labels.append(f"department:{dept_code}:tool_choice")
+                            action_values.append(tool_value)
+
+                if specs:
+                    st.caption("动作维度顺序：" + "，".join(spec_labels))
+
                 run_decision_env = st.button("执行单次调参", key="run_decision_env_button")
                 just_finished_single = False
                 if run_decision_env:
-                    if not selected_agents:
-                        st.warning("请至少选择一个代理进行调参。")
-                    elif not range_valid:
+                    if not specs:
+                        st.warning("请至少配置一个动作维度（代理或部门参数）。")
+                    elif selected_agents and not range_valid:
                         st.error("请确保所有代理的最大权重大于最小权重。")
+                    elif not controls_valid:
+                        st.error("请修正部门参数的取值范围。")
                     else:
                         LOGGER.info(
                             "离线调参（单次）按钮点击，已选择代理=%s 动作=%s disable_departments=%s",
@@ -448,11 +545,11 @@ def render_backtest_review() -> None:
                                         resolved_experiment_id = experiment_id or str(uuid.uuid4())
                                         resolved_strategy = strategy_label or "DecisionEnv"
                                         action_payload = {
-                                            name: value
-                                            for name, value in zip(selected_agents, action_values)
+                                            label: value for label, value in zip(spec_labels, action_values)
                                         }
                                         metrics_payload = dict(observation)
                                         metrics_payload["reward"] = reward
+                                        metrics_payload["department_controls"] = info.get("department_controls")
                                         log_success = False
                                         try:
                                             log_tuning_result(
@@ -477,12 +574,14 @@ def render_backtest_review() -> None:
                                             "observation": dict(observation),
                                             "reward": float(reward),
                                             "weights": info.get("weights", {}),
+                                            "department_controls": info.get("department_controls"),
+                                            "actions": action_payload,
                                             "nav_series": info.get("nav_series"),
                                             "trades": info.get("trades"),
                                             "portfolio_snapshots": info.get("portfolio_snapshots"),
                                             "portfolio_trades": info.get("portfolio_trades"),
                                             "risk_breakdown": info.get("risk_breakdown"),
-                                            "selected_agents": list(selected_agents),
+                                            "spec_labels": list(spec_labels),
                                             "action_values": list(action_values),
                                             "experiment_id": resolved_experiment_id,
                                             "strategy_label": resolved_strategy,
@@ -562,6 +661,16 @@ def render_backtest_review() -> None:
                         with st.expander("风险事件统计", expanded=False):
                             st.json(risk_breakdown)
 
+                    department_info = single_result.get("department_controls") or {}
+                    if department_info:
+                        with st.expander("部门控制参数", expanded=False):
+                            st.json(department_info)
+
+                    action_snapshot = single_result.get("actions") or {}
+                    if action_snapshot:
+                        with st.expander("动作明细", expanded=False):
+                            st.json(action_snapshot)
+
                     if st.button("清除单次调参结果", key="clear_decision_env_single"):
                         st.session_state.pop(_DECISION_ENV_SINGLE_RESULT_KEY, None)
                         st.success("已清除单次调参结果缓存。")
@@ -584,10 +693,12 @@ def render_backtest_review() -> None:
                 run_batch = st.button("批量执行调参", key="run_decision_env_batch")
                 batch_just_ran = False
                 if run_batch:
-                    if not selected_agents:
-                        st.warning("请先选择调参代理。")
-                    elif not range_valid:
+                    if not specs:
+                        st.warning("请至少配置一个动作维度。")
+                    elif selected_agents and not range_valid:
                         st.error("请确保所有代理的最大权重大于最小权重。")
+                    elif not controls_valid:
+                        st.error("请修正部门参数的取值范围。")
                     else:
                         LOGGER.info(
                             "离线调参（批量）按钮点击，已选择代理=%s disable_departments=%s",
@@ -693,11 +804,12 @@ def render_backtest_review() -> None:
                                                     extra=LOG_EXTRA,
                                                 )
                                                 action_payload = {
-                                                    name: value
-                                                    for name, value in zip(selected_agents, action_vals)
+                                                    label: value
+                                                    for label, value in zip(spec_labels, action_vals)
                                                 }
                                                 metrics_payload = dict(observation)
                                                 metrics_payload["reward"] = reward
+                                                metrics_payload["department_controls"] = info.get("department_controls")
                                                 weights_payload = info.get("weights", {})
                                                 try:
                                                     log_tuning_result(
@@ -713,13 +825,14 @@ def render_backtest_review() -> None:
                                                 results.append(
                                                     {
                                                         "序号": idx,
-                                                        "动作": action_vals,
+                                                        "动作": action_payload,
                                                         "状态": "ok",
                                                         "总收益": observation.get("total_return", 0.0),
                                                         "最大回撤": observation.get("max_drawdown", 0.0),
                                                         "波动率": observation.get("volatility", 0.0),
                                                         "奖励": reward,
                                                         "权重": weights_payload,
+                                                        "部门控制": info.get("department_controls"),
                                                     }
                                                 )
                                     st.session_state[_DECISION_ENV_BATCH_RESULTS_KEY] = {

docs/TODO.md

@@ -18,7 +18,7 @@
 - 围绕动量、估值、流动性等核心信号扩展轻量高质量因子集，全部由程序生成，满足端到端自动决策需求。
 
 ## 3. 决策优化与强化学习
-- 扩展 `DecisionEnv` 的动作空间（提示版本、部门温度、function 调用策略等），不仅限于代理权重调节。
+- ✅ 扩展 `DecisionEnv` 的动作空间（提示版本、部门温度、function 调用策略等），不仅限于代理权重调节。
 - 引入 Bandit / 贝叶斯优化或 RL 算法探索动作空间，并将 `portfolio_snapshots`、`portfolio_trades` 指标纳入奖励约束。
 - 构建实时持仓/成交数据写入链路，使线上监控与离线调参共用同一数据源。
 - 借鉴 TradingAgents-CN 的做法：拆分环境与策略、提供训练脚本/配置，并输出丰富的评估指标（如 Sharpe、Sortino、基准对比）。

tests/test_decision_env.py

@@ -7,13 +7,58 @@ import pytest
 
 from app.backtest.decision_env import DecisionEnv, EpisodeMetrics, ParameterSpec
 from app.backtest.engine import BacktestResult, BtConfig
+from app.utils.config import DepartmentSettings, LLMConfig, LLMEndpoint
+
+
+class _StubDepartmentAgent:
+    def __init__(self) -> None:
+        self._tool_choice = "auto"
+        self._max_rounds = 3
+        endpoint = LLMEndpoint(provider="openai", model="mock", temperature=0.2)
+        self.settings = DepartmentSettings(
+            code="momentum",
+            title="Momentum",
+            description="baseline",
+            prompt="baseline",
+            llm=LLMConfig(primary=endpoint),
+        )
+
+    @property
+    def tool_choice(self) -> str:
+        return self._tool_choice
+
+    @tool_choice.setter
+    def tool_choice(self, value) -> None:
+        normalized = str(value).strip().lower()
+        if normalized not in {"auto", "none", "required"}:
+            raise ValueError("invalid tool choice")
+        self._tool_choice = normalized
+
+    @property
+    def max_rounds(self) -> int:
+        return self._max_rounds
+
+    @max_rounds.setter
+    def max_rounds(self, value) -> None:
+        numeric = int(round(float(value)))
+        if numeric < 1:
+            numeric = 1
+        if numeric > 6:
+            numeric = 6
+        self._max_rounds = numeric
+
+
+class _StubManager:
+    def __init__(self) -> None:
+        self.agents = {"momentum": _StubDepartmentAgent()}
 
 
 class _StubEngine:
     def __init__(self, cfg: BtConfig) -> None:  # noqa: D401
         self.cfg = cfg
         self.weights = {}
-        self.department_manager = None
+        self.department_manager = _StubManager()
+        _StubEngine.last_instance = self
 
     def run(self) -> BacktestResult:
         result = BacktestResult()
@@ -53,6 +98,9 @@ class _StubEngine:
         return result
 
 
+_StubEngine.last_instance: _StubEngine | None = None
+
+
 def test_decision_env_returns_risk_metrics(monkeypatch):
     cfg = BtConfig(
         id="stub",
@@ -96,3 +144,68 @@ def test_default_reward_penalizes_metrics():
     )
     reward = DecisionEnv._default_reward(metrics)
     assert reward == pytest.approx(0.1 - (0.5 * 0.2 + 0.05 * 2 + 0.1 * 0.3))
+
+
+def test_decision_env_department_controls(monkeypatch):
+    cfg = BtConfig(
+        id="stub",
+        name="stub",
+        start_date=date(2025, 1, 10),
+        end_date=date(2025, 1, 10),
+        universe=["000001.SZ"],
+        params={},
+    )
+    specs = [
+        ParameterSpec(name="w_mom", target="agent_weights.A_mom", minimum=0.0, maximum=1.0),
+        ParameterSpec(
+            name="dept_prompt",
+            target="department.momentum.prompt",
+            values=["baseline", "aggressive"],
+        ),
+        ParameterSpec(
+            name="dept_temp",
+            target="department.momentum.temperature",
+            minimum=0.1,
+            maximum=0.9,
+        ),
+        ParameterSpec(
+            name="dept_tool",
+            target="department.momentum.function_policy",
+            values=["none", "auto", "required"],
+        ),
+        ParameterSpec(
+            name="dept_rounds",
+            target="department.momentum.max_rounds",
+            minimum=1,
+            maximum=5,
+        ),
+    ]
+
+    env = DecisionEnv(bt_config=cfg, parameter_specs=specs, baseline_weights={"A_mom": 0.5})
+
+    monkeypatch.setattr("app.backtest.decision_env.BacktestEngine", _StubEngine)
+    monkeypatch.setattr(DecisionEnv, "_clear_portfolio_records", lambda self: None)
+    monkeypatch.setattr(DecisionEnv, "_fetch_portfolio_records", lambda self: ([], []))
+
+    obs, reward, done, info = env.step([0.3, 1.0, 0.75, 0.0, 1.0])
+
+    assert done is True
+    assert obs["total_return"] == pytest.approx(0.0)
+
+    controls = info["department_controls"]
+    assert "momentum" in controls
+    momentum_ctrl = controls["momentum"]
+    assert momentum_ctrl["prompt"] == "aggressive"
+    assert momentum_ctrl["temperature"] == pytest.approx(0.7, abs=1e-6)
+    assert momentum_ctrl["tool_choice"] == "none"
+    assert momentum_ctrl["max_rounds"] == 5
+
+    assert env.last_department_controls == controls
+
+    engine = _StubEngine.last_instance
+    assert engine is not None
+    agent = engine.department_manager.agents["momentum"]
+    assert agent.settings.prompt == "aggressive"
+    assert agent.settings.llm.primary.temperature == pytest.approx(0.7, abs=1e-6)
+    assert agent.tool_choice == "none"
+    assert agent.max_rounds == 5

tests/test_llm_templates.py

@@ -169,6 +169,12 @@ def test_default_templates():
     assert momentum is not None
     assert "动量研究部门" in momentum.name
 
+    assert TemplateRegistry.get("value_dept") is not None
+    assert TemplateRegistry.get("news_dept") is not None
+    assert TemplateRegistry.get("liquidity_dept") is not None
+    assert TemplateRegistry.get("macro_dept") is not None
+    assert TemplateRegistry.get("risk_dept") is not None
+
     # Validate template content
     assert all("{" + var + "}" in dept_base.template for var in dept_base.variables)
     assert all("{" + var + "}" in momentum.template for var in momentum.variables)