add comprehensive logging to decision workflow and risk assessment

app/agents/beliefs.py

@@ -4,8 +4,13 @@ from __future__ import annotations
 from dataclasses import dataclass
 from typing import Dict, Iterable, List, Optional
 
+from app.utils.logging import get_logger
+
 from .base import AgentAction
 
+LOGGER = get_logger(__name__)
+LOG_EXTRA = {"stage": "decision_belief"}
+
 
 @dataclass
 class BeliefRevisionResult:
@@ -46,12 +51,21 @@ def revise_beliefs(belief_updates: Dict[str, "BeliefUpdate"], default_action: Ag
         "votes": {action.value: count for action, count in action_votes.items()},
         "reasons": reasons,
     }
-    return BeliefRevisionResult(
+    result = BeliefRevisionResult(
         consensus_action=consensus_action,
         consensus_confidence=consensus_confidence,
         conflicts=conflicts,
         notes=notes,
     )
+    LOGGER.debug(
+        "信念修正完成 consensus=%s confidence=%.3f conflicts=%s vote_counts=%s",
+        result.consensus_action.value if result.consensus_action else None,
+        result.consensus_confidence,
+        result.conflicts,
+        notes["votes"],
+        extra=LOG_EXTRA,
+    )
+    return result
 
 
 # avoid circular import typing

app/agents/game.py

@@ -5,6 +5,8 @@ from dataclasses import dataclass, field
 from math import log
 from typing import Dict, Iterable, List, Mapping, Optional, Tuple
 
+from app.utils.logging import get_logger
+
 from .base import Agent, AgentAction, AgentContext, UtilityMatrix
 from .departments import DepartmentContext, DepartmentDecision, DepartmentManager
 from .registry import weight_map
@@ -20,6 +22,10 @@ from .protocols import (
 )
 
 
+LOGGER = get_logger(__name__)
+LOG_EXTRA = {"stage": "decision_workflow"}
+
+
 ACTIONS: Tuple[AgentAction, ...] = (
     AgentAction.SELL,
     AgentAction.HOLD,
@@ -188,9 +194,30 @@ class DecisionWorkflow:
         self.norm_weights: Dict[str, float] = {}
         self.filtered_utilities: Dict[AgentAction, Dict[str, float]] = {}
         self.belief_revision: Optional[BeliefRevisionResult] = None
+        LOGGER.debug(
+            "初始化决策流程 ts_code=%s trade_date=%s method=%s agents=%s departments=%s",
+            context.ts_code,
+            context.trade_date,
+            method,
+            len(self.agent_list),
+            bool(self.department_manager),
+            extra=LOG_EXTRA,
+        )
 
     def run(self) -> Decision:
+        LOGGER.debug(
+            "执行决策流程 ts_code=%s method=%s feasible=%s",
+            self.context.ts_code,
+            self.method,
+            [action.value for action in self.feasible_actions],
+            extra=LOG_EXTRA,
+        )
         if not self.feasible_actions:
+            LOGGER.warning(
+                "无可行动作，回退到 HOLD ts_code=%s",
+                self.context.ts_code,
+                extra=LOG_EXTRA,
+            )
             return Decision(
                 action=AgentAction.HOLD,
                 confidence=0.0,
@@ -201,6 +228,13 @@ class DecisionWorkflow:
 
         self._evaluate_departments()
         action, confidence = self._select_action()
+        LOGGER.debug(
+            "初步动作选择完成 ts_code=%s action=%s confidence=%.3f",
+            self.context.ts_code,
+            action.value,
+            confidence,
+            extra=LOG_EXTRA,
+        )
         risk_assessment = self._apply_risk(action)
         exec_action = self._finalize_execution(action, risk_assessment)
         self._finalize_conflicts(exec_action)
@@ -210,7 +244,7 @@ class DecisionWorkflow:
             self.department_votes,
         )
 
-        return Decision(
+        decision = Decision(
             action=action,
             confidence=confidence,
             target_weight=target_weight_for_action(action),
@@ -224,6 +258,16 @@ class DecisionWorkflow:
             belief_updates=self.belief_updates,
             belief_revision=self.belief_revision,
         )
+        LOGGER.info(
+            "决策完成 ts_code=%s action=%s confidence=%.3f review=%s risk_status=%s",
+            self.context.ts_code,
+            decision.action.value,
+            decision.confidence,
+            decision.requires_review,
+            risk_assessment.status,
+            extra=LOG_EXTRA,
+        )
+        return decision
 
     def _evaluate_departments(self) -> None:
         if not self.department_manager:
@@ -236,7 +280,19 @@ class DecisionWorkflow:
             market_snapshot=dict(getattr(self.context, "market_snapshot", {}) or {}),
             raw=dict(getattr(self.context, "raw", {}) or {}),
         )
+        LOGGER.debug(
+            "开始部门评估 ts_code=%s departments=%s",
+            self.context.ts_code,
+            list(self.department_manager.agents.keys()),
+            extra=LOG_EXTRA,
+        )
         self.department_decisions = self.department_manager.evaluate(dept_context)
+        LOGGER.debug(
+            "部门评估完成 ts_code=%s decisions=%s",
+            self.context.ts_code,
+            list(self.department_decisions.keys()),
+            extra=LOG_EXTRA,
+        )
         if self.department_decisions:
             self.department_round = self.host.start_round(
                 self.host_trace,
@@ -285,11 +341,32 @@ class DecisionWorkflow:
             )
 
         if self.method == "vote":
-            return vote(self.filtered_utilities, self.norm_weights)
+            action, confidence = vote(self.filtered_utilities, self.norm_weights)
+            LOGGER.debug(
+                "采用投票机制 ts_code=%s action=%s confidence=%.3f",
+                self.context.ts_code,
+                action.value,
+                confidence,
+                extra=LOG_EXTRA,
+            )
+            return action, confidence
 
         action, confidence = nash_bargain(self.filtered_utilities, self.norm_weights, hold_scores)
         if action not in self.feasible_actions:
-            return vote(self.filtered_utilities, self.norm_weights)
+            LOGGER.debug(
+                "纳什解不可行，改用投票 ts_code=%s invalid_action=%s",
+                self.context.ts_code,
+                action.value,
+                extra=LOG_EXTRA,
+            )
+            action, confidence = vote(self.filtered_utilities, self.norm_weights)
+        LOGGER.debug(
+            "纳什解计算完成 ts_code=%s action=%s confidence=%.3f",
+            self.context.ts_code,
+            action.value,
+            confidence,
+            extra=LOG_EXTRA,
+        )
         return action, confidence
 
     def _apply_risk(self, action: AgentAction) -> RiskAssessment:
@@ -307,6 +384,17 @@ class DecisionWorkflow:
             self.department_round.outcome = action.value
             self.host.finalize_round(self.department_round)
 
+        LOGGER.debug(
+            "风险评估结果 ts_code=%s action=%s status=%s reason=%s conflict=%s votes=%s",
+            self.context.ts_code,
+            action.value,
+            assessment.status,
+            assessment.reason,
+            conflict_flag,
+            dict(self.department_votes),
+            extra=LOG_EXTRA,
+        )
+
         if assessment.status != "ok":
             self.risk_round = self.host.ensure_round(
                 self.host_trace,
@@ -398,12 +486,28 @@ class DecisionWorkflow:
         )
         self.host.finalize_round(self.execution_round)
         self.execution_round.notes.setdefault("target_weight", exec_weight)
+        LOGGER.info(
+            "执行阶段结论 ts_code=%s final_action=%s original=%s status=%s target_weight=%.4f review=%s",
+            self.context.ts_code,
+            exec_action.value,
+            action.value,
+            exec_status,
+            exec_weight,
+            requires_review,
+            extra=LOG_EXTRA,
+        )
         return exec_action
 
     def _finalize_conflicts(self, exec_action: AgentAction) -> None:
         self.host.close(self.host_trace)
         self.belief_revision = revise_beliefs(self.belief_updates, exec_action)
         if self.belief_revision.conflicts:
+            LOGGER.warning(
+                "发现信念冲突 ts_code=%s conflicts=%s",
+                self.context.ts_code,
+                self.belief_revision.conflicts,
+                extra=LOG_EXTRA,
+            )
             conflict_round = self.host.ensure_round(
                 self.host_trace,
                 agenda="conflict_resolution",
@@ -436,15 +540,35 @@ def decide(
     department_manager: Optional[DepartmentManager] = None,
     department_context: Optional[DepartmentContext] = None,
 ) -> Decision:
+    agent_list = list(agents)
+    LOGGER.debug(
+        "进入多智能体决策 ts_code=%s trade_date=%s agents=%s method=%s",
+        context.ts_code,
+        context.trade_date,
+        len(agent_list),
+        method,
+        extra=LOG_EXTRA,
+    )
     workflow = DecisionWorkflow(
         context,
-        agents,
+        agent_list,
         weights,
         method,
         department_manager,
         department_context,
     )
-    return workflow.run()
+    decision = workflow.run()
+    LOGGER.info(
+        "完成多智能体决策 ts_code=%s trade_date=%s action=%s confidence=%.3f review=%s method=%s",
+        context.ts_code,
+        context.trade_date,
+        decision.action.value,
+        decision.confidence,
+        decision.requires_review,
+        method,
+        extra=LOG_EXTRA,
+    )
+    return decision
 
 
 def _department_scores(decision: DepartmentDecision) -> Dict[AgentAction, float]:

app/agents/risk.py

@@ -1,8 +1,13 @@
 """Risk agent acts as leader with veto rights."""
 from __future__ import annotations
 
+from app.utils.logging import get_logger
+
 from .base import Agent, AgentAction, AgentContext
 
+LOGGER = get_logger(__name__)
+LOG_EXTRA = {"stage": "decision_risk"}
+
 
 class RiskRecommendation:
     """Represents structured recommendation from the risk agent."""
@@ -68,21 +73,44 @@ class RiskAgent(Agent):
         features = dict(context.features or {})
         risk_penalty = float(features.get("risk_penalty") or 0.0)
 
+        def finalize(
+            recommendation: RiskRecommendation,
+            trigger: str,
+        ) -> RiskRecommendation:
+            LOGGER.debug(
+                "风险代理评估 ts_code=%s action=%s status=%s reason=%s trigger=%s penalty=%.3f conflict=%s",
+                context.ts_code,
+                decision_action.value,
+                recommendation.status,
+                recommendation.reason,
+                trigger,
+                risk_penalty,
+                conflict_flag,
+                extra=LOG_EXTRA,
+            )
+            return recommendation
+
         if bool(features.get("is_suspended")):
-            return RiskRecommendation(
+            return finalize(
-                status="blocked",
+                RiskRecommendation(
-                reason="suspended",
+                    status="blocked",
-                recommended_action=AgentAction.HOLD,
+                    reason="suspended",
-                notes={"trigger": "is_suspended"},
+                    recommended_action=AgentAction.HOLD,
+                    notes={"trigger": "is_suspended"},
+                ),
+                "is_suspended",
             )
 
         if bool(features.get("is_blacklisted")):
             fallback = AgentAction.SELL if decision_action is AgentAction.SELL else AgentAction.HOLD
-            return RiskRecommendation(
+            return finalize(
-                status="blocked",
+                RiskRecommendation(
-                reason="blacklist",
+                    status="blocked",
-                recommended_action=fallback,
+                    reason="blacklist",
-                notes={"trigger": "is_blacklisted"},
+                    recommended_action=fallback,
+                    notes={"trigger": "is_blacklisted"},
+                ),
+                "is_blacklisted",
             )
 
         if bool(features.get("limit_up")) and decision_action in {
@@ -90,22 +118,28 @@ class RiskAgent(Agent):
             AgentAction.BUY_M,
             AgentAction.BUY_L,
         }:
-            return RiskRecommendation(
+            return finalize(
-                status="blocked",
+                RiskRecommendation(
-                reason="limit_up",
+                    status="blocked",
-                recommended_action=AgentAction.HOLD,
+                    reason="limit_up",
-                notes={"trigger": "limit_up"},
+                    recommended_action=AgentAction.HOLD,
+                    notes={"trigger": "limit_up"},
+                ),
+                "limit_up",
             )
 
         if bool(features.get("position_limit")) and decision_action in {
             AgentAction.BUY_M,
             AgentAction.BUY_L,
         }:
-            return RiskRecommendation(
+            return finalize(
-                status="pending_review",
+                RiskRecommendation(
-                reason="position_limit",
+                    status="pending_review",
-                recommended_action=AgentAction.BUY_S,
+                    reason="position_limit",
-                notes={"trigger": "position_limit"},
+                    recommended_action=AgentAction.BUY_S,
+                    notes={"trigger": "position_limit"},
+                ),
+                "position_limit",
             )
 
         if risk_penalty >= 0.9 and decision_action in {
@@ -113,28 +147,40 @@ class RiskAgent(Agent):
             AgentAction.BUY_M,
             AgentAction.BUY_L,
         }:
-            return RiskRecommendation(
+            return finalize(
-                status="blocked",
+                RiskRecommendation(
-                reason="risk_penalty_extreme",
+                    status="blocked",
-                recommended_action=AgentAction.HOLD,
+                    reason="risk_penalty_extreme",
-                notes={"risk_penalty": risk_penalty},
+                    recommended_action=AgentAction.HOLD,
+                    notes={"risk_penalty": risk_penalty},
+                ),
+                "risk_penalty_extreme",
             )
         if risk_penalty >= 0.7 and decision_action in {
             AgentAction.BUY_S,
             AgentAction.BUY_M,
             AgentAction.BUY_L,
         }:
-            return RiskRecommendation(
+            return finalize(
-                status="pending_review",
+                RiskRecommendation(
-                reason="risk_penalty_high",
+                    status="pending_review",
-                recommended_action=AgentAction.HOLD,
+                    reason="risk_penalty_high",
-                notes={"risk_penalty": risk_penalty},
+                    recommended_action=AgentAction.HOLD,
+                    notes={"risk_penalty": risk_penalty},
+                ),
+                "risk_penalty_high",
             )
 
         if conflict_flag:
-            return RiskRecommendation(
+            return finalize(
-                status="pending_review",
+                RiskRecommendation(
-                reason="conflict_threshold",
+                    status="pending_review",
+                    reason="conflict_threshold",
+                ),
+                "conflict_threshold",
             )
 
-        return RiskRecommendation(status="ok", reason="clear")
+        return finalize(
+            RiskRecommendation(status="ok", reason="clear"),
+            "clear",
+        )

app/ingest/job_logger.py

@@ -2,14 +2,17 @@
 from __future__ import annotations
 
 import json
-from datetime import datetime
 from typing import Any, Dict, Optional
 
 from app.utils.db import db_session
+from app.utils.logging import get_logger
+
+LOGGER = get_logger(__name__)
+LOG_EXTRA = {"stage": "data_ingest"}
 
 
 class JobLogger:
-    """任务记录器。"""
+    """任务记录器，通过数据库记录抓取作业运行情况。"""
 
     def __init__(self, job_type: str) -> None:
         """初始化任务记录器。
@@ -28,17 +31,36 @@ class JobLogger:
                 INSERT INTO fetch_jobs (job_type, status, created_at, updated_at)
                 VALUES (?, 'running', CURRENT_TIMESTAMP, CURRENT_TIMESTAMP)
                 """,
-                (self.job_type,)
+                (self.job_type,),
             )
             self.job_id = cursor.lastrowid
             session.commit()
+        LOGGER.info(
+            "抓取任务启动 job_type=%s job_id=%s",
+            self.job_type,
+            self.job_id,
+            extra=LOG_EXTRA,
+        )
         return self
 
     def __exit__(self, exc_type: Any, exc_val: Any, exc_tb: Any) -> None:
         """结束任务记录。"""
         if exc_val:
+            LOGGER.exception(
+                "抓取任务失败 job_type=%s job_id=%s err=%s",
+                self.job_type,
+                self.job_id,
+                exc_val,
+                extra=LOG_EXTRA,
+            )
             self.update_status("failed", str(exc_val))
         else:
+            LOGGER.info(
+                "抓取任务完成 job_type=%s job_id=%s",
+                self.job_type,
+                self.job_id,
+                extra=LOG_EXTRA,
+            )
             self.update_status("success")
 
     def update_status(self, status: str, error_msg: Optional[str] = None) -> None:
@@ -49,6 +71,7 @@ class JobLogger:
             error_msg: 错误信息（如果有）
         """
         if not self.job_id:
+            LOGGER.debug("忽略无效任务状态更新 job_type=%s status=%s", self.job_type, status, extra=LOG_EXTRA)
             return
 
         with db_session() as session:
@@ -60,9 +83,17 @@ class JobLogger:
                     updated_at = CURRENT_TIMESTAMP
                 WHERE id = ?
                 """,
-                (status, error_msg, self.job_id)
+                (status, error_msg, self.job_id),
             )
             session.commit()
+        LOGGER.debug(
+            "更新任务状态 job_type=%s job_id=%s status=%s error=%s",
+            self.job_type,
+            self.job_id,
+            status,
+            error_msg,
+            extra=LOG_EXTRA,
+        )
 
     def update_metadata(self, metadata: Dict[str, Any]) -> None:
         """更新任务元数据。
@@ -71,6 +102,11 @@ class JobLogger:
             metadata: 元数据字典
         """
         if not self.job_id:
+            LOGGER.debug(
+                "忽略元数据更新（尚未初始化） job_type=%s",
+                self.job_type,
+                extra=LOG_EXTRA,
+            )
             return
 
         with db_session() as session:
@@ -80,6 +116,13 @@ class JobLogger:
                 SET metadata = ?
                 WHERE id = ?
                 """,
-                (json.dumps(metadata), self.job_id)
+                (json.dumps(metadata), self.job_id),
             )
             session.commit()
+        LOGGER.debug(
+            "记录任务元数据 job_type=%s job_id=%s keys=%s",
+            self.job_type,
+            self.job_id,
+            sorted(metadata.keys()),
+            extra=LOG_EXTRA,
+        )

app/rl/adapters.py

@@ -7,6 +7,10 @@ from typing import Dict, Iterable, List, Mapping, Sequence, Tuple
 import numpy as np
 
 from app.backtest.decision_env import DecisionEnv
+from app.utils.logging import get_logger
+
+LOGGER = get_logger(__name__)
+LOG_EXTRA = {"stage": "decision_env"}
 
 
 @dataclass
@@ -26,6 +30,13 @@ class DecisionEnvAdapter:
         else:
             self._keys = list(self.observation_keys)
             self._last_reset_obs = None
+        LOGGER.debug(
+            "初始化 DecisionEnvAdapter obs_dim=%s action_dim=%s keys=%s",
+            len(self._keys),
+            self.env.action_dim,
+            self._keys,
+            extra=LOG_EXTRA,
+        )
 
     @property
     def action_dim(self) -> int:
@@ -38,12 +49,24 @@ class DecisionEnvAdapter:
     def reset(self) -> Tuple[np.ndarray, Dict[str, float]]:
         raw = self.env.reset()
         self._last_reset_obs = raw
+        LOGGER.debug(
+            "环境重置完成 episode=%s",
+            raw.get("episode"),
+            extra=LOG_EXTRA,
+        )
         return self._to_array(raw), raw
 
     def step(
         self, action: Sequence[float]
     ) -> Tuple[np.ndarray, float, bool, Mapping[str, object], Mapping[str, float]]:
         obs_dict, reward, done, info = self.env.step(action)
+        LOGGER.debug(
+            "环境执行动作 action=%s reward=%.4f done=%s",
+            [round(float(a), 4) for a in action],
+            reward,
+            done,
+            extra=LOG_EXTRA,
+        )
         return self._to_array(obs_dict), reward, done, info, obs_dict
 
     def _to_array(self, payload: Mapping[str, float]) -> np.ndarray:

app/rl/ppo.py

@@ -10,8 +10,13 @@ import torch
 from torch import nn
 from torch.distributions import Beta
 
+from app.utils.logging import get_logger
+
 from .adapters import DecisionEnvAdapter
 
+LOGGER = get_logger(__name__)
+LOG_EXTRA = {"stage": "rl_ppo"}
+
 
 def _init_layer(layer: nn.Module, std: float = 1.0) -> nn.Module:
     if isinstance(layer, nn.Linear):
@@ -168,6 +173,15 @@ class PPOTrainer:
         if config.seed is not None:
             torch.manual_seed(config.seed)
             np.random.seed(config.seed)
+        LOGGER.info(
+            "初始化 PPOTrainer obs_dim=%s action_dim=%s total_timesteps=%s rollout=%s device=%s",
+            obs_dim,
+            action_dim,
+            config.total_timesteps,
+            config.rollout_steps,
+            config.device,
+            extra=LOG_EXTRA,
+        )
 
     def train(self) -> TrainingSummary:
         cfg = self.config
@@ -180,6 +194,14 @@ class PPOTrainer:
         diagnostics: List[Dict[str, float]] = []
         current_return = 0.0
         current_length = 0
+        LOGGER.info(
+            "开始 PPO 训练 total_timesteps=%s rollout_steps=%s epochs=%s minibatch=%s",
+            cfg.total_timesteps,
+            cfg.rollout_steps,
+            cfg.epochs,
+            cfg.minibatch_size,
+            extra=LOG_EXTRA,
+        )
 
         while timesteps < cfg.total_timesteps:
             rollout.reset()
@@ -203,6 +225,14 @@ class PPOTrainer:
                 if done:
                     episode_rewards.append(current_return)
                     episode_lengths.append(current_length)
+                    LOGGER.info(
+                        "episode 完成 reward=%.4f length=%s episodes=%s timesteps=%s",
+                        episode_rewards[-1],
+                        episode_lengths[-1],
+                        len(episode_rewards),
+                        timesteps,
+                        extra=LOG_EXTRA,
+                    )
                     current_return = 0.0
                     current_length = 0
                     next_obs_array, _ = self.adapter.reset()
@@ -216,7 +246,17 @@ class PPOTrainer:
             with torch.no_grad():
                 next_value = self.critic(obs.unsqueeze(0)).squeeze(0).item()
             rollout.finish(last_value=next_value, gamma=cfg.gamma, gae_lambda=cfg.gae_lambda)
+            LOGGER.debug(
+                "完成样本收集 batch_size=%s timesteps=%s remaining=%s",
+                rollout._pos,
+                timesteps,
+                cfg.total_timesteps - timesteps,
+                extra=LOG_EXTRA,
+            )
 
+            last_policy_loss = None
+            last_value_loss = None
+            last_entropy = None
             for _ in range(cfg.epochs):
                 for (mb_obs, mb_actions, mb_log_probs, mb_adv, mb_returns, _) in rollout.get_minibatches(
                     cfg.minibatch_size
@@ -241,6 +281,9 @@ class PPOTrainer:
                     value_loss.backward()
                     nn.utils.clip_grad_norm_(self.critic.parameters(), cfg.max_grad_norm)
                     self.value_optimizer.step()
+                    last_policy_loss = float(policy_loss.detach().cpu())
+                    last_value_loss = float(value_loss.detach().cpu())
+                    last_entropy = float(entropy.mean().detach().cpu())
 
                     diagnostics.append(
                         {
@@ -249,13 +292,30 @@ class PPOTrainer:
                             "entropy": float(entropy.mean().detach().cpu()),
                         }
                     )
+            LOGGER.info(
+                "优化轮次完成 timesteps=%s/%s policy_loss=%.4f value_loss=%.4f entropy=%.4f",
+                timesteps,
+                cfg.total_timesteps,
+                last_policy_loss if last_policy_loss is not None else 0.0,
+                last_value_loss if last_value_loss is not None else 0.0,
+                last_entropy if last_entropy is not None else 0.0,
+                extra=LOG_EXTRA,
+            )
 
-        return TrainingSummary(
+        summary = TrainingSummary(
             timesteps=timesteps,
             episode_rewards=episode_rewards,
             episode_lengths=episode_lengths,
             diagnostics=diagnostics,
         )
+        LOGGER.info(
+            "PPO 训练结束 timesteps=%s episodes=%s mean_reward=%.4f",
+            summary.timesteps,
+            len(summary.episode_rewards),
+            float(np.mean(summary.episode_rewards)) if summary.episode_rewards else 0.0,
+            extra=LOG_EXTRA,
+        )
+        return summary
 
 
 def train_ppo(adapter: DecisionEnvAdapter, config: PPOConfig) -> TrainingSummary:

app/utils/feature_snapshots.py

@@ -5,6 +5,10 @@ from dataclasses import dataclass
 from typing import Dict, Iterable, Mapping, Optional, Sequence
 
 from .data_access import DataBroker
+from .logging import get_logger
+
+LOGGER = get_logger(__name__)
+LOG_EXTRA = {"stage": "feature_snapshot"}
 
 
 @dataclass
@@ -15,6 +19,11 @@ class FeatureSnapshotService:
 
     def __init__(self, broker: Optional[DataBroker] = None) -> None:
         self.broker = broker or DataBroker()
+        LOGGER.debug(
+            "初始化特征快照服务 broker=%s",
+            type(self.broker).__name__,
+            extra=LOG_EXTRA,
+        )
 
     def load_latest(
         self,
@@ -27,13 +36,34 @@ class FeatureSnapshotService:
         """Fetch a snapshot of feature values for the given universe."""
 
         if not ts_codes:
+            LOGGER.debug(
+                "跳过快照加载（标的为空） trade_date=%s",
+                trade_date,
+                extra=LOG_EXTRA,
+            )
             return {}
-        return self.broker.fetch_batch_latest(
+        field_count = len(fields)
+        LOGGER.debug(
+            "加载特征快照 trade_date=%s universe=%s fields=%s auto_refresh=%s",
+            trade_date,
+            len(ts_codes),
+            field_count,
+            auto_refresh,
+            extra=LOG_EXTRA,
+        )
+        snapshot = self.broker.fetch_batch_latest(
             list(ts_codes),
             trade_date,
             fields,
             auto_refresh=auto_refresh,
         )
+        LOGGER.debug(
+            "特征快照加载完成 trade_date=%s universe=%s",
+            trade_date,
+            len(snapshot),
+            extra=LOG_EXTRA,
+        )
+        return snapshot
 
     def load_single(
         self,
@@ -45,14 +75,30 @@ class FeatureSnapshotService:
     ) -> Mapping[str, object]:
         """Convenience wrapper to reuse the snapshot logic for a single symbol."""
 
+        field_list = list(fields)
+        LOGGER.debug(
+            "加载单标的快照 trade_date=%s ts_code=%s fields=%s auto_refresh=%s",
+            trade_date,
+            ts_code,
+            len(field_list),
+            auto_refresh,
+            extra=LOG_EXTRA,
+        )
         snapshot = self.load_latest(
             trade_date,
-            list(fields),
+            field_list,
             [ts_code],
             auto_refresh=auto_refresh,
         )
-        return snapshot.get(ts_code, {})
+        result = snapshot.get(ts_code, {})
+        if not result:
+            LOGGER.debug(
+                "单标的快照为空 trade_date=%s ts_code=%s",
+                trade_date,
+                ts_code,
+                extra=LOG_EXTRA,
+            )
+        return result
 
 
 __all__ = ["FeatureSnapshotService"]
-