diff --git a/app/ui/views/backtest.py b/app/ui/views/backtest.py index 66273b4..0b6b323 100644 --- a/app/ui/views/backtest.py +++ b/app/ui/views/backtest.py @@ -180,892 +180,886 @@ def render_backtest_review() -> None: st.divider() # ADD: Comparison view for multiple backtest configurations - with st.expander("历史回测结果对比", expanded=False): - st.caption("从历史回测配置中选择多个进行净值曲线与指标对比。") - normalize_to_one = st.checkbox("归一化到 1 起点", value=True, key="bt_cmp_normalize") - use_log_y = st.checkbox("对数坐标", value=False, key="bt_cmp_log_y") - metric_options = ["总收益", "最大回撤", "交易数", "平均换手", "风险事件"] - selected_metrics = st.multiselect("显示指标列", metric_options, default=metric_options, key="bt_cmp_metrics") + st.caption("从历史回测配置中选择多个进行净值曲线与指标对比。") + normalize_to_one = st.checkbox("归一化到 1 起点", value=True, key="bt_cmp_normalize") + use_log_y = st.checkbox("对数坐标", value=False, key="bt_cmp_log_y") + metric_options = ["总收益", "最大回撤", "交易数", "平均换手", "风险事件"] + selected_metrics = st.multiselect("显示指标列", metric_options, default=metric_options, key="bt_cmp_metrics") + try: + with db_session(read_only=True) as conn: + cfg_rows = conn.execute( + "SELECT id, name FROM bt_config ORDER BY rowid DESC LIMIT 50" + ).fetchall() + except Exception: # noqa: BLE001 + LOGGER.exception("读取 bt_config 失败", extra=LOG_EXTRA) + cfg_rows = [] + cfg_options = [f"{row['id']} | {row['name']}" for row in cfg_rows] + selected_labels = st.multiselect("选择配置", cfg_options, default=cfg_options[:2], key="bt_cmp_configs") + selected_ids = [label.split(" | ")[0].strip() for label in selected_labels] + nav_df = pd.DataFrame() + rpt_df = pd.DataFrame() + if selected_ids: try: with db_session(read_only=True) as conn: - cfg_rows = conn.execute( - "SELECT id, name FROM bt_config ORDER BY rowid DESC LIMIT 50" - ).fetchall() + nav_df = pd.read_sql_query( + "SELECT cfg_id, trade_date, nav FROM bt_nav WHERE cfg_id IN (%s)" % (",".join(["?"]*len(selected_ids))), + conn, + params=tuple(selected_ids), + ) + rpt_df = pd.read_sql_query( + "SELECT cfg_id, summary FROM bt_report WHERE cfg_id IN (%s)" % (",".join(["?"]*len(selected_ids))), + conn, + params=tuple(selected_ids), + ) except Exception: # noqa: BLE001 - LOGGER.exception("读取 bt_config 失败", extra=LOG_EXTRA) - cfg_rows = [] - cfg_options = [f"{row['id']} | {row['name']}" for row in cfg_rows] - selected_labels = st.multiselect("选择配置", cfg_options, default=cfg_options[:2], key="bt_cmp_configs") - selected_ids = [label.split(" | ")[0].strip() for label in selected_labels] - nav_df = pd.DataFrame() - rpt_df = pd.DataFrame() - if selected_ids: + LOGGER.exception("读取回测结果失败", extra=LOG_EXTRA) + st.error("读取回测结果失败") + nav_df = pd.DataFrame() + rpt_df = pd.DataFrame() + if not nav_df.empty: try: - with db_session(read_only=True) as conn: - nav_df = pd.read_sql_query( - "SELECT cfg_id, trade_date, nav FROM bt_nav WHERE cfg_id IN (%s)" % (",".join(["?"]*len(selected_ids))), - conn, - params=tuple(selected_ids), - ) - rpt_df = pd.read_sql_query( - "SELECT cfg_id, summary FROM bt_report WHERE cfg_id IN (%s)" % (",".join(["?"]*len(selected_ids))), - conn, - params=tuple(selected_ids), - ) - except Exception: # noqa: BLE001 - LOGGER.exception("读取回测结果失败", extra=LOG_EXTRA) - st.error("读取回测结果失败") - nav_df = pd.DataFrame() - rpt_df = pd.DataFrame() - if not nav_df.empty: + nav_df["trade_date"] = pd.to_datetime(nav_df["trade_date"], errors="coerce") + # ADD: date window filter + overall_min = pd.to_datetime(nav_df["trade_date"].min()).date() + overall_max = pd.to_datetime(nav_df["trade_date"].max()).date() + col_d1, col_d2 = st.columns(2) + start_filter = col_d1.date_input("起始日期", value=overall_min, key="bt_cmp_start") + end_filter = col_d2.date_input("结束日期", value=overall_max, key="bt_cmp_end") + if start_filter > end_filter: + start_filter, end_filter = end_filter, start_filter + mask = (nav_df["trade_date"].dt.date >= start_filter) & (nav_df["trade_date"].dt.date <= end_filter) + nav_df = nav_df.loc[mask] + pivot = nav_df.pivot_table(index="trade_date", columns="cfg_id", values="nav") + if normalize_to_one: + pivot = pivot.apply(lambda s: s / s.dropna().iloc[0] if s.dropna().size else s) + import plotly.graph_objects as go + fig = go.Figure() + for col in pivot.columns: + fig.add_trace(go.Scatter(x=pivot.index, y=pivot[col], mode="lines", name=str(col))) + fig.update_layout(height=300, margin=dict(l=10, r=10, t=30, b=10)) + if use_log_y: + fig.update_yaxes(type="log") + st.plotly_chart(fig, width='stretch') + # ADD: export pivot try: - nav_df["trade_date"] = pd.to_datetime(nav_df["trade_date"], errors="coerce") - # ADD: date window filter - overall_min = pd.to_datetime(nav_df["trade_date"].min()).date() - overall_max = pd.to_datetime(nav_df["trade_date"].max()).date() - col_d1, col_d2 = st.columns(2) - start_filter = col_d1.date_input("起始日期", value=overall_min, key="bt_cmp_start") - end_filter = col_d2.date_input("结束日期", value=overall_max, key="bt_cmp_end") - if start_filter > end_filter: - start_filter, end_filter = end_filter, start_filter - mask = (nav_df["trade_date"].dt.date >= start_filter) & (nav_df["trade_date"].dt.date <= end_filter) - nav_df = nav_df.loc[mask] - pivot = nav_df.pivot_table(index="trade_date", columns="cfg_id", values="nav") - if normalize_to_one: - pivot = pivot.apply(lambda s: s / s.dropna().iloc[0] if s.dropna().size else s) - import plotly.graph_objects as go - fig = go.Figure() - for col in pivot.columns: - fig.add_trace(go.Scatter(x=pivot.index, y=pivot[col], mode="lines", name=str(col))) - fig.update_layout(height=300, margin=dict(l=10, r=10, t=30, b=10)) - if use_log_y: - fig.update_yaxes(type="log") - st.plotly_chart(fig, width='stretch') - # ADD: export pivot + csv_buf = pivot.reset_index() + csv_buf.columns = ["trade_date"] + [str(c) for c in pivot.columns] + st.download_button( + "下载曲线(CSV)", + data=csv_buf.to_csv(index=False), + file_name="bt_nav_compare.csv", + mime="text/csv", + key="dl_nav_compare", + ) + except Exception: + pass + except Exception: # noqa: BLE001 + LOGGER.debug("绘制对比曲线失败", extra=LOG_EXTRA) + if not rpt_df.empty: + try: + metrics_rows: List[Dict[str, object]] = [] + for _, row in rpt_df.iterrows(): + cfg_id = row["cfg_id"] + try: + summary = json.loads(row["summary"]) if isinstance(row["summary"], str) else (row["summary"] or {}) + except json.JSONDecodeError: + summary = {} + record = { + "cfg_id": cfg_id, + "总收益": summary.get("total_return"), + "最大回撤": summary.get("max_drawdown"), + "交易数": summary.get("trade_count"), + "平均换手": summary.get("avg_turnover"), + "风险事件": summary.get("risk_events"), + } + metrics_rows.append({k: v for k, v in record.items() if (k == "cfg_id" or k in selected_metrics)}) + if metrics_rows: + dfm = pd.DataFrame(metrics_rows) + st.dataframe(dfm, hide_index=True, width='stretch') try: - csv_buf = pivot.reset_index() - csv_buf.columns = ["trade_date"] + [str(c) for c in pivot.columns] st.download_button( - "下载曲线(CSV)", - data=csv_buf.to_csv(index=False), - file_name="bt_nav_compare.csv", + "下载指标(CSV)", + data=dfm.to_csv(index=False), + file_name="bt_metrics_compare.csv", mime="text/csv", - key="dl_nav_compare", + key="dl_metrics_compare", ) except Exception: pass - except Exception: # noqa: BLE001 - LOGGER.debug("绘制对比曲线失败", extra=LOG_EXTRA) - if not rpt_df.empty: - try: - metrics_rows: List[Dict[str, object]] = [] - for _, row in rpt_df.iterrows(): - cfg_id = row["cfg_id"] - try: - summary = json.loads(row["summary"]) if isinstance(row["summary"], str) else (row["summary"] or {}) - except json.JSONDecodeError: - summary = {} - record = { - "cfg_id": cfg_id, - "总收益": summary.get("total_return"), - "最大回撤": summary.get("max_drawdown"), - "交易数": summary.get("trade_count"), - "平均换手": summary.get("avg_turnover"), - "风险事件": summary.get("risk_events"), - } - metrics_rows.append({k: v for k, v in record.items() if (k == "cfg_id" or k in selected_metrics)}) - if metrics_rows: - dfm = pd.DataFrame(metrics_rows) - st.dataframe(dfm, hide_index=True, width='stretch') - try: - st.download_button( - "下载指标(CSV)", - data=dfm.to_csv(index=False), - file_name="bt_metrics_compare.csv", - mime="text/csv", - key="dl_metrics_compare", - ) - except Exception: - pass - except Exception: # noqa: BLE001 - LOGGER.debug("渲染指标表失败", extra=LOG_EXTRA) - else: - st.info("请选择至少一个配置进行对比。") + except Exception: # noqa: BLE001 + LOGGER.debug("渲染指标表失败", extra=LOG_EXTRA) + else: + st.info("请选择至少一个配置进行对比。") with tab_rl: st.caption("使用 DecisionEnv 对代理权重进行强化学习调参，支持单次与批量实验。") - with st.expander("离线调参实验 (DecisionEnv)", expanded=False): - st.caption( - "使用 DecisionEnv 对代理权重做离线调参。请选择需要优化的代理并设定权重范围，" - "系统会运行一次回测并返回收益、回撤等指标。若 LLM 网络不可用，将返回失败标记。" + + disable_departments = st.checkbox( + "禁用部门 LLM（仅规则代理，适合离线快速评估）", + value=True, + help="关闭部门调用后不依赖外部 LLM 网络，仅根据规则代理权重模拟。", + ) + + default_experiment_id = f"streamlit_{datetime.now().strftime('%Y%m%d_%H%M%S')}" + experiment_id = st.text_input( + "实验 ID", + value=default_experiment_id, + help="用于在 tuning_results 表中区分不同实验。", + ) + strategy_label = st.text_input( + "策略说明", + value="DecisionEnv", + help="可选：为本次调参记录一个策略名称或备注。", + ) + + agent_objects = default_agents() + agent_names = [agent.name for agent in agent_objects] + if not agent_names: + st.info("暂无可调整的代理。") + else: + selected_agents = st.multiselect( + "选择调参的代理权重", + agent_names, + default=agent_names[:2], + key="decision_env_agents", ) - disable_departments = st.checkbox( - "禁用部门 LLM（仅规则代理，适合离线快速评估）", - value=True, - help="关闭部门调用后不依赖外部 LLM 网络，仅根据规则代理权重模拟。", - ) - - default_experiment_id = f"streamlit_{datetime.now().strftime('%Y%m%d_%H%M%S')}" - experiment_id = st.text_input( - "实验 ID", - value=default_experiment_id, - help="用于在 tuning_results 表中区分不同实验。", - ) - strategy_label = st.text_input( - "策略说明", - value="DecisionEnv", - help="可选：为本次调参记录一个策略名称或备注。", - ) - - agent_objects = default_agents() - agent_names = [agent.name for agent in agent_objects] - if not agent_names: - st.info("暂无可调整的代理。") - else: - selected_agents = st.multiselect( - "选择调参的代理权重", - agent_names, - default=agent_names[:2], - key="decision_env_agents", + specs: List[ParameterSpec] = [] + spec_labels: List[str] = [] + action_values: List[float] = [] + range_valid = True + for idx, agent_name in enumerate(selected_agents): + col_min, col_max, col_action = st.columns([1, 1, 2]) + min_key = f"decision_env_min_{agent_name}" + max_key = f"decision_env_max_{agent_name}" + action_key = f"decision_env_action_{agent_name}" + default_min = 0.0 + default_max = 1.0 + min_val = col_min.number_input( + f"{agent_name} 最小权重", + min_value=0.0, + max_value=1.0, + value=default_min, + step=0.05, + key=min_key, ) + max_val = col_max.number_input( + f"{agent_name} 最大权重", + min_value=0.0, + max_value=1.0, + value=default_max, + step=0.05, + key=max_key, + ) + if max_val <= min_val: + range_valid = False + action_val = col_action.slider( + f"{agent_name} 动作 (0-1)", + min_value=0.0, + max_value=1.0, + value=0.5, + step=0.01, + key=action_key, + ) + specs.append( + ParameterSpec( + name=f"weight_{agent_name}", + target=f"agent_weights.{agent_name}", + minimum=min_val, + maximum=max_val, + ) + ) + spec_labels.append(f"agent:{agent_name}") + action_values.append(action_val) - specs: List[ParameterSpec] = [] - spec_labels: List[str] = [] - action_values: List[float] = [] - range_valid = True - for idx, agent_name in enumerate(selected_agents): - col_min, col_max, col_action = st.columns([1, 1, 2]) - min_key = f"decision_env_min_{agent_name}" - max_key = f"decision_env_max_{agent_name}" - action_key = f"decision_env_action_{agent_name}" - default_min = 0.0 - default_max = 1.0 - min_val = col_min.number_input( - f"{agent_name} 最小权重", - min_value=0.0, - max_value=1.0, - value=default_min, - step=0.05, - key=min_key, + 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", ) - max_val = col_max.number_input( - f"{agent_name} 最大权重", - min_value=0.0, - max_value=1.0, - value=default_max, - step=0.05, - key=max_key, - ) - if max_val <= min_val: - range_valid = False - action_val = col_action.slider( - f"{agent_name} 动作 (0-1)", - min_value=0.0, - max_value=1.0, - value=0.5, - step=0.01, - key=action_key, - ) - specs.append( - ParameterSpec( - name=f"weight_{agent_name}", - target=f"agent_weights.{agent_name}", - minimum=min_val, - maximum=max_val, + 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", ) - ) - 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", + 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", ) - 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", + 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, ) - 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) + ) + 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_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, ) - 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) + ) + 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)) + 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 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。") + 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 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", + selected_agents, + action_values, + disable_departments, + extra=LOG_EXTRA, + ) + baseline_weights = cfg.agent_weights.as_dict() + for agent in agent_objects: + baseline_weights.setdefault(agent.name, 1.0) + + universe_env = [code.strip() for code in universe_text.split(',') if code.strip()] + if not universe_env: + st.error("请先指定至少一个股票代码。") 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", - selected_agents, - action_values, - disable_departments, + bt_cfg_env = BtConfig( + id="decision_env_streamlit", + name="DecisionEnv Streamlit", + start_date=start_date, + end_date=end_date, + universe=universe_env, + params={ + "target": target, + "stop": stop, + "hold_days": int(hold_days), + }, + method=cfg.decision_method, + ) + env = DecisionEnv( + bt_config=bt_cfg_env, + parameter_specs=specs, + baseline_weights=baseline_weights, + disable_departments=disable_departments, + ) + env.reset() + LOGGER.debug( + "离线调参（单次）启动 DecisionEnv：cfg=%s 参数维度=%s", + bt_cfg_env, + len(specs), extra=LOG_EXTRA, ) - baseline_weights = cfg.agent_weights.as_dict() - for agent in agent_objects: - baseline_weights.setdefault(agent.name, 1.0) - - universe_env = [code.strip() for code in universe_text.split(',') if code.strip()] - if not universe_env: - st.error("请先指定至少一个股票代码。") - else: - bt_cfg_env = BtConfig( - id="decision_env_streamlit", - name="DecisionEnv Streamlit", - start_date=start_date, - end_date=end_date, - universe=universe_env, - params={ - "target": target, - "stop": stop, - "hold_days": int(hold_days), - }, - method=cfg.decision_method, - ) - env = DecisionEnv( - bt_config=bt_cfg_env, - parameter_specs=specs, - baseline_weights=baseline_weights, - disable_departments=disable_departments, - ) - env.reset() - LOGGER.debug( - "离线调参（单次）启动 DecisionEnv：cfg=%s 参数维度=%s", - bt_cfg_env, - len(specs), - extra=LOG_EXTRA, - ) - with st.spinner("正在执行离线调参……"): - try: - observation, reward, done, info = env.step(action_values) - LOGGER.info( - "离线调参（单次）完成，obs=%s reward=%.4f done=%s", - observation, - reward, - done, - extra=LOG_EXTRA, - ) - except Exception as exc: # noqa: BLE001 - LOGGER.exception("DecisionEnv 调用失败", extra=LOG_EXTRA) - st.error(f"离线调参失败：{exc}") + with st.spinner("正在执行离线调参……"): + try: + observation, reward, done, info = env.step(action_values) + LOGGER.info( + "离线调参（单次）完成，obs=%s reward=%.4f done=%s", + observation, + reward, + done, + extra=LOG_EXTRA, + ) + except Exception as exc: # noqa: BLE001 + LOGGER.exception("DecisionEnv 调用失败", extra=LOG_EXTRA) + st.error(f"离线调参失败：{exc}") + st.session_state.pop(_DECISION_ENV_SINGLE_RESULT_KEY, None) + else: + if observation.get("failure"): + st.error("调参失败：回测执行未完成，可能是 LLM 网络不可用或参数异常。") + st.json(observation) st.session_state.pop(_DECISION_ENV_SINGLE_RESULT_KEY, None) else: - if observation.get("failure"): - st.error("调参失败：回测执行未完成，可能是 LLM 网络不可用或参数异常。") - st.json(observation) - st.session_state.pop(_DECISION_ENV_SINGLE_RESULT_KEY, None) + resolved_experiment_id = experiment_id or str(uuid.uuid4()) + resolved_strategy = strategy_label or "DecisionEnv" + action_payload = { + 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( + experiment_id=resolved_experiment_id, + strategy=resolved_strategy, + action=action_payload, + reward=reward, + metrics=metrics_payload, + weights=info.get("weights", {}), + ) + except Exception: # noqa: BLE001 + LOGGER.exception("记录调参结果失败", extra=LOG_EXTRA) else: - resolved_experiment_id = experiment_id or str(uuid.uuid4()) - resolved_strategy = strategy_label or "DecisionEnv" - action_payload = { - 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( - experiment_id=resolved_experiment_id, - strategy=resolved_strategy, - action=action_payload, - reward=reward, - metrics=metrics_payload, - weights=info.get("weights", {}), - ) - except Exception: # noqa: BLE001 - LOGGER.exception("记录调参结果失败", extra=LOG_EXTRA) - else: - log_success = True - LOGGER.info( - "离线调参（单次）日志写入成功：experiment=%s strategy=%s", - resolved_experiment_id, - resolved_strategy, - extra=LOG_EXTRA, - ) - st.session_state[_DECISION_ENV_SINGLE_RESULT_KEY] = { - "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"), - "spec_labels": list(spec_labels), - "action_values": list(action_values), - "experiment_id": resolved_experiment_id, - "strategy_label": resolved_strategy, - "logged": log_success, - } - just_finished_single = True - single_result = st.session_state.get(_DECISION_ENV_SINGLE_RESULT_KEY) - if single_result: - if just_finished_single: - st.success("离线调参完成") + log_success = True + LOGGER.info( + "离线调参（单次）日志写入成功：experiment=%s strategy=%s", + resolved_experiment_id, + resolved_strategy, + extra=LOG_EXTRA, + ) + st.session_state[_DECISION_ENV_SINGLE_RESULT_KEY] = { + "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"), + "spec_labels": list(spec_labels), + "action_values": list(action_values), + "experiment_id": resolved_experiment_id, + "strategy_label": resolved_strategy, + "logged": log_success, + } + just_finished_single = True + single_result = st.session_state.get(_DECISION_ENV_SINGLE_RESULT_KEY) + if single_result: + if just_finished_single: + st.success("离线调参完成") + else: + st.success("离线调参结果（最近一次运行）") + st.caption( + f"实验 ID：{single_result.get('experiment_id', '-') } | 策略：{single_result.get('strategy_label', 'DecisionEnv')}" + ) + observation = single_result.get("observation", {}) + reward = float(single_result.get("reward", 0.0)) + col_metrics = st.columns(4) + col_metrics[0].metric("总收益", f"{observation.get('total_return', 0.0):+.2%}") + col_metrics[1].metric("最大回撤", f"{observation.get('max_drawdown', 0.0):+.2%}") + col_metrics[2].metric("波动率", f"{observation.get('volatility', 0.0):+.2%}") + col_metrics[3].metric("奖励", f"{reward:+.4f}") + + turnover_ratio = float(observation.get("turnover", 0.0) or 0.0) + turnover_value = float(observation.get("turnover_value", 0.0) or 0.0) + risk_count = float(observation.get("risk_count", 0.0) or 0.0) + col_metrics_extra = st.columns(3) + col_metrics_extra[0].metric("平均换手率", f"{turnover_ratio:.2%}") + col_metrics_extra[1].metric("成交额", f"{turnover_value:,.0f}") + col_metrics_extra[2].metric("风险事件数", f"{int(risk_count)}") + + weights_dict = single_result.get("weights") or {} + if weights_dict: + st.write("调参后权重：") + st.json(weights_dict) + if st.button("保存这些权重为默认配置", key="save_decision_env_weights_single"): + try: + cfg.agent_weights.update_from_dict(weights_dict) + save_config(cfg) + except Exception as exc: # noqa: BLE001 + LOGGER.exception("保存权重失败", extra={**LOG_EXTRA, "error": str(exc)}) + st.error(f"写入配置失败：{exc}") + else: + st.success("代理权重已写入 config.json") + + if single_result.get("logged"): + st.caption("调参结果已写入 tuning_results 表。") + + nav_series = single_result.get("nav_series") or [] + if nav_series: + try: + nav_df = pd.DataFrame(nav_series) + if {"trade_date", "nav"}.issubset(nav_df.columns): + nav_df = nav_df.sort_values("trade_date") + nav_df["trade_date"] = pd.to_datetime(nav_df["trade_date"]) + st.line_chart(nav_df.set_index("trade_date")["nav"], height=220) + except Exception: # noqa: BLE001 + LOGGER.debug("导航曲线绘制失败", extra=LOG_EXTRA) + + trades = single_result.get("trades") or [] + if trades: + st.write("成交记录：") + st.dataframe(pd.DataFrame(trades), hide_index=True, width='stretch') + + snapshots = single_result.get("portfolio_snapshots") or [] + if snapshots: + with st.expander("投资组合快照", expanded=False): + st.dataframe(pd.DataFrame(snapshots), hide_index=True, width='stretch') + + portfolio_trades = single_result.get("portfolio_trades") or [] + if portfolio_trades: + with st.expander("组合成交明细", expanded=False): + st.dataframe(pd.DataFrame(portfolio_trades), hide_index=True, width='stretch') + + risk_breakdown = single_result.get("risk_breakdown") or {} + if risk_breakdown: + 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。") + + st.divider() + st.subheader("自动探索（epsilon-greedy）") + col_ep, col_eps, col_seed = st.columns([1, 1, 1]) + bandit_episodes = int( + col_ep.number_input( + "迭代次数", + min_value=1, + max_value=200, + value=10, + step=1, + key="decision_env_bandit_episodes", + help="探索的回合数，越大越充分但耗时越久。", + ) + ) + bandit_epsilon = float( + col_eps.slider( + "探索比例 ε", + min_value=0.0, + max_value=1.0, + value=0.2, + step=0.05, + key="decision_env_bandit_epsilon", + help="ε 越大，随机探索概率越高。", + ) + ) + seed_text = col_seed.text_input( + "随机种子（可选）", + value="", + key="decision_env_bandit_seed", + help="填写整数可复现实验，不填写则随机。", + ).strip() + bandit_seed = None + if seed_text: + try: + bandit_seed = int(seed_text) + except ValueError: + st.warning("随机种子需为整数，已忽略该值。") + bandit_seed = None + + run_bandit = st.button("执行自动探索", key="run_decision_env_bandit") + if run_bandit: + if not specs: + st.warning("请至少配置一个动作维度再执行探索。") + 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: + baseline_weights = cfg.agent_weights.as_dict() + for agent in agent_objects: + baseline_weights.setdefault(agent.name, 1.0) + + universe_env = [code.strip() for code in universe_text.split(',') if code.strip()] + if not universe_env: + st.error("请先指定至少一个股票代码。") else: - st.success("离线调参结果（最近一次运行）") - st.caption( - f"实验 ID：{single_result.get('experiment_id', '-') } | 策略：{single_result.get('strategy_label', 'DecisionEnv')}" + bt_cfg_env = BtConfig( + id="decision_env_bandit", + name="DecisionEnv Bandit", + start_date=start_date, + end_date=end_date, + universe=universe_env, + params={ + "target": target, + "stop": stop, + "hold_days": int(hold_days), + }, + method=cfg.decision_method, + ) + env = DecisionEnv( + bt_config=bt_cfg_env, + parameter_specs=specs, + baseline_weights=baseline_weights, + disable_departments=disable_departments, + ) + config = BanditConfig( + experiment_id=experiment_id or f"bandit_{datetime.now().strftime('%Y%m%d_%H%M%S')}", + strategy=strategy_label or "DecisionEnv", + episodes=bandit_episodes, + epsilon=bandit_epsilon, + seed=bandit_seed, + ) + bandit = EpsilonGreedyBandit(env, config) + with st.spinner("自动探索进行中，请稍候..."): + summary = bandit.run() + + episodes_dump: List[Dict[str, object]] = [] + for idx, episode in enumerate(summary.episodes, start=1): + episodes_dump.append( + { + "序号": idx, + "奖励": episode.reward, + "动作(raw)": json.dumps(episode.action, ensure_ascii=False), + "参数值": json.dumps(episode.resolved_action, ensure_ascii=False), + "总收益": episode.metrics.total_return, + "最大回撤": episode.metrics.max_drawdown, + "波动率": episode.metrics.volatility, + "权重": json.dumps(episode.weights or {}, ensure_ascii=False), + "部门控制": json.dumps(episode.department_controls or {}, ensure_ascii=False), + } + ) + + best_episode = summary.best_episode + best_index = summary.episodes.index(best_episode) + 1 if best_episode else None + st.session_state[_DECISION_ENV_BANDIT_RESULTS_KEY] = { + "episodes": episodes_dump, + "best_index": best_index, + "best": { + "reward": best_episode.reward if best_episode else None, + "action": best_episode.action if best_episode else None, + "resolved_action": best_episode.resolved_action if best_episode else None, + "weights": best_episode.weights if best_episode else None, + "department_controls": best_episode.department_controls if best_episode else None, + }, + "experiment_id": config.experiment_id, + "strategy": config.strategy, + } + st.success(f"自动探索完成，共执行 {len(episodes_dump)} 轮。") + + bandit_state = st.session_state.get(_DECISION_ENV_BANDIT_RESULTS_KEY) + if bandit_state: + st.caption( + f"实验 ID：{bandit_state.get('experiment_id')} | 策略：{bandit_state.get('strategy')}" + ) + episodes_dump = bandit_state.get("episodes") or [] + if episodes_dump: + st.dataframe(pd.DataFrame(episodes_dump), hide_index=True, width='stretch') + best_payload = bandit_state.get("best") or {} + if best_payload.get("reward") is not None: + st.success( + f"最佳结果：第 {bandit_state.get('best_index')} 轮，奖励 {best_payload['reward']:+.4f}" ) - observation = single_result.get("observation", {}) - reward = float(single_result.get("reward", 0.0)) - col_metrics = st.columns(4) - col_metrics[0].metric("总收益", f"{observation.get('total_return', 0.0):+.2%}") - col_metrics[1].metric("最大回撤", f"{observation.get('max_drawdown', 0.0):+.2%}") - col_metrics[2].metric("波动率", f"{observation.get('volatility', 0.0):+.2%}") - col_metrics[3].metric("奖励", f"{reward:+.4f}") - - turnover_ratio = float(observation.get("turnover", 0.0) or 0.0) - turnover_value = float(observation.get("turnover_value", 0.0) or 0.0) - risk_count = float(observation.get("risk_count", 0.0) or 0.0) - col_metrics_extra = st.columns(3) - col_metrics_extra[0].metric("平均换手率", f"{turnover_ratio:.2%}") - col_metrics_extra[1].metric("成交额", f"{turnover_value:,.0f}") - col_metrics_extra[2].metric("风险事件数", f"{int(risk_count)}") - - weights_dict = single_result.get("weights") or {} - if weights_dict: - st.write("调参后权重：") - st.json(weights_dict) - if st.button("保存这些权重为默认配置", key="save_decision_env_weights_single"): + col_best1, col_best2 = st.columns(2) + col_best1.write("动作(raw)：") + col_best1.json(best_payload.get("action") or {}) + col_best2.write("参数值：") + col_best2.json(best_payload.get("resolved_action") or {}) + weights_payload = best_payload.get("weights") or {} + if weights_payload: + st.write("对应代理权重：") + st.json(weights_payload) + if st.button( + "将最佳权重写入默认配置", + key="save_decision_env_bandit_weights", + ): try: - cfg.agent_weights.update_from_dict(weights_dict) + cfg.agent_weights.update_from_dict(weights_payload) save_config(cfg) except Exception as exc: # noqa: BLE001 - LOGGER.exception("保存权重失败", extra={**LOG_EXTRA, "error": str(exc)}) + LOGGER.exception( + "保存 bandit 权重失败", + extra={**LOG_EXTRA, "error": str(exc)}, + ) st.error(f"写入配置失败：{exc}") else: - st.success("代理权重已写入 config.json") + st.success("最佳权重已写入 config.json") + dept_ctrl = best_payload.get("department_controls") or {} + if dept_ctrl: + with st.expander("最佳部门控制参数", expanded=False): + st.json(dept_ctrl) + if st.button("清除自动探索结果", key="clear_decision_env_bandit"): + st.session_state.pop(_DECISION_ENV_BANDIT_RESULTS_KEY, None) + st.success("已清除自动探索结果。") - if single_result.get("logged"): - st.caption("调参结果已写入 tuning_results 表。") - - nav_series = single_result.get("nav_series") or [] - if nav_series: - try: - nav_df = pd.DataFrame(nav_series) - if {"trade_date", "nav"}.issubset(nav_df.columns): - nav_df = nav_df.sort_values("trade_date") - nav_df["trade_date"] = pd.to_datetime(nav_df["trade_date"]) - st.line_chart(nav_df.set_index("trade_date")["nav"], height=220) - except Exception: # noqa: BLE001 - LOGGER.debug("导航曲线绘制失败", extra=LOG_EXTRA) - - trades = single_result.get("trades") or [] - if trades: - st.write("成交记录：") - st.dataframe(pd.DataFrame(trades), hide_index=True, width='stretch') - - snapshots = single_result.get("portfolio_snapshots") or [] - if snapshots: - with st.expander("投资组合快照", expanded=False): - st.dataframe(pd.DataFrame(snapshots), hide_index=True, width='stretch') - - portfolio_trades = single_result.get("portfolio_trades") or [] - if portfolio_trades: - with st.expander("组合成交明细", expanded=False): - st.dataframe(pd.DataFrame(portfolio_trades), hide_index=True, width='stretch') - - risk_breakdown = single_result.get("risk_breakdown") or {} - if risk_breakdown: - 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。") - - st.divider() - st.subheader("自动探索（epsilon-greedy）") - col_ep, col_eps, col_seed = st.columns([1, 1, 1]) - bandit_episodes = int( - col_ep.number_input( - "迭代次数", - min_value=1, - max_value=200, - value=10, - step=1, - key="decision_env_bandit_episodes", - help="探索的回合数，越大越充分但耗时越久。", + st.divider() + st.caption("批量调参：在下方输入多组动作，每行表示一组 0-1 之间的值，用逗号分隔。") + default_grid = "\n".join( + [ + ",".join(["0.2" for _ in specs]), + ",".join(["0.5" for _ in specs]), + ",".join(["0.8" for _ in specs]), + ] + ) if specs else "" + action_grid_raw = st.text_area( + "动作列表", + value=default_grid, + height=120, + key="decision_env_batch_actions", + ) + run_batch = st.button("批量执行调参", key="run_decision_env_batch") + batch_just_ran = False + if run_batch: + 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", + selected_agents, + disable_departments, + extra=LOG_EXTRA, ) - ) - bandit_epsilon = float( - col_eps.slider( - "探索比例 ε", - min_value=0.0, - max_value=1.0, - value=0.2, - step=0.05, - key="decision_env_bandit_epsilon", - help="ε 越大，随机探索概率越高。", - ) - ) - seed_text = col_seed.text_input( - "随机种子（可选）", - value="", - key="decision_env_bandit_seed", - help="填写整数可复现实验，不填写则随机。", - ).strip() - bandit_seed = None - if seed_text: - try: - bandit_seed = int(seed_text) - except ValueError: - st.warning("随机种子需为整数，已忽略该值。") - bandit_seed = None - - run_bandit = st.button("执行自动探索", key="run_decision_env_bandit") - if run_bandit: - if not specs: - st.warning("请至少配置一个动作维度再执行探索。") - 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。") + lines = [line.strip() for line in action_grid_raw.splitlines() if line.strip()] + if not lines: + st.warning("请在文本框中输入至少一组动作。") else: - baseline_weights = cfg.agent_weights.as_dict() - for agent in agent_objects: - baseline_weights.setdefault(agent.name, 1.0) - - universe_env = [code.strip() for code in universe_text.split(',') if code.strip()] - if not universe_env: - st.error("请先指定至少一个股票代码。") - else: - bt_cfg_env = BtConfig( - id="decision_env_bandit", - name="DecisionEnv Bandit", - start_date=start_date, - end_date=end_date, - universe=universe_env, - params={ - "target": target, - "stop": stop, - "hold_days": int(hold_days), - }, - method=cfg.decision_method, - ) - env = DecisionEnv( - bt_config=bt_cfg_env, - parameter_specs=specs, - baseline_weights=baseline_weights, - disable_departments=disable_departments, - ) - config = BanditConfig( - experiment_id=experiment_id or f"bandit_{datetime.now().strftime('%Y%m%d_%H%M%S')}", - strategy=strategy_label or "DecisionEnv", - episodes=bandit_episodes, - epsilon=bandit_epsilon, - seed=bandit_seed, - ) - bandit = EpsilonGreedyBandit(env, config) - with st.spinner("自动探索进行中，请稍候..."): - summary = bandit.run() - - episodes_dump: List[Dict[str, object]] = [] - for idx, episode in enumerate(summary.episodes, start=1): - episodes_dump.append( - { - "序号": idx, - "奖励": episode.reward, - "动作(raw)": json.dumps(episode.action, ensure_ascii=False), - "参数值": json.dumps(episode.resolved_action, ensure_ascii=False), - "总收益": episode.metrics.total_return, - "最大回撤": episode.metrics.max_drawdown, - "波动率": episode.metrics.volatility, - "权重": json.dumps(episode.weights or {}, ensure_ascii=False), - "部门控制": json.dumps(episode.department_controls or {}, ensure_ascii=False), - } - ) - - best_episode = summary.best_episode - best_index = summary.episodes.index(best_episode) + 1 if best_episode else None - st.session_state[_DECISION_ENV_BANDIT_RESULTS_KEY] = { - "episodes": episodes_dump, - "best_index": best_index, - "best": { - "reward": best_episode.reward if best_episode else None, - "action": best_episode.action if best_episode else None, - "resolved_action": best_episode.resolved_action if best_episode else None, - "weights": best_episode.weights if best_episode else None, - "department_controls": best_episode.department_controls if best_episode else None, - }, - "experiment_id": config.experiment_id, - "strategy": config.strategy, - } - st.success(f"自动探索完成，共执行 {len(episodes_dump)} 轮。") - - bandit_state = st.session_state.get(_DECISION_ENV_BANDIT_RESULTS_KEY) - if bandit_state: - st.caption( - f"实验 ID：{bandit_state.get('experiment_id')} | 策略：{bandit_state.get('strategy')}" - ) - episodes_dump = bandit_state.get("episodes") or [] - if episodes_dump: - st.dataframe(pd.DataFrame(episodes_dump), hide_index=True, width='stretch') - best_payload = bandit_state.get("best") or {} - if best_payload.get("reward") is not None: - st.success( - f"最佳结果：第 {bandit_state.get('best_index')} 轮，奖励 {best_payload['reward']:+.4f}" - ) - col_best1, col_best2 = st.columns(2) - col_best1.write("动作(raw)：") - col_best1.json(best_payload.get("action") or {}) - col_best2.write("参数值：") - col_best2.json(best_payload.get("resolved_action") or {}) - weights_payload = best_payload.get("weights") or {} - if weights_payload: - st.write("对应代理权重：") - st.json(weights_payload) - if st.button( - "将最佳权重写入默认配置", - key="save_decision_env_bandit_weights", - ): - try: - cfg.agent_weights.update_from_dict(weights_payload) - save_config(cfg) - except Exception as exc: # noqa: BLE001 - LOGGER.exception( - "保存 bandit 权重失败", - extra={**LOG_EXTRA, "error": str(exc)}, - ) - st.error(f"写入配置失败：{exc}") - else: - st.success("最佳权重已写入 config.json") - dept_ctrl = best_payload.get("department_controls") or {} - if dept_ctrl: - with st.expander("最佳部门控制参数", expanded=False): - st.json(dept_ctrl) - if st.button("清除自动探索结果", key="clear_decision_env_bandit"): - st.session_state.pop(_DECISION_ENV_BANDIT_RESULTS_KEY, None) - st.success("已清除自动探索结果。") - - st.divider() - st.caption("批量调参：在下方输入多组动作，每行表示一组 0-1 之间的值，用逗号分隔。") - default_grid = "\n".join( - [ - ",".join(["0.2" for _ in specs]), - ",".join(["0.5" for _ in specs]), - ",".join(["0.8" for _ in specs]), - ] - ) if specs else "" - action_grid_raw = st.text_area( - "动作列表", - value=default_grid, - height=120, - key="decision_env_batch_actions", - ) - run_batch = st.button("批量执行调参", key="run_decision_env_batch") - batch_just_ran = False - if run_batch: - 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", - selected_agents, - disable_departments, + LOGGER.debug( + "离线调参（批量）原始输入=%s", + lines, extra=LOG_EXTRA, ) - lines = [line.strip() for line in action_grid_raw.splitlines() if line.strip()] - if not lines: - st.warning("请在文本框中输入至少一组动作。") - else: - LOGGER.debug( - "离线调参（批量）原始输入=%s", - lines, + parsed_actions: List[List[float]] = [] + for line in lines: + try: + values = [float(val.strip()) for val in line.split(',') if val.strip()] + except ValueError: + st.error(f"无法解析动作行：{line}") + parsed_actions = [] + break + if len(values) != len(specs): + st.error(f"动作维度不匹配（期望 {len(specs)} 个值）：{line}") + parsed_actions = [] + break + parsed_actions.append(values) + if parsed_actions: + LOGGER.info( + "离线调参（批量）解析动作成功，数量=%s", + len(parsed_actions), extra=LOG_EXTRA, ) - parsed_actions: List[List[float]] = [] - for line in lines: - try: - values = [float(val.strip()) for val in line.split(',') if val.strip()] - except ValueError: - st.error(f"无法解析动作行：{line}") - parsed_actions = [] - break - if len(values) != len(specs): - st.error(f"动作维度不匹配（期望 {len(specs)} 个值）：{line}") - parsed_actions = [] - break - parsed_actions.append(values) - if parsed_actions: - LOGGER.info( - "离线调参（批量）解析动作成功，数量=%s", + baseline_weights = cfg.agent_weights.as_dict() + for agent in agent_objects: + baseline_weights.setdefault(agent.name, 1.0) + + universe_env = [code.strip() for code in universe_text.split(',') if code.strip()] + if not universe_env: + st.error("请先指定至少一个股票代码。") + else: + bt_cfg_env = BtConfig( + id="decision_env_streamlit_batch", + name="DecisionEnv Batch", + start_date=start_date, + end_date=end_date, + universe=universe_env, + params={ + "target": target, + "stop": stop, + "hold_days": int(hold_days), + }, + method=cfg.decision_method, + ) + env = DecisionEnv( + bt_config=bt_cfg_env, + parameter_specs=specs, + baseline_weights=baseline_weights, + disable_departments=disable_departments, + ) + results: List[Dict[str, object]] = [] + resolved_experiment_id = experiment_id or str(uuid.uuid4()) + resolved_strategy = strategy_label or "DecisionEnv" + LOGGER.debug( + "离线调参（批量）启动 DecisionEnv：cfg=%s 动作组=%s", + bt_cfg_env, len(parsed_actions), extra=LOG_EXTRA, ) - baseline_weights = cfg.agent_weights.as_dict() - for agent in agent_objects: - baseline_weights.setdefault(agent.name, 1.0) - - universe_env = [code.strip() for code in universe_text.split(',') if code.strip()] - if not universe_env: - st.error("请先指定至少一个股票代码。") - else: - bt_cfg_env = BtConfig( - id="decision_env_streamlit_batch", - name="DecisionEnv Batch", - start_date=start_date, - end_date=end_date, - universe=universe_env, - params={ - "target": target, - "stop": stop, - "hold_days": int(hold_days), - }, - method=cfg.decision_method, - ) - env = DecisionEnv( - bt_config=bt_cfg_env, - parameter_specs=specs, - baseline_weights=baseline_weights, - disable_departments=disable_departments, - ) - results: List[Dict[str, object]] = [] - resolved_experiment_id = experiment_id or str(uuid.uuid4()) - resolved_strategy = strategy_label or "DecisionEnv" - LOGGER.debug( - "离线调参（批量）启动 DecisionEnv：cfg=%s 动作组=%s", - bt_cfg_env, - len(parsed_actions), - extra=LOG_EXTRA, - ) - with st.spinner("正在批量执行调参……"): - for idx, action_vals in enumerate(parsed_actions, start=1): - env.reset() - try: - observation, reward, done, info = env.step(action_vals) - except Exception as exc: # noqa: BLE001 - LOGGER.exception("批量调参失败", extra=LOG_EXTRA) - results.append( - { - "序号": idx, - "动作": action_vals, - "状态": "error", - "错误": str(exc), - } - ) - continue - if observation.get("failure"): - results.append( - { - "序号": idx, - "动作": action_vals, - "状态": "failure", - "奖励": -1.0, - } - ) - else: - LOGGER.info( - "离线调参（批量）第 %s 组完成，reward=%.4f obs=%s", - idx, - reward, - observation, - extra=LOG_EXTRA, - ) - action_payload = { - label: value - for label, value in zip(spec_labels, action_vals) + with st.spinner("正在批量执行调参……"): + for idx, action_vals in enumerate(parsed_actions, start=1): + env.reset() + try: + observation, reward, done, info = env.step(action_vals) + except Exception as exc: # noqa: BLE001 + LOGGER.exception("批量调参失败", extra=LOG_EXTRA) + results.append( + { + "序号": idx, + "动作": action_vals, + "状态": "error", + "错误": str(exc), } - 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( - experiment_id=resolved_experiment_id, - strategy=resolved_strategy, - action=action_payload, - reward=reward, - metrics=metrics_payload, - weights=weights_payload, - ) - except Exception: # noqa: BLE001 - LOGGER.exception("记录调参结果失败", extra=LOG_EXTRA) - results.append( - { - "序号": idx, - "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"), - } + ) + continue + if observation.get("failure"): + results.append( + { + "序号": idx, + "动作": action_vals, + "状态": "failure", + "奖励": -1.0, + } + ) + else: + LOGGER.info( + "离线调参（批量）第 %s 组完成，reward=%.4f obs=%s", + idx, + reward, + observation, + extra=LOG_EXTRA, + ) + action_payload = { + 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( + experiment_id=resolved_experiment_id, + strategy=resolved_strategy, + action=action_payload, + reward=reward, + metrics=metrics_payload, + weights=weights_payload, ) - st.session_state[_DECISION_ENV_BATCH_RESULTS_KEY] = { - "results": results, - "selectable": [ - row - for row in results - if row.get("状态") == "ok" and row.get("权重") - ], - "experiment_id": resolved_experiment_id, - "strategy_label": resolved_strategy, - } - batch_just_ran = True - LOGGER.info( - "离线调参（批量）执行结束，总结果条数=%s", - len(results), - extra=LOG_EXTRA, - ) - batch_state = st.session_state.get(_DECISION_ENV_BATCH_RESULTS_KEY) - if batch_state: - results = batch_state.get("results") or [] - if results: - if batch_just_ran: - st.success("批量调参完成") - else: - st.success("批量调参结果（最近一次运行）") - st.caption( - f"实验 ID：{batch_state.get('experiment_id', '-') } | 策略：{batch_state.get('strategy_label', 'DecisionEnv')}" - ) - results_df = pd.DataFrame(results) - st.write("批量调参结果：") - st.dataframe(results_df, hide_index=True, width='stretch') - selectable = batch_state.get("selectable") or [] - if selectable: - option_labels = [ - f"序号 {row['序号']} | 奖励 {row.get('奖励', 0.0):+.4f}" - for row in selectable - ] - selected_label = st.selectbox( - "选择要保存的记录", - option_labels, - key="decision_env_batch_select", - ) - selected_row = None - for label, row in zip(option_labels, selectable): - if label == selected_label: - selected_row = row - break - if selected_row and st.button( - "保存所选权重为默认配置", - key="save_decision_env_weights_batch", - ): - try: - cfg.agent_weights.update_from_dict(selected_row.get("权重", {})) - save_config(cfg) - except Exception as exc: # noqa: BLE001 - LOGGER.exception("批量保存权重失败", extra={**LOG_EXTRA, "error": str(exc)}) - st.error(f"写入配置失败：{exc}") - else: - st.success( - f"已将序号 {selected_row['序号']} 的权重写入 config.json" - ) - else: - st.caption("暂无成功的结果可供保存。") + except Exception: # noqa: BLE001 + LOGGER.exception("记录调参结果失败", extra=LOG_EXTRA) + results.append( + { + "序号": idx, + "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] = { + "results": results, + "selectable": [ + row + for row in results + if row.get("状态") == "ok" and row.get("权重") + ], + "experiment_id": resolved_experiment_id, + "strategy_label": resolved_strategy, + } + batch_just_ran = True + LOGGER.info( + "离线调参（批量）执行结束，总结果条数=%s", + len(results), + extra=LOG_EXTRA, + ) + batch_state = st.session_state.get(_DECISION_ENV_BATCH_RESULTS_KEY) + if batch_state: + results = batch_state.get("results") or [] + if results: + if batch_just_ran: + st.success("批量调参完成") else: - st.caption("批量调参在最近一次执行中未产生结果。") - if st.button("清除批量调参结果", key="clear_decision_env_batch"): - st.session_state.pop(_DECISION_ENV_BATCH_RESULTS_KEY, None) - st.session_state.pop("decision_env_batch_select", None) - st.success("已清除批量调参结果缓存。") + st.success("批量调参结果（最近一次运行）") + st.caption( + f"实验 ID：{batch_state.get('experiment_id', '-') } | 策略：{batch_state.get('strategy_label', 'DecisionEnv')}" + ) + results_df = pd.DataFrame(results) + st.write("批量调参结果：") + st.dataframe(results_df, hide_index=True, width='stretch') + selectable = batch_state.get("selectable") or [] + if selectable: + option_labels = [ + f"序号 {row['序号']} | 奖励 {row.get('奖励', 0.0):+.4f}" + for row in selectable + ] + selected_label = st.selectbox( + "选择要保存的记录", + option_labels, + key="decision_env_batch_select", + ) + selected_row = None + for label, row in zip(option_labels, selectable): + if label == selected_label: + selected_row = row + break + if selected_row and st.button( + "保存所选权重为默认配置", + key="save_decision_env_weights_batch", + ): + try: + cfg.agent_weights.update_from_dict(selected_row.get("权重", {})) + save_config(cfg) + except Exception as exc: # noqa: BLE001 + LOGGER.exception("批量保存权重失败", extra={**LOG_EXTRA, "error": str(exc)}) + st.error(f"写入配置失败：{exc}") + else: + st.success( + f"已将序号 {selected_row['序号']} 的权重写入 config.json" + ) + else: + st.caption("暂无成功的结果可供保存。") + else: + st.caption("批量调参在最近一次执行中未产生结果。") + if st.button("清除批量调参结果", key="clear_decision_env_batch"): + st.session_state.pop(_DECISION_ENV_BATCH_RESULTS_KEY, None) + st.session_state.pop("decision_env_batch_select", None) + st.success("已清除批量调参结果缓存。")