From a492d6a9f71ce86ceea5ec91193fb9531446e89c Mon Sep 17 00:00:00 2001 From: sam Date: Sun, 5 Oct 2025 20:57:58 +0800 Subject: [PATCH] update --- app/ui/streamlit_app.py | 1402 ++++++++++++++++++++------------------- 1 file changed, 710 insertions(+), 692 deletions(-) diff --git a/app/ui/streamlit_app.py b/app/ui/streamlit_app.py index ff3bae6..181d48d 100644 --- a/app/ui/streamlit_app.py +++ b/app/ui/streamlit_app.py @@ -631,7 +631,7 @@ def render_today_plan() -> None: "BUY_M": score_map.get("BUY_M", 0.0), "BUY_L": score_map.get("BUY_L", 0.0), } - ) + ) if feasible_actions: st.caption(f"可行操作集合：{', '.join(feasible_actions)}") @@ -1260,7 +1260,8 @@ def render_log_viewer() -> None: def render_backtest_review() -> None: """渲染回测执行、调参与结果复盘页面。""" st.header("回测与复盘") - cfg = get_config() + st.caption("1. 基于历史数据复盘当前策略；2. 借助强化学习/调参探索更优参数组合。") + app_cfg = get_config() default_start, default_end = _default_backtest_range(window_days=60) LOGGER.debug( "回测默认参数：start=%s end=%s universe=%s target=%s stop=%s hold_days=%s", @@ -1273,13 +1274,15 @@ def render_backtest_review() -> None: extra=LOG_EXTRA, ) + st.markdown("### 回测参数") col1, col2 = st.columns(2) - start_date = col1.date_input("开始日期", value=default_start) - end_date = col2.date_input("结束日期", value=default_end) - universe_text = st.text_input("股票列表（逗号分隔）", value="000001.SZ") - target = st.number_input("目标收益（例：0.035 表示 3.5%）", value=0.035, step=0.005, format="%.3f") - stop = st.number_input("止损收益（例：-0.015 表示 -1.5%）", value=-0.015, step=0.005, format="%.3f") - hold_days = st.number_input("持有期（交易日）", value=10, step=1) + start_date = col1.date_input("开始日期", value=default_start, key="bt_start_date") + end_date = col2.date_input("结束日期", value=default_end, key="bt_end_date") + universe_text = st.text_input("股票列表（逗号分隔）", value="000001.SZ", key="bt_universe") + col_target, col_stop, col_hold = st.columns(3) + target = col_target.number_input("目标收益（例：0.035 表示 3.5%）", value=0.035, step=0.005, format="%.3f", key="bt_target") + stop = col_stop.number_input("止损收益（例：-0.015 表示 -1.5%）", value=-0.015, step=0.005, format="%.3f", key="bt_stop") + hold_days = col_hold.number_input("持有期（交易日）", value=10, step=1, key="bt_hold_days") LOGGER.debug( "当前回测表单输入：start=%s end=%s universe_text=%s target=%.3f stop=%.3f hold_days=%s", start_date, @@ -1291,710 +1294,725 @@ def render_backtest_review() -> None: extra=LOG_EXTRA, ) - if st.button("运行回测"): - LOGGER.info("用户点击运行回测按钮", extra=LOG_EXTRA) - decision_log_container = st.container() - status_box = st.status("准备执行回测...", expanded=True) - llm_stats_placeholder = st.empty() - decision_entries: List[str] = [] + tab_backtest, tab_rl = st.tabs(["回测验证", "强化学习调参"]) - def _decision_callback(ts_code: str, trade_dt: date, ctx: AgentContext, decision: Decision) -> None: - ts_label = trade_dt.isoformat() - summary = "" - for dept_decision in decision.department_decisions.values(): - if getattr(dept_decision, "summary", ""): - summary = str(dept_decision.summary) - break - entry_lines = [ - f"**{ts_label} {ts_code}** → {decision.action.value} (信心 {decision.confidence:.2f})", - ] - if summary: - entry_lines.append(f"摘要：{summary}") - dep_highlights = [] - for dept_code, dept_decision in decision.department_decisions.items(): - dep_highlights.append( - f"{dept_code}:{dept_decision.action.value}({dept_decision.confidence:.2f})" - ) - if dep_highlights: - entry_lines.append("部门意见：" + "；".join(dep_highlights)) - decision_entries.append(" \n".join(entry_lines)) - decision_log_container.markdown("\n\n".join(decision_entries[-200:])) - status_box.write(f"{ts_label} {ts_code} → {decision.action.value} (信心 {decision.confidence:.2f})") - stats = snapshot_llm_metrics() - llm_stats_placeholder.json( - { - "LLM 调用次数": stats.get("total_calls", 0), - "Prompt Tokens": stats.get("total_prompt_tokens", 0), - "Completion Tokens": stats.get("total_completion_tokens", 0), - "按 Provider": stats.get("provider_calls", {}), - "按模型": stats.get("model_calls", {}), - } - ) - _update_dashboard_sidebar(stats) + with tab_backtest: + st.markdown("#### 回测执行") + if st.button("运行回测", key="bt_run_button"): + LOGGER.info("用户点击运行回测按钮", extra=LOG_EXTRA) + decision_log_container = st.container() + status_box = st.status("准备执行回测...", expanded=True) + llm_stats_placeholder = st.empty() + decision_entries: List[str] = [] - reset_llm_metrics() - status_box.update(label="执行回测中...", state="running") - try: - universe = [code.strip() for code in universe_text.split(',') if code.strip()] - LOGGER.info( - "回测参数：start=%s end=%s universe=%s target=%s stop=%s hold_days=%s", - start_date, - end_date, - universe, - target, - stop, - hold_days, - extra=LOG_EXTRA, - ) - cfg = BtConfig( - id="streamlit_demo", - name="Streamlit Demo Strategy", - start_date=start_date, - end_date=end_date, - universe=universe, - params={ - "target": target, - "stop": stop, - "hold_days": int(hold_days), - }, - ) - result = run_backtest(cfg, decision_callback=_decision_callback) - LOGGER.info( - "回测完成：nav_records=%s trades=%s", - len(result.nav_series), - len(result.trades), - extra=LOG_EXTRA, - ) - status_box.update(label="回测执行完成", state="complete") - st.success("回测执行完成，详见下方结果与统计。") - metrics = snapshot_llm_metrics() - llm_stats_placeholder.json( - { - "LLM 调用次数": metrics.get("total_calls", 0), - "Prompt Tokens": metrics.get("total_prompt_tokens", 0), - "Completion Tokens": metrics.get("total_completion_tokens", 0), - "按 Provider": metrics.get("provider_calls", {}), - "按模型": metrics.get("model_calls", {}), - } - ) - _update_dashboard_sidebar(metrics) - st.json({"nav_records": result.nav_series, "trades": result.trades}) - except Exception as exc: # noqa: BLE001 - LOGGER.exception("回测执行失败", extra=LOG_EXTRA) - status_box.update(label="回测执行失败", state="error") - st.error(f"回测执行失败：{exc}") - - 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", - ) - - specs: List[ParameterSpec] = [] - 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, - ) - ) - action_values.append(action_val) - - 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: - st.error("请确保所有代理的最大权重大于最小权重。") - 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: - 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}") - 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: - 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) - } - metrics_payload = dict(observation) - metrics_payload["reward"] = reward - 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", {}), - "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), - "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) - - 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.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]), + def _decision_callback(ts_code: str, trade_dt: date, ctx: AgentContext, decision: Decision) -> None: + ts_label = trade_dt.isoformat() + summary = "" + for dept_decision in decision.department_decisions.values(): + if getattr(dept_decision, "summary", ""): + summary = str(dept_decision.summary) + break + entry_lines = [ + f"**{ts_label} {ts_code}** → {decision.action.value} (信心 {decision.confidence:.2f})", ] - ) 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 selected_agents: - st.warning("请先选择调参代理。") - elif not range_valid: - st.error("请确保所有代理的最大权重大于最小权重。") - 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, + if summary: + entry_lines.append(f"摘要：{summary}") + dep_highlights = [] + for dept_code, dept_decision in decision.department_decisions.items(): + dep_highlights.append( + f"{dept_code}:{dept_decision.action.value}({dept_decision.confidence:.2f})" ) - 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, - 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", - 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) + if dep_highlights: + entry_lines.append("部门意见：" + "；".join(dep_highlights)) + decision_entries.append(" \n".join(entry_lines)) + decision_log_container.markdown("\n\n".join(decision_entries[-200:])) + status_box.write(f"{ts_label} {ts_code} → {decision.action.value} (信心 {decision.confidence:.2f})") + stats = snapshot_llm_metrics() + llm_stats_placeholder.json( + { + "LLM 调用次数": stats.get("total_calls", 0), + "Prompt Tokens": stats.get("total_prompt_tokens", 0), + "Completion Tokens": stats.get("total_completion_tokens", 0), + "按 Provider": stats.get("provider_calls", {}), + "按模型": stats.get("model_calls", {}), + } + ) + _update_dashboard_sidebar(stats) - 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 = { - name: value - for name, value in zip(selected_agents, action_vals) - } - metrics_payload = dict(observation) - metrics_payload["reward"] = reward - 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_vals, - "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, - } - ) - 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。") - 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。") + reset_llm_metrics() + status_box.update(label="执行回测中...", state="running") + try: + universe = [code.strip() for code in universe_text.split(',') if code.strip()] + LOGGER.info( + "回测参数：start=%s end=%s universe=%s target=%s stop=%s hold_days=%s", + start_date, + end_date, + universe, + target, + stop, + hold_days, + extra=LOG_EXTRA, + ) + backtest_cfg = BtConfig( + id="streamlit_demo", + name="Streamlit Demo Strategy", + start_date=start_date, + end_date=end_date, + universe=universe, + params={ + "target": target, + "stop": stop, + "hold_days": int(hold_days), + }, + ) + result = run_backtest(backtest_cfg, decision_callback=_decision_callback) + LOGGER.info( + "回测完成：nav_records=%s trades=%s", + len(result.nav_series), + len(result.trades), + extra=LOG_EXTRA, + ) + status_box.update(label="回测执行完成", state="complete") + st.success("回测执行完成，详见下方结果与统计。") + metrics = snapshot_llm_metrics() + llm_stats_placeholder.json( + { + "LLM 调用次数": metrics.get("total_calls", 0), + "Prompt Tokens": metrics.get("total_prompt_tokens", 0), + "Completion Tokens": metrics.get("total_completion_tokens", 0), + "按 Provider": metrics.get("provider_calls", {}), + "按模型": metrics.get("model_calls", {}), + } + ) + _update_dashboard_sidebar(metrics) + st.session_state["backtest_last_result"] = {"nav_records": result.nav_series, "trades": result.trades} + st.json(st.session_state["backtest_last_result"]) + except Exception as exc: # noqa: BLE001 + LOGGER.exception("回测执行失败", extra=LOG_EXTRA) + status_box.update(label="回测执行失败", state="error") + st.error(f"回测执行失败：{exc}") - # ADD: Comparison view for multiple backtest configurations - with st.expander("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) - use_log_y = st.checkbox("对数坐标", value=False) - 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) - 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]) - selected_ids = [label.split(" | ")[0].strip() for label in selected_labels] - nav_df = pd.DataFrame() - rpt_df = pd.DataFrame() - if selected_ids: + last_result = st.session_state.get("backtest_last_result") + if last_result: + st.markdown("#### 最近回测输出") + st.json(last_result) + + 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") 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), - ) + 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: + 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: - 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) - end_filter = col_d2.date_input("结束日期", value=overall_max) - 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: - 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", + 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: - 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') + 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: + 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: + csv_buf = pivot.reset_index() + csv_buf.columns = ["trade_date"] + [str(c) for c in pivot.columns] st.download_button( - "下载指标(CSV)", - data=dfm.to_csv(index=False), - file_name="bt_metrics_compare.csv", + "下载曲线(CSV)", + data=csv_buf.to_csv(index=False), + file_name="bt_nav_compare.csv", mime="text/csv", - key="dl_metrics_compare", + key="dl_nav_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("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。") + + 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", + ) + + specs: List[ParameterSpec] = [] + 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, + ) + ) + action_values.append(action_val) + + 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: + st.error("请确保所有代理的最大权重大于最小权重。") + 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: + 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}") + 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: + 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) + } + metrics_payload = dict(observation) + metrics_payload["reward"] = reward + 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", {}), + "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), + "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) + + 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.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 selected_agents: + st.warning("请先选择调参代理。") + elif not range_valid: + st.error("请确保所有代理的最大权重大于最小权重。") + 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, + ) + 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, + 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", + 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 = { + name: value + for name, value in zip(selected_agents, action_vals) + } + metrics_payload = dict(observation) + metrics_payload["reward"] = reward + 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_vals, + "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, + } + ) + 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。") + 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。") + + def render_config_overview() -> None: """Render a concise overview of persisted configuration values."""