697 lines
28 KiB
Python
697 lines
28 KiB
Python
"""今日计划页面视图。"""
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from __future__ import annotations
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import json
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from collections import Counter
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from datetime import date, datetime, timedelta
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from typing import Dict, List, Optional
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import numpy as np
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import pandas as pd
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import streamlit as st
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from app.backtest.engine import BacktestEngine, PortfolioState, BtConfig
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from app.utils.portfolio import list_investment_pool
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from app.utils.db import db_session
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from app.ui.shared import (
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LOGGER,
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LOG_EXTRA,
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get_latest_trade_date,
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get_query_params,
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set_query_params,
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)
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def _fetch_agent_actions(trade_date: str, symbols: List[str]) -> Dict[str, Dict[str, Optional[str]]]:
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unique_symbols = list(dict.fromkeys(symbols))
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if not unique_symbols:
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return {}
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placeholder = ", ".join("?" for _ in unique_symbols)
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sql = (
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f"""
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SELECT ts_code, agent, action
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FROM agent_utils
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WHERE trade_date = ?
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AND ts_code IN ({placeholder})
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"""
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)
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params: List[object] = [trade_date, *unique_symbols]
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with db_session(read_only=True) as conn:
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rows = conn.execute(sql, params).fetchall()
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mapping: Dict[str, Dict[str, Optional[str]]] = {}
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for row in rows:
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ts_code = row["ts_code"]
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agent = row["agent"]
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action = row["action"]
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mapping.setdefault(ts_code, {})[agent] = action
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return mapping
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def _reevaluate_symbols(
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trade_date_obj: date,
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symbols: List[str],
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cfg_id: str,
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cfg_name: str,
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) -> List[Dict[str, object]]:
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unique_symbols = list(dict.fromkeys(symbols))
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if not unique_symbols:
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return []
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trade_date_str = trade_date_obj.isoformat()
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before_map = _fetch_agent_actions(trade_date_str, unique_symbols)
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engine_params: Dict[str, object] = {}
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target_val = st.session_state.get("bt_target")
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if target_val is not None:
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try:
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engine_params["target"] = float(target_val)
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except (TypeError, ValueError):
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pass
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stop_val = st.session_state.get("bt_stop")
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if stop_val is not None:
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try:
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engine_params["stop"] = float(stop_val)
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except (TypeError, ValueError):
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pass
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hold_val = st.session_state.get("bt_hold_days")
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if hold_val is not None:
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try:
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engine_params["hold_days"] = int(hold_val)
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except (TypeError, ValueError):
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pass
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capital_val = st.session_state.get("bt_initial_capital")
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if capital_val is not None:
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try:
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engine_params["initial_capital"] = float(capital_val)
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except (TypeError, ValueError):
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pass
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cfg = BtConfig(
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id=cfg_id,
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name=cfg_name,
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start_date=trade_date_obj,
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end_date=trade_date_obj,
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universe=unique_symbols,
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params=engine_params,
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)
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engine = BacktestEngine(cfg)
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state = PortfolioState(cash=engine.initial_cash)
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engine.simulate_day(trade_date_obj, state)
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after_map = _fetch_agent_actions(trade_date_str, unique_symbols)
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changes: List[Dict[str, object]] = []
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for code in unique_symbols:
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before_agents = before_map.get(code, {})
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after_agents = after_map.get(code, {})
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for agent, new_action in after_agents.items():
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old_action = before_agents.get(agent)
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if new_action != old_action:
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changes.append(
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{
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"代码": code,
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"代理": agent,
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"原动作": old_action,
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"新动作": new_action,
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}
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)
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for agent, old_action in before_agents.items():
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if agent not in after_agents:
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changes.append(
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{
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"代码": code,
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"代理": agent,
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"原动作": old_action,
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"新动作": None,
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}
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)
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return changes
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def render_today_plan() -> None:
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LOGGER.info("渲染今日计划页面", extra=LOG_EXTRA)
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st.header("今日计划")
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latest_trade_date = get_latest_trade_date()
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if latest_trade_date:
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st.caption(f"最新交易日：{latest_trade_date.isoformat()}（统计数据请见左侧系统监控）")
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else:
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st.caption("统计与决策概览现已移至左侧'系统监控'侧栏。")
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try:
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with db_session(read_only=True) as conn:
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date_rows = conn.execute(
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"""
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SELECT DISTINCT trade_date
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FROM agent_utils
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ORDER BY trade_date DESC
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LIMIT 30
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"""
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).fetchall()
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except Exception: # noqa: BLE001
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LOGGER.exception("加载 agent_utils 失败", extra=LOG_EXTRA)
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st.warning("暂未写入部门/代理决策，请先运行回测或策略评估流程。")
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return
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trade_dates = [row["trade_date"] for row in date_rows]
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if not trade_dates:
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st.info("暂无决策记录，完成一次回测后即可在此查看部门意见与投票结果。")
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return
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query = get_query_params()
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# 确保默认日期格式正确
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default_trade_date = str(query.get("date", [trade_dates[0]])[0])
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try:
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default_idx = trade_dates.index(default_trade_date)
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except ValueError:
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default_idx = 0
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# 确保日期格式统一为 YYYYMMDD
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formatted_trade_dates = [str(td) for td in trade_dates]
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trade_date = st.selectbox("交易日", formatted_trade_dates, index=default_idx)
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with db_session(read_only=True) as conn:
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code_rows = conn.execute(
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"""
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SELECT DISTINCT ts_code
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FROM agent_utils
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WHERE trade_date = ?
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ORDER BY ts_code
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""",
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(trade_date,),
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).fetchall()
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symbols = [row["ts_code"] for row in code_rows]
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detail_tab, assistant_tab = st.tabs(["标的详情", "投资助理模式"])
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with assistant_tab:
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_render_today_plan_assistant_view(trade_date)
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with detail_tab:
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if not symbols:
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st.info("所选交易日暂无 agent_utils 记录。")
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else:
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_render_today_plan_symbol_view(trade_date, symbols, query)
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def _render_today_plan_assistant_view(trade_date: str | int | date) -> None:
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# 确保日期格式为字符串
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if isinstance(trade_date, date):
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trade_date = trade_date.strftime("%Y%m%d")
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st.info("已开启投资助理模式：以下内容为组合级（去标的）建议，不包含任何具体标的代码。")
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try:
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candidates = list_investment_pool(trade_date=trade_date)
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if candidates:
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scores = [float(item.score or 0.0) for item in candidates]
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statuses = [item.status or "UNKNOWN" for item in candidates]
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tags: List[str] = []
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rationales: List[str] = []
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for item in candidates:
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if getattr(item, "tags", None):
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tags.extend(item.tags)
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if getattr(item, "rationale", None):
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rationales.append(str(item.rationale))
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cnt = Counter(statuses)
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tag_cnt = Counter(tags)
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st.subheader("候选池聚合概览（已匿名化）")
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col_a, col_b, col_c = st.columns(3)
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col_a.metric("候选数", f"{len(candidates)}")
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col_b.metric("平均评分", f"{np.mean(scores):.3f}" if scores else "-")
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col_c.metric("中位评分", f"{np.median(scores):.3f}" if scores else "-")
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st.write("状态分布：")
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st.json(dict(cnt))
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if tag_cnt:
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st.write("常见标签（示例）：")
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st.json(dict(tag_cnt.most_common(10)))
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if rationales:
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st.write("汇总理由（节选，不含代码）：")
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seen = set()
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excerpts = []
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for rationale in rationales:
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text = rationale.strip()
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if text and text not in seen:
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seen.add(text)
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excerpts.append(text)
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if len(excerpts) >= 3:
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break
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for idx, excerpt in enumerate(excerpts, start=1):
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st.markdown(f"**理由 {idx}:** {excerpt}")
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avg_score = float(np.mean(scores)) if scores else 0.0
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suggest_pct = max(0.0, min(0.3, 0.10 + (avg_score - 0.5) * 0.2))
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st.subheader("组合级建议（不指定标的）")
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st.write(
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f"基于候选池平均评分 {avg_score:.3f}，建议今日用于新增买入的现金比例约为 {suggest_pct:.0%}。"
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)
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st.write(
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"建议分配思路：在候选池中挑选若干得分较高的标的按目标权重等比例分配，或以分批买入的方式分摊入场时点。"
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)
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if st.button("生成组合级操作建议（仅输出，不执行）"):
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st.success("已生成组合级建议（仅供参考）。")
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st.write({
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"候选数": len(candidates),
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"平均评分": avg_score,
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"建议新增买入比例": f"{suggest_pct:.0%}",
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})
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else:
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st.info("所选交易日暂无候选投资池数据。")
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except Exception: # noqa: BLE001
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LOGGER.exception("加载候选池聚合信息失败", extra=LOG_EXTRA)
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st.error("加载候选池数据时发生错误。")
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def _render_today_plan_symbol_view(
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trade_date: str | int | date,
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symbols: List[str],
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query_params: Dict[str, List[str]],
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) -> None:
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default_ts = query_params.get("code", [symbols[0]])[0]
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try:
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default_ts_idx = symbols.index(default_ts)
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except ValueError:
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default_ts_idx = 0
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ts_code = st.selectbox("标的", symbols, index=default_ts_idx)
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batch_symbols = st.multiselect("批量重评估（可多选）", symbols, default=[])
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if st.button("一键重评估所有标的", type="primary", width='stretch'):
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with st.spinner("正在对所有标的进行重评估，请稍候..."):
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try:
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trade_date_obj: Optional[date] = None
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try:
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trade_date_obj = date.fromisoformat(str(trade_date))
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except Exception:
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try:
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trade_date_obj = datetime.strptime(str(trade_date), "%Y%m%d").date()
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except Exception:
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pass
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if trade_date_obj is None:
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raise ValueError(f"无法解析交易日：{trade_date}")
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progress = st.progress(0.0)
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progress.progress(0.3 if symbols else 1.0)
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changes_all = _reevaluate_symbols(
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trade_date_obj,
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symbols,
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"reeval_ui_all",
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"UI All Re-eval",
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)
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progress.progress(1.0)
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st.success(f"一键重评估完成：共处理 {len(symbols)} 个标的")
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if changes_all:
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st.write("检测到以下动作变更：")
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st.dataframe(pd.DataFrame(changes_all), hide_index=True, width='stretch')
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st.rerun()
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except Exception as exc: # noqa: BLE001
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LOGGER.exception("一键重评估失败", extra=LOG_EXTRA)
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st.error(f"一键重评估执行过程中发生错误：{exc}")
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set_query_params(date=str(trade_date), code=str(ts_code))
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with db_session(read_only=True) as conn:
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rows = conn.execute(
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"""
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SELECT agent, action, utils, feasible, weight
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FROM agent_utils
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WHERE trade_date = ? AND ts_code = ?
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ORDER BY CASE WHEN agent = 'global' THEN 1 ELSE 0 END, agent
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""",
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(trade_date, ts_code),
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).fetchall()
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if not rows:
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st.info("未查询到详细决策记录，稍后再试。")
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return
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try:
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feasible_actions = json.loads(rows[0]["feasible"] or "[]")
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except (KeyError, TypeError, json.JSONDecodeError):
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feasible_actions = []
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global_info = None
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dept_records: List[Dict[str, object]] = []
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dept_details: Dict[str, Dict[str, object]] = {}
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agent_records: List[Dict[str, object]] = []
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for item in rows:
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agent_name = item["agent"]
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action = item["action"]
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weight = float(item["weight"] or 0.0)
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try:
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utils = json.loads(item["utils"] or "{}")
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except json.JSONDecodeError:
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utils = {}
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if agent_name == "global":
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global_info = {
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"action": action,
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"confidence": float(utils.get("_confidence", 0.0)),
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"target_weight": float(utils.get("_target_weight", 0.0)),
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"department_votes": utils.get("_department_votes", {}),
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"requires_review": bool(utils.get("_requires_review", False)),
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"scope_values": utils.get("_scope_values", {}),
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"close_series": utils.get("_close_series", []),
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"turnover_series": utils.get("_turnover_series", []),
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"department_supplements": utils.get("_department_supplements", {}),
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"department_dialogue": utils.get("_department_dialogue", {}),
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"department_telemetry": utils.get("_department_telemetry", {}),
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}
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continue
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if agent_name.startswith("dept_"):
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code = agent_name.split("dept_", 1)[-1]
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signals = utils.get("_signals", [])
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risks = utils.get("_risks", [])
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supplements = utils.get("_supplements", [])
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dialogue = utils.get("_dialogue", [])
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telemetry = utils.get("_telemetry", {})
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dept_records.append(
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{
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"部门": code,
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"行动": action,
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"信心": float(utils.get("_confidence", 0.0)),
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"权重": weight,
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"摘要": utils.get("_summary", ""),
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"核心信号": "；".join(signals) if isinstance(signals, list) else signals,
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"风险提示": "；".join(risks) if isinstance(risks, list) else risks,
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"补充次数": len(supplements) if isinstance(supplements, list) else 0,
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}
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)
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dept_details[code] = {
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"supplements": supplements if isinstance(supplements, list) else [],
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"dialogue": dialogue if isinstance(dialogue, list) else [],
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"summary": utils.get("_summary", ""),
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"signals": signals,
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"risks": risks,
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"telemetry": telemetry if isinstance(telemetry, dict) else {},
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}
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else:
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score_map = {
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key: float(val)
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for key, val in utils.items()
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if not str(key).startswith("_")
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}
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agent_records.append(
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{
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"代理": agent_name,
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"建议动作": action,
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"权重": weight,
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"SELL": score_map.get("SELL", 0.0),
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"HOLD": score_map.get("HOLD", 0.0),
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"BUY_S": score_map.get("BUY_S", 0.0),
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"BUY_M": score_map.get("BUY_M", 0.0),
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"BUY_L": score_map.get("BUY_L", 0.0),
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}
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)
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if feasible_actions:
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st.caption(f"可行操作集合：{', '.join(feasible_actions)}")
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st.subheader("全局策略")
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if global_info:
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col1, col2, col3 = st.columns(3)
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col1.metric("最终行动", global_info["action"])
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col2.metric("信心", f"{global_info['confidence']:.2f}")
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col3.metric("目标权重", f"{global_info['target_weight']:+.2%}")
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if global_info["department_votes"]:
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st.json(global_info["department_votes"])
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if global_info["requires_review"]:
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st.warning("部门分歧较大，已标记为需人工复核。")
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with st.expander("基础上下文数据", expanded=False):
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scope = global_info.get("scope_values") or {}
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close_series = global_info.get("close_series") or []
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turnover_series = global_info.get("turnover_series") or []
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st.write("最新字段：")
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if scope:
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st.json(scope)
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st.download_button(
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"下载字段(JSON)",
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data=json.dumps(scope, ensure_ascii=False, indent=2),
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file_name=f"{ts_code}_{trade_date}_scope.json",
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mime="application/json",
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key="dl_scope_json",
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)
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if close_series:
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st.write("收盘价时间序列 (最近窗口)：")
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st.json(close_series)
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try:
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import csv
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import io
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buf = io.StringIO()
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writer = csv.writer(buf)
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writer.writerow(["trade_date", "close"])
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for dt_val, val in close_series:
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writer.writerow([dt_val, val])
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st.download_button(
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"下载收盘价(CSV)",
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data=buf.getvalue(),
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file_name=f"{ts_code}_{trade_date}_close_series.csv",
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mime="text/csv",
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key="dl_close_csv",
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)
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except Exception: # noqa: BLE001
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pass
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||
if turnover_series:
|
||
st.write("换手率时间序列 (最近窗口)：")
|
||
st.json(turnover_series)
|
||
dept_sup = global_info.get("department_supplements") or {}
|
||
dept_dialogue = global_info.get("department_dialogue") or {}
|
||
dept_telemetry = global_info.get("department_telemetry") or {}
|
||
if dept_sup or dept_dialogue:
|
||
with st.expander("部门补数与对话记录", expanded=False):
|
||
if dept_sup:
|
||
st.write("补充数据：")
|
||
st.json(dept_sup)
|
||
if dept_dialogue:
|
||
st.write("对话片段：")
|
||
st.json(dept_dialogue)
|
||
if dept_telemetry:
|
||
with st.expander("部门 LLM 元数据", expanded=False):
|
||
st.json(dept_telemetry)
|
||
else:
|
||
st.info("暂未写入全局策略摘要。")
|
||
|
||
st.subheader("部门意见")
|
||
if dept_records:
|
||
keyword = st.text_input("筛选摘要/信号关键词", value="")
|
||
filtered = dept_records
|
||
if keyword.strip():
|
||
kw = keyword.strip()
|
||
filtered = [
|
||
item
|
||
for item in dept_records
|
||
if kw in str(item.get("摘要", "")) or kw in str(item.get("核心信号", ""))
|
||
]
|
||
min_conf = st.slider("最低信心过滤", 0.0, 1.0, 0.0, 0.05)
|
||
sort_col = st.selectbox("排序列", ["信心", "权重"], index=0)
|
||
filtered = [row for row in filtered if float(row.get("信心", 0.0)) >= min_conf]
|
||
filtered = sorted(filtered, key=lambda r: float(r.get(sort_col, 0.0)), reverse=True)
|
||
dept_df = pd.DataFrame(filtered)
|
||
st.dataframe(dept_df, width='stretch', hide_index=True)
|
||
try:
|
||
st.download_button(
|
||
"下载部门意见(CSV)",
|
||
data=dept_df.to_csv(index=False),
|
||
file_name=f"{trade_date}_{ts_code}_departments.csv",
|
||
mime="text/csv",
|
||
key="dl_dept_csv",
|
||
)
|
||
except Exception: # noqa: BLE001
|
||
pass
|
||
for code, details in dept_details.items():
|
||
with st.expander(f"{code} 补充详情", expanded=False):
|
||
supplements = details.get("supplements", [])
|
||
dialogue = details.get("dialogue", [])
|
||
if supplements:
|
||
st.write("补充数据：")
|
||
st.json(supplements)
|
||
else:
|
||
st.caption("无补充数据请求。")
|
||
if dialogue:
|
||
st.write("对话记录：")
|
||
for idx, line in enumerate(dialogue, start=1):
|
||
st.markdown(f"**回合 {idx}:** {line}")
|
||
else:
|
||
st.caption("无额外对话。")
|
||
telemetry = details.get("telemetry") or {}
|
||
if telemetry:
|
||
st.write("LLM 元数据：")
|
||
st.json(telemetry)
|
||
else:
|
||
st.info("暂无部门记录。")
|
||
|
||
st.subheader("代理评分")
|
||
if agent_records:
|
||
sort_agent_by = st.selectbox(
|
||
"代理排序",
|
||
["权重", "SELL", "HOLD", "BUY_S", "BUY_M", "BUY_L"],
|
||
index=1,
|
||
)
|
||
agent_df = pd.DataFrame(agent_records)
|
||
if sort_agent_by in agent_df.columns:
|
||
agent_df = agent_df.sort_values(sort_agent_by, ascending=False)
|
||
st.dataframe(agent_df, width='stretch', hide_index=True)
|
||
try:
|
||
st.download_button(
|
||
"下载代理评分(CSV)",
|
||
data=agent_df.to_csv(index=False),
|
||
file_name=f"{trade_date}_{ts_code}_agents.csv",
|
||
mime="text/csv",
|
||
key="dl_agent_csv",
|
||
)
|
||
except Exception: # noqa: BLE001
|
||
pass
|
||
else:
|
||
st.info("暂无基础代理评分。")
|
||
|
||
st.divider()
|
||
st.subheader("相关新闻")
|
||
try:
|
||
with db_session(read_only=True) as conn:
|
||
try:
|
||
trade_date_obj = date.fromisoformat(str(trade_date))
|
||
except Exception:
|
||
try:
|
||
trade_date_obj = datetime.strptime(str(trade_date), "%Y%m%d").date()
|
||
except Exception:
|
||
trade_date_obj = date.today() - timedelta(days=7)
|
||
|
||
news_query = """
|
||
SELECT id, title, source, pub_time, sentiment, heat, entities
|
||
FROM news
|
||
WHERE ts_code = ? AND pub_time >= ?
|
||
ORDER BY pub_time DESC
|
||
LIMIT 10
|
||
"""
|
||
seven_days_ago = (trade_date_obj - timedelta(days=7)).strftime("%Y-%m-%d")
|
||
news_rows = conn.execute(news_query, (ts_code, seven_days_ago)).fetchall()
|
||
|
||
if news_rows:
|
||
news_data = []
|
||
for row in news_rows:
|
||
entities_info = {}
|
||
try:
|
||
if row["entities"]:
|
||
entities_info = json.loads(row["entities"])
|
||
except (json.JSONDecodeError, TypeError):
|
||
pass
|
||
|
||
news_item = {
|
||
"标题": row["title"],
|
||
"来源": row["source"],
|
||
"发布时间": row["pub_time"],
|
||
"情感指数": f"{row['sentiment']:.2f}" if row["sentiment"] is not None else "-",
|
||
"热度评分": f"{row['heat']:.2f}" if row["heat"] is not None else "-",
|
||
}
|
||
|
||
industries = entities_info.get("industries", [])
|
||
if industries:
|
||
news_item["相关行业"] = "、".join(industries[:3])
|
||
|
||
news_data.append(news_item)
|
||
|
||
news_df = pd.DataFrame(news_data)
|
||
for col in news_df.columns:
|
||
news_df[col] = news_df[col].astype(str)
|
||
st.dataframe(news_df, width='stretch', hide_index=True)
|
||
|
||
st.write("详细新闻内容：")
|
||
for idx, row in enumerate(news_rows):
|
||
with st.expander(f"{idx+1}. {row['title']}", expanded=False):
|
||
st.write(f"**来源：** {row['source']}")
|
||
st.write(f"**发布时间：** {row['pub_time']}")
|
||
|
||
entities_info = {}
|
||
try:
|
||
if row["entities"]:
|
||
entities_info = json.loads(row["entities"])
|
||
except (json.JSONDecodeError, TypeError):
|
||
pass
|
||
|
||
sentiment_display = f"{row['sentiment']:.2f}" if row["sentiment"] is not None else "-"
|
||
heat_display = f"{row['heat']:.2f}" if row["heat"] is not None else "-"
|
||
st.write(f"**情感指数：** {sentiment_display} | **热度评分：** {heat_display}")
|
||
|
||
industries = entities_info.get("industries", [])
|
||
if industries:
|
||
st.write(f"**相关行业：** {'、'.join(industries)}")
|
||
|
||
important_keywords = entities_info.get("important_keywords", [])
|
||
if important_keywords:
|
||
st.write(f"**重要关键词：** {'、'.join(important_keywords)}")
|
||
|
||
url = entities_info.get("source_url", "")
|
||
if url:
|
||
st.markdown(f"[查看原文]({url})", unsafe_allow_html=True)
|
||
else:
|
||
st.info(f"近7天内暂无关于 {ts_code} 的新闻。")
|
||
except Exception as exc: # noqa: BLE001
|
||
LOGGER.exception("获取新闻数据失败", extra=LOG_EXTRA)
|
||
st.error(f"获取新闻数据时发生错误：{exc}")
|
||
|
||
st.divider()
|
||
st.info("投资池与仓位概览已移至单独页面。请在侧边或页面导航中选择“投资池/仓位”以查看详细信息。")
|
||
|
||
st.divider()
|
||
st.subheader("策略重评估")
|
||
st.caption("对当前选中的交易日与标的，立即触发一次策略评估并回写 agent_utils。")
|
||
cols_re = st.columns([1, 1])
|
||
if cols_re[0].button("对该标的重评估", key="reevaluate_current_symbol"):
|
||
with st.spinner("正在重评估..."):
|
||
try:
|
||
trade_date_obj: Optional[date] = None
|
||
try:
|
||
trade_date_obj = date.fromisoformat(str(trade_date))
|
||
except Exception:
|
||
try:
|
||
trade_date_obj = datetime.strptime(str(trade_date), "%Y%m%d").date()
|
||
except Exception:
|
||
pass
|
||
if trade_date_obj is None:
|
||
raise ValueError(f"无法解析交易日：{trade_date}")
|
||
changes = _reevaluate_symbols(
|
||
trade_date_obj,
|
||
[ts_code],
|
||
"reeval_ui",
|
||
"UI Re-evaluation",
|
||
)
|
||
if changes:
|
||
for change in changes:
|
||
change.setdefault("代码", ts_code)
|
||
df_changes = pd.DataFrame(changes)
|
||
if "代码" in df_changes.columns:
|
||
df_changes = df_changes[["代码", "代理", "原动作", "新动作"]]
|
||
st.success("重评估完成，检测到动作变更：")
|
||
st.dataframe(df_changes, hide_index=True, width='stretch')
|
||
else:
|
||
st.success("重评估完成，无动作变更。")
|
||
st.rerun()
|
||
except Exception as exc: # noqa: BLE001
|
||
LOGGER.exception("重评估失败", extra=LOG_EXTRA)
|
||
st.error(f"重评估失败：{exc}")
|
||
if cols_re[1].button("批量重评估（所选）", key="reevaluate_batch", disabled=not batch_symbols):
|
||
with st.spinner("批量重评估执行中..."):
|
||
try:
|
||
trade_date_obj: Optional[date] = None
|
||
try:
|
||
trade_date_obj = date.fromisoformat(str(trade_date))
|
||
except Exception:
|
||
try:
|
||
trade_date_obj = datetime.strptime(str(trade_date), "%Y%m%d").date()
|
||
except Exception:
|
||
pass
|
||
if trade_date_obj is None:
|
||
raise ValueError(f"无法解析交易日：{trade_date}")
|
||
progress = st.progress(0.0)
|
||
progress.progress(0.3 if batch_symbols else 1.0)
|
||
changes_all = _reevaluate_symbols(
|
||
trade_date_obj,
|
||
batch_symbols,
|
||
"reeval_ui_batch",
|
||
"UI Batch Re-eval",
|
||
)
|
||
progress.progress(1.0)
|
||
st.success(f"批量重评估完成：共处理 {len(batch_symbols)} 个标的")
|
||
if changes_all:
|
||
st.dataframe(pd.DataFrame(changes_all), hide_index=True, width='stretch')
|
||
st.rerun()
|
||
except Exception as exc: # noqa: BLE001
|
||
LOGGER.exception("批量重评估失败", extra=LOG_EXTRA)
|
||
st.error(f"批量重评估失败：{exc}")
|