diff --git a/app/ui/shared.py b/app/ui/shared.py
new file mode 100644
index 0000000..3986cd4
--- /dev/null
+++ b/app/ui/shared.py
@@ -0,0 +1,65 @@
+"""Shared utilities and constants for Streamlit UI views."""
+from __future__ import annotations
+
+from datetime import date, datetime, timedelta
+from typing import Optional
+
+import streamlit as st
+
+from app.utils.db import db_session
+from app.utils.logging import get_logger
+
+LOGGER = get_logger(__name__)
+LOG_EXTRA = {"stage": "ui"}
+
+
+def get_query_params() -> dict[str, list[str]]:
+ """Safely read URL query parameters from Streamlit."""
+ try:
+ return dict(st.query_params)
+ except Exception: # noqa: BLE001
+ return {}
+
+
+def set_query_params(**kwargs: object) -> None:
+ """Update URL query parameters, ignoring failures in unsupported contexts."""
+ try:
+ payload = {k: v for k, v in kwargs.items() if v is not None}
+ if payload:
+ st.query_params.update(payload)
+ except Exception: # noqa: BLE001
+ pass
+
+
+def get_latest_trade_date() -> Optional[date]:
+ """Fetch the most recent trade date from the database."""
+ try:
+ with db_session(read_only=True) as conn:
+ row = conn.execute(
+ "SELECT trade_date FROM daily ORDER BY trade_date DESC LIMIT 1"
+ ).fetchone()
+ except Exception: # noqa: BLE001
+ LOGGER.exception("查询最新交易日失败", extra=LOG_EXTRA)
+ return None
+ if not row:
+ return None
+ raw_value = row["trade_date"]
+ if not raw_value:
+ return None
+ try:
+ return datetime.strptime(str(raw_value), "%Y%m%d").date()
+ except ValueError:
+ try:
+ return datetime.fromisoformat(str(raw_value)).date()
+ except ValueError:
+ LOGGER.warning("无法解析交易日：%s", raw_value, extra=LOG_EXTRA)
+ return None
+
+
+def default_backtest_range(window_days: int = 60) -> tuple[date, date]:
+ """Return a sensible (end, start) date range for backtests."""
+ latest = get_latest_trade_date() or date.today()
+ start = latest - timedelta(days=window_days)
+ if start > latest:
+ start = latest
+ return start, latest
diff --git a/app/ui/streamlit_app.py b/app/ui/streamlit_app.py
index 7027c3a..84b982c 100644
--- a/app/ui/streamlit_app.py
+++ b/app/ui/streamlit_app.py
@@ -2,2989 +2,76 @@
from __future__ import annotations
import sys
-from dataclasses import asdict
-from datetime import date, datetime, timedelta
from pathlib import Path
-from typing import Dict, List, Optional
+
+import streamlit as st
ROOT = Path(__file__).resolve().parents[2]
if str(ROOT) not in sys.path:
sys.path.insert(0, str(ROOT))
-import json
-from datetime import datetime
-import uuid
-
-import pandas as pd
-import plotly.express as px
-import plotly.graph_objects as go
-import requests
-from requests.exceptions import RequestException
-import streamlit as st
-import numpy as np
-from collections import Counter
-
-from app.agents.base import AgentContext
-from app.agents.game import Decision
-from app.backtest.engine import BtConfig, run_backtest
-from app.ui.portfolio_config import render_portfolio_config
-from app.backtest.decision_env import DecisionEnv, ParameterSpec
from app.data.schema import initialize_database
from app.ingest.checker import run_boot_check
-from app.ingest.tushare import FetchJob, run_ingestion
-from app.llm.client import llm_config_snapshot, run_llm
-from app.llm.metrics import (
- recent_decisions as llm_recent_decisions,
- register_listener as register_llm_metrics_listener,
- reset as reset_llm_metrics,
- snapshot as snapshot_llm_metrics,
+from app.ingest.rss import ingest_configured_rss
+from app.ui.portfolio_config import render_portfolio_config
+from app.ui.shared import LOGGER, LOG_EXTRA
+from app.ui.views import (
+ render_backtest_review,
+ render_config_overview,
+ render_data_settings,
+ render_global_dashboard,
+ render_llm_settings,
+ render_log_viewer,
+ render_market_visualization,
+ render_pool_overview,
+ render_tests,
+ render_today_plan,
)
-from app.utils import alerts
-from app.utils.config import (
- ALLOWED_LLM_STRATEGIES,
- DEFAULT_LLM_BASE_URLS,
- DEFAULT_LLM_MODEL_OPTIONS,
- DEFAULT_LLM_MODELS,
- DepartmentSettings,
- LLMEndpoint,
- LLMProvider,
- get_config,
- save_config,
-)
-from app.utils.db import db_session
-from app.utils.logging import get_logger
-from app.utils.portfolio import (
- get_latest_snapshot,
- list_investment_pool,
- list_positions,
- list_recent_trades,
-)
-from app.agents.registry import default_agents
-from app.utils.tuning import log_tuning_result
-from app.backtest.engine import BacktestEngine, PortfolioState
-
-
-LOGGER = get_logger(__name__)
-LOG_EXTRA = {"stage": "ui"}
-_DECISION_ENV_SINGLE_RESULT_KEY = "decision_env_single_result"
-_DECISION_ENV_BATCH_RESULTS_KEY = "decision_env_batch_results"
-_DASHBOARD_CONTAINERS: Optional[tuple[object, object]] = None
-_DASHBOARD_ELEMENTS: Optional[Dict[str, object]] = None
-_SIDEBAR_LISTENER_ATTACHED = False
-# ADD: simple in-memory cache for provider model discovery
-_MODEL_CACHE: Dict[str, Dict[str, object]] = {}
-_CACHE_TTL_SECONDS = 300
-_WARNINGS_CONTAINER = None
-_WARNINGS_PLACEHOLDER = None
-
-# ADD: query param helpers
-def _get_query_params() -> Dict[str, List[str]]:
- try:
- return dict(st.query_params)
- except Exception:
- return {}
-
-def _set_query_params(**kwargs: object) -> None:
- try:
- payload = {k: v for k, v in kwargs.items() if v is not None}
- if payload:
- st.query_params.update(payload)
- except Exception:
- pass
-
-
-def _sidebar_metrics_listener(metrics: Dict[str, object]) -> None:
- try:
- _update_dashboard_sidebar(metrics)
- except Exception: # noqa: BLE001
- LOGGER.debug("侧边栏监听器刷新失败", exc_info=True, extra=LOG_EXTRA)
-
-
-def render_global_dashboard() -> None:
- """Render a persistent sidebar with realtime LLM stats and recent decisions."""
-
- global _DASHBOARD_CONTAINERS
- global _DASHBOARD_ELEMENTS
- global _SIDEBAR_LISTENER_ATTACHED
- global _WARNINGS_CONTAINER
- global _WARNINGS_PLACEHOLDER
-
- # ADD: warning badge on top
- warnings = alerts.get_warnings()
- badge = f" ({len(warnings)})" if warnings else ""
- st.sidebar.header(f"系统监控{badge}")
-
- metrics_container = st.sidebar.container()
- decisions_container = st.sidebar.container()
- _WARNINGS_CONTAINER = st.sidebar.container()
- _WARNINGS_PLACEHOLDER = st.sidebar.empty()
- _DASHBOARD_CONTAINERS = (metrics_container, decisions_container)
- _DASHBOARD_ELEMENTS = _ensure_dashboard_elements(metrics_container, decisions_container)
- if not _SIDEBAR_LISTENER_ATTACHED:
- register_llm_metrics_listener(_sidebar_metrics_listener)
- _SIDEBAR_LISTENER_ATTACHED = True
- _update_dashboard_sidebar()
-
-
-def _update_dashboard_sidebar(
- metrics: Optional[Dict[str, object]] = None,
-) -> None:
- global _DASHBOARD_CONTAINERS
- global _DASHBOARD_ELEMENTS
- global _WARNINGS_CONTAINER
- global _WARNINGS_PLACEHOLDER
-
- containers = _DASHBOARD_CONTAINERS
- if not containers:
- return
- metrics_container, decisions_container = containers
- elements = _DASHBOARD_ELEMENTS
- if elements is None:
- elements = _ensure_dashboard_elements(metrics_container, decisions_container)
- _DASHBOARD_ELEMENTS = elements
-
- if metrics is None:
- metrics = snapshot_llm_metrics()
-
- elements["metrics_calls"].metric("LLM 调用", metrics.get("total_calls", 0))
- elements["metrics_prompt"].metric("Prompt Tokens", metrics.get("total_prompt_tokens", 0))
- elements["metrics_completion"].metric(
- "Completion Tokens", metrics.get("total_completion_tokens", 0)
- )
-
- provider_calls = metrics.get("provider_calls", {})
- model_calls = metrics.get("model_calls", {})
- provider_placeholder = elements["provider_distribution"]
- provider_placeholder.empty()
- if provider_calls:
- provider_placeholder.json(provider_calls)
- else:
- provider_placeholder.info("暂无 Provider 分布数据。")
-
- model_placeholder = elements["model_distribution"]
- model_placeholder.empty()
- if model_calls:
- model_placeholder.json(model_calls)
- else:
- model_placeholder.info("暂无模型分布数据。")
-
- decisions = metrics.get("recent_decisions") or llm_recent_decisions(10)
- if decisions:
- lines = []
- for record in reversed(decisions[-10:]):
- ts_code = record.get("ts_code")
- trade_date = record.get("trade_date")
- action = record.get("action")
- confidence = record.get("confidence", 0.0)
- summary = record.get("summary")
- line = f"**{trade_date} {ts_code}** → {action} (置信度 {confidence:.2f})"
- if summary:
- line += f"\n{summary}"
- lines.append(line)
- decisions_placeholder = elements["decisions_list"]
- decisions_placeholder.empty()
- decisions_placeholder.markdown("\n\n".join(lines), unsafe_allow_html=True)
- else:
- decisions_placeholder = elements["decisions_list"]
- decisions_placeholder.empty()
- decisions_placeholder.info("暂无决策记录。执行回测或实时评估后可在此查看。")
- # Render warnings section in-place (clear then write)
- if _WARNINGS_PLACEHOLDER is not None:
- _WARNINGS_PLACEHOLDER.empty()
- with _WARNINGS_PLACEHOLDER.container():
- st.subheader("数据告警")
- warn_list = alerts.get_warnings()
- if warn_list:
- lines = []
- for warning in warn_list[-10:]:
- detail = warning.get("detail")
- appendix = f" {detail}" if detail else ""
- lines.append(
- f"- **{warning['source']}** {warning['message']}{appendix}\n{warning['timestamp']}"
- )
- st.markdown("\n".join(lines), unsafe_allow_html=True)
- btn_cols = st.columns([1,1])
- if btn_cols[0].button("清除数据告警", key="clear_data_alerts_sibling"):
- alerts.clear_warnings()
- _update_dashboard_sidebar()
- try:
- st.download_button(
- "导出告警(JSON)",
- data=json.dumps(warn_list, ensure_ascii=False, indent=2),
- file_name="data_warnings.json",
- mime="application/json",
- key="dl_warnings_json_sibling",
- )
- except Exception:
- pass
- else:
- st.info("暂无数据告警。")
-
-
-def _ensure_dashboard_elements(metrics_container, decisions_container) -> Dict[str, object]:
- metrics_container.header("系统监控")
- col_a, col_b, col_c = metrics_container.columns(3)
- metrics_calls = col_a.empty()
- metrics_prompt = col_b.empty()
- metrics_completion = col_c.empty()
- distribution_expander = metrics_container.expander("调用分布", expanded=False)
- provider_distribution = distribution_expander.empty()
- model_distribution = distribution_expander.empty()
-
- decisions_container.subheader("最新决策")
- decisions_list = decisions_container.empty()
-
- elements = {
- "metrics_calls": metrics_calls,
- "metrics_prompt": metrics_prompt,
- "metrics_completion": metrics_completion,
- "provider_distribution": provider_distribution,
- "model_distribution": model_distribution,
- "decisions_list": decisions_list,
- }
- return elements
-
-def _discover_provider_models(provider: LLMProvider, base_override: str = "", api_override: Optional[str] = None) -> tuple[list[str], Optional[str]]:
- """Attempt to query provider API and return available model ids."""
-
- base_url = (base_override or provider.base_url or DEFAULT_LLM_BASE_URLS.get(provider.key, "")).strip()
- if not base_url:
- return [], "请先填写 Base URL"
- timeout = float(provider.default_timeout or 30.0)
- mode = provider.mode or ("ollama" if provider.key == "ollama" else "openai")
-
- # ADD: simple cache by provider+base URL
- cache_key = f"{provider.key}|{base_url}"
- now = datetime.now()
- cached = _MODEL_CACHE.get(cache_key)
- if cached:
- ts = cached.get("ts")
- if isinstance(ts, float) and (now.timestamp() - ts) < _CACHE_TTL_SECONDS:
- models = list(cached.get("models") or [])
- return models, None
-
- try:
- if mode == "ollama":
- url = base_url.rstrip('/') + "/api/tags"
- response = requests.get(url, timeout=timeout)
- response.raise_for_status()
- data = response.json()
- models = []
- for item in data.get("models", []) or data.get("data", []):
- name = item.get("name") or item.get("model") or item.get("tag")
- if name:
- models.append(str(name).strip())
- _MODEL_CACHE[cache_key] = {"ts": now.timestamp(), "models": sorted(set(models))}
- return sorted(set(models)), None
-
- api_key = (api_override or provider.api_key or "").strip()
- if not api_key:
- return [], "缺少 API Key"
- url = base_url.rstrip('/') + "/v1/models"
- headers = {
- "Authorization": f"Bearer {api_key}",
- "Content-Type": "application/json",
- }
- response = requests.get(url, headers=headers, timeout=timeout)
- response.raise_for_status()
- payload = response.json()
- models = [
- str(item.get("id")).strip()
- for item in payload.get("data", [])
- if item.get("id")
- ]
- _MODEL_CACHE[cache_key] = {"ts": now.timestamp(), "models": sorted(set(models))}
- return sorted(set(models)), None
- except RequestException as exc: # noqa: BLE001
- return [], f"HTTP 错误：{exc}"
- except Exception as exc: # noqa: BLE001
- return [], f"解析失败：{exc}"
-
-def _load_stock_options(limit: int = 500) -> list[str]:
- try:
- with db_session(read_only=True) as conn:
- rows = conn.execute(
- "SELECT ts_code, name FROM stock_basic WHERE list_status = 'L' ORDER BY ts_code"
- ).fetchall()
- except Exception:
- LOGGER.exception("加载股票列表失败", extra=LOG_EXTRA)
- return []
- options: list[str] = []
- for row in rows[:limit]:
- code = row["ts_code"]
- name = row["name"] or ""
- label = f"{code} | {name}" if name else code
- options.append(label)
- LOGGER.info("加载股票选项完成，数量=%s", len(options), extra=LOG_EXTRA)
- return options
-
-
-def _parse_ts_code(selection: str) -> str:
- return selection.split(' | ')[0].strip().upper()
-
-
-def _load_daily_frame(ts_code: str, start: date, end: date) -> pd.DataFrame:
- LOGGER.info(
- "加载行情数据：ts_code=%s start=%s end=%s",
- ts_code,
- start,
- end,
- extra=LOG_EXTRA,
- )
- start_str = start.strftime('%Y%m%d')
- end_str = end.strftime('%Y%m%d')
- range_query = (
- "SELECT trade_date, open, high, low, close, vol, amount "
- "FROM daily WHERE ts_code = ? AND trade_date BETWEEN ? AND ? ORDER BY trade_date"
- )
- fallback_query = (
- "SELECT trade_date, open, high, low, close, vol, amount "
- "FROM daily WHERE ts_code = ? ORDER BY trade_date DESC LIMIT 200"
- )
- with db_session(read_only=True) as conn:
- df = pd.read_sql_query(range_query, conn, params=(ts_code, start_str, end_str))
- if df.empty:
- df = pd.read_sql_query(fallback_query, conn, params=(ts_code,))
- if df.empty:
- LOGGER.warning(
- "行情数据为空：ts_code=%s start=%s end=%s",
- ts_code,
- start,
- end,
- extra=LOG_EXTRA,
- )
- return df
- df = df.sort_values('trade_date')
- df['trade_date'] = pd.to_datetime(df['trade_date'])
- df.set_index('trade_date', inplace=True)
- LOGGER.info("行情数据加载完成：条数=%s", len(df), extra=LOG_EXTRA)
- return df
-
-
-def _get_latest_trade_date() -> Optional[date]:
- try:
- with db_session(read_only=True) as conn:
- row = conn.execute(
- "SELECT trade_date FROM daily ORDER BY trade_date DESC LIMIT 1"
- ).fetchone()
- except Exception: # noqa: BLE001
- LOGGER.exception("查询最新交易日失败", extra=LOG_EXTRA)
- return None
- if not row:
- return None
- raw_value = row["trade_date"]
- if not raw_value:
- return None
- try:
- return datetime.strptime(str(raw_value), "%Y%m%d").date()
- except ValueError:
- try:
- return datetime.fromisoformat(str(raw_value)).date()
- except ValueError:
- LOGGER.warning("无法解析交易日：%s", raw_value, extra=LOG_EXTRA)
- return None
-
-
-def _default_backtest_range(window_days: int = 60) -> tuple[date, date]:
- latest = _get_latest_trade_date() or date.today()
- start = latest - timedelta(days=window_days)
- if start > latest:
- start = latest
- return start, latest
-
-
-def render_today_plan() -> None:
- LOGGER.info("渲染今日计划页面", extra=LOG_EXTRA)
- st.header("今日计划")
- latest_trade_date = _get_latest_trade_date()
- if latest_trade_date:
- st.caption(f"最新交易日：{latest_trade_date.isoformat()}（统计数据请见左侧系统监控）")
- else:
- st.caption("统计与决策概览现已移至左侧'系统监控'侧栏。")
- try:
- with db_session(read_only=True) as conn:
- date_rows = conn.execute(
- """
- SELECT DISTINCT trade_date
- FROM agent_utils
- ORDER BY trade_date DESC
- LIMIT 30
- """
- ).fetchall()
- except Exception: # noqa: BLE001
- LOGGER.exception("加载 agent_utils 失败", extra=LOG_EXTRA)
- 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。")
- return
- trade_dates = [row["trade_date"] for row in date_rows]
- if not trade_dates:
- 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。")
- return
- q = _get_query_params()
- default_trade_date = q.get("date", [trade_dates[0]])[0]
- try:
- default_idx = trade_dates.index(default_trade_date)
- except ValueError:
- default_idx = 0
- trade_date = st.selectbox("交易日", trade_dates, index=default_idx)
- with db_session(read_only=True) as conn:
- code_rows = conn.execute(
- """
- SELECT DISTINCT ts_code
- FROM agent_utils
- WHERE trade_date = ?
- ORDER BY ts_code
- """,
- (trade_date,),
- ).fetchall()
- symbols = [row["ts_code"] for row in code_rows]
- detail_tab, assistant_tab = st.tabs(["标的详情", "投资助理模式"])
- with assistant_tab:
- 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。")
- try:
- candidates = list_investment_pool(trade_date=trade_date)
- if candidates:
- scores = [float(item.score or 0.0) for item in candidates]
- statuses = [item.status or "UNKNOWN" for item in candidates]
- tags = []
- rationales = []
- for item in candidates:
- if getattr(item, "tags", None):
- tags.extend(item.tags)
- if getattr(item, "rationale", None):
- rationales.append(str(item.rationale))
- cnt = Counter(statuses)
- tag_cnt = Counter(tags)
- st.subheader("候选池聚合概览（已匿名化）")
- col_a, col_b, col_c = st.columns(3)
- col_a.metric("候选数", f"{len(candidates)}")
- col_b.metric("平均评分", f"{np.mean(scores):.3f}" if scores else "-")
- col_c.metric("中位评分", f"{np.median(scores):.3f}" if scores else "-")
-
- st.write("状态分布：")
- st.json(dict(cnt))
-
- if tag_cnt:
- st.write("常见标签（示例）：")
- st.json(dict(tag_cnt.most_common(10)))
-
- if rationales:
- st.write("汇总理由（节选，不含代码）：")
- # 显示前三条去重理由摘要
- seen = set()
- excerpts = []
- for r in rationales:
- s = r.strip()
- if not s:
- continue
- if s in seen:
- continue
- seen.add(s)
- excerpts.append(s)
- if len(excerpts) >= 3:
- break
- for idx, ex in enumerate(excerpts, start=1):
- st.markdown(f"**理由 {idx}:** {ex}")
-
- # 简单生成组合级建议（示例逻辑，可后续替换为更复杂策略）
- avg_score = float(np.mean(scores)) if scores else 0.0
- # 建议使用现金比例：基线 10%，根据平均评分上下调整，限制在[0, 30%]
- suggest_pct = max(0.0, min(0.3, 0.10 + (avg_score - 0.5) * 0.2))
- st.subheader("组合级建议（不指定标的）")
- st.write(
- 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%}。"
- )
- st.write(
- "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。"
- )
- if st.button("生成组合级操作建议（仅输出，不执行）"):
- st.success("已生成组合级建议（仅供参考）。")
- st.write({
- "候选数": len(candidates),
- "平均评分": avg_score,
- "建议新增买入比例": f"{suggest_pct:.0%}",
- })
- else:
- st.info("所选交易日暂无候选投资池数据。")
- except Exception:
- LOGGER.exception("加载候选池聚合信息失败", extra=LOG_EXTRA)
- st.error("加载候选池数据时发生错误。")
- with detail_tab:
- if not symbols:
- st.info("所选交易日暂无 agent_utils 记录。")
- else:
- default_ts = q.get("code", [symbols[0]])[0]
- try:
- default_ts_idx = symbols.index(default_ts)
- except ValueError:
- default_ts_idx = 0
- ts_code = st.selectbox("标的", symbols, index=default_ts_idx)
- batch_symbols = st.multiselect("批量重评估（可多选）", symbols, default=[])
-
- if st.button("一键重评估所有标的", type="primary", width='stretch'):
- with st.spinner("正在对所有标的进行重评估，请稍候..."):
- try:
- # 解析交易日
- trade_date_obj = 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)
- changes_all = []
- success_count = 0
- error_count = 0
-
- # 遍历所有标的
- for idx, code in enumerate(symbols, start=1):
- try:
- # 保存重评估前的状态
- with db_session(read_only=True) as conn:
- before_rows = conn.execute(
- "SELECT agent, action FROM agent_utils WHERE trade_date = ? AND ts_code = ?",
- (trade_date, code),
- ).fetchall()
- before_map = {row["agent"]: row["action"] for row in before_rows}
-
- # 执行重评估
- cfg = BtConfig(
- id="reeval_ui_all",
- name="UI All Re-eval",
- start_date=trade_date_obj,
- end_date=trade_date_obj,
- universe=[code],
- params={},
- )
- engine = BacktestEngine(cfg)
- state = PortfolioState()
- _ = engine.simulate_day(trade_date_obj, state)
-
- # 检查变化
- with db_session(read_only=True) as conn:
- after_rows = conn.execute(
- "SELECT agent, action FROM agent_utils WHERE trade_date = ? AND ts_code = ?",
- (trade_date, code),
- ).fetchall()
- for row in after_rows:
- agent = row["agent"]
- new_action = row["action"]
- old_action = before_map.get(agent)
- if new_action != old_action:
- changes_all.append({"代码": code, "代理": agent, "原动作": old_action, "新动作": new_action})
- success_count += 1
- except Exception as e:
- LOGGER.exception(f"重评估 {code} 失败", extra=LOG_EXTRA)
- error_count += 1
-
- # 更新进度
- progress.progress(idx / len(symbols))
-
- # 显示结果
- if error_count > 0:
- st.error(f"一键重评估完成：成功 {success_count} 个，失败 {error_count} 个")
- else:
- st.success(f"一键重评估完成：所有 {success_count} 个标的重评估成功")
-
- # 显示变更记录
- if changes_all:
- st.write("检测到以下动作变更：")
- st.dataframe(pd.DataFrame(changes_all), hide_index=True, width='stretch')
-
- # 刷新页面数据
- st.rerun()
- except Exception as exc:
- LOGGER.exception("一键重评估失败", extra=LOG_EXTRA)
- st.error(f"一键重评估执行过程中发生错误：{exc}")
- _set_query_params(date=str(trade_date), code=str(ts_code))
- with db_session(read_only=True) as conn:
- rows = conn.execute(
- """
- SELECT agent, action, utils, feasible, weight
- FROM agent_utils
- WHERE trade_date = ? AND ts_code = ?
- ORDER BY CASE WHEN agent = 'global' THEN 1 ELSE 0 END, agent
- """,
- (trade_date, ts_code),
- ).fetchall()
- if not rows:
- st.info("未查询到详细决策记录，稍后再试。")
- return
- try:
- feasible_actions = json.loads(rows[0]["feasible"] or "[]")
- except (KeyError, TypeError, json.JSONDecodeError):
- feasible_actions = []
- global_info = None
- dept_records: List[Dict[str, object]] = []
- dept_details: Dict[str, Dict[str, object]] = {}
- agent_records: List[Dict[str, object]] = []
- for item in rows:
- agent_name = item["agent"]
- action = item["action"]
- weight = float(item["weight"] or 0.0)
- try:
- utils = json.loads(item["utils"] or "{}")
- except json.JSONDecodeError:
- utils = {}
-
- if agent_name == "global":
- global_info = {
- "action": action,
- "confidence": float(utils.get("_confidence", 0.0)),
- "target_weight": float(utils.get("_target_weight", 0.0)),
- "department_votes": utils.get("_department_votes", {}),
- "requires_review": bool(utils.get("_requires_review", False)),
- "scope_values": utils.get("_scope_values", {}),
- "close_series": utils.get("_close_series", []),
- "turnover_series": utils.get("_turnover_series", []),
- "department_supplements": utils.get("_department_supplements", {}),
- "department_dialogue": utils.get("_department_dialogue", {}),
- "department_telemetry": utils.get("_department_telemetry", {}),
- }
- continue
-
- if agent_name.startswith("dept_"):
- code = agent_name.split("dept_", 1)[-1]
- signals = utils.get("_signals", [])
- risks = utils.get("_risks", [])
- supplements = utils.get("_supplements", [])
- dialogue = utils.get("_dialogue", [])
- telemetry = utils.get("_telemetry", {})
- dept_records.append(
- {
- "部门": code,
- "行动": action,
- "信心": float(utils.get("_confidence", 0.0)),
- "权重": weight,
- "摘要": utils.get("_summary", ""),
- "核心信号": "；".join(signals) if isinstance(signals, list) else signals,
- "风险提示": "；".join(risks) if isinstance(risks, list) else risks,
- "补充次数": len(supplements) if isinstance(supplements, list) else 0,
- }
- )
- dept_details[code] = {
- "supplements": supplements if isinstance(supplements, list) else [],
- "dialogue": dialogue if isinstance(dialogue, list) else [],
- "summary": utils.get("_summary", ""),
- "signals": signals,
- "risks": risks,
- "telemetry": telemetry if isinstance(telemetry, dict) else {},
- }
- else:
- score_map = {
- key: float(val)
- for key, val in utils.items()
- if not str(key).startswith("_")
- }
- agent_records.append(
- {
- "代理": agent_name,
- "建议动作": action,
- "权重": weight,
- "SELL": score_map.get("SELL", 0.0),
- "HOLD": score_map.get("HOLD", 0.0),
- "BUY_S": score_map.get("BUY_S", 0.0),
- "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)}")
- st.subheader("全局策略")
- if global_info:
- col1, col2, col3 = st.columns(3)
- col1.metric("最终行动", global_info["action"])
- col2.metric("信心", f"{global_info['confidence']:.2f}")
- col3.metric("目标权重", f"{global_info['target_weight']:+.2%}")
- if global_info["department_votes"]:
- st.json(global_info["department_votes"])
- if global_info["requires_review"]:
- st.warning("部门分歧较大，已标记为需人工复核。")
- with st.expander("基础上下文数据", expanded=False):
- # ADD: export buttons
- scope = global_info.get("scope_values") or {}
- close_series = global_info.get("close_series") or []
- turnover_series = global_info.get("turnover_series") or []
- st.write("最新字段：")
- if scope:
- st.json(scope)
- st.download_button(
- "下载字段(JSON)",
- data=json.dumps(scope, ensure_ascii=False, indent=2),
- file_name=f"{ts_code}_{trade_date}_scope.json",
- mime="application/json",
- key="dl_scope_json",
- )
- if close_series:
- st.write("收盘价时间序列 (最近窗口)：")
- st.json(close_series)
- try:
- import io, csv
- buf = io.StringIO()
- writer = csv.writer(buf)
- writer.writerow(["trade_date", "close"])
- for dt, val in close_series:
- writer.writerow([dt, val])
- st.download_button(
- "下载收盘价(CSV)",
- data=buf.getvalue(),
- file_name=f"{ts_code}_{trade_date}_close_series.csv",
- mime="text/csv",
- key="dl_close_csv",
- )
- except Exception:
- pass
- 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:
- # ADD: keyword filter for department summaries
- 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", ""))
- ]
- # ADD: confidence filter and sort
- 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:
- 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:
- # ADD: sorting and CSV export for agents
- 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:
- 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:
- # 解析当前trade_date为datetime对象
- try:
- trade_date_obj = date.fromisoformat(str(trade_date))
- except:
- try:
- trade_date_obj = datetime.strptime(str(trade_date), "%Y%m%d").date()
- except:
- # 如果解析失败，使用当前日期向前推7天
- trade_date_obj = date.today() - timedelta(days=7)
-
- # 查询近7天内与当前标的相关的新闻，按发布时间降序排列
- 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
- """
- # 计算7天前的日期字符串
- 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:
- # 解析entities字段获取更多信息
- entities_info = {}
- try:
- if row["entities"]:
- entities_info = json.loads(row["entities"])
- except (json.JSONDecodeError, TypeError):
- pass
-
- # 准备新闻数据
- 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 "-"
- }
-
- # 如果有行业信息，添加到展示数据中
- industries = entities_info.get("industries", [])
- if industries:
- news_item["相关行业"] = "、".join(industries[:3]) # 只显示前3个行业
-
- news_data.append(news_item)
-
- # 显示新闻表格
- news_df = pd.DataFrame(news_data)
- # 确保所有列都是字符串类型，避免PyArrow序列化错误
- for col in news_df.columns:
- news_df[col] = news_df[col].astype(str)
- st.dataframe(news_df, width='stretch', hide_index=True)
-
- # 添加新闻详情展开视图
- 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']}")
-
- # 解析entities获取更多详细信息
- entities_info = {}
- try:
- if row["entities"]:
- entities_info = json.loads(row["entities"])
- except (json.JSONDecodeError, TypeError):
- pass
-
- # 显示情感和热度信息
- 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}")
-
- # 显示行业信息
- industries = entities_info.get("industries", [])
- if industries:
- st.write(f"**相关行业：** {'、'.join(industries)}")
-
- # 显示重要关键词
- important_keywords = entities_info.get("important_keywords", [])
- if important_keywords:
- st.write(f"**重要关键词：** {'、'.join(important_keywords)}")
-
- # 显示URL链接（如果有）
- 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 e:
- 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：{e}")
- 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 = 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}")
- # snapshot before
- with db_session(read_only=True) as conn:
- before_rows = conn.execute(
- """
- SELECT agent, action, utils FROM agent_utils
- WHERE trade_date = ? AND ts_code = ?
- """,
- (trade_date, ts_code),
- ).fetchall()
- before_map = {row["agent"]: (row["action"], row["utils"]) for row in before_rows}
- cfg = BtConfig(
- id="reeval_ui",
- name="UI Re-evaluation",
- start_date=trade_date_obj,
- end_date=trade_date_obj,
- universe=[ts_code],
- params={},
- )
- engine = BacktestEngine(cfg)
- state = PortfolioState()
- _ = engine.simulate_day(trade_date_obj, state)
- # compare after
- with db_session(read_only=True) as conn:
- after_rows = conn.execute(
- """
- SELECT agent, action, utils FROM agent_utils
- WHERE trade_date = ? AND ts_code = ?
- """,
- (trade_date, ts_code),
- ).fetchall()
- changes = []
- for row in after_rows:
- agent = row["agent"]
- new_action = row["action"]
- old_action, _old_utils = before_map.get(agent, (None, None))
- if new_action != old_action:
- changes.append({"代理": agent, "原动作": old_action, "新动作": new_action})
- if changes:
- st.success("重评估完成，检测到动作变更：")
- st.dataframe(pd.DataFrame(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 = 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)
- changes_all: List[Dict[str, object]] = []
- for idx, code in enumerate(batch_symbols, start=1):
- with db_session(read_only=True) as conn:
- before_rows = conn.execute(
- "SELECT agent, action FROM agent_utils WHERE trade_date = ? AND ts_code = ?",
- (trade_date, code),
- ).fetchall()
- before_map = {row["agent"]: row["action"] for row in before_rows}
- cfg = BtConfig(
- id="reeval_ui_batch",
- name="UI Batch Re-eval",
- start_date=trade_date_obj,
- end_date=trade_date_obj,
- universe=[code],
- params={},
- )
- engine = BacktestEngine(cfg)
- state = PortfolioState()
- _ = engine.simulate_day(trade_date_obj, state)
- with db_session(read_only=True) as conn:
- after_rows = conn.execute(
- "SELECT agent, action FROM agent_utils WHERE trade_date = ? AND ts_code = ?",
- (trade_date, code),
- ).fetchall()
- for row in after_rows:
- agent = row["agent"]
- new_action = row["action"]
- old_action = before_map.get(agent)
- if new_action != old_action:
- changes_all.append({"代码": code, "代理": agent, "原动作": old_action, "新动作": new_action})
- progress.progress(idx / max(1, len(batch_symbols)))
- st.success("批量重评估完成。")
- 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}")
-
-
-
-def render_pool_overview() -> None:
- """单独的投资池与仓位概览页面（从今日计划中提取）。"""
- LOGGER.info("渲染投资池与仓位概览页面", extra=LOG_EXTRA)
- st.header("投资池与仓位概览")
-
- snapshot = get_latest_snapshot()
- if snapshot:
- col_a, col_b, col_c = st.columns(3)
- if snapshot.total_value is not None:
- col_a.metric("组合净值", f"{snapshot.total_value:,.2f}")
- if snapshot.cash is not None:
- col_b.metric("现金余额", f"{snapshot.cash:,.2f}")
- if snapshot.invested_value is not None:
- col_c.metric("持仓市值", f"{snapshot.invested_value:,.2f}")
- detail_cols = st.columns(4)
- if snapshot.unrealized_pnl is not None:
- detail_cols[0].metric("浮盈", f"{snapshot.unrealized_pnl:,.2f}")
- if snapshot.realized_pnl is not None:
- detail_cols[1].metric("已实现盈亏", f"{snapshot.realized_pnl:,.2f}")
- if snapshot.net_flow is not None:
- detail_cols[2].metric("净流入", f"{snapshot.net_flow:,.2f}")
- if snapshot.exposure is not None:
- detail_cols[3].metric("风险敞口", f"{snapshot.exposure:.2%}")
- if snapshot.notes:
- st.caption(f"备注：{snapshot.notes}")
- else:
- st.info("暂无组合快照，请在执行回测或实盘同步后写入 portfolio_snapshots。")
-
- # 候选池
- try:
- # trade_date param optional; use latest available if not provided
- latest_date = _get_latest_trade_date()
- candidates = list_investment_pool(trade_date=latest_date)
- except Exception:
- LOGGER.exception("加载候选池失败", extra=LOG_EXTRA)
- candidates = []
-
- if candidates:
- candidate_df = pd.DataFrame(
- [
- {
- "交易日": item.trade_date,
- "代码": item.ts_code,
- "评分": item.score,
- "状态": item.status,
- "标签": "、".join(item.tags) if item.tags else "-",
- "理由": item.rationale or "",
- }
- for item in candidates
- ]
- )
- st.write("候选投资池：")
- st.dataframe(candidate_df, width='stretch', hide_index=True)
- else:
- st.caption("候选投资池暂无数据。")
-
- # 持仓
- positions = list_positions(active_only=False)
- if positions:
- position_df = pd.DataFrame(
- [
- {
- "ID": pos.id,
- "代码": pos.ts_code,
- "开仓日": pos.opened_date,
- "平仓日": pos.closed_date or "-",
- "状态": pos.status,
- "数量": pos.quantity,
- "成本": pos.cost_price,
- "现价": pos.market_price,
- "市值": pos.market_value,
- "浮盈": pos.unrealized_pnl,
- "已实现": pos.realized_pnl,
- "目标权重": pos.target_weight,
- }
- for pos in positions
- ]
- )
- st.write("组合持仓：")
- st.dataframe(position_df, width='stretch', hide_index=True)
- else:
- st.caption("组合持仓暂无记录。")
-
- trades = list_recent_trades(limit=20)
- if trades:
- trades_df = pd.DataFrame(trades)
- st.write("近期成交：")
- st.dataframe(trades_df, width='stretch', hide_index=True)
- else:
- st.caption("近期成交暂无记录。")
-
- st.caption("数据来源：agent_utils、investment_pool、portfolio_positions、portfolio_trades、portfolio_snapshots。")
-
- if st.button("执行对比", type="secondary"):
- with st.spinner("执行日志对比分析中..."):
- try:
- with db_session(read_only=True) as conn:
- # 获取两个日期的日志统计
- query_date1 = f"{compare_date1.isoformat()}T00:00:00Z"
- query_date2 = f"{compare_date1.isoformat()}T23:59:59Z"
- logs1 = conn.execute(
- "SELECT level, COUNT(*) as count FROM run_log WHERE ts BETWEEN ? AND ? GROUP BY level",
- (query_date1, query_date2)
- ).fetchall()
-
- query_date3 = f"{compare_date2.isoformat()}T00:00:00Z"
- query_date4 = f"{compare_date2.isoformat()}T23:59:59Z"
- logs2 = conn.execute(
- "SELECT level, COUNT(*) as count FROM run_log WHERE ts BETWEEN ? AND ? GROUP BY level",
- (query_date3, query_date4)
- ).fetchall()
-
- # 转换为DataFrame并可视化
- df1 = pd.DataFrame(logs1, columns=["level", "count"])
- df1["date"] = compare_date1.strftime("%Y-%m-%d")
- df2 = pd.DataFrame(logs2, columns=["level", "count"])
- df2["date"] = compare_date2.strftime("%Y-%m-%d")
-
- # 确保所有列的数据类型一致，避免PyArrow序列化错误
- for df in [df1, df2]:
- for col in df.columns:
- if col != "level": # level列保持字符串类型
- df[col] = df[col].astype(object)
-
- compare_df = pd.concat([df1, df2])
-
- # 绘制对比图表
- fig = px.bar(
- compare_df,
- x="level",
- y="count",
- color="date",
- barmode="group",
- title=f"日志级别分布对比 ({compare_date1} vs {compare_date2})"
- )
- st.plotly_chart(fig, width='stretch')
-
- # 显示详细对比表格
- st.write("日志统计对比：")
- # 使用不含连字符的日期格式作为列名后缀，避免Arrow类型转换错误
- date1_str = compare_date1.strftime("%Y%m%d")
- date2_str = compare_date2.strftime("%Y%m%d")
- merged_df = df1.merge(df2, on="level", suffixes=(f"_{date1_str}", f"_{date2_str}"), how="outer").fillna(0)
- st.dataframe(merged_df, hide_index=True, width="stretch")
- except Exception as e:
- LOGGER.exception("日志对比失败", extra=LOG_EXTRA)
- st.error(f"日志对比分析失败：{e}")
-
- return
-
-
-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")
- 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",
- default_start,
- default_end,
- "000001.SZ",
- 0.035,
- -0.015,
- 10,
- 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, 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,
- end_date,
- universe_text,
- target,
- stop,
- hold_days,
- extra=LOG_EXTRA,
- )
-
- tab_backtest, tab_rl = st.tabs(["回测验证", "强化学习调参"])
-
- 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] = []
-
- 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)
-
- 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}")
-
- 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:
- 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:
- 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:
- 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:
- 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."""
-
- LOGGER.info("渲染配置概览页", extra=LOG_EXTRA)
- cfg = get_config()
-
- st.subheader("核心配置概览")
- col1, col2, col3 = st.columns(3)
- col1.metric("决策方式", cfg.decision_method.upper())
- col2.metric("自动更新数据", "启用" if cfg.auto_update_data else "关闭")
- col3.metric("数据更新间隔(天)", cfg.data_update_interval)
-
- col4, col5, col6 = st.columns(3)
- col4.metric("强制刷新", "开启" if cfg.force_refresh else "关闭")
- col5.metric("TuShare Token", "已配置" if cfg.tushare_token else "未配置")
- col6.metric("配置文件", cfg.data_paths.config_file.name)
- st.caption(f"配置文件路径：{cfg.data_paths.config_file}")
-
- st.divider()
- st.subheader("RSS 数据源状态")
- rss_sources = cfg.rss_sources or {}
- if rss_sources:
- rows: List[Dict[str, object]] = []
- for name, payload in rss_sources.items():
- if isinstance(payload, dict):
- rows.append(
- {
- "名称": name,
- "启用": "是" if payload.get("enabled", True) else "否",
- "URL": payload.get("url", "-"),
- "关键词数": len(payload.get("keywords", []) or []),
- }
- )
- elif isinstance(payload, bool):
- rows.append(
- {
- "名称": name,
- "启用": "是" if payload else "否",
- "URL": "-",
- "关键词数": 0,
- }
- )
- if rows:
- st.dataframe(pd.DataFrame(rows), hide_index=True, width="stretch")
- else:
- st.info("未配置 RSS 数据源。")
- else:
- st.info("未在配置文件中找到 RSS 数据源。")
-
- st.divider()
- st.subheader("部门配置")
- dept_rows: List[Dict[str, object]] = []
- for code, dept in cfg.departments.items():
- dept_rows.append(
- {
- "部门": dept.title or code,
- "代码": code,
- "权重": dept.weight,
- "LLM 策略": dept.llm.strategy,
- "模板": dept.prompt_template_id or f"{code}_dept",
- "模板版本": dept.prompt_template_version or "(激活版本)",
- }
- )
- if dept_rows:
- st.dataframe(pd.DataFrame(dept_rows), hide_index=True, width="stretch")
- else:
- st.info("尚未配置任何部门。")
-
- st.divider()
- st.subheader("LLM 成本控制")
- cost = cfg.llm_cost
- col_a, col_b, col_c, col_d = st.columns(4)
- col_a.metric("成本控制", "启用" if cost.enabled else "关闭")
- col_b.metric("小时预算($)", f"{cost.hourly_budget:.2f}")
- col_c.metric("日预算($)", f"{cost.daily_budget:.2f}")
- col_d.metric("月预算($)", f"{cost.monthly_budget:.2f}")
-
- if cost.model_weights:
- weight_rows = (
- pd.DataFrame(
- [
- {"模型": model, "占比上限": f"{limit * 100:.0f}%"}
- for model, limit in cost.model_weights.items()
- ]
- )
- )
- st.dataframe(weight_rows, hide_index=True, width="stretch")
- else:
- st.caption("未配置模型占比限制。")
-
- st.divider()
- st.caption("提示：数据源、LLM 及投资组合设置可在对应标签页中调整。")
-
-
-def render_llm_settings() -> None:
- cfg = get_config()
- st.subheader("LLM 设置")
- providers = cfg.llm_providers
- provider_keys = sorted(providers.keys())
- st.caption("先在 Provider 中维护基础连接（URL、Key、模型），再为全局与各部门设置个性化参数。")
-
- provider_select_col, provider_manage_col = st.columns([3, 1])
- if provider_keys:
- try:
- default_provider = cfg.llm.primary.provider or provider_keys[0]
- provider_index = provider_keys.index(default_provider)
- except ValueError:
- provider_index = 0
- selected_provider = provider_select_col.selectbox(
- "选择 Provider",
- provider_keys,
- index=provider_index,
- key="llm_provider_select",
- )
- else:
- selected_provider = None
- provider_select_col.info("尚未配置 Provider，请先创建。")
-
- new_provider_name = provider_manage_col.text_input("新增 Provider", key="new_provider_name")
- if provider_manage_col.button("创建 Provider", key="create_provider_btn"):
- key = (new_provider_name or "").strip().lower()
- if not key:
- st.warning("请输入有效的 Provider 名称。")
- elif key in providers:
- st.warning(f"Provider {key} 已存在。")
- else:
- providers[key] = LLMProvider(key=key)
- cfg.llm_providers = providers
- save_config()
- st.success(f"已创建 Provider {key}。")
- st.rerun()
-
- if selected_provider:
- provider_cfg = providers.get(selected_provider, LLMProvider(key=selected_provider))
- title_key = f"provider_title_{selected_provider}"
- base_key = f"provider_base_{selected_provider}"
- api_key_key = f"provider_api_{selected_provider}"
- mode_key = f"provider_mode_{selected_provider}"
- enabled_key = f"provider_enabled_{selected_provider}"
-
- title_val = st.text_input("备注名称", value=provider_cfg.title or "", key=title_key)
- base_val = st.text_input("Base URL", value=provider_cfg.base_url or "", key=base_key, help="调用地址，例如：https://api.openai.com")
- api_val = st.text_input("API Key", value=provider_cfg.api_key or "", key=api_key_key, type="password")
-
- enabled_val = st.checkbox("启用", value=provider_cfg.enabled, key=enabled_key)
- mode_val = st.selectbox("模式", options=["openai", "ollama"], index=0 if provider_cfg.mode == "openai" else 1, key=mode_key)
- st.markdown("可用模型：")
- if provider_cfg.models:
- st.code("\n".join(provider_cfg.models), language="text")
- else:
- st.info("尚未获取模型列表，可点击下方按钮自动拉取。")
- try:
- cache_key = f"{selected_provider}|{(base_val or '').strip()}"
- entry = _MODEL_CACHE.get(cache_key)
- if entry and isinstance(entry.get("ts"), float):
- ts = datetime.fromtimestamp(entry["ts"]).strftime("%Y-%m-%d %H:%M:%S")
- st.caption(f"最近拉取时间：{ts}")
- except Exception:
- pass
-
- fetch_key = f"fetch_models_{selected_provider}"
- if st.button("获取模型列表", key=fetch_key):
- with st.spinner("正在获取模型列表..."):
- models, error = _discover_provider_models(provider_cfg, base_val, api_val)
- if error:
- st.error(error)
- else:
- provider_cfg.models = models
- if models and (not provider_cfg.default_model or provider_cfg.default_model not in models):
- provider_cfg.default_model = models[0]
- providers[selected_provider] = provider_cfg
- cfg.llm_providers = providers
- cfg.sync_runtime_llm()
- save_config()
- st.success(f"共获取 {len(models)} 个模型。")
- st.rerun()
-
- if st.button("保存 Provider", key=f"save_provider_{selected_provider}"):
- provider_cfg.title = title_val.strip()
- provider_cfg.base_url = base_val.strip()
- provider_cfg.api_key = api_val.strip() or None
- provider_cfg.enabled = enabled_val
- provider_cfg.mode = mode_val
- providers[selected_provider] = provider_cfg
- cfg.llm_providers = providers
- cfg.sync_runtime_llm()
- save_config()
- st.success("Provider 已保存。")
- st.session_state[title_key] = provider_cfg.title or ""
-
- provider_in_use = (cfg.llm.primary.provider == selected_provider) or any(
- ep.provider == selected_provider for ep in cfg.llm.ensemble
- )
- if not provider_in_use:
- for dept in cfg.departments.values():
- if dept.llm.primary.provider == selected_provider or any(ep.provider == selected_provider for ep in dept.llm.ensemble):
- provider_in_use = True
- break
- if st.button(
- "删除 Provider",
- key=f"delete_provider_{selected_provider}",
- disabled=provider_in_use or len(providers) <= 1,
- ):
- providers.pop(selected_provider, None)
- cfg.llm_providers = providers
- cfg.sync_runtime_llm()
- save_config()
- st.success("Provider 已删除。")
- st.rerun()
-
- st.markdown("##### 全局推理配置")
- if not provider_keys:
- st.warning("请先配置至少一个 Provider。")
- else:
- global_cfg = cfg.llm
- primary = global_cfg.primary
- try:
- provider_index = provider_keys.index(primary.provider or provider_keys[0])
- except ValueError:
- provider_index = 0
- selected_global_provider = st.selectbox(
- "主模型 Provider",
- provider_keys,
- index=provider_index,
- key="global_provider_select",
- )
- provider_cfg = providers.get(selected_global_provider)
- available_models = provider_cfg.models if provider_cfg else []
- default_model = primary.model or (provider_cfg.default_model if provider_cfg else None)
- if available_models:
- options = available_models + ["自定义"]
- try:
- model_index = available_models.index(default_model)
- model_choice = st.selectbox("主模型", options, index=model_index, key="global_model_choice")
- except ValueError:
- model_choice = st.selectbox("主模型", options, index=len(options) - 1, key="global_model_choice")
- if model_choice == "自定义":
- model_val = st.text_input("自定义模型", value=default_model or "", key="global_model_custom").strip()
- else:
- model_val = model_choice
- else:
- model_val = st.text_input("主模型", value=default_model or "", key="global_model_custom").strip()
-
- temp_default = primary.temperature if primary.temperature is not None else (provider_cfg.default_temperature if provider_cfg else 0.2)
- temp_val = st.slider("主模型温度", min_value=0.0, max_value=2.0, value=float(temp_default), step=0.05, key="global_temp")
- timeout_default = primary.timeout if primary.timeout is not None else (provider_cfg.default_timeout if provider_cfg else 30.0)
- timeout_val = st.number_input("主模型超时(秒)", min_value=5, max_value=300, value=int(timeout_default), step=5, key="global_timeout")
- prompt_template_val = st.text_area(
- "主模型 Prompt 模板（可选）",
- value=primary.prompt_template or provider_cfg.prompt_template if provider_cfg else "",
- height=120,
- key="global_prompt_template",
- )
-
- strategy_val = st.selectbox("推理策略", sorted(ALLOWED_LLM_STRATEGIES), index=sorted(ALLOWED_LLM_STRATEGIES).index(global_cfg.strategy) if global_cfg.strategy in ALLOWED_LLM_STRATEGIES else 0, key="global_strategy")
- show_ensemble = strategy_val != "single"
- majority_threshold_val = st.number_input(
- "多数投票门槛",
- min_value=1,
- max_value=10,
- value=int(global_cfg.majority_threshold),
- step=1,
- key="global_majority",
- disabled=not show_ensemble,
- )
- if not show_ensemble:
- majority_threshold_val = 1
-
- ensemble_rows: List[Dict[str, str]] = []
- if show_ensemble:
- ensemble_rows = [
- {
- "provider": ep.provider,
- "model": ep.model or "",
- "temperature": "" if ep.temperature is None else f"{ep.temperature:.3f}",
- "timeout": "" if ep.timeout is None else str(int(ep.timeout)),
- "prompt_template": ep.prompt_template or "",
- }
- for ep in global_cfg.ensemble
- ] or [{"provider": primary.provider or selected_global_provider, "model": "", "temperature": "", "timeout": "", "prompt_template": ""}]
-
- ensemble_editor = st.data_editor(
- ensemble_rows,
- num_rows="dynamic",
- key="global_ensemble_editor",
- width='stretch',
- hide_index=True,
- column_config={
- "provider": st.column_config.SelectboxColumn("Provider", options=provider_keys),
- "model": st.column_config.TextColumn("模型"),
- "temperature": st.column_config.TextColumn("温度"),
- "timeout": st.column_config.TextColumn("超时(秒)"),
- "prompt_template": st.column_config.TextColumn("Prompt 模板"),
- },
- )
- if hasattr(ensemble_editor, "to_dict"):
- ensemble_rows = ensemble_editor.to_dict("records")
- else:
- ensemble_rows = ensemble_editor
- else:
- st.info("当前策略为单模型，未启用协作模型。")
-
- if st.button("保存全局配置", key="save_global_llm"):
- primary.provider = selected_global_provider
- primary.model = model_val or None
- primary.temperature = float(temp_val)
- primary.timeout = float(timeout_val)
- primary.prompt_template = prompt_template_val.strip() or None
- primary.base_url = None
- primary.api_key = None
-
- new_ensemble: List[LLMEndpoint] = []
- if show_ensemble:
- for row in ensemble_rows:
- provider_val = (row.get("provider") or "").strip().lower()
- if not provider_val:
- continue
- model_raw = (row.get("model") or "").strip() or None
- temp_raw = (row.get("temperature") or "").strip()
- timeout_raw = (row.get("timeout") or "").strip()
- prompt_raw = (row.get("prompt_template") or "").strip()
- new_ensemble.append(
- LLMEndpoint(
- provider=provider_val,
- model=model_raw,
- temperature=float(temp_raw) if temp_raw else None,
- timeout=float(timeout_raw) if timeout_raw else None,
- prompt_template=prompt_raw or None,
- )
- )
- cfg.llm.ensemble = new_ensemble
- cfg.llm.strategy = strategy_val
- cfg.llm.majority_threshold = int(majority_threshold_val)
- cfg.sync_runtime_llm()
- save_config()
- st.success("全局 LLM 配置已保存。")
- st.json(llm_config_snapshot())
-
- st.markdown("##### 部门配置")
- dept_settings = cfg.departments or {}
- dept_rows = [
- {
- "code": code,
- "title": dept.title,
- "description": dept.description,
- "weight": float(dept.weight),
- "strategy": dept.llm.strategy,
- "majority_threshold": dept.llm.majority_threshold,
- "provider": dept.llm.primary.provider or (provider_keys[0] if provider_keys else ""),
- "model": dept.llm.primary.model or "",
- "temperature": "" if dept.llm.primary.temperature is None else f"{dept.llm.primary.temperature:.3f}",
- "timeout": "" if dept.llm.primary.timeout is None else str(int(dept.llm.primary.timeout)),
- "prompt_template": dept.llm.primary.prompt_template or "",
- }
- for code, dept in sorted(dept_settings.items())
- ]
-
- if not dept_rows:
- st.info("当前未配置部门，可在 config.json 中添加。")
- dept_rows = []
-
- dept_editor = st.data_editor(
- dept_rows,
- num_rows="fixed",
- key="department_editor",
- width='stretch',
- hide_index=True,
- column_config={
- "code": st.column_config.TextColumn("编码", disabled=True),
- "title": st.column_config.TextColumn("名称"),
- "description": st.column_config.TextColumn("说明"),
- "weight": st.column_config.NumberColumn("权重", min_value=0.0, max_value=10.0, step=0.1),
- "strategy": st.column_config.SelectboxColumn("策略", options=sorted(ALLOWED_LLM_STRATEGIES)),
- "majority_threshold": st.column_config.NumberColumn("投票阈值", min_value=1, max_value=10, step=1),
- "provider": st.column_config.SelectboxColumn("Provider", options=provider_keys or [""]),
- "model": st.column_config.TextColumn("模型"),
- "temperature": st.column_config.TextColumn("温度"),
- "timeout": st.column_config.TextColumn("超时(秒)"),
- "prompt_template": st.column_config.TextColumn("Prompt 模板"),
- },
- )
-
- if hasattr(dept_editor, "to_dict"):
- dept_rows = dept_editor.to_dict("records")
- else:
- dept_rows = dept_editor
-
- col_reset, col_save = st.columns([1, 1])
-
- if col_save.button("保存部门配置"):
- updated_departments: Dict[str, DepartmentSettings] = {}
- for row in dept_rows:
- code = row.get("code")
- if not code:
- continue
- existing = dept_settings.get(code) or DepartmentSettings(code=code, title=code)
- existing.title = row.get("title") or existing.title
- existing.description = row.get("description") or ""
- try:
- existing.weight = max(0.0, float(row.get("weight", existing.weight)))
- except (TypeError, ValueError):
- pass
-
- strategy_val = (row.get("strategy") or existing.llm.strategy).lower()
- if strategy_val in ALLOWED_LLM_STRATEGIES:
- existing.llm.strategy = strategy_val
- if existing.llm.strategy == "single":
- existing.llm.majority_threshold = 1
- existing.llm.ensemble = []
- else:
- majority_raw = row.get("majority_threshold")
- try:
- majority_val = int(majority_raw)
- if majority_val > 0:
- existing.llm.majority_threshold = majority_val
- except (TypeError, ValueError):
- pass
-
- provider_val = (row.get("provider") or existing.llm.primary.provider or (provider_keys[0] if provider_keys else "ollama")).strip().lower()
- model_val = (row.get("model") or "").strip() or None
- temp_raw = (row.get("temperature") or "").strip()
- timeout_raw = (row.get("timeout") or "").strip()
- prompt_raw = (row.get("prompt_template") or "").strip()
-
- endpoint = existing.llm.primary or LLMEndpoint()
- endpoint.provider = provider_val
- endpoint.model = model_val
- endpoint.temperature = float(temp_raw) if temp_raw else None
- endpoint.timeout = float(timeout_raw) if timeout_raw else None
- endpoint.prompt_template = prompt_raw or None
- endpoint.base_url = None
- endpoint.api_key = None
- existing.llm.primary = endpoint
- if existing.llm.strategy != "single":
- existing.llm.ensemble = []
-
- updated_departments[code] = existing
-
- if updated_departments:
- cfg.departments = updated_departments
- cfg.sync_runtime_llm()
- save_config()
- st.success("部门配置已更新。")
- else:
- st.warning("未能解析部门配置输入。")
-
- if col_reset.button("恢复默认部门"):
- from app.utils.config import _default_departments
-
- cfg.departments = _default_departments()
- cfg.sync_runtime_llm()
- save_config()
- st.success("已恢复默认部门配置。")
- st.rerun()
-
- st.caption("部门配置存储为独立 LLM 参数，执行时会自动套用对应 Provider 的连接信息。")
-
-def render_market_visualization() -> None:
- """Render a standalone market data visualization dashboard."""
-
- st.header("股票行情可视化")
- options = _load_stock_options()
- default_code = options[0] if options else "000001.SZ"
-
- if options:
- selection = st.selectbox("选择股票", options, index=0)
- ts_code = _parse_ts_code(selection)
- LOGGER.debug("选择股票：%s", ts_code, extra=LOG_EXTRA)
- else:
- ts_code = st.text_input("输入股票代码（如 000001.SZ）", value=default_code).strip().upper()
- LOGGER.debug("输入股票：%s", ts_code, extra=LOG_EXTRA)
-
- col_start, col_end = st.columns(2)
- default_start = date.today() - timedelta(days=180)
- start_date = col_start.date_input("开始日期", value=default_start, key="viz_start")
- end_date = col_end.date_input("结束日期", value=date.today(), key="viz_end")
- LOGGER.debug("行情可视化日期范围：%s-%s", start_date, end_date, extra=LOG_EXTRA)
-
- if start_date > end_date:
- LOGGER.warning("无效日期范围：%s>%s", start_date, end_date, extra=LOG_EXTRA)
- st.error("开始日期不能晚于结束日期")
- return
-
- with st.spinner("正在加载行情数据..."):
- try:
- df = _load_daily_frame(ts_code, start_date, end_date)
- except Exception as exc: # noqa: BLE001
- LOGGER.exception("加载行情数据失败", extra=LOG_EXTRA)
- st.error(f"读取数据失败：{exc}")
- return
-
- if df.empty:
- LOGGER.warning("指定区间无行情数据：%s %s-%s", ts_code, start_date, end_date, extra=LOG_EXTRA)
- st.warning("未查询到该区间的交易数据，请确认数据库已拉取对应日线。")
- return
-
- price_df = df[["close"]].rename(columns={"close": "收盘价"})
- volume_df = df[["vol"]].rename(columns={"vol": "成交量(手)"})
-
- sampled = price_df.resample("3D").last().dropna() if price_df.shape[0] > 180 else price_df
- volume_sampled = volume_df.resample("3D").mean().dropna() if volume_df.shape[0] > 180 else volume_df
-
- first_close = sampled.iloc[0, 0]
- last_close = sampled.iloc[-1, 0]
- delta_abs = last_close - first_close
- delta_pct = (delta_abs / first_close * 100) if first_close else 0.0
-
- metric_col1, metric_col2, metric_col3 = st.columns(3)
- metric_col1.metric("最新收盘价", f"{last_close:.2f}", delta=f"{delta_abs:+.2f}")
- metric_col2.metric("区间涨跌幅", f"{delta_pct:+.2f}%")
- metric_col3.metric("平均成交量", f"{volume_sampled['成交量(手)'].mean():.0f}")
-
- df_reset = df.reset_index().rename(columns={
- "trade_date": "交易日",
- "open": "开盘价",
- "high": "最高价",
- "low": "最低价",
- "close": "收盘价",
- "vol": "成交量(手)",
- "amount": "成交额(千元)",
- })
- df_reset["成交额(千元)"] = df_reset["成交额(千元)"] / 1000
-
- numeric_columns = ["开盘价", "最高价", "最低价", "收盘价", "成交量(手)", "成交额(千元)"]
- for col in numeric_columns:
- if col in df_reset.columns:
- df_reset[col] = pd.to_numeric(df_reset[col], errors="coerce")
- df_reset["交易日"] = pd.to_datetime(df_reset["交易日"])
-
- candle_fig = go.Figure(
- data=[
- go.Candlestick(
- x=df_reset["交易日"],
- open=df_reset["开盘价"],
- high=df_reset["最高价"],
- low=df_reset["最低价"],
- close=df_reset["收盘价"],
- name="K线",
- )
- ]
- )
- candle_fig.update_layout(height=420, margin=dict(l=10, r=10, t=40, b=10))
- st.plotly_chart(candle_fig, width="stretch")
-
- vol_fig = px.bar(
- df_reset,
- x="交易日",
- y="成交量(手)",
- labels={"成交量(手)": "成交量(手)"},
- title="成交量",
- )
- vol_fig.update_layout(height=280, margin=dict(l=10, r=10, t=40, b=10))
- st.plotly_chart(vol_fig, width="stretch")
-
- amt_fig = px.bar(
- df_reset,
- x="交易日",
- y="成交额(千元)",
- labels={"成交额(千元)": "成交额(千元)"},
- title="成交额",
- )
- amt_fig.update_layout(height=280, margin=dict(l=10, r=10, t=40, b=10))
- st.plotly_chart(amt_fig, width="stretch")
-
- df_reset["月份"] = df_reset["交易日"].dt.to_period("M").astype(str)
- df_reset["收盘价"] = pd.to_numeric(df_reset["收盘价"], errors="coerce")
- box_fig = px.box(
- df_reset,
- x="月份",
- y="收盘价",
- points="outliers",
- title="月度收盘价分布",
- )
- box_fig.update_layout(height=320, margin=dict(l=10, r=10, t=40, b=10))
- st.plotly_chart(box_fig, width="stretch")
-
- st.caption("提示：成交量单位为手，成交额以千元显示。箱线图按月展示收盘价分布。")
- st.dataframe(df_reset.tail(20), width="stretch")
- LOGGER.info("行情可视化完成，展示行数=%s", len(df_reset), extra=LOG_EXTRA)
-
-
-def render_tests() -> None:
- LOGGER.info("渲染自检页面", extra=LOG_EXTRA)
- st.header("自检测试")
- st.write("用于快速检查数据库与数据拉取是否正常工作。")
-
- if st.button("测试数据库初始化"):
- LOGGER.info("点击测试数据库初始化按钮", extra=LOG_EXTRA)
- with st.spinner("正在检查数据库..."):
- result = initialize_database()
- if result.skipped:
- LOGGER.info("数据库已存在，无需初始化", extra=LOG_EXTRA)
- st.success("数据库已存在，检查通过。")
- else:
- LOGGER.info("数据库初始化完成，执行语句数=%s", result.executed, extra=LOG_EXTRA)
- st.success(f"数据库初始化完成，共执行 {result.executed} 条语句。")
-
- st.divider()
-
- if st.button("测试 TuShare 拉取（示例 2024-01-01 至 2024-01-03）"):
- LOGGER.info("点击示例 TuShare 拉取按钮", extra=LOG_EXTRA)
- with st.spinner("正在调用 TuShare 接口..."):
- try:
- run_ingestion(
- FetchJob(
- name="streamlit_self_test",
- start=date(2024, 1, 1),
- end=date(2024, 1, 3),
- ts_codes=("000001.SZ",),
- ),
- include_limits=False,
- )
- LOGGER.info("示例 TuShare 拉取成功", extra=LOG_EXTRA)
- st.success("TuShare 示例拉取完成，数据已写入数据库。")
- except Exception as exc: # noqa: BLE001
- LOGGER.exception("示例 TuShare 拉取失败", extra=LOG_EXTRA)
- st.error(f"拉取失败：{exc}")
- alerts.add_warning("TuShare", "示例拉取失败", str(exc))
- _update_dashboard_sidebar()
-
- st.info("注意：TuShare 拉取依赖网络与 Token，若环境未配置将出现错误提示。")
-
- st.divider()
-
- st.subheader("RSS 数据测试")
- st.write("用于验证 RSS 配置是否能够正常抓取新闻并写入数据库。")
- rss_url = st.text_input(
- "测试 RSS 地址",
- value="https://rsshub.app/cls/depth/1000",
- help="留空则使用默认配置的全部 RSS 来源。",
- ).strip()
- rss_hours = int(
- st.number_input(
- "回溯窗口（小时）",
- min_value=1,
- max_value=168,
- value=24,
- step=6,
- help="仅抓取最近指定小时内的新闻。",
- )
- )
- rss_limit = int(
- st.number_input(
- "单源抓取条数",
- min_value=1,
- max_value=200,
- value=50,
- step=10,
- )
- )
- if st.button("运行 RSS 测试"):
- from app.ingest import rss as rss_ingest
-
- LOGGER.info(
- "点击 RSS 测试按钮 rss_url=%s hours=%s limit=%s",
- rss_url,
- rss_hours,
- rss_limit,
- extra=LOG_EXTRA,
- )
- with st.spinner("正在抓取 RSS 新闻..."):
- try:
- if rss_url:
- items = rss_ingest.fetch_rss_feed(
- rss_url,
- hours_back=rss_hours,
- max_items=rss_limit,
- )
- count = rss_ingest.save_news_items(items)
- else:
- count = rss_ingest.ingest_configured_rss(
- hours_back=rss_hours,
- max_items_per_feed=rss_limit,
- )
- st.success(f"RSS 测试完成，新增 {count} 条新闻记录。")
- except Exception as exc: # noqa: BLE001
- LOGGER.exception("RSS 测试失败", extra=LOG_EXTRA)
- st.error(f"RSS 测试失败：{exc}")
- alerts.add_warning("RSS", "RSS 测试执行失败", str(exc))
- _update_dashboard_sidebar()
-
- st.divider()
- days = int(
- st.number_input(
- "检查窗口（天数）",
- min_value=30,
- max_value=10950,
- value=365,
- step=30,
- )
- )
- LOGGER.debug("检查窗口天数=%s", days, extra=LOG_EXTRA)
- cfg = get_config()
- force_refresh = st.checkbox(
- "强制刷新数据（关闭增量跳过）",
- value=cfg.force_refresh,
- help="勾选后将重新拉取所选区间全部数据",
- )
- if force_refresh != cfg.force_refresh:
- cfg.force_refresh = force_refresh
- LOGGER.info("更新 force_refresh=%s", force_refresh, extra=LOG_EXTRA)
- save_config()
-
- if st.button("执行手动数据同步"):
- LOGGER.info("点击执行手动数据同步按钮", extra=LOG_EXTRA)
- progress_bar = st.progress(0.0)
- status_placeholder = st.empty()
- log_placeholder = st.empty()
- messages: list[str] = []
-
- def hook(message: str, value: float) -> None:
- progress_bar.progress(min(max(value, 0.0), 1.0))
- status_placeholder.write(message)
- messages.append(message)
- LOGGER.debug("手动数据同步进度：%s -> %.2f", message, value, extra=LOG_EXTRA)
-
- with st.spinner("正在执行手动数据同步..."):
- try:
- report = run_boot_check(
- days=days,
- progress_hook=hook,
- force_refresh=force_refresh,
- )
- LOGGER.info("手动数据同步成功", extra=LOG_EXTRA)
- st.success("手动数据同步完成，以下为数据覆盖摘要。")
- st.json(report.to_dict())
- if messages:
- log_placeholder.markdown("\n".join(f"- {msg}" for msg in messages))
- except Exception as exc: # noqa: BLE001
- LOGGER.exception("手动数据同步失败", extra=LOG_EXTRA)
- st.error(f"手动数据同步失败：{exc}")
- alerts.add_warning("数据同步", "手动数据同步失败", str(exc))
- _update_dashboard_sidebar()
- if messages:
- log_placeholder.markdown("\n".join(f"- {msg}" for msg in messages))
- finally:
- progress_bar.progress(1.0)
-
- st.divider()
- st.subheader("LLM 接口测试")
- st.json(llm_config_snapshot())
- llm_prompt = st.text_area("测试 Prompt", value="请概述今天的市场重点。", height=160)
- system_prompt = st.text_area(
- "System Prompt (可选)",
- value="你是一名量化策略研究助手，用简洁中文回答。",
- height=100,
- )
- if st.button("执行 LLM 测试"):
- with st.spinner("正在调用 LLM..."):
- try:
- response = run_llm(llm_prompt, system=system_prompt or None)
- except Exception as exc: # noqa: BLE001
- LOGGER.exception("LLM 测试失败", extra=LOG_EXTRA)
- st.error(f"LLM 调用失败：{exc}")
- else:
- LOGGER.info("LLM 测试成功", extra=LOG_EXTRA)
- st.success("LLM 调用成功，以下为返回内容：")
- st.write(response)
-
-
-def render_log_viewer() -> None:
- """渲染日志钻取与历史对比视图页面。"""
- LOGGER.info("渲染日志视图页面", extra=LOG_EXTRA)
- st.header("日志钻取与历史对比")
- st.write("查看系统运行日志，支持时间范围筛选、关键词搜索和历史对比功能。")
-
- col1, col2 = st.columns(2)
- with col1:
- start_date = st.date_input("开始日期", value=date.today() - timedelta(days=7))
- with col2:
- end_date = st.date_input("结束日期", value=date.today())
-
- log_levels = ["ALL", "DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"]
- selected_level = st.selectbox("日志级别", log_levels, index=1)
-
- search_query = st.text_input("搜索关键词")
-
- with db_session(read_only=True) as conn:
- stages = [row["stage"] for row in conn.execute("SELECT DISTINCT stage FROM run_log").fetchall()]
- stages = [s for s in stages if s]
- stages.insert(0, "ALL")
- selected_stage = st.selectbox("执行阶段", stages)
-
- with st.spinner("加载日志数据中..."):
- try:
- with db_session(read_only=True) as conn:
- query_parts = ["SELECT ts, stage, level, msg FROM run_log WHERE 1=1"]
- params: list[object] = []
-
- start_ts = f"{start_date.isoformat()}T00:00:00Z"
- end_ts = f"{end_date.isoformat()}T23:59:59Z"
- query_parts.append("AND ts BETWEEN ? AND ?")
- params.extend([start_ts, end_ts])
-
- if selected_level != "ALL":
- query_parts.append("AND level = ?")
- params.append(selected_level)
-
- if search_query:
- query_parts.append("AND msg LIKE ?")
- params.append(f"%{search_query}%")
-
- if selected_stage != "ALL":
- query_parts.append("AND stage = ?")
- params.append(selected_stage)
-
- query_parts.append("ORDER BY ts DESC")
-
- query = " ".join(query_parts)
- rows = conn.execute(query, params).fetchall()
-
- if rows:
- rows_dict = [{key: row[key] for key in row.keys()} for row in rows]
- log_df = pd.DataFrame(rows_dict)
- log_df["ts"] = pd.to_datetime(log_df["ts"]).dt.strftime("%Y-%m-%d %H:%M:%S")
- for col in log_df.columns:
- log_df[col] = log_df[col].astype(str)
- else:
- log_df = pd.DataFrame(columns=["ts", "stage", "level", "msg"])
-
- st.dataframe(
- log_df,
- hide_index=True,
- width="stretch",
- column_config={
- "ts": st.column_config.TextColumn("时间"),
- "stage": st.column_config.TextColumn("执行阶段"),
- "level": st.column_config.TextColumn("日志级别"),
- "msg": st.column_config.TextColumn("日志消息", width="large"),
- },
- )
-
- if not log_df.empty:
- csv_data = log_df.to_csv(index=False).encode("utf-8")
- st.download_button(
- label="下载日志CSV",
- data=csv_data,
- file_name=f"logs_{start_date}_{end_date}.csv",
- mime="text/csv",
- key="download_logs",
- )
-
- json_data = log_df.to_json(orient="records", force_ascii=False, indent=2)
- st.download_button(
- label="下载日志JSON",
- data=json_data,
- file_name=f"logs_{start_date}_{end_date}.json",
- mime="application/json",
- key="download_logs_json",
- )
- except Exception as exc: # noqa: BLE001
- LOGGER.exception("加载日志失败", extra=LOG_EXTRA)
- st.error(f"加载日志数据失败：{exc}")
-
- st.subheader("历史对比")
- st.write("选择两个时间点的日志进行对比分析。")
-
- col3, col4 = st.columns(2)
- with col3:
- compare_date1 = st.date_input("对比日期1", value=date.today() - timedelta(days=1))
- with col4:
- compare_date2 = st.date_input("对比日期2", value=date.today())
-
- comparison_stage = st.selectbox("对比阶段", stages, key="compare_stage")
- st.write("选择需要比较的日志数量。")
- compare_limit = st.slider("对比日志数量", min_value=10, max_value=200, value=50, step=10)
-
- if st.button("生成历史对比报告"):
- with st.spinner("生成对比报告中..."):
- try:
- with db_session(read_only=True) as conn:
- def load_logs(d: date) -> pd.DataFrame:
- start_ts = f"{d.isoformat()}T00:00:00Z"
- end_ts = f"{d.isoformat()}T23:59:59Z"
- query = ["SELECT ts, level, msg FROM run_log WHERE ts BETWEEN ? AND ?"]
- params: list[object] = [start_ts, end_ts]
- if comparison_stage != "ALL":
- query.append("AND stage = ?")
- params.append(comparison_stage)
- query.append("ORDER BY ts DESC LIMIT ?")
- params.append(compare_limit)
- sql = " ".join(query)
- rows = conn.execute(sql, params).fetchall()
- if not rows:
- return pd.DataFrame(columns=["ts", "level", "msg"])
- df = pd.DataFrame([{k: row[k] for k in row.keys()} for row in rows])
- df["ts"] = pd.to_datetime(df["ts"]).dt.strftime("%Y-%m-%d %H:%M:%S")
- return df
-
- df1 = load_logs(compare_date1)
- df2 = load_logs(compare_date2)
-
- if df1.empty and df2.empty:
- st.info("选定日期暂无日志可对比。")
- else:
- st.write("### 对比结果")
- col_a, col_b = st.columns(2)
- with col_a:
- st.write(f"{compare_date1} 日日志")
- st.dataframe(df1, hide_index=True, width="stretch")
- with col_b:
- st.write(f"{compare_date2} 日日志")
- st.dataframe(df2, hide_index=True, width="stretch")
-
- summary = {
- "日期1日志条数": int(len(df1)),
- "日期2日志条数": int(len(df2)),
- "新增日志条数": max(len(df2) - len(df1), 0),
- }
- st.write("摘要：", summary)
- except Exception as exc: # noqa: BLE001
- LOGGER.exception("历史对比生成失败", extra=LOG_EXTRA)
- st.error(f"生成历史对比失败：{exc}")
-
-
-def render_data_settings() -> None:
- """渲染数据源配置界面."""
- st.subheader("Tushare 数据源")
- cfg = get_config()
-
- col1, col2 = st.columns(2)
- with col1:
- tushare_token = st.text_input(
- "Tushare Token",
- value=cfg.tushare_token or "",
- type="password",
- help="从 tushare.pro 获取的 API token"
- )
-
- with col2:
- auto_update = st.checkbox(
- "启动时自动更新数据",
- value=cfg.auto_update_data,
- help="启动应用时自动检查并更新数据"
- )
-
- update_interval = st.slider(
- "数据更新间隔(天)",
- min_value=1,
- max_value=30,
- value=cfg.data_update_interval,
- help="自动更新时检查的数据时间范围"
- )
-
- if st.button("保存数据源配置"):
- cfg.tushare_token = tushare_token
- cfg.auto_update_data = auto_update
- cfg.data_update_interval = update_interval
- save_config(cfg)
- st.success("数据源配置已更新！")
-
- st.divider()
- st.subheader("数据更新记录")
-
- with db_session() as session:
- df = pd.read_sql_query(
- """
- SELECT job_type, status, created_at, updated_at, error_msg
- FROM fetch_jobs
- ORDER BY created_at DESC
- LIMIT 50
- """,
- session
- )
-
- if not df.empty:
- df["duration"] = (df["updated_at"] - df["created_at"]).dt.total_seconds().round(2)
- df = df.drop(columns=["updated_at"])
- df = df.rename(columns={
- "job_type": "数据类型",
- "status": "状态",
- "created_at": "开始时间",
- "error_msg": "错误信息",
- "duration": "耗时(秒)"
- })
- st.dataframe(df, width='stretch')
- else:
- st.info("暂无数据更新记录")
+from app.utils.config import get_config
def main() -> None:
LOGGER.info("初始化 Streamlit UI", extra=LOG_EXTRA)
st.set_page_config(page_title="多智能体个人投资助理", layout="wide")
-
- # 确保数据库表已创建
- from app.data.schema import initialize_database
+
initialize_database()
-
- # 检查是否需要自动更新数据
+
cfg = get_config()
if cfg.auto_update_data:
LOGGER.info("检测到自动更新数据选项已启用，开始执行数据拉取", extra=LOG_EXTRA)
try:
- # 初始化数据库
- from app.data.schema import initialize_database
- initialize_database()
-
- # 执行开机检查（包含数据拉取）
- from app.ingest.checker import run_boot_check
with st.spinner("正在自动更新数据..."):
def progress_hook(message: str, progress: float) -> None:
st.write(f"📊 {message} ({progress:.1%})")
-
+
report = run_boot_check(
- days=30, # 最近30天
+ days=30,
auto_fetch=True,
progress_hook=progress_hook,
- force_refresh=False
+ force_refresh=False,
)
-
- # 执行RSS新闻拉取
- from app.ingest.rss import ingest_configured_rss
rss_count = ingest_configured_rss(hours_back=24, max_items_per_feed=50)
-
- LOGGER.info("自动数据更新完成：日线数据覆盖%s-%s，RSS新闻%s条",
- report.start, report.end, rss_count, extra=LOG_EXTRA)
+ LOGGER.info(
+ "自动数据更新完成：日线数据覆盖%s-%s，RSS新闻%s条",
+ report.start,
+ report.end,
+ rss_count,
+ extra=LOG_EXTRA,
+ )
st.success(f"✅ 自动数据更新完成：获取RSS新闻 {rss_count} 条")
-
- except Exception as exc:
+ except Exception as exc: # noqa: BLE001
LOGGER.exception("自动数据更新失败", extra=LOG_EXTRA)
st.error(f"❌ 自动数据更新失败：{exc}")
-
+
render_global_dashboard()
+
tabs = st.tabs(["今日计划", "投资池/仓位", "回测与复盘", "行情可视化", "日志钻取", "数据与设置", "自检测试"])
LOGGER.debug(
"Tabs 初始化完成：%s",
- ["今日计划", "回测与复盘", "行情可视化", "日志钻取", "数据与设置", "自检测试"],
+ ["今日计划", "投资池/仓位", "回测与复盘", "行情可视化", "日志钻取", "数据与设置", "自检测试"],
extra=LOG_EXTRA,
)
+
with tabs[0]:
render_today_plan()
with tabs[1]:
@@ -2998,17 +85,12 @@ def main() -> None:
with tabs[5]:
st.header("系统设置")
settings_tabs = st.tabs(["配置概览", "LLM 设置", "投资组合", "数据源"])
-
with settings_tabs[0]:
render_config_overview()
-
with settings_tabs[1]:
render_llm_settings()
-
with settings_tabs[2]:
- from app.ui.portfolio_config import render_portfolio_config
render_portfolio_config()
-
with settings_tabs[3]:
render_data_settings()
with tabs[6]:
diff --git a/app/ui/views/__init__.py b/app/ui/views/__init__.py
new file mode 100644
index 0000000..e4f945a
--- /dev/null
+++ b/app/ui/views/__init__.py
@@ -0,0 +1,24 @@
+"""View modules for Streamlit UI tabs."""
+
+from .today import render_today_plan
+from .pool import render_pool_overview
+from .backtest import render_backtest_review
+from .market import render_market_visualization
+from .logs import render_log_viewer
+from .settings import render_config_overview, render_llm_settings, render_data_settings
+from .tests import render_tests
+from .dashboard import render_global_dashboard, update_dashboard_sidebar
+
+__all__ = [
+ "render_today_plan",
+ "render_pool_overview",
+ "render_backtest_review",
+ "render_market_visualization",
+ "render_log_viewer",
+ "render_config_overview",
+ "render_llm_settings",
+ "render_data_settings",
+ "render_tests",
+ "render_global_dashboard",
+ "update_dashboard_sidebar",
+]
diff --git a/app/ui/views/backtest.py b/app/ui/views/backtest.py
new file mode 100644
index 0000000..6f8513a
--- /dev/null
+++ b/app/ui/views/backtest.py
@@ -0,0 +1,790 @@
+"""回测与复盘相关视图。"""
+from __future__ import annotations
+
+import json
+import uuid
+from dataclasses import asdict
+from datetime import date
+from typing import Dict, List, Optional
+
+import numpy as np
+import pandas as pd
+import plotly.express as px
+import requests
+from requests.exceptions import RequestException
+import streamlit as st
+
+from app.agents.base import AgentContext
+from app.agents.game import Decision
+from app.agents.registry import default_agents
+from app.backtest.decision_env import DecisionEnv, ParameterSpec
+from app.backtest.engine import BacktestEngine, PortfolioState, BtConfig, run_backtest
+from app.ingest.checker import run_boot_check
+from app.ingest.tushare import run_ingestion
+from app.llm.client import run_llm
+from app.llm.metrics import reset as reset_llm_metrics
+from app.llm.metrics import snapshot as snapshot_llm_metrics
+from app.utils import alerts
+from app.utils.config import get_config, save_config
+from app.utils.tuning import log_tuning_result
+
+from app.ui.shared import LOGGER, LOG_EXTRA, default_backtest_range
+from app.ui.views.dashboard import update_dashboard_sidebar
+
+_DECISION_ENV_SINGLE_RESULT_KEY = "decision_env_single_result"
+_DECISION_ENV_BATCH_RESULTS_KEY = "decision_env_batch_results"
+
+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")
+ 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",
+ default_start,
+ default_end,
+ "000001.SZ",
+ 0.035,
+ -0.015,
+ 10,
+ 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, 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,
+ end_date,
+ universe_text,
+ target,
+ stop,
+ hold_days,
+ extra=LOG_EXTRA,
+ )
+
+ tab_backtest, tab_rl = st.tabs(["回测验证", "强化学习调参"])
+
+ 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] = []
+
+ 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)
+
+ 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}")
+
+ 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:
+ 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:
+ 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:
+ 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:
+ 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。")
diff --git a/app/ui/views/dashboard.py b/app/ui/views/dashboard.py
new file mode 100644
index 0000000..90edac5
--- /dev/null
+++ b/app/ui/views/dashboard.py
@@ -0,0 +1,147 @@
+"""Sidebar dashboard for the Streamlit UI."""
+from __future__ import annotations
+
+from typing import Dict, Optional
+
+import streamlit as st
+
+from app.llm.metrics import (
+ recent_decisions as llm_recent_decisions,
+ register_listener as register_llm_metrics_listener,
+ snapshot as snapshot_llm_metrics,
+)
+from app.utils import alerts
+
+from app.ui.shared import LOGGER, LOG_EXTRA
+
+_DASHBOARD_CONTAINERS: Optional[tuple[object, object]] = None
+_DASHBOARD_ELEMENTS: Optional[Dict[str, object]] = None
+_SIDEBAR_LISTENER_ATTACHED = False
+_WARNINGS_PLACEHOLDER = None
+
+
+def _ensure_dashboard_elements(metrics_container: object, decisions_container: object) -> Dict[str, object]:
+ elements = {
+ "metrics_calls": metrics_container.metric,
+ "metrics_prompt": metrics_container.metric,
+ "metrics_completion": metrics_container.metric,
+ "provider_distribution": metrics_container.empty(),
+ "model_distribution": metrics_container.empty(),
+ "decisions_list": decisions_container.empty(),
+ }
+ return elements
+
+
+def update_dashboard_sidebar(metrics: Optional[Dict[str, object]] = None) -> None:
+ """Refresh sidebar metrics and warnings."""
+ global _DASHBOARD_CONTAINERS
+ global _DASHBOARD_ELEMENTS
+ global _WARNINGS_PLACEHOLDER
+
+ containers = _DASHBOARD_CONTAINERS
+ if not containers:
+ return
+ metrics_container, decisions_container = containers
+ elements = _DASHBOARD_ELEMENTS
+ if elements is None:
+ elements = _ensure_dashboard_elements(metrics_container, decisions_container)
+ _DASHBOARD_ELEMENTS = elements
+
+ if metrics is None:
+ metrics = snapshot_llm_metrics()
+
+ elements["metrics_calls"]("LLM 调用", metrics.get("total_calls", 0))
+ elements["metrics_prompt"]("Prompt Tokens", metrics.get("total_prompt_tokens", 0))
+ elements["metrics_completion"]("Completion Tokens", metrics.get("total_completion_tokens", 0))
+
+ provider_calls = metrics.get("provider_calls", {})
+ model_calls = metrics.get("model_calls", {})
+
+ provider_placeholder = elements["provider_distribution"]
+ provider_placeholder.empty()
+ if provider_calls:
+ provider_placeholder.json(provider_calls)
+ else:
+ provider_placeholder.info("暂无 Provider 分布数据。")
+
+ model_placeholder = elements["model_distribution"]
+ model_placeholder.empty()
+ if model_calls:
+ model_placeholder.json(model_calls)
+ else:
+ model_placeholder.info("暂无模型分布数据。")
+
+ decisions = metrics.get("recent_decisions") or llm_recent_decisions(10)
+ decisions_placeholder = elements["decisions_list"]
+ decisions_placeholder.empty()
+ if decisions:
+ lines = []
+ for record in reversed(decisions[-10:]):
+ ts_code = record.get("ts_code")
+ trade_date = record.get("trade_date")
+ action = record.get("action")
+ confidence = record.get("confidence", 0.0)
+ summary = record.get("summary")
+ line = f"**{trade_date} {ts_code}** → {action} (置信度 {confidence:.2f})"
+ if summary:
+ line += f"\n{summary}"
+ lines.append(line)
+ decisions_placeholder.markdown("\n\n".join(lines), unsafe_allow_html=True)
+ else:
+ decisions_placeholder.info("暂无决策记录。执行回测或实时评估后可在此查看。")
+
+ if _WARNINGS_PLACEHOLDER is not None:
+ _WARNINGS_PLACEHOLDER.empty()
+ with _WARNINGS_PLACEHOLDER.container():
+ st.subheader("数据告警")
+ warn_list = alerts.get_warnings()
+ if warn_list:
+ lines = []
+ for warning in warn_list[-10:]:
+ detail = warning.get("detail")
+ source = warning.get("source")
+ ts = warning.get("ts")
+ label = warning.get("label")
+ line = f"- **{source or '未知来源'}** {label or ''}"
+ if detail:
+ line += f"：{detail}"
+ if ts:
+ line += f"（{ts}）"
+ lines.append(line)
+ st.markdown("\n".join(lines))
+ else:
+ st.caption("暂无数据告警。")
+
+
+def _sidebar_metrics_listener(metrics: Dict[str, object]) -> None:
+ try:
+ update_dashboard_sidebar(metrics)
+ except Exception: # noqa: BLE001
+ LOGGER.debug("侧边栏监听器刷新失败", exc_info=True, extra=LOG_EXTRA)
+
+
+def render_global_dashboard() -> None:
+ """Render a persistent sidebar with realtime LLM stats and recent decisions."""
+
+ global _DASHBOARD_CONTAINERS
+ global _DASHBOARD_ELEMENTS
+ global _SIDEBAR_LISTENER_ATTACHED
+ global _WARNINGS_PLACEHOLDER
+
+ warnings = alerts.get_warnings()
+ badge = f" ({len(warnings)})" if warnings else ""
+ st.sidebar.header(f"系统监控{badge}")
+
+ metrics_container = st.sidebar.container()
+ decisions_container = st.sidebar.container()
+ st.sidebar.container() # legacy placeholder for layout spacing
+ warn_placeholder = st.sidebar.empty()
+
+ _DASHBOARD_CONTAINERS = (metrics_container, decisions_container)
+ _DASHBOARD_ELEMENTS = _ensure_dashboard_elements(metrics_container, decisions_container)
+ _WARNINGS_PLACEHOLDER = warn_placeholder
+
+ if not _SIDEBAR_LISTENER_ATTACHED:
+ register_llm_metrics_listener(_sidebar_metrics_listener)
+ _SIDEBAR_LISTENER_ATTACHED = True
+ update_dashboard_sidebar()
diff --git a/app/ui/views/logs.py b/app/ui/views/logs.py
new file mode 100644
index 0000000..16acef1
--- /dev/null
+++ b/app/ui/views/logs.py
@@ -0,0 +1,164 @@
+"""日志钻取视图。"""
+from __future__ import annotations
+
+from datetime import date, timedelta
+import pandas as pd
+import streamlit as st
+
+from app.utils.db import db_session
+
+from app.ui.shared import LOGGER, LOG_EXTRA
+
+def render_log_viewer() -> None:
+ """渲染日志钻取与历史对比视图页面。"""
+ LOGGER.info("渲染日志视图页面", extra=LOG_EXTRA)
+ st.header("日志钻取与历史对比")
+ st.write("查看系统运行日志，支持时间范围筛选、关键词搜索和历史对比功能。")
+
+ col1, col2 = st.columns(2)
+ with col1:
+ start_date = st.date_input("开始日期", value=date.today() - timedelta(days=7))
+ with col2:
+ end_date = st.date_input("结束日期", value=date.today())
+
+ log_levels = ["ALL", "DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"]
+ selected_level = st.selectbox("日志级别", log_levels, index=1)
+
+ search_query = st.text_input("搜索关键词")
+
+ with db_session(read_only=True) as conn:
+ stages = [row["stage"] for row in conn.execute("SELECT DISTINCT stage FROM run_log").fetchall()]
+ stages = [s for s in stages if s]
+ stages.insert(0, "ALL")
+ selected_stage = st.selectbox("执行阶段", stages)
+
+ with st.spinner("加载日志数据中..."):
+ try:
+ with db_session(read_only=True) as conn:
+ query_parts = ["SELECT ts, stage, level, msg FROM run_log WHERE 1=1"]
+ params: list[object] = []
+
+ start_ts = f"{start_date.isoformat()}T00:00:00Z"
+ end_ts = f"{end_date.isoformat()}T23:59:59Z"
+ query_parts.append("AND ts BETWEEN ? AND ?")
+ params.extend([start_ts, end_ts])
+
+ if selected_level != "ALL":
+ query_parts.append("AND level = ?")
+ params.append(selected_level)
+
+ if search_query:
+ query_parts.append("AND msg LIKE ?")
+ params.append(f"%{search_query}%")
+
+ if selected_stage != "ALL":
+ query_parts.append("AND stage = ?")
+ params.append(selected_stage)
+
+ query_parts.append("ORDER BY ts DESC")
+
+ query = " ".join(query_parts)
+ rows = conn.execute(query, params).fetchall()
+
+ if rows:
+ rows_dict = [{key: row[key] for key in row.keys()} for row in rows]
+ log_df = pd.DataFrame(rows_dict)
+ log_df["ts"] = pd.to_datetime(log_df["ts"]).dt.strftime("%Y-%m-%d %H:%M:%S")
+ for col in log_df.columns:
+ log_df[col] = log_df[col].astype(str)
+ else:
+ log_df = pd.DataFrame(columns=["ts", "stage", "level", "msg"])
+
+ st.dataframe(
+ log_df,
+ hide_index=True,
+ width="stretch",
+ column_config={
+ "ts": st.column_config.TextColumn("时间"),
+ "stage": st.column_config.TextColumn("执行阶段"),
+ "level": st.column_config.TextColumn("日志级别"),
+ "msg": st.column_config.TextColumn("日志消息", width="large"),
+ },
+ )
+
+ if not log_df.empty:
+ csv_data = log_df.to_csv(index=False).encode("utf-8")
+ st.download_button(
+ label="下载日志CSV",
+ data=csv_data,
+ file_name=f"logs_{start_date}_{end_date}.csv",
+ mime="text/csv",
+ key="download_logs",
+ )
+
+ json_data = log_df.to_json(orient="records", force_ascii=False, indent=2)
+ st.download_button(
+ label="下载日志JSON",
+ data=json_data,
+ file_name=f"logs_{start_date}_{end_date}.json",
+ mime="application/json",
+ key="download_logs_json",
+ )
+ except Exception as exc: # noqa: BLE001
+ LOGGER.exception("加载日志失败", extra=LOG_EXTRA)
+ st.error(f"加载日志数据失败：{exc}")
+
+ st.subheader("历史对比")
+ st.write("选择两个时间点的日志进行对比分析。")
+
+ col3, col4 = st.columns(2)
+ with col3:
+ compare_date1 = st.date_input("对比日期1", value=date.today() - timedelta(days=1))
+ with col4:
+ compare_date2 = st.date_input("对比日期2", value=date.today())
+
+ comparison_stage = st.selectbox("对比阶段", stages, key="compare_stage")
+ st.write("选择需要比较的日志数量。")
+ compare_limit = st.slider("对比日志数量", min_value=10, max_value=200, value=50, step=10)
+
+ if st.button("生成历史对比报告"):
+ with st.spinner("生成对比报告中..."):
+ try:
+ with db_session(read_only=True) as conn:
+ def load_logs(d: date) -> pd.DataFrame:
+ start_ts = f"{d.isoformat()}T00:00:00Z"
+ end_ts = f"{d.isoformat()}T23:59:59Z"
+ query = ["SELECT ts, level, msg FROM run_log WHERE ts BETWEEN ? AND ?"]
+ params: list[object] = [start_ts, end_ts]
+ if comparison_stage != "ALL":
+ query.append("AND stage = ?")
+ params.append(comparison_stage)
+ query.append("ORDER BY ts DESC LIMIT ?")
+ params.append(compare_limit)
+ sql = " ".join(query)
+ rows = conn.execute(sql, params).fetchall()
+ if not rows:
+ return pd.DataFrame(columns=["ts", "level", "msg"])
+ df = pd.DataFrame([{k: row[k] for k in row.keys()} for row in rows])
+ df["ts"] = pd.to_datetime(df["ts"]).dt.strftime("%Y-%m-%d %H:%M:%S")
+ return df
+
+ df1 = load_logs(compare_date1)
+ df2 = load_logs(compare_date2)
+
+ if df1.empty and df2.empty:
+ st.info("选定日期暂无日志可对比。")
+ else:
+ st.write("### 对比结果")
+ col_a, col_b = st.columns(2)
+ with col_a:
+ st.write(f"{compare_date1} 日日志")
+ st.dataframe(df1, hide_index=True, width="stretch")
+ with col_b:
+ st.write(f"{compare_date2} 日日志")
+ st.dataframe(df2, hide_index=True, width="stretch")
+
+ summary = {
+ "日期1日志条数": int(len(df1)),
+ "日期2日志条数": int(len(df2)),
+ "新增日志条数": max(len(df2) - len(df1), 0),
+ }
+ st.write("摘要：", summary)
+ except Exception as exc: # noqa: BLE001
+ LOGGER.exception("历史对比生成失败", extra=LOG_EXTRA)
+ st.error(f"生成历史对比失败：{exc}")
diff --git a/app/ui/views/market.py b/app/ui/views/market.py
new file mode 100644
index 0000000..db97641
--- /dev/null
+++ b/app/ui/views/market.py
@@ -0,0 +1,115 @@
+"""行情可视化页面。"""
+from __future__ import annotations
+
+from datetime import date, timedelta
+
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+import streamlit as st
+
+from app.utils.db import db_session
+
+from app.ui.shared import LOGGER, LOG_EXTRA
+
+
+def _load_stock_options(limit: int = 500) -> list[str]:
+ try:
+ with db_session(read_only=True) as conn:
+ rows = conn.execute(
+ """
+ SELECT DISTINCT ts_code
+ FROM daily
+ ORDER BY trade_date DESC
+ LIMIT ?
+ """,
+ (limit,),
+ ).fetchall()
+ except Exception: # noqa: BLE001
+ LOGGER.exception("加载股票列表失败", extra=LOG_EXTRA)
+ return []
+ return [row["ts_code"] for row in rows]
+
+
+def _parse_ts_code(selection: str) -> str:
+ return selection.split(" ", 1)[0]
+
+
+def _load_daily_frame(ts_code: str, start: date, end: date) -> pd.DataFrame:
+ with db_session(read_only=True) as conn:
+ df = pd.read_sql_query(
+ """
+ SELECT trade_date, open, high, low, close, vol, amount
+ FROM daily
+ WHERE ts_code = ? AND trade_date BETWEEN ? AND ?
+ ORDER BY trade_date
+ """,
+ conn,
+ params=(ts_code, start.strftime("%Y%m%d"), end.strftime("%Y%m%d")),
+ )
+ if df.empty:
+ return df
+ df["trade_date"] = pd.to_datetime(df["trade_date"], format="%Y%m%d")
+ return df
+
+
+def render_market_visualization() -> None:
+ st.header("行情可视化")
+ st.caption("按标的查看 K 线、成交量以及常用指标。")
+
+ options = _load_stock_options()
+ if not options:
+ st.warning("暂未加载到可用的行情标的，请先执行数据同步。")
+ return
+
+ selection = st.selectbox("选择标的", options, index=0)
+ ts_code = _parse_ts_code(selection)
+ col1, col2 = st.columns(2)
+ with col1:
+ start_date = st.date_input("开始日期", value=date.today() - timedelta(days=120))
+ with col2:
+ end_date = st.date_input("结束日期", value=date.today())
+
+ if start_date > end_date:
+ st.error("开始日期不能晚于结束日期。")
+ return
+
+ try:
+ df = _load_daily_frame(ts_code, start_date, end_date)
+ except Exception as exc: # noqa: BLE001
+ LOGGER.exception("加载行情数据失败", extra=LOG_EXTRA)
+ st.error(f"加载行情数据失败：{exc}")
+ return
+
+ if df.empty:
+ st.info("所选区间内无行情数据。")
+ return
+
+ st.metric("最新收盘价", f"{df['close'].iloc[-1]:.2f}")
+ fig = go.Figure(
+ data=[
+ go.Candlestick(
+ x=df["trade_date"],
+ open=df["open"],
+ high=df["high"],
+ low=df["low"],
+ close=df["close"],
+ name="K线",
+ )
+ ]
+ )
+ fig.update_layout(title=f"{ts_code} K线图", xaxis_title="日期", yaxis_title="价格")
+ st.plotly_chart(fig, use_container_width=True)
+
+ fig_vol = px.bar(df, x="trade_date", y="vol", title="成交量")
+ st.plotly_chart(fig_vol, use_container_width=True)
+
+ df_ma = df.copy()
+ df_ma["MA5"] = df_ma["close"].rolling(window=5).mean()
+ df_ma["MA20"] = df_ma["close"].rolling(window=20).mean()
+ df_ma["MA60"] = df_ma["close"].rolling(window=60).mean()
+
+ fig_ma = px.line(df_ma, x="trade_date", y=["close", "MA5", "MA20", "MA60"], title="均线对比")
+ st.plotly_chart(fig_ma, use_container_width=True)
+
+ st.dataframe(df, hide_index=True, width='stretch')
diff --git a/app/ui/views/pool.py b/app/ui/views/pool.py
new file mode 100644
index 0000000..5956704
--- /dev/null
+++ b/app/ui/views/pool.py
@@ -0,0 +1,162 @@
+"""投资池与仓位概览页面。"""
+from __future__ import annotations
+
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+
+from app.utils.db import db_session
+from app.utils.portfolio import (
+ get_latest_snapshot,
+ list_investment_pool,
+ list_positions,
+ list_recent_trades,
+)
+
+from app.ui.shared import LOGGER, LOG_EXTRA, get_latest_trade_date
+
+
+def render_pool_overview() -> None:
+ """单独的投资池与仓位概览页面（从今日计划中提取）。"""
+ LOGGER.info("渲染投资池与仓位概览页面", extra=LOG_EXTRA)
+ st.header("投资池与仓位概览")
+
+ snapshot = get_latest_snapshot()
+ if snapshot:
+ col_a, col_b, col_c = st.columns(3)
+ if snapshot.total_value is not None:
+ col_a.metric("组合净值", f"{snapshot.total_value:,.2f}")
+ if snapshot.cash is not None:
+ col_b.metric("现金余额", f"{snapshot.cash:,.2f}")
+ if snapshot.invested_value is not None:
+ col_c.metric("持仓市值", f"{snapshot.invested_value:,.2f}")
+ detail_cols = st.columns(4)
+ if snapshot.unrealized_pnl is not None:
+ detail_cols[0].metric("浮盈", f"{snapshot.unrealized_pnl:,.2f}")
+ if snapshot.realized_pnl is not None:
+ detail_cols[1].metric("已实现盈亏", f"{snapshot.realized_pnl:,.2f}")
+ if snapshot.net_flow is not None:
+ detail_cols[2].metric("净流入", f"{snapshot.net_flow:,.2f}")
+ if snapshot.exposure is not None:
+ detail_cols[3].metric("风险敞口", f"{snapshot.exposure:.2%}")
+ if snapshot.notes:
+ st.caption(f"备注：{snapshot.notes}")
+ else:
+ st.info("暂无组合快照，请在执行回测或实盘同步后写入 portfolio_snapshots。")
+
+ try:
+ latest_date = get_latest_trade_date()
+ candidates = list_investment_pool(trade_date=latest_date)
+ except Exception: # noqa: BLE001
+ LOGGER.exception("加载候选池失败", extra=LOG_EXTRA)
+ candidates = []
+
+ if candidates:
+ candidate_df = pd.DataFrame(
+ [
+ {
+ "交易日": item.trade_date,
+ "代码": item.ts_code,
+ "评分": item.score,
+ "状态": item.status,
+ "标签": "、".join(item.tags) if item.tags else "-",
+ "理由": item.rationale or "",
+ }
+ for item in candidates
+ ]
+ )
+ st.write("候选投资池：")
+ st.dataframe(candidate_df, width='stretch', hide_index=True)
+ else:
+ st.caption("候选投资池暂无数据。")
+
+ positions = list_positions(active_only=False)
+ if positions:
+ position_df = pd.DataFrame(
+ [
+ {
+ "ID": pos.id,
+ "代码": pos.ts_code,
+ "开仓日": pos.opened_date,
+ "平仓日": pos.closed_date or "-",
+ "状态": pos.status,
+ "数量": pos.quantity,
+ "成本": pos.cost_price,
+ "现价": pos.market_price,
+ "市值": pos.market_value,
+ "浮盈": pos.unrealized_pnl,
+ "已实现": pos.realized_pnl,
+ "目标权重": pos.target_weight,
+ }
+ for pos in positions
+ ]
+ )
+ st.write("组合持仓：")
+ st.dataframe(position_df, width='stretch', hide_index=True)
+ else:
+ st.caption("组合持仓暂无记录。")
+
+ trades = list_recent_trades(limit=20)
+ if trades:
+ trades_df = pd.DataFrame(trades)
+ st.write("近期成交：")
+ st.dataframe(trades_df, width='stretch', hide_index=True)
+ else:
+ st.caption("近期成交暂无记录。")
+
+ st.caption("数据来源：agent_utils、investment_pool、portfolio_positions、portfolio_trades、portfolio_snapshots。")
+
+ if st.button("执行对比", type="secondary"):
+ with st.spinner("执行日志对比分析中..."):
+ try:
+ with db_session(read_only=True) as conn:
+ query_date1 = f"{compare_date1.isoformat()}T00:00:00Z" # type: ignore[name-defined]
+ query_date2 = f"{compare_date1.isoformat()}T23:59:59Z" # type: ignore[name-defined]
+ logs1 = conn.execute(
+ "SELECT level, COUNT(*) as count FROM run_log WHERE ts BETWEEN ? AND ? GROUP BY level",
+ (query_date1, query_date2),
+ ).fetchall()
+
+ query_date3 = f"{compare_date2.isoformat()}T00:00:00Z" # type: ignore[name-defined]
+ query_date4 = f"{compare_date2.isoformat()}T23:59:59Z" # type: ignore[name-defined]
+ logs2 = conn.execute(
+ "SELECT level, COUNT(*) as count FROM run_log WHERE ts BETWEEN ? AND ? GROUP BY level",
+ (query_date3, query_date4),
+ ).fetchall()
+
+ df1 = pd.DataFrame(logs1, columns=["level", "count"])
+ df1["date"] = compare_date1.strftime("%Y-%m-%d") # type: ignore[name-defined]
+ df2 = pd.DataFrame(logs2, columns=["level", "count"])
+ df2["date"] = compare_date2.strftime("%Y-%m-%d") # type: ignore[name-defined]
+
+ for df in (df1, df2):
+ for col in df.columns:
+ if col != "level":
+ df[col] = df[col].astype(object)
+
+ compare_df = pd.concat([df1, df2])
+ fig = px.bar(
+ compare_df,
+ x="level",
+ y="count",
+ color="date",
+ barmode="group",
+ title=f"日志级别分布对比 ({compare_date1} vs {compare_date2})", # type: ignore[name-defined]
+ )
+ st.plotly_chart(fig, width='stretch')
+
+ st.write("日志统计对比：")
+ date1_str = compare_date1.strftime("%Y%m%d") # type: ignore[name-defined]
+ date2_str = compare_date2.strftime("%Y%m%d") # type: ignore[name-defined]
+ merged_df = df1.merge(
+ df2,
+ on="level",
+ suffixes=(f"_{date1_str}", f"_{date2_str}"),
+ how="outer",
+ ).fillna(0)
+ st.dataframe(merged_df, hide_index=True, width="stretch")
+ except Exception as exc: # noqa: BLE001
+ LOGGER.exception("日志对比失败", extra=LOG_EXTRA)
+ st.error(f"日志对比分析失败：{exc}")
+
+ return
diff --git a/app/ui/views/settings.py b/app/ui/views/settings.py
new file mode 100644
index 0000000..0d2c738
--- /dev/null
+++ b/app/ui/views/settings.py
@@ -0,0 +1,603 @@
+"""系统设置相关视图。"""
+from __future__ import annotations
+
+from datetime import datetime, timedelta
+from typing import Dict, List, Optional
+
+import pandas as pd
+import requests
+from requests.exceptions import RequestException
+import streamlit as st
+
+from app.llm.client import llm_config_snapshot
+from app.utils.config import (
+ ALLOWED_LLM_STRATEGIES,
+ DEFAULT_LLM_BASE_URLS,
+ DepartmentSettings,
+ LLMEndpoint,
+ LLMProvider,
+ get_config,
+ save_config,
+)
+from app.utils.db import db_session
+
+from app.ui.shared import LOGGER, LOG_EXTRA
+
+_MODEL_CACHE: Dict[str, Dict[str, object]] = {}
+_CACHE_TTL_SECONDS = 300
+
+def _discover_provider_models(provider: LLMProvider, base_override: str = "", api_override: Optional[str] = None) -> tuple[list[str], Optional[str]]:
+ """Attempt to query provider API and return available model ids."""
+
+ base_url = (base_override or provider.base_url or DEFAULT_LLM_BASE_URLS.get(provider.key, "")).strip()
+ if not base_url:
+ return [], "请先填写 Base URL"
+ timeout = float(provider.default_timeout or 30.0)
+ mode = provider.mode or ("ollama" if provider.key == "ollama" else "openai")
+
+ # ADD: simple cache by provider+base URL
+ cache_key = f"{provider.key}|{base_url}"
+ now = datetime.now()
+ cached = _MODEL_CACHE.get(cache_key)
+ if cached:
+ ts = cached.get("ts")
+ if isinstance(ts, float) and (now.timestamp() - ts) < _CACHE_TTL_SECONDS:
+ models = list(cached.get("models") or [])
+ return models, None
+
+ try:
+ if mode == "ollama":
+ url = base_url.rstrip('/') + "/api/tags"
+ response = requests.get(url, timeout=timeout)
+ response.raise_for_status()
+ data = response.json()
+ models = []
+ for item in data.get("models", []) or data.get("data", []):
+ name = item.get("name") or item.get("model") or item.get("tag")
+ if name:
+ models.append(str(name).strip())
+ _MODEL_CACHE[cache_key] = {"ts": now.timestamp(), "models": sorted(set(models))}
+ return sorted(set(models)), None
+
+ api_key = (api_override or provider.api_key or "").strip()
+ if not api_key:
+ return [], "缺少 API Key"
+ url = base_url.rstrip('/') + "/v1/models"
+ headers = {
+ "Authorization": f"Bearer {api_key}",
+ "Content-Type": "application/json",
+ }
+ response = requests.get(url, headers=headers, timeout=timeout)
+ response.raise_for_status()
+ payload = response.json()
+ models = [
+ str(item.get("id")).strip()
+ for item in payload.get("data", [])
+ if item.get("id")
+ ]
+ _MODEL_CACHE[cache_key] = {"ts": now.timestamp(), "models": sorted(set(models))}
+ return sorted(set(models)), None
+ except RequestException as exc: # noqa: BLE001
+ return [], f"HTTP 错误：{exc}"
+ except Exception as exc: # noqa: BLE001
+ return [], f"解析失败：{exc}"
+
+def render_config_overview() -> None:
+ """Render a concise overview of persisted configuration values."""
+
+ LOGGER.info("渲染配置概览页", extra=LOG_EXTRA)
+ cfg = get_config()
+
+ st.subheader("核心配置概览")
+ col1, col2, col3 = st.columns(3)
+ col1.metric("决策方式", cfg.decision_method.upper())
+ col2.metric("自动更新数据", "启用" if cfg.auto_update_data else "关闭")
+ col3.metric("数据更新间隔(天)", cfg.data_update_interval)
+
+ col4, col5, col6 = st.columns(3)
+ col4.metric("强制刷新", "开启" if cfg.force_refresh else "关闭")
+ col5.metric("TuShare Token", "已配置" if cfg.tushare_token else "未配置")
+ col6.metric("配置文件", cfg.data_paths.config_file.name)
+ st.caption(f"配置文件路径：{cfg.data_paths.config_file}")
+
+ st.divider()
+ st.subheader("RSS 数据源状态")
+ rss_sources = cfg.rss_sources or {}
+ if rss_sources:
+ rows: List[Dict[str, object]] = []
+ for name, payload in rss_sources.items():
+ if isinstance(payload, dict):
+ rows.append(
+ {
+ "名称": name,
+ "启用": "是" if payload.get("enabled", True) else "否",
+ "URL": payload.get("url", "-"),
+ "关键词数": len(payload.get("keywords", []) or []),
+ }
+ )
+ elif isinstance(payload, bool):
+ rows.append(
+ {
+ "名称": name,
+ "启用": "是" if payload else "否",
+ "URL": "-",
+ "关键词数": 0,
+ }
+ )
+ if rows:
+ st.dataframe(pd.DataFrame(rows), hide_index=True, width="stretch")
+ else:
+ st.info("未配置 RSS 数据源。")
+ else:
+ st.info("未在配置文件中找到 RSS 数据源。")
+
+ st.divider()
+ st.subheader("部门配置")
+ dept_rows: List[Dict[str, object]] = []
+ for code, dept in cfg.departments.items():
+ dept_rows.append(
+ {
+ "部门": dept.title or code,
+ "代码": code,
+ "权重": dept.weight,
+ "LLM 策略": dept.llm.strategy,
+ "模板": dept.prompt_template_id or f"{code}_dept",
+ "模板版本": dept.prompt_template_version or "(激活版本)",
+ }
+ )
+ if dept_rows:
+ st.dataframe(pd.DataFrame(dept_rows), hide_index=True, width="stretch")
+ else:
+ st.info("尚未配置任何部门。")
+
+ st.divider()
+ st.subheader("LLM 成本控制")
+ cost = cfg.llm_cost
+ col_a, col_b, col_c, col_d = st.columns(4)
+ col_a.metric("成本控制", "启用" if cost.enabled else "关闭")
+ col_b.metric("小时预算($)", f"{cost.hourly_budget:.2f}")
+ col_c.metric("日预算($)", f"{cost.daily_budget:.2f}")
+ col_d.metric("月预算($)", f"{cost.monthly_budget:.2f}")
+
+ if cost.model_weights:
+ weight_rows = (
+ pd.DataFrame(
+ [
+ {"模型": model, "占比上限": f"{limit * 100:.0f}%"}
+ for model, limit in cost.model_weights.items()
+ ]
+ )
+ )
+ st.dataframe(weight_rows, hide_index=True, width="stretch")
+ else:
+ st.caption("未配置模型占比限制。")
+
+ st.divider()
+ st.caption("提示：数据源、LLM 及投资组合设置可在对应标签页中调整。")
+
+def render_llm_settings() -> None:
+ cfg = get_config()
+ st.subheader("LLM 设置")
+ providers = cfg.llm_providers
+ provider_keys = sorted(providers.keys())
+ st.caption("先在 Provider 中维护基础连接（URL、Key、模型），再为全局与各部门设置个性化参数。")
+
+ provider_select_col, provider_manage_col = st.columns([3, 1])
+ if provider_keys:
+ try:
+ default_provider = cfg.llm.primary.provider or provider_keys[0]
+ provider_index = provider_keys.index(default_provider)
+ except ValueError:
+ provider_index = 0
+ selected_provider = provider_select_col.selectbox(
+ "选择 Provider",
+ provider_keys,
+ index=provider_index,
+ key="llm_provider_select",
+ )
+ else:
+ selected_provider = None
+ provider_select_col.info("尚未配置 Provider，请先创建。")
+
+ new_provider_name = provider_manage_col.text_input("新增 Provider", key="new_provider_name")
+ if provider_manage_col.button("创建 Provider", key="create_provider_btn"):
+ key = (new_provider_name or "").strip().lower()
+ if not key:
+ st.warning("请输入有效的 Provider 名称。")
+ elif key in providers:
+ st.warning(f"Provider {key} 已存在。")
+ else:
+ providers[key] = LLMProvider(key=key)
+ cfg.llm_providers = providers
+ save_config()
+ st.success(f"已创建 Provider {key}。")
+ st.rerun()
+
+ if selected_provider:
+ provider_cfg = providers.get(selected_provider, LLMProvider(key=selected_provider))
+ title_key = f"provider_title_{selected_provider}"
+ base_key = f"provider_base_{selected_provider}"
+ api_key_key = f"provider_api_{selected_provider}"
+ mode_key = f"provider_mode_{selected_provider}"
+ enabled_key = f"provider_enabled_{selected_provider}"
+
+ title_val = st.text_input("备注名称", value=provider_cfg.title or "", key=title_key)
+ base_val = st.text_input("Base URL", value=provider_cfg.base_url or "", key=base_key, help="调用地址，例如：https://api.openai.com")
+ api_val = st.text_input("API Key", value=provider_cfg.api_key or "", key=api_key_key, type="password")
+
+ enabled_val = st.checkbox("启用", value=provider_cfg.enabled, key=enabled_key)
+ mode_val = st.selectbox("模式", options=["openai", "ollama"], index=0 if provider_cfg.mode == "openai" else 1, key=mode_key)
+ st.markdown("可用模型：")
+ if provider_cfg.models:
+ st.code("\n".join(provider_cfg.models), language="text")
+ else:
+ st.info("尚未获取模型列表，可点击下方按钮自动拉取。")
+ try:
+ cache_key = f"{selected_provider}|{(base_val or '').strip()}"
+ entry = _MODEL_CACHE.get(cache_key)
+ if entry and isinstance(entry.get("ts"), float):
+ ts = datetime.fromtimestamp(entry["ts"]).strftime("%Y-%m-%d %H:%M:%S")
+ st.caption(f"最近拉取时间：{ts}")
+ except Exception:
+ pass
+
+ fetch_key = f"fetch_models_{selected_provider}"
+ if st.button("获取模型列表", key=fetch_key):
+ with st.spinner("正在获取模型列表..."):
+ models, error = _discover_provider_models(provider_cfg, base_val, api_val)
+ if error:
+ st.error(error)
+ else:
+ provider_cfg.models = models
+ if models and (not provider_cfg.default_model or provider_cfg.default_model not in models):
+ provider_cfg.default_model = models[0]
+ providers[selected_provider] = provider_cfg
+ cfg.llm_providers = providers
+ cfg.sync_runtime_llm()
+ save_config()
+ st.success(f"共获取 {len(models)} 个模型。")
+ st.rerun()
+
+ if st.button("保存 Provider", key=f"save_provider_{selected_provider}"):
+ provider_cfg.title = title_val.strip()
+ provider_cfg.base_url = base_val.strip()
+ provider_cfg.api_key = api_val.strip() or None
+ provider_cfg.enabled = enabled_val
+ provider_cfg.mode = mode_val
+ providers[selected_provider] = provider_cfg
+ cfg.llm_providers = providers
+ cfg.sync_runtime_llm()
+ save_config()
+ st.success("Provider 已保存。")
+ st.session_state[title_key] = provider_cfg.title or ""
+
+ provider_in_use = (cfg.llm.primary.provider == selected_provider) or any(
+ ep.provider == selected_provider for ep in cfg.llm.ensemble
+ )
+ if not provider_in_use:
+ for dept in cfg.departments.values():
+ if dept.llm.primary.provider == selected_provider or any(ep.provider == selected_provider for ep in dept.llm.ensemble):
+ provider_in_use = True
+ break
+ if st.button(
+ "删除 Provider",
+ key=f"delete_provider_{selected_provider}",
+ disabled=provider_in_use or len(providers) <= 1,
+ ):
+ providers.pop(selected_provider, None)
+ cfg.llm_providers = providers
+ cfg.sync_runtime_llm()
+ save_config()
+ st.success("Provider 已删除。")
+ st.rerun()
+
+ st.markdown("##### 全局推理配置")
+ if not provider_keys:
+ st.warning("请先配置至少一个 Provider。")
+ else:
+ global_cfg = cfg.llm
+ primary = global_cfg.primary
+ try:
+ provider_index = provider_keys.index(primary.provider or provider_keys[0])
+ except ValueError:
+ provider_index = 0
+ selected_global_provider = st.selectbox(
+ "主模型 Provider",
+ provider_keys,
+ index=provider_index,
+ key="global_provider_select",
+ )
+ provider_cfg = providers.get(selected_global_provider)
+ available_models = provider_cfg.models if provider_cfg else []
+ default_model = primary.model or (provider_cfg.default_model if provider_cfg else None)
+ if available_models:
+ options = available_models + ["自定义"]
+ try:
+ model_index = available_models.index(default_model)
+ model_choice = st.selectbox("主模型", options, index=model_index, key="global_model_choice")
+ except ValueError:
+ model_choice = st.selectbox("主模型", options, index=len(options) - 1, key="global_model_choice")
+ if model_choice == "自定义":
+ model_val = st.text_input("自定义模型", value=default_model or "", key="global_model_custom").strip()
+ else:
+ model_val = model_choice
+ else:
+ model_val = st.text_input("主模型", value=default_model or "", key="global_model_custom").strip()
+
+ temp_default = primary.temperature if primary.temperature is not None else (provider_cfg.default_temperature if provider_cfg else 0.2)
+ temp_val = st.slider("主模型温度", min_value=0.0, max_value=2.0, value=float(temp_default), step=0.05, key="global_temp")
+ timeout_default = primary.timeout if primary.timeout is not None else (provider_cfg.default_timeout if provider_cfg else 30.0)
+ timeout_val = st.number_input("主模型超时(秒)", min_value=5, max_value=300, value=int(timeout_default), step=5, key="global_timeout")
+ prompt_template_val = st.text_area(
+ "主模型 Prompt 模板（可选）",
+ value=primary.prompt_template or provider_cfg.prompt_template if provider_cfg else "",
+ height=120,
+ key="global_prompt_template",
+ )
+
+ strategy_val = st.selectbox("推理策略", sorted(ALLOWED_LLM_STRATEGIES), index=sorted(ALLOWED_LLM_STRATEGIES).index(global_cfg.strategy) if global_cfg.strategy in ALLOWED_LLM_STRATEGIES else 0, key="global_strategy")
+ show_ensemble = strategy_val != "single"
+ majority_threshold_val = st.number_input(
+ "多数投票门槛",
+ min_value=1,
+ max_value=10,
+ value=int(global_cfg.majority_threshold),
+ step=1,
+ key="global_majority",
+ disabled=not show_ensemble,
+ )
+ if not show_ensemble:
+ majority_threshold_val = 1
+
+ ensemble_rows: List[Dict[str, str]] = []
+ if show_ensemble:
+ ensemble_rows = [
+ {
+ "provider": ep.provider,
+ "model": ep.model or "",
+ "temperature": "" if ep.temperature is None else f"{ep.temperature:.3f}",
+ "timeout": "" if ep.timeout is None else str(int(ep.timeout)),
+ "prompt_template": ep.prompt_template or "",
+ }
+ for ep in global_cfg.ensemble
+ ] or [{"provider": primary.provider or selected_global_provider, "model": "", "temperature": "", "timeout": "", "prompt_template": ""}]
+
+ ensemble_editor = st.data_editor(
+ ensemble_rows,
+ num_rows="dynamic",
+ key="global_ensemble_editor",
+ width='stretch',
+ hide_index=True,
+ column_config={
+ "provider": st.column_config.SelectboxColumn("Provider", options=provider_keys),
+ "model": st.column_config.TextColumn("模型"),
+ "temperature": st.column_config.TextColumn("温度"),
+ "timeout": st.column_config.TextColumn("超时(秒)"),
+ "prompt_template": st.column_config.TextColumn("Prompt 模板"),
+ },
+ )
+ if hasattr(ensemble_editor, "to_dict"):
+ ensemble_rows = ensemble_editor.to_dict("records")
+ else:
+ ensemble_rows = ensemble_editor
+ else:
+ st.info("当前策略为单模型，未启用协作模型。")
+
+ if st.button("保存全局配置", key="save_global_llm"):
+ primary.provider = selected_global_provider
+ primary.model = model_val or None
+ primary.temperature = float(temp_val)
+ primary.timeout = float(timeout_val)
+ primary.prompt_template = prompt_template_val.strip() or None
+ primary.base_url = None
+ primary.api_key = None
+
+ new_ensemble: List[LLMEndpoint] = []
+ if show_ensemble:
+ for row in ensemble_rows:
+ provider_val = (row.get("provider") or "").strip().lower()
+ if not provider_val:
+ continue
+ model_raw = (row.get("model") or "").strip() or None
+ temp_raw = (row.get("temperature") or "").strip()
+ timeout_raw = (row.get("timeout") or "").strip()
+ prompt_raw = (row.get("prompt_template") or "").strip()
+ new_ensemble.append(
+ LLMEndpoint(
+ provider=provider_val,
+ model=model_raw,
+ temperature=float(temp_raw) if temp_raw else None,
+ timeout=float(timeout_raw) if timeout_raw else None,
+ prompt_template=prompt_raw or None,
+ )
+ )
+ cfg.llm.ensemble = new_ensemble
+ cfg.llm.strategy = strategy_val
+ cfg.llm.majority_threshold = int(majority_threshold_val)
+ cfg.sync_runtime_llm()
+ save_config()
+ st.success("全局 LLM 配置已保存。")
+ st.json(llm_config_snapshot())
+
+ st.markdown("##### 部门配置")
+ dept_settings = cfg.departments or {}
+ dept_rows = [
+ {
+ "code": code,
+ "title": dept.title,
+ "description": dept.description,
+ "weight": float(dept.weight),
+ "strategy": dept.llm.strategy,
+ "majority_threshold": dept.llm.majority_threshold,
+ "provider": dept.llm.primary.provider or (provider_keys[0] if provider_keys else ""),
+ "model": dept.llm.primary.model or "",
+ "temperature": "" if dept.llm.primary.temperature is None else f"{dept.llm.primary.temperature:.3f}",
+ "timeout": "" if dept.llm.primary.timeout is None else str(int(dept.llm.primary.timeout)),
+ "prompt_template": dept.llm.primary.prompt_template or "",
+ }
+ for code, dept in sorted(dept_settings.items())
+ ]
+
+ if not dept_rows:
+ st.info("当前未配置部门，可在 config.json 中添加。")
+ dept_rows = []
+
+ dept_editor = st.data_editor(
+ dept_rows,
+ num_rows="fixed",
+ key="department_editor",
+ width='stretch',
+ hide_index=True,
+ column_config={
+ "code": st.column_config.TextColumn("编码", disabled=True),
+ "title": st.column_config.TextColumn("名称"),
+ "description": st.column_config.TextColumn("说明"),
+ "weight": st.column_config.NumberColumn("权重", min_value=0.0, max_value=10.0, step=0.1),
+ "strategy": st.column_config.SelectboxColumn("策略", options=sorted(ALLOWED_LLM_STRATEGIES)),
+ "majority_threshold": st.column_config.NumberColumn("投票阈值", min_value=1, max_value=10, step=1),
+ "provider": st.column_config.SelectboxColumn("Provider", options=provider_keys or [""]),
+ "model": st.column_config.TextColumn("模型"),
+ "temperature": st.column_config.TextColumn("温度"),
+ "timeout": st.column_config.TextColumn("超时(秒)"),
+ "prompt_template": st.column_config.TextColumn("Prompt 模板"),
+ },
+ )
+
+ if hasattr(dept_editor, "to_dict"):
+ dept_rows = dept_editor.to_dict("records")
+ else:
+ dept_rows = dept_editor
+
+ col_reset, col_save = st.columns([1, 1])
+
+ if col_save.button("保存部门配置"):
+ updated_departments: Dict[str, DepartmentSettings] = {}
+ for row in dept_rows:
+ code = row.get("code")
+ if not code:
+ continue
+ existing = dept_settings.get(code) or DepartmentSettings(code=code, title=code)
+ existing.title = row.get("title") or existing.title
+ existing.description = row.get("description") or ""
+ try:
+ existing.weight = max(0.0, float(row.get("weight", existing.weight)))
+ except (TypeError, ValueError):
+ pass
+
+ strategy_val = (row.get("strategy") or existing.llm.strategy).lower()
+ if strategy_val in ALLOWED_LLM_STRATEGIES:
+ existing.llm.strategy = strategy_val
+ if existing.llm.strategy == "single":
+ existing.llm.majority_threshold = 1
+ existing.llm.ensemble = []
+ else:
+ majority_raw = row.get("majority_threshold")
+ try:
+ majority_val = int(majority_raw)
+ if majority_val > 0:
+ existing.llm.majority_threshold = majority_val
+ except (TypeError, ValueError):
+ pass
+
+ provider_val = (row.get("provider") or existing.llm.primary.provider or (provider_keys[0] if provider_keys else "ollama")).strip().lower()
+ model_val = (row.get("model") or "").strip() or None
+ temp_raw = (row.get("temperature") or "").strip()
+ timeout_raw = (row.get("timeout") or "").strip()
+ prompt_raw = (row.get("prompt_template") or "").strip()
+
+ endpoint = existing.llm.primary or LLMEndpoint()
+ endpoint.provider = provider_val
+ endpoint.model = model_val
+ endpoint.temperature = float(temp_raw) if temp_raw else None
+ endpoint.timeout = float(timeout_raw) if timeout_raw else None
+ endpoint.prompt_template = prompt_raw or None
+ endpoint.base_url = None
+ endpoint.api_key = None
+ existing.llm.primary = endpoint
+ if existing.llm.strategy != "single":
+ existing.llm.ensemble = []
+
+ updated_departments[code] = existing
+
+ if updated_departments:
+ cfg.departments = updated_departments
+ cfg.sync_runtime_llm()
+ save_config()
+ st.success("部门配置已更新。")
+ else:
+ st.warning("未能解析部门配置输入。")
+
+ if col_reset.button("恢复默认部门"):
+ from app.utils.config import _default_departments
+
+ cfg.departments = _default_departments()
+ cfg.sync_runtime_llm()
+ save_config()
+ st.success("已恢复默认部门配置。")
+ st.rerun()
+
+ st.caption("部门配置存储为独立 LLM 参数，执行时会自动套用对应 Provider 的连接信息。")
+
+def render_data_settings() -> None:
+ """渲染数据源配置界面."""
+ st.subheader("Tushare 数据源")
+ cfg = get_config()
+
+ col1, col2 = st.columns(2)
+ with col1:
+ tushare_token = st.text_input(
+ "Tushare Token",
+ value=cfg.tushare_token or "",
+ type="password",
+ help="从 tushare.pro 获取的 API token"
+ )
+
+ with col2:
+ auto_update = st.checkbox(
+ "启动时自动更新数据",
+ value=cfg.auto_update_data,
+ help="启动应用时自动检查并更新数据"
+ )
+
+ update_interval = st.slider(
+ "数据更新间隔(天)",
+ min_value=1,
+ max_value=30,
+ value=cfg.data_update_interval,
+ help="自动更新时检查的数据时间范围"
+ )
+
+ if st.button("保存数据源配置"):
+ cfg.tushare_token = tushare_token
+ cfg.auto_update_data = auto_update
+ cfg.data_update_interval = update_interval
+ save_config(cfg)
+ st.success("数据源配置已更新！")
+
+ st.divider()
+ st.subheader("数据更新记录")
+
+ with db_session() as session:
+ df = pd.read_sql_query(
+ """
+ SELECT job_type, status, created_at, updated_at, error_msg
+ FROM fetch_jobs
+ ORDER BY created_at DESC
+ LIMIT 50
+ """,
+ session
+ )
+
+ if not df.empty:
+ df["duration"] = (df["updated_at"] - df["created_at"]).dt.total_seconds().round(2)
+ df = df.drop(columns=["updated_at"])
+ df = df.rename(columns={
+ "job_type": "数据类型",
+ "status": "状态",
+ "created_at": "开始时间",
+ "error_msg": "错误信息",
+ "duration": "耗时(秒)"
+ })
+ st.dataframe(df, width='stretch')
+ else:
+ st.info("暂无数据更新记录")
diff --git a/app/ui/views/tests.py b/app/ui/views/tests.py
new file mode 100644
index 0000000..f5e0253
--- /dev/null
+++ b/app/ui/views/tests.py
@@ -0,0 +1,194 @@
+"""自检测试视图。"""
+from __future__ import annotations
+
+from datetime import date
+
+import streamlit as st
+
+from app.data.schema import initialize_database
+from app.ingest.checker import run_boot_check
+from app.ingest.tushare import FetchJob, run_ingestion
+from app.llm.client import llm_config_snapshot, run_llm
+from app.utils import alerts
+from app.utils.config import get_config, save_config
+
+from app.ui.shared import LOGGER, LOG_EXTRA
+from app.ui.views.dashboard import update_dashboard_sidebar
+
+def render_tests() -> None:
+ LOGGER.info("渲染自检页面", extra=LOG_EXTRA)
+ st.header("自检测试")
+ st.write("用于快速检查数据库与数据拉取是否正常工作。")
+
+ if st.button("测试数据库初始化"):
+ LOGGER.info("点击测试数据库初始化按钮", extra=LOG_EXTRA)
+ with st.spinner("正在检查数据库..."):
+ result = initialize_database()
+ if result.skipped:
+ LOGGER.info("数据库已存在，无需初始化", extra=LOG_EXTRA)
+ st.success("数据库已存在，检查通过。")
+ else:
+ LOGGER.info("数据库初始化完成，执行语句数=%s", result.executed, extra=LOG_EXTRA)
+ st.success(f"数据库初始化完成，共执行 {result.executed} 条语句。")
+
+ st.divider()
+
+ if st.button("测试 TuShare 拉取（示例 2024-01-01 至 2024-01-03）"):
+ LOGGER.info("点击示例 TuShare 拉取按钮", extra=LOG_EXTRA)
+ with st.spinner("正在调用 TuShare 接口..."):
+ try:
+ run_ingestion(
+ FetchJob(
+ name="streamlit_self_test",
+ start=date(2024, 1, 1),
+ end=date(2024, 1, 3),
+ ts_codes=("000001.SZ",),
+ ),
+ include_limits=False,
+ )
+ LOGGER.info("示例 TuShare 拉取成功", extra=LOG_EXTRA)
+ st.success("TuShare 示例拉取完成，数据已写入数据库。")
+ except Exception as exc: # noqa: BLE001
+ LOGGER.exception("示例 TuShare 拉取失败", extra=LOG_EXTRA)
+ st.error(f"拉取失败：{exc}")
+ alerts.add_warning("TuShare", "示例拉取失败", str(exc))
+ update_dashboard_sidebar()
+
+ st.info("注意：TuShare 拉取依赖网络与 Token，若环境未配置将出现错误提示。")
+
+ st.divider()
+
+ st.subheader("RSS 数据测试")
+ st.write("用于验证 RSS 配置是否能够正常抓取新闻并写入数据库。")
+ rss_url = st.text_input(
+ "测试 RSS 地址",
+ value="https://rsshub.app/cls/depth/1000",
+ help="留空则使用默认配置的全部 RSS 来源。",
+ ).strip()
+ rss_hours = int(
+ st.number_input(
+ "回溯窗口（小时）",
+ min_value=1,
+ max_value=168,
+ value=24,
+ step=6,
+ help="仅抓取最近指定小时内的新闻。",
+ )
+ )
+ rss_limit = int(
+ st.number_input(
+ "单源抓取条数",
+ min_value=1,
+ max_value=200,
+ value=50,
+ step=10,
+ )
+ )
+ if st.button("运行 RSS 测试"):
+ from app.ingest import rss as rss_ingest
+
+ LOGGER.info(
+ "点击 RSS 测试按钮 rss_url=%s hours=%s limit=%s",
+ rss_url,
+ rss_hours,
+ rss_limit,
+ extra=LOG_EXTRA,
+ )
+ with st.spinner("正在抓取 RSS 新闻..."):
+ try:
+ if rss_url:
+ items = rss_ingest.fetch_rss_feed(
+ rss_url,
+ hours_back=rss_hours,
+ max_items=rss_limit,
+ )
+ count = rss_ingest.save_news_items(items)
+ else:
+ count = rss_ingest.ingest_configured_rss(
+ hours_back=rss_hours,
+ max_items_per_feed=rss_limit,
+ )
+ st.success(f"RSS 测试完成，新增 {count} 条新闻记录。")
+ except Exception as exc: # noqa: BLE001
+ LOGGER.exception("RSS 测试失败", extra=LOG_EXTRA)
+ st.error(f"RSS 测试失败：{exc}")
+ alerts.add_warning("RSS", "RSS 测试执行失败", str(exc))
+ update_dashboard_sidebar()
+
+ st.divider()
+ days = int(
+ st.number_input(
+ "检查窗口（天数）",
+ min_value=30,
+ max_value=10950,
+ value=365,
+ step=30,
+ )
+ )
+ LOGGER.debug("检查窗口天数=%s", days, extra=LOG_EXTRA)
+ cfg = get_config()
+ force_refresh = st.checkbox(
+ "强制刷新数据（关闭增量跳过）",
+ value=cfg.force_refresh,
+ help="勾选后将重新拉取所选区间全部数据",
+ )
+ if force_refresh != cfg.force_refresh:
+ cfg.force_refresh = force_refresh
+ LOGGER.info("更新 force_refresh=%s", force_refresh, extra=LOG_EXTRA)
+ save_config()
+
+ if st.button("执行手动数据同步"):
+ LOGGER.info("点击执行手动数据同步按钮", extra=LOG_EXTRA)
+ progress_bar = st.progress(0.0)
+ status_placeholder = st.empty()
+ log_placeholder = st.empty()
+ messages: list[str] = []
+
+ def hook(message: str, value: float) -> None:
+ progress_bar.progress(min(max(value, 0.0), 1.0))
+ status_placeholder.write(message)
+ messages.append(message)
+ LOGGER.debug("手动数据同步进度：%s -> %.2f", message, value, extra=LOG_EXTRA)
+
+ with st.spinner("正在执行手动数据同步..."):
+ try:
+ report = run_boot_check(
+ days=days,
+ progress_hook=hook,
+ force_refresh=force_refresh,
+ )
+ LOGGER.info("手动数据同步成功", extra=LOG_EXTRA)
+ st.success("手动数据同步完成，以下为数据覆盖摘要。")
+ st.json(report.to_dict())
+ if messages:
+ log_placeholder.markdown("\n".join(f"- {msg}" for msg in messages))
+ except Exception as exc: # noqa: BLE001
+ LOGGER.exception("手动数据同步失败", extra=LOG_EXTRA)
+ st.error(f"手动数据同步失败：{exc}")
+ alerts.add_warning("数据同步", "手动数据同步失败", str(exc))
+ update_dashboard_sidebar()
+ if messages:
+ log_placeholder.markdown("\n".join(f"- {msg}" for msg in messages))
+ finally:
+ progress_bar.progress(1.0)
+
+ st.divider()
+ st.subheader("LLM 接口测试")
+ st.json(llm_config_snapshot())
+ llm_prompt = st.text_area("测试 Prompt", value="请概述今天的市场重点。", height=160)
+ system_prompt = st.text_area(
+ "System Prompt (可选)",
+ value="你是一名量化策略研究助手，用简洁中文回答。",
+ height=100,
+ )
+ if st.button("执行 LLM 测试"):
+ with st.spinner("正在调用 LLM..."):
+ try:
+ response = run_llm(llm_prompt, system=system_prompt or None)
+ except Exception as exc: # noqa: BLE001
+ LOGGER.exception("LLM 测试失败", extra=LOG_EXTRA)
+ st.error(f"LLM 调用失败：{exc}")
+ else:
+ LOGGER.info("LLM 测试成功", extra=LOG_EXTRA)
+ st.success("LLM 调用成功，以下为返回内容：")
+ st.write(response)
diff --git a/app/ui/views/today.py b/app/ui/views/today.py
new file mode 100644
index 0000000..808a5a8
--- /dev/null
+++ b/app/ui/views/today.py
@@ -0,0 +1,677 @@
+"""今日计划页面视图。"""
+from __future__ import annotations
+
+import json
+from collections import Counter
+from datetime import date, datetime, timedelta
+from typing import Dict, List, Optional
+
+import numpy as np
+import pandas as pd
+import streamlit as st
+
+from app.backtest.engine import BacktestEngine, PortfolioState, BtConfig
+from app.utils.portfolio import list_investment_pool
+from app.utils.db import db_session
+
+from app.ui.shared import (
+ LOGGER,
+ LOG_EXTRA,
+ get_latest_trade_date,
+ get_query_params,
+ set_query_params,
+)
+
+
+def render_today_plan() -> None:
+ LOGGER.info("渲染今日计划页面", extra=LOG_EXTRA)
+ st.header("今日计划")
+ latest_trade_date = get_latest_trade_date()
+ if latest_trade_date:
+ st.caption(f"最新交易日：{latest_trade_date.isoformat()}（统计数据请见左侧系统监控）")
+ else:
+ st.caption("统计与决策概览现已移至左侧'系统监控'侧栏。")
+ try:
+ with db_session(read_only=True) as conn:
+ date_rows = conn.execute(
+ """
+ SELECT DISTINCT trade_date
+ FROM agent_utils
+ ORDER BY trade_date DESC
+ LIMIT 30
+ """
+ ).fetchall()
+ except Exception: # noqa: BLE001
+ LOGGER.exception("加载 agent_utils 失败", extra=LOG_EXTRA)
+ 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。")
+ return
+
+ trade_dates = [row["trade_date"] for row in date_rows]
+ if not trade_dates:
+ 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。")
+ return
+
+ query = get_query_params()
+ default_trade_date = query.get("date", [trade_dates[0]])[0]
+ try:
+ default_idx = trade_dates.index(default_trade_date)
+ except ValueError:
+ default_idx = 0
+ trade_date = st.selectbox("交易日", trade_dates, index=default_idx)
+
+ with db_session(read_only=True) as conn:
+ code_rows = conn.execute(
+ """
+ SELECT DISTINCT ts_code
+ FROM agent_utils
+ WHERE trade_date = ?
+ ORDER BY ts_code
+ """,
+ (trade_date,),
+ ).fetchall()
+ symbols = [row["ts_code"] for row in code_rows]
+
+ detail_tab, assistant_tab = st.tabs(["标的详情", "投资助理模式"])
+ with assistant_tab:
+ _render_today_plan_assistant_view(trade_date)
+
+ with detail_tab:
+ if not symbols:
+ st.info("所选交易日暂无 agent_utils 记录。")
+ else:
+ _render_today_plan_symbol_view(trade_date, symbols, query)
+
+
+def _render_today_plan_assistant_view(trade_date: str | int | date) -> None:
+ 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。")
+ try:
+ candidates = list_investment_pool(trade_date=trade_date)
+ if candidates:
+ scores = [float(item.score or 0.0) for item in candidates]
+ statuses = [item.status or "UNKNOWN" for item in candidates]
+ tags: List[str] = []
+ rationales: List[str] = []
+ for item in candidates:
+ if getattr(item, "tags", None):
+ tags.extend(item.tags)
+ if getattr(item, "rationale", None):
+ rationales.append(str(item.rationale))
+ cnt = Counter(statuses)
+ tag_cnt = Counter(tags)
+ st.subheader("候选池聚合概览（已匿名化）")
+ col_a, col_b, col_c = st.columns(3)
+ col_a.metric("候选数", f"{len(candidates)}")
+ col_b.metric("平均评分", f"{np.mean(scores):.3f}" if scores else "-")
+ col_c.metric("中位评分", f"{np.median(scores):.3f}" if scores else "-")
+
+ st.write("状态分布：")
+ st.json(dict(cnt))
+
+ if tag_cnt:
+ st.write("常见标签（示例）：")
+ st.json(dict(tag_cnt.most_common(10)))
+
+ if rationales:
+ st.write("汇总理由（节选，不含代码）：")
+ seen = set()
+ excerpts = []
+ for rationale in rationales:
+ text = rationale.strip()
+ if text and text not in seen:
+ seen.add(text)
+ excerpts.append(text)
+ if len(excerpts) >= 3:
+ break
+ for idx, excerpt in enumerate(excerpts, start=1):
+ st.markdown(f"**理由 {idx}:** {excerpt}")
+
+ avg_score = float(np.mean(scores)) if scores else 0.0
+ suggest_pct = max(0.0, min(0.3, 0.10 + (avg_score - 0.5) * 0.2))
+ st.subheader("组合级建议（不指定标的）")
+ st.write(
+ 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%}。"
+ )
+ st.write(
+ "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。"
+ )
+ if st.button("生成组合级操作建议（仅输出，不执行）"):
+ st.success("已生成组合级建议（仅供参考）。")
+ st.write({
+ "候选数": len(candidates),
+ "平均评分": avg_score,
+ "建议新增买入比例": f"{suggest_pct:.0%}",
+ })
+ else:
+ st.info("所选交易日暂无候选投资池数据。")
+ except Exception: # noqa: BLE001
+ LOGGER.exception("加载候选池聚合信息失败", extra=LOG_EXTRA)
+ st.error("加载候选池数据时发生错误。")
+
+
+def _render_today_plan_symbol_view(
+ trade_date: str | int | date,
+ symbols: List[str],
+ query_params: Dict[str, List[str]],
+) -> None:
+ default_ts = query_params.get("code", [symbols[0]])[0]
+ try:
+ default_ts_idx = symbols.index(default_ts)
+ except ValueError:
+ default_ts_idx = 0
+ ts_code = st.selectbox("标的", symbols, index=default_ts_idx)
+ batch_symbols = st.multiselect("批量重评估（可多选）", symbols, default=[])
+
+ if st.button("一键重评估所有标的", type="primary", width='stretch'):
+ with st.spinner("正在对所有标的进行重评估，请稍候..."):
+ 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)
+ changes_all: List[Dict[str, object]] = []
+ success_count = 0
+ error_count = 0
+
+ for idx, code in enumerate(symbols, start=1):
+ try:
+ with db_session(read_only=True) as conn:
+ before_rows = conn.execute(
+ "SELECT agent, action FROM agent_utils WHERE trade_date = ? AND ts_code = ?",
+ (trade_date, code),
+ ).fetchall()
+ before_map = {row["agent"]: row["action"] for row in before_rows}
+
+ cfg = BtConfig(
+ id="reeval_ui_all",
+ name="UI All Re-eval",
+ start_date=trade_date_obj,
+ end_date=trade_date_obj,
+ universe=[code],
+ params={},
+ )
+ engine = BacktestEngine(cfg)
+ state = PortfolioState()
+ engine.simulate_day(trade_date_obj, state)
+
+ with db_session(read_only=True) as conn:
+ after_rows = conn.execute(
+ "SELECT agent, action FROM agent_utils WHERE trade_date = ? AND ts_code = ?",
+ (trade_date, code),
+ ).fetchall()
+ for row in after_rows:
+ agent = row["agent"]
+ new_action = row["action"]
+ old_action = before_map.get(agent)
+ if new_action != old_action:
+ changes_all.append(
+ {"代码": code, "代理": agent, "原动作": old_action, "新动作": new_action}
+ )
+ success_count += 1
+ except Exception: # noqa: BLE001
+ LOGGER.exception("重评估 %s 失败", code, extra=LOG_EXTRA)
+ error_count += 1
+
+ progress.progress(idx / len(symbols))
+
+ if error_count > 0:
+ st.error(f"一键重评估完成：成功 {success_count} 个，失败 {error_count} 个")
+ else:
+ st.success(f"一键重评估完成：所有 {success_count} 个标的重评估成功")
+
+ if changes_all:
+ st.write("检测到以下动作变更：")
+ st.dataframe(pd.DataFrame(changes_all), hide_index=True, width='stretch')
+
+ st.rerun()
+ except Exception as exc: # noqa: BLE001
+ LOGGER.exception("一键重评估失败", extra=LOG_EXTRA)
+ st.error(f"一键重评估执行过程中发生错误：{exc}")
+
+ set_query_params(date=str(trade_date), code=str(ts_code))
+
+ with db_session(read_only=True) as conn:
+ rows = conn.execute(
+ """
+ SELECT agent, action, utils, feasible, weight
+ FROM agent_utils
+ WHERE trade_date = ? AND ts_code = ?
+ ORDER BY CASE WHEN agent = 'global' THEN 1 ELSE 0 END, agent
+ """,
+ (trade_date, ts_code),
+ ).fetchall()
+
+ if not rows:
+ st.info("未查询到详细决策记录，稍后再试。")
+ return
+
+ try:
+ feasible_actions = json.loads(rows[0]["feasible"] or "[]")
+ except (KeyError, TypeError, json.JSONDecodeError):
+ feasible_actions = []
+
+ global_info = None
+ dept_records: List[Dict[str, object]] = []
+ dept_details: Dict[str, Dict[str, object]] = {}
+ agent_records: List[Dict[str, object]] = []
+
+ for item in rows:
+ agent_name = item["agent"]
+ action = item["action"]
+ weight = float(item["weight"] or 0.0)
+ try:
+ utils = json.loads(item["utils"] or "{}")
+ except json.JSONDecodeError:
+ utils = {}
+
+ if agent_name == "global":
+ global_info = {
+ "action": action,
+ "confidence": float(utils.get("_confidence", 0.0)),
+ "target_weight": float(utils.get("_target_weight", 0.0)),
+ "department_votes": utils.get("_department_votes", {}),
+ "requires_review": bool(utils.get("_requires_review", False)),
+ "scope_values": utils.get("_scope_values", {}),
+ "close_series": utils.get("_close_series", []),
+ "turnover_series": utils.get("_turnover_series", []),
+ "department_supplements": utils.get("_department_supplements", {}),
+ "department_dialogue": utils.get("_department_dialogue", {}),
+ "department_telemetry": utils.get("_department_telemetry", {}),
+ }
+ continue
+
+ if agent_name.startswith("dept_"):
+ code = agent_name.split("dept_", 1)[-1]
+ signals = utils.get("_signals", [])
+ risks = utils.get("_risks", [])
+ supplements = utils.get("_supplements", [])
+ dialogue = utils.get("_dialogue", [])
+ telemetry = utils.get("_telemetry", {})
+ dept_records.append(
+ {
+ "部门": code,
+ "行动": action,
+ "信心": float(utils.get("_confidence", 0.0)),
+ "权重": weight,
+ "摘要": utils.get("_summary", ""),
+ "核心信号": "；".join(signals) if isinstance(signals, list) else signals,
+ "风险提示": "；".join(risks) if isinstance(risks, list) else risks,
+ "补充次数": len(supplements) if isinstance(supplements, list) else 0,
+ }
+ )
+ dept_details[code] = {
+ "supplements": supplements if isinstance(supplements, list) else [],
+ "dialogue": dialogue if isinstance(dialogue, list) else [],
+ "summary": utils.get("_summary", ""),
+ "signals": signals,
+ "risks": risks,
+ "telemetry": telemetry if isinstance(telemetry, dict) else {},
+ }
+ else:
+ score_map = {
+ key: float(val)
+ for key, val in utils.items()
+ if not str(key).startswith("_")
+ }
+ agent_records.append(
+ {
+ "代理": agent_name,
+ "建议动作": action,
+ "权重": weight,
+ "SELL": score_map.get("SELL", 0.0),
+ "HOLD": score_map.get("HOLD", 0.0),
+ "BUY_S": score_map.get("BUY_S", 0.0),
+ "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)}")
+
+ st.subheader("全局策略")
+ if global_info:
+ col1, col2, col3 = st.columns(3)
+ col1.metric("最终行动", global_info["action"])
+ col2.metric("信心", f"{global_info['confidence']:.2f}")
+ col3.metric("目标权重", f"{global_info['target_weight']:+.2%}")
+ if global_info["department_votes"]:
+ st.json(global_info["department_votes"])
+ if global_info["requires_review"]:
+ st.warning("部门分歧较大，已标记为需人工复核。")
+ with st.expander("基础上下文数据", expanded=False):
+ scope = global_info.get("scope_values") or {}
+ close_series = global_info.get("close_series") or []
+ turnover_series = global_info.get("turnover_series") or []
+ st.write("最新字段：")
+ if scope:
+ st.json(scope)
+ st.download_button(
+ "下载字段(JSON)",
+ data=json.dumps(scope, ensure_ascii=False, indent=2),
+ file_name=f"{ts_code}_{trade_date}_scope.json",
+ mime="application/json",
+ key="dl_scope_json",
+ )
+ if close_series:
+ st.write("收盘价时间序列 (最近窗口)：")
+ st.json(close_series)
+ try:
+ import csv
+ import io
+
+ buf = io.StringIO()
+ writer = csv.writer(buf)
+ writer.writerow(["trade_date", "close"])
+ for dt_val, val in close_series:
+ writer.writerow([dt_val, val])
+ st.download_button(
+ "下载收盘价(CSV)",
+ data=buf.getvalue(),
+ file_name=f"{ts_code}_{trade_date}_close_series.csv",
+ mime="text/csv",
+ key="dl_close_csv",
+ )
+ except Exception: # noqa: BLE001
+ pass
+ 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}")
+ with db_session(read_only=True) as conn:
+ before_rows = conn.execute(
+ """
+ SELECT agent, action, utils FROM agent_utils
+ WHERE trade_date = ? AND ts_code = ?
+ """,
+ (trade_date, ts_code),
+ ).fetchall()
+ before_map = {row["agent"]: (row["action"], row["utils"]) for row in before_rows}
+ cfg = BtConfig(
+ id="reeval_ui",
+ name="UI Re-evaluation",
+ start_date=trade_date_obj,
+ end_date=trade_date_obj,
+ universe=[ts_code],
+ params={},
+ )
+ engine = BacktestEngine(cfg)
+ state = PortfolioState()
+ engine.simulate_day(trade_date_obj, state)
+ with db_session(read_only=True) as conn:
+ after_rows = conn.execute(
+ """
+ SELECT agent, action, utils FROM agent_utils
+ WHERE trade_date = ? AND ts_code = ?
+ """,
+ (trade_date, ts_code),
+ ).fetchall()
+ changes = []
+ for row in after_rows:
+ agent = row["agent"]
+ new_action = row["action"]
+ old_action, _old_utils = before_map.get(agent, (None, None))
+ if new_action != old_action:
+ changes.append({"代理": agent, "原动作": old_action, "新动作": new_action})
+ if changes:
+ st.success("重评估完成，检测到动作变更：")
+ st.dataframe(pd.DataFrame(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)
+ changes_all: List[Dict[str, object]] = []
+ for idx, code in enumerate(batch_symbols, start=1):
+ with db_session(read_only=True) as conn:
+ before_rows = conn.execute(
+ "SELECT agent, action FROM agent_utils WHERE trade_date = ? AND ts_code = ?",
+ (trade_date, code),
+ ).fetchall()
+ before_map = {row["agent"]: row["action"] for row in before_rows}
+ cfg = BtConfig(
+ id="reeval_ui_batch",
+ name="UI Batch Re-eval",
+ start_date=trade_date_obj,
+ end_date=trade_date_obj,
+ universe=[code],
+ params={},
+ )
+ engine = BacktestEngine(cfg)
+ state = PortfolioState()
+ engine.simulate_day(trade_date_obj, state)
+ with db_session(read_only=True) as conn:
+ after_rows = conn.execute(
+ "SELECT agent, action FROM agent_utils WHERE trade_date = ? AND ts_code = ?",
+ (trade_date, code),
+ ).fetchall()
+ for row in after_rows:
+ agent = row["agent"]
+ new_action = row["action"]
+ old_action = before_map.get(agent)
+ if new_action != old_action:
+ changes_all.append({"代码": code, "代理": agent, "原动作": old_action, "新动作": new_action})
+ progress.progress(idx / max(1, len(batch_symbols)))
+ st.success("批量重评估完成。")
+ 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}")