"""Streamlit UI scaffold for the investment assistant."""
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
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
from app.agents.base import AgentContext
from app.agents.game import Decision
from app.backtest.engine import BtConfig, run_backtest
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.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
# ADD: read default selection from URL
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]
if not symbols:
st.info("所选交易日暂无 agent_utils 记录。")
return
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)
# ADD: batch selection for re-evaluation
batch_symbols = st.multiselect("批量重评估（可多选）", symbols, default=[])
# sync URL params
_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("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。")
candidates = list_investment_pool(trade_date=trade_date)
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。")
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_backtest() -> None:
LOGGER.info("渲染回测页面", extra=LOG_EXTRA)
st.header("回测与复盘")
st.write("在此运行回测、展示净值曲线与代理贡献。")
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,
)
col1, col2 = st.columns(2)
start_date = col1.date_input("开始日期", value=default_start)
end_date = col2.date_input("结束日期", value=default_end)
universe_text = st.text_input("股票列表（逗号分隔）", value="000001.SZ")
target = st.number_input("目标收益（例：0.035 表示 3.5%）", value=0.035, step=0.005, format="%.3f")
stop = st.number_input("止损收益（例：-0.015 表示 -1.5%）", value=-0.015, step=0.005, format="%.3f")
hold_days = st.number_input("持有期（交易日）", value=10, step=1)
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,
)
if st.button("运行回测"):
LOGGER.info("用户点击运行回测按钮", extra=LOG_EXTRA)
decision_log_container = st.container()
status_box = st.status("准备执行回测...", expanded=True)
llm_stats_placeholder = st.empty()
decision_entries: List[str] = []
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,
)
cfg = BtConfig(
id="streamlit_demo",
name="Streamlit Demo Strategy",
start_date=start_date,
end_date=end_date,
universe=universe,
params={
"target": target,
"stop": stop,
"hold_days": int(hold_days),
},
)
result = run_backtest(cfg, decision_callback=_decision_callback)
LOGGER.info(
"回测完成：nav_records=%s trades=%s",
len(result.nav_series),
len(result.trades),
extra=LOG_EXTRA,
)
status_box.update(label="回测执行完成", state="complete")
st.success("回测执行完成，详见下方结果与统计。")
metrics = snapshot_llm_metrics()
llm_stats_placeholder.json(
{
"LLM 调用次数": metrics.get("total_calls", 0),
"Prompt Tokens": metrics.get("total_prompt_tokens", 0),
"Completion Tokens": metrics.get("total_completion_tokens", 0),
"按 Provider": metrics.get("provider_calls", {}),
"按模型": metrics.get("model_calls", {}),
}
)
_update_dashboard_sidebar(metrics)
st.json({"nav_records": result.nav_series, "trades": result.trades})
except Exception as exc: # noqa: BLE001
LOGGER.exception("回测执行失败", extra=LOG_EXTRA)
status_box.update(label="回测执行失败", state="error")
st.error(f"回测执行失败：{exc}")
with st.expander("离线调参实验 (DecisionEnv)", expanded=False):
st.caption(
"使用 DecisionEnv 对代理权重做离线调参。请选择需要优化的代理并设定权重范围，"
"系统会运行一次回测并返回收益、回撤等指标。若 LLM 网络不可用，将返回失败标记。"
)
disable_departments = st.checkbox(
"禁用部门 LLM（仅规则代理，适合离线快速评估）",
value=True,
help="关闭部门调用后不依赖外部 LLM 网络，仅根据规则代理权重模拟。",
)
default_experiment_id = f"streamlit_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
experiment_id = st.text_input(
"实验 ID",
value=default_experiment_id,
help="用于在 tuning_results 表中区分不同实验。",
)
strategy_label = st.text_input(
"策略说明",
value="DecisionEnv",
help="可选：为本次调参记录一个策略名称或备注。",
)
agent_objects = default_agents()
agent_names = [agent.name for agent in agent_objects]
if not agent_names:
st.info("暂无可调整的代理。")
else:
selected_agents = st.multiselect(
"选择调参的代理权重",
agent_names,
default=agent_names[:2],
key="decision_env_agents",
)
specs: List[ParameterSpec] = []
action_values: List[float] = []
range_valid = True
for idx, agent_name in enumerate(selected_agents):
col_min, col_max, col_action = st.columns([1, 1, 2])
min_key = f"decision_env_min_{agent_name}"
max_key = f"decision_env_max_{agent_name}"
action_key = f"decision_env_action_{agent_name}"
default_min = 0.0
default_max = 1.0
min_val = col_min.number_input(
f"{agent_name} 最小权重",
min_value=0.0,
max_value=1.0,
value=default_min,
step=0.05,
key=min_key,
)
max_val = col_max.number_input(
f"{agent_name} 最大权重",
min_value=0.0,
max_value=1.0,
value=default_max,
step=0.05,
key=max_key,
)
if max_val <= min_val:
range_valid = False
action_val = col_action.slider(
f"{agent_name} 动作 (0-1)",
min_value=0.0,
max_value=1.0,
value=0.5,
step=0.01,
key=action_key,
)
specs.append(
ParameterSpec(
name=f"weight_{agent_name}",
target=f"agent_weights.{agent_name}",
minimum=min_val,
maximum=max_val,
)
)
action_values.append(action_val)
run_decision_env = st.button("执行单次调参", key="run_decision_env_button")
just_finished_single = False
if run_decision_env:
if not selected_agents:
st.warning("请至少选择一个代理进行调参。")
elif not range_valid:
st.error("请确保所有代理的最大权重大于最小权重。")
else:
LOGGER.info(
"离线调参（单次）按钮点击，已选择代理=%s 动作=%s disable_departments=%s",
selected_agents,
action_values,
disable_departments,
extra=LOG_EXTRA,
)
baseline_weights = cfg.agent_weights.as_dict()
for agent in agent_objects:
baseline_weights.setdefault(agent.name, 1.0)
universe_env = [code.strip() for code in universe_text.split(',') if code.strip()]
if not universe_env:
st.error("请先指定至少一个股票代码。")
else:
bt_cfg_env = BtConfig(
id="decision_env_streamlit",
name="DecisionEnv Streamlit",
start_date=start_date,
end_date=end_date,
universe=universe_env,
params={
"target": target,
"stop": stop,
"hold_days": int(hold_days),
},
method=cfg.decision_method,
)
env = DecisionEnv(
bt_config=bt_cfg_env,
parameter_specs=specs,
baseline_weights=baseline_weights,
disable_departments=disable_departments,
)
env.reset()
LOGGER.debug(
"离线调参（单次）启动 DecisionEnv：cfg=%s 参数维度=%s",
bt_cfg_env,
len(specs),
extra=LOG_EXTRA,
)
with st.spinner("正在执行离线调参……"):
try:
observation, reward, done, info = env.step(action_values)
LOGGER.info(
"离线调参（单次）完成，obs=%s reward=%.4f done=%s",
observation,
reward,
done,
extra=LOG_EXTRA,
)
except Exception as exc: # noqa: BLE001
LOGGER.exception("DecisionEnv 调用失败", extra=LOG_EXTRA)
st.error(f"离线调参失败：{exc}")
st.session_state.pop(_DECISION_ENV_SINGLE_RESULT_KEY, None)
else:
if observation.get("failure"):
st.error("调参失败：回测执行未完成，可能是 LLM 网络不可用或参数异常。")
st.json(observation)
st.session_state.pop(_DECISION_ENV_SINGLE_RESULT_KEY, None)
else:
resolved_experiment_id = experiment_id or str(uuid.uuid4())
resolved_strategy = strategy_label or "DecisionEnv"
action_payload = {
name: value
for name, value in zip(selected_agents, action_values)
}
metrics_payload = dict(observation)
metrics_payload["reward"] = reward
log_success = False
try:
log_tuning_result(
experiment_id=resolved_experiment_id,
strategy=resolved_strategy,
action=action_payload,
reward=reward,
metrics=metrics_payload,
weights=info.get("weights", {}),
)
except Exception: # noqa: BLE001
LOGGER.exception("记录调参结果失败", extra=LOG_EXTRA)
else:
log_success = True
LOGGER.info(
"离线调参（单次）日志写入成功：experiment=%s strategy=%s",
resolved_experiment_id,
resolved_strategy,
extra=LOG_EXTRA,
)
st.session_state[_DECISION_ENV_SINGLE_RESULT_KEY] = {
"observation": dict(observation),
"reward": float(reward),
"weights": info.get("weights", {}),
"nav_series": info.get("nav_series"),
"trades": info.get("trades"),
"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}")
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')
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。")
# ADD: Comparison view for multiple backtest configurations
with st.expander("回测结果对比", expanded=False):
st.caption("从历史回测配置中选择多个进行净值曲线与指标对比。")
normalize_to_one = st.checkbox("归一化到 1 起点", value=True)
use_log_y = st.checkbox("对数坐标", value=False)
metric_options = ["总收益", "最大回撤", "交易数", "平均换手", "风险事件"]
selected_metrics = st.multiselect("显示指标列", metric_options, default=metric_options)
try:
with db_session(read_only=True) as conn:
cfg_rows = conn.execute(
"SELECT id, name FROM bt_config ORDER BY rowid DESC LIMIT 50"
).fetchall()
except Exception: # noqa: BLE001
LOGGER.exception("读取 bt_config 失败", extra=LOG_EXTRA)
cfg_rows = []
cfg_options = [f"{row['id']} | {row['name']}" for row in cfg_rows]
selected_labels = st.multiselect("选择配置", cfg_options, default=cfg_options[:2])
selected_ids = [label.split(" | ")[0].strip() for label in selected_labels]
nav_df = pd.DataFrame()
rpt_df = pd.DataFrame()
if selected_ids:
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)
end_filter = col_d2.date_input("结束日期", value=overall_max)
if start_filter > end_filter:
start_filter, end_filter = end_filter, start_filter
mask = (nav_df["trade_date"].dt.date >= start_filter) & (nav_df["trade_date"].dt.date <= end_filter)
nav_df = nav_df.loc[mask]
pivot = nav_df.pivot_table(index="trade_date", columns="cfg_id", values="nav")
if normalize_to_one:
pivot = pivot.apply(lambda s: s / s.dropna().iloc[0] if s.dropna().size else s)
import plotly.graph_objects as go
fig = go.Figure()
for col in pivot.columns:
fig.add_trace(go.Scatter(x=pivot.index, y=pivot[col], mode="lines", name=str(col)))
fig.update_layout(height=300, margin=dict(l=10, r=10, t=30, b=10))
if use_log_y:
fig.update_yaxes(type="log")
st.plotly_chart(fig, use_container_width=True)
# 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。")
def render_settings() -> None:
LOGGER.info("渲染设置页面", extra=LOG_EXTRA)
st.header("数据与设置")
cfg = get_config()
LOGGER.debug("当前 TuShare Token 是否已配置=%s", bool(cfg.tushare_token), extra=LOG_EXTRA)
token = st.text_input("TuShare Token", value=cfg.tushare_token or "", type="password")
if st.button("保存设置"):
LOGGER.info("保存设置按钮被点击", extra=LOG_EXTRA)
cfg.tushare_token = token.strip() or None
LOGGER.info("TuShare Token 更新，是否为空=%s", cfg.tushare_token is None, extra=LOG_EXTRA)
save_config()
st.success("设置已保存，仅在当前会话生效。")
st.write("新闻源开关与数据库备份将在此配置。")
st.divider()
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 management -------------------------------------------------
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}"
default_model_key = f"provider_default_model_{selected_provider}"
mode_key = f"provider_mode_{selected_provider}"
temp_key = f"provider_temp_{selected_provider}"
timeout_key = f"provider_timeout_{selected_provider}"
prompt_key = f"provider_prompt_{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")
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。")
# ADD: show cache last updated if available
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
if provider_cfg.models and default_model_val in provider_cfg.models:
provider_cfg.default_model = default_model_val
else:
provider_cfg.default_model = default_model_val
provider_cfg.default_temperature = float(temp_val)
provider_cfg.default_timeout = float(timeout_val)
provider_cfg.prompt_template = prompt_template_val.strip()
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 ""
st.session_state[default_model_key] = provider_cfg.default_model 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())
# Department configuration -------------------------------------------
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_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("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)
viz_col1, viz_col2 = st.columns(2)
default_start = date.today() - timedelta(days=180)
start_date = viz_col1.date_input("开始日期", value=default_start, key="viz_start")
end_date = viz_col2.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)"})
if price_df.shape[0] > 180:
sampled = price_df.resample('3D').last().dropna()
else:
sampled = price_df
if volume_df.shape[0] > 180:
volume_sampled = volume_df.resample('3D').mean().dropna()
else:
volume_sampled = 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
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, use_container_width=True)
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, use_container_width=True)
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, use_container_width=True)
df_reset["月份"] = df_reset["交易日"].dt.to_period("M").astype(str)
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, use_container_width=True)
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)
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 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")
render_global_dashboard()
tabs = st.tabs(["今日计划", "回测与复盘", "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", "数据与设置", "自检测试"], extra=LOG_EXTRA)
with tabs[0]:
render_today_plan()
with tabs[1]:
render_backtest()
with tabs[2]:
render_settings()
with tabs[3]:
render_tests()
if __name__ == "__main__":
main()