899 lines
34 KiB
Python
899 lines
34 KiB
Python
"""Streamlit UI scaffold for the investment assistant."""
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from __future__ import annotations
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import sys
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from dataclasses import asdict
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from datetime import date, timedelta
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from pathlib import Path
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from typing import Dict, List
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ROOT = Path(__file__).resolve().parents[2]
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if str(ROOT) not in sys.path:
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sys.path.insert(0, str(ROOT))
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import json
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import pandas as pd
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import plotly.express as px
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import plotly.graph_objects as go
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import streamlit as st
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from app.backtest.engine import BtConfig, run_backtest
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from app.data.schema import initialize_database
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from app.ingest.checker import run_boot_check
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from app.ingest.tushare import FetchJob, run_ingestion
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from app.llm.client import llm_config_snapshot, run_llm
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from app.utils.config import (
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ALLOWED_LLM_STRATEGIES,
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DEFAULT_LLM_BASE_URLS,
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DEFAULT_LLM_MODEL_OPTIONS,
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DEFAULT_LLM_MODELS,
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DepartmentSettings,
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LLMEndpoint,
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get_config,
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save_config,
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)
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from app.utils.db import db_session
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from app.utils.logging import get_logger
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LOGGER = get_logger(__name__)
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LOG_EXTRA = {"stage": "ui"}
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def _load_stock_options(limit: int = 500) -> list[str]:
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try:
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with db_session(read_only=True) as conn:
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rows = conn.execute(
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"SELECT ts_code, name FROM stock_basic WHERE list_status = 'L' ORDER BY ts_code"
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).fetchall()
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except Exception:
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LOGGER.exception("加载股票列表失败", extra=LOG_EXTRA)
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return []
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options: list[str] = []
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for row in rows[:limit]:
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code = row["ts_code"]
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name = row["name"] or ""
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label = f"{code} | {name}" if name else code
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options.append(label)
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LOGGER.info("加载股票选项完成，数量=%s", len(options), extra=LOG_EXTRA)
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return options
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def _parse_ts_code(selection: str) -> str:
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return selection.split(' | ')[0].strip().upper()
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def _load_daily_frame(ts_code: str, start: date, end: date) -> pd.DataFrame:
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LOGGER.info(
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"加载行情数据：ts_code=%s start=%s end=%s",
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ts_code,
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start,
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end,
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extra=LOG_EXTRA,
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)
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start_str = start.strftime('%Y%m%d')
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end_str = end.strftime('%Y%m%d')
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range_query = (
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"SELECT trade_date, open, high, low, close, vol, amount "
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"FROM daily WHERE ts_code = ? AND trade_date BETWEEN ? AND ? ORDER BY trade_date"
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)
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fallback_query = (
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"SELECT trade_date, open, high, low, close, vol, amount "
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"FROM daily WHERE ts_code = ? ORDER BY trade_date DESC LIMIT 200"
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)
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with db_session(read_only=True) as conn:
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df = pd.read_sql_query(range_query, conn, params=(ts_code, start_str, end_str))
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if df.empty:
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df = pd.read_sql_query(fallback_query, conn, params=(ts_code,))
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if df.empty:
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LOGGER.warning(
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"行情数据为空：ts_code=%s start=%s end=%s",
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ts_code,
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start,
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end,
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extra=LOG_EXTRA,
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)
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return df
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df = df.sort_values('trade_date')
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df['trade_date'] = pd.to_datetime(df['trade_date'])
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df.set_index('trade_date', inplace=True)
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LOGGER.info("行情数据加载完成：条数=%s", len(df), extra=LOG_EXTRA)
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return df
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def render_today_plan() -> None:
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LOGGER.info("渲染今日计划页面", extra=LOG_EXTRA)
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st.header("今日计划")
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try:
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with db_session(read_only=True) as conn:
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date_rows = conn.execute(
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"""
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SELECT DISTINCT trade_date
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FROM agent_utils
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ORDER BY trade_date DESC
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LIMIT 30
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"""
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).fetchall()
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except Exception: # noqa: BLE001
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LOGGER.exception("加载 agent_utils 失败", extra=LOG_EXTRA)
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st.warning("暂未写入部门/代理决策，请先运行回测或策略评估流程。")
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return
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trade_dates = [row["trade_date"] for row in date_rows]
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if not trade_dates:
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st.info("暂无决策记录，完成一次回测后即可在此查看部门意见与投票结果。")
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return
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trade_date = st.selectbox("交易日", trade_dates, index=0)
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with db_session(read_only=True) as conn:
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code_rows = conn.execute(
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"""
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SELECT DISTINCT ts_code
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FROM agent_utils
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WHERE trade_date = ?
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ORDER BY ts_code
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""",
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(trade_date,),
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).fetchall()
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symbols = [row["ts_code"] for row in code_rows]
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if not symbols:
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st.info("所选交易日暂无 agent_utils 记录。")
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return
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ts_code = st.selectbox("标的", symbols, index=0)
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with db_session(read_only=True) as conn:
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rows = conn.execute(
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"""
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SELECT agent, action, utils, feasible, weight
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FROM agent_utils
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WHERE trade_date = ? AND ts_code = ?
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ORDER BY CASE WHEN agent = 'global' THEN 1 ELSE 0 END, agent
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""",
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(trade_date, ts_code),
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).fetchall()
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if not rows:
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st.info("未查询到详细决策记录，稍后再试。")
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return
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try:
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feasible_actions = json.loads(rows[0]["feasible"] or "[]")
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except (KeyError, TypeError, json.JSONDecodeError):
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feasible_actions = []
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global_info = None
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dept_records: List[Dict[str, object]] = []
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agent_records: List[Dict[str, object]] = []
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for item in rows:
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agent_name = item["agent"]
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action = item["action"]
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weight = float(item["weight"] or 0.0)
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try:
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utils = json.loads(item["utils"] or "{}")
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except json.JSONDecodeError:
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utils = {}
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if agent_name == "global":
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global_info = {
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"action": action,
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"confidence": float(utils.get("_confidence", 0.0)),
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"target_weight": float(utils.get("_target_weight", 0.0)),
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"department_votes": utils.get("_department_votes", {}),
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"requires_review": bool(utils.get("_requires_review", False)),
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}
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continue
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if agent_name.startswith("dept_"):
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code = agent_name.split("dept_", 1)[-1]
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signals = utils.get("_signals", [])
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risks = utils.get("_risks", [])
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dept_records.append(
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{
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"部门": code,
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"行动": action,
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"信心": float(utils.get("_confidence", 0.0)),
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"权重": weight,
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"摘要": utils.get("_summary", ""),
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"核心信号": "；".join(signals) if isinstance(signals, list) else signals,
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"风险提示": "；".join(risks) if isinstance(risks, list) else risks,
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}
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)
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else:
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score_map = {
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key: float(val)
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for key, val in utils.items()
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if not str(key).startswith("_")
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}
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agent_records.append(
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{
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"代理": agent_name,
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"建议动作": action,
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"权重": weight,
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"SELL": score_map.get("SELL", 0.0),
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"HOLD": score_map.get("HOLD", 0.0),
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"BUY_S": score_map.get("BUY_S", 0.0),
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"BUY_M": score_map.get("BUY_M", 0.0),
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"BUY_L": score_map.get("BUY_L", 0.0),
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}
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)
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if feasible_actions:
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st.caption(f"可行操作集合：{', '.join(feasible_actions)}")
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st.subheader("全局策略")
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if global_info:
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col1, col2, col3 = st.columns(3)
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col1.metric("最终行动", global_info["action"])
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col2.metric("信心", f"{global_info['confidence']:.2f}")
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col3.metric("目标权重", f"{global_info['target_weight']:+.2%}")
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if global_info["department_votes"]:
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st.json(global_info["department_votes"])
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if global_info["requires_review"]:
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st.warning("部门分歧较大，已标记为需人工复核。")
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else:
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st.info("暂未写入全局策略摘要。")
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st.subheader("部门意见")
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if dept_records:
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dept_df = pd.DataFrame(dept_records)
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st.dataframe(dept_df, use_container_width=True, hide_index=True)
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else:
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st.info("暂无部门记录。")
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st.subheader("代理评分")
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if agent_records:
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agent_df = pd.DataFrame(agent_records)
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st.dataframe(agent_df, use_container_width=True, hide_index=True)
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else:
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st.info("暂无基础代理评分。")
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st.caption("以上内容来源于 agent_utils 表，可通过回测或实时评估自动更新。")
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def render_backtest() -> None:
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LOGGER.info("渲染回测页面", extra=LOG_EXTRA)
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st.header("回测与复盘")
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st.write("在此运行回测、展示净值曲线与代理贡献。")
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default_start = date(2020, 1, 1)
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default_end = date(2020, 3, 31)
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LOGGER.debug(
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"回测默认参数：start=%s end=%s universe=%s target=%s stop=%s hold_days=%s",
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default_start,
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default_end,
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"000001.SZ",
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0.035,
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-0.015,
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10,
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extra=LOG_EXTRA,
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)
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col1, col2 = st.columns(2)
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start_date = col1.date_input("开始日期", value=default_start)
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end_date = col2.date_input("结束日期", value=default_end)
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universe_text = st.text_input("股票列表（逗号分隔）", value="000001.SZ")
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target = st.number_input("目标收益（例：0.035 表示 3.5%）", value=0.035, step=0.005, format="%.3f")
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stop = st.number_input("止损收益（例：-0.015 表示 -1.5%）", value=-0.015, step=0.005, format="%.3f")
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hold_days = st.number_input("持有期（交易日）", value=10, step=1)
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LOGGER.debug(
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"当前回测表单输入：start=%s end=%s universe_text=%s target=%.3f stop=%.3f hold_days=%s",
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start_date,
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||
end_date,
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universe_text,
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||
target,
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stop,
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hold_days,
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||
extra=LOG_EXTRA,
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||
)
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||
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if st.button("运行回测"):
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LOGGER.info("用户点击运行回测按钮", extra=LOG_EXTRA)
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with st.spinner("正在执行回测..."):
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try:
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universe = [code.strip() for code in universe_text.split(',') if code.strip()]
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LOGGER.info(
|
||
"回测参数：start=%s end=%s universe=%s target=%s stop=%s hold_days=%s",
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start_date,
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end_date,
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universe,
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||
target,
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stop,
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hold_days,
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||
extra=LOG_EXTRA,
|
||
)
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cfg = BtConfig(
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||
id="streamlit_demo",
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||
name="Streamlit Demo Strategy",
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||
start_date=start_date,
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||
end_date=end_date,
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||
universe=universe,
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||
params={
|
||
"target": target,
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"stop": stop,
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"hold_days": int(hold_days),
|
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},
|
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)
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result = run_backtest(cfg)
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||
LOGGER.info(
|
||
"回测完成：nav_records=%s trades=%s",
|
||
len(result.nav_series),
|
||
len(result.trades),
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||
extra=LOG_EXTRA,
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)
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st.success("回测执行完成，详见回测结果摘要。")
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||
st.json({"nav_records": result.nav_series, "trades": result.trades})
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||
except Exception as exc: # noqa: BLE001
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||
LOGGER.exception("回测执行失败", extra=LOG_EXTRA)
|
||
st.error(f"回测执行失败：{exc}")
|
||
|
||
|
||
def render_settings() -> None:
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||
LOGGER.info("渲染设置页面", extra=LOG_EXTRA)
|
||
st.header("数据与设置")
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||
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")
|
||
llm_cfg = cfg.llm
|
||
primary = llm_cfg.primary
|
||
providers = sorted(DEFAULT_LLM_MODELS.keys())
|
||
try:
|
||
provider_index = providers.index((primary.provider or "ollama").lower())
|
||
except ValueError:
|
||
provider_index = 0
|
||
selected_provider = st.selectbox("LLM Provider", providers, index=provider_index)
|
||
provider_info = DEFAULT_LLM_MODEL_OPTIONS.get(selected_provider, {})
|
||
model_options = provider_info.get("models", [])
|
||
custom_model_label = "自定义模型"
|
||
default_model_hint = DEFAULT_LLM_MODELS.get(selected_provider, DEFAULT_LLM_MODELS["ollama"])
|
||
|
||
if model_options:
|
||
options_with_custom = model_options + [custom_model_label]
|
||
if primary.provider == selected_provider and primary.model in model_options:
|
||
model_index = options_with_custom.index(primary.model)
|
||
else:
|
||
model_index = 0
|
||
selected_model_option = st.selectbox(
|
||
"LLM 模型",
|
||
options_with_custom,
|
||
index=model_index,
|
||
help=f"可选模型：{', '.join(model_options)}",
|
||
)
|
||
if selected_model_option == custom_model_label:
|
||
custom_model_value = st.text_input(
|
||
"自定义模型名称",
|
||
value="" if primary.provider != selected_provider or primary.model in model_options else primary.model,
|
||
)
|
||
chosen_model = custom_model_value.strip() or default_model_hint
|
||
else:
|
||
chosen_model = selected_model_option
|
||
else:
|
||
chosen_model = st.text_input(
|
||
"LLM 模型",
|
||
value=primary.model or default_model_hint,
|
||
help="未预设该 Provider 的模型列表，请手动填写",
|
||
).strip() or default_model_hint
|
||
default_base_hint = DEFAULT_LLM_BASE_URLS.get(selected_provider, "")
|
||
provider_default_temp = float(provider_info.get("temperature", 0.2))
|
||
provider_default_timeout = int(provider_info.get("timeout", 30.0))
|
||
|
||
if primary.provider == selected_provider:
|
||
base_value = primary.base_url or default_base_hint or ""
|
||
temp_value = float(primary.temperature)
|
||
timeout_value = int(primary.timeout)
|
||
else:
|
||
base_value = default_base_hint or ""
|
||
temp_value = provider_default_temp
|
||
timeout_value = provider_default_timeout
|
||
|
||
llm_base = st.text_input(
|
||
"LLM Base URL",
|
||
value=base_value,
|
||
help=f"默认推荐：{default_base_hint or '按供应商要求填写'}",
|
||
)
|
||
llm_api_key = st.text_input(
|
||
"LLM API Key",
|
||
value=primary.api_key or "",
|
||
type="password",
|
||
help="点击右侧小图标可查看当前 Key，该值会写入 config.json（已被 gitignore 排除）",
|
||
)
|
||
llm_temperature = st.slider(
|
||
"LLM 温度",
|
||
min_value=0.0,
|
||
max_value=2.0,
|
||
value=temp_value,
|
||
step=0.05,
|
||
)
|
||
llm_timeout = st.number_input(
|
||
"请求超时时间 (秒)",
|
||
min_value=5,
|
||
max_value=120,
|
||
value=timeout_value,
|
||
step=5,
|
||
)
|
||
|
||
strategy_options = ["single", "majority", "leader"]
|
||
try:
|
||
strategy_index = strategy_options.index(llm_cfg.strategy)
|
||
except ValueError:
|
||
strategy_index = 0
|
||
selected_strategy = st.selectbox("LLM 推理策略", strategy_options, index=strategy_index)
|
||
majority_threshold = st.number_input(
|
||
"多数投票门槛",
|
||
min_value=1,
|
||
max_value=10,
|
||
value=int(llm_cfg.majority_threshold),
|
||
step=1,
|
||
format="%d",
|
||
)
|
||
|
||
existing_api_keys = {ep.provider: ep.api_key or None for ep in llm_cfg.ensemble}
|
||
|
||
available_providers = sorted(DEFAULT_LLM_MODEL_OPTIONS.keys())
|
||
ensemble_rows = [
|
||
{
|
||
"provider": ep.provider or "",
|
||
"model": ep.model or DEFAULT_LLM_MODELS.get(ep.provider, DEFAULT_LLM_MODELS["ollama"]),
|
||
"base_url": ep.base_url or DEFAULT_LLM_BASE_URLS.get(ep.provider, ""),
|
||
"api_key": "***" if ep.api_key else "",
|
||
"temperature": float(ep.temperature),
|
||
"timeout": float(ep.timeout),
|
||
}
|
||
for ep in llm_cfg.ensemble
|
||
] or [
|
||
{
|
||
"provider": "",
|
||
"model": "",
|
||
"base_url": "",
|
||
"api_key": "",
|
||
"temperature": provider_default_temp,
|
||
"timeout": provider_default_timeout,
|
||
}
|
||
]
|
||
|
||
edited = st.data_editor(
|
||
ensemble_rows,
|
||
num_rows="dynamic",
|
||
key="llm_ensemble_editor",
|
||
column_config={
|
||
"provider": st.column_config.SelectboxColumn(
|
||
"Provider",
|
||
options=available_providers,
|
||
help="选择 LLM 供应商"
|
||
),
|
||
"model": st.column_config.TextColumn("模型", help="留空时使用该 Provider 的默认模型"),
|
||
"base_url": st.column_config.TextColumn("Base URL", help="留空时使用默认地址"),
|
||
"api_key": st.column_config.TextColumn("API Key", help="留空表示使用环境变量或不配置"),
|
||
"temperature": st.column_config.NumberColumn("温度", min_value=0.0, max_value=2.0, step=0.05),
|
||
"timeout": st.column_config.NumberColumn("超时(秒)", min_value=5.0, max_value=120.0, step=5.0),
|
||
},
|
||
hide_index=True,
|
||
use_container_width=True,
|
||
)
|
||
if hasattr(edited, "to_dict"):
|
||
ensemble_rows = edited.to_dict("records")
|
||
else:
|
||
ensemble_rows = edited
|
||
|
||
if st.button("保存 LLM 设置"):
|
||
primary.provider = selected_provider
|
||
primary.model = chosen_model
|
||
primary.base_url = llm_base.strip() or DEFAULT_LLM_BASE_URLS.get(selected_provider)
|
||
primary.temperature = llm_temperature
|
||
primary.timeout = llm_timeout
|
||
api_key_value = llm_api_key.strip()
|
||
if api_key_value:
|
||
primary.api_key = api_key_value
|
||
|
||
new_ensemble: List[LLMEndpoint] = []
|
||
for row in ensemble_rows:
|
||
provider = (row.get("provider") or "").strip().lower()
|
||
if not provider:
|
||
continue
|
||
provider_defaults = DEFAULT_LLM_MODEL_OPTIONS.get(provider, {})
|
||
default_model = DEFAULT_LLM_MODELS.get(provider, DEFAULT_LLM_MODELS["ollama"])
|
||
default_base = DEFAULT_LLM_BASE_URLS.get(provider)
|
||
temp_default = float(provider_defaults.get("temperature", 0.2))
|
||
timeout_default = float(provider_defaults.get("timeout", 30.0))
|
||
|
||
model_val = (row.get("model") or "").strip() or default_model
|
||
base_val = (row.get("base_url") or "").strip() or default_base
|
||
api_raw = (row.get("api_key") or "").strip()
|
||
if api_raw == "***":
|
||
api_value = existing_api_keys.get(provider)
|
||
else:
|
||
api_value = api_raw or None
|
||
|
||
temp_val = row.get("temperature")
|
||
timeout_val = row.get("timeout")
|
||
endpoint = LLMEndpoint(
|
||
provider=provider,
|
||
model=model_val,
|
||
base_url=base_val,
|
||
api_key=api_value,
|
||
temperature=float(temp_val) if temp_val is not None else temp_default,
|
||
timeout=float(timeout_val) if timeout_val is not None else timeout_default,
|
||
)
|
||
new_ensemble.append(endpoint)
|
||
|
||
llm_cfg.ensemble = new_ensemble
|
||
llm_cfg.strategy = selected_strategy
|
||
llm_cfg.majority_threshold = int(majority_threshold)
|
||
save_config()
|
||
LOGGER.info("LLM 配置已更新：%s", llm_config_snapshot(), extra=LOG_EXTRA)
|
||
st.success("LLM 设置已保存，仅在当前会话生效。")
|
||
st.json(llm_config_snapshot())
|
||
|
||
st.divider()
|
||
st.subheader("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,
|
||
"primary_provider": (dept.llm.primary.provider or "ollama"),
|
||
"primary_model": dept.llm.primary.model or "",
|
||
"ensemble_size": len(dept.llm.ensemble),
|
||
}
|
||
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",
|
||
use_container_width=True,
|
||
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),
|
||
help="single=单模型, majority=多数投票, leader=顾问-决策者模式",
|
||
),
|
||
"primary_provider": st.column_config.SelectboxColumn(
|
||
"主模型 Provider",
|
||
options=sorted(DEFAULT_LLM_MODEL_OPTIONS.keys()),
|
||
),
|
||
"primary_model": st.column_config.TextColumn("主模型名称"),
|
||
"ensemble_size": st.column_config.NumberColumn(
|
||
"协作模型数量",
|
||
disabled=True,
|
||
help="在 config.json 中编辑 ensemble 详情",
|
||
),
|
||
},
|
||
)
|
||
|
||
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):
|
||
existing.weight = existing.weight
|
||
|
||
strategy_val = (row.get("strategy") or existing.llm.strategy).lower()
|
||
if strategy_val in ALLOWED_LLM_STRATEGIES:
|
||
existing.llm.strategy = strategy_val
|
||
|
||
provider_before = existing.llm.primary.provider or ""
|
||
provider_val = (row.get("primary_provider") or provider_before or "ollama").lower()
|
||
existing.llm.primary.provider = provider_val
|
||
|
||
model_val = (row.get("primary_model") or "").strip()
|
||
if model_val:
|
||
existing.llm.primary.model = model_val
|
||
else:
|
||
existing.llm.primary.model = DEFAULT_LLM_MODELS.get(provider_val, existing.llm.primary.model)
|
||
|
||
if provider_before != provider_val:
|
||
default_base = DEFAULT_LLM_BASE_URLS.get(provider_val)
|
||
existing.llm.primary.base_url = default_base or existing.llm.primary.base_url
|
||
|
||
existing.llm.primary.__post_init__()
|
||
updated_departments[code] = existing
|
||
|
||
if updated_departments:
|
||
cfg.departments = updated_departments
|
||
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()
|
||
save_config()
|
||
st.success("已恢复默认部门配置。")
|
||
st.experimental_rerun()
|
||
|
||
st.caption("部门协作模型（ensemble）请在 config.json 中手动编辑，UI 将在后续版本补充。")
|
||
|
||
|
||
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}")
|
||
|
||
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()
|
||
days = int(st.number_input("检查窗口（天数）", min_value=30, max_value=1095, 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("执行开机检查"):
|
||
LOGGER.info("点击执行开机检查按钮", 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("开机检查进度：%s -> %.2f", message, value, extra=LOG_EXTRA)
|
||
|
||
with st.spinner("正在执行开机检查..."):
|
||
try:
|
||
report = run_boot_check(
|
||
days=days,
|
||
progress_hook=hook,
|
||
force_refresh=force_refresh,
|
||
)
|
||
LOGGER.info("开机检查成功", extra=LOG_EXTRA)
|
||
st.success("开机检查完成，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("开机检查失败", extra=LOG_EXTRA)
|
||
st.error(f"开机检查失败：{exc}")
|
||
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="多智能体投资助理", layout="wide")
|
||
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()
|