1026 lines
42 KiB
Python
1026 lines
42 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, Optional
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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 requests
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from requests.exceptions import RequestException
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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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LLMProvider,
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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 _discover_provider_models(provider: LLMProvider, base_override: str = "", api_override: Optional[str] = None) -> tuple[list[str], Optional[str]]:
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"""Attempt to query provider API and return available model ids."""
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base_url = (base_override or provider.base_url or DEFAULT_LLM_BASE_URLS.get(provider.key, "")).strip()
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if not base_url:
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return [], "请先填写 Base URL"
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timeout = float(provider.default_timeout or 30.0)
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mode = provider.mode or ("ollama" if provider.key == "ollama" else "openai")
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try:
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if mode == "ollama":
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url = base_url.rstrip('/') + "/api/tags"
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response = requests.get(url, timeout=timeout)
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response.raise_for_status()
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data = response.json()
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models = []
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for item in data.get("models", []) or data.get("data", []):
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name = item.get("name") or item.get("model") or item.get("tag")
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if name:
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models.append(str(name).strip())
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return sorted(set(models)), None
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api_key = (api_override or provider.api_key or "").strip()
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if not api_key:
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return [], "缺少 API Key"
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url = base_url.rstrip('/') + "/v1/models"
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headers = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json",
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}
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response = requests.get(url, headers=headers, timeout=timeout)
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response.raise_for_status()
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payload = response.json()
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models = [
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str(item.get("id")).strip()
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for item in payload.get("data", [])
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if item.get("id")
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]
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return sorted(set(models)), None
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except RequestException as exc: # noqa: BLE001
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return [], f"HTTP 错误：{exc}"
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except Exception as exc: # noqa: BLE001
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return [], f"解析失败：{exc}"
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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。")
|
||
else:
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st.info("暂未写入全局策略摘要。")
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||
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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。")
|
||
|
||
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:
|
||
LOGGER.info("渲染回测页面", extra=LOG_EXTRA)
|
||
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。")
|
||
|
||
default_start = date(2020, 1, 1)
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||
default_end = date(2020, 3, 31)
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||
LOGGER.debug(
|
||
"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,
|
||
default_end,
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||
"000001.SZ",
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||
0.035,
|
||
-0.015,
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10,
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||
extra=LOG_EXTRA,
|
||
)
|
||
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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)
|
||
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")
|
||
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)
|
||
with st.spinner("正在执行回测..."):
|
||
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)
|
||
LOGGER.info(
|
||
"回测完成：nav_records=%s trades=%s",
|
||
len(result.nav_series),
|
||
len(result.trades),
|
||
extra=LOG_EXTRA,
|
||
)
|
||
st.success("回测执行完成，详见回测结果摘要。")
|
||
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)
|
||
st.error(f"回测执行失败：{exc}")
|
||
|
||
|
||
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。")
|
||
|
||
model_choice_key = f"{default_model_key}_choice"
|
||
if provider_cfg.models:
|
||
options = provider_cfg.models + ["自定义"]
|
||
default_choice = provider_cfg.default_model if provider_cfg.default_model in provider_cfg.models else "自定义"
|
||
model_choice = st.selectbox("默认模型", options, index=options.index(default_choice), key=model_choice_key)
|
||
if model_choice == "自定义":
|
||
default_model_val = st.text_input("自定义默认模型", value=provider_cfg.default_model or "", key=default_model_key).strip() or None
|
||
else:
|
||
default_model_val = model_choice
|
||
else:
|
||
default_model_val = st.text_input("默认模型", value=provider_cfg.default_model or "", key=default_model_key).strip() or None
|
||
mode_val = st.selectbox("调用模式", ["openai", "ollama"], index=0 if provider_cfg.mode == "openai" else 1, key=mode_key)
|
||
temp_val = st.slider("默认温度", min_value=0.0, max_value=2.0, value=float(provider_cfg.default_temperature), step=0.05, key=temp_key)
|
||
timeout_val = st.number_input("默认超时(秒)", min_value=5, max_value=300, value=int(provider_cfg.default_timeout or 30), step=5, key=timeout_key)
|
||
prompt_template_val = st.text_area("默认 Prompt 模板（可选，使用 {prompt} 占位）", value=provider_cfg.prompt_template or "", key=prompt_key, height=120)
|
||
enabled_val = st.checkbox("启用", value=provider_cfg.enabled, key=enabled_key)
|
||
|
||
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",
|
||
use_container_width=True,
|
||
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",
|
||
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)),
|
||
"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}")
|
||
|
||
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()
|