605 lines
23 KiB
Python
605 lines
23 KiB
Python
"""Streamlit UI scaffold for the investment assistant."""
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from __future__ import annotations
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import json
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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 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 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.llm.explain import make_human_card
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from app.utils.config import (
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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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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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st.write("待接入候选池筛选与多智能体决策结果。")
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sample = make_human_card("000001.SZ", "2025-01-01", {"decisions": []})
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LOGGER.debug("示例卡片内容：%s", sample, extra=LOG_EXTRA)
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st.json(sample)
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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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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(
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"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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)
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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={
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"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(
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"回测完成：nav_records=%s trades=%s",
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len(result.nav_series),
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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)
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st.error(f"回测执行失败：{exc}")
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def render_settings() -> None:
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LOGGER.info("渲染设置页面", extra=LOG_EXTRA)
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st.header("数据与设置")
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cfg = get_config()
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LOGGER.debug("当前 TuShare Token 是否已配置=%s", bool(cfg.tushare_token), extra=LOG_EXTRA)
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token = st.text_input("TuShare Token", value=cfg.tushare_token or "", type="password")
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if st.button("保存设置"):
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LOGGER.info("保存设置按钮被点击", extra=LOG_EXTRA)
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cfg.tushare_token = token.strip() or None
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LOGGER.info("TuShare Token 更新，是否为空=%s", cfg.tushare_token is None, extra=LOG_EXTRA)
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save_config()
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st.success("设置已保存，仅在当前会话生效。")
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st.write("新闻源开关与数据库备份将在此配置。")
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st.divider()
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st.subheader("LLM 设置")
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llm_cfg = cfg.llm
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primary = llm_cfg.primary
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providers = sorted(DEFAULT_LLM_MODELS.keys())
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try:
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provider_index = providers.index((primary.provider or "ollama").lower())
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except ValueError:
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provider_index = 0
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selected_provider = st.selectbox("LLM Provider", providers, index=provider_index)
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provider_info = DEFAULT_LLM_MODEL_OPTIONS.get(selected_provider, {})
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model_options = provider_info.get("models", [])
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custom_model_label = "自定义模型"
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default_model_hint = DEFAULT_LLM_MODELS.get(selected_provider, DEFAULT_LLM_MODELS["ollama"])
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if model_options:
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options_with_custom = model_options + [custom_model_label]
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if primary.model in model_options:
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model_index = options_with_custom.index(primary.model)
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else:
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model_index = len(options_with_custom) - 1
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selected_model_option = st.selectbox(
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"LLM 模型",
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options_with_custom,
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index=model_index,
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help=f"可选模型：{', '.join(model_options)}",
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)
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if selected_model_option == custom_model_label:
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custom_model_value = st.text_input(
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"自定义模型名称",
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value=primary.model if primary.model not in model_options else "",
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)
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else:
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custom_model_value = selected_model_option
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else:
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custom_model_value = st.text_input(
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"LLM 模型",
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value=primary.model or default_model_hint,
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help="未预设该 Provider 的模型列表，请手动填写",
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)
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selected_model_option = custom_model_label
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default_base_hint = DEFAULT_LLM_BASE_URLS.get(selected_provider, "")
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provider_default_temp = float(provider_info.get("temperature", 0.2))
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provider_default_timeout = int(provider_info.get("timeout", 30.0))
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if primary.provider == selected_provider:
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base_value = primary.base_url or default_base_hint or ""
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temp_value = float(primary.temperature)
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timeout_value = int(primary.timeout)
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else:
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base_value = default_base_hint or ""
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temp_value = provider_default_temp
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timeout_value = provider_default_timeout
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llm_base = st.text_input(
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"LLM Base URL (可选)",
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value=base_value,
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help=f"默认推荐：{default_base_hint or '按供应商要求填写'}",
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)
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llm_api_key = st.text_input("LLM API Key (OpenAI 类需要)", value=primary.api_key or "", type="password")
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llm_temperature = st.slider(
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"LLM 温度",
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min_value=0.0,
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max_value=2.0,
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value=temp_value,
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step=0.05,
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)
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llm_timeout = st.number_input(
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"请求超时时间 (秒)",
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min_value=5,
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max_value=120,
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value=timeout_value,
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step=5,
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)
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strategy_options = ["single", "majority"]
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try:
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strategy_index = strategy_options.index(llm_cfg.strategy)
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except ValueError:
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strategy_index = 0
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selected_strategy = st.selectbox("LLM 推理策略", strategy_options, index=strategy_index)
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majority_threshold = st.number_input(
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"多数投票门槛",
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min_value=1,
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max_value=10,
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value=int(llm_cfg.majority_threshold),
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step=1,
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format="%d",
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)
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ensemble_display = []
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for endpoint in llm_cfg.ensemble:
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data = asdict(endpoint)
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if data.get("api_key"):
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data["api_key"] = ""
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ensemble_display.append(data)
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ensemble_text = st.text_area(
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"LLM 集群配置 (JSON 数组)",
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value=json.dumps(ensemble_display or [], ensure_ascii=False, indent=2),
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height=220,
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)
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if st.button("保存 LLM 设置"):
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original_provider = primary.provider
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original_model = primary.model
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primary.provider = selected_provider
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if model_options:
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if selected_model_option == custom_model_label:
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model_input = custom_model_value.strip()
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primary.model = model_input or DEFAULT_LLM_MODELS.get(
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selected_provider, DEFAULT_LLM_MODELS["ollama"]
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)
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else:
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primary.model = selected_model_option
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else:
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primary.model = custom_model_value.strip() or DEFAULT_LLM_MODELS.get(
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selected_provider, DEFAULT_LLM_MODELS["ollama"]
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)
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primary.base_url = llm_base.strip() or None
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primary.temperature = llm_temperature
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primary.timeout = llm_timeout
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api_key_value = llm_api_key.strip()
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primary.api_key = api_key_value or None
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try:
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parsed = json.loads(ensemble_text or "[]")
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if not isinstance(parsed, list):
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raise ValueError("ensemble 配置必须是数组")
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except Exception as exc: # noqa: BLE001
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LOGGER.exception("解析 LLM 集群配置失败", extra=LOG_EXTRA)
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st.error(f"LLM 集群配置解析失败：{exc}")
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else:
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new_ensemble: List[LLMEndpoint] = []
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invalid = False
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for item in parsed:
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if not isinstance(item, dict):
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st.error("LLM 集群配置中的每个元素都必须是对象")
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invalid = True
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break
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fields = {key: item.get(key) for key in ("provider", "model", "base_url", "api_key", "temperature", "timeout")}
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endpoint = LLMEndpoint(**{k: v for k, v in fields.items() if v not in (None, "")})
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if not endpoint.provider:
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endpoint.provider = "ollama"
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new_ensemble.append(endpoint)
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if not invalid:
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llm_cfg.ensemble = new_ensemble
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llm_cfg.strategy = selected_strategy
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llm_cfg.majority_threshold = int(majority_threshold)
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save_config()
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LOGGER.info("LLM 配置已更新：%s", llm_config_snapshot(), extra=LOG_EXTRA)
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st.success("LLM 设置已保存，仅在当前会话生效。")
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st.json(llm_config_snapshot())
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def render_tests() -> None:
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LOGGER.info("渲染自检页面", extra=LOG_EXTRA)
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st.header("自检测试")
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st.write("用于快速检查数据库与数据拉取是否正常工作。")
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if st.button("测试数据库初始化"):
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LOGGER.info("点击测试数据库初始化按钮", extra=LOG_EXTRA)
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with st.spinner("正在检查数据库..."):
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result = initialize_database()
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if result.skipped:
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LOGGER.info("数据库已存在，无需初始化", extra=LOG_EXTRA)
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st.success("数据库已存在，检查通过。")
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else:
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LOGGER.info("数据库初始化完成，执行语句数=%s", result.executed, extra=LOG_EXTRA)
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st.success(f"数据库初始化完成，共执行 {result.executed} 条语句。")
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st.divider()
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if st.button("测试 TuShare 拉取（示例 2024-01-01 至 2024-01-03）"):
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LOGGER.info("点击示例 TuShare 拉取按钮", extra=LOG_EXTRA)
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with st.spinner("正在调用 TuShare 接口..."):
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try:
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run_ingestion(
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FetchJob(
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name="streamlit_self_test",
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start=date(2024, 1, 1),
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end=date(2024, 1, 3),
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ts_codes=("000001.SZ",),
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),
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include_limits=False,
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)
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LOGGER.info("示例 TuShare 拉取成功", extra=LOG_EXTRA)
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st.success("TuShare 示例拉取完成，数据已写入数据库。")
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except Exception as exc: # noqa: BLE001
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LOGGER.exception("示例 TuShare 拉取失败", extra=LOG_EXTRA)
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st.error(f"拉取失败：{exc}")
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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。")
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st.divider()
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days = int(st.number_input("检查窗口（天数）", min_value=30, max_value=1095, value=365, step=30))
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LOGGER.debug("检查窗口天数=%s", days, extra=LOG_EXTRA)
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cfg = get_config()
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force_refresh = st.checkbox(
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"强制刷新数据（关闭增量跳过）",
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value=cfg.force_refresh,
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help="勾选后将重新拉取所选区间全部数据",
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)
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if force_refresh != cfg.force_refresh:
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cfg.force_refresh = force_refresh
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LOGGER.info("更新 force_refresh=%s", force_refresh, extra=LOG_EXTRA)
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save_config()
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if st.button("执行开机检查"):
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LOGGER.info("点击执行开机检查按钮", extra=LOG_EXTRA)
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progress_bar = st.progress(0.0)
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status_placeholder = st.empty()
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log_placeholder = st.empty()
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messages: list[str] = []
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def hook(message: str, value: float) -> None:
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progress_bar.progress(min(max(value, 0.0), 1.0))
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status_placeholder.write(message)
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messages.append(message)
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LOGGER.debug("开机检查进度：%s -> %.2f", message, value, extra=LOG_EXTRA)
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with st.spinner("正在执行开机检查..."):
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try:
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report = run_boot_check(
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days=days,
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progress_hook=hook,
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force_refresh=force_refresh,
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)
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LOGGER.info("开机检查成功", extra=LOG_EXTRA)
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st.success("开机检查完成，以下为数据覆盖摘要。")
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st.json(report.to_dict())
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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()
|