"""Streamlit UI scaffold for the investment assistant.""" from __future__ import annotations import sys from dataclasses import asdict from datetime import date, timedelta from pathlib import Path from typing import Dict, List ROOT = Path(__file__).resolve().parents[2] if str(ROOT) not in sys.path: sys.path.insert(0, str(ROOT)) import json import pandas as pd import plotly.express as px import plotly.graph_objects as go import streamlit as st from app.backtest.engine import BtConfig, run_backtest from app.data.schema import initialize_database from app.ingest.checker import run_boot_check from app.ingest.tushare import FetchJob, run_ingestion from app.llm.client import llm_config_snapshot, run_llm from app.utils.config import ( ALLOWED_LLM_STRATEGIES, DEFAULT_LLM_BASE_URLS, DEFAULT_LLM_MODEL_OPTIONS, DEFAULT_LLM_MODELS, DepartmentSettings, LLMEndpoint, get_config, save_config, ) from app.utils.db import db_session from app.utils.logging import get_logger LOGGER = get_logger(__name__) LOG_EXTRA = {"stage": "ui"} def _load_stock_options(limit: int = 500) -> list[str]: try: with db_session(read_only=True) as conn: rows = conn.execute( "SELECT ts_code, name FROM stock_basic WHERE list_status = 'L' ORDER BY ts_code" ).fetchall() except Exception: LOGGER.exception("加载股票列表失败", extra=LOG_EXTRA) return [] options: list[str] = [] for row in rows[:limit]: code = row["ts_code"] name = row["name"] or "" label = f"{code} | {name}" if name else code options.append(label) LOGGER.info("加载股票选项完成，数量=%s", len(options), extra=LOG_EXTRA) return options def _parse_ts_code(selection: str) -> str: return selection.split(' | ')[0].strip().upper() def _load_daily_frame(ts_code: str, start: date, end: date) -> pd.DataFrame: LOGGER.info( "加载行情数据：ts_code=%s start=%s end=%s", ts_code, start, end, extra=LOG_EXTRA, ) start_str = start.strftime('%Y%m%d') end_str = end.strftime('%Y%m%d') range_query = ( "SELECT trade_date, open, high, low, close, vol, amount " "FROM daily WHERE ts_code = ? AND trade_date BETWEEN ? AND ? ORDER BY trade_date" ) fallback_query = ( "SELECT trade_date, open, high, low, close, vol, amount " "FROM daily WHERE ts_code = ? ORDER BY trade_date DESC LIMIT 200" ) with db_session(read_only=True) as conn: df = pd.read_sql_query(range_query, conn, params=(ts_code, start_str, end_str)) if df.empty: df = pd.read_sql_query(fallback_query, conn, params=(ts_code,)) if df.empty: LOGGER.warning( "行情数据为空：ts_code=%s start=%s end=%s", ts_code, start, end, extra=LOG_EXTRA, ) return df df = df.sort_values('trade_date') df['trade_date'] = pd.to_datetime(df['trade_date']) df.set_index('trade_date', inplace=True) LOGGER.info("行情数据加载完成：条数=%s", len(df), extra=LOG_EXTRA) return df def render_today_plan() -> None: LOGGER.info("渲染今日计划页面", extra=LOG_EXTRA) st.header("今日计划") try: with db_session(read_only=True) as conn: date_rows = conn.execute( """ SELECT DISTINCT trade_date FROM agent_utils ORDER BY trade_date DESC LIMIT 30 """ ).fetchall() except Exception: # noqa: BLE001 LOGGER.exception("加载 agent_utils 失败", extra=LOG_EXTRA) st.warning("暂未写入部门/代理决策，请先运行回测或策略评估流程。") return trade_dates = [row["trade_date"] for row in date_rows] if not trade_dates: st.info("暂无决策记录，完成一次回测后即可在此查看部门意见与投票结果。") return trade_date = st.selectbox("交易日", trade_dates, index=0) with db_session(read_only=True) as conn: code_rows = conn.execute( """ SELECT DISTINCT ts_code FROM agent_utils WHERE trade_date = ? ORDER BY ts_code """, (trade_date,), ).fetchall() symbols = [row["ts_code"] for row in code_rows] if not symbols: st.info("所选交易日暂无 agent_utils 记录。") return ts_code = st.selectbox("标的", symbols, index=0) with db_session(read_only=True) as conn: rows = conn.execute( """ SELECT agent, action, utils, feasible, weight FROM agent_utils WHERE trade_date = ? AND ts_code = ? ORDER BY CASE WHEN agent = 'global' THEN 1 ELSE 0 END, agent """, (trade_date, ts_code), ).fetchall() if not rows: st.info("未查询到详细决策记录，稍后再试。") return try: feasible_actions = json.loads(rows[0]["feasible"] or "[]") except (KeyError, TypeError, json.JSONDecodeError): feasible_actions = [] global_info = None dept_records: List[Dict[str, object]] = [] agent_records: List[Dict[str, object]] = [] for item in rows: agent_name = item["agent"] action = item["action"] weight = float(item["weight"] or 0.0) try: utils = json.loads(item["utils"] or "{}") except json.JSONDecodeError: utils = {} if agent_name == "global": global_info = { "action": action, "confidence": float(utils.get("_confidence", 0.0)), "target_weight": float(utils.get("_target_weight", 0.0)), "department_votes": utils.get("_department_votes", {}), "requires_review": bool(utils.get("_requires_review", False)), } continue if agent_name.startswith("dept_"): code = agent_name.split("dept_", 1)[-1] signals = utils.get("_signals", []) risks = utils.get("_risks", []) dept_records.append( { "部门": code, "行动": action, "信心": float(utils.get("_confidence", 0.0)), "权重": weight, "摘要": utils.get("_summary", ""), "核心信号": "；".join(signals) if isinstance(signals, list) else signals, "风险提示": "；".join(risks) if isinstance(risks, list) else risks, } ) else: score_map = { key: float(val) for key, val in utils.items() if not str(key).startswith("_") } agent_records.append( { "代理": agent_name, "建议动作": action, "权重": weight, "SELL": score_map.get("SELL", 0.0), "HOLD": score_map.get("HOLD", 0.0), "BUY_S": score_map.get("BUY_S", 0.0), "BUY_M": score_map.get("BUY_M", 0.0), "BUY_L": score_map.get("BUY_L", 0.0), } ) if feasible_actions: st.caption(f"可行操作集合：{', '.join(feasible_actions)}") st.subheader("全局策略") if global_info: col1, col2, col3 = st.columns(3) col1.metric("最终行动", global_info["action"]) col2.metric("信心", f"{global_info['confidence']:.2f}") col3.metric("目标权重", f"{global_info['target_weight']:+.2%}") if global_info["department_votes"]: st.json(global_info["department_votes"]) if global_info["requires_review"]: st.warning("部门分歧较大，已标记为需人工复核。") else: st.info("暂未写入全局策略摘要。") st.subheader("部门意见") if dept_records: dept_df = pd.DataFrame(dept_records) st.dataframe(dept_df, use_container_width=True, hide_index=True) else: st.info("暂无部门记录。") st.subheader("代理评分") if agent_records: agent_df = pd.DataFrame(agent_records) st.dataframe(agent_df, use_container_width=True, hide_index=True) else: 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。") def render_backtest() -> None: LOGGER.info("渲染回测页面", extra=LOG_EXTRA) st.header("回测与复盘") st.write("在此运行回测、展示净值曲线与代理贡献。") default_start = date(2020, 1, 1) default_end = date(2020, 3, 31) LOGGER.debug( "回测默认参数：start=%s end=%s universe=%s target=%s stop=%s hold_days=%s", default_start, default_end, "000001.SZ", 0.035, -0.015, 10, extra=LOG_EXTRA, ) col1, col2 = st.columns(2) start_date = col1.date_input("开始日期", value=default_start) end_date = col2.date_input("结束日期", value=default_end) universe_text = st.text_input("股票列表（逗号分隔）", value="000001.SZ") target = st.number_input("目标收益（例：0.035 表示 3.5%）", value=0.035, step=0.005, format="%.3f") stop = st.number_input("止损收益（例：-0.015 表示 -1.5%）", value=-0.015, step=0.005, format="%.3f") hold_days = st.number_input("持有期（交易日）", value=10, step=1) LOGGER.debug( "当前回测表单输入：start=%s end=%s universe_text=%s target=%.3f stop=%.3f hold_days=%s", start_date, end_date, universe_text, target, stop, hold_days, extra=LOG_EXTRA, ) if st.button("运行回测"): LOGGER.info("用户点击运行回测按钮", extra=LOG_EXTRA) 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") 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()