"""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, Optional 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 requests from requests.exceptions import RequestException 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, LLMProvider, 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 _discover_provider_models(provider: LLMProvider, base_override: str = "", api_override: Optional[str] = None) -> tuple[list[str], Optional[str]]: """Attempt to query provider API and return available model ids.""" base_url = (base_override or provider.base_url or DEFAULT_LLM_BASE_URLS.get(provider.key, "")).strip() if not base_url: return [], "请先填写 Base URL" timeout = float(provider.default_timeout or 30.0) mode = provider.mode or ("ollama" if provider.key == "ollama" else "openai") try: if mode == "ollama": url = base_url.rstrip('/') + "/api/tags" response = requests.get(url, timeout=timeout) response.raise_for_status() data = response.json() models = [] for item in data.get("models", []) or data.get("data", []): name = item.get("name") or item.get("model") or item.get("tag") if name: models.append(str(name).strip()) return sorted(set(models)), None api_key = (api_override or provider.api_key or "").strip() if not api_key: return [], "缺少 API Key" url = base_url.rstrip('/') + "/v1/models" headers = { "Authorization": f"Bearer {api_key}", "Content-Type": "application/json", } response = requests.get(url, headers=headers, timeout=timeout) response.raise_for_status() payload = response.json() models = [ str(item.get("id")).strip() for item in payload.get("data", []) if item.get("id") ] return sorted(set(models)), None except RequestException as exc: # noqa: BLE001 return [], f"HTTP 错误：{exc}" except Exception as exc: # noqa: BLE001 return [], f"解析失败：{exc}" def _load_stock_options(limit: int = 500) -> list[str]: try: with db_session(read_only=True) as conn: rows = conn.execute( "SELECT ts_code, name FROM stock_basic WHERE list_status = 'L' ORDER BY ts_code" ).fetchall() except Exception: LOGGER.exception("加载股票列表失败", extra=LOG_EXTRA) return [] options: list[str] = [] for row in rows[:limit]: code = row["ts_code"] name = row["name"] or "" label = f"{code} | {name}" if name else code options.append(label) LOGGER.info("加载股票选项完成，数量=%s", len(options), extra=LOG_EXTRA) return options def _parse_ts_code(selection: str) -> str: return selection.split(' | ')[0].strip().upper() def _load_daily_frame(ts_code: str, start: date, end: date) -> pd.DataFrame: LOGGER.info( "加载行情数据：ts_code=%s start=%s end=%s", ts_code, start, end, extra=LOG_EXTRA, ) start_str = start.strftime('%Y%m%d') end_str = end.strftime('%Y%m%d') range_query = ( "SELECT trade_date, open, high, low, close, vol, amount " "FROM daily WHERE ts_code = ? AND trade_date BETWEEN ? AND ? ORDER BY trade_date" ) fallback_query = ( "SELECT trade_date, open, high, low, close, vol, amount " "FROM daily WHERE ts_code = ? ORDER BY trade_date DESC LIMIT 200" ) with db_session(read_only=True) as conn: df = pd.read_sql_query(range_query, conn, params=(ts_code, start_str, end_str)) if df.empty: df = pd.read_sql_query(fallback_query, conn, params=(ts_code,)) if df.empty: LOGGER.warning( "行情数据为空：ts_code=%s start=%s end=%s", ts_code, start, end, extra=LOG_EXTRA, ) return df df = df.sort_values('trade_date') df['trade_date'] = pd.to_datetime(df['trade_date']) df.set_index('trade_date', inplace=True) LOGGER.info("行情数据加载完成：条数=%s", len(df), extra=LOG_EXTRA) return df def 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") 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()