import streamlit as st
import plotly.graph_objects as go
import plotly.express as px
import pandas as pd
import json
from datetime import datetime
import time
import base64
import os
from stock_data import StockDataFetcher
from ai_agents import StockAnalysisAgents
from pdf_generator import display_pdf_export_section
from database import db
from monitor_manager import display_monitor_manager, get_monitor_summary
from monitor_service import monitor_service
from notification_service import notification_service
from config_manager import config_manager
from main_force_ui import display_main_force_selector
from sector_strategy_ui import display_sector_strategy
from longhubang_ui import display_longhubang
from smart_monitor_ui import smart_monitor_ui
# 页面配置
st.set_page_config(
page_title="复合多AI智能体股票团队分析系统",
page_icon="📈",
layout="wide",
initial_sidebar_state="expanded"
)
# 模型选择器
def model_selector():
"""模型选择器"""
st.sidebar.markdown("---")
st.sidebar.subheader("🤖 AI模型选择")
model_options = {
"deepseek-chat": "DeepSeek Chat (默认)",
"deepseek-reasoner": "DeepSeek Reasoner (推理增强)"
}
selected_model = st.sidebar.selectbox(
"选择AI模型",
options=list(model_options.keys()),
format_func=lambda x: model_options[x],
help="DeepSeek Reasoner提供更强的推理能力，但响应时间可能更长"
)
return selected_model
# 自定义CSS样式 - 专业版
st.markdown("""
""", unsafe_allow_html=True)
def main():
# 顶部标题栏
st.markdown("""
📈 复合多AI智能体股票团队分析系统
基于DeepSeek的专业量化投资分析平台 | Multi-Agent Stock Analysis System
""", unsafe_allow_html=True)
# 侧边栏
with st.sidebar:
# 快捷导航 - 移到顶部
st.markdown("### 🔍 功能导航")
# 🏠 单股分析（首页）
if st.button("🏠 股票分析", width='stretch', key="nav_home", help="返回首页，进行单只股票的深度分析"):
# 清除所有功能页面标志
for key in ['show_history', 'show_monitor', 'show_config', 'show_main_force',
'show_sector_strategy', 'show_longhubang', 'show_portfolio']:
if key in st.session_state:
del st.session_state[key]
st.markdown("---")
# 🎯 选股板块
with st.expander("🎯 选股板块", expanded=False):
st.markdown("**根据不同策略筛选优质股票**")
if st.button("💰 主力选股", width='stretch', key="nav_main_force", help="基于主力资金流向的选股策略"):
st.session_state.show_main_force = True
for key in ['show_history', 'show_monitor', 'show_config', 'show_sector_strategy',
'show_longhubang', 'show_portfolio']:
if key in st.session_state:
del st.session_state[key]
# 📊 策略分析
with st.expander("📊 策略分析", expanded=False):
st.markdown("**AI驱动的板块和龙虎榜策略**")
if st.button("🎯 智策板块", width='stretch', key="nav_sector_strategy", help="AI板块策略分析"):
st.session_state.show_sector_strategy = True
for key in ['show_history', 'show_monitor', 'show_config', 'show_main_force',
'show_longhubang', 'show_portfolio', 'show_smart_monitor']:
if key in st.session_state:
del st.session_state[key]
if st.button("🐉 智瞰龙虎", width='stretch', key="nav_longhubang", help="龙虎榜深度分析"):
st.session_state.show_longhubang = True
for key in ['show_history', 'show_monitor', 'show_config', 'show_main_force',
'show_sector_strategy', 'show_portfolio', 'show_smart_monitor']:
if key in st.session_state:
del st.session_state[key]
# 💼 投资管理
with st.expander("💼 投资管理", expanded=False):
st.markdown("**持仓跟踪与实时监测**")
if st.button("📊 持仓分析", width='stretch', key="nav_portfolio", help="投资组合分析与定时跟踪"):
st.session_state.show_portfolio = True
for key in ['show_history', 'show_monitor', 'show_config', 'show_main_force',
'show_sector_strategy', 'show_longhubang', 'show_smart_monitor']:
if key in st.session_state:
del st.session_state[key]
if st.button("🤖 AI盯盘", width='stretch', key="nav_smart_monitor", help="DeepSeek AI自动盯盘决策交易（支持A股T+1）"):
st.session_state.show_smart_monitor = True
for key in ['show_history', 'show_monitor', 'show_config', 'show_main_force',
'show_sector_strategy', 'show_longhubang', 'show_portfolio']:
if key in st.session_state:
del st.session_state[key]
if st.button("📡 实时监测", width='stretch', key="nav_monitor", help="价格监控与预警提醒"):
st.session_state.show_monitor = True
for key in ['show_history', 'show_main_force', 'show_longhubang', 'show_portfolio',
'show_config', 'show_sector_strategy', 'show_smart_monitor']:
if key in st.session_state:
del st.session_state[key]
st.markdown("---")
# 📖 历史记录
if st.button("📖 历史记录", width='stretch', key="nav_history", help="查看历史分析记录"):
st.session_state.show_history = True
for key in ['show_monitor', 'show_longhubang', 'show_portfolio', 'show_config',
'show_main_force', 'show_sector_strategy']:
if key in st.session_state:
del st.session_state[key]
# ⚙️ 环境配置
if st.button("⚙️ 环境配置", width='stretch', key="nav_config", help="系统设置与API配置"):
st.session_state.show_config = True
for key in ['show_history', 'show_monitor', 'show_main_force', 'show_sector_strategy',
'show_longhubang', 'show_portfolio']:
if key in st.session_state:
del st.session_state[key]
st.markdown("---")
# 系统配置
st.markdown("### ⚙️ 系统配置")
# API密钥检查
api_key_status = check_api_key()
if api_key_status:
st.success("✅ API已连接")
else:
st.error("❌ API未配置")
st.caption("请在.env中配置API密钥")
st.markdown("---")
# 模型选择器
selected_model = model_selector()
st.session_state.selected_model = selected_model
st.markdown("---")
# 系统状态面板
st.markdown("### 📊 系统状态")
monitor_status = "🟢 运行中" if monitor_service.running else "🔴 已停止"
st.markdown(f"**监测服务**: {monitor_status}")
try:
from monitor_db import monitor_db
stocks = monitor_db.get_monitored_stocks()
notifications = monitor_db.get_pending_notifications()
record_count = db.get_record_count()
st.markdown(f"**分析记录**: {record_count}条")
st.markdown(f"**监测股票**: {len(stocks)}只")
st.markdown(f"**待处理**: {len(notifications)}条")
except:
pass
st.markdown("---")
# 分析参数设置
st.markdown("### 📊 分析参数")
period = st.selectbox(
"数据周期",
["1y", "6mo", "3mo", "1mo"],
index=0,
help="选择历史数据的时间范围"
)
st.markdown("---")
# 帮助信息
with st.expander("💡 使用帮助"):
st.markdown("""
**股票代码格式**
- 🇨🇳 A股：6位数字（如600519）
- 🇭🇰 港股：1-5位数字（如700、00700）或HK前缀（如HK00700）
- 🇺🇸 美股：字母代码（如AAPL）
**功能说明**
- **股票分析**：AI团队深度分析个股
- **选股板块**：主力资金选股策略
- **策略分析**：智策板块、智瞰龙虎
- **投资管理**：持仓分析、实时监测
- **历史记录**：查看分析历史
**AI分析流程**
1. 数据获取 → 2. 技术分析
3. 基本面分析 → 4. 资金分析
5. 情绪数据(ARBR) → 6. 新闻(qstock)
7. AI团队分析 → 8. 团队讨论 → 9. 决策
""")
# 检查是否显示历史记录
if 'show_history' in st.session_state and st.session_state.show_history:
display_history_records()
return
# 检查是否显示监测面板
if 'show_monitor' in st.session_state and st.session_state.show_monitor:
display_monitor_manager()
return
# 检查是否显示主力选股
if 'show_main_force' in st.session_state and st.session_state.show_main_force:
display_main_force_selector()
return
# 检查是否显示智策板块
if 'show_sector_strategy' in st.session_state and st.session_state.show_sector_strategy:
display_sector_strategy()
return
# 检查是否显示智瞰龙虎
if 'show_longhubang' in st.session_state and st.session_state.show_longhubang:
display_longhubang()
return
# 检查是否显示AI盯盘
if 'show_smart_monitor' in st.session_state and st.session_state.show_smart_monitor:
smart_monitor_ui()
return
# 检查是否显示持仓分析
if 'show_portfolio' in st.session_state and st.session_state.show_portfolio:
from portfolio_ui import display_portfolio_manager
display_portfolio_manager()
return
# 检查是否显示环境配置
if 'show_config' in st.session_state and st.session_state.show_config:
display_config_manager()
return
# 主界面
# 添加单个/批量分析切换
col_mode1, col_mode2 = st.columns([1, 3])
with col_mode1:
analysis_mode = st.radio(
"分析模式",
["单个分析", "批量分析"],
horizontal=True,
help="单个分析：分析单只股票；批量分析：同时分析多只股票"
)
with col_mode2:
if analysis_mode == "批量分析":
batch_mode = st.radio(
"批量模式",
["顺序分析", "多线程并行"],
horizontal=True,
help="顺序分析：按次序分析，稳定但较慢；多线程并行：同时分析多只，快速但消耗资源"
)
st.session_state.batch_mode = batch_mode
st.markdown("---")
if analysis_mode == "单个分析":
# 单个股票分析界面
col1, col2, col3 = st.columns([2, 1, 1])
with col1:
stock_input = st.text_input(
"🔍 请输入股票代码或名称",
placeholder="例如: AAPL, 000001, 00700",
help="支持A股(如000001)、港股(如00700)和美股(如AAPL)"
)
with col2:
analyze_button = st.button("🚀 开始分析", type="primary", width='stretch')
with col3:
if st.button("🔄 清除缓存", width='stretch'):
st.cache_data.clear()
st.success("缓存已清除")
else:
# 批量股票分析界面
stock_input = st.text_area(
"🔍 请输入多个股票代码（每行一个或用逗号分隔）",
placeholder="例如:\n000001\n600036\n00700\n\n或者: 000001, 600036, 00700, AAPL",
height=120,
help="支持多种格式：每行一个代码或用逗号分隔。支持A股、港股、美股"
)
col1, col2, col3 = st.columns(3)
with col1:
analyze_button = st.button("🚀 开始批量分析", type="primary", width='stretch')
with col2:
if st.button("🔄 清除缓存", width='stretch'):
st.cache_data.clear()
st.success("缓存已清除")
with col3:
if st.button("🗑️ 清除结果", width='stretch'):
if 'batch_analysis_results' in st.session_state:
del st.session_state.batch_analysis_results
st.success("已清除批量分析结果")
# 分析师团队选择
st.markdown("---")
st.subheader("👥 选择分析师团队")
col1, col2, col3 = st.columns(3)
with col1:
enable_technical = st.checkbox("📊 技术分析师", value=True,
help="负责技术指标分析、图表形态识别、趋势判断")
enable_fundamental = st.checkbox("💼 基本面分析师", value=True,
help="负责公司财务分析、行业研究、估值分析")
with col2:
enable_fund_flow = st.checkbox("💰 资金面分析师", value=True,
help="负责资金流向分析、主力行为研究")
enable_risk = st.checkbox("⚠️ 风险管理师", value=True,
help="负责风险识别、风险评估、风险控制策略制定")
with col3:
enable_sentiment = st.checkbox("📈 市场情绪分析师", value=False,
help="负责市场情绪研究、ARBR指标分析（仅A股）")
enable_news = st.checkbox("📰 新闻分析师", value=False,
help="负责新闻事件分析、舆情研究（仅A股，qstock数据源）")
# 显示已选择的分析师
selected_analysts = []
if enable_technical:
selected_analysts.append("技术分析师")
if enable_fundamental:
selected_analysts.append("基本面分析师")
if enable_fund_flow:
selected_analysts.append("资金面分析师")
if enable_risk:
selected_analysts.append("风险管理师")
if enable_sentiment:
selected_analysts.append("市场情绪分析师")
if enable_news:
selected_analysts.append("新闻分析师")
if selected_analysts:
st.info(f"✅ 已选择 {len(selected_analysts)} 位分析师: {', '.join(selected_analysts)}")
else:
st.warning("⚠️ 请至少选择一位分析师")
# 保存选择到session_state
st.session_state.enable_technical = enable_technical
st.session_state.enable_fundamental = enable_fundamental
st.session_state.enable_fund_flow = enable_fund_flow
st.session_state.enable_risk = enable_risk
st.session_state.enable_sentiment = enable_sentiment
st.session_state.enable_news = enable_news
st.markdown("---")
if analyze_button and stock_input:
if not api_key_status:
st.error("❌ 请先配置 DeepSeek API Key")
return
# 检查是否至少选择了一位分析师
if not selected_analysts:
st.error("❌ 请至少选择一位分析师参与分析")
return
if analysis_mode == "单个分析":
# 单个股票分析
# 清除之前的分析结果
if 'analysis_completed' in st.session_state:
del st.session_state.analysis_completed
if 'stock_info' in st.session_state:
del st.session_state.stock_info
if 'agents_results' in st.session_state:
del st.session_state.agents_results
if 'discussion_result' in st.session_state:
del st.session_state.discussion_result
if 'final_decision' in st.session_state:
del st.session_state.final_decision
if 'just_completed' in st.session_state:
del st.session_state.just_completed
run_stock_analysis(stock_input, period)
else:
# 批量股票分析
# 解析股票代码列表
stock_list = parse_stock_list(stock_input)
if not stock_list:
st.error("❌ 请输入有效的股票代码")
return
if len(stock_list) > 20:
st.warning(f"⚠️ 检测到 {len(stock_list)} 只股票，建议一次分析不超过20只")
st.info(f"📊 准备分析 {len(stock_list)} 只股票: {', '.join(stock_list)}")
# 清除之前的分析结果（包括单个和批量）
if 'batch_analysis_results' in st.session_state:
del st.session_state.batch_analysis_results
if 'analysis_completed' in st.session_state:
del st.session_state.analysis_completed
if 'stock_info' in st.session_state:
del st.session_state.stock_info
if 'agents_results' in st.session_state:
del st.session_state.agents_results
if 'discussion_result' in st.session_state:
del st.session_state.discussion_result
if 'final_decision' in st.session_state:
del st.session_state.final_decision
if 'just_completed' in st.session_state:
del st.session_state.just_completed
# 获取批量模式
batch_mode = st.session_state.get('batch_mode', '顺序分析')
# 运行批量分析
run_batch_analysis(stock_list, period, batch_mode)
# 检查是否有已完成的批量分析结果（优先显示批量结果）
if 'batch_analysis_results' in st.session_state and st.session_state.batch_analysis_results:
display_batch_analysis_results(st.session_state.batch_analysis_results, period)
# 检查是否有已完成的单个分析结果（但不是刚刚完成的，避免重复显示）
elif 'analysis_completed' in st.session_state and st.session_state.analysis_completed:
# 如果是刚刚完成的分析，清除标志，避免重复显示
if st.session_state.get('just_completed', False):
st.session_state.just_completed = False
else:
# 重新显示之前的分析结果（页面刷新后）
stock_info = st.session_state.stock_info
agents_results = st.session_state.agents_results
discussion_result = st.session_state.discussion_result
final_decision = st.session_state.final_decision
# 重新获取股票数据用于显示图表
stock_info_current, stock_data, indicators = get_stock_data(stock_info['symbol'], period)
# 显示股票基本信息
display_stock_info(stock_info, indicators)
# 显示股票图表
if stock_data is not None:
display_stock_chart(stock_data, stock_info)
# 显示各分析师报告
display_agents_analysis(agents_results)
# 显示团队讨论
display_team_discussion(discussion_result)
# 显示最终决策
display_final_decision(final_decision, stock_info, agents_results, discussion_result)
# 示例和说明
elif not stock_input:
show_example_interface()
def check_api_key():
"""检查API密钥是否配置"""
try:
import config
return bool(config.DEEPSEEK_API_KEY and config.DEEPSEEK_API_KEY.strip())
except:
return False
@st.cache_data(ttl=300) # 缓存5分钟
def get_stock_data(symbol, period):
"""获取股票数据（带缓存）"""
fetcher = StockDataFetcher()
stock_info = fetcher.get_stock_info(symbol)
stock_data = fetcher.get_stock_data(symbol, period)
if isinstance(stock_data, dict) and "error" in stock_data:
return stock_info, None, None
stock_data_with_indicators = fetcher.calculate_technical_indicators(stock_data)
indicators = fetcher.get_latest_indicators(stock_data_with_indicators)
return stock_info, stock_data_with_indicators, indicators
def parse_stock_list(stock_input):
"""解析股票代码列表
支持的格式：
- 每行一个代码
- 逗号分隔
- 空格分隔
"""
if not stock_input or not stock_input.strip():
return []
# 先按换行符分割
lines = stock_input.strip().split('\n')
# 处理每一行
stock_list = []
for line in lines:
line = line.strip()
if not line:
continue
# 检查是否包含逗号
if ',' in line:
codes = [code.strip() for code in line.split(',')]
stock_list.extend([code for code in codes if code])
# 检查是否包含空格
elif ' ' in line:
codes = [code.strip() for code in line.split()]
stock_list.extend([code for code in codes if code])
else:
stock_list.append(line)
# 去重并保持顺序
seen = set()
unique_list = []
for code in stock_list:
if code not in seen:
seen.add(code)
unique_list.append(code)
return unique_list
def analyze_single_stock_for_batch(symbol, period, enabled_analysts_config=None, selected_model='deepseek-chat'):
"""单个股票分析（用于批量分析）
Args:
symbol: 股票代码
period: 数据周期
enabled_analysts_config: 分析师配置字典
selected_model: 选择的AI模型
返回分析结果或错误信息
"""
try:
# 使用默认配置
if enabled_analysts_config is None:
enabled_analysts_config = {
'technical': True,
'fundamental': True,
'fund_flow': True,
'risk': True,
'sentiment': False,
'news': False
}
# 1. 获取股票数据
stock_info, stock_data, indicators = get_stock_data(symbol, period)
if "error" in stock_info:
return {"symbol": symbol, "error": stock_info['error'], "success": False}
if stock_data is None:
return {"symbol": symbol, "error": "无法获取股票历史数据", "success": False}
# 2. 获取财务数据
fetcher = StockDataFetcher()
financial_data = fetcher.get_financial_data(symbol)
# 2.5 获取季报数据（仅A股）
quarterly_data = None
enable_fundamental = enabled_analysts_config.get('fundamental', True)
if enable_fundamental and fetcher._is_chinese_stock(symbol):
try:
from quarterly_report_data import QuarterlyReportDataFetcher
quarterly_fetcher = QuarterlyReportDataFetcher()
quarterly_data = quarterly_fetcher.get_quarterly_reports(symbol)
except:
pass
# 获取分析师选择状态（从参数而不是session_state）
enable_fund_flow = enabled_analysts_config.get('fund_flow', True)
enable_sentiment = enabled_analysts_config.get('sentiment', False)
enable_news = enabled_analysts_config.get('news', False)
# 3. 获取资金流向数据（akshare数据源，可选）
fund_flow_data = None
if enable_fund_flow and fetcher._is_chinese_stock(symbol):
try:
from fund_flow_akshare import FundFlowAkshareDataFetcher
fund_flow_fetcher = FundFlowAkshareDataFetcher()
fund_flow_data = fund_flow_fetcher.get_fund_flow_data(symbol)
except:
pass
# 4. 获取市场情绪数据（可选）
sentiment_data = None
if enable_sentiment and fetcher._is_chinese_stock(symbol):
try:
from market_sentiment_data import MarketSentimentDataFetcher
sentiment_fetcher = MarketSentimentDataFetcher()
sentiment_data = sentiment_fetcher.get_market_sentiment_data(symbol, stock_data)
except:
pass
# 5. 获取新闻数据（qstock数据源，可选）
news_data = None
if enable_news and fetcher._is_chinese_stock(symbol):
try:
from qstock_news_data import QStockNewsDataFetcher
news_fetcher = QStockNewsDataFetcher()
news_data = news_fetcher.get_stock_news(symbol)
except:
pass
# 5.5 获取风险数据（限售解禁、大股东减持、重要事件，可选）
risk_data = None
enable_risk = enabled_analysts_config.get('risk', True)
if enable_risk and fetcher._is_chinese_stock(symbol):
try:
risk_data = fetcher.get_risk_data(symbol)
except:
pass
# 6. 初始化AI分析系统
agents = StockAnalysisAgents(model=selected_model)
# 使用传入的分析师配置
enabled_analysts = enabled_analysts_config
# 7. 运行多智能体分析
agents_results = agents.run_multi_agent_analysis(
stock_info, stock_data, indicators, financial_data,
fund_flow_data, sentiment_data, news_data, quarterly_data, risk_data,
enabled_analysts=enabled_analysts_config
)
# 8. 团队讨论
discussion_result = agents.conduct_team_discussion(agents_results, stock_info)
# 9. 最终决策
final_decision = agents.make_final_decision(discussion_result, stock_info, indicators)
# 保存到数据库
saved_to_db = False
db_error = None
try:
record_id = db.save_analysis(
symbol=stock_info.get('symbol', ''),
stock_name=stock_info.get('name', ''),
period=period,
stock_info=stock_info,
agents_results=agents_results,
discussion_result=discussion_result,
final_decision=final_decision
)
saved_to_db = True
print(f"✅ {symbol} 成功保存到数据库，记录ID: {record_id}")
except Exception as e:
db_error = str(e)
print(f"❌ {symbol} 保存到数据库失败: {db_error}")
return {
"symbol": symbol,
"success": True,
"stock_info": stock_info,
"indicators": indicators,
"agents_results": agents_results,
"discussion_result": discussion_result,
"final_decision": final_decision,
"saved_to_db": saved_to_db,
"db_error": db_error
}
except Exception as e:
return {"symbol": symbol, "error": str(e), "success": False}
def run_batch_analysis(stock_list, period, batch_mode="顺序分析"):
"""运行批量股票分析"""
import concurrent.futures
import threading
# 在开始分析前获取配置（从session_state）
enabled_analysts_config = {
'technical': st.session_state.get('enable_technical', True),
'fundamental': st.session_state.get('enable_fundamental', True),
'fund_flow': st.session_state.get('enable_fund_flow', True),
'risk': st.session_state.get('enable_risk', True),
'sentiment': st.session_state.get('enable_sentiment', False),
'news': st.session_state.get('enable_news', False)
}
selected_model = st.session_state.get('selected_model', 'deepseek-chat')
# 创建进度显示
st.subheader(f"📊 批量分析进行中 ({batch_mode})")
progress_bar = st.progress(0)
status_text = st.empty()
# 存储结果
results = []
total = len(stock_list)
if batch_mode == "多线程并行":
# 多线程并行分析
status_text.text(f"🚀 使用多线程并行分析 {total} 只股票...")
# 创建线程锁用于更新进度
lock = threading.Lock()
completed = [0] # 使用列表以便在闭包中修改
progress_status = [{}] # 存储进度状态
def analyze_with_progress(symbol):
"""包装分析函数，不在线程中访问Streamlit上下文"""
try:
result = analyze_single_stock_for_batch(symbol, period, enabled_analysts_config, selected_model)
with lock:
completed[0] += 1
progress_status[0][symbol] = result
return result
except Exception as e:
with lock:
completed[0] += 1
error_result = {"symbol": symbol, "error": str(e), "success": False}
progress_status[0][symbol] = error_result
return error_result
# 使用线程池执行，限制最大并发数为3以避免API限流
with concurrent.futures.ThreadPoolExecutor(max_workers=3) as executor:
future_to_symbol = {executor.submit(analyze_with_progress, symbol): symbol
for symbol in stock_list}
for future in concurrent.futures.as_completed(future_to_symbol):
symbol = future_to_symbol[future]
try:
result = future.result(timeout=300) # 5分钟超时
results.append(result)
# 在主线程中更新UI
progress = len(results) / total
progress_bar.progress(progress)
if result['success']:
status_text.text(f"✅ [{len(results)}/{total}] {symbol} 分析完成")
else:
status_text.text(f"❌ [{len(results)}/{total}] {symbol} 分析失败: {result.get('error', '未知错误')}")
except concurrent.futures.TimeoutError:
results.append({"symbol": symbol, "error": "分析超时（5分钟）", "success": False})
progress_bar.progress(len(results) / total)
status_text.text(f"⏱️ [{len(results)}/{total}] {symbol} 分析超时")
except Exception as e:
results.append({"symbol": symbol, "error": str(e), "success": False})
progress_bar.progress(len(results) / total)
status_text.text(f"❌ [{len(results)}/{total}] {symbol} 出现错误")
else:
# 顺序分析
status_text.text(f"📝 按顺序分析 {total} 只股票...")
for i, symbol in enumerate(stock_list, 1):
status_text.text(f"🔍 [{i}/{total}] 正在分析 {symbol}...")
try:
result = analyze_single_stock_for_batch(symbol, period, enabled_analysts_config, selected_model)
except Exception as e:
result = {"symbol": symbol, "error": str(e), "success": False}
results.append(result)
# 更新进度
progress = i / total
progress_bar.progress(progress)
if result['success']:
status_text.text(f"✅ [{i}/{total}] {symbol} 分析完成")
else:
status_text.text(f"❌ [{i}/{total}] {symbol} 分析失败: {result.get('error', '未知错误')}")
# 完成
progress_bar.progress(1.0)
# 统计结果
success_count = sum(1 for r in results if r['success'])
failed_count = total - success_count
saved_count = sum(1 for r in results if r.get('saved_to_db', False))
# 显示完成信息
if success_count > 0:
status_text.success(f"✅ 批量分析完成！成功 {success_count} 只，失败 {failed_count} 只，已保存 {saved_count} 只到历史记录")
# 显示保存失败的股票
save_failed = [r['symbol'] for r in results if r.get('success') and not r.get('saved_to_db', False)]
if save_failed:
st.warning(f"⚠️ 以下股票分析成功但保存失败: {', '.join(save_failed)}")
else:
status_text.error(f"❌ 批量分析完成，但所有股票都分析失败")
# 保存结果到session_state
st.session_state.batch_analysis_results = results
st.session_state.batch_analysis_mode = batch_mode
time.sleep(1)
progress_bar.empty()
# 自动显示结果
st.rerun()
def run_stock_analysis(symbol, period):
"""运行股票分析"""
# 进度条
progress_bar = st.progress(0)
status_text = st.empty()
try:
# 1. 获取股票数据
status_text.text("📈 正在获取股票数据...")
progress_bar.progress(10)
stock_info, stock_data, indicators = get_stock_data(symbol, period)
if "error" in stock_info:
st.error(f"❌ {stock_info['error']}")
return
if stock_data is None:
st.error("❌ 无法获取股票历史数据")
return
# 显示股票基本信息
display_stock_info(stock_info, indicators)
progress_bar.progress(20)
# 显示股票图表
display_stock_chart(stock_data, stock_info)
progress_bar.progress(30)
# 2. 获取财务数据
status_text.text("📊 正在获取财务数据...")
fetcher = StockDataFetcher() # 创建fetcher实例
financial_data = fetcher.get_financial_data(symbol)
progress_bar.progress(35)
# 2.5 获取季报数据（仅在选择了基本面分析师且为A股时）
enable_fundamental = st.session_state.get('enable_fundamental', True)
quarterly_data = None
if enable_fundamental and fetcher._is_chinese_stock(symbol):
status_text.text("📊 正在获取季报数据（akshare数据源）...")
try:
from quarterly_report_data import QuarterlyReportDataFetcher
quarterly_fetcher = QuarterlyReportDataFetcher()
quarterly_data = quarterly_fetcher.get_quarterly_reports(symbol)
if quarterly_data and quarterly_data.get('data_success'):
income_count = quarterly_data.get('income_statement', {}).get('periods', 0) if quarterly_data.get('income_statement') else 0
balance_count = quarterly_data.get('balance_sheet', {}).get('periods', 0) if quarterly_data.get('balance_sheet') else 0
cash_flow_count = quarterly_data.get('cash_flow', {}).get('periods', 0) if quarterly_data.get('cash_flow') else 0
st.info(f"✅ 成功获取季报数据：利润表{income_count}期，资产负债表{balance_count}期，现金流量表{cash_flow_count}期")
else:
st.warning("⚠️ 未能获取季报数据，将基于基本财务数据分析")
except Exception as e:
st.warning(f"⚠️ 获取季报数据时出错: {str(e)}")
quarterly_data = None
elif enable_fundamental and not fetcher._is_chinese_stock(symbol):
st.info("ℹ️ 美股暂不支持季报数据")
progress_bar.progress(37)
# 获取分析师选择状态
enable_fund_flow = st.session_state.get('enable_fund_flow', True)
enable_sentiment = st.session_state.get('enable_sentiment', False)
enable_news = st.session_state.get('enable_news', False)
# 3. 获取资金流向数据（仅在选择了资金面分析师时，使用akshare数据源）
fund_flow_data = None
if enable_fund_flow and fetcher._is_chinese_stock(symbol):
status_text.text("💰 正在获取资金流向数据（akshare数据源）...")
try:
from fund_flow_akshare import FundFlowAkshareDataFetcher
fund_flow_fetcher = FundFlowAkshareDataFetcher()
fund_flow_data = fund_flow_fetcher.get_fund_flow_data(symbol)
if fund_flow_data and fund_flow_data.get('data_success'):
days = fund_flow_data.get('fund_flow_data', {}).get('days', 0) if fund_flow_data.get('fund_flow_data') else 0
st.info(f"✅ 成功获取 {days} 个交易日的资金流向数据")
else:
st.warning("⚠️ 未能获取资金流向数据，将基于技术指标进行资金面分析")
except Exception as e:
st.warning(f"⚠️ 获取资金流向数据时出错: {str(e)}")
fund_flow_data = None
elif enable_fund_flow and not fetcher._is_chinese_stock(symbol):
st.info("ℹ️ 美股暂不支持资金流向数据")
progress_bar.progress(40)
# 4. 获取市场情绪数据（仅在选择了市场情绪分析师时）
sentiment_data = None
if enable_sentiment and fetcher._is_chinese_stock(symbol):
status_text.text("📊 正在获取市场情绪数据（ARBR等指标）...")
try:
from market_sentiment_data import MarketSentimentDataFetcher
sentiment_fetcher = MarketSentimentDataFetcher()
sentiment_data = sentiment_fetcher.get_market_sentiment_data(symbol, stock_data)
if sentiment_data and sentiment_data.get('data_success'):
st.info("✅ 成功获取市场情绪数据（ARBR、换手率、涨跌停等）")
else:
st.warning("⚠️ 未能获取完整的市场情绪数据，将基于基本信息进行分析")
except Exception as e:
st.warning(f"⚠️ 获取市场情绪数据时出错: {str(e)}")
sentiment_data = None
elif enable_sentiment and not fetcher._is_chinese_stock(symbol):
st.info("ℹ️ 美股暂不支持市场情绪数据（ARBR等指标）")
progress_bar.progress(45)
# 5. 获取新闻数据（仅在选择了新闻分析师时，使用qstock数据源）
news_data = None
if enable_news and fetcher._is_chinese_stock(symbol):
status_text.text("📰 正在获取新闻数据...")
try:
from qstock_news_data import QStockNewsDataFetcher
news_fetcher = QStockNewsDataFetcher()
news_data = news_fetcher.get_stock_news(symbol)
if news_data and news_data.get('data_success'):
news_count = news_data.get('news_data', {}).get('count', 0) if news_data.get('news_data') else 0
st.info(f"✅ 成功从东方财富获取个股 {news_count} 条新闻")
else:
st.warning("⚠️ 未能获取新闻数据，将基于基本信息进行分析")
except Exception as e:
st.warning(f"⚠️ 获取新闻数据时出错: {str(e)}")
news_data = None
elif enable_news and not fetcher._is_chinese_stock(symbol):
st.info("ℹ️ 美股暂不支持新闻数据")
progress_bar.progress(45)
# 5.5 获取风险数据（仅在选择了风险管理师时，使用问财数据源）
enable_risk = st.session_state.get('enable_risk', True)
risk_data = None
if enable_risk and fetcher._is_chinese_stock(symbol):
status_text.text("⚠️ 正在获取风险数据（限售解禁、大股东减持、重要事件）...")
try:
risk_data = fetcher.get_risk_data(symbol)
if risk_data and risk_data.get('data_success'):
# 统计获取到的风险数据类型
risk_types = []
if risk_data.get('lifting_ban') and risk_data['lifting_ban'].get('has_data'):
risk_types.append("限售解禁")
if risk_data.get('shareholder_reduction') and risk_data['shareholder_reduction'].get('has_data'):
risk_types.append("大股东减持")
if risk_data.get('important_events') and risk_data['important_events'].get('has_data'):
risk_types.append("重要事件")
if risk_types:
st.info(f"✅ 成功获取风险数据：{', '.join(risk_types)}")
else:
st.info("ℹ️ 暂无风险相关数据")
else:
st.info("ℹ️ 暂无风险相关数据，将基于基本信息进行风险分析")
except Exception as e:
st.warning(f"⚠️ 获取风险数据时出错: {str(e)}")
risk_data = None
elif enable_risk and not fetcher._is_chinese_stock(symbol):
st.info("ℹ️ 美股暂不支持风险数据（限售解禁、大股东减持等）")
progress_bar.progress(50)
# 6. 初始化AI分析系统
status_text.text("🤖 正在初始化AI分析系统...")
# 使用选择的模型
selected_model = st.session_state.get('selected_model', 'deepseek-chat')
agents = StockAnalysisAgents(model=selected_model)
progress_bar.progress(55)
# 获取所有分析师选择状态
enable_technical = st.session_state.get('enable_technical', True)
enable_fundamental = st.session_state.get('enable_fundamental', True)
enable_risk = st.session_state.get('enable_risk', True)
# 创建分析师启用字典
enabled_analysts = {
'technical': enable_technical,
'fundamental': enable_fundamental,
'fund_flow': enable_fund_flow,
'risk': enable_risk,
'sentiment': enable_sentiment,
'news': enable_news
}
# 7. 运行多智能体分析（传入所有数据和分析师选择）
status_text.text("🔍 AI分析师团队正在分析,请耐心等待几分钟...")
agents_results = agents.run_multi_agent_analysis(
stock_info, stock_data, indicators, financial_data,
fund_flow_data, sentiment_data, news_data, quarterly_data, risk_data,
enabled_analysts=enabled_analysts
)
progress_bar.progress(75)
# 显示各分析师报告
display_agents_analysis(agents_results)
# 8. 团队讨论
status_text.text("🤝 分析团队正在讨论...")
discussion_result = agents.conduct_team_discussion(agents_results, stock_info)
progress_bar.progress(88)
# 显示团队讨论
display_team_discussion(discussion_result)
# 9. 最终决策
status_text.text("📋 正在制定最终投资决策...")
final_decision = agents.make_final_decision(discussion_result, stock_info, indicators)
progress_bar.progress(100)
# 显示最终决策
display_final_decision(final_decision, stock_info, agents_results, discussion_result)
# 保存分析结果到session_state（用于页面刷新后显示）
st.session_state.analysis_completed = True
st.session_state.stock_info = stock_info
st.session_state.agents_results = agents_results
st.session_state.discussion_result = discussion_result
st.session_state.final_decision = final_decision
st.session_state.just_completed = True # 标记刚刚完成分析
# 保存到数据库
try:
db.save_analysis(
symbol=stock_info.get('symbol', ''),
stock_name=stock_info.get('name', ''),
period=period,
stock_info=stock_info,
agents_results=agents_results,
discussion_result=discussion_result,
final_decision=final_decision
)
st.success("✅ 分析记录已保存到数据库")
except Exception as e:
st.warning(f"⚠️ 保存到数据库时出现错误: {str(e)}")
status_text.text("✅ 分析完成！")
time.sleep(1)
status_text.empty()
progress_bar.empty()
except Exception as e:
st.error(f"❌ 分析过程中出现错误: {str(e)}")
progress_bar.empty()
status_text.empty()
def display_stock_info(stock_info, indicators):
"""显示股票基本信息"""
st.subheader(f"📊 {stock_info.get('name', 'N/A')} ({stock_info.get('symbol', 'N/A')})")
# 基本信息卡片
col1, col2, col3, col4, col5 = st.columns(5)
with col1:
current_price = stock_info.get('current_price', 'N/A')
st.metric("当前价格", f"{current_price}")
with col2:
change_percent = stock_info.get('change_percent', 'N/A')
if isinstance(change_percent, (int, float)):
st.metric("涨跌幅", f"{change_percent:.2f}%", f"{change_percent:.2f}%")
else:
st.metric("涨跌幅", f"{change_percent}")
with col3:
pe_ratio = stock_info.get('pe_ratio', 'N/A')
st.metric("市盈率", f"{pe_ratio}")
with col4:
pb_ratio = stock_info.get('pb_ratio', 'N/A')
st.metric("市净率", f"{pb_ratio}")
with col5:
market_cap = stock_info.get('market_cap', 'N/A')
if isinstance(market_cap, (int, float)):
market_cap_str = f"{market_cap/1e9:.2f}B" if market_cap > 1e9 else f"{market_cap/1e6:.2f}M"
st.metric("市值", market_cap_str)
else:
st.metric("市值", f"{market_cap}")
# 技术指标
if indicators and not isinstance(indicators, dict) or "error" not in indicators:
st.subheader("📈 关键技术指标")
col1, col2, col3, col4 = st.columns(4)
with col1:
rsi = indicators.get('rsi', 'N/A')
if isinstance(rsi, (int, float)):
rsi_color = "normal"
if rsi > 70:
rsi_color = "inverse"
elif rsi < 30:
rsi_color = "off"
st.metric("RSI", f"{rsi:.2f}")
else:
st.metric("RSI", f"{rsi}")
with col2:
ma20 = indicators.get('ma20', 'N/A')
if isinstance(ma20, (int, float)):
st.metric("MA20", f"{ma20:.2f}")
else:
st.metric("MA20", f"{ma20}")
with col3:
volume_ratio = indicators.get('volume_ratio', 'N/A')
if isinstance(volume_ratio, (int, float)):
st.metric("量比", f"{volume_ratio:.2f}")
else:
st.metric("量比", f"{volume_ratio}")
with col4:
macd = indicators.get('macd', 'N/A')
if isinstance(macd, (int, float)):
st.metric("MACD", f"{macd:.4f}")
else:
st.metric("MACD", f"{macd}")
def display_stock_chart(stock_data, stock_info):
"""显示股票图表"""
st.subheader("📈 股价走势图")
# 创建蜡烛图
fig = go.Figure()
# 添加蜡烛图
fig.add_trace(go.Candlestick(
x=stock_data.index,
open=stock_data['Open'],
high=stock_data['High'],
low=stock_data['Low'],
close=stock_data['Close'],
name="K线"
))
# 添加移动平均线
if 'MA5' in stock_data.columns:
fig.add_trace(go.Scatter(
x=stock_data.index,
y=stock_data['MA5'],
name="MA5",
line=dict(color='orange', width=1)
))
if 'MA20' in stock_data.columns:
fig.add_trace(go.Scatter(
x=stock_data.index,
y=stock_data['MA20'],
name="MA20",
line=dict(color='blue', width=1)
))
if 'MA60' in stock_data.columns:
fig.add_trace(go.Scatter(
x=stock_data.index,
y=stock_data['MA60'],
name="MA60",
line=dict(color='purple', width=1)
))
# 布林带
if 'BB_upper' in stock_data.columns and 'BB_lower' in stock_data.columns:
fig.add_trace(go.Scatter(
x=stock_data.index,
y=stock_data['BB_upper'],
name="布林上轨",
line=dict(color='red', width=1, dash='dash')
))
fig.add_trace(go.Scatter(
x=stock_data.index,
y=stock_data['BB_lower'],
name="布林下轨",
line=dict(color='green', width=1, dash='dash'),
fill='tonexty',
fillcolor='rgba(0,100,80,0.1)'
))
fig.update_layout(
title=f"{stock_info.get('name', 'N/A')} 股价走势",
xaxis_title="日期",
yaxis_title="价格",
height=500,
showlegend=True
)
# 生成唯一的key
chart_key = f"main_stock_chart_{stock_info.get('symbol', 'unknown')}_{int(time.time())}"
st.plotly_chart(fig, width='stretch', key=chart_key)
# 成交量图
if 'Volume' in stock_data.columns:
fig_volume = go.Figure()
fig_volume.add_trace(go.Bar(
x=stock_data.index,
y=stock_data['Volume'],
name="成交量",
marker_color='lightblue'
))
fig_volume.update_layout(
title="成交量",
xaxis_title="日期",
yaxis_title="成交量",
height=200
)
# 生成唯一的key
volume_key = f"volume_chart_{stock_info.get('symbol', 'unknown')}_{int(time.time())}"
st.plotly_chart(fig_volume, width='stretch', key=volume_key)
def display_agents_analysis(agents_results):
"""显示各分析师报告"""
st.subheader("🤖 AI分析师团队报告")
# 创建标签页
tab_names = []
tab_contents = []
for agent_key, agent_result in agents_results.items():
agent_name = agent_result.get('agent_name', '未知分析师')
tab_names.append(agent_name)
tab_contents.append(agent_result)
tabs = st.tabs(tab_names)
for i, tab in enumerate(tabs):
with tab:
agent_result = tab_contents[i]
# 分析师信息
st.markdown(f"""
👨💼 {agent_result.get('agent_name', '未知')}
职责：{agent_result.get('agent_role', '未知')}
关注领域：{', '.join(agent_result.get('focus_areas', []))}
分析时间：{agent_result.get('timestamp', '未知')}
""", unsafe_allow_html=True)
# 分析报告
st.markdown("**📄 分析报告:**")
st.write(agent_result.get('analysis', '暂无分析'))
def display_team_discussion(discussion_result):
"""显示团队讨论"""
st.subheader("🤝 分析团队讨论")
st.markdown("""
💭 团队综合讨论
各位分析师正在就该股票进行深入讨论，整合不同维度的分析观点...
""", unsafe_allow_html=True)
st.write(discussion_result)
def display_final_decision(final_decision, stock_info, agents_results=None, discussion_result=None):
"""显示最终投资决策"""
st.subheader("📋 最终投资决策")
if isinstance(final_decision, dict) and "decision_text" not in final_decision:
# JSON格式的决策
col1, col2 = st.columns([1, 2])
with col1:
# 投资评级
rating = final_decision.get('rating', '未知')
rating_color = {"买入": "🟢", "持有": "🟡", "卖出": "🔴"}.get(rating, "⚪")
st.markdown(f"""
{rating_color} {rating}
投资评级
""", unsafe_allow_html=True)
# 关键指标
confidence = final_decision.get('confidence_level', 'N/A')
st.metric("信心度", f"{confidence}/10")
target_price = final_decision.get('target_price', 'N/A')
st.metric("目标价格", f"{target_price}")
position_size = final_decision.get('position_size', 'N/A')
st.metric("建议仓位", f"{position_size}")
with col2:
# 详细建议
st.markdown("**🎯 操作建议:**")
st.write(final_decision.get('operation_advice', '暂无建议'))
st.markdown("**📍 关键位置:**")
col2_1, col2_2 = st.columns(2)
with col2_1:
st.write(f"**进场区间:** {final_decision.get('entry_range', 'N/A')}")
st.write(f"**止盈位:** {final_decision.get('take_profit', 'N/A')}")
with col2_2:
st.write(f"**止损位:** {final_decision.get('stop_loss', 'N/A')}")
st.write(f"**持有周期:** {final_decision.get('holding_period', 'N/A')}")
# 风险提示
risk_warning = final_decision.get('risk_warning', '')
if risk_warning:
st.markdown(f"""
""", unsafe_allow_html=True)
else:
# 文本格式的决策
decision_text = final_decision.get('decision_text', str(final_decision))
st.write(decision_text)
# 添加PDF导出功能
st.markdown("---")
if agents_results and discussion_result:
display_pdf_export_section(stock_info, agents_results, discussion_result, final_decision)
else:
st.warning("⚠️ PDF导出功能需要完整的分析数据")
def show_example_interface():
"""显示示例界面"""
st.subheader("💡 使用说明")
col1, col2 = st.columns(2)
with col1:
st.markdown("""
### 🚀 如何使用
1. **输入股票代码**：支持A股(如000001)、港股(如00700)和美股(如AAPL)
2. **点击开始分析**：系统将启动AI分析师团队
3. **查看分析报告**：多位专业分析师将从不同角度分析
4. **获得投资建议**：获得最终的投资评级和操作建议
### 📊 分析维度
- **技术面**：趋势、指标、支撑阻力
- **基本面**：财务、估值、行业分析
- **资金面**：资金流向、主力行为
- **风险管理**：风险识别与控制
- **市场情绪**：情绪指标、热点分析
""")
with col2:
st.markdown("""
### 📈 示例股票代码
**A股热门**
- 000001 (平安银行)
- 600036 (招商银行)
- 600519 (贵州茅台)
**港股热门**
- 00700 或 700 (腾讯控股)
- 09988 或 9988 (阿里巴巴-SW)
- 01810 或 1810 (小米集团-W)
**美股热门**
- AAPL (苹果)
- MSFT (微软)
- NVDA (英伟达)
""")
st.info("💡 提示：首次运行需要配置DeepSeek API Key，请在.env中设置DEEPSEEK_API_KEY")
st.markdown("---")
st.markdown("""
### 🌏 市场支持说明
- **A股**：完整支持（技术分析、财务数据、资金流向、市场情绪、新闻数据qstock）
- **港股**：部分支持（技术分析、21项财务指标）⭐️
- **美股**：完整支持（技术分析、财务数据）
### 📊 港股支持的财务指标
盈利能力（6项）、营运能力（3项）、偿债能力（2项）、市场表现（4项）、分红指标（3项）、股本结构（3项）
""")
def display_history_records():
"""显示历史分析记录"""
st.subheader("📚 历史分析记录")
# 获取所有记录
records = db.get_all_records()
if not records:
st.info("📭 暂无历史分析记录")
return
st.write(f"📊 共找到 {len(records)} 条分析记录")
# 搜索和筛选
col1, col2 = st.columns([3, 1])
with col1:
search_term = st.text_input("🔍 搜索股票代码或名称", placeholder="输入股票代码或名称进行搜索")
with col2:
st.write("")
st.write("")
if st.button("🔄 刷新列表"):
st.rerun()
# 筛选记录
filtered_records = records
if search_term:
filtered_records = [
record for record in records
if search_term.lower() in record['symbol'].lower() or
search_term.lower() in record['stock_name'].lower()
]
if not filtered_records:
st.warning("🔍 未找到匹配的记录")
return
# 显示记录列表
for record in filtered_records:
# 根据评级设置颜色和图标
rating = record.get('rating', '未知')
rating_color = {
"买入": "🟢",
"持有": "🟡",
"卖出": "🔴",
"强烈买入": "🟢",
"强烈卖出": "🔴"
}.get(rating, "⚪")
with st.expander(f"{rating_color} {record['stock_name']} ({record['symbol']}) - {record['analysis_date']}"):
col1, col2, col3, col4 = st.columns([2, 2, 1, 1])
with col1:
st.write(f"**股票代码:** {record['symbol']}")
st.write(f"**股票名称:** {record['stock_name']}")
with col2:
st.write(f"**分析时间:** {record['analysis_date']}")
st.write(f"**数据周期:** {record['period']}")
st.write(f"**投资评级:** **{rating}**")
with col3:
if st.button("👀 查看详情", key=f"view_{record['id']}"):
st.session_state.viewing_record_id = record['id']
with col4:
if st.button("➕ 监测", key=f"add_monitor_{record['id']}"):
st.session_state.add_to_monitor_id = record['id']
st.session_state.viewing_record_id = record['id']
# 删除按钮（新增一行）
col5, _, _, _ = st.columns(4)
with col5:
if st.button("🗑️ 删除", key=f"delete_{record['id']}"):
if db.delete_record(record['id']):
st.success("✅ 记录已删除")
st.rerun()
else:
st.error("❌ 删除失败")
# 查看详细记录
if 'viewing_record_id' in st.session_state:
display_record_detail(st.session_state.viewing_record_id)
def display_add_to_monitor_dialog(record):
"""显示加入监测的对话框"""
st.markdown("---")
st.subheader("➕ 加入监测")
final_decision = record['final_decision']
# 从final_decision中提取关键数据
if isinstance(final_decision, dict):
# 解析进场区间
entry_range_str = final_decision.get('entry_range', 'N/A')
entry_min = 0.0
entry_max = 0.0
# 尝试解析进场区间字符串，支持多种格式
if entry_range_str and entry_range_str != 'N/A':
try:
import re
# 移除常见的前缀和单位
clean_str = str(entry_range_str).replace('¥', '').replace('元', '').replace('$', '')
# 使用正则表达式提取数字
# 支持格式：10.5-12.0, 10.5 - 12.0, 10.5~12.0, 10.5至12.0 等
numbers = re.findall(r'\d+\.?\d*', clean_str)
if len(numbers) >= 2:
entry_min = float(numbers[0])
entry_max = float(numbers[1])
except:
# 如果解析失败，尝试用分隔符split
try:
clean_str = str(entry_range_str).replace('¥', '').replace('元', '').replace('$', '')
# 尝试多种分隔符
for sep in ['-', '~', '至', '到']:
if sep in clean_str:
parts = clean_str.split(sep)
if len(parts) == 2:
entry_min = float(parts[0].strip())
entry_max = float(parts[1].strip())
break
except:
pass
# 提取止盈和止损
take_profit_str = final_decision.get('take_profit', 'N/A')
stop_loss_str = final_decision.get('stop_loss', 'N/A')
take_profit = 0.0
stop_loss = 0.0
# 解析止盈位
if take_profit_str and take_profit_str != 'N/A':
try:
import re
# 移除单位和符号
clean_str = str(take_profit_str).replace('¥', '').replace('元', '').replace('$', '').strip()
# 提取第一个数字
numbers = re.findall(r'\d+\.?\d*', clean_str)
if numbers:
take_profit = float(numbers[0])
except:
pass
# 解析止损位
if stop_loss_str and stop_loss_str != 'N/A':
try:
import re
# 移除单位和符号
clean_str = str(stop_loss_str).replace('¥', '').replace('元', '').replace('$', '').strip()
# 提取第一个数字
numbers = re.findall(r'\d+\.?\d*', clean_str)
if numbers:
stop_loss = float(numbers[0])
except:
pass
# 获取评级
rating = final_decision.get('rating', '买入')
# 检查是否已经在监测列表中
from monitor_db import monitor_db
existing_stocks = monitor_db.get_monitored_stocks()
is_duplicate = any(stock['symbol'] == record['symbol'] for stock in existing_stocks)
if is_duplicate:
st.warning(f"⚠️ {record['symbol']} 已经在监测列表中。继续添加将创建重复监测项。")
st.info(f"""
**从分析结果中提取的数据：**
- 进场区间: {entry_min} - {entry_max}
- 止盈位: {take_profit if take_profit > 0 else '未设置'}
- 止损位: {stop_loss if stop_loss > 0 else '未设置'}
- 投资评级: {rating}
""")
# 显示表单供用户确认或修改
with st.form(key=f"monitor_form_{record['id']}"):
st.markdown("**请确认或修改监测参数：**")
col1, col2 = st.columns([1, 1])
with col1:
st.subheader("🎯 关键位置")
new_entry_min = st.number_input("进场区间最低价", value=float(entry_min), step=0.01, format="%.2f")
new_entry_max = st.number_input("进场区间最高价", value=float(entry_max), step=0.01, format="%.2f")
new_take_profit = st.number_input("止盈价位", value=float(take_profit), step=0.01, format="%.2f")
new_stop_loss = st.number_input("止损价位", value=float(stop_loss), step=0.01, format="%.2f")
with col2:
st.subheader("⚙️ 监测设置")
check_interval = st.slider("监测间隔(分钟)", 5, 120, 30)
notification_enabled = st.checkbox("启用通知", value=True)
new_rating = st.selectbox("投资评级", ["买入", "持有", "卖出"],
index=["买入", "持有", "卖出"].index(rating) if rating in ["买入", "持有", "卖出"] else 0)
col_a, col_b, col_c = st.columns(3)
with col_a:
submit = st.form_submit_button("✅ 确认加入监测", type="primary", width='stretch')
with col_b:
cancel = st.form_submit_button("❌ 取消", width='stretch')
if submit:
if new_entry_min > 0 and new_entry_max > 0 and new_entry_max > new_entry_min:
try:
# 添加到监测数据库
entry_range = {"min": new_entry_min, "max": new_entry_max}
stock_id = monitor_db.add_monitored_stock(
symbol=record['symbol'],
name=record['stock_name'],
rating=new_rating,
entry_range=entry_range,
take_profit=new_take_profit if new_take_profit > 0 else None,
stop_loss=new_stop_loss if new_stop_loss > 0 else None,
check_interval=check_interval,
notification_enabled=notification_enabled
)
st.success(f"✅ 已成功将 {record['symbol']} 加入监测列表！")
st.balloons()
# 立即更新一次价格
from monitor_service import monitor_service
monitor_service.manual_update_stock(stock_id)
# 清理session state并跳转到监测页面
if 'add_to_monitor_id' in st.session_state:
del st.session_state.add_to_monitor_id
if 'viewing_record_id' in st.session_state:
del st.session_state.viewing_record_id
if 'show_history' in st.session_state:
del st.session_state.show_history
# 设置跳转到监测页面
st.session_state.show_monitor = True
st.session_state.monitor_jump_highlight = record['symbol'] # 标记要高亮显示的股票
time.sleep(1.5)
st.rerun()
except Exception as e:
st.error(f"❌ 加入监测失败: {str(e)}")
else:
st.error("❌ 请输入有效的进场区间（最低价应小于最高价，且都大于0）")
if cancel:
if 'add_to_monitor_id' in st.session_state:
del st.session_state.add_to_monitor_id
st.rerun()
else:
st.warning("⚠️ 无法从分析结果中提取关键数据")
if st.button("❌ 取消"):
if 'add_to_monitor_id' in st.session_state:
del st.session_state.add_to_monitor_id
st.rerun()
def display_record_detail(record_id):
"""显示单条记录的详细信息"""
st.markdown("---")
st.subheader("📋 详细分析记录")
record = db.get_record_by_id(record_id)
if not record:
st.error("❌ 记录不存在")
return
# 基本信息
col1, col2, col3 = st.columns(3)
with col1:
st.metric("股票代码", record['symbol'])
with col2:
st.metric("股票名称", record['stock_name'])
with col3:
st.metric("分析时间", record['analysis_date'])
# 股票基本信息
st.subheader("📊 股票基本信息")
stock_info = record['stock_info']
if stock_info:
col1, col2, col3, col4, col5 = st.columns(5)
with col1:
current_price = stock_info.get('current_price', 'N/A')
st.metric("当前价格", f"{current_price}")
with col2:
change_percent = stock_info.get('change_percent', 'N/A')
if isinstance(change_percent, (int, float)):
st.metric("涨跌幅", f"{change_percent:.2f}%", f"{change_percent:.2f}%")
else:
st.metric("涨跌幅", f"{change_percent}")
with col3:
pe_ratio = stock_info.get('pe_ratio', 'N/A')
st.metric("市盈率", f"{pe_ratio}")
with col4:
pb_ratio = stock_info.get('pb_ratio', 'N/A')
st.metric("市净率", f"{pb_ratio}")
with col5:
market_cap = stock_info.get('market_cap', 'N/A')
if isinstance(market_cap, (int, float)):
market_cap_str = f"{market_cap/1e9:.2f}B" if market_cap > 1e9 else f"{market_cap/1e6:.2f}M"
st.metric("市值", market_cap_str)
else:
st.metric("市值", f"{market_cap}")
# 各分析师报告
st.subheader("🤖 AI分析师团队报告")
agents_results = record['agents_results']
if agents_results:
tab_names = []
tab_contents = []
for agent_key, agent_result in agents_results.items():
agent_name = agent_result.get('agent_name', '未知分析师')
tab_names.append(agent_name)
tab_contents.append(agent_result)
tabs = st.tabs(tab_names)
for i, tab in enumerate(tabs):
with tab:
agent_result = tab_contents[i]
st.markdown(f"""
👨💼 {agent_result.get('agent_name', '未知')}
职责：{agent_result.get('agent_role', '未知')}
关注领域：{', '.join(agent_result.get('focus_areas', []))}
""", unsafe_allow_html=True)
st.markdown("**📄 分析报告:**")
st.write(agent_result.get('analysis', '暂无分析'))
# 团队讨论
st.subheader("🤝 分析团队讨论")
discussion_result = record['discussion_result']
if discussion_result:
st.markdown("""
💭 团队综合讨论
""", unsafe_allow_html=True)
st.write(discussion_result)
# 最终决策
st.subheader("📋 最终投资决策")
final_decision = record['final_decision']
if final_decision:
if isinstance(final_decision, dict) and "decision_text" not in final_decision:
col1, col2 = st.columns([1, 2])
with col1:
rating = final_decision.get('rating', '未知')
rating_color = {"买入": "🟢", "持有": "🟡", "卖出": "🔴"}.get(rating, "⚪")
st.markdown(f"""
{rating_color} {rating}
投资评级
""", unsafe_allow_html=True)
confidence = final_decision.get('confidence_level', 'N/A')
st.metric("信心度", f"{confidence}/10")
target_price = final_decision.get('target_price', 'N/A')
st.metric("目标价格", f"{target_price}")
position_size = final_decision.get('position_size', 'N/A')
st.metric("建议仓位", f"{position_size}")
with col2:
st.markdown("**🎯 操作建议:**")
st.write(final_decision.get('operation_advice', '暂无建议'))
st.markdown("**📍 关键位置:**")
col2_1, col2_2 = st.columns(2)
with col2_1:
st.write(f"**进场区间:** {final_decision.get('entry_range', 'N/A')}")
st.write(f"**止盈位:** {final_decision.get('take_profit', 'N/A')}")
with col2_2:
st.write(f"**止损位:** {final_decision.get('stop_loss', 'N/A')}")
st.write(f"**持有周期:** {final_decision.get('holding_period', 'N/A')}")
else:
decision_text = final_decision.get('decision_text', str(final_decision))
st.write(decision_text)
# 加入监测功能
st.markdown("---")
st.subheader("🎯 操作")
# 检查是否需要显示加入监测的对话框
if 'add_to_monitor_id' in st.session_state and st.session_state.add_to_monitor_id == record_id:
display_add_to_monitor_dialog(record)
else:
# 只有在不显示对话框时才显示按钮
col1, col2 = st.columns([1, 3])
with col1:
if st.button("➕ 加入监测", type="primary", width='stretch'):
st.session_state.add_to_monitor_id = record_id
st.rerun()
# 返回按钮
st.markdown("---")
if st.button("⬅️ 返回历史记录列表"):
if 'viewing_record_id' in st.session_state:
del st.session_state.viewing_record_id
if 'add_to_monitor_id' in st.session_state:
del st.session_state.add_to_monitor_id
st.rerun()
def display_config_manager():
"""显示环境配置管理界面"""
st.subheader("⚙️ 环境配置管理")
st.markdown("""
在这里可以配置系统的环境变量，包括API密钥、数据源配置、量化交易配置等。
注意：配置修改后需要重启应用才能生效。
""", unsafe_allow_html=True)
# 获取当前配置
config_info = config_manager.get_config_info()
# 创建标签页
tab1, tab2, tab3, tab4 = st.tabs(["📝 基本配置", "📊 数据源配置", "🤖 量化交易配置", "📢 通知配置"])
# 使用session_state保存临时配置
if 'temp_config' not in st.session_state:
st.session_state.temp_config = {key: info["value"] for key, info in config_info.items()}
with tab1:
st.markdown("### DeepSeek API配置")
st.markdown("DeepSeek是系统的核心AI引擎，必须配置才能使用分析功能。")
# DeepSeek API Key
api_key_info = config_info["DEEPSEEK_API_KEY"]
current_api_key = st.session_state.temp_config.get("DEEPSEEK_API_KEY", "")
new_api_key = st.text_input(
f"🔑 {api_key_info['description']} {'*' if api_key_info['required'] else ''}",
value=current_api_key,
type="password",
help="从 https://platform.deepseek.com 获取API密钥",
key="input_deepseek_api_key"
)
st.session_state.temp_config["DEEPSEEK_API_KEY"] = new_api_key
# 显示当前状态
if new_api_key:
masked_key = new_api_key[:8] + "*" * (len(new_api_key) - 12) + new_api_key[-4:] if len(new_api_key) > 12 else "***"
st.success(f"✅ API密钥已设置: {masked_key}")
else:
st.warning("⚠️ 未设置API密钥，系统无法使用AI分析功能")
st.markdown("---")
# DeepSeek Base URL
base_url_info = config_info["DEEPSEEK_BASE_URL"]
current_base_url = st.session_state.temp_config.get("DEEPSEEK_BASE_URL", "")
new_base_url = st.text_input(
f"🌐 {base_url_info['description']}",
value=current_base_url,
help="一般无需修改，保持默认即可",
key="input_deepseek_base_url"
)
st.session_state.temp_config["DEEPSEEK_BASE_URL"] = new_base_url
st.info("💡 如何获取DeepSeek API密钥？\n\n1. 访问 https://platform.deepseek.com\n2. 注册/登录账号\n3. 进入API密钥管理页面\n4. 创建新的API密钥\n5. 复制密钥并粘贴到上方输入框")
with tab2:
st.markdown("### Tushare数据接口（可选）")
st.markdown("Tushare提供更丰富的A股财务数据，配置后可以获取更详细的财务分析。")
tushare_info = config_info["TUSHARE_TOKEN"]
current_tushare = st.session_state.temp_config.get("TUSHARE_TOKEN", "")
new_tushare = st.text_input(
f"🎫 {tushare_info['description']}",
value=current_tushare,
type="password",
help="从 https://tushare.pro 获取Token",
key="input_tushare_token"
)
st.session_state.temp_config["TUSHARE_TOKEN"] = new_tushare
if new_tushare:
st.success("✅ Tushare Token已设置")
else:
st.info("ℹ️ 未设置Tushare Token，系统将使用其他数据源")
st.info("💡 如何获取Tushare Token？\n\n1. 访问 https://tushare.pro\n2. 注册账号\n3. 进入个人中心\n4. 获取Token\n5. 复制并粘贴到上方输入框")
with tab3:
st.markdown("### MiniQMT量化交易配置（可选）")
st.markdown("配置后可以使用量化交易功能，自动执行交易策略。")
# 启用开关
miniqmt_enabled_info = config_info["MINIQMT_ENABLED"]
current_enabled = st.session_state.temp_config.get("MINIQMT_ENABLED", "false") == "true"
new_enabled = st.checkbox(
"启用MiniQMT量化交易",
value=current_enabled,
help="开启后可以使用量化交易功能",
key="input_miniqmt_enabled"
)
st.session_state.temp_config["MINIQMT_ENABLED"] = "true" if new_enabled else "false"
# 其他配置
col1, col2 = st.columns(2)
with col1:
account_id_info = config_info["MINIQMT_ACCOUNT_ID"]
current_account_id = st.session_state.temp_config.get("MINIQMT_ACCOUNT_ID", "")
new_account_id = st.text_input(
f"🆔 {account_id_info['description']}",
value=current_account_id,
disabled=not new_enabled,
key="input_miniqmt_account_id"
)
st.session_state.temp_config["MINIQMT_ACCOUNT_ID"] = new_account_id
host_info = config_info["MINIQMT_HOST"]
current_host = st.session_state.temp_config.get("MINIQMT_HOST", "")
new_host = st.text_input(
f"🖥️ {host_info['description']}",
value=current_host,
disabled=not new_enabled,
key="input_miniqmt_host"
)
st.session_state.temp_config["MINIQMT_HOST"] = new_host
with col2:
port_info = config_info["MINIQMT_PORT"]
current_port = st.session_state.temp_config.get("MINIQMT_PORT", "")
new_port = st.text_input(
f"🔌 {port_info['description']}",
value=current_port,
disabled=not new_enabled,
key="input_miniqmt_port"
)
st.session_state.temp_config["MINIQMT_PORT"] = new_port
if new_enabled:
st.success("✅ MiniQMT已启用")
else:
st.info("ℹ️ MiniQMT未启用")
st.warning("⚠️ 警告：量化交易涉及真实资金操作，请谨慎配置和使用！")
with tab4:
st.markdown("### 通知配置")
st.markdown("配置邮件和Webhook通知，用于实时监测和智策定时分析的提醒。")
# 创建两列布局
col_email, col_webhook = st.columns(2)
with col_email:
st.markdown("#### 📧 邮件通知")
# 邮件启用开关
email_enabled_info = config_info.get("EMAIL_ENABLED", {"value": "false"})
current_email_enabled = st.session_state.temp_config.get("EMAIL_ENABLED", "false") == "true"
new_email_enabled = st.checkbox(
"启用邮件通知",
value=current_email_enabled,
help="开启后可以接收邮件提醒",
key="input_email_enabled"
)
st.session_state.temp_config["EMAIL_ENABLED"] = "true" if new_email_enabled else "false"
# SMTP服务器
smtp_server_info = config_info.get("SMTP_SERVER", {"description": "SMTP服务器地址", "value": ""})
current_smtp_server = st.session_state.temp_config.get("SMTP_SERVER", "")
new_smtp_server = st.text_input(
f"📮 {smtp_server_info['description']}",
value=current_smtp_server,
disabled=not new_email_enabled,
placeholder="smtp.qq.com",
key="input_smtp_server"
)
st.session_state.temp_config["SMTP_SERVER"] = new_smtp_server
# SMTP端口
smtp_port_info = config_info.get("SMTP_PORT", {"description": "SMTP端口", "value": "587"})
current_smtp_port = st.session_state.temp_config.get("SMTP_PORT", "587")
new_smtp_port = st.text_input(
f"🔌 {smtp_port_info['description']}",
value=current_smtp_port,
disabled=not new_email_enabled,
placeholder="587 (TLS) 或 465 (SSL)",
key="input_smtp_port"
)
st.session_state.temp_config["SMTP_PORT"] = new_smtp_port
# 发件人邮箱
email_from_info = config_info.get("EMAIL_FROM", {"description": "发件人邮箱", "value": ""})
current_email_from = st.session_state.temp_config.get("EMAIL_FROM", "")
new_email_from = st.text_input(
f"📤 {email_from_info['description']}",
value=current_email_from,
disabled=not new_email_enabled,
placeholder="your-email@qq.com",
key="input_email_from"
)
st.session_state.temp_config["EMAIL_FROM"] = new_email_from
# 邮箱授权码
email_password_info = config_info.get("EMAIL_PASSWORD", {"description": "邮箱授权码", "value": ""})
current_email_password = st.session_state.temp_config.get("EMAIL_PASSWORD", "")
new_email_password = st.text_input(
f"🔐 {email_password_info['description']}",
value=current_email_password,
type="password",
disabled=not new_email_enabled,
help="不是邮箱登录密码，而是SMTP授权码",
key="input_email_password"
)
st.session_state.temp_config["EMAIL_PASSWORD"] = new_email_password
# 收件人邮箱
email_to_info = config_info.get("EMAIL_TO", {"description": "收件人邮箱", "value": ""})
current_email_to = st.session_state.temp_config.get("EMAIL_TO", "")
new_email_to = st.text_input(
f"📥 {email_to_info['description']}",
value=current_email_to,
disabled=not new_email_enabled,
placeholder="receiver@qq.com",
key="input_email_to"
)
st.session_state.temp_config["EMAIL_TO"] = new_email_to
if new_email_enabled and all([new_smtp_server, new_email_from, new_email_password, new_email_to]):
st.success("✅ 邮件配置完整")
elif new_email_enabled:
st.warning("⚠️ 邮件配置不完整")
else:
st.info("ℹ️ 邮件通知未启用")
st.caption("💡 QQ邮箱授权码获取：设置 → 账户 → POP3/IMAP/SMTP → 生成授权码")
with col_webhook:
st.markdown("#### 📱 Webhook通知")
# Webhook启用开关
webhook_enabled_info = config_info.get("WEBHOOK_ENABLED", {"value": "false"})
current_webhook_enabled = st.session_state.temp_config.get("WEBHOOK_ENABLED", "false") == "true"
new_webhook_enabled = st.checkbox(
"启用Webhook通知",
value=current_webhook_enabled,
help="开启后可以发送到钉钉或飞书群",
key="input_webhook_enabled"
)
st.session_state.temp_config["WEBHOOK_ENABLED"] = "true" if new_webhook_enabled else "false"
# Webhook类型选择
webhook_type_info = config_info.get("WEBHOOK_TYPE", {"description": "Webhook类型", "value": "dingtalk", "options": ["dingtalk", "feishu"]})
current_webhook_type = st.session_state.temp_config.get("WEBHOOK_TYPE", "dingtalk")
new_webhook_type = st.selectbox(
f"📲 {webhook_type_info['description']}",
options=webhook_type_info.get('options', ["dingtalk", "feishu"]),
index=0 if current_webhook_type == "dingtalk" else 1,
disabled=not new_webhook_enabled,
key="input_webhook_type"
)
st.session_state.temp_config["WEBHOOK_TYPE"] = new_webhook_type
# Webhook URL
webhook_url_info = config_info.get("WEBHOOK_URL", {"description": "Webhook地址", "value": ""})
current_webhook_url = st.session_state.temp_config.get("WEBHOOK_URL", "")
new_webhook_url = st.text_input(
f"🔗 {webhook_url_info['description']}",
value=current_webhook_url,
disabled=not new_webhook_enabled,
placeholder="https://oapi.dingtalk.com/robot/send?access_token=...",
key="input_webhook_url"
)
st.session_state.temp_config["WEBHOOK_URL"] = new_webhook_url
# Webhook自定义关键词（钉钉安全验证）
webhook_keyword_info = config_info.get("WEBHOOK_KEYWORD", {"description": "自定义关键词（钉钉安全验证）", "value": "aiagents通知"})
current_webhook_keyword = st.session_state.temp_config.get("WEBHOOK_KEYWORD", "aiagents通知")
new_webhook_keyword = st.text_input(
f"🔑 {webhook_keyword_info['description']}",
value=current_webhook_keyword,
disabled=not new_webhook_enabled or new_webhook_type != "dingtalk",
placeholder="aiagents通知",
help="钉钉机器人安全设置中的自定义关键词，飞书不需要此设置",
key="input_webhook_keyword"
)
st.session_state.temp_config["WEBHOOK_KEYWORD"] = new_webhook_keyword
# 测试连通按钮
if new_webhook_enabled and new_webhook_url:
if st.button("🧪 测试Webhook连通", width='stretch', key="test_webhook_btn"):
with st.spinner("正在发送测试消息..."):
# 临时更新配置
temp_env_backup = {}
for key in ["WEBHOOK_ENABLED", "WEBHOOK_TYPE", "WEBHOOK_URL", "WEBHOOK_KEYWORD"]:
temp_env_backup[key] = os.getenv(key)
os.environ[key] = st.session_state.temp_config.get(key, "")
try:
# 创建临时通知服务实例
from notification_service import NotificationService
temp_notification_service = NotificationService()
success, message = temp_notification_service.send_test_webhook()
if success:
st.success(f"✅ {message}")
else:
st.error(f"❌ {message}")
except Exception as e:
st.error(f"❌ 测试失败: {str(e)}")
finally:
# 恢复环境变量
for key, value in temp_env_backup.items():
if value is not None:
os.environ[key] = value
elif key in os.environ:
del os.environ[key]
if new_webhook_enabled and new_webhook_url:
st.success(f"✅ Webhook配置完整 ({new_webhook_type})")
elif new_webhook_enabled:
st.warning("⚠️ 请配置Webhook URL")
else:
st.info("ℹ️ Webhook通知未启用")
# 显示帮助信息
if new_webhook_type == "dingtalk":
st.caption("💡 钉钉机器人配置：\n1. 进入钉钉群 → 设置 → 智能群助手\n2. 添加机器人 → 自定义\n3. 复制Webhook地址\n4. 安全设置选择【自定义关键词】，填写上方的关键词")
else:
st.caption("💡 飞书机器人配置：\n1. 进入飞书群 → 设置 → 群机器人\n2. 添加机器人 → 自定义机器人\n3. 复制Webhook地址")
st.markdown("---")
st.info("💡 **使用说明**：\n- 可以同时启用邮件和Webhook通知\n- 实时监测和智策定时分析都会使用配置的通知方式\n- 配置后建议使用各功能中的测试按钮验证通知是否正常")
# 操作按钮
st.markdown("---")
col1, col2, col3, col4 = st.columns([1, 1, 1, 2])
with col1:
if st.button("💾 保存配置", type="primary", width='stretch'):
# 验证配置
is_valid, message = config_manager.validate_config(st.session_state.temp_config)
if is_valid:
# 保存配置
if config_manager.write_env(st.session_state.temp_config):
st.success("✅ 配置已保存到 .env 文件")
st.info("ℹ️ 请重启应用使配置生效")
# 尝试重新加载配置
try:
config_manager.reload_config()
st.success("✅ 配置已重新加载")
except Exception as e:
st.warning(f"⚠️ 配置重新加载失败: {e}")
time.sleep(2)
st.rerun()
else:
st.error("❌ 保存配置失败")
else:
st.error(f"❌ 配置验证失败: {message}")
with col2:
if st.button("🔄 重置", width='stretch'):
# 重置为当前文件中的值
st.session_state.temp_config = {key: info["value"] for key, info in config_info.items()}
st.success("✅ 已重置为当前配置")
st.rerun()
with col3:
if st.button("⬅️ 返回", width='stretch'):
if 'show_config' in st.session_state:
del st.session_state.show_config
if 'temp_config' in st.session_state:
del st.session_state.temp_config
st.rerun()
# 显示当前.env文件内容
st.markdown("---")
with st.expander("📄 查看当前 .env 文件内容"):
current_config = config_manager.read_env()
st.code(f"""# AI股票分析系统环境配置
# 由系统自动生成和管理
# ========== DeepSeek API配置 ==========
DEEPSEEK_API_KEY="{current_config.get('DEEPSEEK_API_KEY', '')}"
DEEPSEEK_BASE_URL="{current_config.get('DEEPSEEK_BASE_URL', '')}"
# ========== Tushare数据接口（可选）==========
TUSHARE_TOKEN="{current_config.get('TUSHARE_TOKEN', '')}"
# ========== MiniQMT量化交易配置（可选）==========
MINIQMT_ENABLED="{current_config.get('MINIQMT_ENABLED', 'false')}"
MINIQMT_ACCOUNT_ID="{current_config.get('MINIQMT_ACCOUNT_ID', '')}"
MINIQMT_HOST="{current_config.get('MINIQMT_HOST', '127.0.0.1')}"
MINIQMT_PORT="{current_config.get('MINIQMT_PORT', '58610')}"
# ========== 邮件通知配置（可选）==========
EMAIL_ENABLED="{current_config.get('EMAIL_ENABLED', 'false')}"
SMTP_SERVER="{current_config.get('SMTP_SERVER', '')}"
SMTP_PORT="{current_config.get('SMTP_PORT', '587')}"
EMAIL_FROM="{current_config.get('EMAIL_FROM', '')}"
EMAIL_PASSWORD="{current_config.get('EMAIL_PASSWORD', '')}"
EMAIL_TO="{current_config.get('EMAIL_TO', '')}"
# ========== Webhook通知配置（可选）==========
WEBHOOK_ENABLED="{current_config.get('WEBHOOK_ENABLED', 'false')}"
WEBHOOK_TYPE="{current_config.get('WEBHOOK_TYPE', 'dingtalk')}"
WEBHOOK_URL="{current_config.get('WEBHOOK_URL', '')}"
WEBHOOK_KEYWORD="{current_config.get('WEBHOOK_KEYWORD', 'aiagents通知')}"
""", language="bash")
def display_batch_analysis_results(results, period):
"""显示批量分析结果（对比视图）"""
st.subheader("📊 批量分析结果对比")
# 统计信息
total = len(results)
success_results = [r for r in results if r['success']]
failed_results = [r for r in results if not r['success']]
saved_count = sum(1 for r in results if r.get('saved_to_db', False))
# 显示统计
col1, col2, col3, col4 = st.columns(4)
with col1:
st.metric("总数", total)
with col2:
st.metric("成功", len(success_results), delta=None, delta_color="normal")
with col3:
st.metric("失败", len(failed_results), delta=None, delta_color="inverse")
with col4:
st.metric("已保存", saved_count, delta=None, delta_color="normal")
# 提示信息
if saved_count > 0:
st.info(f"💾 已有 {saved_count} 只股票的分析结果保存到历史记录，可在侧边栏点击「📖 历史记录」查看")
st.markdown("---")
# 失败的股票列表
if failed_results:
with st.expander(f"❌ 查看失败的 {len(failed_results)} 只股票", expanded=False):
for result in failed_results:
st.error(f"**{result['symbol']}**: {result.get('error', '未知错误')}")
# 保存失败的股票列表
save_failed_results = [r for r in success_results if not r.get('saved_to_db', False)]
if save_failed_results:
with st.expander(f"⚠️ 查看分析成功但保存失败的 {len(save_failed_results)} 只股票", expanded=False):
for result in save_failed_results:
db_error = result.get('db_error', '未知错误')
st.warning(f"**{result['symbol']} - {result['stock_info'].get('name', 'N/A')}**: {db_error}")
# 成功的股票分析结果
if not success_results:
st.warning("⚠️ 没有成功分析的股票")
return
# 创建对比视图选项
view_mode = st.radio(
"显示模式",
["对比表格", "详细卡片"],
horizontal=True,
help="对比表格：横向对比多只股票；详细卡片：逐个查看详细分析"
)
if view_mode == "对比表格":
# 表格对比视图
display_comparison_table(success_results)
else:
# 详细卡片视图
display_detailed_cards(success_results, period)
def display_comparison_table(results):
"""显示对比表格"""
import pandas as pd
st.subheader("📋 股票对比表格")
# 构建对比数据
comparison_data = []
for result in results:
stock_info = result['stock_info']
indicators = result.get('indicators', {})
final_decision = result['final_decision']
# 解析评级
if isinstance(final_decision, dict):
rating = final_decision.get('rating', 'N/A')
confidence = final_decision.get('confidence_level', 'N/A')
target_price = final_decision.get('target_price', 'N/A')
else:
rating = 'N/A'
confidence = 'N/A'
target_price = 'N/A'
# 确保信心度为字符串类型，避免类型混合导致的序列化错误
if isinstance(confidence, (int, float)):
confidence = str(confidence)
row = {
'股票代码': stock_info.get('symbol', 'N/A'),
'股票名称': stock_info.get('name', 'N/A'),
'当前价格': stock_info.get('current_price', 'N/A'),
'涨跌幅(%)': stock_info.get('change_percent', 'N/A'),
'市盈率': stock_info.get('pe_ratio', 'N/A'),
'市净率': stock_info.get('pb_ratio', 'N/A'),
'RSI': indicators.get('rsi', 'N/A'),
'MACD': indicators.get('macd', 'N/A'),
'投资评级': rating,
'信心度': confidence,
'目标价格': target_price
}
comparison_data.append(row)
# 创建DataFrame
df = pd.DataFrame(comparison_data)
# 应用样式
# 显示表格（不使用样式，避免matplotlib导入问题）
st.dataframe(
df,
width='stretch',
height=400
)
# 添加评级说明
st.caption("💡 投资评级说明：强烈买入 > 买入 > 持有 > 卖出 > 强烈卖出")
# 添加筛选功能
st.markdown("---")
st.subheader("🔍 快速筛选")
col1, col2 = st.columns(2)
with col1:
rating_filter = st.multiselect(
"按评级筛选",
options=df['投资评级'].unique().tolist(),
default=df['投资评级'].unique().tolist()
)
with col2:
# 按涨跌幅排序
sort_by = st.selectbox(
"排序方式",
["默认", "涨跌幅降序", "涨跌幅升序", "信心度降序", "RSI降序"]
)
# 应用筛选
filtered_df = df[df['投资评级'].isin(rating_filter)]
# 应用排序
if sort_by == "涨跌幅降序":
filtered_df = filtered_df.sort_values('涨跌幅(%)', ascending=False)
elif sort_by == "涨跌幅升序":
filtered_df = filtered_df.sort_values('涨跌幅(%)', ascending=True)
elif sort_by == "信心度降序":
filtered_df = filtered_df.sort_values('信心度', ascending=False)
elif sort_by == "RSI降序":
filtered_df = filtered_df.sort_values('RSI', ascending=False)
if not filtered_df.empty:
st.dataframe(filtered_df, width='stretch')
else:
st.info("没有符合条件的股票")
def display_detailed_cards(results, period):
"""显示详细卡片视图"""
st.subheader("📇 详细分析卡片")
# 选择要查看的股票
stock_options = [f"{r['stock_info']['symbol']} - {r['stock_info']['name']}" for r in results]
selected_stock = st.selectbox("选择股票", options=stock_options)
# 找到对应的结果
selected_index = stock_options.index(selected_stock)
result = results[selected_index]
# 显示详细分析
stock_info = result['stock_info']
indicators = result['indicators']
agents_results = result['agents_results']
discussion_result = result['discussion_result']
final_decision = result['final_decision']
# 获取股票数据用于显示图表
try:
stock_info_current, stock_data, _ = get_stock_data(stock_info['symbol'], period)
# 显示股票基本信息
display_stock_info(stock_info, indicators)
# 显示股票图表
if stock_data is not None:
display_stock_chart(stock_data, stock_info)
# 显示各分析师报告
display_agents_analysis(agents_results)
# 显示团队讨论
display_team_discussion(discussion_result)
# 显示最终决策
display_final_decision(final_decision, stock_info, agents_results, discussion_result)
except Exception as e:
st.error(f"显示详细信息时出错: {str(e)}")
if __name__ == "__main__":
main()