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
# 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("### 🔍 快捷导航")
if st.button("📖 历史记录", use_container_width=True, key="nav_history"):
st.session_state.show_history = True
if 'show_monitor' in st.session_state:
del st.session_state.show_monitor
if st.button("📊 实时监测", use_container_width=True, key="nav_monitor"):
st.session_state.show_monitor = True
if 'show_history' in st.session_state:
del st.session_state.show_history
if st.button("🏠 返回首页", use_container_width=True, key="nav_home"):
if 'show_history' in st.session_state:
del st.session_state.show_history
if 'show_monitor' in st.session_state:
del st.session_state.show_monitor
if 'show_config' in st.session_state:
del st.session_state.show_config
if st.button("⚙️ 环境配置", use_container_width=True, key="nav_config"):
st.session_state.show_config = True
if 'show_history' in st.session_state:
del st.session_state.show_history
if 'show_monitor' in st.session_state:
del st.session_state.show_monitor
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）
- 🇺🇸 美股：字母代码（如AAPL）
**功能说明**
- **智能分析**：AI团队深度分析
- **实时监测**：价格监控与提醒
- **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. 新闻公告
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
# 检查是否显示环境配置
if 'show_config' in st.session_state and st.session_state.show_config:
display_config_manager()
return
# 主界面
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, 600036",
help="支持美股代码(如AAPL)和A股代码(如000001)"
)
with col2:
analyze_button = st.button("🚀 开始分析", type="primary", use_container_width=True)
with col3:
if st.button("🔄 清除缓存", use_container_width=True):
st.cache_data.clear()
st.success("缓存已清除")
# 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("📰 新闻公告分析师", value=False,
help="负责新闻事件分析、公司公告解读（仅A股）")
# 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("新闻公告分析师")
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
# 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
run_stock_analysis(stock_input, period)
# 检查是否有已完成的分析结果
if 'analysis_completed' in st.session_state and st.session_state.analysis_completed:
# 重新显示分析结果
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 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)
# 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）
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（主力）...")
try:
fund_flow_data = fetcher.get_fund_flow_data(symbol)
if fund_flow_data and fund_flow_data.get('query_success'):
st.info("✅ 成功获取主力资金流向数据")
else:
st.warning("⚠️ 未能获取主力资金流向数据，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. 获取新闻公告数据（仅在选择了新闻公告分析师时）
news_announcement_data = None
if enable_news and fetcher._is_chinese_stock(symbol):
status_text.text("📰 正在获取新闻公告数据（问财）...")
try:
from news_announcement_data import NewsAnnouncementDataFetcher
news_fetcher = NewsAnnouncementDataFetcher()
news_announcement_data = news_fetcher.get_news_and_announcements(symbol)
if news_announcement_data and news_announcement_data.get('data_success'):
news_count = news_announcement_data.get('news_data', {}).get('count', 0) if news_announcement_data.get('news_data') else 0
announcement_count = news_announcement_data.get('announcement_data', {}).get('count', 0) if news_announcement_data.get('announcement_data') else 0
st.info(f"✅ 成功获取 {news_count} 条新闻，{announcement_count} 条公告")
else:
st.warning("⚠️ 未能获取新闻公告数据，将基于基本信息进行分析")
except Exception as e:
st.warning(f"⚠️ 获取新闻公告数据时出错: {str(e)}")
news_announcement_data = None
elif enable_news and not fetcher._is_chinese_stock(symbol):
st.info("ℹ️ 美股暂不支持新闻公告数据")
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_announcement_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)
# 保存分析结果到session_state
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
# 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)}")
# 显示最终决策
display_final_decision(final_decision, stock_info, agents_results, discussion_result)
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, use_container_width=True, 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, use_container_width=True, 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**：支持美股(如AAPL、MSFT)和A股(如000001、600036)
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**：5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
**美股热门**
- AAPL (苹果)
- MSFT (微软)
- GOOGL (谷歌)
- TSLA (特斯拉)
- NVDA (英伟达)
**A股热门**
- 000001 (平安银行)
- 600036 (招商银行)
- 000002 (万科A)
- 600519 (贵州茅台)
- 000858 (五粮液)
""")
st.info("💡 提示：首次运行需要配置DeepSeek API Key，请在.env中设置DEEPSEEK_API_KEY")
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", use_container_width=True)
with col_b:
cancel = st.form_submit_button("❌ 取消", use_container_width=True)
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", use_container_width=True):
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 = st.tabs(["📝 基本配置", "📊 数据源配置", "🤖 量化交易配置"])
# 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！")
# 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", use_container_width=True):
# 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", use_container_width=True):
# 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", use_container_width=True):
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')}"
""", language="bash")
if __name__ == "__main__":
main()