oficcejo
1119 lines
37 KiB
Python
1119 lines
37 KiB
Python
import streamlit as st
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import plotly.graph_objects as go
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import plotly.express as px
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import pandas as pd
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import json
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from datetime import datetime
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import time
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import base64
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import os
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from stock_data import StockDataFetcher
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from ai_agents import StockAnalysisAgents
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from pdf_generator import display_pdf_export_section
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from database import db
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from monitor_manager import display_monitor_manager, get_monitor_summary
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from monitor_service import monitor_service
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from notification_service import notification_service
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# 页面配置
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st.set_page_config(
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page_title="复合多AI智能体股票团队分析系统",
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page_icon="📈",
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layout="wide",
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initial_sidebar_state="expanded"
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)
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# 模型选择器
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def model_selector():
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"""模型选择器"""
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st.sidebar.markdown("---")
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st.sidebar.subheader("🤖 AI模型选择")
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model_options = {
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"deepseek-chat": "DeepSeek Chat (默认)",
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"deepseek-reasoner": "DeepSeek Reasoner (推理增强)"
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}
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selected_model = st.sidebar.selectbox(
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"选择AI模型",
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options=list(model_options.keys()),
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format_func=lambda x: model_options[x],
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help="DeepSeek Reasoner提供更强的推理能力，但响应时间可能更长"
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)
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return selected_model
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# 自定义CSS样式 - 专业版
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st.markdown("""
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<style>
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/* 全局样式 */
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.main {
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background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
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background-attachment: fixed;
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}
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.stApp {
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background: transparent;
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}
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/* 主容器 */
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.block-container {
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padding-top: 2rem;
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padding-bottom: 2rem;
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background: rgba(255, 255, 255, 0.95);
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border-radius: 20px;
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box-shadow: 0 10px 40px rgba(0, 0, 0, 0.1);
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margin-top: 1rem;
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}
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/* 顶部导航栏 */
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.top-nav {
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background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
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padding: 1.5rem 2rem;
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border-radius: 15px;
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margin-bottom: 2rem;
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box-shadow: 0 8px 32px rgba(102, 126, 234, 0.3);
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}
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.nav-title {
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font-size: 2rem;
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font-weight: 800;
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color: white;
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text-align: center;
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margin: 0;
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text-shadow: 2px 2px 4px rgba(0,0,0,0.2);
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letter-spacing: 1px;
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}
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.nav-subtitle {
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text-align: center;
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color: rgba(255, 255, 255, 0.9);
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font-size: 0.95rem;
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margin-top: 0.5rem;
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font-weight: 300;
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}
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/* 标签页样式 */
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.stTabs [data-baseweb="tab-list"] {
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gap: 2rem;
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background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
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padding: 1rem 2rem;
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border-radius: 15px;
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box-shadow: 0 4px 15px rgba(102, 126, 234, 0.2);
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}
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.stTabs [data-baseweb="tab"] {
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height: 60px;
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background: rgba(255, 255, 255, 0.1);
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border-radius: 10px;
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color: white;
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font-weight: 600;
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font-size: 1.1rem;
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padding: 0 2rem;
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border: none;
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transition: all 0.3s ease;
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}
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.stTabs [data-baseweb="tab"]:hover {
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background: rgba(255, 255, 255, 0.2);
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transform: translateY(-2px);
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}
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.stTabs [aria-selected="true"] {
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background: white !important;
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color: #667eea !important;
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box-shadow: 0 4px 15px rgba(0, 0, 0, 0.1);
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}
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/* 侧边栏美化 */
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.css-1d391kg, [data-testid="stSidebar"] {
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background: linear-gradient(180deg, #667eea 0%, #764ba2 100%);
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padding-top: 2rem;
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}
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.css-1d391kg h1, .css-1d391kg h2, .css-1d391kg h3,
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[data-testid="stSidebar"] h1, [data-testid="stSidebar"] h2, [data-testid="stSidebar"] h3 {
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color: white !important;
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}
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.css-1d391kg .stMarkdown, [data-testid="stSidebar"] .stMarkdown {
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color: rgba(255, 255, 255, 0.95) !important;
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}
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/* 分析师卡片 */
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.agent-card {
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background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
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padding: 1.5rem;
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border-radius: 15px;
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margin: 1rem 0;
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border-left: 5px solid #667eea;
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box-shadow: 0 4px 15px rgba(0, 0, 0, 0.1);
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transition: transform 0.3s ease;
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}
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.agent-card:hover {
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transform: translateX(5px);
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}
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/* 决策卡片 */
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.decision-card {
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background: linear-gradient(135deg, #e8f5e9 0%, #c8e6c9 100%);
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padding: 2rem;
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border-radius: 15px;
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border: 3px solid #4caf50;
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margin: 1.5rem 0;
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box-shadow: 0 8px 30px rgba(76, 175, 80, 0.2);
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}
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/* 警告卡片 */
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.warning-card {
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background: linear-gradient(135deg, #fff3e0 0%, #ffe0b2 100%);
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padding: 1.5rem;
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border-radius: 15px;
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border-left: 5px solid #ff9800;
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box-shadow: 0 4px 15px rgba(255, 152, 0, 0.2);
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}
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/* 指标卡片 */
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.metric-card {
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background: white;
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padding: 1.5rem;
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border-radius: 12px;
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box-shadow: 0 4px 20px rgba(0, 0, 0, 0.08);
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text-align: center;
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transition: transform 0.3s ease, box-shadow 0.3s ease;
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border-top: 4px solid #667eea;
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}
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.metric-card:hover {
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transform: translateY(-5px);
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box-shadow: 0 8px 30px rgba(0, 0, 0, 0.15);
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}
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/* 按钮美化 */
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.stButton>button {
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background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
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color: white;
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border: none;
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border-radius: 10px;
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padding: 0.75rem 2rem;
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font-weight: 600;
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font-size: 1rem;
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transition: all 0.3s ease;
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box-shadow: 0 4px 15px rgba(102, 126, 234, 0.3);
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}
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.stButton>button:hover {
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transform: translateY(-2px);
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box-shadow: 0 6px 25px rgba(102, 126, 234, 0.4);
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}
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/* 输入框美化 */
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.stTextInput>div>div>input {
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border-radius: 10px;
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border: 2px solid #e0e0e0;
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padding: 0.75rem;
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font-size: 1rem;
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transition: border-color 0.3s ease;
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}
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.stTextInput>div>div>input:focus {
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border-color: #667eea;
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box-shadow: 0 0 0 3px rgba(102, 126, 234, 0.1);
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}
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/* 进度条美化 */
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.stProgress > div > div > div > div {
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background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
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}
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/* 成功/错误/警告/信息消息框 */
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.stSuccess, .stError, .stWarning, .stInfo {
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border-radius: 10px;
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padding: 1rem;
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box-shadow: 0 2px 10px rgba(0, 0, 0, 0.1);
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}
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/* 图表容器 */
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.js-plotly-plot {
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border-radius: 15px;
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box-shadow: 0 4px 20px rgba(0, 0, 0, 0.1);
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}
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/* Expander美化 */
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.streamlit-expanderHeader {
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background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
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border-radius: 10px;
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font-weight: 600;
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}
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/* 数据框美化 */
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.dataframe {
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border-radius: 10px;
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overflow: hidden;
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box-shadow: 0 2px 10px rgba(0, 0, 0, 0.1);
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}
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/* 隐藏Streamlit默认元素 */
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#MainMenu {visibility: hidden;}
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footer {visibility: hidden;}
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/* 响应式设计 */
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@media (max-width: 768px) {
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.nav-title {
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font-size: 1.5rem;
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}
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.stTabs [data-baseweb="tab"] {
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font-size: 0.9rem;
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padding: 0 1rem;
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}
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}
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</style>
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""", unsafe_allow_html=True)
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def main():
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# 顶部标题栏
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st.markdown("""
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<div class="top-nav">
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<h1 class="nav-title">📈 复合多AI智能体股票团队分析系统</h1>
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<p class="nav-subtitle">基于DeepSeek的专业量化投资分析平台 | Multi-Agent Stock Analysis System</p>
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</div>
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""", unsafe_allow_html=True)
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# 侧边栏
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with st.sidebar:
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# 快捷导航 - 移到顶部
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st.markdown("### 🔍 快捷导航")
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if st.button("📖 历史记录", use_container_width=True, key="nav_history"):
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st.session_state.show_history = True
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if 'show_monitor' in st.session_state:
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del st.session_state.show_monitor
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if st.button("📊 实时监测", use_container_width=True, key="nav_monitor"):
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st.session_state.show_monitor = True
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if 'show_history' in st.session_state:
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del st.session_state.show_history
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if st.button("🏠 返回首页", use_container_width=True, key="nav_home"):
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if 'show_history' in st.session_state:
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del st.session_state.show_history
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if 'show_monitor' in st.session_state:
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del st.session_state.show_monitor
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st.markdown("---")
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# 系统配置
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st.markdown("### ⚙️ 系统配置")
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# API密钥检查
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api_key_status = check_api_key()
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if api_key_status:
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st.success("✅ API已连接")
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else:
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st.error("❌ API未配置")
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st.caption("请在.env中配置API密钥")
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st.markdown("---")
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# 模型选择器
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selected_model = model_selector()
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st.session_state.selected_model = selected_model
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st.markdown("---")
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# 系统状态面板
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st.markdown("### 📊 系统状态")
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monitor_status = "🟢 运行中" if monitor_service.running else "🔴 已停止"
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st.markdown(f"**监测服务**: {monitor_status}")
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try:
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from monitor_db import monitor_db
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stocks = monitor_db.get_monitored_stocks()
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notifications = monitor_db.get_pending_notifications()
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record_count = db.get_record_count()
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st.markdown(f"**分析记录**: {record_count}条")
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st.markdown(f"**监测股票**: {len(stocks)}只")
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st.markdown(f"**待处理**: {len(notifications)}条")
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except:
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pass
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st.markdown("---")
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# 分析参数设置
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st.markdown("### 📊 分析参数")
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period = st.selectbox(
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"数据周期",
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["1y", "6mo", "3mo", "1mo"],
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index=0,
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help="选择历史数据的时间范围"
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)
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st.markdown("---")
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# 帮助信息
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with st.expander("💡 使用帮助"):
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st.markdown("""
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**股票代码格式**
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- 🇨🇳 A股：6位数字（如600519）
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- 🇺🇸 美股：字母代码（如AAPL）
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**功能说明**
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- **智能分析**：AI团队深度分析
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- **实时监测**：价格监控与提醒
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- **历史记录**：查看分析历史
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**AI分析流程**
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1. 数据获取 → 2. 技术分析
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3. 基本面分析 → 4. 资金分析
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5. 风险评估 → 6. 情绪分析
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7. 团队讨论 → 8. 最终决策
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""")
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# 检查是否显示历史记录
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if 'show_history' in st.session_state and st.session_state.show_history:
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display_history_records()
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return
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# 检查是否显示监测面板
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if 'show_monitor' in st.session_state and st.session_state.show_monitor:
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display_monitor_manager()
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return
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# 主界面
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col1, col2, col3 = st.columns([2, 1, 1])
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with col1:
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stock_input = st.text_input(
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"🔍 请输入股票代码或名称",
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placeholder="例如: AAPL, 000001, 600036",
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help="支持美股代码(如AAPL)和A股代码(如000001)"
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)
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with col2:
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analyze_button = st.button("🚀 开始分析", type="primary", use_container_width=True)
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with col3:
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if st.button("🔄 清除缓存", use_container_width=True):
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st.cache_data.clear()
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st.success("缓存已清除")
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if analyze_button and stock_input:
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if not api_key_status:
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st.error("❌ 请先配置 DeepSeek API Key")
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return
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# 清除之前的分析结果
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if 'analysis_completed' in st.session_state:
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del st.session_state.analysis_completed
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if 'stock_info' in st.session_state:
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del st.session_state.stock_info
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if 'agents_results' in st.session_state:
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del st.session_state.agents_results
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if 'discussion_result' in st.session_state:
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del st.session_state.discussion_result
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if 'final_decision' in st.session_state:
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del st.session_state.final_decision
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run_stock_analysis(stock_input, period)
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# 检查是否有已完成的分析结果
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if 'analysis_completed' in st.session_state and st.session_state.analysis_completed:
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# 重新显示分析结果
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stock_info = st.session_state.stock_info
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agents_results = st.session_state.agents_results
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discussion_result = st.session_state.discussion_result
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final_decision = st.session_state.final_decision
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# 重新获取股票数据用于显示图表
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stock_info_current, stock_data, indicators = get_stock_data(stock_info['symbol'], period)
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# 显示股票基本信息
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display_stock_info(stock_info, indicators)
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# 显示股票图表
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if stock_data is not None:
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display_stock_chart(stock_data, stock_info)
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# 显示各分析师报告
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display_agents_analysis(agents_results)
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# 显示团队讨论
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display_team_discussion(discussion_result)
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# 显示最终决策
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display_final_decision(final_decision, stock_info, agents_results, discussion_result)
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# 示例和说明
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elif not stock_input:
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show_example_interface()
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def check_api_key():
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"""检查API密钥是否配置"""
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try:
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import config
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return bool(config.DEEPSEEK_API_KEY and config.DEEPSEEK_API_KEY.strip())
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except:
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return False
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@st.cache_data(ttl=300) # 缓存5分钟
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def get_stock_data(symbol, period):
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"""获取股票数据（带缓存）"""
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fetcher = StockDataFetcher()
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stock_info = fetcher.get_stock_info(symbol)
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stock_data = fetcher.get_stock_data(symbol, period)
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if isinstance(stock_data, dict) and "error" in stock_data:
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return stock_info, None, None
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stock_data_with_indicators = fetcher.calculate_technical_indicators(stock_data)
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indicators = fetcher.get_latest_indicators(stock_data_with_indicators)
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return stock_info, stock_data_with_indicators, indicators
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def run_stock_analysis(symbol, period):
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"""运行股票分析"""
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# 进度条
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progress_bar = st.progress(0)
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status_text = st.empty()
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try:
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# 1. 获取股票数据
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status_text.text("📈 正在获取股票数据...")
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progress_bar.progress(10)
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stock_info, stock_data, indicators = get_stock_data(symbol, period)
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if "error" in stock_info:
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st.error(f"❌ {stock_info['error']}")
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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)
|
|
|
|
# 3. 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(45)
|
|
|
|
# 4. 运行多智能体分析
|
|
status_text.text("🔍 AI分析师团队正在分析...")
|
|
agents_results = agents.run_multi_agent_analysis(stock_info, stock_data, indicators, financial_data)
|
|
progress_bar.progress(70)
|
|
|
|
# 显示各分析师报告
|
|
display_agents_analysis(agents_results)
|
|
|
|
# 5. 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(85)
|
|
|
|
# 显示团队讨论
|
|
display_team_discussion(discussion_result)
|
|
|
|
# 6. 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"""
|
|
<div class="agent-card">
|
|
<h4>👨💼 {agent_result.get('agent_name', '未知')}</h4>
|
|
<p><strong>职责：</strong>{agent_result.get('agent_role', '未知')}</p>
|
|
<p><strong>关注领域：</strong>{', '.join(agent_result.get('focus_areas', []))}</p>
|
|
<p><strong>分析时间：</strong>{agent_result.get('timestamp', '未知')}</p>
|
|
</div>
|
|
""", 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("""
|
|
<div class="agent-card">
|
|
<h4>💭 团队综合讨论</h4>
|
|
<p>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...</p>
|
|
</div>
|
|
""", 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"""
|
|
<div class="decision-card">
|
|
<h3 style="text-align: center;">{rating_color} {rating}</h3>
|
|
<h4 style="text-align: center;">投资评级</h4>
|
|
</div>
|
|
""", 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"""
|
|
<div class="warning-card">
|
|
<h4>⚠️ 风险提示</h4>
|
|
<p>{risk_warning}</p>
|
|
</div>
|
|
""", 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']}"):
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col1, col2, col3, col4 = st.columns([2, 2, 1, 1])
|
|
|
|
with col1:
|
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st.write(f"**股票代码:** {record['symbol']}")
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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("🗑️ 删除", key=f"delete_{record['id']}"):
|
|
if db.delete_record(record['id']):
|
|
st.success("✅ 记录已删除")
|
|
st.rerun()
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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_record_detail(record_id):
|
|
"""显示单条记录的详细信息"""
|
|
st.markdown("---")
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|
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"""
|
|
<div class="agent-card">
|
|
<h4>👨💼 {agent_result.get('agent_name', '未知')}</h4>
|
|
<p><strong>职责：</strong>{agent_result.get('agent_role', '未知')}</p>
|
|
<p><strong>关注领域：</strong>{', '.join(agent_result.get('focus_areas', []))}</p>
|
|
</div>
|
|
""", 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("""
|
|
<div class="agent-card">
|
|
<h4>💭 团队综合讨论</h4>
|
|
</div>
|
|
""", 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"""
|
|
<div class="decision-card">
|
|
<h3 style="text-align: center;">{rating_color} {rating}</h3>
|
|
<h4 style="text-align: center;">投资评级</h4>
|
|
</div>
|
|
""", 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)
|
|
|
|
# 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
|
|
st.rerun()
|
|
|
|
if __name__ == "__main__":
|
|
main() |