diff --git a/requirements_streamlit.txt b/requirements_streamlit.txt
new file mode 100644
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+++ b/requirements_streamlit.txt
@@ -0,0 +1,5 @@
+# Streamlit 可视化应用依赖
+streamlit>=1.28.0
+plotly>=5.15.0
+pandas>=2.0.0
+requests>=2.31.0
diff --git a/start_streamlit.sh b/start_streamlit.sh
new file mode 100755
index 0000000..7e96f18
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+++ b/start_streamlit.sh
@@ -0,0 +1,24 @@
+#!/bin/bash
+# Streamlit 应用启动脚本
+
+cd "$(dirname "$0")"
+
+echo "🚀 启动 ETF轮动策略可视化应用..."
+echo ""
+
+# 检查虚拟环境
+if [ -d "venv" ]; then
+    source venv/bin/activate
+fi
+
+# 安装依赖（如果需要）
+# pip install -r requirements_streamlit.txt
+
+# 设置环境变量
+export API_BASE_URL=${API_BASE_URL:-"https://k3s.tokenpluse.xyz"}
+
+echo "📊 正在启动 Streamlit 服务..."
+echo "🌐 API地址: $API_BASE_URL"
+echo ""
+
+streamlit run streamlit_app.py --server.port 8501 --server.address 0.0.0.0
diff --git a/streamlit_app.py b/streamlit_app.py
new file mode 100644
index 0000000..45b0ad8
--- /dev/null
+++ b/streamlit_app.py
@@ -0,0 +1,366 @@
+"""
+ETF轮动策略回测结果可视化
+============================
+使用 Streamlit 展示策略回测结果
+
+运行: streamlit run streamlit_app.py
+"""
+
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+import requests
+from datetime import datetime
+import os
+
+# 页面配置
+st.set_page_config(
+    page_title="ETF轮动策略回测结果",
+    page_icon="📈",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# 样式
+st.markdown("""
+<style>
+    .main-header {
+        font-size: 2.5rem;
+        font-weight: bold;
+        color: #1f77b4;
+    }
+    .metric-card {
+        background-color: #f0f2f6;
+        border-radius: 10px;
+        padding: 20px;
+        margin: 10px 0;
+    }
+    .positive { color: #28a745; }
+    .negative { color: #dc3545; }
+</style>
+""", unsafe_allow_html=True)
+
+# 标题
+st.markdown('<p class="main-header">📈 ETF轮动策略回测结果</p>', unsafe_allow_html=True)
+st.markdown("---")
+
+# 侧边栏配置
+st.sidebar.header("⚙️ 配置")
+
+# 数据API配置
+api_base = st.sidebar.text_input(
+    "数据API地址",
+    value=os.getenv('API_BASE_URL', 'https://k3s.tokenpluse.xyz'),
+    help="Flask API服务地址"
+)
+
+# 加载数据
+@st.cache_data(ttl=3600)
+def load_summary_data():
+    """加载策略汇总数据"""
+    try:
+        df = pd.read_csv('results/report_summary.csv')
+        # 转换百分比字符串为数值
+        for col in ['胜率', '平均收益', '累计收益', '最大单次收益', '最大单次亏损']:
+            if col in df.columns:
+                df[col] = df[col].str.rstrip('%').astype(float)
+        return df
+    except Exception as e:
+        st.error(f"加载汇总数据失败: {e}")
+        return None
+
+@st.cache_data(ttl=3600)
+def load_trades_data():
+    """加载交易记录数据"""
+    try:
+        df = pd.read_csv('results/report_trades.csv')
+        df['进场日期'] = pd.to_datetime(df['进场日期'])
+        df['出场日期'] = pd.to_datetime(df['出场日期'])
+        return df
+    except Exception as e:
+        st.error(f"加载交易数据失败: {e}")
+        return None
+
+# 加载数据
+summary_df = load_summary_data()
+trades_df = load_trades_data()
+
+# 主页面标签页
+tab1, tab2, tab3, tab4 = st.tabs(["📊 策略概览", "📈 收益分析", "🔄 调仓记录", "🔍 品种详情"])
+
+# ============ Tab 1: 策略概览 ============
+with tab1:
+    st.header("策略绩效概览")
+    
+    if summary_df is not None:
+        # 关键指标
+        col1, col2, col3, col4 = st.columns(4)
+        
+        with col1:
+            total_trades = summary_df['调仓次数'].sum()
+            st.metric("总调仓次数", f"{total_trades:,}")
+        
+        with col2:
+            avg_win_rate = summary_df['胜率'].mean()
+            st.metric("平均胜率", f"{avg_win_rate:.2f}%")
+        
+        with col3:
+            best_return = summary_df['累计收益'].max()
+            best_code = summary_df.loc[summary_df['累计收益'].idxmax(), '品种代码']
+            st.metric("最佳品种收益", f"{best_return:.2f}%", f"{best_code}")
+        
+        with col4:
+            worst_return = summary_df['累计收益'].min()
+            worst_code = summary_df.loc[summary_df['累计收益'].idxmin(), '品种代码']
+            st.metric("最差品种收益", f"{worst_return:.2f}%", f"{worst_code}")
+        
+        st.markdown("---")
+        
+        # 品种收益对比
+        fig = px.bar(
+            summary_df.sort_values('累计收益', ascending=True),
+            x='累计收益',
+            y='品种名称',
+            orientation='h',
+            color='累计收益',
+            color_continuous_scale=['red', 'yellow', 'green'],
+            title="各品种累计收益对比",
+            labels={'累计收益': '累计收益 (%)', '品种名称': ''}
+        )
+        fig.update_layout(height=400)
+        st.plotly_chart(fig, use_container_width=True)
+        
+        # 胜率 vs 平均收益散点图
+        fig2 = px.scatter(
+            summary_df,
+            x='胜率',
+            y='平均收益',
+            size='调仓次数',
+            color='品种名称',
+            hover_data=['品种代码', '累计收益'],
+            title="胜率 vs 平均收益（气泡大小=调仓次数）",
+            labels={'胜率': '胜率 (%)', '平均收益': '平均收益 (%)'}
+        )
+        st.plotly_chart(fig2, use_container_width=True)
+        
+        # 数据表格
+        st.subheader("详细数据")
+        st.dataframe(
+            summary_df.style.format({
+                '胜率': '{:.2f}%',
+                '平均收益': '{:.2f}%',
+                '累计收益': '{:.2f}%',
+                '最大单次收益': '{:.2f}%',
+                '最大单次亏损': '{:.2f}%'
+            }),
+            use_container_width=True
+        )
+    else:
+        st.warning("暂无汇总数据")
+
+# ============ Tab 2: 收益分析 ============
+with tab2:
+    st.header("收益分析")
+    
+    if trades_df is not None:
+        # 按月份统计收益
+        trades_df['年月'] = trades_df['出场日期'].dt.to_period('M')
+        monthly_returns = trades_df.groupby('年月').agg({
+            '持仓收益': 'sum',
+            '品种代码': 'count'
+        }).rename(columns={'品种代码': '交易次数'})
+        monthly_returns.index = monthly_returns.index.astype(str)
+        
+        # 月度收益柱状图
+        fig = px.bar(
+            monthly_returns.reset_index(),
+            x='年月',
+            y='持仓收益',
+            title="月度累计收益",
+            labels={'持仓收益': '收益 (%)', '年月': '月份'},
+            color='持仓收益',
+            color_continuous_scale=['red', 'yellow', 'green']
+        )
+        st.plotly_chart(fig, use_container_width=True)
+        
+        # 收益分布直方图
+        fig2 = px.histogram(
+            trades_df,
+            x='持仓收益',
+            nbins=50,
+            title="单次交易收益分布",
+            labels={'持仓收益': '收益 (%)', 'count': '次数'}
+        )
+        fig2.add_vline(x=0, line_dash="dash", line_color="red")
+        st.plotly_chart(fig2, use_container_width=True)
+        
+        # 累计收益曲线
+        trades_sorted = trades_df.sort_values('出场日期')
+        trades_sorted['累计收益_曲线'] = trades_sorted['持仓收益'].cumsum()
+        
+        fig3 = px.line(
+            trades_sorted,
+            x='出场日期',
+            y='累计收益_曲线',
+            title="策略累计收益曲线",
+            labels={'累计收益_曲线': '累计收益 (%)', '出场日期': '日期'}
+        )
+        st.plotly_chart(fig3, use_container_width=True)
+    else:
+        st.warning("暂无交易数据")
+
+# ============ Tab 3: 调仓记录 ============
+with tab3:
+    st.header("调仓记录")
+    
+    if trades_df is not None:
+        # 筛选器
+        col1, col2, col3 = st.columns(3)
+        
+        with col1:
+            selected_codes = st.multiselect(
+                "选择品种",
+                options=trades_df['品种代码'].unique(),
+                default=[]
+            )
+        
+        with col2:
+            min_return = st.slider(
+                "最小收益",
+                min_value=float(trades_df['持仓收益'].min()),
+                max_value=float(trades_df['持仓收益'].max()),
+                value=float(trades_df['持仓收益'].min())
+            )
+        
+        with col3:
+            min_days = st.slider(
+                "最小持仓天数",
+                min_value=1,
+                max_value=int(trades_df['持仓天数'].max()),
+                value=1
+            )
+        
+        # 过滤数据
+        filtered_df = trades_df.copy()
+        if selected_codes:
+            filtered_df = filtered_df[filtered_df['品种代码'].isin(selected_codes)]
+        filtered_df = filtered_df[
+            (filtered_df['持仓收益'] >= min_return) &
+            (filtered_df['持仓天数'] >= min_days)
+        ]
+        
+        # 显示表格
+        st.subheader(f"筛选结果 ({len(filtered_df)} 条记录)")
+        
+        # 添加颜色标记
+        def color_return(val):
+            color = 'green' if val > 0 else 'red'
+            return f'color: {color}'
+        
+        styled_df = filtered_df.style.applymap(
+            color_return,
+            subset=['持仓收益']
+        ).format({
+            '进场价格': '{:.2f}',
+            '出场价格': '{:.2f}',
+            '持仓收益': '{:.2f}%',
+            '进场净值': '{:.4f}',
+            '出场净值': '{:.4f}',
+            '净值贡献': '{:.4f}'
+        })
+        
+        st.dataframe(styled_df, use_container_width=True, height=500)
+        
+        # 导出按钮
+        csv = filtered_df.to_csv(index=False).encode('utf-8')
+        st.download_button(
+            label="📥 导出筛选结果",
+            data=csv,
+            file_name=f"trades_filtered_{datetime.now().strftime('%Y%m%d')}.csv",
+            mime="text/csv"
+        )
+    else:
+        st.warning("暂无调仓记录")
+
+# ============ Tab 4: 品种详情 ============
+with tab4:
+    st.header("品种详情分析")
+    
+    if summary_df is not None and trades_df is not None:
+        selected_code = st.selectbox(
+            "选择品种",
+            options=summary_df['品种代码'].tolist(),
+            format_func=lambda x: f"{x} - {summary_df[summary_df['品种代码']==x]['品种名称'].iloc[0]}"
+        )
+        
+        if selected_code:
+            # 品种基本信息
+            code_info = summary_df[summary_df['品种代码'] == selected_code].iloc[0]
+            
+            col1, col2, col3, col4 = st.columns(4)
+            with col1:
+                st.metric("调仓次数", int(code_info['调仓次数']))
+            with col2:
+                st.metric("胜率", f"{code_info['胜率']:.2f}%")
+            with col3:
+                st.metric("累计收益", f"{code_info['累计收益']:.2f}%")
+            with col4:
+                st.metric("平均持仓天数", f"{code_info['平均持仓天数']:.1f}天")
+            
+            # 该品种的交易记录
+            code_trades = trades_df[trades_df['品种代码'] == selected_code].sort_values('出场日期')
+            
+            if len(code_trades) > 0:
+                # 收益时间序列
+                code_trades['累计收益_品种'] = code_trades['持仓收益'].cumsum()
+                
+                fig = make_subplots(
+                    rows=2, cols=1,
+                    subplot_titles=('单次收益', '累计收益'),
+                    vertical_spacing=0.1
+                )
+                
+                fig.add_trace(
+                    go.Bar(
+                        x=code_trades['出场日期'],
+                        y=code_trades['持仓收益'],
+                        name='单次收益',
+                        marker_color=['green' if x > 0 else 'red' for x in code_trades['持仓收益']]
+                    ),
+                    row=1, col=1
+                )
+                
+                fig.add_trace(
+                    go.Scatter(
+                        x=code_trades['出场日期'],
+                        y=code_trades['累计收益_品种'],
+                        name='累计收益',
+                        mode='lines+markers'
+                    ),
+                    row=2, col=1
+                )
+                
+                fig.update_layout(height=600, showlegend=False)
+                st.plotly_chart(fig, use_container_width=True)
+                
+                # 持仓天数分布
+                fig2 = px.box(
+                    code_trades,
+                    y='持仓天数',
+                    title=f"{code_info['品种名称']} 持仓天数分布"
+                )
+                st.plotly_chart(fig2, use_container_width=True)
+            else:
+                st.info("该品种暂无交易记录")
+    else:
+        st.warning("暂无数据")
+
+# 底部信息
+st.markdown("---")
+st.markdown("""
+<div style="text-align: center; color: #666;">
+    <p>ETF轮动策略回测系统 | 数据更新时间: {}</p>
+</div>
+""".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S')), unsafe_allow_html=True)