etf/streamlit_app.py

"""
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)