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