aszerW
5c87bea4fc
问题： - HTML报告错误地将所有品种持仓收益简单累加 - 没有考虑仓位占比权重 - 导致净值曲线和KPI指标与策略实际结果不一致 修复： - 使用出场净值和仓位占比计算每日组合净值 - 净值 = sum(出场净值 * 仓位占比) - 总收益 = (最终净值 - 初始净值) / 初始净值 * 100 - 月度收益使用净值变化率计算 - 最大回撤基于真实净值曲线计算 - 胜率基于每日净值涨跌计算 - 修复pandas FutureWarning警告 现在HTML报告的净值曲线、收益指标与轮动策略完全一致
287 lines
11 KiB
Python
287 lines
11 KiB
Python
"""
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ETF轮动策略报告生成器
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=======================
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从回测数据生成精美的 HTML 策略报告
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使用方法:
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python generate_report.py
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python generate_report.py --start 2024-01-01 --end 2024-12-31
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"""
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import pandas as pd
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import numpy as np
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from jinja2 import Template
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from datetime import datetime
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import argparse
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import os
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import sys
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class ReportGenerator:
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"""策略报告生成器"""
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def __init__(self, results_dir='results'):
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self.results_dir = results_dir
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self.summary_df = None
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self.trades_df = None
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def load_data(self):
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"""加载回测数据"""
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# 加载汇总数据
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summary_path = os.path.join(self.results_dir, 'report_summary.csv')
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if not os.path.exists(summary_path):
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raise FileNotFoundError(f"找不到汇总数据文件: {summary_path}")
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self.summary_df = pd.read_csv(summary_path)
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# 转换百分比
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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 self.summary_df.columns:
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self.summary_df[col] = self.summary_df[col].str.rstrip('%').astype(float)
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# 加载交易记录
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trades_path = os.path.join(self.results_dir, 'report_trades.csv')
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if not os.path.exists(trades_path):
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raise FileNotFoundError(f"找不到交易记录文件: {trades_path}")
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self.trades_df = pd.read_csv(trades_path)
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self.trades_df['进场日期'] = pd.to_datetime(self.trades_df['进场日期'])
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self.trades_df['出场日期'] = pd.to_datetime(self.trades_df['出场日期'])
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print(f"✅ 数据加载成功: {len(self.trades_df)} 条交易记录")
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def calculate_kpis(self, trades_filtered=None):
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"""计算关键指标"""
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df = trades_filtered if trades_filtered is not None else self.trades_df
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# 使用净值计算真实收益
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# 按日期分组计算每日组合净值
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daily_nav = df.groupby('出场日期').apply(
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lambda x: (x['出场净值'].astype(float) * x['仓位占比'].str.rstrip('%').astype(float) / 100).sum(),
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include_groups=False
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).reset_index()
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daily_nav.columns = ['date', 'nav']
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daily_nav = daily_nav.sort_values('date')
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# 总收益 = (最终净值 - 初始净值) / 初始净值 * 100
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initial_nav = daily_nav['nav'].iloc[0]
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final_nav = daily_nav['nav'].iloc[-1]
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total_return = (final_nav - initial_nav) / initial_nav * 100
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# 年化收益
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days = (daily_nav['date'].iloc[-1] - daily_nav['date'].iloc[0]).days
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if days > 0:
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annual_return = total_return / (days / 365.0)
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else:
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annual_return = 0
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# 胜率 - 使用净值变化
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daily_nav['nav_change'] = daily_nav['nav'].pct_change()
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win_count = (daily_nav['nav_change'] > 0).sum()
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total_count = len(daily_nav) - 1 # 减去第一天
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win_rate = (win_count / total_count * 100) if total_count > 0 else 0
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loss_count = total_count - win_count
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# 夏普比率
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if daily_nav['nav_change'].std() > 0:
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sharpe_ratio = daily_nav['nav_change'].mean() / daily_nav['nav_change'].std() * np.sqrt(252)
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else:
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sharpe_ratio = 0
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# 最大回撤
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running_max = daily_nav['nav'].cummax()
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drawdown = (daily_nav['nav'] - running_max) / running_max * 100
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max_drawdown = drawdown.min()
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# 调仓次数
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total_trades = len(df)
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# 最佳品种 - 从 summary 获取
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if self.summary_df is not None:
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symbol_col = self.summary_df['累计收益']
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if symbol_col.dtype == 'object':
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symbol_col_num = symbol_col.str.rstrip('%').astype(float)
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else:
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symbol_col_num = symbol_col
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best_symbol = self.summary_df.loc[symbol_col_num.idxmax(), '品种代码']
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else:
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best_symbol = 'N/A'
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# 平均持仓天数
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avg_holding_days = df['持仓天数'].mean() if len(df) > 0 else 0
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return {
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'total_return': f"{total_return:.2f}",
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'annual_return': f"{annual_return:.2f}",
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'win_rate': f"{win_rate:.2f}",
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'max_drawdown': f"{max_drawdown:.2f}",
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'sharpe_ratio': f"{sharpe_ratio:.2f}",
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'total_trades': str(total_trades),
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'best_symbol': best_symbol,
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'avg_holding_days': f"{avg_holding_days:.1f}",
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'win_count': int(win_count),
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'loss_count': int(loss_count)
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}
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def prepare_chart_data(self, trades_filtered=None):
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"""准备图表数据"""
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df = trades_filtered if trades_filtered is not None else self.trades_df
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# 转换持仓收益为数值
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if df['持仓收益'].dtype == 'object':
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df = df.copy()
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df['持仓收益_num'] = df['持仓收益'].str.rstrip('%').astype(float)
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else:
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df = df.copy()
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df['持仓收益_num'] = df['持仓收益']
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df_sorted = df.sort_values('出场日期')
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# 净值曲线数据 - 使用出场净值（考虑仓位加权）
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# 按日期分组，计算每日的加权平均净值
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daily_nav = df_sorted.groupby('出场日期').apply(
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lambda x: (x['出场净值'].astype(float) * x['仓位占比'].str.rstrip('%').astype(float) / 100).sum(),
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include_groups=False
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).reset_index()
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daily_nav.columns = ['date', 'nav']
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daily_nav = daily_nav.sort_values('date')
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nav_values = daily_nav['nav'].round(4).tolist()
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nav_dates = daily_nav['date'].dt.strftime('%Y-%m-%d').tolist()
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# 月度收益数据 - 使用净值变化计算
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df_copy = df_sorted.copy()
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df_copy['年月'] = df_copy['出场日期'].dt.to_period('M')
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monthly_nav = df_copy.groupby('年月').apply(
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lambda x: (x['出场净值'].astype(float) * x['仓位占比'].str.rstrip('%').astype(float) / 100).sum(),
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include_groups=False
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).reset_index()
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monthly_nav.columns = ['年月', 'nav']
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monthly_nav = monthly_nav.sort_values('年月')
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monthly_nav['nav_change'] = monthly_nav['nav'].pct_change() * 100
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monthly_nav['nav_change'] = monthly_nav['nav_change'].fillna(0)
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monthly_nav['年月_str'] = monthly_nav['年月'].astype(str)
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monthly_dates = monthly_nav['年月_str'].tolist()
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monthly_values = monthly_nav['nav_change'].round(2).tolist()
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# 品种收益排行 - 使用累计收益列
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symbol_returns = self.summary_df.set_index('品种代码')['累计收益']
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if symbol_returns.dtype == 'object':
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symbol_returns = symbol_returns.str.rstrip('%').astype(float)
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symbol_returns = symbol_returns.sort_values()
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symbol_names = []
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symbol_returns_list = []
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for code, ret in symbol_returns.items():
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name = self.summary_df[self.summary_df['品种代码'] == code]
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if len(name) > 0:
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symbol_names.append(name.iloc[0]['品种名称'])
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else:
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symbol_names.append(code)
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symbol_returns_list.append(round(ret, 2))
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# 唯一品种列表
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symbols = df['品种代码'].unique().tolist()
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return {
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'nav_dates': nav_dates,
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'nav_values': [round(v, 2) for v in nav_values],
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'monthly_dates': monthly_dates,
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'monthly_values': monthly_values,
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'symbol_names': symbol_names,
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'symbol_returns': symbol_returns_list,
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'symbols': symbols
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}
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def generate(self, start_date=None, end_date=None, output_dir='reports'):
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"""生成报告"""
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print("🚀 开始生成策略报告...")
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# 加载数据
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self.load_data()
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# 筛选数据
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if start_date:
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start_date = pd.to_datetime(start_date)
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if end_date:
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end_date = pd.to_datetime(end_date)
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trades_filtered = self.trades_df.copy()
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if start_date:
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trades_filtered = trades_filtered[trades_filtered['出场日期'] >= start_date]
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if end_date:
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trades_filtered = trades_filtered[trades_filtered['出场日期'] <= end_date]
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print(f"📊 筛选后数据: {len(trades_filtered)} 条记录")
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# 计算指标
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kpis = self.calculate_kpis(trades_filtered)
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chart_data = self.prepare_chart_data(trades_filtered)
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# 准备交易记录
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trades_display = trades_filtered.copy()
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trades_display['进场日期'] = trades_display['进场日期'].dt.strftime('%Y-%m-%d')
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trades_display['出场日期'] = trades_display['出场日期'].dt.strftime('%Y-%m-%d')
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trades_list = trades_display.to_dict('records')
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# 读取模板
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template_path = os.path.join(os.path.dirname(__file__), 'template.html')
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with open(template_path, 'r', encoding='utf-8') as f:
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template = Template(f.read())
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# 渲染模板
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html = template.render(
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report_date=datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
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start_date=start_date.strftime('%Y-%m-%d') if start_date else trades_filtered['出场日期'].min().strftime('%Y-%m-%d'),
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end_date=end_date.strftime('%Y-%m-%d') if end_date else trades_filtered['出场日期'].max().strftime('%Y-%m-%d'),
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trades=trades_list,
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**kpis,
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**chart_data
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)
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# 创建输出目录
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os.makedirs(output_dir, exist_ok=True)
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# 保存报告
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output_file = os.path.join(
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output_dir,
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f'strategy_report_{datetime.now().strftime("%Y%m%d_%H%M%S")}.html'
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)
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with open(output_file, 'w', encoding='utf-8') as f:
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f.write(html)
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print(f"✅ 报告已生成: {output_file}")
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print(f"📁 文件大小: {os.path.getsize(output_file) / 1024:.1f} KB")
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print(f"🌐 在浏览器中打开: file://{os.path.abspath(output_file)}")
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return output_file
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def main():
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"""主函数"""
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parser = argparse.ArgumentParser(description='生成ETF轮动策略报告')
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parser.add_argument('--start', type=str, help='开始日期 (YYYY-MM-DD)')
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parser.add_argument('--end', type=str, help='结束日期 (YYYY-MM-DD)')
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parser.add_argument('--output', type=str, default='reports', help='输出目录')
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args = parser.parse_args()
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try:
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generator = ReportGenerator()
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generator.generate(
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start_date=args.start,
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end_date=args.end,
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output_dir=args.output
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)
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except Exception as e:
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print(f"❌ 生成失败: {e}")
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import traceback
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traceback.print_exc()
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sys.exit(1)
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if __name__ == '__main__':
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main()
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