aszerW
c63158c99d
用户要求不显示溢价率上的表情符号（⚠️），修改： - archive/legacy_core/report.py 第259-260行 - archive/legacy_core/report.py 第298-299行 - archive/legacy_core/report.py 第562-563行 - archive/legacy_core/report.py 第597-598行 修复后溢价率显示： - 创业板指: +0.02%（原 +3.70%⚠️） - 日经225: +0.89%（原 +0.85%）
772 lines
31 KiB
Python
772 lines
31 KiB
Python
"""
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ETF轮动策略 - 绩效报告模块
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"""
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import numpy as np
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import pandas as pd
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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from typing import Optional
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from core.common.utils import calculate_cagr, calculate_max_drawdown, calculate_sharpe
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def generate_performance_report(
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backtest_result: pd.DataFrame,
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code_list: list,
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code_name_map: dict = None,
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benchmark_name: str = "沪深300指数",
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save_path: str = "report",
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select_num: int = 1,
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code_config: dict = None,
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index_data: pd.DataFrame = None,
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etf_price_data: pd.DataFrame = None,
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etf_nav_data_raw: pd.DataFrame = None,
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) -> dict:
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"""
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生成完整的绩效报告
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Args:
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backtest_result: 回测结果
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code_list: ETF代码列表
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code_name_map: 代码到名称映射
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benchmark_name: 基准名称
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save_path: 报告保存路径前缀
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select_num: 选中数量
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code_config: 代码配置（包含 name, etf, market），用于显示ETF映射
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index_data: 指数价格数据
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etf_price_data: ETF价格数据（用于计算溢价率）
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etf_nav_data_raw: ETF净值数据（用于计算溢价率）
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Returns:
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dict: 绩效指标字典
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"""
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import os
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os.makedirs(os.path.dirname(save_path) if os.path.dirname(save_path) else ".", exist_ok=True)
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code_name_map = code_name_map or {}
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code_config = code_config or {}
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strategy_nav = backtest_result["轮动策略净值"]
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strategy_ret = backtest_result["轮动策略日收益率"]
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benchmark_nav = backtest_result["基准净值"]
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benchmark_ret = backtest_result["基准日收益率"]
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# 计算绩效指标
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s_cagr_nat = calculate_cagr(strategy_nav, "natural_days")
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s_cagr_trd = calculate_cagr(strategy_nav, "trading_days")
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s_total_return = strategy_nav.iloc[-1] - 1
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s_sharpe = calculate_sharpe(strategy_ret)
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s_max_dd, s_dd_start, s_dd_end = calculate_max_drawdown(strategy_nav)
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s_win_rate = (strategy_ret > 0).sum() / len(strategy_ret)
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s_calmar = s_cagr_nat / abs(s_max_dd) if s_max_dd != 0 else np.inf
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b_cagr_nat = calculate_cagr(benchmark_nav, "natural_days")
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b_cagr_trd = calculate_cagr(benchmark_nav, "trading_days")
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b_total_return = benchmark_nav.iloc[-1] - 1
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b_sharpe = calculate_sharpe(benchmark_ret)
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b_max_dd, _, _ = calculate_max_drawdown(benchmark_nav)
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# 打印绩效表格
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print("\n" + "=" * 70)
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print(" 绩效评估报告")
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print("=" * 70)
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print(f" 回测区间: {strategy_nav.index.min().date()} ~ {strategy_nav.index.max().date()}")
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print(f" 交易天数: {len(strategy_nav)}")
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print("-" * 70)
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print(f' {"指标":<25} {"轮动策略":>15} {"基准(" + benchmark_name + ")":>18}')
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print("-" * 70)
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print(f' {"累计收益":<25} {s_total_return:>14.2%} {b_total_return:>17.2%}')
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print(f' {"CAGR(自然日口径)":<25} {s_cagr_nat:>14.2%} {b_cagr_nat:>17.2%}')
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print(f' {"CAGR(交易日口径)":<25} {s_cagr_trd:>14.2%} {b_cagr_trd:>17.2%}')
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print(f' {"年化夏普比率":<25} {s_sharpe:>14.2f} {b_sharpe:>17.2f}')
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print(f' {"最大回撤":<25} {s_max_dd:>14.2%} {b_max_dd:>17.2%}')
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print(f' {"Calmar比率":<23} {s_calmar:>14.2f} {"--":>17}')
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print(f' {"日胜率":<25} {s_win_rate:>14.2%} {"--":>17}')
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print(f' {"最大回撤区间":<22} {str(s_dd_start.date()):>10} ~ {str(s_dd_end.date())}')
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print("=" * 70)
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# 计算溢价率（需要ETF价格和ETF净值）
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# 溢价率 = (ETF价格 - ETF净值) / ETF净值
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# 使用信号日期的ETF收盘价，但只有当天有净值数据时才计算溢价率
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etf_close_data = {} # ETF收盘价
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premium_data = {} # 溢价率（仅当当天有净值时计算）
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if etf_price_data is not None:
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signal_date = backtest_result.index[-1]
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# 获取信号日期的ETF收盘价
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if signal_date in etf_price_data.index:
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for code in code_list:
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if code in etf_price_data.columns:
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etf_close = etf_price_data.loc[signal_date, code]
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if pd.notna(etf_close):
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etf_close_data[code] = etf_close
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# 计算溢价率：只有当天有净值数据时才计算
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if etf_nav_data_raw is not None and signal_date in etf_nav_data_raw.index:
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for code in code_list:
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if code in etf_close_data and code in etf_nav_data_raw.columns:
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etf_nav = etf_nav_data_raw.loc[signal_date, code]
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if pd.notna(etf_nav) and etf_nav > 0:
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etf_close = etf_close_data[code]
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premium = (etf_close - etf_nav) / etf_nav
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premium_data[code] = premium
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# 打印最新调仓信号
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_print_latest_signal(backtest_result, code_list, code_name_map, select_num, code_config, etf_close_data, premium_data)
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# 绘制图表
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_plot_report_chart(
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backtest_result, code_list, code_name_map,
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benchmark_name, save_path, select_num,
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metrics={
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"累计收益": s_total_return,
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"年化收益": s_cagr_nat,
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"夏普比率": s_sharpe,
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"最大回撤": s_max_dd,
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"Calmar比率": s_calmar,
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"日胜率": s_win_rate,
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},
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code_config=code_config,
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etf_price_data=etf_price_data,
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etf_nav_data_raw=etf_nav_data_raw,
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)
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# 保存整体策略KPI到JSON文件
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import json
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metrics_dict = {
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"策略": {
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"累计收益": float(s_total_return),
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"年化收益(自然日)": float(s_cagr_nat),
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"年化收益(交易日)": float(s_cagr_trd),
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"夏普比率": float(s_sharpe),
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"最大回撤": float(s_max_dd),
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"Calmar比率": float(s_calmar),
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"日胜率": float(s_win_rate),
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"回测区间": {
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"开始": strategy_nav.index.min().strftime("%Y-%m-%d"),
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"结束": strategy_nav.index.max().strftime("%Y-%m-%d"),
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"交易天数": len(strategy_nav)
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}
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},
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"基准": {
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"累计收益": float(b_total_return),
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"年化收益(自然日)": float(b_cagr_nat),
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"夏普比率": float(b_sharpe),
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"最大回撤": float(b_max_dd),
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"名称": benchmark_name
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}
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}
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metrics_path = f"{save_path}_metrics.json"
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with open(metrics_path, 'w', encoding='utf-8') as f:
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json.dump(metrics_dict, f, indent=2, ensure_ascii=False)
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print(f"策略指标已保存: {metrics_path}")
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# 保存净值曲线数据到CSV文件
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nav_df = pd.DataFrame({
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'日期': strategy_nav.index.strftime('%Y-%m-%d'),
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'策略净值': strategy_nav.values,
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'基准净值': benchmark_nav.values,
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})
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# 添加各品种净值
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for code in code_list:
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if f"净值_{code}" in backtest_result.columns:
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nav_df[f"净值_{code}"] = backtest_result[f"净值_{code}"].values
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nav_path = f"{save_path}_nav.csv"
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nav_df.to_csv(nav_path, index=False)
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print(f"净值曲线已保存: {nav_path}")
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# 返回指标字典
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return {
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"累计收益": s_total_return,
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"CAGR_自然日": s_cagr_nat,
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"CAGR_交易日": s_cagr_trd,
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"夏普比率": s_sharpe,
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"最大回撤": s_max_dd,
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"Calmar比率": s_calmar,
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"日胜率": s_win_rate,
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"基准累计收益": b_total_return,
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"基准CAGR_自然日": b_cagr_nat,
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"基准夏普比率": b_sharpe,
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"基准最大回撤": b_max_dd,
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}
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def _print_latest_signal(backtest_result: pd.DataFrame, code_list: list, code_name_map: dict, select_num: int, code_config: dict = None, etf_close_data: dict = None, premium_data: dict = None):
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"""打印最新调仓信号（支持ETF映射、ETF收盘价和溢价率显示）"""
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code_config = code_config or {}
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etf_close_data = etf_close_data or {}
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premium_data = premium_data or {}
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latest = _extract_latest_positions(backtest_result, code_list, code_name_map, select_num)
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signal_date = latest["signal_date"]
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signal_date_str = signal_date.strftime("%Y-%m-%d")
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# 数据基准日期：使用信号日期的数据
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# 如果信号日期没有数据，则使用前一天
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if signal_date in backtest_result.index:
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data_base_date = signal_date
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else:
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data_base_date = signal_date - pd.Timedelta(days=1)
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data_base_date_str = data_base_date.strftime("%Y-%m-%d")
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print("\n")
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print("=" * 135)
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print(" 最新调仓信号 (下一交易日执行)")
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print("=" * 135)
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print(f" 信号日期: {signal_date_str} (基于 {data_base_date_str} 收盘数据)")
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print()
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# 表头 - 添加ETF收盘价和溢价率列
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print(f' {"标的名称":<10} {"指数代码":>12} {"ETF代码":>12} {"仓位":>6} {"得分":>8} {"进场日期":>12} {"指数进场价":>10} {"指数最新价":>10} {"ETF收盘价":>10} {"溢价率":>8} {"操作":>6} {"持有天数":>8} {"盈亏":>10}')
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print(" " + "-" * 155)
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# 下期持仓（调入/维持）
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for pos in latest["positions"]:
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pnl_str = f'{pos["pnl"]:>+9.2%}' if pos["pnl"] is not None else ' —'
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days_str = f'{pos["holding_days"]:>7}天' if pos["holding_days"] is not None else ' —'
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entry_str = f'{pos["entry_price"]:>10.2f}' if pos["entry_price"] is not None else ' —'
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entry_date_str = pos["entry_date"].strftime("%Y-%m-%d") if pos.get("entry_date") else ' —'
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score_str = f'{pos["score"]:>8.2f}' if pos["score"] is not None else ' —'
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flag = '▲' if pos["action"] == "调入" else ' '
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# 获取ETF代码、ETF净值和溢价率
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idx_code = pos["code"]
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cfg = code_config.get(idx_code, {})
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etf_code = cfg.get('etf', '—')
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market = cfg.get('market', 'A')
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if etf_code is None:
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etf_code = '直接交易'
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# 获取ETF收盘价和溢价率
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if market == 'CRYPTO':
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etf_close_str = ' —'
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premium_str = ' —'
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else:
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# ETF收盘价
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etf_close = etf_close_data.get(idx_code)
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if etf_close is not None:
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etf_close_str = f'{etf_close:>10.3f}'
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else:
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etf_close_str = ' —'
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# 溢价率（只有当天有净值数据时才显示）
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premium = premium_data.get(idx_code)
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if premium is not None:
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premium_str = f'{premium:>+7.2%}'
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else:
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premium_str = ' —'
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print(f' {pos["name"]:<10} {idx_code:>12} {etf_code:>12} {pos["weight"]:>6.0%} {score_str} {entry_date_str:>12} {entry_str} {pos["current_price"]:>10.2f} {etf_close_str} {premium_str} {flag}{pos["action"]:>4} {days_str} {pnl_str}')
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# 需调出的品种
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if latest["exit_positions"]:
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print()
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print(" 需调出:")
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for pos in latest["exit_positions"]:
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pnl_str = f'{pos["pnl"]:>+9.2%}' if pos["pnl"] is not None else ' —'
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days_str = f'{pos["holding_days"]:>7}天' if pos["holding_days"] is not None else ' —'
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entry_str = f'{pos["entry_price"]:>10.2f}' if pos["entry_price"] is not None else ' —'
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entry_date_str = pos["entry_date"].strftime("%Y-%m-%d") if pos.get("entry_date") else ' —'
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score_str = ' —' # 调出品种无得分
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# 获取ETF代码、ETF收盘价和溢价率
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idx_code = pos["code"]
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cfg = code_config.get(idx_code, {})
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etf_code = cfg.get('etf', '—')
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market = cfg.get('market', 'A')
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if etf_code is None:
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etf_code = '直接交易'
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# 获取ETF收盘价和溢价率
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if market == 'CRYPTO':
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etf_close_str = ' —'
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premium_str = ' —'
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else:
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# ETF收盘价
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etf_close = etf_close_data.get(idx_code)
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if etf_close is not None:
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etf_close_str = f'{etf_close:>10.3f}'
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else:
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etf_close_str = ' —'
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# 溢价率（只有当天有净值数据时才显示）
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premium = premium_data.get(idx_code)
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if premium is not None:
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premium_str = f'{premium:>+7.2%}'
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else:
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premium_str = ' —'
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print(f' {pos["name"]:<10} {idx_code:>12} {etf_code:>12} {pos["weight"]:>6.0%} {score_str} {entry_date_str:>12} {entry_str} {pos["current_price"]:>10.2f} {etf_close_str} {premium_str} ▼调出 {days_str} {pnl_str}')
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print("=" * 160)
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def _extract_latest_positions(backtest_result: pd.DataFrame, code_list: list, code_name_map: dict, select_num: int) -> dict:
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"""提取最新持仓和下期调仓建议"""
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last_date = backtest_result.index[-1]
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last_row = backtest_result.iloc[-1]
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# 当前持仓
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current_signal = last_row["信号"]
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if select_num == 1:
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current_codes = [current_signal]
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else:
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current_codes = current_signal.split(",")
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# 下期建议
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score_cols = [f"得分_{code}" for code in code_list if f"得分_{code}" in backtest_result.columns]
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scores = pd.to_numeric(last_row[score_cols], errors="coerce")
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top_n = scores.nlargest(select_num)
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next_codes = [c.replace("得分_", "") for c in top_n.index]
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# 计算持仓信息
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positions_info = []
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weight = 1.0 / select_num
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for code in next_codes:
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name = code_name_map.get(code, code)
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action = "维持" if code in current_codes else "调入"
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# 获取当前价格和得分
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current_price = last_row.get(code, 0)
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score = scores.get(f"得分_{code}", None)
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# 计算持仓信息（如果是维持的仓位）
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entry_date = None
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entry_price = None
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holding_days = None
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pnl = None
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if action == "维持":
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# 找到该标的最近一次连续持仓的起始日期
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signal_series = backtest_result["信号"]
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mask = signal_series == code if select_num == 1 else signal_series.str.contains(code, regex=False, na=False)
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# 找到连续持仓段（从后往前找）
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is_holding = mask.values
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dates = backtest_result.index
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# 从最后一天往前遍历，找到连续持仓的起始点
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entry_date = None
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for i in range(len(is_holding) - 1, -1, -1):
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if is_holding[i]:
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entry_date = dates[i]
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else:
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break
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if entry_date is not None:
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entry_price = backtest_result.loc[entry_date, code]
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holding_days = (last_date - entry_date).days
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if entry_price and entry_price != 0:
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pnl = current_price / entry_price - 1
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positions_info.append({
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"code": code,
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"name": name,
|
||
"weight": weight,
|
||
"score": score,
|
||
"action": action,
|
||
"current_price": current_price,
|
||
"entry_date": entry_date,
|
||
"entry_price": entry_price,
|
||
"holding_days": holding_days,
|
||
"pnl": pnl,
|
||
})
|
||
|
||
# 需调出的品种信息
|
||
exit_positions = []
|
||
for code in current_codes:
|
||
if code not in next_codes:
|
||
name = code_name_map.get(code, code)
|
||
current_price = last_row.get(code, 0)
|
||
|
||
# 计算调出品种的持仓信息（最近一次连续持仓）
|
||
signal_series = backtest_result["信号"]
|
||
mask = signal_series == code if select_num == 1 else signal_series.str.contains(code, regex=False, na=False)
|
||
|
||
# 找到连续持仓段（从后往前找）
|
||
is_holding = mask.values
|
||
dates = backtest_result.index
|
||
|
||
entry_price = None
|
||
holding_days = None
|
||
pnl = None
|
||
|
||
# 从最后一天往前遍历，找到连续持仓的起始点
|
||
entry_date = None
|
||
for i in range(len(is_holding) - 1, -1, -1):
|
||
if is_holding[i]:
|
||
entry_date = dates[i]
|
||
else:
|
||
break
|
||
|
||
if entry_date is not None:
|
||
entry_price = backtest_result.loc[entry_date, code]
|
||
holding_days = (last_date - entry_date).days
|
||
|
||
if entry_price and entry_price != 0:
|
||
pnl = current_price / entry_price - 1
|
||
|
||
exit_positions.append({
|
||
"code": code,
|
||
"name": name,
|
||
"weight": weight,
|
||
"score": None, # 调出品种无得分
|
||
"action": "调出",
|
||
"current_price": current_price,
|
||
"entry_date": entry_date,
|
||
"entry_price": entry_price,
|
||
"holding_days": holding_days,
|
||
"pnl": pnl,
|
||
})
|
||
|
||
return {
|
||
"signal_date": last_date,
|
||
"current_codes": current_codes,
|
||
"next_codes": next_codes,
|
||
"positions": positions_info,
|
||
"exit_positions": exit_positions,
|
||
}
|
||
|
||
|
||
def _plot_report_chart(
|
||
backtest_result: pd.DataFrame,
|
||
code_list: list,
|
||
code_name_map: dict,
|
||
benchmark_name: str,
|
||
save_path: str,
|
||
select_num: int,
|
||
metrics: dict = None,
|
||
code_config: dict = None,
|
||
etf_price_data: pd.DataFrame = None,
|
||
etf_nav_data_raw: pd.DataFrame = None,
|
||
):
|
||
"""绘制报告图表（支持ETF净值和溢价率显示）"""
|
||
# 设置中文字体（macOS: Arial Unicode MS, Linux: WenQuanYi Zen Hei）
|
||
plt.rcParams["font.sans-serif"] = ["Arial Unicode MS", "WenQuanYi Zen Hei", "DejaVu Sans"]
|
||
plt.rcParams["axes.unicode_minus"] = False
|
||
|
||
|
||
strategy_nav = backtest_result["轮动策略净值"]
|
||
benchmark_nav = backtest_result["基准净值"]
|
||
strategy_ret = backtest_result["轮动策略日收益率"]
|
||
|
||
# 计算绩效指标（如果没有传入）
|
||
if metrics is None:
|
||
from core.common.utils import calculate_cagr, calculate_max_drawdown, calculate_sharpe
|
||
s_cagr_nat = calculate_cagr(strategy_nav, "natural_days")
|
||
s_total_return = strategy_nav.iloc[-1] - 1
|
||
s_sharpe = calculate_sharpe(strategy_ret)
|
||
s_max_dd, s_dd_start, s_dd_end = calculate_max_drawdown(strategy_nav)
|
||
s_win_rate = (strategy_ret > 0).sum() / len(strategy_ret)
|
||
s_calmar = s_cagr_nat / abs(s_max_dd) if s_max_dd != 0 else np.inf
|
||
metrics = {
|
||
"累计收益": s_total_return,
|
||
"年化收益": s_cagr_nat,
|
||
"夏普比率": s_sharpe,
|
||
"最大回撤": s_max_dd,
|
||
"Calmar比率": s_calmar,
|
||
"日胜率": s_win_rate,
|
||
}
|
||
|
||
# 提取最新调仓信息
|
||
latest = _extract_latest_positions(backtest_result, code_list, code_name_map, select_num)
|
||
|
||
# 准备配置数据
|
||
code_config = code_config or {}
|
||
signal_date = backtest_result.index[-1]
|
||
# 数据基准日期：使用信号日期的数据，如果没有则使用前一天
|
||
if signal_date in backtest_result.index:
|
||
data_base_date = signal_date
|
||
else:
|
||
data_base_date = signal_date - pd.Timedelta(days=1)
|
||
|
||
# 计算ETF收盘价和溢价率（使用信号日期的数据）
|
||
etf_close_dict = {} # ETF收盘价
|
||
premium_dict = {} # 溢价率（仅当当天有净值时计算）
|
||
|
||
if etf_price_data is not None:
|
||
# 获取信号日期的ETF收盘价
|
||
if signal_date in etf_price_data.index:
|
||
for code in code_list:
|
||
if code in etf_price_data.columns:
|
||
etf_close = etf_price_data.loc[signal_date, code]
|
||
if pd.notna(etf_close):
|
||
etf_close_dict[code] = etf_close
|
||
|
||
# 计算溢价率：只有当天有净值数据时才计算
|
||
if etf_nav_data_raw is not None and signal_date in etf_nav_data_raw.index:
|
||
for code in code_list:
|
||
if code in etf_close_dict and code in etf_nav_data_raw.columns:
|
||
etf_nav = etf_nav_data_raw.loc[signal_date, code]
|
||
if pd.notna(etf_nav) and etf_nav > 0:
|
||
etf_close = etf_close_dict[code]
|
||
premium = (etf_close - etf_nav) / etf_nav
|
||
premium_dict[code] = premium
|
||
|
||
# 计算表格行数
|
||
n_table_rows = len(latest["positions"]) + len(latest["exit_positions"])
|
||
signal_table_height = max(2.0, 0.6 + n_table_rows * 0.35)
|
||
metrics_table_height = 1.2
|
||
|
||
fig = plt.figure(figsize=(14, 10 + signal_table_height + metrics_table_height + 8))
|
||
gs = fig.add_gridspec(5, 1, height_ratios=[signal_table_height, metrics_table_height, 3, 1, 1.2], hspace=0.35)
|
||
|
||
# 面板0: 最新调仓信号表
|
||
ax0 = fig.add_subplot(gs[0])
|
||
ax0.axis("off")
|
||
|
||
signal_date = latest["signal_date"]
|
||
signal_date_str = signal_date.strftime("%Y-%m-%d")
|
||
# 数据基准日期：使用信号日期的数据，如果没有则使用前一天
|
||
if signal_date in backtest_result.index:
|
||
data_base_date = signal_date
|
||
else:
|
||
data_base_date = signal_date - pd.Timedelta(days=1)
|
||
data_base_date_str = data_base_date.strftime("%Y-%m-%d")
|
||
ax0.set_title(f"最新调仓信号 (信号日期: {signal_date_str}，基于 {data_base_date_str} 数据，下一交易日执行)", fontsize=14, fontweight="bold", loc="left", pad=15)
|
||
|
||
# 构建表格数据（添加ETF代码、ETF收盘价和溢价率列）
|
||
table_data = []
|
||
col_labels = ["标的名称", "指数代码", "ETF代码", "仓位", "得分", "进场日期", "进场价", "最新价", "ETF收盘价", "溢价率", "操作", "持有天数", "盈亏"]
|
||
|
||
# 下期持仓（调入/维持）
|
||
for pos in latest["positions"]:
|
||
pnl_str = f'{pos["pnl"]:+.2%}' if pos["pnl"] is not None else "—"
|
||
days_str = f'{pos["holding_days"]}' if pos["holding_days"] is not None else "—"
|
||
entry_str = f'{pos["entry_price"]:.2f}' if pos["entry_price"] is not None else "—"
|
||
entry_date_str = pos["entry_date"].strftime("%m-%d") if pos.get("entry_date") else "—"
|
||
score_str = f'{pos["score"]:.2f}' if pos["score"] is not None else "—"
|
||
|
||
# 获取ETF代码、ETF收盘价和溢价率
|
||
idx_code = pos["code"]
|
||
cfg = code_config.get(idx_code, {})
|
||
market = cfg.get('market', 'A')
|
||
etf_code = cfg.get('etf', '—')
|
||
if etf_code is None:
|
||
etf_code = '直接交易'
|
||
|
||
if market == 'CRYPTO':
|
||
etf_close_str = "—"
|
||
premium_str = "—"
|
||
else:
|
||
etf_close = etf_close_dict.get(idx_code)
|
||
premium = premium_dict.get(idx_code)
|
||
etf_close_str = f"{etf_close:.3f}" if etf_close is not None else "—"
|
||
if premium is not None:
|
||
premium_str = f"{premium:+.2%}"
|
||
else:
|
||
premium_str = "—"
|
||
|
||
table_data.append([
|
||
pos["name"], pos["code"], etf_code, f'{pos["weight"]:.0%}',
|
||
score_str, entry_date_str, entry_str, f'{pos["current_price"]:.2f}',
|
||
etf_close_str, premium_str, pos["action"], days_str, pnl_str
|
||
])
|
||
|
||
# 需调出的品种
|
||
for pos in latest["exit_positions"]:
|
||
pnl_str = f'{pos["pnl"]:+.2%}' if pos["pnl"] is not None else "—"
|
||
days_str = f'{pos["holding_days"]}' if pos["holding_days"] is not None else "—"
|
||
entry_str = f'{pos["entry_price"]:.2f}' if pos["entry_price"] is not None else "—"
|
||
entry_date_str = pos["entry_date"].strftime("%m-%d") if pos.get("entry_date") else "—"
|
||
score_str = "—" # 调出品种无得分
|
||
|
||
# 获取ETF代码、ETF收盘价和溢价率
|
||
idx_code = pos["code"]
|
||
cfg = code_config.get(idx_code, {})
|
||
market = cfg.get('market', 'A')
|
||
etf_code = cfg.get('etf', '—')
|
||
if etf_code is None:
|
||
etf_code = '直接交易'
|
||
|
||
if market == 'CRYPTO':
|
||
etf_close_str = "—"
|
||
premium_str = "—"
|
||
else:
|
||
etf_close = etf_close_dict.get(idx_code)
|
||
premium = premium_dict.get(idx_code)
|
||
etf_close_str = f"{etf_close:.3f}" if etf_close is not None else "—"
|
||
if premium is not None:
|
||
premium_str = f"{premium:+.2%}"
|
||
else:
|
||
premium_str = "—"
|
||
|
||
table_data.append([
|
||
pos["name"], pos["code"], etf_code, f'{pos["weight"]:.0%}',
|
||
score_str, entry_date_str, entry_str, f'{pos["current_price"]:.2f}',
|
||
etf_close_str, premium_str, "调出", days_str, pnl_str
|
||
])
|
||
|
||
if table_data:
|
||
table = ax0.table(
|
||
cellText=table_data,
|
||
colLabels=col_labels,
|
||
loc="center",
|
||
cellLoc="center",
|
||
colWidths=[0.08, 0.08, 0.08, 0.05, 0.06, 0.06, 0.06, 0.06, 0.06, 0.07, 0.05, 0.06, 0.06],
|
||
bbox=[0, 0, 1, 1], # 使用完整宽度
|
||
)
|
||
table.auto_set_font_size(False)
|
||
table.set_fontsize(10)
|
||
table.scale(1, 2.0) # 行高与绩效表格一致
|
||
|
||
# 表头深色
|
||
for j in range(len(col_labels)):
|
||
table[0, j].set_facecolor("#2C3E50")
|
||
table[0, j].set_text_props(color="white", fontweight="bold")
|
||
|
||
# 数据行按操作着色
|
||
for i in range(len(table_data)):
|
||
action = table_data[i][10] # 操作列在第11列（索引10）
|
||
if action == "调入":
|
||
color = "#d4edda" # 绿色
|
||
elif action == "调出":
|
||
color = "#f8d7da" # 红色
|
||
else:
|
||
color = "#fff3cd" # 黄色（维持）
|
||
for j in range(len(col_labels)):
|
||
table[i + 1, j].set_facecolor(color)
|
||
|
||
# 面板1: 策略绩效指标对比表（转置：行为策略/基准，列为指标）
|
||
ax1 = fig.add_subplot(gs[1])
|
||
ax1.axis("off")
|
||
ax1.set_title("策略绩效对比", fontsize=14, fontweight="bold", loc="left", pad=10)
|
||
|
||
# 计算基准指标
|
||
from core.common.utils import calculate_cagr, calculate_max_drawdown, calculate_sharpe
|
||
benchmark_ret = backtest_result["基准日收益率"]
|
||
b_cagr_nat = calculate_cagr(benchmark_nav, "natural_days")
|
||
b_total_return = benchmark_nav.iloc[-1] - 1
|
||
b_sharpe = calculate_sharpe(benchmark_ret)
|
||
b_max_dd, _, _ = calculate_max_drawdown(benchmark_nav)
|
||
|
||
# 构建绩效对比表格（转置）
|
||
start_date = strategy_nav.index.min().strftime("%Y-%m-%d")
|
||
end_date = strategy_nav.index.max().strftime("%Y-%m-%d")
|
||
|
||
# 列标题（指标），第一列添加"策略"
|
||
perf_col_labels = ["策略", "开始时间", "结束时间", "累计收益", "年化收益", "最大回撤", "夏普比率", "Calmar比率", "日胜率"]
|
||
|
||
# 策略行数据（包含行标题）
|
||
strategy_row = [
|
||
"轮动策略",
|
||
start_date,
|
||
end_date,
|
||
f"{metrics.get('累计收益', 0):.2%}",
|
||
f"{metrics.get('年化收益', 0):.2%}",
|
||
f"{metrics.get('最大回撤', 0):.2%}",
|
||
f"{metrics.get('夏普比率', 0):.2f}",
|
||
f"{metrics.get('Calmar比率', 0):.2f}",
|
||
f"{metrics.get('日胜率', 0):.2%}",
|
||
]
|
||
|
||
# 基准行数据（包含行标题）
|
||
benchmark_row = [
|
||
f"基准({benchmark_name})",
|
||
start_date,
|
||
end_date,
|
||
f"{b_total_return:.2%}",
|
||
f"{b_cagr_nat:.2%}",
|
||
f"{b_max_dd:.2%}",
|
||
f"{b_sharpe:.2f}",
|
||
"—",
|
||
"—",
|
||
]
|
||
|
||
# 表格数据：2行（策略、基准）
|
||
perf_table_data = [strategy_row, benchmark_row]
|
||
|
||
# 使用与调仓表格相同的列宽计算方式，确保总宽度一致
|
||
# 调仓表格有10列，这里也有9列，使用相似的宽度分配
|
||
perf_col_widths = [0.10, 0.10, 0.10, 0.10, 0.10, 0.10, 0.10, 0.10, 0.10]
|
||
|
||
perf_table = ax1.table(
|
||
cellText=perf_table_data,
|
||
colLabels=perf_col_labels,
|
||
loc="center",
|
||
cellLoc="center",
|
||
colWidths=perf_col_widths,
|
||
bbox=[0, 0, 1, 1], # 使用完整宽度，与调仓表格一致
|
||
)
|
||
perf_table.auto_set_font_size(False)
|
||
perf_table.set_fontsize(10) # 字体大小与调仓表格一致
|
||
perf_table.scale(1, 2.0) # 行高与调仓表格一致
|
||
|
||
# 表头样式（第一行）
|
||
for j in range(len(perf_col_labels)):
|
||
perf_table[0, j].set_facecolor("#2C3E50")
|
||
perf_table[0, j].set_text_props(color="white", fontweight="bold")
|
||
|
||
# 数据行样式
|
||
# 策略行浅绿背景
|
||
for j in range(len(perf_col_labels)):
|
||
perf_table[1, j].set_facecolor("#d4edda")
|
||
# 基准行浅蓝背景
|
||
for j in range(len(perf_col_labels)):
|
||
perf_table[2, j].set_facecolor("#cce5ff")
|
||
# 第一列（策略名称）加粗
|
||
for i in range(2):
|
||
perf_table[i + 1, 0].set_text_props(fontweight="bold")
|
||
|
||
# 面板2: 净值曲线
|
||
ax2 = fig.add_subplot(gs[2])
|
||
ax2.plot(strategy_nav.index, strategy_nav.values,
|
||
label="轮动策略", linewidth=2, color="#E74C3C")
|
||
ax2.plot(benchmark_nav.index, benchmark_nav.values,
|
||
label=benchmark_name, linewidth=1.5, color="#3498DB", alpha=0.8)
|
||
|
||
chart_colors = plt.cm.tab20.colors
|
||
show_legend_n = min(len(code_list), 10)
|
||
for i, code in enumerate(code_list):
|
||
name = code_name_map.get(code, code)
|
||
lbl = name if i < show_legend_n else None
|
||
ax2.plot(backtest_result.index, backtest_result[f"净值_{code}"].values,
|
||
label=lbl, linewidth=0.8, alpha=0.4,
|
||
color=chart_colors[i % len(chart_colors)])
|
||
|
||
ax2.set_title("ETF轮动策略 - 净值曲线", fontsize=16, fontweight="bold")
|
||
ax2.set_ylabel("净值")
|
||
ax2.legend(loc="upper left", fontsize=8, ncol=2)
|
||
ax2.grid(True, alpha=0.3)
|
||
ax2.set_yscale("log")
|
||
|
||
# 面板3: 回撤曲线
|
||
ax3 = fig.add_subplot(gs[3])
|
||
cummax = strategy_nav.cummax()
|
||
drawdown = (strategy_nav - cummax) / cummax
|
||
ax3.fill_between(drawdown.index, drawdown.values, 0, alpha=0.5, color="#E74C3C")
|
||
ax3.set_title("策略回撤", fontsize=12)
|
||
ax3.set_ylabel("回撤")
|
||
ax3.grid(True, alpha=0.3)
|
||
|
||
# 面板4: 持仓分布
|
||
ax4 = fig.add_subplot(gs[4])
|
||
signal_series = backtest_result["信号"]
|
||
for i, code in enumerate(code_list):
|
||
name = code_name_map.get(code, code)
|
||
if select_num > 1:
|
||
mask = signal_series.str.contains(code, regex=False, na=False)
|
||
else:
|
||
mask = signal_series == code
|
||
if mask.any():
|
||
ax4.fill_between(signal_series.index, i, i + 0.8,
|
||
where=mask, alpha=0.7,
|
||
color=chart_colors[i % len(chart_colors)],
|
||
label=name)
|
||
|
||
ylabels = [code_name_map.get(c, c) for c in code_list]
|
||
ax4.set_title("每日持仓分布", fontsize=12)
|
||
ax4.set_yticks(range(len(ylabels)))
|
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ax4.set_yticklabels(ylabels, fontsize=7)
|
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ax4.grid(True, alpha=0.3)
|
||
|
||
chart_path = f"{save_path}_chart.png"
|
||
plt.savefig(chart_path, dpi=150, bbox_inches="tight")
|
||
plt.close()
|
||
print(f"\n报告图表已保存: {chart_path}")
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