aszerW
9b154a1a25
- 配置中取消固定end_date，改为默认使用当前日期 - 添加打印最新调仓信号的功能，显示持仓明细及调出品种 - 在报告生成流程中调用最新调仓信号打印函数 - 图表展示中新增最新调仓信号表格，支持颜色区分调入、调出和维持 - 优化报告图表布局，调整画布高度适应信号表内容 - 删除无用test.py测试脚本及相关冗余代码
426 lines
16 KiB
Python
426 lines
16 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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) -> 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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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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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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# 打印最新调仓信号
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_print_latest_signal(backtest_result, code_list, code_name_map, select_num)
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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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)
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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):
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"""打印最新调仓信号"""
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latest = _extract_latest_positions(backtest_result, code_list, code_name_map, select_num)
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signal_date_str = latest["signal_date"].strftime("%Y-%m-%d")
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print("\n")
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print("=" * 100)
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print(" 最新调仓信号 (下一交易日执行)")
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print("=" * 100)
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print(f" 数据截止: {signal_date_str}")
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print()
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# 表头
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print(f' {"品种名称":<8} {"代码":>10} {"仓位":>6} {"得分":>8} {"进场日期":>12} {"进场价":>10} {"最新价":>10} {"操作":>6} {"持有天数":>8} {"盈亏":>10}')
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print(" " + "-" * 115)
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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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print(f' {pos["name"]:<8} {pos["code"]:>10} {pos["weight"]:>6.0%} {score_str} {entry_date_str:>12} {entry_str} {pos["current_price"]:>10.2f} {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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print(f' {pos["name"]:<8} {pos["code"]:>10} {pos["weight"]:>6.0%} {score_str} {entry_date_str:>12} {entry_str} {pos["current_price"]:>10.2f} ▼调出 {days_str} {pnl_str}')
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print("=" * 120)
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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,
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"weight": weight,
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"score": score,
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"action": action,
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"current_price": current_price,
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"entry_date": entry_date,
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"entry_price": entry_price,
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"holding_days": holding_days,
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"pnl": pnl,
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})
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# 需调出的品种信息
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exit_positions = []
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for code in current_codes:
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if code not in next_codes:
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name = code_name_map.get(code, code)
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current_price = last_row.get(code, 0)
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# 计算调出品种的持仓信息（最近一次连续持仓）
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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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entry_price = None
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holding_days = None
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pnl = None
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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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exit_positions.append({
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"code": code,
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"name": name,
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"weight": weight,
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"score": None, # 调出品种无得分
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"action": "调出",
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"current_price": current_price,
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"entry_date": entry_date,
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"entry_price": entry_price,
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"holding_days": holding_days,
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"pnl": pnl,
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})
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return {
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"signal_date": last_date,
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"current_codes": current_codes,
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"next_codes": next_codes,
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"positions": positions_info,
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"exit_positions": exit_positions,
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}
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def _plot_report_chart(
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backtest_result: pd.DataFrame,
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code_list: list,
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code_name_map: dict,
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benchmark_name: str,
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save_path: str,
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select_num: int,
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):
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"""绘制报告图表"""
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plt.rcParams["font.sans-serif"] = ["Arial Unicode MS", "SimHei", "DejaVu Sans"]
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plt.rcParams["axes.unicode_minus"] = False
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strategy_nav = backtest_result["轮动策略净值"]
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benchmark_nav = backtest_result["基准净值"]
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# 提取最新调仓信息
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latest = _extract_latest_positions(backtest_result, code_list, code_name_map, select_num)
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# 计算表格行数
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n_table_rows = len(latest["positions"]) + len(latest["exit_positions"])
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table_height = max(1.5, 0.5 + n_table_rows * 0.28)
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fig = plt.figure(figsize=(14, 10 + table_height + 8))
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gs = fig.add_gridspec(4, 1, height_ratios=[table_height, 3, 1, 1.2], hspace=0.35)
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# 面板0: 最新调仓信号表
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ax0 = fig.add_subplot(gs[0])
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ax0.axis("off")
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signal_date_str = latest["signal_date"].strftime("%Y-%m-%d")
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ax0.set_title(f"最新调仓信号 (数据截止: {signal_date_str}，下一交易日执行)", fontsize=14, fontweight="bold", loc="left", pad=15)
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# 构建表格数据
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table_data = []
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col_labels = ["品种名称", "代码", "仓位", "得分", "进场日期", "进场价", "最新价", "操作", "持有天数", "盈亏"]
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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"]:+.2%}' if pos["pnl"] is not None else "—"
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days_str = f'{pos["holding_days"]}' if pos["holding_days"] is not None else "—"
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entry_str = f'{pos["entry_price"]:.2f}' if pos["entry_price"] is not None else "—"
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entry_date_str = pos["entry_date"].strftime("%m-%d") if pos.get("entry_date") else "—"
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score_str = f'{pos["score"]:.2f}' if pos["score"] is not None else "—"
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table_data.append([
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pos["name"], pos["code"], f'{pos["weight"]:.0%}',
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score_str, entry_date_str, entry_str, f'{pos["current_price"]:.2f}',
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pos["action"], days_str, pnl_str
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])
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# 需调出的品种
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for pos in latest["exit_positions"]:
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pnl_str = f'{pos["pnl"]:+.2%}' if pos["pnl"] is not None else "—"
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days_str = f'{pos["holding_days"]}' if pos["holding_days"] is not None else "—"
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entry_str = f'{pos["entry_price"]:.2f}' if pos["entry_price"] is not None else "—"
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entry_date_str = pos["entry_date"].strftime("%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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table_data.append([
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pos["name"], pos["code"], f'{pos["weight"]:.0%}',
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score_str, entry_date_str, entry_str, f'{pos["current_price"]:.2f}',
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"调出", days_str, pnl_str
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])
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if table_data:
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table = ax0.table(
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cellText=table_data,
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colLabels=col_labels,
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loc="upper center",
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cellLoc="center",
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colWidths=[0.08, 0.08, 0.05, 0.06, 0.06, 0.07, 0.07, 0.05, 0.06, 0.07],
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)
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table.auto_set_font_size(False)
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table.set_fontsize(10)
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table.scale(1, 2.0)
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# 表头深色
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for j in range(len(col_labels)):
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table[0, j].set_facecolor("#2C3E50")
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table[0, j].set_text_props(color="white", fontweight="bold")
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# 数据行按操作着色
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for i in range(len(table_data)):
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action = table_data[i][3]
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if action == "调入":
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color = "#d4edda" # 绿色
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elif action == "调出":
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color = "#f8d7da" # 红色
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else:
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color = "#fff3cd" # 黄色
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for j in range(len(col_labels)):
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table[i + 1, j].set_facecolor(color)
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# 面板1: 净值曲线
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ax1 = fig.add_subplot(gs[1])
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ax1.plot(strategy_nav.index, strategy_nav.values,
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label="轮动策略", linewidth=2, color="#E74C3C")
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ax1.plot(benchmark_nav.index, benchmark_nav.values,
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label=benchmark_name, linewidth=1.5, color="#3498DB", alpha=0.8)
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chart_colors = plt.cm.tab20.colors
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show_legend_n = min(len(code_list), 10)
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for i, code in enumerate(code_list):
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name = code_name_map.get(code, code)
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lbl = name if i < show_legend_n else None
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ax1.plot(backtest_result.index, backtest_result[f"净值_{code}"].values,
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label=lbl, linewidth=0.8, alpha=0.4,
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color=chart_colors[i % len(chart_colors)])
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ax1.set_title("ETF轮动策略 - 净值曲线", fontsize=16, fontweight="bold")
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ax1.set_ylabel("净值")
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ax1.legend(loc="upper left", fontsize=8, ncol=2)
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ax1.grid(True, alpha=0.3)
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ax1.set_yscale("log")
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|
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# 面板2: 回撤曲线
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ax2 = fig.add_subplot(gs[2])
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cummax = strategy_nav.cummax()
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drawdown = (strategy_nav - cummax) / cummax
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ax2.fill_between(drawdown.index, drawdown.values, 0, alpha=0.5, color="#E74C3C")
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ax2.set_title("策略回撤", fontsize=12)
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ax2.set_ylabel("回撤")
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ax2.grid(True, alpha=0.3)
|
|
|
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# 面板3: 持仓分布
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|
ax3 = fig.add_subplot(gs[3])
|
|
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():
|
|
ax3.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]
|
|
ax3.set_title("每日持仓分布", fontsize=12)
|
|
ax3.set_yticks(range(len(ylabels)))
|
|
ax3.set_yticklabels(ylabels, fontsize=7)
|
|
ax3.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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