chore(config): 添加环境变量示例及.gitignore更新

- 新增 .env.example，包含 Tushare API、钉钉机器人和PostgreSQL数据库配置模板 - 更新.gitignore，忽略本地配置文件如 .env.local 和 config_local.py - 添加对报表文件命名规则的支持，保留示例文件不忽略 - 删除废弃的 chart.py 及相关图表模块代码 - 新增 config/settings.py，实现从环境变量读取配置的统一接口 - 设置数据目录及缓存目录，确保目录存在，提高配置管理规范性
2026-03-18 23:33:40 +08:00
parent 7c93be4b41
commit 988c2335fb
39 changed files with 2983 additions and 1011 deletions
--- a/strategies/rotation/report.py
+++ b/strategies/rotation/report.py
@@ -0,0 +1,175 @@
+"""
+ETF轮动策略 - 绩效报告模块
+"""
+
+import numpy as np
+import pandas as pd
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+from typing import Optional
+
+from core.common.utils import calculate_cagr, calculate_max_drawdown, calculate_sharpe
+
+
+def generate_performance_report(
+    backtest_result: pd.DataFrame,
+    code_list: list,
+    code_name_map: dict = None,
+    benchmark_name: str = "沪深300指数",
+    save_path: str = "report",
+    select_num: int = 1,
+) -> dict:
+    """
+    生成完整的绩效报告
+
+    Args:
+        backtest_result: 回测结果
+        code_list: ETF代码列表
+        code_name_map: 代码到名称映射
+        benchmark_name: 基准名称
+        save_path: 报告保存路径前缀
+        select_num: 选中数量
+
+    Returns:
+        dict: 绩效指标字典
+    """
+    import os
+    os.makedirs(os.path.dirname(save_path) if os.path.dirname(save_path) else ".", exist_ok=True)
+
+    code_name_map = code_name_map or {}
+    strategy_nav = backtest_result["轮动策略净值"]
+    strategy_ret = backtest_result["轮动策略日收益率"]
+    benchmark_nav = backtest_result["基准净值"]
+    benchmark_ret = backtest_result["基准日收益率"]
+
+    # 计算绩效指标
+    s_cagr_nat = calculate_cagr(strategy_nav, "natural_days")
+    s_cagr_trd = calculate_cagr(strategy_nav, "trading_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
+
+    b_cagr_nat = calculate_cagr(benchmark_nav, "natural_days")
+    b_cagr_trd = calculate_cagr(benchmark_nav, "trading_days")
+    b_total_return = benchmark_nav.iloc[-1] - 1
+    b_sharpe = calculate_sharpe(benchmark_ret)
+    b_max_dd, _, _ = calculate_max_drawdown(benchmark_nav)
+
+    # 打印绩效表格
+    print("\n" + "=" * 70)
+    print("                    绩效评估报告")
+    print("=" * 70)
+    print(f"  回测区间: {strategy_nav.index.min().date()} ~ {strategy_nav.index.max().date()}")
+    print(f"  交易天数: {len(strategy_nav)}")
+    print("-" * 70)
+    print(f'  {"指标":<25} {"轮动策略":>15} {"基准(" + benchmark_name + ")":>18}')
+    print("-" * 70)
+    print(f'  {"累计收益":<25} {s_total_return:>14.2%} {b_total_return:>17.2%}')
+    print(f'  {"CAGR(自然日口径)":<25} {s_cagr_nat:>14.2%} {b_cagr_nat:>17.2%}')
+    print(f'  {"CAGR(交易日口径)":<25} {s_cagr_trd:>14.2%} {b_cagr_trd:>17.2%}')
+    print(f'  {"年化夏普比率":<25} {s_sharpe:>14.2f} {b_sharpe:>17.2f}')
+    print(f'  {"最大回撤":<25} {s_max_dd:>14.2%} {b_max_dd:>17.2%}')
+    print(f'  {"Calmar比率":<23} {s_calmar:>14.2f} {"--":>17}')
+    print(f'  {"日胜率":<25} {s_win_rate:>14.2%} {"--":>17}')
+    print(f'  {"最大回撤区间":<22} {str(s_dd_start.date()):>10} ~ {str(s_dd_end.date())}')
+    print("=" * 70)
+
+    # 绘制图表
+    _plot_report_chart(
+        backtest_result, code_list, code_name_map,
+        benchmark_name, save_path, select_num
+    )
+
+    # 返回指标字典
+    return {
+        "累计收益": s_total_return,
+        "CAGR_自然日": s_cagr_nat,
+        "CAGR_交易日": s_cagr_trd,
+        "夏普比率": s_sharpe,
+        "最大回撤": s_max_dd,
+        "Calmar比率": s_calmar,
+        "日胜率": s_win_rate,
+        "基准累计收益": b_total_return,
+        "基准CAGR_自然日": b_cagr_nat,
+        "基准夏普比率": b_sharpe,
+        "基准最大回撤": b_max_dd,
+    }
+
+
+def _plot_report_chart(
+    backtest_result: pd.DataFrame,
+    code_list: list,
+    code_name_map: dict,
+    benchmark_name: str,
+    save_path: str,
+    select_num: int,
+):
+    """绘制报告图表"""
+    plt.rcParams["font.sans-serif"] = ["Arial Unicode MS", "SimHei", "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"]
+
+    fig, axes = plt.subplots(3, 1, figsize=(14, 12))
+
+    # 面板1: 净值曲线
+    ax1 = axes[0]
+    ax1.plot(strategy_nav.index, strategy_nav.values,
+             label="轮动策略", linewidth=2, color="#E74C3C")
+    ax1.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
+        ax1.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)])
+
+    ax1.set_title("ETF轮动策略 - 净值曲线", fontsize=16, fontweight="bold")
+    ax1.set_ylabel("净值")
+    ax1.legend(loc="upper left", fontsize=8, ncol=2)
+    ax1.grid(True, alpha=0.3)
+    ax1.set_yscale("log")
+
+    # 面板2: 回撤曲线
+    ax2 = axes[1]
+    cummax = strategy_nav.cummax()
+    drawdown = (strategy_nav - cummax) / cummax
+    ax2.fill_between(drawdown.index, drawdown.values, 0, alpha=0.5, color="#E74C3C")
+    ax2.set_title("策略回撤", fontsize=12)
+    ax2.set_ylabel("回撤")
+    ax2.grid(True, alpha=0.3)
+
+    # 面板3: 持仓分布
+    ax3 = axes[2]
+    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)
+
+    plt.tight_layout()
+    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}")