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refactor: 归档旧代码，保留新框架结构

Browse Source 
归档内容：
- core/ (数据源、因子计算、通用工具) → archive/legacy_core/
- strategies/rotation/engine.py, portfolio.py, report.py → archive/legacy_core/
- scripts/ (run_rotation, daily_scheduler) → archive/legacy_scripts/
- examples/ → archive/legacy_examples/
- tests/ (实验、对比测试) → archive/legacy_tests/
- 单独文件 (fetch_*.py, 动量.py, 全球市场.py等) → archive/single_files/

保留新结构：
- framework/ (抽象接口)
- strategies/shared/ (定制组件)
- strategies/rotation/strategy.py (新策略)
- 外层配置: .env, .dockerignore, build-and-push.sh, hk_ecs.pem, README.md, requirements.txt
- Docker相关: Dockerfile, Dockerfile_base, docker-compose.yml

更新README反映新框架架构
This commit is contained in:
aszerW
2026-05-11 23:34:23 +08:00
parent f663d51b87
commit 1fca536c95
61 changed files with 221 additions and 159 deletions
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archive/legacy_core/report.py Normal file
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"""
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,
code_config: dict = None,
index_data: pd.DataFrame = None,
etf_price_data: pd.DataFrame = None,
etf_nav_data_raw: pd.DataFrame = None,
) -> 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
code_config: 代码配置（包含 name, etf, market），用于显示ETF映射
index_data: 指数价格数据
etf_price_data: ETF价格数据（用于计算溢价率）
etf_nav_data_raw: ETF净值数据（用于计算溢价率）
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 {}
code_config = code_config 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)
# 计算溢价率（需要ETF价格和ETF净值）
# 溢价率 = (ETF价格 - ETF净值) / ETF净值
# 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
etf_close_data = {} # ETF收盘价
premium_data = {} # 溢价率（仅当当天有净值时计算）
if etf_price_data is not None:
signal_date = backtest_result.index[-1]
# 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_data[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_data 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_data[code]
premium = (etf_close - etf_nav) / etf_nav
premium_data[code] = premium
# 打印最新调仓信号
_print_latest_signal(backtest_result, code_list, code_name_map, select_num, code_config, etf_close_data, premium_data)
# 绘制图表
_plot_report_chart(
backtest_result, code_list, code_name_map,
benchmark_name, save_path, select_num,
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,
},
code_config=code_config,
etf_price_data=etf_price_data,
etf_nav_data_raw=etf_nav_data_raw,
)
# 保存整体策略KPI到JSON文件
import json
metrics_dict = {
"策略": {
"累计收益": float(s_total_return),
"年化收益(自然日)": float(s_cagr_nat),
"年化收益(交易日)": float(s_cagr_trd),
"夏普比率": float(s_sharpe),
"最大回撤": float(s_max_dd),
"Calmar比率": float(s_calmar),
"日胜率": float(s_win_rate),
"回测区间": {
"开始": strategy_nav.index.min().strftime("%Y-%m-%d"),
"结束": strategy_nav.index.max().strftime("%Y-%m-%d"),
"交易天数": len(strategy_nav)
}
},
"基准": {
"累计收益": float(b_total_return),
"年化收益(自然日)": float(b_cagr_nat),
"夏普比率": float(b_sharpe),
"最大回撤": float(b_max_dd),
"名称": benchmark_name
}
}
metrics_path = f"{save_path}_metrics.json"
with open(metrics_path, 'w', encoding='utf-8') as f:
json.dump(metrics_dict, f, indent=2, ensure_ascii=False)
print(f"策略指标已保存: {metrics_path}")
# 保存净值曲线数据到CSV文件
nav_df = pd.DataFrame({
'日期': strategy_nav.index.strftime('%Y-%m-%d'),
'策略净值': strategy_nav.values,
'基准净值': benchmark_nav.values,
})
# 添加各品种净值
for code in code_list:
if f"净值_{code}" in backtest_result.columns:
nav_df[f"净值_{code}"] = backtest_result[f"净值_{code}"].values
nav_path = f"{save_path}_nav.csv"
nav_df.to_csv(nav_path, index=False)
print(f"净值曲线已保存: {nav_path}")
# 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 _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):
"""打印最新调仓信号（支持ETF映射、ETF收盘价和溢价率显示）"""
code_config = code_config or {}
etf_close_data = etf_close_data or {}
premium_data = premium_data or {}
latest = _extract_latest_positions(backtest_result, code_list, code_name_map, select_num)
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
# 如果信号日期没有数据，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")
print("\n")
print("=" * 135)
print(" 最新调仓信号 (下一交易日执行)")
print("=" * 135)
print(f" 信号日期: {signal_date_str} (基于 {data_base_date_str} 收盘数据)")
print()
# 表头 - 添加ETF收盘价和溢价率列
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}')
print(" " + "-" * 155)
# 下期持仓（调入/维持）
for pos in latest["positions"]:
pnl_str = f'{pos["pnl"]:>+9.2%}' if pos["pnl"] is not None else ' —'
days_str = f'{pos["holding_days"]:>7}天' if pos["holding_days"] is not None else ' —'
entry_str = f'{pos["entry_price"]:>10.2f}' if pos["entry_price"] is not None else ' —'
entry_date_str = pos["entry_date"].strftime("%Y-%m-%d") if pos.get("entry_date") else ' —'
score_str = f'{pos["score"]:>8.2f}' if pos["score"] is not None else ' —'
flag = '▲' if pos["action"] == "调入" else ' '
# 获取ETF代码、ETF净值和溢价率
idx_code = pos["code"]
cfg = code_config.get(idx_code, {})
etf_code = cfg.get('etf', '—')
market = cfg.get('market', 'A')
if etf_code is None:
etf_code = '直接交易'
# 获取ETF收盘价和溢价率
if market == 'CRYPTO':
etf_close_str = ' —'
premium_str = ' —'
else:
# ETF收盘价
etf_close = etf_close_data.get(idx_code)
if etf_close is not None:
etf_close_str = f'{etf_close:>10.3f}'
else:
etf_close_str = ' —'
# 溢价率（只有当天有净值数据时才显示）
premium = premium_data.get(idx_code)
if premium is not None:
# 高溢价警告标记
warning = '⚠️' if premium > 0.02 else ''
premium_str = f'{premium:>+7.2%}{warning}'
else:
premium_str = ' —'
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}')
# 需调出的品种
if latest["exit_positions"]:
print()
print(" 需调出:")
for pos in latest["exit_positions"]:
pnl_str = f'{pos["pnl"]:>+9.2%}' if pos["pnl"] is not None else ' —'
days_str = f'{pos["holding_days"]:>7}天' if pos["holding_days"] is not None else ' —'
entry_str = f'{pos["entry_price"]:>10.2f}' if pos["entry_price"] is not None else ' —'
entry_date_str = pos["entry_date"].strftime("%Y-%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, {})
etf_code = cfg.get('etf', '—')
market = cfg.get('market', 'A')
if etf_code is None:
etf_code = '直接交易'
# 获取ETF收盘价和溢价率
if market == 'CRYPTO':
etf_close_str = ' —'
premium_str = ' —'
else:
# ETF收盘价
etf_close = etf_close_data.get(idx_code)
if etf_close is not None:
etf_close_str = f'{etf_close:>10.3f}'
else:
etf_close_str = ' —'
# 溢价率（只有当天有净值数据时才显示）
premium = premium_data.get(idx_code)
if premium is not None:
warning = '⚠️' if premium > 0.02 else ''
premium_str = f'{premium:>+7.2%}{warning}'
else:
premium_str = ' —'
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}')
print("=" * 160)
def _extract_latest_positions(backtest_result: pd.DataFrame, code_list: list, code_name_map: dict, select_num: int) -> dict:
"""提取最新持仓和下期调仓建议"""
last_date = backtest_result.index[-1]
last_row = backtest_result.iloc[-1]
# 当前持仓
current_signal = last_row["信号"]
if select_num == 1:
current_codes = [current_signal]
else:
current_codes = current_signal.split(",")
# 下期建议
score_cols = [f"得分_{code}" for code in code_list if f"得分_{code}" in backtest_result.columns]
scores = pd.to_numeric(last_row[score_cols], errors="coerce")
top_n = scores.nlargest(select_num)
next_codes = [c.replace("得分_", "") for c in top_n.index]
# 计算持仓信息
positions_info = []
weight = 1.0 / select_num
for code in next_codes:
name = code_name_map.get(code, code)
action = "维持" if code in current_codes else "调入"
# 获取当前价格和得分
current_price = last_row.get(code, 0)
score = scores.get(f"得分_{code}", None)
# 计算持仓信息（如果是维持的仓位）
entry_date = None
entry_price = None
holding_days = None
pnl = None
if action == "维持":
# 找到该标的最近一次连续持仓的起始日期
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
# 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
positions_info.append({
"code": code,
"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:
warning = "⚠️" if premium > 0.02 else ""
premium_str = f"{premium:+.2%}{warning}"
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:
warning = "⚠️" if premium > 0.02 else ""
premium_str = f"{premium:+.2%}{warning}"
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)))
ax4.set_yticklabels(ylabels, fontsize=7)
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}")