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refactor: 归档旧代码，保留新框架结构

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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/portfolio.py Normal file
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"""
ETF轮动策略 - 持仓跟踪模块
"""
import pandas as pd
from typing import Optional
def track_positions(
backtest_result: pd.DataFrame,
code_name_map: dict = None,
select_num: int = 1,
) -> tuple[pd.DataFrame, pd.DataFrame]:
"""
从回测结果中提取每笔持仓记录
Args:
backtest_result: 回测结果（含 '信号' 列）
code_name_map: 代码→名称映射
select_num: 每次选中的品种数量
Returns:
tuple: (trades_df, summary_df)
"""
code_name_map = code_name_map or {}
data = backtest_result.copy()
dates = data.index.tolist()
signals = data["信号"].tolist()
trades = []
if select_num == 1:
# 单品种轮动
current_code = signals[0]
entry_date = dates[0]
entry_price = data.loc[entry_date, current_code]
entry_nav = data.loc[entry_date, "轮动策略净值"]
for i in range(1, len(dates)):
today_code = signals[i]
if today_code != current_code:
exit_date = dates[i - 1]
exit_price = data.loc[exit_date, current_code]
exit_nav = data.loc[exit_date, "轮动策略净值"]
holding_days = (i - 1) - dates.index(entry_date) + 1
trade_return = exit_price / entry_price - 1 if entry_price != 0 else 0
nav_contrib = exit_nav - entry_nav
trades.append({
"序号": len(trades) + 1,
"品种代码": current_code,
"品种名称": code_name_map.get(current_code, current_code),
"进场日期": entry_date,
"出场日期": exit_date,
"持仓天数": holding_days,
"仓位占比": "100%",
"进场价格": round(entry_price, 2),
"出场价格": round(exit_price, 2),
"持仓收益": trade_return,
"进场净值": round(entry_nav, 4),
"出场净值": round(exit_nav, 4),
"净值贡献": round(nav_contrib, 4),
})
current_code = today_code
entry_date = dates[i]
entry_price = data.loc[entry_date, current_code]
entry_nav = data.loc[entry_date, "轮动策略净值"]
# 最后一笔
exit_date = dates[-1]
exit_price = data.loc[exit_date, current_code]
exit_nav = data.loc[exit_date, "轮动策略净值"]
holding_days = len(dates) - dates.index(entry_date)
trade_return = exit_price / entry_price - 1 if entry_price != 0 else 0
nav_contrib = exit_nav - entry_nav
trades.append({
"序号": len(trades) + 1,
"品种代码": current_code,
"品种名称": code_name_map.get(current_code, current_code),
"进场日期": entry_date,
"出场日期": exit_date,
"持仓天数": holding_days,
"仓位占比": "100%",
"进场价格": round(entry_price, 2),
"出场价格": round(exit_price, 2),
"持仓收益": trade_return,
"进场净值": round(entry_nav, 4),
"出场净值": round(exit_nav, 4),
"净值贡献": round(nav_contrib, 4),
})
else:
# 多品种等权轮动
current_signal = signals[0]
entry_date = dates[0]
codes = [c for c in current_signal.split(",") if c] # 过滤空字符串
if not codes:
# 空信号，返回空结果
return pd.DataFrame(trades), pd.DataFrame()
weight = 1.0 / len(codes)
entry_prices = {c: data.loc[entry_date, c] for c in codes}
entry_nav = data.loc[entry_date, "轮动策略净值"]
for i in range(1, len(dates)):
today_signal = signals[i]
if today_signal != current_signal:
exit_date = dates[i - 1]
exit_nav = data.loc[exit_date, "轮动策略净值"]
holding_days = (i - 1) - dates.index(entry_date) + 1
for c in codes:
exit_price = data.loc[exit_date, c]
ep = entry_prices[c]
trade_return = exit_price / ep - 1 if ep != 0 else 0
trades.append({
"序号": len(trades) + 1,
"品种代码": c,
"品种名称": code_name_map.get(c, c),
"进场日期": entry_date,
"出场日期": exit_date,
"持仓天数": holding_days,
"仓位占比": f"{weight:.0%}",
"进场价格": round(ep, 2),
"出场价格": round(exit_price, 2),
"持仓收益": trade_return,
"进场净值": round(entry_nav, 4),
"出场净值": round(exit_nav, 4),
"净值贡献": round((exit_nav - entry_nav) * weight, 4),
})
current_signal = today_signal
entry_date = dates[i]
codes = [c for c in current_signal.split(",") if c] # 过滤空字符串
if not codes:
break # 空信号，结束循环
weight = 1.0 / len(codes)
entry_prices = {c: data.loc[entry_date, c] for c in codes}
entry_nav = data.loc[entry_date, "轮动策略净值"]
# 最后一笔
exit_date = dates[-1]
exit_nav = data.loc[exit_date, "轮动策略净值"]
holding_days = len(dates) - dates.index(entry_date)
for c in codes:
exit_price = data.loc[exit_date, c]
ep = entry_prices[c]
trade_return = exit_price / ep - 1 if ep != 0 else 0
trades.append({
"序号": len(trades) + 1,
"品种代码": c,
"品种名称": code_name_map.get(c, c),
"进场日期": entry_date,
"出场日期": exit_date,
"持仓天数": holding_days,
"仓位占比": f"{weight:.0%}",
"进场价格": round(ep, 2),
"出场价格": round(exit_price, 2),
"持仓收益": trade_return,
"进场净值": round(entry_nav, 4),
"出场净值": round(exit_nav, 4),
"净值贡献": round((exit_nav - entry_nav) * weight, 4),
})
trades_df = pd.DataFrame(trades)
summary = _summarize_by_code(trades_df, code_name_map)
return trades_df, summary
def _summarize_by_code(trades_df: pd.DataFrame, code_name_map: dict) -> pd.DataFrame:
"""按品种汇总持仓统计"""
if trades_df.empty:
return pd.DataFrame()
groups = trades_df.groupby("品种代码")
rows = []
for code, grp in groups:
total_trades = len(grp)
total_days = grp["持仓天数"].sum()
avg_days = grp["持仓天数"].mean()
win_trades = (grp["持仓收益"] > 0).sum()
win_rate = win_trades / total_trades if total_trades > 0 else 0
avg_return = grp["持仓收益"].mean()
total_return = (1 + grp["持仓收益"]).prod() - 1
max_return = grp["持仓收益"].max()
min_return = grp["持仓收益"].min()
rows.append({
"品种代码": code,
"品种名称": code_name_map.get(code, code),
"调仓次数": total_trades,
"总持仓天数": total_days,
"平均持仓天数": round(avg_days, 1),
"胜率": win_rate,
"平均收益": avg_return,
"累计收益": total_return,
"最大单次收益": max_return,
"最大单次亏损": min_return,
})
summary = pd.DataFrame(rows)
summary = summary.sort_values("总持仓天数", ascending=False).reset_index(drop=True)
return summary
def save_trades(
trades_df: pd.DataFrame,
summary_df: pd.DataFrame,
save_path: str = "report",
) -> None:
"""保存调仓明细和汇总到CSV"""
import os
os.makedirs(os.path.dirname(save_path) if os.path.dirname(save_path) else ".", exist_ok=True)
# 保存调仓明细
trades_path = f"{save_path}_trades.csv"
if not trades_df.empty:
trades_out = trades_df.copy()
if "持仓收益" in trades_out.columns:
trades_out["持仓收益"] = trades_out["持仓收益"].apply(lambda x: f"{x:.2%}")
if "进场日期" in trades_out.columns:
trades_out["进场日期"] = trades_out["进场日期"].apply(
lambda x: x.strftime("%Y-%m-%d") if hasattr(x, "strftime") else str(x)[:10]
)
if "出场日期" in trades_out.columns:
trades_out["出场日期"] = trades_out["出场日期"].apply(
lambda x: x.strftime("%Y-%m-%d") if hasattr(x, "strftime") else str(x)[:10]
)
trades_out.to_csv(trades_path, index=False, encoding="utf-8-sig")
print(f"\n调仓明细已保存: {trades_path}")
else:
# 创建空文件
pd.DataFrame().to_csv(trades_path, index=False, encoding="utf-8-sig")
print(f"\n调仓明细为空: {trades_path}")
# 保存品种汇总
summary_path = f"{save_path}_summary.csv"
if not summary_df.empty:
summary_out = summary_df.copy()
for col in ["胜率", "平均收益", "累计收益", "最大单次收益", "最大单次亏损"]:
if col in summary_out.columns:
summary_out[col] = summary_out[col].apply(lambda x: f"{x:.2%}")
summary_out.to_csv(summary_path, index=False, encoding="utf-8-sig")
print(f"品种汇总已保存: {summary_path}")
else:
# 创建空文件
pd.DataFrame().to_csv(summary_path, index=False, encoding="utf-8-sig")
print(f"品种汇总为空: {summary_path}")