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clean(rotation): add simple rotation strategy and remove unused files

Browse Source 
New:
- rotation/simple_rotation.py: daily-iteration rotation strategy (584 lines)
- rotation/config_loader.py: standalone config loader
- rotation/config_simple.yaml: 11 assets, 7 groups
- rotation/README_SIMPLE.md: usage guide
- scripts/get_trading_calendar.py: trading calendar fetcher

Removed:
- rotation/example_usage.py, run_strategy.py (replaced by simple_rotation.py)
- rotation/results/ output files (gitignored)
- scripts/verify_*.py, calculate_returns_from_detail.py (one-off scripts)
- scripts/README_TRADING_CALENDAR.md

Backtest result (2020-01-10 ~ 2026-06-01):
- Total return: 1237.6%, Annual: 52.66%
- Max drawdown: -11.71%, Sharpe: 2.50
This commit is contained in:
aszerW
2026-06-01 22:28:26 +08:00
parent 3b0208d7d3
commit 451ffa33d2
8 changed files with 1720 additions and 19 deletions
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13
framework_v2/scripts/backtest_global_rotation.py
View File

@@ -72,7 +72,18 @@ def run_backtest():
print("=" * 70)
strategy = GlobalRotationStrategy(config)
result = strategy.run()
# 运行回测并导出 detail
output_dir = project_root / "framework_v2" / "results"
output_dir.mkdir(exist_ok=True)
detail_path = output_dir / "backtest_detail_v2.json"
print(f"\n导出 detail JSON: {detail_path}")
result = strategy.run(
export_detail=True,
detail_path=str(detail_path)
)
# 打印结果
print("\n" + "=" * 70)

83
framework_v2/strategies/rotation/rotation.py
View File

@@ -391,31 +391,55 @@ class GlobalRotationStrategy(StrategyBase):
aligner = CrossMarketAligner(target_calendar=trading_calendar)
# 提取交易标的的收盘价，并对齐到 A 股日历
print(" [对齐] 对齐 ETF 价格到 A 股日历...")
close_dict = {}
print(" [对齐] 构建可实现价格序列（模拟真实交易）...")
executable_close_dict = {}
for signal_code, trade_code in signal_to_trade.items():
if trade_code in data:
# 提取收盘价
close_series = data[trade_code]['close']
# 使用 signal_code 作为键（与 positions 列名一致）
close_dict[signal_code] = close_series
# 提取开盘价和收盘价
etf_df = data[trade_code]
open_series = etf_df['open'].reindex(trading_calendar, method='ffill')
close_series = etf_df['close'].reindex(trading_calendar, method='ffill')
# 默认使用收盘价
exec_close = close_series.copy()
# 检测调仓日，调整价格以反映真实交易
for i in range(1, len(trading_calendar)):
date = trading_calendar[i]
prev_date = trading_calendar[i-1]
# 获取仓位变化
prev_pos = positions.loc[prev_date, signal_code] if signal_code in positions.columns else 0
curr_pos = positions.loc[date, signal_code] if signal_code in positions.columns else 0
# 买入日：修改前一天价格为当日开盘价
# 这样收益率 = (close[t] - open[t]) / open[t] = 日内收益
if pd.isna(prev_pos) or prev_pos == 0:
if pd.notna(curr_pos) and curr_pos > 0:
exec_close.loc[prev_date] = open_series.loc[date]
# 卖出日：不需要修改（因为 positions[t]=0，不会计算收益）
executable_close_dict[signal_code] = exec_close
else:
print(f" 警告: {trade_code} 数据不存在，跳过")
# 使用 CrossMarketAligner 对齐多标的收益率
# 内部逻辑：先 ffill 价格到 A 股日历，再计算收益率
print(" [对齐] 计算收益率（先对齐价格，再计算）...")
returns_df = aligner.align_multi_asset(close_dict)
print(" [对齐] 计算收益率（使用可实现价格）...")
returns_df = aligner.align_multi_asset(executable_close_dict)
print(f" [对齐] 收益率数据: {len(returns_df)} 天, {len(returns_df.columns)} 个标的")
# 对齐 positions 到 A 股日历
# 注意：必须先 reindex 再 ffill，因为 reindex(method='ffill') 不会填充已有的 NaN
positions = positions.reindex(trading_calendar)
positions = positions.ffill()
# 卖出日不向前填充（保持 0）
positions = positions.ffill().fillna(0)
# 计算策略收益（仓位加权，T+1 执行）
positions_delayed = positions.shift(1).fillna(0)
strategy_returns = (positions_delayed * returns_df).sum(axis=1)
# 计算策略收益（仓位加权，无需延迟）
# 因为 positions[t] 已表示 t 日的实际持仓，且价格已调整为可实现价格
strategy_returns = (positions * returns_df).sum(axis=1)
# 扣除交易成本
strategy_returns, rebalance_count = self._apply_trade_cost(
@@ -649,6 +673,33 @@ class GlobalRotationStrategy(StrategyBase):
index_return_dict = {}
etf_return_dict = {}
# 构建 ETF 可实现价格序列（与回测一致）
executable_etf_close = {}
for signal_code, trade_code in signal_to_trade.items():
if trade_code in self._data:
etf_df = self._data[trade_code]
open_series = etf_df['open'].reindex(trading_calendar, method='ffill')
close_series = etf_df['close'].reindex(trading_calendar, method='ffill')
# 默认使用 close
exec_close = close_series.copy()
# 检测调仓日，调整价格
for i in range(1, len(trading_calendar)):
date = trading_calendar[i]
prev_date = trading_calendar[i-1]
# 获取仓位变化
prev_pos = positions.loc[prev_date, signal_code] if signal_code in positions.columns else 0
curr_pos = positions.loc[date, signal_code] if signal_code in positions.columns else 0
# 买入日：修改前一天价格为 open
if pd.isna(prev_pos) or prev_pos == 0:
if pd.notna(curr_pos) and curr_pos > 0:
exec_close.loc[prev_date] = open_series.loc[date]
executable_etf_close[signal_code] = exec_close
for signal_code, trade_code in signal_to_trade.items():
# 指数收益率
if signal_code in index_close_dict:
@@ -656,10 +707,10 @@ class GlobalRotationStrategy(StrategyBase):
idx_return = idx_close.pct_change(fill_method=None).fillna(0)
index_return_dict[signal_code] = idx_return
# ETF 收益率
if signal_code in etf_close_dict:
etf_close = etf_close_dict[signal_code].reindex(trading_calendar, method='ffill')
etf_return = etf_close.pct_change(fill_method=None).fillna(0)
# ETF 收益率（使用可实现价格）
if signal_code in executable_etf_close:
etf_exec = executable_etf_close[signal_code]
etf_return = etf_exec.pct_change(fill_method=None).fillna(0)
etf_return_dict[signal_code] = etf_return
# 对齐因子