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fix(core): 修复计算与数据对齐等多处逻辑问题

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- 修正CAGR计算，去除NaN并检查起始值有效性以避免异常结果
- 优化混合数据源的数据对齐逻辑，使用配置结束日期与A股最新数据日期的较早者
- 计算因子时对齐A股交易日历，重新基于对齐价格计算日收益率，改进因子对齐准确度
- 轮动策略中跳过空信号，避免空信号影响持仓和调仓逻辑
- 调整信号处理，过滤空字符串和NaN，保证轮动信号数据有效性
- 多品种轮动持仓中加入空信号判断，避免无效信号导致错误
- 调整调仓明细和品种汇总保存逻辑，增加空文件创建以保证输出路径文件稳定生成
- 完善多处打印信息和注释，增强代码可读性与调试便利性
This commit is contained in:
aszerW
2026-03-26 22:21:38 +08:00
parent 2faea1517f
commit 70bb69fd98
5 changed files with 114 additions and 49 deletions
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43
strategies/rotation/engine.py
View File

@@ -115,6 +115,10 @@ class RotationStrategy(BacktestStrategy):
target = daily_target.iloc[i]
if current_held is None:
# 跳过空信号，直到找到第一个有效信号
if not target:
held_signals.append(None) # 添加None占位，保持长度一致
continue
current_held = target
last_rebalance_idx = i
held_signals.append(current_held)
@@ -122,20 +126,24 @@ class RotationStrategy(BacktestStrategy):
days_since = i - last_rebalance_idx
if days_since >= rebalance_days:
should = self._check_rebalance(
result.iloc[i], current_held, target,
select_num, rebalance_threshold
)
if should:
current_held = target
last_rebalance_idx = i
# 目标信号为空时不调仓
if target: # 只在目标有效时才检查是否调仓
should = self._check_rebalance(
result.iloc[i], current_held, target,
select_num, rebalance_threshold
)
if should:
current_held = target
last_rebalance_idx = i
held_signals.append(current_held)
result["信号_raw"] = held_signals
result["信号"] = result["信号_raw"].shift(1)
result = result.drop(columns=["信号_raw"])
# 删除信号为 NaN 或空字符串的行
result = result.dropna(subset=["信号"])
result = result[result["信号"] != ""]
self.signals = result
self._print_signal_stats(result, select_num, rebalance_days, rebalance_threshold)
@@ -152,12 +160,14 @@ class RotationStrategy(BacktestStrategy):
return (new_score / old_score - 1) >= threshold
return new_score > 0
else:
new_codes = target.split(",")
old_codes = current_held.split(",")
new_codes = [c for c in target.split(",") if c] # 过滤空字符串
old_codes = [c for c in current_held.split(",") if c] # 过滤空字符串
if not new_codes or not old_codes:
return True # 有空持仓，需要调仓
if set(new_codes) == set(old_codes):
return False
new_total = sum(float(row[f"得分_{c}"]) for c in new_codes)
old_total = sum(float(row[f"得分_{c}"]) for c in old_codes)
new_total = sum(float(row.get(f"得分_{c}", 0)) for c in new_codes)
old_total = sum(float(row.get(f"得分_{c}", 0)) for c in old_codes)
if old_total > 0:
return (new_total / old_total - 1) >= threshold
return new_total > 0
@@ -206,12 +216,17 @@ class RotationStrategy(BacktestStrategy):
# 计算策略日收益率
if select_num == 1:
def calc_return(row):
return row[f"日收益率_{row['信号']}"]
signal = row['信号']
if not signal or pd.isna(signal):
return 0.0
return row.get(f"日收益率_{signal}", 0.0)
result["轮动策略日收益率"] = result.apply(calc_return, axis=1)
else:
def calc_multi_return(row):
codes = row["信号"].split(",")
returns = [row[f"日收益率_{c}"] for c in codes]
codes = [c for c in row["信号"].split(",") if c] # 过滤空字符串
if not codes:
return 0.0
returns = [row.get(f"日收益率_{c}", 0.0) for c in codes]
return np.mean(returns)
result["轮动策略日收益率"] = result.apply(calc_multi_return, axis=1)

57
strategies/rotation/portfolio.py
View File

@@ -95,7 +95,10 @@ def track_positions(
# 多品种等权轮动
current_signal = signals[0]
entry_date = dates[0]
codes = current_signal.split(",")
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"]
@@ -131,7 +134,9 @@ def track_positions(
current_signal = today_signal
entry_date = dates[i]
codes = current_signal.split(",")
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"]
@@ -211,23 +216,37 @@ def save_trades(
import os
os.makedirs(os.path.dirname(save_path) if os.path.dirname(save_path) else ".", exist_ok=True)
trades_out = trades_df.copy()
trades_out["持仓收益"] = trades_out["持仓收益"].apply(lambda x: f"{x:.2%}")
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]
)
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]
)
# 保存调仓明细
trades_path = f"{save_path}_trades.csv"
trades_out.to_csv(trades_path, index=False, encoding="utf-8-sig")
print(f"\n调仓明细已保存: {trades_path}")
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"]:
summary_out[col] = summary_out[col].apply(lambda x: f"{x:.2%}")
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"
summary_out.to_csv(summary_path, index=False, encoding="utf-8-sig")
print(f"品种汇总已保存: {summary_path}")
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}")