aszerW
70bb69fd98
- 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
306 lines
12 KiB
Python
306 lines
12 KiB
Python
"""
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ETF轮动策略引擎
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整合信号生成和回测逻辑
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使用 YFinance 数据源（支持 SSH 隧道）
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"""
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import pandas as pd
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import numpy as np
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from typing import Optional
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from strategies.base import BacktestStrategy
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from core.datasource.hybrid_source import HybridDataSource
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from core.factors.momentum import compute_factors, calculate_daily_return
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class RotationStrategy(BacktestStrategy):
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"""ETF轮动策略"""
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def __init__(self, config: dict):
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super().__init__("ETF轮动策略", config)
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# 初始化混合数据源
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ssh_config = config.get("ssh_tunnel", {})
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self.data_source = HybridDataSource(
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ssh_config=ssh_config,
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use_cache=config.get("use_cache", True)
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)
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print(f"使用混合数据源: Tushare(中国A股) + YFinance(港股/美股/加密货币)")
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print(f"SSH隧道: {ssh_config.get('enabled', False)}")
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self.data = None
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self.signals = None
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self.backtest_result = None
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def fetch_data(self) -> pd.DataFrame:
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"""获取数据（支持指数-ETF双轨数据）"""
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from config.settings import DEFAULT_BENCHMARK_CODE
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# 从配置中读取基准代码，或使用默认值
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benchmark_code = self.config.get("benchmark", {}).get("code", DEFAULT_BENCHMARK_CODE)
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# 获取代码配置（包含 name, etf, market）
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code_config = self.config.get("code_list", {})
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# 使用上下文管理器管理 SSH 隧道
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with self.data_source:
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index_data, etf_data, etf_nav_data, benchmark_data, valid_codes = self.data_source.fetch_all(
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code_config,
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benchmark_code,
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self.config["start_date"],
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self.config["end_date"],
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)
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# 存储数据和配置
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self.index_data = index_data # 指数数据（用于因子计算）
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self.etf_data = etf_data # ETF价格数据（用于收益计算）
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self.etf_nav_data = etf_nav_data # ETF净值数据（用于溢价率计算）
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self.benchmark_data = benchmark_data
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self.valid_codes = valid_codes
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self.code_config = code_config # 代码配置（用于判断市场类型）
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# 计算因子（传入两套数据：指数数据用于因子，ETF数据用于收益）
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factor_data, valid_codes = compute_factors(
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index_data,
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valid_codes,
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n=self.config["n_days"],
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factor_type=self.config["factor_type"],
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etf_data=etf_data, # 传入ETF数据用于收益计算
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code_config=code_config, # 传入配置以判断加密货币
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)
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self.data = factor_data
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self.valid_codes = valid_codes
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return factor_data
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def generate_signals(self) -> pd.DataFrame:
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"""生成轮动信号"""
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if self.data is None:
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self.fetch_data()
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result = self.data.copy()
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score_cols = [f"得分_{code}" for code in self.valid_codes]
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select_num = self.config["select_num"]
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rebalance_days = self.config["rebalance_days"]
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rebalance_threshold = self.config["rebalance_threshold"]
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# Step 1: 每日目标组合
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if not score_cols:
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raise ValueError("没有有效的指数代码，无法生成信号")
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if select_num == 1:
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daily_target = (
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result[score_cols]
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.idxmax(axis=1)
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.str.replace("得分_", "", regex=False)
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)
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else:
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def top_n_codes(row):
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scores = pd.to_numeric(row[score_cols], errors="coerce")
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# 过滤掉 NaN 值
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scores = scores.dropna()
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if len(scores) == 0:
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return ""
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top = scores.nlargest(min(select_num, len(scores))).index.tolist()
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return ",".join([c.replace("得分_", "") for c in top])
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daily_target = result.apply(top_n_codes, axis=1)
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# Step 2: 逐日生成信号（调仓周期控制）
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held_signals = []
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current_held = None
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last_rebalance_idx = 0
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for i in range(len(result)):
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target = daily_target.iloc[i]
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if current_held is None:
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# 跳过空信号，直到找到第一个有效信号
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if not target:
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held_signals.append(None) # 添加None占位，保持长度一致
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continue
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current_held = target
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last_rebalance_idx = i
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held_signals.append(current_held)
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continue
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days_since = i - last_rebalance_idx
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if days_since >= rebalance_days:
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# 目标信号为空时不调仓
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if target: # 只在目标有效时才检查是否调仓
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should = self._check_rebalance(
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result.iloc[i], current_held, target,
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select_num, rebalance_threshold
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)
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if should:
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current_held = target
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last_rebalance_idx = i
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held_signals.append(current_held)
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result["信号_raw"] = held_signals
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result["信号"] = result["信号_raw"].shift(1)
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result = result.drop(columns=["信号_raw"])
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# 删除信号为 NaN 或空字符串的行
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result = result.dropna(subset=["信号"])
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result = result[result["信号"] != ""]
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self.signals = result
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self._print_signal_stats(result, select_num, rebalance_days, rebalance_threshold)
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return result
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def _check_rebalance(self, row, current_held, target, select_num, threshold):
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"""检查是否应该调仓"""
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if select_num == 1:
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if target == current_held:
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return False
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new_score = float(row[f"得分_{target}"])
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old_score = float(row[f"得分_{current_held}"])
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if old_score > 0:
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return (new_score / old_score - 1) >= threshold
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return new_score > 0
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else:
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new_codes = [c for c in target.split(",") if c] # 过滤空字符串
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old_codes = [c for c in current_held.split(",") if c] # 过滤空字符串
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if not new_codes or not old_codes:
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return True # 有空持仓，需要调仓
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if set(new_codes) == set(old_codes):
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return False
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new_total = sum(float(row.get(f"得分_{c}", 0)) for c in new_codes)
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old_total = sum(float(row.get(f"得分_{c}", 0)) for c in old_codes)
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if old_total > 0:
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return (new_total / old_total - 1) >= threshold
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return new_total > 0
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def _print_signal_stats(self, result, select_num, rebalance_days, rebalance_threshold):
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"""打印信号统计"""
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total_days = len(result)
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if select_num == 1:
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rebalance_count = (result["信号"] != result["信号"].shift(1)).sum() - 1
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else:
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prev = None
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rebalance_count = 0
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for s in result["信号"]:
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if prev is not None and s != prev:
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if set(s.split(",")) != set(prev.split(",")):
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rebalance_count += 1
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prev = s
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rebalance_count = max(rebalance_count, 0)
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avg_hold = total_days / max(rebalance_count, 1)
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years = total_days / 252
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annual_rebalances = rebalance_count / max(years, 0.1)
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print(f"\n信号生成完成:")
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print(f" 调仓周期: {rebalance_days} 天 | 阈值: {rebalance_threshold:.1%}")
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print(f" 交易天数: {total_days}")
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print(f" 调仓次数: {rebalance_count} | 平均持仓: {avg_hold:.1f} 天 | 年均调仓: {annual_rebalances:.1f} 次")
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if select_num == 1:
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signal_counts = result["信号"].value_counts()
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print(f" 品种持仓分布 (前10):")
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for code, count in signal_counts.head(10).items():
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pct = count / total_days * 100
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print(f" {code}: {count}天 ({pct:.1f}%)")
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def run_backtest(self) -> pd.DataFrame:
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"""执行回测"""
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if self.signals is None:
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self.generate_signals()
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result = self.signals.copy()
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select_num = self.config["select_num"]
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trade_cost = self.config["trade_cost"]
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# 计算策略日收益率
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if select_num == 1:
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def calc_return(row):
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signal = row['信号']
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if not signal or pd.isna(signal):
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return 0.0
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return row.get(f"日收益率_{signal}", 0.0)
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result["轮动策略日收益率"] = result.apply(calc_return, axis=1)
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else:
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def calc_multi_return(row):
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codes = [c for c in row["信号"].split(",") if c] # 过滤空字符串
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if not codes:
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return 0.0
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returns = [row.get(f"日收益率_{c}", 0.0) for c in codes]
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return np.mean(returns)
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result["轮动策略日收益率"] = result.apply(calc_multi_return, axis=1)
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# 扣除交易成本
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if trade_cost > 0:
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prev_signal = result["信号"].shift(1)
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if select_num == 1:
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changed = (result["信号"] != prev_signal) & prev_signal.notna()
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result.loc[changed, "轮动策略日收益率"] -= trade_cost
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else:
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turnover_list = []
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for curr, prev in zip(result["信号"], prev_signal):
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if pd.isna(prev) or curr == prev:
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turnover_list.append(0.0)
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else:
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old = set(prev.split(","))
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new = set(curr.split(","))
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swapped = len(old - new)
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turnover_list.append(swapped / len(old))
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result["换手率"] = turnover_list
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result["轮动策略日收益率"] -= result["换手率"] * trade_cost
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# 计算净值
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result["轮动策略净值"] = (1 + result["轮动策略日收益率"]).cumprod()
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# 各ETF单独净值
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for code in self.valid_codes:
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first_price = result[code].iloc[0]
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result[f"净值_{code}"] = result[code] / first_price
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# 基准净值
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# benchmark_data 是 DataFrame，需要提取 close 列
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if isinstance(self.benchmark_data, pd.DataFrame):
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if 'close' in self.benchmark_data.columns:
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bench_close = self.benchmark_data['close']
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else:
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# 宽格式数据
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bench_close = self.benchmark_data.iloc[:, 0]
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else:
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bench_close = self.benchmark_data
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bench_ret = bench_close.pct_change().dropna()
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common_dates = result.index.intersection(bench_ret.index)
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bench_ret = bench_ret.loc[common_dates]
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result["基准日收益率"] = bench_ret.reindex(result.index, fill_value=0)
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result["基准净值"] = (1 + result["基准日收益率"]).cumprod()
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self.backtest_result = result
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# 打印摘要
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total_days = len(result)
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strategy_total_return = result["轮动策略净值"].iloc[-1] - 1
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benchmark_total_return = result["基准净值"].iloc[-1] - 1
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print(f"\n回测完成:")
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print(f" 回测区间: {result.index.min().date()} ~ {result.index.max().date()}")
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print(f" 交易天数: {total_days}")
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print(f" 策略累计收益: {strategy_total_return:.2%}")
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print(f" 基准累计收益: {benchmark_total_return:.2%}")
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return result
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def run(self) -> dict:
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"""运行完整流程"""
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self.fetch_data()
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self.generate_signals()
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self.run_backtest()
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return self.backtest_result
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def get_signals(self) -> pd.DataFrame:
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"""获取当前信号"""
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if self.signals is None:
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self.generate_signals()
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return self.signals
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