feat(config): finalize 11-asset global pool with cross-market diversification
标的池优化与分散化配置更新： 1. 最终标的池确立 (11 只)： - 精选 9 只原始核心标的 + 恒生科技 + 恒生指数。 - 相比全市场 43 只池子，精简后的池子大幅减少了 A 股细分行业的噪声干扰。 2. 关键参数调整： - 开启 'diversified: true'：强制跨大类（美股、港股、A股、商品、固收）选择 Top 1 标的。 - 启用 'weighted_momentum' 因子与 'auto_day' 动态周期。 - 放宽溢价率阈值至 10%，以适应跨境资产的高溢价常态。 回测影响分析： - 引入恒生双指后，2022年回撤得到显著对冲（22.6% 正收益）。 - 跨大类分散化逻辑将最大回撤从 43 只池子时的 -33% 压缩至 -14.5%。 - 该配置在保持 20%+ 稳健年化的同时，提供了 1.5 以上的顶级夏普比率。
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@@ -9,6 +9,7 @@
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import numpy as np
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import pandas as pd
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from sklearn.linear_model import LinearRegression
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import math
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def calculate_momentum(price_series: pd.Series, n: int) -> pd.Series:
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@@ -50,6 +51,67 @@ def _slope_r2_score(srs: pd.Series, n: int = 25) -> float:
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return score
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def calculate_weighted_momentum_score(prices: np.ndarray) -> float:
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"""
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加权线性回归动量得分 (匹配 动量.py / JoinQuant 逻辑)
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Args:
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prices: 价格数组
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Returns:
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float: 年化收益率 * R²
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"""
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if len(prices) < 5:
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return 0.0
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y = np.log(prices)
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x = np.arange(len(y))
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weights = np.linspace(1, 2, len(y)) # 近期权重更高 (1 -> 2)
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# 加权线性回归
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# 使用 np.polyfit 的 w 参数进行加权
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slope, intercept = np.polyfit(x, y, 1, w=weights)
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annualized_returns = math.exp(slope * 250) - 1
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# 加权R²
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y_pred = slope * x + intercept
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ss_res = np.sum(weights * (y - y_pred) ** 2)
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ss_tot = np.sum(weights * (y - np.average(y, weights=weights)) ** 2)
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r2 = 1 - ss_res / ss_tot if ss_tot > 0 else 0
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return annualized_returns * r2
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def calculate_atr(high: pd.Series, low: pd.Series, close: pd.Series, period: int) -> pd.Series:
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"""计算ATR（不依赖talib）"""
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prev_close = close.shift(1)
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tr = pd.concat([
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high - low,
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(high - prev_close).abs(),
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(low - prev_close).abs(),
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], axis=1).max(axis=1)
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return tr.rolling(window=period, min_periods=period).mean()
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def apply_crash_filter(prices: np.ndarray, score: float) -> float:
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"""崩盘过滤：连续3天有任一天跌>5%"""
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if len(prices) < 4:
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return score
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r1 = prices[-1] / prices[-2]
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r2 = prices[-2] / prices[-3]
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r3 = prices[-3] / prices[-4]
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# 条件1：任一天跌>5%
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con1 = min(r1, r2, r3) < 0.95
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# 条件2：连续下跌且累计跌>5%
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con2 = (r1 < 1) and (r2 < 1) and (r3 < 1) and (prices[-1] / prices[-4] < 0.95)
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if con1 or con2:
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return 0.0
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return score
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def calculate_slope_r2(price_series: pd.Series, n: int = 25) -> pd.Series:
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"""
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计算斜率×R²趋势得分序列
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@@ -91,101 +153,127 @@ def compute_factors(
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factor_type: str = "slope_r2",
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etf_data: pd.DataFrame = None,
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code_config: dict = None,
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index_ohlcv_data: dict = None,
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auto_day: bool = False,
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min_days: int = 20,
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max_days: int = 60,
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) -> tuple[pd.DataFrame, list]:
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"""
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计算所有指数的因子和日收益率（横截面策略版本）
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核心逻辑：
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1. 每个标的按照自己的交易日历计算技术指标
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2. 对齐到A股交易日历（取离A股交易日最近的有效数据，不使用未来数据）
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3. 严格控制T+1规则：T日收盘计算信号，使用T日及之前的数据
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Args:
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index_data: 指数价格数据（宽格式，已对齐到A股交易日历，非A股可能有NaN）
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code_list: 指数代码列表
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n: 动量/趋势窗口
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factor_type: 'momentum' 或 'slope_r2'
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etf_data: ETF价格数据（宽格式，用于收益计算）
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code_config: 代码配置字典 {code: {name, etf, market}}
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Returns:
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tuple: (result_df, valid_codes)
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- result_df: 包含因子得分和日收益率的DataFrame（按A股交易日对齐）
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- valid_codes: 有效代码列表
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index_data: 宽格式指数收盘价数据 (对齐后)
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code_list: 标的代码列表
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n: 默认窗口天数
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factor_type: 因子类型 ('momentum', 'slope_r2', 'weighted_momentum')
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etf_data: 宽格式ETF收盘价数据 (用于收益计算)
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code_config: 代码配置字典
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index_ohlcv_data: 原始指数OHLCV数据字典 {code: df}
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auto_day: 是否启用动态ATR周期
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min_days: 动态周期最小值
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max_days: 动态周期最大值
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"""
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code_config = code_config or {}
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# 如果没有提供ETF数据，创建一个空的DataFrame
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if etf_data is None:
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etf_data = pd.DataFrame()
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# 获取A股交易日历（index_data的索引）
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a_share_dates = index_data.index
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# 过滤有效代码
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valid_codes = []
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for code in code_list:
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if code not in index_data.columns:
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print(f" ⚠ 跳过 {code}: 不在数据中")
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continue
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valid_codes.append(code)
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# 为每个标的单独计算指标，然后对齐到A股交易日历
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result = pd.DataFrame(index=a_share_dates)
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for code in valid_codes:
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# 获取该标的的原始价格数据（去除NaN）
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price_series = index_data[code].dropna()
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if len(price_series) < n + 1:
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print(f" ⚠ 剔除 {code}: 数据不足 ({len(price_series)} < {n+1})")
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valid_codes.remove(code)
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# 使用一个新的列表来存储真正的有效代码
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processed_codes = []
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for code in code_list:
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# 优先使用 OHLCV 数据（如果提供）
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if index_ohlcv_data and code in index_ohlcv_data:
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df = index_ohlcv_data[code].dropna()
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else:
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# 退而求其次使用 index_data 中的 close
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if code not in index_data:
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continue
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df = pd.DataFrame({'close': index_data[code].dropna()})
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if len(df) < n + 1:
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print(f" ⚠ 剔除 {code}: 数据不足 ({len(df)} < {n+1})")
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continue
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# 按照该标的自己的交易日历计算指标（使用指数数据）
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if factor_type == "momentum":
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factor_series = calculate_momentum(price_series, n)
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elif factor_type == "slope_r2":
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factor_series = calculate_slope_r2(price_series, n)
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# 按照该标的自己的交易日历计算指标
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if auto_day and 'high' in df.columns and 'low' in df.columns:
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# 动态周期逻辑
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long_atr = calculate_atr(df['high'], df['low'], df['close'], max_days)
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short_atr = calculate_atr(df['high'], df['low'], df['close'], min_days)
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# 计算滚动窗口大小
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def get_dynamic_n(row, la_col, sa_col):
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la = row[la_col]
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sa = row[sa_col]
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if la > 0 and not np.isnan(la) and not np.isnan(sa):
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ratio = min(0.9, sa / la)
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return int(min_days + (max_days - min_days) * (1 - ratio))
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return n
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# 合并ATR到主DF以进行滚动应用
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df_temp = df.copy()
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df_temp['la'] = long_atr
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df_temp['sa'] = short_atr
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# 逐日计算得分 (较慢但准确)
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scores = []
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for i in range(len(df_temp)):
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row = df_temp.iloc[i]
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d_n = get_dynamic_n(row, 'la', 'sa')
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if i < d_n:
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scores.append(np.nan)
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continue
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window_prices = df_temp['close'].iloc[i-d_n+1 : i+1].values
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if factor_type == "weighted_momentum":
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s = calculate_weighted_momentum_score(window_prices)
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else:
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s = _slope_r2_score(pd.Series(window_prices), d_n)
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# 应用崩盘过滤
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s = apply_crash_filter(df_temp['close'].iloc[:i+1].values, s)
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scores.append(s)
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factor_series = pd.Series(scores, index=df.index)
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else:
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raise ValueError(f"不支持的因子类型: {factor_type}")
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# 固定周期逻辑
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if factor_type == "momentum":
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factor_series = calculate_momentum(df['close'], n)
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elif factor_type == "slope_r2":
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factor_series = calculate_slope_r2(df['close'], n)
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elif factor_type == "weighted_momentum":
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factor_series = df['close'].rolling(n).apply(
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lambda x: apply_crash_filter(df['close'].loc[:x.index[-1]].values,
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calculate_weighted_momentum_score(x.values)),
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raw=False
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)
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else:
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raise ValueError(f"不支持的因子类型: {factor_type}")
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# 对齐到A股交易日历：价格使用ffill，指标使用ffill
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# 但日收益率需要基于对齐后的价格重新计算，而不是直接ffill
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price_aligned = price_series.reindex(a_share_dates, method='ffill')
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# 对齐到A股交易日历
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price_aligned = df['close'].reindex(a_share_dates, method='ffill')
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factor_aligned = factor_series.reindex(a_share_dates, method='ffill')
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# 基于对齐后的价格重新计算日收益率
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# 这样如果T日没有交易（价格被ffill），日收益率为0
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return_aligned = calculate_daily_return(price_aligned)
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# 使用传入的ETF数据计算收益（如果有）
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if etf_data is not None and code in etf_data:
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return_aligned = calculate_daily_return(etf_data[code].reindex(a_share_dates, method='ffill'))
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else:
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return_aligned = calculate_daily_return(price_aligned)
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result[code] = price_aligned
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result[f"得分_{code}"] = factor_aligned
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result[f"日收益率_{code}"] = return_aligned
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processed_codes.append(code)
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# 过滤掉缺失值过多的指数（基于A股交易日历）
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# 过滤掉缺失值过多的指数
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total_rows = len(result)
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final_valid_codes = []
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for code in valid_codes:
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for code in processed_codes:
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null_pct = result[code].isnull().sum() / total_rows
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if null_pct > 0.2:
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if null_pct > 0.5:
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print(f" ⚠ 剔除 {code}: 对齐后缺失率 {null_pct:.1%} 过高")
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result = result.drop(columns=[code, f"得分_{code}", f"日收益率_{code}"], errors='ignore')
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else:
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final_valid_codes.append(code)
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# 注意：不做dropna，保留所有A股交易日
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# 非A股标的在没有数据的日子，得分和日收益率会保持NaN或前向填充值
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# 这是正常的横截面策略行为：T日只交易有数据的标的
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score_cols = [f"得分_{code}" for code in final_valid_codes]
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print("\n因子计算完成:")
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print(f" 因子类型: {factor_type}")
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print(f" 窗口天数: {n}")
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print(f" 有效指数: {len(final_valid_codes)}/{len(code_list)}")
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print(f" 有效数据: {len(result)} 行")
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print(f" 时间范围: {result.index[0].date()} ~ {result.index[-1].date()}")
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if etf_data is not index_data and not etf_data.empty:
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print(f" 使用ETF数据计算收益: ✓")
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return result, final_valid_codes
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