1533 lines
72 KiB
Python
1533 lines
72 KiB
Python
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import numpy as np
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import pandas as pd
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import talib
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def get_rolling_factor(df):
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old_columns = df.columns.tolist()[:]
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# 按股票和日期排序（如果尚未排序）
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df = df.sort_values(by=['ts_code', 'trade_date'])
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grouped = df.groupby('ts_code', group_keys=False)
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epsilon = 1e-8
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df['lg_elg_net_buy_vol'] = df['buy_lg_vol'] + df['buy_elg_vol'] - df['sell_lg_vol'] - df['sell_elg_vol']
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# 检查 'volume' 列是否存在且有效
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df['flow_lg_elg_intensity'] = df['lg_elg_net_buy_vol'] / (df['vol'] + epsilon)
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# 2. 散户与主力背离度 (Retail vs Institutional Divergence)
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# 衡量小单净流入与（大单+超大单）净流入的差异或比率
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df['sm_net_buy_vol'] = df['buy_sm_vol'] - df['sell_sm_vol']
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df['flow_divergence_diff'] = df['sm_net_buy_vol'] - df['lg_elg_net_buy_vol']
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# 比率形式可能更稳定
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df['flow_divergence_ratio'] = df['sm_net_buy_vol'] / (
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df['lg_elg_net_buy_vol'] + np.sign(df['lg_elg_net_buy_vol']) * epsilon + epsilon) # 复杂处理避免0/0
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# 3. 资金流结构变动 (Flow Structure Change - Relative Strength of Large Flow)
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# 大单+超大单买入额占总买入额的比例的变化
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df['total_buy_vol'] = df['buy_sm_vol'] + df['buy_lg_vol'] + df['buy_elg_vol']
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df['lg_elg_buy_prop'] = (df['buy_lg_vol'] + df['buy_elg_vol']) / (df['total_buy_vol'] + epsilon)
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df['flow_struct_buy_change'] = grouped['lg_elg_buy_prop'].diff(1) # 1日变化
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# 4. 资金流加速度 (Flow Acceleration)
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# 净主力资金流的变化率（二阶导）
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df['lg_elg_net_buy_vol_change'] = grouped['lg_elg_net_buy_vol'].diff(1)
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df['flow_lg_elg_accel'] = grouped['lg_elg_net_buy_vol_change'].diff(1)
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# # 5. 极端资金流事件 (Categorical: Extreme Flow Event)
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# # 定义主力资金流强度是否处于其历史极端水平（例如，过去N天的90分位数以上或10分位数以下）
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# rolling_window = 20 # 可调整窗口期
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# # Step 1: Calculate the rolling quantiles separately
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# rolling_high = grouped['flow_lg_elg_intensity'].rolling(rolling_window, min_periods=1).quantile(0.9) # min_periods=1 保证窗口未满时也有输出
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# rolling_low = grouped['flow_lg_elg_intensity'].rolling(rolling_window, min_periods=1).quantile(0.1)
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# # Step 2: Assign the results to the DataFrame
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# # 确保 df 和 rolling_high/low 的索引是一致的
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# # 如果 df 的索引在此期间没有被修改过，这通常是安全的
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# df['flow_lg_elg_intensity_rolling_high'] = rolling_high
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# df['flow_lg_elg_intensity_rolling_low'] = rolling_low
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# # Step 3: Continue with the logic using the new columns
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# conditions_flow = [
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# df['flow_lg_elg_intensity'] > df['flow_lg_elg_intensity_rolling_high'],
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# df['flow_lg_elg_intensity'] < df['flow_lg_elg_intensity_rolling_low']
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# ]
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# choices_flow = [1, -1] # 1: 极端流入, -1: 极端流出
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# df['cat_extreme_flow'] = np.select(conditions_flow, choices_flow, default=0)
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# --- 筹码分布因子 ---
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# 6. 筹码集中度 (Chip Concentration)
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# 衡量筹码分布的紧密程度，例如 95% 与 5% 成本价的差距，相对于当前价格进行标准化
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# 检查 'close' 列是否存在且有效
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df['chip_concentration_range'] = (df['cost_95pct'] - df['cost_5pct']) / (df['close'] + epsilon)
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# 7. 筹码分布偏度 (Chip Distribution Skewness Proxy)
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# 比较中位数成本 (cost_50pct) 和加权平均成本 (weight_avg)
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# weight_avg > cost_50pct 暗示高成本区有较多筹码（右偏）
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df['chip_skewness'] = (df['weight_avg'] - df['cost_50pct']) / (df['cost_50pct'] + epsilon)
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# 8. 浮筹比例 (Floating Chips Proxy)
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# 衡量短期内（例如15%成本线以下）的筹码比例与总获利盘比例的关系
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# winner_rate 高但 cost_15pct 接近当前价，可能意味着大部分获利盘成本不高，易浮动
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# 这里简化为：获利盘比例 与 （当前价-15%成本价）/当前价 的乘积
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price_dist_cost15 = (df['close'] - df['cost_15pct']) / (df['close'] + epsilon)
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df['floating_chip_proxy'] = df['winner_rate'] * np.maximum(0, price_dist_cost15) # 只考虑价格高于15%成本线的情况
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# 9. 成本支撑强度变化 (Cost Support Strength Change)
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# 观察低位筹码成本（如 5% 或 15% 分位点）的变化率，看支撑位是上移还是下移
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df['cost_support_15pct_change'] = grouped['cost_15pct'].pct_change(1) * 100 # 百分比变化
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# 10. 获利盘压力/支撑区 (Categorical: Winner Rate Zone & Price Position)
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# 结合获利盘比例和当前价格相对于筹码成本的位置
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# 例如: 价格在 85% 成本线之上 & 获利盘 > 0.8 -> 高位派发风险区?
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# 价格在 15% 成本线之下 & 获利盘 < 0.2 -> 低位吸筹潜力区?
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conditions_winner = [
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(df['close'] > df['cost_85pct']) & (df['winner_rate'] > 0.8), # 高位 & 高获利盘
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(df['close'] < df['cost_15pct']) & (df['winner_rate'] < 0.2), # 低位 & 低获利盘
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(df['close'] > df['cost_50pct']) & (df['winner_rate'] > 0.5), # 中高位 & 多数获利
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(df['close'] < df['cost_50pct']) & (df['winner_rate'] < 0.5), # 中低位 & 多数亏损
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]
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choices_winner = [1, 2, 3, 4] # 1:高风险区, 2:低潜力区, 3:中上获利区, 4:中下亏损区
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df['cat_winner_price_zone'] = np.select(conditions_winner, choices_winner, default=0) # 0: 其他
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# --- 结合因子 ---
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# 11. 主力行为与筹码结构一致性 (Flow-Chip Consistency)
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# 例如：主力净买入发生在价格接近下方筹码密集区（如 cost_15pct 到 cost_50pct）时
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price_near_low_support = (df['close'] > df['cost_15pct']) & (df['close'] < df['cost_50pct'])
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df['flow_chip_consistency'] = df['lg_elg_net_buy_vol'] * price_near_low_support.astype(int)
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# 可以进一步标准化或做成 categorical
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# 12. 获利了结压力/承接盘强度 (Profit-Taking Pressure vs Absorption)
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# 在高获利盘(winner_rate > 0.7)的情况下，观察主力资金是净流出（了结）还是净流入（高位换手/承接）
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high_winner_rate_flag = (df['winner_rate'] > 0.7).astype(int)
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df['profit_taking_vs_absorb'] = df['lg_elg_net_buy_vol'] * high_winner_rate_flag
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# 正值表示高获利盘下主力仍在买入（承接），负值表示主力在卖出（了结）
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# 清理临时列和可能产生的 NaN (可选，根据需要处理)
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cols_to_drop = ['lg_elg_net_buy_vol', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop',
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'lg_elg_net_buy_vol_change', 'flow_lg_elg_intensity_rolling_high',
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'flow_lg_elg_intensity_rolling_low']
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# df = df.drop(columns=cols_to_drop)
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window = 20
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df['_is_positive'] = (df['pct_chg'] > 0).astype(int)
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df['_is_negative'] = (df['pct_chg'] < 0).astype(int)
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df['cat_is_positive'] = (df['pct_chg'] > 0).astype(int)
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# 分离正负收益率 (用于计算各自的均值和平方均值)
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# 注意：这里我们保留原始收益率用于计算，而不是 clip 到 0
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df['_pos_returns'] = df['pct_chg'].where(df['pct_chg'] > 0, 0) # 非正设为0，便于求和
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df['_neg_returns'] = df['pct_chg'].where(df['pct_chg'] < 0, 0) # 非负设为0，便于求和
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# 计算收益率的平方 (用于计算 E[X^2])
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df['_pos_returns_sq'] = np.square(df['_pos_returns'])
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df['_neg_returns_sq'] = np.square(df['_neg_returns']) # 平方后负数变正
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# 4. 计算滚动统计量 (使用内置函数，速度较快)
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# 计算正收益日的统计量
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rolling_pos_count = grouped['_is_positive'].rolling(window, min_periods=max(1, window // 2)).sum()
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rolling_pos_sum = grouped['_pos_returns'].rolling(window, min_periods=max(1, window // 2)).sum()
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rolling_pos_sum_sq = grouped['_pos_returns_sq'].rolling(window, min_periods=max(1, window // 2)).sum()
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# 计算负收益日的统计量
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rolling_neg_count = grouped['_is_negative'].rolling(window, min_periods=max(1, window // 2)).sum()
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rolling_neg_sum = grouped['_neg_returns'].rolling(window, min_periods=max(1, window // 2)).sum()
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rolling_neg_sum_sq = grouped['_neg_returns_sq'].rolling(window, min_periods=max(1, window // 2)).sum()
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# 5. 计算方差和标准差
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pos_mean_sq = rolling_pos_sum_sq / rolling_pos_count
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pos_mean = rolling_pos_sum / rolling_pos_count
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pos_var = pos_mean_sq - np.square(pos_mean)
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pos_var = pos_var.where(rolling_pos_count >= 2, np.nan).clip(lower=0)
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upside_vol = np.sqrt(pos_var)
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neg_mean_sq = rolling_neg_sum_sq / rolling_neg_count
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neg_mean = rolling_neg_sum / rolling_neg_count # 注意 neg_mean 是负数
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neg_var = neg_mean_sq - np.square(neg_mean)
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neg_var = neg_var.where(rolling_neg_count >= 2, np.nan).clip(lower=0)
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downside_vol = np.sqrt(neg_var)
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# rolling 操作后结果带有 MultiIndex，需要去除股票代码层级以便合并
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df['upside_vol'] = upside_vol.reset_index(level=0, drop=True)
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df['downside_vol'] = downside_vol.reset_index(level=0, drop=True)
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df['vol_ratio'] = df['upside_vol'] / df['downside_vol']
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df['vol_ratio'] = df['vol_ratio'].replace([np.inf, -np.inf], np.nan).fillna(0) # 或 fillna(np.nan)
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df['return_skew'] = grouped['pct_chg'].rolling(window=5).skew().reset_index(0, drop=True)
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df['return_kurtosis'] = grouped['pct_chg'].rolling(window=5).kurt().reset_index(0, drop=True)
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# 因子 1：短期成交量变化率
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df['volume_change_rate'] = (
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grouped['vol'].rolling(window=2).mean() /
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grouped['vol'].rolling(window=10).mean() - 1
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).reset_index(level=0, drop=True) # 确保索引对齐
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# 因子 2：成交量突破信号
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max_volume = grouped['vol'].rolling(window=5).max().reset_index(level=0, drop=True) # 确保索引对齐
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df['cat_volume_breakout'] = (df['vol'] > max_volume)
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# 因子 3：换手率均线偏离度
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mean_turnover = grouped['turnover_rate'].rolling(window=3).mean().reset_index(level=0, drop=True)
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std_turnover = grouped['turnover_rate'].rolling(window=3).std().reset_index(level=0, drop=True)
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df['turnover_deviation'] = (df['turnover_rate'] - mean_turnover) / std_turnover
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# 因子 4：换手率激增信号
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df['cat_turnover_spike'] = (df['turnover_rate'] > mean_turnover + 2 * std_turnover)
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# 因子 5：量比均值
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df['avg_volume_ratio'] = grouped['volume_ratio'].rolling(window=3).mean().reset_index(level=0, drop=True)
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# 因子 6：量比突破信号
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max_volume_ratio = grouped['volume_ratio'].rolling(window=5).max().reset_index(level=0, drop=True)
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df['cat_volume_ratio_breakout'] = (df['volume_ratio'] > max_volume_ratio)
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df['vol_spike'] = grouped.apply(
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lambda x: pd.Series(x['vol'].rolling(20).mean(), index=x.index)
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)
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df['vol_std_5'] = grouped['vol'].pct_change().rolling(window=5).std()
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# 计算 ATR
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df['atr_14'] = grouped.apply(
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lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=14),
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index=x.index)
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)
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df['atr_6'] = grouped.apply(
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lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=6),
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index=x.index)
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)
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# 计算 OBV 及其均线
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df['obv'] = grouped.apply(
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lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)
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)
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print(df.columns)
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df['maobv_6'] = grouped.apply(
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lambda x: pd.Series(talib.SMA(x['obv'].values, timeperiod=6), index=x.index)
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)
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df['rsi_3'] = grouped.apply(
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lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=3), index=x.index)
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)
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# df['rsi_6'] = grouped.apply(
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# lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=6), index=x.index)
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# )
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# df['rsi_9'] = grouped.apply(
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# lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=9), index=x.index)
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# )
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# 计算 return_10 和 return_20
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df['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1)
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# df['return_10'] = grouped['close'].apply(lambda x: x / x.shift(10) - 1)
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df['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)
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# df['avg_close_5'] = grouped['close'].apply(lambda x: x.rolling(window=5).mean() / x)
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# 计算标准差指标
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df['std_return_5'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=5).std())
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# df['std_return_15'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=15).std())
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# df['std_return_25'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=25).std())
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df['std_return_90'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=90).std())
|
|||
|
|
df['std_return_90_2'] = grouped['close'].apply(lambda x: x.shift(10).pct_change().rolling(window=90).std())
|
|||
|
|
|
|||
|
|
# 计算 EMA 指标
|
|||
|
|
df['_ema_5'] = grouped['close'].apply(
|
|||
|
|
lambda x: pd.Series(talib.EMA(x.values, timeperiod=5), index=x.index)
|
|||
|
|
)
|
|||
|
|
df['_ema_13'] = grouped['close'].apply(
|
|||
|
|
lambda x: pd.Series(talib.EMA(x.values, timeperiod=13), index=x.index)
|
|||
|
|
)
|
|||
|
|
df['_ema_20'] = grouped['close'].apply(
|
|||
|
|
lambda x: pd.Series(talib.EMA(x.values, timeperiod=20), index=x.index)
|
|||
|
|
)
|
|||
|
|
df['_ema_60'] = grouped['close'].apply(
|
|||
|
|
lambda x: pd.Series(talib.EMA(x.values, timeperiod=60), index=x.index)
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
# 计算 act_factor1, act_factor2, act_factor3, act_factor4
|
|||
|
|
df['act_factor1'] = grouped['_ema_5'].apply(
|
|||
|
|
lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 50
|
|||
|
|
)
|
|||
|
|
df['act_factor2'] = grouped['_ema_13'].apply(
|
|||
|
|
lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 40
|
|||
|
|
)
|
|||
|
|
df['act_factor3'] = grouped['_ema_20'].apply(
|
|||
|
|
lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 21
|
|||
|
|
)
|
|||
|
|
df['act_factor4'] = grouped['_ema_60'].apply(
|
|||
|
|
lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 10
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
# 根据 trade_date 截面计算排名
|
|||
|
|
df['rank_act_factor1'] = df.groupby('trade_date', group_keys=False)['act_factor1'].rank(ascending=False, pct=True)
|
|||
|
|
df['rank_act_factor2'] = df.groupby('trade_date', group_keys=False)['act_factor2'].rank(ascending=False, pct=True)
|
|||
|
|
df['rank_act_factor3'] = df.groupby('trade_date', group_keys=False)['act_factor3'].rank(ascending=False, pct=True)
|
|||
|
|
|
|||
|
|
df['log_circ_mv'] = np.log(df['circ_mv'])
|
|||
|
|
|
|||
|
|
window_high_volume = 5
|
|||
|
|
window_close_stddev = 20
|
|||
|
|
period_delta = 5
|
|||
|
|
|
|||
|
|
# 计算每只股票的滚动协方差
|
|||
|
|
def calculate_rolling_cov(group):
|
|||
|
|
return group['high'].rolling(window_high_volume).cov(group['vol'])
|
|||
|
|
|
|||
|
|
df['cov'] = grouped.apply(calculate_rolling_cov)
|
|||
|
|
|
|||
|
|
# 计算每只股票的协方差差分
|
|||
|
|
def calculate_delta_cov(group):
|
|||
|
|
return group['cov'].diff(period_delta)
|
|||
|
|
|
|||
|
|
df['delta_cov'] = grouped.apply(calculate_delta_cov)
|
|||
|
|
|
|||
|
|
# 计算每只股票的滚动标准差
|
|||
|
|
def calculate_stddev_close(group):
|
|||
|
|
return group['close'].rolling(window_close_stddev).std()
|
|||
|
|
|
|||
|
|
df['_stddev_close'] = grouped.apply(calculate_stddev_close)
|
|||
|
|
df['_rank_stddev'] = df.groupby('trade_date')['_stddev_close'].rank(pct=True)
|
|||
|
|
df['alpha_22_improved'] = -1 * df['delta_cov'] * df['_rank_stddev']
|
|||
|
|
|
|||
|
|
df['alpha_003'] = np.where(df['high'] != df['low'],
|
|||
|
|
(df['close'] - df['open']) / (df['high'] - df['low']),
|
|||
|
|
0)
|
|||
|
|
|
|||
|
|
df['alpha_007'] = grouped.apply(lambda x: x['close'].rolling(5).corr(x['vol']))
|
|||
|
|
df['alpha_007'] = df.groupby('trade_date', group_keys=False)['alpha_007'].rank(ascending=True, pct=True)
|
|||
|
|
|
|||
|
|
df['alpha_013'] = grouped['close'].transform(lambda x: x.rolling(5).sum() - x.rolling(20).sum())
|
|||
|
|
df['alpha_013'] = df.groupby('trade_date', group_keys=False)['alpha_013'].rank(ascending=True, pct=True)
|
|||
|
|
|
|||
|
|
df['cat_up_limit'] = (df['close'] == df['up_limit']) # 是否涨停（1表示涨停，0表示未涨停）
|
|||
|
|
df['cat_down_limit'] = (df['close'] == df['down_limit']) # 是否跌停（1表示跌停，0表示未跌停）
|
|||
|
|
df['up_limit_count_10d'] = grouped['cat_up_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0,
|
|||
|
|
drop=True)
|
|||
|
|
df['down_limit_count_10d'] = grouped['cat_down_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0,
|
|||
|
|
drop=True)
|
|||
|
|
|
|||
|
|
# 3. 最近连续涨跌停天数
|
|||
|
|
def calculate_consecutive_limits(series):
|
|||
|
|
"""
|
|||
|
|
计算连续涨停/跌停天数。
|
|||
|
|
"""
|
|||
|
|
consecutive_up = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1)
|
|||
|
|
consecutive_down = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1)
|
|||
|
|
return consecutive_up, consecutive_down
|
|||
|
|
|
|||
|
|
# 连续涨停天数
|
|||
|
|
df['consecutive_up_limit'] = grouped['cat_up_limit'].apply(
|
|||
|
|
lambda x: calculate_consecutive_limits(x)[0]
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
df['vol_break'] = np.where((df['close'] > df['cost_85pct']) & (df['volume_ratio'] > 2), 1, 0)
|
|||
|
|
|
|||
|
|
df['weight_roc5'] = grouped['weight_avg'].apply(lambda x: x.pct_change(5))
|
|||
|
|
|
|||
|
|
def rolling_corr(group):
|
|||
|
|
roc_close = group['close'].pct_change()
|
|||
|
|
roc_weight = group['weight_avg'].pct_change()
|
|||
|
|
return roc_close.rolling(10).corr(roc_weight)
|
|||
|
|
|
|||
|
|
df['price_cost_divergence'] = grouped.apply(rolling_corr)
|
|||
|
|
|
|||
|
|
df['smallcap_concentration'] = (1 / df['log_circ_mv']) * (df['cost_85pct'] - df['cost_15pct'])
|
|||
|
|
|
|||
|
|
# 16. 筹码稳定性指数 (20日波动率)
|
|||
|
|
df['weight_std20'] = grouped['weight_avg'].apply(lambda x: x.rolling(20).std())
|
|||
|
|
df['cost_stability'] = df['weight_std20'] / grouped['weight_avg'].transform(lambda x: x.rolling(20).mean())
|
|||
|
|
|
|||
|
|
# 17. 成本区间突破标记
|
|||
|
|
df['high_cost_break_days'] = grouped.apply(lambda g: g['close'].gt(g['cost_95pct']).rolling(5).sum())
|
|||
|
|
|
|||
|
|
# 20. 筹码-流动性风险
|
|||
|
|
df['liquidity_risk'] = (df['cost_95pct'] - df['cost_5pct']) * (
|
|||
|
|
1 / grouped['vol'].transform(lambda x: x.rolling(10).mean()))
|
|||
|
|
|
|||
|
|
# 7. 市值波动率因子 (使用 grouped)
|
|||
|
|
df['turnover_std'] = grouped['turnover_rate'].transform(lambda x: x.rolling(window=20).std())
|
|||
|
|
df['mv_volatility'] = grouped.apply(lambda x: x['turnover_std'] / x['log_circ_mv'])
|
|||
|
|
|
|||
|
|
# 8. 市值成长性因子
|
|||
|
|
df['volume_growth'] = grouped['vol'].pct_change(periods=20)
|
|||
|
|
df['mv_growth'] = df['volume_growth'] / df['log_circ_mv']
|
|||
|
|
|
|||
|
|
# AR 指标
|
|||
|
|
df["ar"] = grouped.apply(
|
|||
|
|
lambda x: (x["high"].div(x["open"]).rolling(3).sum()) / (x["open"].div(x["low"]).rolling(3).sum()) * 100)
|
|||
|
|
|
|||
|
|
# BR 指标
|
|||
|
|
df["pre_close"] = grouped["close"].shift(1)
|
|||
|
|
df["br_up"] = (df["high"] - df["pre_close"]).clip(lower=0)
|
|||
|
|
df["br_down"] = (df["pre_close"] - df["low"]).clip(lower=0)
|
|||
|
|
df["br"] = grouped.apply(lambda x: (x["br_up"].rolling(3).sum()) / (x["br_down"].rolling(3).sum()) * 100)
|
|||
|
|
|
|||
|
|
# ARBR
|
|||
|
|
df['arbr'] = df['ar'] - df['br']
|
|||
|
|
df.drop(columns=["pre_close", "br_up", "br_down", 'ar', 'br'], inplace=True)
|
|||
|
|
|
|||
|
|
df.drop(columns=['weight_std20'], inplace=True, errors='ignore')
|
|||
|
|
df.drop(
|
|||
|
|
columns=['_is_positive', '_is_negative', '_pos_returns', '_neg_returns', '_pos_returns_sq', '_neg_returns_sq'],
|
|||
|
|
inplace=True, errors='ignore')
|
|||
|
|
new_columns = [col for col in df.columns.tolist()[:] if col not in old_columns]
|
|||
|
|
|
|||
|
|
return df, new_columns
|
|||
|
|
|
|||
|
|
|
|||
|
|
def get_simple_factor(df):
|
|||
|
|
old_columns = df.columns.tolist()[:]
|
|||
|
|
df = df.sort_values(by=['ts_code', 'trade_date'])
|
|||
|
|
|
|||
|
|
alpha = 0.5
|
|||
|
|
df['momentum_factor'] = df['volume_change_rate'] + alpha * df['turnover_deviation']
|
|||
|
|
df['resonance_factor'] = df['volume_ratio'] * df['pct_chg']
|
|||
|
|
df['log_close'] = np.log(df['close'])
|
|||
|
|
|
|||
|
|
df['cat_vol_spike'] = df['vol'] > 2 * df['vol_spike']
|
|||
|
|
|
|||
|
|
df['up'] = (df['high'] - df[['close', 'open']].max(axis=1)) / df['close']
|
|||
|
|
df['down'] = (df[['close', 'open']].min(axis=1) - df['low']) / df['close']
|
|||
|
|
|
|||
|
|
df['obv_maobv_6'] = df['obv'] - df['maobv_6']
|
|||
|
|
|
|||
|
|
# 计算比值指标
|
|||
|
|
df['std_return_5_over_std_return_90'] = df['std_return_5'] / df['std_return_90']
|
|||
|
|
# df['std_return_5 / std_return_25'] = df['std_return_5'] / df['std_return_25']
|
|||
|
|
|
|||
|
|
# 计算标准差差值
|
|||
|
|
df['std_return_90_minus_std_return_90_2'] = df['std_return_90'] - df['std_return_90_2']
|
|||
|
|
|
|||
|
|
# df['cat_af1'] = df['act_factor1'] > 0
|
|||
|
|
df['cat_af2'] = df['act_factor2'] > df['act_factor1']
|
|||
|
|
df['cat_af3'] = df['act_factor3'] > df['act_factor2']
|
|||
|
|
df['cat_af4'] = df['act_factor4'] > df['act_factor3']
|
|||
|
|
|
|||
|
|
# 计算 act_factor5 和 act_factor6
|
|||
|
|
df['act_factor5'] = df['act_factor1'] + df['act_factor2'] + df['act_factor3'] + df['act_factor4']
|
|||
|
|
df['act_factor6'] = (df['act_factor1'] - df['act_factor2']) / np.sqrt(
|
|||
|
|
df['act_factor1'] ** 2 + df['act_factor2'] ** 2)
|
|||
|
|
|
|||
|
|
df['active_buy_volume_large'] = df['buy_lg_vol'] / df['net_mf_vol']
|
|||
|
|
df['active_buy_volume_big'] = df['buy_elg_vol'] / df['net_mf_vol']
|
|||
|
|
df['active_buy_volume_small'] = df['buy_sm_vol'] / df['net_mf_vol']
|
|||
|
|
|
|||
|
|
df['buy_lg_vol_minus_sell_lg_vol'] = (df['buy_lg_vol'] - df['sell_lg_vol']) / df['net_mf_vol']
|
|||
|
|
df['buy_elg_vol_minus_sell_elg_vol'] = (df['buy_elg_vol'] - df['sell_elg_vol']) / df['net_mf_vol']
|
|||
|
|
|
|||
|
|
df['log_circ_mv'] = np.log(df['circ_mv'])
|
|||
|
|
|
|||
|
|
df['ctrl_strength'] = (df['cost_85pct'] - df['cost_15pct']) / (df['his_high'] - df['his_low'])
|
|||
|
|
|
|||
|
|
df['low_cost_dev'] = (df['close'] - df['cost_5pct']) / (df['cost_50pct'] - df['cost_5pct'])
|
|||
|
|
|
|||
|
|
df['asymmetry'] = (df['cost_95pct'] - df['cost_50pct']) / (df['cost_50pct'] - df['cost_5pct'])
|
|||
|
|
|
|||
|
|
df['lock_factor'] = df['turnover_rate'] * (
|
|||
|
|
1 - (df['cost_95pct'] - df['cost_5pct']) / (df['his_high'] - df['his_low']))
|
|||
|
|
|
|||
|
|
df['cat_vol_break'] = (df['close'] > df['cost_85pct']) & (df['volume_ratio'] > 2)
|
|||
|
|
|
|||
|
|
df['cost_atr_adj'] = (df['cost_95pct'] - df['cost_5pct']) / df['atr_14']
|
|||
|
|
|
|||
|
|
# 12. 小盘股筹码集中度
|
|||
|
|
df['smallcap_concentration'] = (1 / df['log_circ_mv']) * (df['cost_85pct'] - df['cost_15pct'])
|
|||
|
|
|
|||
|
|
df['cat_golden_resonance'] = ((df['close'] > df['weight_avg']) &
|
|||
|
|
(df['volume_ratio'] > 1.5) &
|
|||
|
|
(df['winner_rate'] > 0.7))
|
|||
|
|
|
|||
|
|
df['mv_turnover_ratio'] = df['turnover_rate'] / df['log_circ_mv']
|
|||
|
|
|
|||
|
|
df['mv_adjusted_volume'] = df['vol'] / df['log_circ_mv']
|
|||
|
|
|
|||
|
|
df['mv_weighted_turnover'] = df['turnover_rate'] * (1 / df['log_circ_mv'])
|
|||
|
|
|
|||
|
|
df['nonlinear_mv_volume'] = df['vol'] / df['log_circ_mv']
|
|||
|
|
|
|||
|
|
df['mv_volume_ratio'] = df['volume_ratio'] / df['log_circ_mv']
|
|||
|
|
|
|||
|
|
df['mv_momentum'] = df['turnover_rate'] * df['volume_ratio'] / df['log_circ_mv']
|
|||
|
|
|
|||
|
|
drop_columns = [col for col in df.columns if col.startswith('_')]
|
|||
|
|
df.drop(columns=drop_columns, inplace=True, errors='ignore')
|
|||
|
|
|
|||
|
|
new_columns = [col for col in df.columns.tolist()[:] if col not in old_columns]
|
|||
|
|
return df, new_columns
|
|||
|
|
|
|||
|
|
|
|||
|
|
import pandas as pd
|
|||
|
|
import numpy as np
|
|||
|
|
from scipy.stats import linregress # For factor 4 (if implementing slope directly)
|
|||
|
|
# from hurst import compute_Hc # For factor 18, needs pip install hurst
|
|||
|
|
# import statsmodels.api as sm # For factor 16, needs pip install statsmodels
|
|||
|
|
|
|||
|
|
# --- Constants ---
|
|||
|
|
epsilon = 1e-10 # Prevent division by zero
|
|||
|
|
|
|||
|
|
# --- Helper Functions ---
|
|||
|
|
def _safe_divide(a, b, default_val=0):
|
|||
|
|
"""Safe division, returns default_val for division by zero or NaN/inf results."""
|
|||
|
|
with np.errstate(divide='ignore', invalid='ignore'):
|
|||
|
|
result = a / b
|
|||
|
|
# Replace NaN, Inf, -Inf resulting from division or invalid ops
|
|||
|
|
result[~np.isfinite(result)] = default_val
|
|||
|
|
return result
|
|||
|
|
|
|||
|
|
# --- Factor Calculation Functions (In-Place Modification) ---
|
|||
|
|
|
|||
|
|
# Category 1: Large Player Intent & Behavior
|
|||
|
|
def lg_flow_mom_corr(df: pd.DataFrame, N: int = 20, M: int = 60, factor_name: str = None):
|
|||
|
|
"""
|
|||
|
|
Calculates Factor 1: Large Flow & Price Momentum Concordance (In-place).
|
|||
|
|
WARNING: Modifies df in-place.
|
|||
|
|
"""
|
|||
|
|
if factor_name is None:
|
|||
|
|
factor_name = f'lg_flow_mom_corr_{N}_{M}'
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
_temp_cols = ['_net_lg_flow_val', '_rolling_net_lg_flow', '_price_mom']
|
|||
|
|
try:
|
|||
|
|
df['_net_lg_flow_val'] = (df['buy_lg_vol'] + df['buy_elg_vol'] - df['sell_lg_vol'] - df['sell_elg_vol']) * df['close']
|
|||
|
|
df['_rolling_net_lg_flow'] = df.groupby('ts_code')['_net_lg_flow_val'].rolling(N, min_periods=max(1, N // 2)).sum().reset_index(level=0, drop=True)
|
|||
|
|
df['_price_mom'] = df.groupby('ts_code')['close'].pct_change(N)
|
|||
|
|
# Calculate correlation on the temporary Series to handle alignment
|
|||
|
|
factor_series = df['_rolling_net_lg_flow'].rolling(M, min_periods=max(1, M // 2)).corr(df['_price_mom'])
|
|||
|
|
df[factor_name] = factor_series
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"Error calculating {factor_name}: {e}")
|
|||
|
|
df[factor_name] = np.nan # Assign NaN on error
|
|||
|
|
finally:
|
|||
|
|
# Cleanup intermediate columns
|
|||
|
|
cols_to_drop = [col for col in _temp_cols if col in df.columns]
|
|||
|
|
if cols_to_drop:
|
|||
|
|
df.drop(columns=cols_to_drop, inplace=True)
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|
|||
|
|
def lg_buy_consolidation(df: pd.DataFrame, N: int = 20, vol_quantile: float = 0.2, factor_name: str = None):
|
|||
|
|
"""
|
|||
|
|
Calculates Factor 2: Large Buying during Consolidation (In-place).
|
|||
|
|
WARNING: Modifies df in-place.
|
|||
|
|
"""
|
|||
|
|
if factor_name is None:
|
|||
|
|
factor_name = f'lg_buy_consolidation_{N}'
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
_temp_cols = ['_rolling_std', '_net_lg_flow_ratio', '_rolling_net_lg_flow_ratio_mean', '_std_threshold']
|
|||
|
|
try:
|
|||
|
|
df['_rolling_std'] = df.groupby('ts_code')['close'].rolling(N, min_periods=max(1, N // 2)).std().reset_index(level=0, drop=True)
|
|||
|
|
df['_net_lg_flow_ratio'] = _safe_divide(
|
|||
|
|
(df['buy_lg_vol'] + df['buy_elg_vol'] - df['sell_lg_vol'] - df['sell_elg_vol']),
|
|||
|
|
df['vol']
|
|||
|
|
)
|
|||
|
|
df['_rolling_net_lg_flow_ratio_mean'] = df.groupby('ts_code')['_net_lg_flow_ratio'].rolling(N, min_periods=max(1, N // 2)).mean().reset_index(level=0, drop=True)
|
|||
|
|
df['_std_threshold'] = df.groupby('trade_date')['_rolling_std'].transform(lambda x: x.quantile(vol_quantile))
|
|||
|
|
df[factor_name] = df['_rolling_net_lg_flow_ratio_mean'].where(df['_rolling_std'] < df['_std_threshold'])
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"Error calculating {factor_name}: {e}")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
finally:
|
|||
|
|
cols_to_drop = [col for col in _temp_cols if col in df.columns]
|
|||
|
|
if cols_to_drop:
|
|||
|
|
df.drop(columns=cols_to_drop, inplace=True)
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|
|||
|
|
def lg_flow_accel(df: pd.DataFrame, factor_name: str = 'lg_flow_accel'):
|
|||
|
|
"""
|
|||
|
|
Calculates Factor 3: Large Flow Acceleration (In-place).
|
|||
|
|
WARNING: Modifies df in-place.
|
|||
|
|
"""
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
_temp_cols = ['_net_lg_flow_vol']
|
|||
|
|
try:
|
|||
|
|
df['_net_lg_flow_vol'] = df['buy_lg_vol'] + df['buy_elg_vol'] - df['sell_lg_vol'] - df['sell_elg_vol']
|
|||
|
|
df[factor_name] = df.groupby('ts_code')['_net_lg_flow_vol'].diff(1).diff(1)
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"Error calculating {factor_name}: {e}")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
finally:
|
|||
|
|
cols_to_drop = [col for col in _temp_cols if col in df.columns]
|
|||
|
|
if cols_to_drop:
|
|||
|
|
df.drop(columns=cols_to_drop, inplace=True)
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|
|||
|
|
def intraday_lg_flow_corr(df: pd.DataFrame, N: int = 20, factor_name: str = None):
|
|||
|
|
"""
|
|||
|
|
Calculates Factor 4: (Approx) Intraday Trend & Large Flow Correlation (In-place).
|
|||
|
|
NOTE: Direct rolling correlation between two rolling series is complex/slow in pandas.
|
|||
|
|
This provides a placeholder or requires significant optimization/pre-calculation.
|
|||
|
|
WARNING: Modifies df in-place. Placeholder implementation returns NaN.
|
|||
|
|
"""
|
|||
|
|
if factor_name is None:
|
|||
|
|
factor_name = f'intraday_lg_flow_corr_{N}'
|
|||
|
|
print(f"Calculating {factor_name} (Placeholder - complex implementation)...")
|
|||
|
|
df[factor_name] = np.nan # Placeholder, see previous thought process for detailed logic needed
|
|||
|
|
print(f"Finished {factor_name} (Placeholder).")
|
|||
|
|
|
|||
|
|
|
|||
|
|
# Category 2: Cost Basis & PnL Status
|
|||
|
|
def profit_pressure(df: pd.DataFrame, factor_name: str = 'profit_pressure'):
|
|||
|
|
"""
|
|||
|
|
Calculates Factor 5: Profit Pressure Index (In-place).
|
|||
|
|
WARNING: Modifies df in-place.
|
|||
|
|
"""
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
_temp_cols = ['_profit_margin_85', '_profit_margin_95']
|
|||
|
|
try:
|
|||
|
|
df['_profit_margin_85'] = _safe_divide(df['close'], df['cost_85pct']) - 1
|
|||
|
|
df['_profit_margin_95'] = _safe_divide(df['close'], df['cost_95pct']) - 1
|
|||
|
|
df[factor_name] = df['winner_rate'] * 0.5 * (df['_profit_margin_85'] + df['_profit_margin_95'])
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"Error calculating {factor_name}: {e}")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
finally:
|
|||
|
|
cols_to_drop = [col for col in _temp_cols if col in df.columns]
|
|||
|
|
if cols_to_drop:
|
|||
|
|
df.drop(columns=cols_to_drop, inplace=True)
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|
|||
|
|
def underwater_resistance(df: pd.DataFrame, factor_name: str = 'underwater_resistance'):
|
|||
|
|
"""
|
|||
|
|
Calculates Factor 6: Resistance from Underwater Positions (In-place).
|
|||
|
|
WARNING: Modifies df in-place.
|
|||
|
|
"""
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
_temp_cols = ['_underwater_ratio', '_dist_to_cost_15']
|
|||
|
|
try:
|
|||
|
|
df['_underwater_ratio'] = 1.0 - df['winner_rate']
|
|||
|
|
df['_dist_to_cost_15'] = np.maximum(0, df['cost_15pct'] - df['close']) / (df['close'] + epsilon)
|
|||
|
|
df[factor_name] = df['_underwater_ratio'] * df['_dist_to_cost_15']
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"Error calculating {factor_name}: {e}")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
finally:
|
|||
|
|
cols_to_drop = [col for col in _temp_cols if col in df.columns]
|
|||
|
|
if cols_to_drop:
|
|||
|
|
df.drop(columns=cols_to_drop, inplace=True)
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|
|||
|
|
def cost_conc_std(df: pd.DataFrame, N: int = 20, factor_name: str = None):
|
|||
|
|
"""
|
|||
|
|
Calculates Factor 7: Cost Concentration Change (Std Dev) (In-place).
|
|||
|
|
WARNING: Modifies df in-place.
|
|||
|
|
"""
|
|||
|
|
if factor_name is None:
|
|||
|
|
factor_name = f'cost_conc_std_{N}'
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
_temp_cols = ['_cost_range_norm']
|
|||
|
|
try:
|
|||
|
|
df['_cost_range_norm'] = _safe_divide(
|
|||
|
|
(df['cost_85pct'] - df['cost_15pct']),
|
|||
|
|
(df['weight_avg'] + epsilon)
|
|||
|
|
)
|
|||
|
|
# Need to calculate rolling std on the temp col before dropping it
|
|||
|
|
factor_series = df.groupby('ts_code')['_cost_range_norm'].rolling(N, min_periods=max(1, N//2)).std().reset_index(level=0, drop=True)
|
|||
|
|
df[factor_name] = factor_series
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"Error calculating {factor_name}: {e}")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
finally:
|
|||
|
|
cols_to_drop = [col for col in _temp_cols if col in df.columns]
|
|||
|
|
if cols_to_drop:
|
|||
|
|
df.drop(columns=cols_to_drop, inplace=True)
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|
|||
|
|
def profit_decay(df: pd.DataFrame, N: int = 20, factor_name: str = None):
|
|||
|
|
"""
|
|||
|
|
Calculates Factor 8: Profit Expectation Decay (In-place).
|
|||
|
|
WARNING: Modifies df in-place.
|
|||
|
|
"""
|
|||
|
|
if factor_name is None:
|
|||
|
|
factor_name = f'profit_decay_{N}'
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
_temp_cols = ['_ret_N', '_winner_rate_change_N']
|
|||
|
|
try:
|
|||
|
|
df['_ret_N'] = _safe_divide(df['close'], df.groupby('ts_code')['close'].shift(N)) - 1
|
|||
|
|
df['_winner_rate_change_N'] = df.groupby('ts_code')['winner_rate'].diff(N)
|
|||
|
|
df[factor_name] = _safe_divide(df['_ret_N'], df['_winner_rate_change_N'])
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"Error calculating {factor_name}: {e}")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
finally:
|
|||
|
|
cols_to_drop = [col for col in _temp_cols if col in df.columns]
|
|||
|
|
if cols_to_drop:
|
|||
|
|
df.drop(columns=cols_to_drop, inplace=True)
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|
|||
|
|
|
|||
|
|
# Category 3: Volatility Source & Market State
|
|||
|
|
def vol_amp_loss(df: pd.DataFrame, N: int = 20, factor_name: str = None):
|
|||
|
|
"""
|
|||
|
|
Calculates Factor 9: Volatility Amplification when Underwater (In-place).
|
|||
|
|
WARNING: Modifies df in-place.
|
|||
|
|
"""
|
|||
|
|
if factor_name is None:
|
|||
|
|
factor_name = f'vol_amp_loss_{N}'
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
_temp_cols = ['_vol_N', '_loss_degree']
|
|||
|
|
try:
|
|||
|
|
df['_vol_N'] = df.groupby('ts_code')['pct_chg'].rolling(N, min_periods=max(1, N // 2)).std().reset_index(level=0, drop=True)
|
|||
|
|
df['_loss_degree'] = np.maximum(0, df['weight_avg'] - df['close']) / (df['close'] + epsilon)
|
|||
|
|
df[factor_name] = df['_vol_N'] * df['_loss_degree']
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"Error calculating {factor_name}: {e}")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
finally:
|
|||
|
|
cols_to_drop = [col for col in _temp_cols if col in df.columns]
|
|||
|
|
if cols_to_drop:
|
|||
|
|
df.drop(columns=cols_to_drop, inplace=True)
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|
|||
|
|
def vol_drop_profit_cnt(df: pd.DataFrame, N: int = 20, M: int = 5, profit_thresh: float = 0.1, drop_thresh: float = -0.03, vol_multiple: float = 2.0, factor_name: str = None):
|
|||
|
|
"""
|
|||
|
|
Calculates Factor 10: High Volume Drop when Profitable (Count over M days) (In-place).
|
|||
|
|
WARNING: Modifies df in-place.
|
|||
|
|
"""
|
|||
|
|
if factor_name is None:
|
|||
|
|
factor_name = f'vol_drop_profit_cnt_{M}'
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
_temp_cols = ['_is_profitable', '_is_dropping', '_rolling_mean_vol', '_rolling_std_vol', '_is_high_vol', '_event']
|
|||
|
|
try:
|
|||
|
|
df['_is_profitable'] = df['close'] > df['weight_avg'] * (1 + profit_thresh)
|
|||
|
|
df['_is_dropping'] = df['pct_chg'] < drop_thresh
|
|||
|
|
df['_rolling_mean_vol'] = df.groupby('ts_code')['vol'].rolling(N, min_periods=1).mean().reset_index(level=0, drop=True)
|
|||
|
|
df['_rolling_std_vol'] = df.groupby('ts_code')['vol'].rolling(N, min_periods=2).std().reset_index(level=0, drop=True).fillna(0)
|
|||
|
|
df['_is_high_vol'] = df['vol'] > (df['_rolling_mean_vol'] + vol_multiple * df['_rolling_std_vol'])
|
|||
|
|
df['_event'] = (df['_is_profitable'] & df['_is_dropping'] & df['_is_high_vol']).astype(int)
|
|||
|
|
factor_series = df.groupby('ts_code')['_event'].rolling(M, min_periods=1).sum().reset_index(level=0, drop=True)
|
|||
|
|
df[factor_name] = factor_series
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"Error calculating {factor_name}: {e}")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
finally:
|
|||
|
|
cols_to_drop = [col for col in _temp_cols if col in df.columns]
|
|||
|
|
if cols_to_drop:
|
|||
|
|
df.drop(columns=cols_to_drop, inplace=True)
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|
|||
|
|
def lg_flow_vol_interact(df: pd.DataFrame, N: int = 20, factor_name: str = None):
|
|||
|
|
"""
|
|||
|
|
Calculates Factor 11: Large Flow Driven Volatility (Interaction Term) (In-place).
|
|||
|
|
WARNING: Modifies df in-place.
|
|||
|
|
"""
|
|||
|
|
if factor_name is None:
|
|||
|
|
factor_name = f'lg_flow_vol_interact_{N}'
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
_temp_cols = ['_vol_N', '_net_lg_flow_val', '_total_val', '_abs_net_lg_flow_ratio', '_abs_net_lg_flow_ratio_N']
|
|||
|
|
try:
|
|||
|
|
df['_vol_N'] = df.groupby('ts_code')['pct_chg'].rolling(N, min_periods=max(1, N // 2)).std().reset_index(level=0, drop=True)
|
|||
|
|
df['_net_lg_flow_val'] = (df['buy_lg_vol'] + df['buy_elg_vol'] - df['sell_lg_vol'] - df['sell_elg_vol']) * df['close']
|
|||
|
|
df['_total_val'] = df['vol'] * df['close']
|
|||
|
|
df['_abs_net_lg_flow_ratio'] = abs(df['_net_lg_flow_val']) / (df['_total_val'] + epsilon)
|
|||
|
|
df['_abs_net_lg_flow_ratio_N'] = df.groupby('ts_code')['_abs_net_lg_flow_ratio'].rolling(N, min_periods=max(1, N // 2)).mean().reset_index(level=0, drop=True)
|
|||
|
|
df[factor_name] = df['_vol_N'] * df['_abs_net_lg_flow_ratio_N']
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"Error calculating {factor_name}: {e}")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
finally:
|
|||
|
|
cols_to_drop = [col for col in _temp_cols if col in df.columns]
|
|||
|
|
if cols_to_drop:
|
|||
|
|
df.drop(columns=cols_to_drop, inplace=True)
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|
|||
|
|
def cost_break_confirm_cnt(df: pd.DataFrame, M: int = 5, factor_name: str = None):
|
|||
|
|
"""
|
|||
|
|
Calculates Factor 12: Cost Breakout Confirmation Count (In-place).
|
|||
|
|
WARNING: Modifies df in-place.
|
|||
|
|
"""
|
|||
|
|
if factor_name is None:
|
|||
|
|
factor_name = f'cost_break_confirm_cnt_{M}'
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
_temp_cols = ['_prev_cost_85', '_prev_cost_15', '_break_up', '_break_down', '_net_lg_flow_vol', '_confirm_up', '_confirm_down', '_net_confirm']
|
|||
|
|
try:
|
|||
|
|
df['_prev_cost_85'] = df.groupby('ts_code')['cost_85pct'].shift(1)
|
|||
|
|
df['_prev_cost_15'] = df.groupby('ts_code')['cost_15pct'].shift(1)
|
|||
|
|
df['_break_up'] = df['close'] > df['_prev_cost_85']
|
|||
|
|
df['_break_down'] = df['close'] < df['_prev_cost_15']
|
|||
|
|
df['_net_lg_flow_vol'] = df['buy_lg_vol'] + df['buy_elg_vol'] - df['sell_lg_vol'] - df['sell_elg_vol']
|
|||
|
|
df['_confirm_up'] = (df['_break_up'] & (df['_net_lg_flow_vol'] > 0)).astype(int)
|
|||
|
|
df['_confirm_down'] = (df['_break_down'] & (df['_net_lg_flow_vol'] < 0)).astype(int)
|
|||
|
|
df['_net_confirm'] = df['_confirm_up'] - df['_confirm_down']
|
|||
|
|
factor_series = df.groupby('ts_code')['_net_confirm'].rolling(M, min_periods=1).sum().reset_index(level=0, drop=True)
|
|||
|
|
df[factor_name] = factor_series
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"Error calculating {factor_name}: {e}")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
finally:
|
|||
|
|
cols_to_drop = [col for col in _temp_cols if col in df.columns]
|
|||
|
|
if cols_to_drop:
|
|||
|
|
df.drop(columns=cols_to_drop, inplace=True)
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|
|||
|
|
|
|||
|
|
# Category 4: Technical Indicators & Market Behavior
|
|||
|
|
def atr_norm_channel_pos(df: pd.DataFrame, N: int = 14, factor_name: str = None):
|
|||
|
|
"""
|
|||
|
|
Calculates Factor 13: ATR Normalized Channel Position (In-place).
|
|||
|
|
WARNING: Modifies df in-place.
|
|||
|
|
"""
|
|||
|
|
if factor_name is None:
|
|||
|
|
factor_name = f'atr_norm_channel_pos_{N}'
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
_temp_cols = ['_prev_close', '_h_l', '_h_pc', '_l_pc', '_tr', '_atr_N', '_roll_low_N']
|
|||
|
|
try:
|
|||
|
|
df['_prev_close'] = df.groupby('ts_code')['close'].shift(1)
|
|||
|
|
df['_h_l'] = df['high'] - df['low']
|
|||
|
|
df['_h_pc'] = abs(df['high'] - df['_prev_close'])
|
|||
|
|
df['_l_pc'] = abs(df['low'] - df['_prev_close'])
|
|||
|
|
df['_tr'] = df[['_h_l', '_h_pc', '_l_pc']].max(axis=1)
|
|||
|
|
df['_atr_N'] = df.groupby('ts_code')['_tr'].rolling(N, min_periods=max(1, N//2)).mean().reset_index(level=0, drop=True)
|
|||
|
|
df['_roll_low_N'] = df.groupby('ts_code')['low'].rolling(N, min_periods=max(1, N//2)).min().reset_index(level=0, drop=True)
|
|||
|
|
df[factor_name] = _safe_divide((df['close'] - df['_roll_low_N']), df['_atr_N'])
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"Error calculating {factor_name}: {e}")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
finally:
|
|||
|
|
cols_to_drop = [col for col in _temp_cols if col in df.columns]
|
|||
|
|
if cols_to_drop:
|
|||
|
|
df.drop(columns=cols_to_drop, inplace=True)
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|
|||
|
|
def turnover_diff_skew(df: pd.DataFrame, N: int = 20, factor_name: str = None):
|
|||
|
|
"""
|
|||
|
|
Calculates Factor 14: Skewness of Turnover Rate Change (In-place).
|
|||
|
|
WARNING: Modifies df in-place.
|
|||
|
|
"""
|
|||
|
|
if factor_name is None:
|
|||
|
|
factor_name = f'turnover_diff_skew_{N}'
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
_temp_cols = ['_turnover_diff']
|
|||
|
|
try:
|
|||
|
|
# Assuming turnover_rate is in percentage points, diff is fine
|
|||
|
|
df['_turnover_diff'] = df.groupby('ts_code')['turnover_rate'].diff(1)
|
|||
|
|
factor_series = df.groupby('ts_code')['_turnover_diff'].rolling(N, min_periods=max(3, N//2)).skew().reset_index(level=0, drop=True)
|
|||
|
|
df[factor_name] = factor_series
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"Error calculating {factor_name}: {e}")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
finally:
|
|||
|
|
cols_to_drop = [col for col in _temp_cols if col in df.columns]
|
|||
|
|
if cols_to_drop:
|
|||
|
|
df.drop(columns=cols_to_drop, inplace=True)
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|
|||
|
|
def lg_sm_flow_diverge(df: pd.DataFrame, N: int = 20, factor_name: str = None):
|
|||
|
|
"""
|
|||
|
|
Calculates Factor 15: Divergence between Large and Small Flow (In-place).
|
|||
|
|
WARNING: Modifies df in-place.
|
|||
|
|
"""
|
|||
|
|
if factor_name is None:
|
|||
|
|
factor_name = f'lg_sm_flow_diverge_{N}'
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
_temp_cols = ['_lg_flow_ratio', '_sm_flow_ratio', '_lg_flow_ratio_N', '_sm_flow_ratio_N']
|
|||
|
|
try:
|
|||
|
|
df['_lg_flow_ratio'] = _safe_divide(
|
|||
|
|
(df['buy_lg_vol'] + df['buy_elg_vol'] - df['sell_lg_vol'] - df['sell_elg_vol']),
|
|||
|
|
df['vol']
|
|||
|
|
)
|
|||
|
|
df['_sm_flow_ratio'] = _safe_divide(
|
|||
|
|
(df['buy_sm_vol'] - df['sell_sm_vol']),
|
|||
|
|
df['vol']
|
|||
|
|
)
|
|||
|
|
df['_lg_flow_ratio_N'] = df.groupby('ts_code')['_lg_flow_ratio'].rolling(N, min_periods=max(1, N // 2)).mean().reset_index(level=0, drop=True)
|
|||
|
|
df['_sm_flow_ratio_N'] = df.groupby('ts_code')['_sm_flow_ratio'].rolling(N, min_periods=max(1, N // 2)).mean().reset_index(level=0, drop=True)
|
|||
|
|
df[factor_name] = df['_lg_flow_ratio_N'] - df['_sm_flow_ratio_N']
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"Error calculating {factor_name}: {e}")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
finally:
|
|||
|
|
cols_to_drop = [col for col in _temp_cols if col in df.columns]
|
|||
|
|
if cols_to_drop:
|
|||
|
|
df.drop(columns=cols_to_drop, inplace=True)
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|
|||
|
|
|
|||
|
|
def cap_neutral_cost_metric(df: pd.DataFrame, factor_name: str = 'cap_neutral_cost_metric'):
|
|||
|
|
"""
|
|||
|
|
Calculates Factor 16: Market Cap Neutralized Cost Metric (Placeholder).
|
|||
|
|
Requires statsmodels and complex implementation.
|
|||
|
|
WARNING: Modifies df in-place. Placeholder implementation returns NaN.
|
|||
|
|
"""
|
|||
|
|
print(f"Calculating {factor_name} (Placeholder - requires statsmodels)...")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
print(f"Finished {factor_name} (Placeholder).")
|
|||
|
|
|
|||
|
|
|
|||
|
|
def pullback_strong(df: pd.DataFrame, N: int = 20, M: int = 20, gain_thresh: float = 0.2, factor_name: str = None):
|
|||
|
|
"""
|
|||
|
|
Calculates Factor 17: Pullback Depth from Recent High for Strong Stocks (In-place).
|
|||
|
|
WARNING: Modifies df in-place.
|
|||
|
|
"""
|
|||
|
|
if factor_name is None:
|
|||
|
|
factor_name = f'pullback_strong_{N}_{M}'
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
_temp_cols = ['_high_N', '_pullback_depth', '_recent_gain_M']
|
|||
|
|
try:
|
|||
|
|
df['_high_N'] = df.groupby('ts_code')['high'].rolling(N, min_periods=max(1, N // 2)).max().reset_index(level=0, drop=True)
|
|||
|
|
df['_pullback_depth'] = _safe_divide((df['_high_N'] - df['close']), df['_high_N'])
|
|||
|
|
df['_recent_gain_M'] = _safe_divide(df['close'], df.groupby('ts_code')['close'].shift(M)) - 1
|
|||
|
|
df[factor_name] = _safe_divide(df['_pullback_depth'], df['_recent_gain_M'])
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"Error calculating {factor_name}: {e}")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
finally:
|
|||
|
|
cols_to_drop = [col for col in _temp_cols if col in df.columns]
|
|||
|
|
if cols_to_drop:
|
|||
|
|
df.drop(columns=cols_to_drop, inplace=True)
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|
|||
|
|
def hurst_exponent_flow(df: pd.DataFrame, N: int = 60, flow_col: str = 'net_mf_vol', factor_name: str = None):
|
|||
|
|
"""
|
|||
|
|
Calculates Factor 18: Hurst Exponent of Money Flow (Placeholder).
|
|||
|
|
Requires 'hurst' library and potentially slow rolling apply.
|
|||
|
|
WARNING: Modifies df in-place. Placeholder implementation returns NaN.
|
|||
|
|
"""
|
|||
|
|
if factor_name is None:
|
|||
|
|
factor_name = f'hurst_{flow_col}_{N}'
|
|||
|
|
print(f"Calculating {factor_name} (Placeholder - requires hurst library)...")
|
|||
|
|
try:
|
|||
|
|
from hurst import compute_Hc
|
|||
|
|
# Logic would go here, likely using rolling().apply() which is slow
|
|||
|
|
# factor_series = df.groupby('ts_code')[flow_col]....apply(hurst_calc_func...)
|
|||
|
|
df[factor_name] = np.nan # Placeholder
|
|||
|
|
except ImportError:
|
|||
|
|
print("Error: 'hurst' library not installed. Cannot calculate factor.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"Error calculating {factor_name}: {e}")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
print(f"Finished {factor_name} (Placeholder).")
|
|||
|
|
|
|||
|
|
|
|||
|
|
def vol_wgt_hist_pos(df: pd.DataFrame, N: int = 20, factor_name: str = None):
|
|||
|
|
"""
|
|||
|
|
Calculates Factor 19: Volume Weighting at Historical Price Level (In-place).
|
|||
|
|
WARNING: Modifies df in-place.
|
|||
|
|
"""
|
|||
|
|
if factor_name is None:
|
|||
|
|
factor_name = f'vol_wgt_hist_pos_{N}'
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
_temp_cols = ['_hist_pos', '_rolling_mean_vol', '_vol_rel_strength']
|
|||
|
|
try:
|
|||
|
|
df['_hist_pos'] = _safe_divide((df['close'] - df['his_low']), (df['his_high'] - df['his_low'])).clip(0, 1)
|
|||
|
|
df['_rolling_mean_vol'] = df.groupby('ts_code')['vol'].rolling(N, min_periods=max(1, N // 2)).mean().reset_index(level=0, drop=True)
|
|||
|
|
df['_vol_rel_strength'] = _safe_divide(df['vol'], df['_rolling_mean_vol'])
|
|||
|
|
df[factor_name] = df['_hist_pos'] * df['_vol_rel_strength']
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"Error calculating {factor_name}: {e}")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
finally:
|
|||
|
|
cols_to_drop = [col for col in _temp_cols if col in df.columns]
|
|||
|
|
if cols_to_drop:
|
|||
|
|
df.drop(columns=cols_to_drop, inplace=True)
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|
|||
|
|
def vol_adj_roc(df: pd.DataFrame, N: int = 20, factor_name: str = None):
|
|||
|
|
"""
|
|||
|
|
Calculates Factor 20: Volatility-Adjusted ROC (In-place).
|
|||
|
|
WARNING: Modifies df in-place.
|
|||
|
|
"""
|
|||
|
|
if factor_name is None:
|
|||
|
|
factor_name = f'vol_adj_roc_{N}'
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
_temp_cols = ['_roc_N', '_vol_N']
|
|||
|
|
try:
|
|||
|
|
df['_roc_N'] = _safe_divide(df['close'], df.groupby('ts_code')['close'].shift(N)) - 1
|
|||
|
|
df['_vol_N'] = df.groupby('ts_code')['pct_chg'].rolling(N, min_periods=max(2, N // 2)).std().reset_index(level=0, drop=True).fillna(0)
|
|||
|
|
df[factor_name] = _safe_divide(df['_roc_N'], df['_vol_N'])
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"Error calculating {factor_name}: {e}")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
finally:
|
|||
|
|
cols_to_drop = [col for col in _temp_cols if col in df.columns]
|
|||
|
|
if cols_to_drop:
|
|||
|
|
df.drop(columns=cols_to_drop, inplace=True)
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|
|||
|
|
def calculate_complex_factor(df: pd.DataFrame, factor_name: str = "complex_factor_deap_1"):
|
|||
|
|
"""
|
|||
|
|
表达式: sub(protected_div_torch(A, B), C)
|
|||
|
|
其中 A, B, C 及内部组件依赖于多个预计算因子列。
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
df (pd.DataFrame): 包含所有必需基础因子列的 DataFrame。
|
|||
|
|
factor_name (str): 要在 df 中创建的新因子列的名称。
|
|||
|
|
|
|||
|
|
WARNING: 此函数会原地修改输入的 DataFrame 'df'。
|
|||
|
|
如果在计算过程中缺少任何必需的列，将打印错误并填充 NaN。
|
|||
|
|
"""
|
|||
|
|
print(f"开始计算因子: {factor_name} (原地修改)...")
|
|||
|
|
_temp_cols_list = [] # 用于记录中间计算列的名称
|
|||
|
|
|
|||
|
|
try:
|
|||
|
|
# --- 分解计算表达式的各个部分 ---
|
|||
|
|
|
|||
|
|
# 计算组件 D
|
|||
|
|
# D = sub(mul(pullback_strong_20_20, div(log_close, industry_return_5)), div(add(vol_adj_roc_20, vol_drop_profit_cnt_5), sub(nonlinear_mv_volume, alpha_007)))
|
|||
|
|
_temp_d_term1_div = _safe_divide(df['log_close'], df['industry_return_5'])
|
|||
|
|
_temp_d_term1 = df['pullback_strong_20_20'] * _temp_d_term1_div
|
|||
|
|
_temp_d_term2_sub = df['nonlinear_mv_volume'] - df['alpha_007']
|
|||
|
|
_temp_d_term2_add = df['vol_adj_roc_20'] + df['vol_drop_profit_cnt_5']
|
|||
|
|
_temp_d_term2 = _safe_divide(_temp_d_term2_add, _temp_d_term2_sub)
|
|||
|
|
df['_temp_D'] = _temp_d_term1 - _temp_d_term2
|
|||
|
|
_temp_cols_list.extend(['_temp_D', '_temp_d_term1_div', '_temp_d_term1', '_temp_d_term2_sub', '_temp_d_term2_add', '_temp_d_term2'])
|
|||
|
|
|
|||
|
|
# 计算组件 A
|
|||
|
|
# A = add(add(mul(D, lg_buy_consolidation_20), lg_buy_consolidation_20), pullback_strong_20_20)
|
|||
|
|
_temp_a_term1 = df['_temp_D'] * df['lg_buy_consolidation_20']
|
|||
|
|
_temp_a_term2 = _temp_a_term1 + df['lg_buy_consolidation_20']
|
|||
|
|
df['_temp_A'] = _temp_a_term2 + df['pullback_strong_20_20']
|
|||
|
|
_temp_cols_list.extend(['_temp_A', '_temp_a_term1', '_temp_a_term2'])
|
|||
|
|
|
|||
|
|
# 计算组件 F
|
|||
|
|
# F = mul(add(net_mf_vol, std_return_5), sub(arbr, industry_act_factor5))
|
|||
|
|
_temp_f_term1 = df['net_mf_vol'] + df['std_return_5']
|
|||
|
|
_temp_f_term2 = df['arbr'] - df['industry_act_factor5']
|
|||
|
|
df['_temp_F'] = _temp_f_term1 * _temp_f_term2
|
|||
|
|
_temp_cols_list.extend(['_temp_F', '_temp_f_term1', '_temp_f_term2'])
|
|||
|
|
|
|||
|
|
# 计算组件 H
|
|||
|
|
# H = add(add(industry_act_factor1, low_cost_dev), mul(mv_weighted_turnover, act_factor4))
|
|||
|
|
_temp_h_term1 = df['industry_act_factor1'] + df['low_cost_dev']
|
|||
|
|
_temp_h_term2 = df['mv_weighted_turnover'] * df['act_factor4']
|
|||
|
|
df['_temp_H'] = _temp_h_term1 + _temp_h_term2
|
|||
|
|
_temp_cols_list.extend(['_temp_H', '_temp_h_term1', '_temp_h_term2'])
|
|||
|
|
|
|||
|
|
# 计算组件 B
|
|||
|
|
# B = div(add(add(F, vol), H), lg_elg_buy_prop)
|
|||
|
|
_temp_b_term1 = df['_temp_F'] + df['vol']
|
|||
|
|
_temp_b_term2 = _temp_b_term1 + df['_temp_H']
|
|||
|
|
df['_temp_B'] = _safe_divide(_temp_b_term2, df['lg_elg_buy_prop'])
|
|||
|
|
_temp_cols_list.extend(['_temp_B', '_temp_b_term1', '_temp_b_term2'])
|
|||
|
|
|
|||
|
|
# 计算组件 C
|
|||
|
|
# C = div(div(intraday_lg_flow_corr_20, lg_elg_buy_prop), lg_elg_buy_prop)
|
|||
|
|
# 注意: intraday_lg_flow_corr_20 可能本身就是 NaN 或需要特殊处理
|
|||
|
|
_temp_c_term1 = _safe_divide(df.get('intraday_lg_flow_corr_20', np.nan), df['lg_elg_buy_prop']) # 使用 .get 处理可能不存在的列
|
|||
|
|
df['_temp_C'] = _safe_divide(_temp_c_term1, df['lg_elg_buy_prop'])
|
|||
|
|
_temp_cols_list.extend(['_temp_C', '_temp_c_term1'])
|
|||
|
|
|
|||
|
|
# --- 计算最终表达式 ---
|
|||
|
|
# final = sub(div(A, B), C)
|
|||
|
|
_temp_final_term1 = _safe_divide(df['_temp_A'], df['_temp_B'])
|
|||
|
|
final_factor_series = _temp_final_term1 - df['_temp_C']
|
|||
|
|
|
|||
|
|
# --- 将最终结果赋值给 df 的新列 (原地修改) ---
|
|||
|
|
df[factor_name] = final_factor_series
|
|||
|
|
|
|||
|
|
print(f"因子 {factor_name} 计算成功。")
|
|||
|
|
|
|||
|
|
except KeyError as e:
|
|||
|
|
# 捕获因为缺少列而产生的错误
|
|||
|
|
print(f"错误: 计算 {factor_name} 时缺少必需的列: {e}")
|
|||
|
|
print("请确保输入的 DataFrame 包含所有表达式中引用的因子列。")
|
|||
|
|
print("将为因子 {factor_name} 填充 NaN。")
|
|||
|
|
df[factor_name] = np.nan # 出错时填充 NaN
|
|||
|
|
except Exception as e:
|
|||
|
|
# 捕获其他可能的计算错误
|
|||
|
|
print(f"错误: 计算 {factor_name} 时发生意外错误: {e}")
|
|||
|
|
print(f"将为因子 {factor_name} 填充 NaN。")
|
|||
|
|
df[factor_name] = np.nan # 出错时填充 NaN
|
|||
|
|
finally:
|
|||
|
|
# --- 清理所有中间计算列 ---
|
|||
|
|
cols_to_drop = [col for col in _temp_cols_list if col in df.columns]
|
|||
|
|
if cols_to_drop:
|
|||
|
|
df.drop(columns=cols_to_drop, inplace=True)
|
|||
|
|
# print(f"已清理 {len(cols_to_drop)} 个临时列 for {factor_name}.")
|
|||
|
|
print(f"因子 {factor_name} 计算流程结束。")
|
|||
|
|
# 函数不返回任何值，因为 df 是原地修改的
|
|||
|
|
|
|||
|
|
import pandas as pd
|
|||
|
|
import numpy as np
|
|||
|
|
# from scipy.stats import rankdata # rankdata is not needed if using pandas rank
|
|||
|
|
# import statsmodels.api as sm # Needed for factor 19
|
|||
|
|
|
|||
|
|
# --- Constants ---
|
|||
|
|
epsilon = 1e-10 # Prevent division by zero
|
|||
|
|
|
|||
|
|
# --- Helper Functions ---
|
|||
|
|
def _safe_divide(numerator, denominator, default_val=0):
|
|||
|
|
"""
|
|||
|
|
安全的除法函数，处理分母为零或接近零，以及NaN/Inf的情况。
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
numerator (pd.Series): 分子.
|
|||
|
|
denominator (pd.Series): 分母.
|
|||
|
|
default_val (float): 当分母为零或结果无效时返回的默认值.
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
pd.Series: 除法结果.
|
|||
|
|
"""
|
|||
|
|
with np.errstate(divide='ignore', invalid='ignore'):
|
|||
|
|
# Convert inputs to numeric, coercing errors to NaN before division
|
|||
|
|
num = pd.to_numeric(numerator, errors='coerce')
|
|||
|
|
den = pd.to_numeric(denominator, errors='coerce')
|
|||
|
|
# Perform division where denominator is not close to zero and inputs are valid numbers
|
|||
|
|
result = np.where(np.abs(den) > epsilon, num / den, default_val)
|
|||
|
|
# Ensure result is float, handle potential NaNs from coercion or division
|
|||
|
|
result = pd.to_numeric(result, errors='coerce')
|
|||
|
|
# Fill remaining NaNs if necessary
|
|||
|
|
result = np.nan_to_num(result, nan=default_val, posinf=default_val, neginf=default_val)
|
|||
|
|
# Ensure result index matches numerator's index if numerator is a Series
|
|||
|
|
if isinstance(numerator, pd.Series):
|
|||
|
|
return pd.Series(result, index=numerator.index)
|
|||
|
|
else:
|
|||
|
|
return pd.Series(result) # Fallback if numerator is not a Series (less likely)
|
|||
|
|
|
|||
|
|
# --- Cross-Sectional Factor Calculation Functions (In-Place Modification) ---
|
|||
|
|
|
|||
|
|
# Category 1: Cross-Sectional Flow & Behavior Strength
|
|||
|
|
def cs_rank_net_lg_flow_val(df: pd.DataFrame, factor_name: str = 'cs_rank_net_lg_flow_val'):
|
|||
|
|
"""
|
|||
|
|
Factor 1: 大单净额截面排序 (In-place).
|
|||
|
|
WARNING: Modifies df in-place.
|
|||
|
|
"""
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
_temp_cols = ['_net_lg_flow_val']
|
|||
|
|
try:
|
|||
|
|
df['_net_lg_flow_val'] = (df['buy_lg_vol'] + df['buy_elg_vol'] - df['sell_lg_vol'] - df['sell_elg_vol']) * df['close']
|
|||
|
|
df[factor_name] = df.groupby('trade_date')['_net_lg_flow_val'].rank(pct=True)
|
|||
|
|
except KeyError as e:
|
|||
|
|
print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
finally:
|
|||
|
|
cols_to_drop = [col for col in _temp_cols if col in df.columns]
|
|||
|
|
if cols_to_drop:
|
|||
|
|
df.drop(columns=cols_to_drop, inplace=True)
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|
|||
|
|
def cs_rank_flow_divergence(df: pd.DataFrame, factor_name: str = 'cs_rank_flow_divergence'):
|
|||
|
|
"""
|
|||
|
|
Factor 2: 大小单流向背离度截面排序 (In-place).
|
|||
|
|
WARNING: Modifies df in-place.
|
|||
|
|
"""
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
_temp_cols = ['_lg_ratio', '_sm_ratio', '_divergence']
|
|||
|
|
try:
|
|||
|
|
df['_lg_ratio'] = _safe_divide(
|
|||
|
|
(df['buy_lg_vol'] + df['buy_elg_vol'] - df['sell_lg_vol'] - df['sell_elg_vol']),
|
|||
|
|
df['vol']
|
|||
|
|
)
|
|||
|
|
df['_sm_ratio'] = _safe_divide(
|
|||
|
|
(df['buy_sm_vol'] - df['sell_sm_vol']),
|
|||
|
|
df['vol']
|
|||
|
|
)
|
|||
|
|
df['_divergence'] = df['_lg_ratio'] - df['_sm_ratio']
|
|||
|
|
df[factor_name] = df.groupby('trade_date')['_divergence'].rank(pct=True)
|
|||
|
|
except KeyError as e:
|
|||
|
|
print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
finally:
|
|||
|
|
cols_to_drop = [col for col in _temp_cols if col in df.columns]
|
|||
|
|
if cols_to_drop:
|
|||
|
|
df.drop(columns=cols_to_drop, inplace=True)
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|
|||
|
|
def cs_rank_industry_adj_lg_flow(df: pd.DataFrame, factor_name: str = 'cs_rank_ind_adj_lg_flow'):
|
|||
|
|
"""
|
|||
|
|
Factor 3: 行业内大单流强度排序 (In-place). Requires 'cat_l2_code'.
|
|||
|
|
WARNING: Modifies df in-place.
|
|||
|
|
"""
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
_temp_cols = ['_net_lg_flow_vol', '_industry_avg_flow', '_deviation']
|
|||
|
|
if 'cat_l2_code' not in df.columns:
|
|||
|
|
print(f"Error calculating {factor_name}: Missing 'cat_l2_code' column. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
return
|
|||
|
|
try:
|
|||
|
|
df['_net_lg_flow_vol'] = (df['buy_lg_vol'] + df['buy_elg_vol'] - df['sell_lg_vol'] - df['sell_elg_vol']) * df['close'] # Or use vol
|
|||
|
|
df['_industry_avg_flow'] = df.groupby(['trade_date', 'cat_l2_code'])['_net_lg_flow_vol'].transform('mean')
|
|||
|
|
df['_deviation'] = df['_net_lg_flow_vol'] - df['_industry_avg_flow']
|
|||
|
|
df[factor_name] = df.groupby('trade_date')['_deviation'].rank(pct=True)
|
|||
|
|
except KeyError as e:
|
|||
|
|
print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
finally:
|
|||
|
|
cols_to_drop = [col for col in _temp_cols if col in df.columns]
|
|||
|
|
if cols_to_drop:
|
|||
|
|
df.drop(columns=cols_to_drop, inplace=True)
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|
|||
|
|
def cs_rank_elg_buy_ratio(df: pd.DataFrame, factor_name: str = 'cs_rank_elg_buy_ratio'):
|
|||
|
|
"""
|
|||
|
|
Factor 4: 超大单买入占比排序 (In-place).
|
|||
|
|
WARNING: Modifies df in-place.
|
|||
|
|
"""
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
_temp_cols = ['_elg_buy_ratio']
|
|||
|
|
try:
|
|||
|
|
df['_elg_buy_ratio'] = _safe_divide(df['buy_elg_vol'], df['vol'])
|
|||
|
|
df[factor_name] = df.groupby('trade_date')['_elg_buy_ratio'].rank(pct=True)
|
|||
|
|
except KeyError as e:
|
|||
|
|
print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
finally:
|
|||
|
|
cols_to_drop = [col for col in _temp_cols if col in df.columns]
|
|||
|
|
if cols_to_drop:
|
|||
|
|
df.drop(columns=cols_to_drop, inplace=True)
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|
|||
|
|
# Category 2: Cross-Sectional Cost Basis & PnL Status
|
|||
|
|
def cs_rank_rel_profit_margin(df: pd.DataFrame, factor_name: str = 'cs_rank_rel_profit_margin'):
|
|||
|
|
"""
|
|||
|
|
Factor 5: 相对盈利幅度排序 (In-place).
|
|||
|
|
WARNING: Modifies df in-place.
|
|||
|
|
"""
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
_temp_cols = ['_profit_margin']
|
|||
|
|
try:
|
|||
|
|
df['_profit_margin'] = _safe_divide((df['close'] - df['weight_avg']), df['close'])
|
|||
|
|
df[factor_name] = df.groupby('trade_date')['_profit_margin'].rank(pct=True)
|
|||
|
|
except KeyError as e:
|
|||
|
|
print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
finally:
|
|||
|
|
cols_to_drop = [col for col in _temp_cols if col in df.columns]
|
|||
|
|
if cols_to_drop:
|
|||
|
|
df.drop(columns=cols_to_drop, inplace=True)
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|
|||
|
|
def cs_rank_cost_breadth(df: pd.DataFrame, factor_name: str = 'cs_rank_cost_breadth'):
|
|||
|
|
"""
|
|||
|
|
Factor 6: 成本分布宽度截面排序 (In-place).
|
|||
|
|
WARNING: Modifies df in-place.
|
|||
|
|
"""
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
_temp_cols = ['_cost_breadth']
|
|||
|
|
try:
|
|||
|
|
df['_cost_breadth'] = _safe_divide((df['cost_85pct'] - df['cost_15pct']), df['weight_avg'])
|
|||
|
|
df[factor_name] = df.groupby('trade_date')['_cost_breadth'].rank(pct=True)
|
|||
|
|
except KeyError as e:
|
|||
|
|
print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
finally:
|
|||
|
|
cols_to_drop = [col for col in _temp_cols if col in df.columns]
|
|||
|
|
if cols_to_drop:
|
|||
|
|
df.drop(columns=cols_to_drop, inplace=True)
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|
|||
|
|
def cs_rank_dist_to_upper_cost(df: pd.DataFrame, factor_name: str = 'cs_rank_dist_to_upper_cost'):
|
|||
|
|
"""
|
|||
|
|
Factor 7: 股价相对高成本位距离排序 (In-place).
|
|||
|
|
WARNING: Modifies df in-place.
|
|||
|
|
"""
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
_temp_cols = ['_dist_to_95']
|
|||
|
|
try:
|
|||
|
|
df['_dist_to_95'] = _safe_divide(df['close'], df['cost_95pct'])
|
|||
|
|
df[factor_name] = df.groupby('trade_date')['_dist_to_95'].rank(pct=True)
|
|||
|
|
except KeyError as e:
|
|||
|
|
print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
finally:
|
|||
|
|
cols_to_drop = [col for col in _temp_cols if col in df.columns]
|
|||
|
|
if cols_to_drop:
|
|||
|
|
df.drop(columns=cols_to_drop, inplace=True)
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|
|||
|
|
def cs_rank_winner_rate(df: pd.DataFrame, factor_name: str = 'cs_rank_winner_rate'):
|
|||
|
|
"""
|
|||
|
|
Factor 8: 获利盘比例截面排序 (In-place).
|
|||
|
|
WARNING: Modifies df in-place.
|
|||
|
|
"""
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
try:
|
|||
|
|
df[factor_name] = df.groupby('trade_date')['winner_rate'].rank(pct=True)
|
|||
|
|
except KeyError as e:
|
|||
|
|
print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
finally:
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|
|||
|
|
|
|||
|
|
# Category 3: Cross-Sectional Price Action & Volatility
|
|||
|
|
def cs_rank_intraday_range(df: pd.DataFrame, factor_name: str = 'cs_rank_intraday_range'):
|
|||
|
|
"""
|
|||
|
|
Factor 9: 日内相对振幅排序 (In-place).
|
|||
|
|
WARNING: Modifies df in-place.
|
|||
|
|
"""
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
_temp_cols = ['_norm_range']
|
|||
|
|
try:
|
|||
|
|
df['_norm_range'] = _safe_divide((df['high'] - df['low']), df['close'])
|
|||
|
|
df[factor_name] = df.groupby('trade_date')['_norm_range'].rank(pct=True)
|
|||
|
|
except KeyError as e:
|
|||
|
|
print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
finally:
|
|||
|
|
cols_to_drop = [col for col in _temp_cols if col in df.columns]
|
|||
|
|
if cols_to_drop:
|
|||
|
|
df.drop(columns=cols_to_drop, inplace=True)
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|
|||
|
|
def cs_rank_close_pos_in_range(df: pd.DataFrame, factor_name: str = 'cs_rank_close_pos_in_range'):
|
|||
|
|
"""
|
|||
|
|
Factor 10: 收盘价在日内位置排序 (In-place).
|
|||
|
|
WARNING: Modifies df in-place.
|
|||
|
|
"""
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
_temp_cols = ['_close_pos']
|
|||
|
|
try:
|
|||
|
|
df['_close_pos'] = _safe_divide((df['close'] - df['low']), (df['high'] - df['low']), default_val=0.5) # Assign 0.5 if high==low
|
|||
|
|
df[factor_name] = df.groupby('trade_date')['_close_pos'].rank(pct=True)
|
|||
|
|
except KeyError as e:
|
|||
|
|
print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
finally:
|
|||
|
|
cols_to_drop = [col for col in _temp_cols if col in df.columns]
|
|||
|
|
if cols_to_drop:
|
|||
|
|
df.drop(columns=cols_to_drop, inplace=True)
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|
|||
|
|
def cs_rank_opening_gap(df: pd.DataFrame, factor_name: str = 'cs_rank_opening_gap'):
|
|||
|
|
"""
|
|||
|
|
Factor 11: 开盘相对跳空幅度排序 (In-place). Needs pre_close.
|
|||
|
|
WARNING: Modifies df in-place. Assumes 'pre_close' exists.
|
|||
|
|
"""
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
_temp_cols = ['_gap']
|
|||
|
|
if 'pre_close' not in df.columns:
|
|||
|
|
print(f"Error calculating {factor_name}: Missing 'pre_close' column. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
return
|
|||
|
|
try:
|
|||
|
|
df['_gap'] = _safe_divide(df['open'], df['pre_close']) - 1
|
|||
|
|
df[factor_name] = df.groupby('trade_date')['_gap'].rank(pct=True)
|
|||
|
|
except KeyError as e:
|
|||
|
|
print(f"Error calculating {factor_name}: Missing column {e} (likely 'open'). Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
finally:
|
|||
|
|
cols_to_drop = [col for col in _temp_cols if col in df.columns]
|
|||
|
|
if cols_to_drop:
|
|||
|
|
df.drop(columns=cols_to_drop, inplace=True)
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|
|||
|
|
def cs_rank_pos_in_hist_range(df: pd.DataFrame, factor_name: str = 'cs_rank_pos_in_hist_range'):
|
|||
|
|
"""
|
|||
|
|
Factor 12: 相对历史波动位置排序 (In-place).
|
|||
|
|
WARNING: Modifies df in-place.
|
|||
|
|
"""
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
_temp_cols = ['_hist_pos']
|
|||
|
|
try:
|
|||
|
|
df['_hist_pos'] = _safe_divide((df['close'] - df['his_low']), (df['his_high'] - df['his_low'])).clip(0, 1) # Clip to 0-1 range
|
|||
|
|
df[factor_name] = df.groupby('trade_date')['_hist_pos'].rank(pct=True)
|
|||
|
|
except KeyError as e:
|
|||
|
|
print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
finally:
|
|||
|
|
cols_to_drop = [col for col in _temp_cols if col in df.columns]
|
|||
|
|
if cols_to_drop:
|
|||
|
|
df.drop(columns=cols_to_drop, inplace=True)
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|
|||
|
|
|
|||
|
|
# Category 4: Cross-Sectional Interaction & Composite Indicators
|
|||
|
|
def cs_rank_vol_x_profit_margin(df: pd.DataFrame, factor_name: str = 'cs_rank_vol_x_profit_margin'):
|
|||
|
|
"""
|
|||
|
|
Factor 13: 波动率与盈亏状态交互排序 (In-place).
|
|||
|
|
WARNING: Modifies df in-place.
|
|||
|
|
"""
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
_temp_cols = ['_daily_vol', '_profit_margin', '_interaction']
|
|||
|
|
try:
|
|||
|
|
df['_daily_vol'] = abs(df['pct_chg'])
|
|||
|
|
df['_profit_margin'] = _safe_divide((df['close'] - df['weight_avg']), df['close'])
|
|||
|
|
df['_interaction'] = df['_daily_vol'] * df['_profit_margin']
|
|||
|
|
df[factor_name] = df.groupby('trade_date')['_interaction'].rank(pct=True)
|
|||
|
|
except KeyError as e:
|
|||
|
|
print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
finally:
|
|||
|
|
cols_to_drop = [col for col in _temp_cols if col in df.columns]
|
|||
|
|
if cols_to_drop:
|
|||
|
|
df.drop(columns=cols_to_drop, inplace=True)
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|
|||
|
|
def cs_rank_lg_flow_price_concordance(df: pd.DataFrame, factor_name: str = 'cs_rank_lg_flow_price_concordance'):
|
|||
|
|
"""
|
|||
|
|
Factor 14: 大单流向与价格变动一致性排序 (In-place).
|
|||
|
|
WARNING: Modifies df in-place.
|
|||
|
|
"""
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
_temp_cols = ['_net_lg_flow_vol', '_concordance']
|
|||
|
|
try:
|
|||
|
|
df['_net_lg_flow_vol'] = df['buy_lg_vol'] + df['buy_elg_vol'] - df['sell_lg_vol'] - df['sell_elg_vol']
|
|||
|
|
df['_concordance'] = df['_net_lg_flow_vol'] * df['pct_chg']
|
|||
|
|
df[factor_name] = df.groupby('trade_date')['_concordance'].rank(pct=True)
|
|||
|
|
except KeyError as e:
|
|||
|
|
print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
finally:
|
|||
|
|
cols_to_drop = [col for col in _temp_cols if col in df.columns]
|
|||
|
|
if cols_to_drop:
|
|||
|
|
df.drop(columns=cols_to_drop, inplace=True)
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|
|||
|
|
def cs_rank_turnover_per_winner(df: pd.DataFrame, factor_name: str = 'cs_rank_turnover_per_winner'):
|
|||
|
|
"""
|
|||
|
|
Factor 15: 高换手获利盘占比排序 (In-place).
|
|||
|
|
WARNING: Modifies df in-place.
|
|||
|
|
"""
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
_temp_cols = ['_turnover_per_winner']
|
|||
|
|
try:
|
|||
|
|
df['_turnover_per_winner'] = _safe_divide(df['turnover_rate'], df['winner_rate'])
|
|||
|
|
df[factor_name] = df.groupby('trade_date')['_turnover_per_winner'].rank(pct=True)
|
|||
|
|
except KeyError as e:
|
|||
|
|
print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
finally:
|
|||
|
|
cols_to_drop = [col for col in _temp_cols if col in df.columns]
|
|||
|
|
if cols_to_drop:
|
|||
|
|
df.drop(columns=cols_to_drop, inplace=True)
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|
|||
|
|
def cs_rank_ind_cap_neutral_pe(df: pd.DataFrame, factor_name: str = 'cs_rank_ind_cap_neutral_pe'):
|
|||
|
|
"""
|
|||
|
|
Factor 16: 行业市值中性化PE排序 (Placeholder).
|
|||
|
|
Requires statsmodels and complex cross-sectional regression implementation.
|
|||
|
|
WARNING: Modifies df in-place. Placeholder implementation returns NaN.
|
|||
|
|
"""
|
|||
|
|
print(f"Calculating {factor_name} (Placeholder - requires statsmodels)...")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
print(f"Finished {factor_name} (Placeholder).")
|
|||
|
|
|
|||
|
|
def cs_rank_volume_ratio(df: pd.DataFrame, factor_name: str = 'cs_rank_volume_ratio'):
|
|||
|
|
"""
|
|||
|
|
Factor 17: 成交量相对强度排序 (In-place).
|
|||
|
|
WARNING: Modifies df in-place.
|
|||
|
|
"""
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
try:
|
|||
|
|
# Assumes 'volume_ratio' (量比) column already exists
|
|||
|
|
df[factor_name] = df.groupby('trade_date')['volume_ratio'].rank(pct=True)
|
|||
|
|
except KeyError as e:
|
|||
|
|
print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
finally:
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|
|||
|
|
def cs_rank_elg_buy_sell_sm_ratio(df: pd.DataFrame, factor_name: str = 'cs_rank_elg_buy_sell_sm_ratio'):
|
|||
|
|
"""
|
|||
|
|
Factor 18: 超大单买入与小单卖出比排序 (In-place).
|
|||
|
|
WARNING: Modifies df in-place.
|
|||
|
|
"""
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
_temp_cols = ['_ratio']
|
|||
|
|
try:
|
|||
|
|
df['_ratio'] = _safe_divide(df['buy_elg_vol'], df['sell_sm_vol'])
|
|||
|
|
df[factor_name] = df.groupby('trade_date')['_ratio'].rank(pct=True)
|
|||
|
|
except KeyError as e:
|
|||
|
|
print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
finally:
|
|||
|
|
cols_to_drop = [col for col in _temp_cols if col in df.columns]
|
|||
|
|
if cols_to_drop:
|
|||
|
|
df.drop(columns=cols_to_drop, inplace=True)
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|
|||
|
|
def cs_rank_cost_dist_vol_ratio(df: pd.DataFrame, factor_name: str = 'cs_rank_cost_dist_vol_ratio'):
|
|||
|
|
"""
|
|||
|
|
Factor 19: 价格偏离成本程度与成交量放大交互排序 (In-place).
|
|||
|
|
WARNING: Modifies df in-place.
|
|||
|
|
"""
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
_temp_cols = ['_dist', '_interaction']
|
|||
|
|
if 'volume_ratio' not in df.columns:
|
|||
|
|
print(f"Error calculating {factor_name}: Missing 'volume_ratio' column. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
return
|
|||
|
|
try:
|
|||
|
|
df['_dist'] = abs(df['close'] - df['weight_avg']) / (df['close'] + epsilon)
|
|||
|
|
df['_interaction'] = df['_dist'] * df['volume_ratio']
|
|||
|
|
df[factor_name] = df.groupby('trade_date')['_interaction'].rank(pct=True)
|
|||
|
|
except KeyError as e:
|
|||
|
|
print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
finally:
|
|||
|
|
cols_to_drop = [col for col in _temp_cols if col in df.columns]
|
|||
|
|
if cols_to_drop:
|
|||
|
|
df.drop(columns=cols_to_drop, inplace=True)
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|
|||
|
|
def cs_rank_size(df: pd.DataFrame, factor_name: str = 'cs_rank_size'):
|
|||
|
|
"""
|
|||
|
|
Factor 20: 市值因子暴露度排序 (Log of circ_mv) (In-place).
|
|||
|
|
WARNING: Modifies df in-place.
|
|||
|
|
"""
|
|||
|
|
print(f"Calculating {factor_name}...")
|
|||
|
|
_temp_cols = ['_log_circ_mv']
|
|||
|
|
try:
|
|||
|
|
# Use log1p for stability if circ_mv can be zero or very small
|
|||
|
|
df['_log_circ_mv'] = np.log1p(df['circ_mv'])
|
|||
|
|
df[factor_name] = df.groupby('trade_date')['_log_circ_mv'].rank(pct=True)
|
|||
|
|
except KeyError as e:
|
|||
|
|
print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.")
|
|||
|
|
df[factor_name] = np.nan
|
|||
|
|
finally:
|
|||
|
|
cols_to_drop = [col for col in _temp_cols if col in df.columns]
|
|||
|
|
if cols_to_drop:
|
|||
|
|
df.drop(columns=cols_to_drop, inplace=True)
|
|||
|
|
print(f"Finished {factor_name}.")
|
|||
|
|
|