diff --git a/main/factor/__pycache__/money_factor.cpython-313.pyc b/main/factor/__pycache__/money_factor.cpython-313.pyc index de80b08..36a061c 100644 Binary files a/main/factor/__pycache__/money_factor.cpython-313.pyc and b/main/factor/__pycache__/money_factor.cpython-313.pyc differ diff --git a/main/factor/money_factor.py b/main/factor/money_factor.py index 3466188..d3dec15 100644 --- a/main/factor/money_factor.py +++ b/main/factor/money_factor.py @@ -1,115 +1,112 @@ import pandas as pd import numpy as np -def holder_trade_factors(all_data_df: pd.DataFrame, stk_holdertrade_df: pd.DataFrame) -> pd.DataFrame: +def holder_trade_factors(all_data_df: pd.DataFrame, + stk_holdertrade_df: pd.DataFrame) -> pd.DataFrame: """ - 生成合并的股东增减持因子以及 change_ratio 相关因子（优化版）。 + 计算基于股东增减持数据的因子。 Args: - stk_holdertrade_df (pd.DataFrame): 股东增减持数据，包含 'ts_code', 'ann_date', 'in_de', 'change_ratio'。 - all_data_df (pd.DataFrame): 所有日期所有股票数据，包含 'ts_code', 'trade_date'。 + all_data_df (pd.DataFrame): 包含每日股票数据的 DataFrame， + 必须包含 'ts_code' 和 'trade_date' 列。 + stk_holdertrade_df (pd.DataFrame): 包含股东增减持信息的 DataFrame。 + 必须包含 'ts_code', 'ann_date', + 'in_de' (例如, '增持', '减持'), + 和 'change_ratio'。 Returns: - pd.DataFrame: 包含增减持因子的 all_data_df。 + pd.DataFrame: 添加了新的股东增减持因子的 all_data_df DataFrame。 """ - print('正在计算股东增减持因子（优化版）...') + print("开始计算股东增减持因子...") - # 1. 确保日期列为 datetime 类型 - stk_holdertrade_df['ann_date'] = pd.to_datetime(stk_holdertrade_df['ann_date']) - all_data_df['trade_date'] = pd.to_datetime(all_data_df['trade_date']) + # --- 1. 预处理 stk_holdertrade_df --- + # 创建副本以避免修改原始传入的DataFrame + stk_trade_processed_df = stk_holdertrade_df.copy() - # 2. 对增减持数据进行预处理和排序（排序在此阶段可能不是严格必需的，但保持良好习惯） - holder_data_processed = stk_holdertrade_df.copy() - holder_data_processed['change_ratio_in_agg'] = holder_data_processed['change_ratio'].where(holder_data_processed['in_de'] == 'IN', 0) - holder_data_processed['change_ratio_de_agg'] = holder_data_processed['change_ratio'].where(holder_data_processed['in_de'] == 'DE', 0) - holder_data_processed['change_ratio_total_agg'] = holder_data_processed['change_ratio'] - holder_data_processed['in_de_numeric'] = holder_data_processed['in_de'].map({'IN': 1, 'DE': -1}).fillna(0) # 用于判断类型 + # 确保 'ann_date' 是 datetime 类型 + stk_trade_processed_df['ann_date'] = pd.to_datetime(stk_trade_processed_df['ann_date']) - # 提前获取所有唯一的交易日期集合，以提高查找效率 - all_trade_dates_set = set(all_data_df['trade_date'].unique()) + # 将 'in_de' 映射为数值: 1 代表 '增持', -1 代表 '减持' + # 请根据你数据中实际的 'in_de' 字符串调整 + in_de_map = {'增持': 1, '减持': -1} # 假设是这两个值 + # 如果你的值是 '1' 和 '2' (1代表增持, 2代表减持)，则映射应相应调整 + # 或者如果 'in_de' 已经是 1 和 -1 (或类似数值)，则可以跳过映射，但要确保类型正确 + stk_trade_processed_df['_direction'] = stk_trade_processed_df['in_de'].map(in_de_map) + # 如果 _direction 列在映射后可能产生NaN (因为in_de中有未覆盖的值)，需要处理 + if stk_trade_processed_df['_direction'].isnull().any(): + print("警告: 'in_de' 列中存在未映射的值，可能导致 _direction 列出现NaN。") + # 可以选择填充NaN，例如用0填充，或者移除这些行 + # stk_trade_processed_df['_direction'].fillna(0, inplace=True) - # 3. 构建一个辅助DataFrame，记录每个公告在未来10个日历日（且是交易日）的影响 - expanded_holder_events = [] - for _, row in holder_data_processed.iterrows(): - ts_code = row['ts_code'] - ann_date = row['ann_date'] - - # 生成从公告日期开始的未来10个日历日的日期范围（包括公告日本身） - # pd.Timedelta(days=10) 表示从公告日+10天 - # pd.date_range(start=ann_date, end=ann_date + pd.Timedelta(days=10), freq='D') - # 更精确地生成11个日期，涵盖公告日及其后的10个日历日 - future_dates = pd.date_range(start=ann_date, periods=11, freq='D') + # 计算有效变动比例 (方向 * 变动比例) + # 确保 change_ratio 是数值类型。假设 change_ratio 是一个正确的比例值 (例如 0.005 代表 0.5%)。 + # 如果 change_ratio 是百分点 (例如 0.5 代表 0.5%)，你可能需要除以 100。 + stk_trade_processed_df['change_ratio'] = pd.to_numeric(stk_trade_processed_df['change_ratio'], errors='coerce') + stk_trade_processed_df['_effective_change'] = stk_trade_processed_df['_direction'] * stk_trade_processed_df['change_ratio'] - for date_in_window in future_dates: - # 只有当日期是实际交易日时才添加 - if date_in_window in all_trade_dates_set: - expanded_holder_events.append({ - 'ts_code': ts_code, - 'trade_date': date_in_window, - 'in_de_numeric': row['in_de_numeric'], - 'change_ratio_total_agg': row['change_ratio_total_agg'], - 'change_ratio_in_agg': row['change_ratio_in_agg'], - 'change_ratio_de_agg': row['change_ratio_de_agg'] - }) - - if not expanded_holder_events: # 如果没有事件，直接返回原始 df - # 确保返回的DataFrame与原始df具有相同的列和顺序 - # 并填充为默认值 - default_factors = pd.DataFrame({ - 'holder_trade_type_10d': None, - 'holder_change_ratio_sum_10d': 0.0, - 'holder_in_change_ratio_sum_10d': 0.0, - 'holder_de_change_ratio_sum_10d': 0.0 - }, index=all_data_df.index) - return pd.concat([all_data_df, default_factors], axis=1) - - - expanded_holder_events_df = pd.DataFrame(expanded_holder_events) - - # 4. 聚合每个 (ts_code, trade_date) 对上的事件 - # 可能会有重复的 (ts_code, trade_date) 对，因为一个交易日可能受多个公告影响 - daily_aggregated_factors = expanded_holder_events_df.groupby(['ts_code', 'trade_date']).agg( - holder_change_ratio_sum_10d=('change_ratio_total_agg', 'sum'), - holder_in_change_ratio_sum_10d=('change_ratio_in_agg', 'sum'), - holder_de_change_ratio_sum_10d=('change_ratio_de_agg', 'sum'), - # 对于 holder_trade_type_10d，聚合 in_de_numeric 的唯一值集合 - _in_de_types_unique=('in_de_numeric', lambda x: set(x)) # 获取该日期窗口内所有独特的增减持类型 + # 按股票代码和公告日期聚合当日的多次增减持操作 + daily_trade_agg = stk_trade_processed_df.groupby(['ts_code', 'ann_date']).agg( + net_change_ratio_daily=('_effective_change', 'sum'), + any_increase_daily=('_direction', lambda x: (x == 1).any().astype(int)), + any_decrease_daily=('_direction', lambda x: (x == -1).any().astype(int)) ).reset_index() - # 根据 _in_de_types_unique 确定 holder_trade_type_10d - def get_trade_type(unique_types_set): - if 1 in unique_types_set and -1 in unique_types_set: - return 'BOTH' - elif 1 in unique_types_set: - return 'IN' - elif -1 in unique_types_set: - return 'DE' - else: - return None # 理论上不应该发生，除非 unique_types_set 为空或只包含0 - - daily_aggregated_factors['holder_trade_type_10d'] = daily_aggregated_factors['_in_de_types_unique'].apply(get_trade_type) + # 将 'ann_date' 重命名为 'trade_date' 以便与 all_data_df 合并 + daily_trade_agg = daily_trade_agg.rename(columns={'ann_date': 'trade_date'}) - # 移除辅助列 - daily_aggregated_factors.drop(columns=['_in_de_types_unique'], inplace=True) + # --- 2. 与 all_data_df 合并 --- + # 创建 all_data_df 的副本进行操作 + df_merged = all_data_df.copy() + df_merged['trade_date'] = pd.to_datetime(df_merged['trade_date']) # 确保日期类型一致 - # 5. 将计算得到的因子合并回 all_data_df - # 确保 all_data_df 也按 ts_code, trade_date 排序，以便 merge 高效 - all_data_df_sorted = all_data_df.sort_values(['ts_code', 'trade_date']).reset_index(drop=True) - - final_df = pd.merge( - all_data_df_sorted, - daily_aggregated_factors, - on=['ts_code', 'trade_date'], - how='left' - ) + # 使用左合并保留 all_data_df 的所有行，并在有增减持公告的日期添加信息 + df_merged = pd.merge(df_merged, daily_trade_agg, on=['ts_code', 'trade_date'], how='left') - # 6. 对于没有增减持记录的日期，因子值为 None 或 0 - # 在 merge_asof 中确实需要排序，但在这种事件展开的方法中，merge 是普通的 left merge，不需要预排序。 - # 考虑到最终的 merge，最好还是保持 `all_data_df` 和 `daily_aggregated_factors` 的键排序。 - # 所以在 `merge` 前对 `all_data_df` 进行一次排序是好的实践。 - - final_df['holder_trade_type_10d'] = final_df['holder_trade_type_10d'].fillna(None) - fillna_ratio_cols = ['holder_change_ratio_sum_10d', 'holder_in_change_ratio_sum_10d', 'holder_de_change_ratio_sum_10d'] - final_df[fillna_ratio_cols] = final_df[fillna_ratio_cols].fillna(0.0) + # 对于没有增减持公告的日期，填充NaN值为0（表示当天无变动或无公告） + df_merged['net_change_ratio_daily'] = df_merged['net_change_ratio_daily'].fillna(0) + df_merged['any_increase_daily'] = df_merged['any_increase_daily'].fillna(0) + df_merged['any_decrease_daily'] = df_merged['any_decrease_daily'].fillna(0) + + # --- 3. 计算滚动因子 --- + # !!! 关键步骤：确保在 groupby().rolling() 之前按分组键和时间键排序 !!! + # 这一步至关重要，以保证滚动计算后的 Series 在 reset_index 后能正确对齐 + df_merged = df_merged.sort_values(['ts_code', 'trade_date']).reset_index(drop=True) + + grouped = df_merged.groupby('ts_code', group_keys=False) # group_keys=False 避免在结果中保留分组键作为索引层级 + + # 因子: 过去N日净变动比例之和 + for N in [10]: + rolling_series = grouped['net_change_ratio_daily'].rolling(window=N, min_periods=1).sum() + df_merged[f'holder_net_change_sum_{N}d'] = rolling_series.reset_index(level=0, drop=True) + + # 因子: 过去N日发生增持的天数 + for N in [10]: + rolling_series = grouped['any_increase_daily'].rolling(window=N, min_periods=1).sum() + df_merged[f'holder_increase_days_{N}d'] = rolling_series.reset_index(level=0, drop=True) + + # 因子: 过去N日发生减持的天数 + for N in [10]: + rolling_series = grouped['any_decrease_daily'].rolling(window=N, min_periods=1).sum() + df_merged[f'holder_decrease_days_{N}d'] = rolling_series.reset_index(level=0, drop=True) + + # 因子: 过去N日是否发生过增持 (布尔标志) + for N in [10]: + rolling_series_sum = grouped['any_increase_daily'].rolling(window=N, min_periods=1).sum() + df_merged[f'holder_any_increase_flag_{N}d'] = (rolling_series_sum > 0).astype(int).reset_index(level=0, drop=True) + + # 因子: 过去N日是否发生过减持 (布尔标志) + for N in [10]: + rolling_series_sum = grouped['any_decrease_daily'].rolling(window=N, min_periods=1).sum() + df_merged[f'holder_any_decrease_flag_{N}d'] = (rolling_series_sum > 0).astype(int).reset_index(level=0, drop=True) + + # 因子: 过去N日净“活动”得分 (增持天数 - 减持天数) + for N in [10]: + # 直接使用已经计算好且索引对齐的列 + df_merged[f'holder_direction_score_{N}d'] = df_merged[f'holder_increase_days_{N}d'] - df_merged[f'holder_decrease_days_{N}d'] + + # 清理在合并过程中产生的每日临时列（如果不再需要它们） + df_merged.drop(columns=['net_change_ratio_daily', 'any_increase_daily', 'any_decrease_daily'], inplace=True, errors='ignore') + + print("股东增减持因子计算完成。") + return df_merged - return final_df \ No newline at end of file diff --git a/main/train/Rank2.ipynb b/main/train/Rank2.ipynb index 69bd026..785c257 100644 --- a/main/train/Rank2.ipynb +++ b/main/train/Rank2.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 9, + "execution_count": 1, "id": "79a7758178bafdd3", "metadata": { "ExecuteTime": { @@ -18,8 +18,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n", "/mnt/d/PyProject/NewStock\n" ] } @@ -46,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 2, "id": "a79cafb06a7e0e43", "metadata": { "ExecuteTime": { @@ -146,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 3, "id": "cac01788dac10678", "metadata": { "ExecuteTime": { @@ -214,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "id": "c4e9e1d31da6dba6", "metadata": { "ExecuteTime": { @@ -314,7 +312,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 5, "id": "a735bc02ceb4d872", "metadata": { "ExecuteTime": { @@ -330,7 +328,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 6, "id": "53f86ddc0677a6d7", "metadata": { "ExecuteTime": { @@ -397,7 +395,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 7, "id": "dbe2fd8021b9417f", "metadata": { "ExecuteTime": { @@ -425,7 +423,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 8, "id": "85c3e3d0235ffffa", "metadata": { "ExecuteTime": { @@ -457,7 +455,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 9, "id": "92d84ce15a562ec6", "metadata": { "ExecuteTime": { @@ -470,21 +468,153 @@ "name": "stdout", "output_type": "stream", "text": [ - "正在计算股东增减持因子（优化版）...\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[18]\u001b[39m\u001b[32m, line 30\u001b[39m\n\u001b[32m 23\u001b[39m df = df.sort_values(by=[\u001b[33m\"\u001b[39m\u001b[33mts_code\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mtrade_date\u001b[39m\u001b[33m\"\u001b[39m])\n\u001b[32m 25\u001b[39m \u001b[38;5;66;03m# df = price_minus_deduction_price(df, n=120)\u001b[39;00m\n\u001b[32m 26\u001b[39m \u001b[38;5;66;03m# df = price_deduction_price_diff_ratio_to_sma(df, n=120)\u001b[39;00m\n\u001b[32m 27\u001b[39m \u001b[38;5;66;03m# df = cat_price_vs_sma_vs_deduction_price(df, n=120)\u001b[39;00m\n\u001b[32m 28\u001b[39m \u001b[38;5;66;03m# df = cat_reason(df, top_list_df)\u001b[39;00m\n\u001b[32m 29\u001b[39m \u001b[38;5;66;03m# df = cat_is_on_top_list(df, top_list_df)\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m30\u001b[39m df = \u001b[43mholder_trade_factors\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstk_holdertrade_df\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 32\u001b[39m df = cat_senti_mom_vol_spike(\n\u001b[32m 33\u001b[39m df,\n\u001b[32m 34\u001b[39m return_period=\u001b[32m3\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 38\u001b[39m current_pct_chg_max=\u001b[32m0.05\u001b[39m,\n\u001b[32m 39\u001b[39m ) \u001b[38;5;66;03m# 当日涨幅不宜过大\u001b[39;00m\n\u001b[32m 41\u001b[39m df = cat_senti_pre_breakout(\n\u001b[32m 42\u001b[39m df,\n\u001b[32m 43\u001b[39m atr_short_N=\u001b[32m10\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 51\u001b[39m volume_ratio_signal_threshold=\u001b[32m1.1\u001b[39m,\n\u001b[32m 52\u001b[39m )\n", - "\u001b[36mFile \u001b[39m\u001b[32m/mnt/d/PyProject/NewStock/main/factor/money_factor.py:50\u001b[39m, in \u001b[36mholder_trade_factors\u001b[39m\u001b[34m(all_data_df, stk_holdertrade_df)\u001b[39m\n\u001b[32m 43\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m date_in_window \u001b[38;5;129;01min\u001b[39;00m future_dates:\n\u001b[32m 44\u001b[39m \u001b[38;5;66;03m# 只有当日期是实际交易日时才添加\u001b[39;00m\n\u001b[32m 45\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m date_in_window \u001b[38;5;129;01min\u001b[39;00m all_trade_dates_set:\n\u001b[32m 46\u001b[39m expanded_holder_events.append({\n\u001b[32m 47\u001b[39m \u001b[33m'\u001b[39m\u001b[33mts_code\u001b[39m\u001b[33m'\u001b[39m: ts_code,\n\u001b[32m 48\u001b[39m \u001b[33m'\u001b[39m\u001b[33mtrade_date\u001b[39m\u001b[33m'\u001b[39m: date_in_window,\n\u001b[32m 49\u001b[39m \u001b[33m'\u001b[39m\u001b[33min_de_numeric\u001b[39m\u001b[33m'\u001b[39m: row[\u001b[33m'\u001b[39m\u001b[33min_de_numeric\u001b[39m\u001b[33m'\u001b[39m],\n\u001b[32m---> \u001b[39m\u001b[32m50\u001b[39m \u001b[33m'\u001b[39m\u001b[33mchange_ratio_total_agg\u001b[39m\u001b[33m'\u001b[39m: \u001b[43mrow\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mchange_ratio_total_agg\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m]\u001b[49m,\n\u001b[32m 51\u001b[39m \u001b[33m'\u001b[39m\u001b[33mchange_ratio_in_agg\u001b[39m\u001b[33m'\u001b[39m: row[\u001b[33m'\u001b[39m\u001b[33mchange_ratio_in_agg\u001b[39m\u001b[33m'\u001b[39m],\n\u001b[32m 52\u001b[39m \u001b[33m'\u001b[39m\u001b[33mchange_ratio_de_agg\u001b[39m\u001b[33m'\u001b[39m: row[\u001b[33m'\u001b[39m\u001b[33mchange_ratio_de_agg\u001b[39m\u001b[33m'\u001b[39m]\n\u001b[32m 53\u001b[39m })\n\u001b[32m 55\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m expanded_holder_events: \u001b[38;5;66;03m# 如果没有事件，直接返回原始 df\u001b[39;00m\n\u001b[32m 56\u001b[39m \u001b[38;5;66;03m# 确保返回的DataFrame与原始df具有相同的列和顺序\u001b[39;00m\n\u001b[32m 57\u001b[39m \u001b[38;5;66;03m# 并填充为默认值\u001b[39;00m\n\u001b[32m 58\u001b[39m default_factors = pd.DataFrame({\n\u001b[32m 59\u001b[39m \u001b[33m'\u001b[39m\u001b[33mholder_trade_type_10d\u001b[39m\u001b[33m'\u001b[39m: \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 60\u001b[39m \u001b[33m'\u001b[39m\u001b[33mholder_change_ratio_sum_10d\u001b[39m\u001b[33m'\u001b[39m: \u001b[32m0.0\u001b[39m,\n\u001b[32m 61\u001b[39m \u001b[33m'\u001b[39m\u001b[33mholder_in_change_ratio_sum_10d\u001b[39m\u001b[33m'\u001b[39m: \u001b[32m0.0\u001b[39m,\n\u001b[32m 62\u001b[39m \u001b[33m'\u001b[39m\u001b[33mholder_de_change_ratio_sum_10d\u001b[39m\u001b[33m'\u001b[39m: \u001b[32m0.0\u001b[39m\n\u001b[32m 63\u001b[39m }, index=all_data_df.index)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/stock/lib/python3.13/site-packages/pandas/core/series.py:1121\u001b[39m, in \u001b[36mSeries.__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 1118\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._values[key]\n\u001b[32m 1120\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m key_is_scalar:\n\u001b[32m-> \u001b[39m\u001b[32m1121\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_get_value\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1123\u001b[39m \u001b[38;5;66;03m# Convert generator to list before going through hashable part\u001b[39;00m\n\u001b[32m 1124\u001b[39m \u001b[38;5;66;03m# (We will iterate through the generator there to check for slices)\u001b[39;00m\n\u001b[32m 1125\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/stock/lib/python3.13/site-packages/pandas/core/series.py:1239\u001b[39m, in \u001b[36mSeries._get_value\u001b[39m\u001b[34m(self, label, takeable)\u001b[39m\n\u001b[32m 1236\u001b[39m \u001b[38;5;66;03m# Similar to Index.get_value, but we do not fall back to positional\u001b[39;00m\n\u001b[32m 1237\u001b[39m loc = \u001b[38;5;28mself\u001b[39m.index.get_loc(label)\n\u001b[32m-> \u001b[39m\u001b[32m1239\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mis_integer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloc\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[32m 1240\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._values[loc]\n\u001b[32m 1242\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m.index, MultiIndex):\n", - "\u001b[31mKeyboardInterrupt\u001b[39m: " + "开始计算股东增减持因子...\n", + "警告: 'in_de' 列中存在未映射的值，可能导致 _direction 列出现NaN。\n", + "股东增减持因子计算完成。\n", + "Calculating cat_senti_mom_vol_spike...\n", + "Finished cat_senti_mom_vol_spike.\n", + "Calculating cat_senti_pre_breakout...\n", + "Calculating atr_10 as it's missing...\n", + "Calculating atr_40 as it's missing...\n", + "Finished cat_senti_pre_breakout.\n", + "计算因子 ts_turnover_rate_acceleration_5_20\n", + "计算因子 ts_vol_sustain_10_30\n", + "计算因子 cs_amount_outlier_10\n", + "计算因子 ts_ff_to_total_turnover_ratio\n", + "计算因子 ts_price_volume_trend_coherence_5_20\n", + "计算因子 ts_ff_turnover_rate_surge_10\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n", + "开始计算因子: AR, BR (原地修改)...\n", + "因子 AR, BR 计算成功。\n", + "因子 AR, BR 计算流程结束。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n", + "计算 BBI...\n", + "--- 计算日级别偏离度 (使用 pct_chg) ---\n", + "--- 计算日级别动量基准 (使用 pct_chg) ---\n", + "日级别动量基准计算完成 (使用 pct_chg)。\n", + "日级别偏离度计算完成 (使用 pct_chg)。\n", + "--- 计算日级别行业偏离度 (使用 pct_chg 和行业基准) ---\n", + "--- 计算日级别行业动量基准 (使用 pct_chg 和 cat_l2_code) ---\n", + "错误: 计算日级别行业动量基准需要以下列: ['pct_chg', 'cat_l2_code', 'trade_date', 'ts_code']。\n", + "错误: 计算日级别行业偏离度需要以下列: ['pct_chg', 'daily_industry_positive_benchmark', 'daily_industry_negative_benchmark']。请先运行 daily_industry_momentum_benchmark(df)。\n", + "Index(['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol',\n", + " 'pct_chg', 'amount', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv',\n", + " 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol',\n", + " 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol',\n", + " 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct',\n", + " 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg',\n", + " 'winner_rate', 'l2_code', 'holder_net_change_sum_10d',\n", + " 'holder_increase_days_10d', 'holder_decrease_days_10d',\n", + " 'holder_any_increase_flag_10d', 'holder_any_decrease_flag_10d',\n", + " 'holder_direction_score_10d', 'cat_senti_mom_vol_spike',\n", + " 'cat_senti_pre_breakout', 'ts_turnover_rate_acceleration_5_20',\n", + " 'ts_vol_sustain_10_30', 'cs_amount_outlier_10',\n", + " 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20',\n", + " 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR',\n", + " 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio',\n", + " 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor',\n", + " 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity',\n", + " 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio',\n", + " 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change',\n", + " 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel',\n", + " 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy',\n", + " 'cost_support_15pct_change', 'cat_winner_price_zone',\n", + " 'flow_chip_consistency', 'profit_taking_vs_absorb', '_is_positive',\n", + " '_is_negative', 'cat_is_positive', '_pos_returns', '_neg_returns',\n", + " '_pos_returns_sq', '_neg_returns_sq', 'upside_vol', 'downside_vol',\n", + " 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate',\n", + " 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike',\n", + " 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike',\n", + " 'vol_std_5', 'atr_14', 'atr_6', 'obv'],\n", + " dtype='object')\n", + "Calculating senti_strong_inflow...\n", + "Finished senti_strong_inflow.\n", + "Calculating lg_flow_mom_corr_20_60...\n", + "Finished lg_flow_mom_corr_20_60.\n", + "Calculating lg_flow_accel...\n", + "Finished lg_flow_accel.\n", + "Calculating profit_pressure...\n", + "Finished profit_pressure.\n", + "Calculating underwater_resistance...\n", + "Finished underwater_resistance.\n", + "Calculating cost_conc_std_20...\n", + "Finished cost_conc_std_20.\n", + "Calculating profit_decay_20...\n", + "Finished profit_decay_20.\n", + "Calculating vol_amp_loss_20...\n", + "Finished vol_amp_loss_20.\n", + "Calculating vol_drop_profit_cnt_5...\n", + "Finished vol_drop_profit_cnt_5.\n", + "Calculating lg_flow_vol_interact_20...\n", + "Finished lg_flow_vol_interact_20.\n", + "Calculating cost_break_confirm_cnt_5...\n", + "Finished cost_break_confirm_cnt_5.\n", + "Calculating atr_norm_channel_pos_14...\n", + "Finished atr_norm_channel_pos_14.\n", + "Calculating turnover_diff_skew_20...\n", + "Finished turnover_diff_skew_20.\n", + "Calculating lg_sm_flow_diverge_20...\n", + "Finished lg_sm_flow_diverge_20.\n", + "Calculating pullback_strong_20_20...\n", + "Finished pullback_strong_20_20.\n", + "Calculating vol_wgt_hist_pos_20...\n", + "Finished vol_wgt_hist_pos_20.\n", + "Calculating vol_adj_roc_20...\n", + "Finished vol_adj_roc_20.\n", + "Calculating cs_rank_net_lg_flow_val...\n", + "Finished cs_rank_net_lg_flow_val.\n", + "Calculating cs_rank_flow_divergence...\n", + "Finished cs_rank_flow_divergence.\n", + "Calculating cs_rank_ind_adj_lg_flow...\n", + "Finished cs_rank_ind_adj_lg_flow.\n", + "Calculating cs_rank_elg_buy_ratio...\n", + "Finished cs_rank_elg_buy_ratio.\n", + "Calculating cs_rank_rel_profit_margin...\n", + "Finished cs_rank_rel_profit_margin.\n", + "Calculating cs_rank_cost_breadth...\n", + "Finished cs_rank_cost_breadth.\n", + "Calculating cs_rank_dist_to_upper_cost...\n", + "Finished cs_rank_dist_to_upper_cost.\n", + "Calculating cs_rank_winner_rate...\n", + "Finished cs_rank_winner_rate.\n", + "Calculating cs_rank_intraday_range...\n", + "Finished cs_rank_intraday_range.\n", + "Calculating cs_rank_close_pos_in_range...\n", + "Finished cs_rank_close_pos_in_range.\n", + "Calculating cs_rank_opening_gap...\n", + "Error calculating cs_rank_opening_gap: Missing 'pre_close' column. Assigning NaN.\n", + "Calculating cs_rank_pos_in_hist_range...\n", + "Finished cs_rank_pos_in_hist_range.\n", + "Calculating cs_rank_vol_x_profit_margin...\n", + "Finished cs_rank_vol_x_profit_margin.\n", + "Calculating cs_rank_lg_flow_price_concordance...\n", + "Finished cs_rank_lg_flow_price_concordance.\n", + "Calculating cs_rank_turnover_per_winner...\n", + "Finished cs_rank_turnover_per_winner.\n", + "Calculating cs_rank_ind_cap_neutral_pe (Placeholder - requires statsmodels)...\n", + "Finished cs_rank_ind_cap_neutral_pe (Placeholder).\n", + "Calculating cs_rank_volume_ratio...\n", + "Finished cs_rank_volume_ratio.\n", + "Calculating cs_rank_elg_buy_sell_sm_ratio...\n", + "Finished cs_rank_elg_buy_sell_sm_ratio.\n", + "Calculating cs_rank_cost_dist_vol_ratio...\n", + "Finished cs_rank_cost_dist_vol_ratio.\n", + "Calculating cs_rank_size...\n", + "Finished cs_rank_size.\n", + "\n", + "RangeIndex: 4554725 entries, 0 to 4554724\n", + "Columns: 194 entries, ts_code to cs_rank_size\n", + "dtypes: bool(10), datetime64[ns](1), float64(173), int64(6), object(4)\n", + "memory usage: 6.3+ GB\n", + "None\n", + "['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'pct_chg', 'amount', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate', 'cat_l2_code', 'holder_net_change_sum_10d', 'holder_increase_days_10d', 'holder_decrease_days_10d', 'holder_any_increase_flag_10d', 'holder_any_decrease_flag_10d', 'holder_direction_score_10d', 'cat_senti_mom_vol_spike', 'cat_senti_pre_breakout', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'price_cost_divergence', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'senti_strong_inflow', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_flow_divergence', 'cs_rank_ind_adj_lg_flow', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_opening_gap', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_ind_cap_neutral_pe', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size']\n" ] } ], @@ -1260,9 +1390,9 @@ "# .fillna(0) # 填充每个股票组最后的 NaN\n", "# .astype(int)\n", "# .reset_index(level=0, drop=True))\n", - "# df['label'] = df.groupby('trade_date', group_keys=False)['future_return'].transform(\n", - "# lambda x: pd.qcut(x, q=50, labels=False, duplicates='drop')\n", - "# )\n", + "df['label'] = df.groupby('trade_date', group_keys=False)['future_return'].transform(\n", + " lambda x: pd.qcut(x, q=50, labels=False, duplicates='drop')\n", + ")\n", "# filter_index = df['future_return'].between(df['future_return'].quantile(0.01), df['future_return'].quantile(0.99))\n", "filter_index = df['future_return'].between(df['future_return'].quantile(0.001), 0.6)\n", "\n", @@ -1281,7 +1411,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "200\n" + "205\n" ] } ], @@ -1345,7 +1475,7 @@ "0 000001.SZ 2019-01-02 16.574219\n", "1 000001.SZ 2019-01-03 16.583965\n", "2 000001.SZ 2019-01-04 16.633371\n", - "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'cat_senti_mom_vol_spike', 'cat_senti_pre_breakout', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'senti_strong_inflow', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n", + "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'holder_net_change_sum_10d', 'holder_increase_days_10d', 'holder_decrease_days_10d', 'holder_any_increase_flag_10d', 'holder_any_decrease_flag_10d', 'cat_senti_mom_vol_spike', 'cat_senti_pre_breakout', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'senti_strong_inflow', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n", "去除极值\n", "开始截面 MAD 去极值处理 (k=3.0)...\n" ] @@ -1354,7 +1484,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "MAD Filtering: 100%|██████████| 139/139 [00:06<00:00, 21.25it/s]\n" + "MAD Filtering: 100%|██████████| 144/144 [00:07<00:00, 19.10it/s]\n" ] }, { @@ -1362,6 +1492,22 @@ "output_type": "stream", "text": [ "截面 MAD 去极值处理完成。\n", + "标准化\n", + "开始截面 Z-Score 标准化...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Standardizing: 100%|██████████| 144/144 [00:02<00:00, 58.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "截面 Z-Score 标准化完成。\n", "开始截面 MAD 去极值处理 (k=3.0)...\n" ] }, @@ -1369,7 +1515,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "MAD Filtering: 100%|██████████| 139/139 [00:05<00:00, 26.68it/s]\n" + "MAD Filtering: 100%|██████████| 144/144 [00:05<00:00, 25.72it/s]\n" ] }, { @@ -1377,6 +1523,21 @@ "output_type": "stream", "text": [ "截面 MAD 去极值处理完成。\n", + "开始截面 Z-Score 标准化...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Standardizing: 100%|██████████| 144/144 [00:01<00:00, 81.71it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "截面 Z-Score 标准化完成。\n", "开始截面 MAD 去极值处理 (k=3.0)...\n" ] }, @@ -1407,7 +1568,7 @@ "output_type": "stream", "text": [ "截面 MAD 去极值处理完成。\n", - "feature_columns: ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'cat_senti_mom_vol_spike', 'cat_senti_pre_breakout', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'senti_strong_inflow', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n", + "feature_columns: ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'holder_net_change_sum_10d', 'holder_increase_days_10d', 'holder_decrease_days_10d', 'holder_any_increase_flag_10d', 'holder_any_decrease_flag_10d', 'cat_senti_mom_vol_spike', 'cat_senti_pre_breakout', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'senti_strong_inflow', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n", "df最小日期: 2019-01-02\n", "df最大日期: 2025-05-30\n", "1091062\n", @@ -1442,12 +1603,12 @@ "\n", "train_data, test_data = train_data.replace([np.inf, -np.inf], np.nan), test_data.replace([np.inf, -np.inf], np.nan)\n", "\n", - "train_data['label'] = train_data.groupby('trade_date', group_keys=False)['future_return'].transform(\n", - " lambda x: pd.qcut(x, q=100, labels=False, duplicates='drop')\n", - ")\n", - "test_data['label'] = test_data.groupby('trade_date', group_keys=False)['future_return'].transform(\n", - " lambda x: pd.qcut(x, q=100, labels=False, duplicates='drop')\n", - ")\n", + "# train_data['label'] = train_data.groupby('trade_date', group_keys=False)['future_return'].transform(\n", + "# lambda x: pd.qcut(x, q=100, labels=False, duplicates='drop')\n", + "# )\n", + "# test_data['label'] = test_data.groupby('trade_date', group_keys=False)['future_return'].transform(\n", + "# lambda x: pd.qcut(x, q=100, labels=False, duplicates='drop')\n", + "# )\n", "\n", "# feature_columns_new = feature_columns[:]\n", "# train_data, _ = create_deviation_within_dates(train_data, [col for col in feature_columns if col in train_data.columns])\n", @@ -1526,12 +1687,12 @@ "cs_mad_filter(train_data, transform_feature_columns)\n", "# print('中性化')\n", "# cs_neutralize_industry_cap(train_data, transform_feature_columns)\n", - "# print('标准化')\n", - "# cs_zscore_standardize(train_data, transform_feature_columns)\n", + "print('标准化')\n", + "cs_zscore_standardize(train_data, transform_feature_columns)\n", "\n", "cs_mad_filter(test_data, transform_feature_columns)\n", "# cs_neutralize_industry_cap(test_data, transform_feature_columns)\n", - "# cs_zscore_standardize(test_data, transform_feature_columns)\n", + "cs_zscore_standardize(test_data, transform_feature_columns)\n", "\n", "mad_filter_feature_columns = [col for col in feature_columns if col not in transform_feature_columns and not col.startswith('cat') and col in train_data.columns]\n", "cs_mad_filter(train_data, mad_filter_feature_columns)\n", @@ -1560,7 +1721,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 17, "id": "3ff2d1c5", "metadata": {}, "outputs": [], @@ -1673,15 +1834,15 @@ " 'label_gain': [gain * gain for gain in label_gain],\n", " 'objective': 'lambdarank',\n", " 'metric': 'ndcg',\n", - " 'learning_rate': 0.05,\n", - " 'num_leaves': 1024,\n", - " 'min_data_in_leaf': 256,\n", + " 'learning_rate': 0.01,\n", + " # 'num_leaves': 1024,\n", + " # 'min_data_in_leaf': 256,\n", " # 'max_depth': 10,\n", - " 'max_bin': 1024,\n", + " # 'max_bin': 1024,\n", " 'feature_fraction': 0.5,\n", " 'bagging_fraction': 0.5,\n", " 'bagging_freq': 5,\n", - " 'lambda_l1': 5,\n", + " # 'lambda_l1': 1,\n", " 'lambda_l2': 50,\n", " 'boosting': 'gbdt',\n", " 'verbosity': -1,\n", @@ -1690,11 +1851,11 @@ " 'ndcg_at': '5',\n", " 'quant_train_renew_leaf': True,\n", " 'lambdarank_truncation_level': 10,\n", - " 'lambdarank_position_bias_regularization': 1,\n", + " # 'lambdarank_position_bias_regularization': 1,\n", " 'seed': 7\n", " }\n", - " feature_contri = [2 if feat.startswith('act_factor') or 'buy' in feat or 'sell' in feat else 1 for feat in feature_columns]\n", - " params['feature_contri'] = feature_contri\n", + " # feature_contri = [2 if feat.startswith('act_factor') or 'buy' in feat or 'sell' in feat else 1 for feat in feature_columns]\n", + " # params['feature_contri'] = feature_contri\n", "\n", " train_groups = train_data_split.groupby('trade_date').size().tolist()\n", " val_groups = val_data_split.groupby('trade_date').size().tolist()\n", @@ -1714,7 +1875,7 @@ " evals = {}\n", " callbacks = [lgb.log_evaluation(period=1000),\n", " lgb.callback.record_evaluation(evals),\n", - " # lgb.early_stopping(300, first_metric_only=False)\n", + " lgb.early_stopping(300, first_metric_only=False)\n", " ]\n", " # 训练模型\n", " model = lgb.train(\n", @@ -1759,7 +1920,7 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 18, "id": "c6eb5cd4-e714-420a-ac48-39af3e11ee81", "metadata": { "ExecuteTime": { @@ -1772,28 +1933,30 @@ "name": "stdout", "output_type": "stream", "text": [ - "train data size: 1091062\n", - " ts_code trade_date log_circ_mv\n", - "0 600306.SH 2020-01-02 11.552040\n", - "1 603269.SH 2020-01-02 11.324801\n", - "2 002633.SZ 2020-01-02 11.759023\n", - "3 603991.SH 2020-01-02 11.181150\n", - "4 000691.SZ 2020-01-02 11.677910\n", - "... ... ... ...\n", - "1091057 603533.SH 2022-12-30 13.362893\n", - "1091058 603416.SH 2022-12-30 13.364553\n", - "1091059 002277.SZ 2022-12-30 13.364740\n", - "1091060 002140.SZ 2022-12-30 13.086924\n", - "1091061 002374.SZ 2022-12-30 13.347147\n", + "train data size: 728000\n", + " ts_code trade_date log_circ_mv\n", + "0 600306.SH 2020-01-02 -1.625251\n", + "1 603269.SH 2020-01-02 -2.029142\n", + "2 002633.SZ 2020-01-02 -1.257360\n", + "3 603991.SH 2020-01-02 -2.284466\n", + "4 000691.SZ 2020-01-02 -1.401531\n", + "... ... ... ...\n", + "727995 002578.SZ 2022-12-30 0.648406\n", + "727996 002766.SZ 2022-12-30 0.571533\n", + "727997 600495.SH 2022-12-30 0.802632\n", + "727998 002119.SZ 2022-12-30 0.751421\n", + "727999 000151.SZ 2022-12-30 0.371616\n", "\n", - "[1091062 rows x 3 columns]\n", - "原始样本数: 1091062, 去除标签为空后样本数: 1091062\n", - "[1000]\ttrain's ndcg@5: 0.667175\tvalid's ndcg@5: 0.357008\n" + "[728000 rows x 3 columns]\n", + "原始样本数: 728000, 去除标签为空后样本数: 728000\n", + "Training until validation scores don't improve for 300 rounds\n", + "Early stopping, best iteration is:\n", + "[22]\ttrain's ndcg@5: 0.428245\tvalid's ndcg@5: 0.432426\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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", 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" ] @@ -1818,20 +1981,28 @@ "\n", "use_pca = False\n", "type = 'light'\n", + "\n", + "train_data['label2'] = train_data.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(1000, 'total_mv'))['future_return'].transform(\n", + " lambda x: pd.qcut(x, q=50, labels=False, duplicates='drop')\n", + ")\n", + "test_data['label2'] = test_data.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(1000, 'total_mv'))['future_return'].transform(\n", + " lambda x: pd.qcut(x, q=50, labels=False, duplicates='drop')\n", + ")\n", + "\n", "# feature_contri = [2 if feat.startswith('act_factor') or 'buy' in feat or 'sell' in feat else 1 for feat in feature_columns]\n", "# light_params['feature_contri'] = feature_contri\n", "# print(f'feature_contri: {feature_contri}')\n", "model, scaler, pca = train_model(train_data\n", " .dropna(subset=['label']).groupby('trade_date', group_keys=False)\n", - " .apply(lambda x: x.nsmallest(3000, 'total_mv'))\n", + " .apply(lambda x: x.nsmallest(1000, 'total_mv'))\n", " .merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", " .merge(index_data, on='trade_date', how='left'), \n", - " feature_columns, type=type, target_column='label')\n" + " feature_columns, type=type, target_column='label2')\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "5d1522a7538db91b", "metadata": { "ExecuteTime": { @@ -1841,7 +2012,7 @@ }, "outputs": [], "source": [ - "score_df = test_data.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(3000, 'total_mv'))\n", + "score_df = test_data.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(1000, 'total_mv'))\n", "# score_df = fill_nan_with_daily_median(score_df, ['pe_ttm'])\n", "# score_df = score_df[score_df['pe_ttm'] > 0]\n", "score_df = score_df.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", @@ -1873,7 +2044,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, + "id": "fed2d6c3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-01-03 00:00:00\n" + ] + } + ], + "source": [ + "print(test_data['trade_date'].min())" + ] + }, + { + "cell_type": "code", + "execution_count": 21, "id": "1f3c1331", "metadata": {}, "outputs": [ @@ -1888,72 +2077,85 @@ "b'\\x80\\x04\\x95\\xbf\\x04\\x01\\x00\\x00\\x00\\x00\\x00\\x8c\\x11pandas.'\n", "\n", "从 Redis 加载的 DataFrame (使用 Pickle):\n", - " index ts_code trade_date open close high low vol \\\n", - "4 25 002802.SZ 2023-01-03 21.53 22.14 22.35 21.31 15974.43 \n", - "3 20 600235.SH 2023-01-03 12.23 12.41 12.50 12.23 35791.00 \n", - "2 13 002691.SZ 2023-01-03 9.13 9.32 9.37 9.13 35566.80 \n", - "1 35 603779.SH 2023-01-03 9.74 9.88 10.16 9.69 78641.45 \n", - "0 131 600228.SH 2023-01-03 15.68 15.75 15.79 15.60 17076.00 \n", - "... ... ... ... ... ... ... ... ... \n", - "2904 87098 000931.SZ 2025-05-30 8.02 7.96 8.13 7.92 170963.38 \n", - "2903 87001 605122.SH 2025-05-30 16.86 16.73 17.11 16.68 20146.40 \n", - "2902 87041 002942.SZ 2025-05-30 24.10 23.95 24.34 23.84 10821.00 \n", - "2901 87122 603170.SH 2025-05-30 14.69 14.34 14.83 14.32 26536.80 \n", - "2900 86987 603177.SH 2025-05-30 8.72 8.55 8.77 8.49 25875.00 \n", + " index ts_code trade_date open close high low vol \\\n", + "4 36 002247.SZ 2023-01-03 16.15 16.80 16.87 16.09 0.514578 \n", + "3 26 002513.SZ 2023-01-03 15.41 15.77 15.95 15.41 -0.499029 \n", + "2 14 002629.SZ 2023-01-03 15.17 15.54 15.58 15.13 0.631716 \n", + "1 5 603030.SH 2023-01-03 8.08 8.32 8.32 8.05 -0.033641 \n", + "0 3 000691.SZ 2023-01-03 13.87 14.24 14.36 13.63 -0.030740 \n", + "... ... ... ... ... ... ... ... ... \n", + "2904 58031 002524.SZ 2025-05-30 19.49 19.40 20.27 19.31 0.455783 \n", + "2903 58034 002084.SZ 2025-05-30 11.88 11.68 11.88 11.58 0.610122 \n", + "2902 58029 600159.SH 2025-05-30 13.66 13.55 13.86 13.50 -0.170606 \n", + "2901 58035 002775.SZ 2025-05-30 17.84 17.55 17.84 17.41 -0.705858 \n", + "2900 58019 600408.SH 2025-05-30 8.64 8.51 8.64 8.43 0.684604 \n", "\n", - " pct_chg amount ... 000905.SH_up_ratio_20d 399006.SZ_up_ratio_20d \\\n", - "4 3.12 23729.134 ... 0.3 0.40 \n", - "3 0.73 20011.747 ... 0.3 0.40 \n", - "2 2.08 19174.296 ... 0.3 0.40 \n", - "1 1.65 53043.254 ... 0.3 0.40 \n", - "0 1.42 12366.664 ... 0.3 0.40 \n", - "... ... ... ... ... ... \n", - "2904 -0.38 89186.130 ... 0.6 0.45 \n", - "2903 -1.18 23723.010 ... 0.6 0.45 \n", - "2902 -1.07 18204.020 ... 0.6 0.45 \n", - "2901 -2.45 36665.356 ... 0.6 0.45 \n", - "2900 -1.38 21916.553 ... 0.6 0.45 \n", + " pct_chg amount ... 000905.SH_up_ratio_20d \\\n", + "4 0.715898 31834.487 ... 0.3 \n", + "3 0.121763 25452.447 ... 0.3 \n", + "2 0.299045 55379.071 ... 0.3 \n", + "1 0.241548 22271.706 ... 0.3 \n", + "0 0.279880 38602.205 ... 0.3 \n", + "... ... ... ... ... \n", + "2904 0.552905 76113.818 ... 0.6 \n", + "2903 -0.070682 67595.352 ... 0.6 \n", + "2902 0.005989 28573.069 ... 0.6 \n", + "2901 0.241111 19025.635 ... 0.6 \n", + "2900 0.788027 40827.689 ... 0.6 \n", "\n", - " 000852.SH_volatility 000905.SH_volatility 399006.SZ_volatility \\\n", - "4 1.036997 0.828596 0.935322 \n", - "3 1.036997 0.828596 0.935322 \n", - "2 1.036997 0.828596 0.935322 \n", - "1 1.036997 0.828596 0.935322 \n", - "0 1.036997 0.828596 0.935322 \n", - "... ... ... ... \n", - "2904 1.089861 0.850444 1.195355 \n", - "2903 1.089861 0.850444 1.195355 \n", - "2902 1.089861 0.850444 1.195355 \n", - "2901 1.089861 0.850444 1.195355 \n", - "2900 1.089861 0.850444 1.195355 \n", + " 399006.SZ_up_ratio_20d 000852.SH_volatility 000905.SH_volatility \\\n", + "4 0.40 1.036997 0.828596 \n", + "3 0.40 1.036997 0.828596 \n", + "2 0.40 1.036997 0.828596 \n", + "1 0.40 1.036997 0.828596 \n", + "0 0.40 1.036997 0.828596 \n", + "... ... ... ... \n", + "2904 0.45 1.089861 0.850444 \n", + "2903 0.45 1.089861 0.850444 \n", + "2902 0.45 1.089861 0.850444 \n", + "2901 0.45 1.089861 0.850444 \n", + "2900 0.45 1.089861 0.850444 \n", "\n", - " 000852.SH_volume_change_rate 000905.SH_volume_change_rate \\\n", - "4 5.203088 -0.750721 \n", - "3 5.203088 -0.750721 \n", - "2 5.203088 -0.750721 \n", - "1 5.203088 -0.750721 \n", - "0 5.203088 -0.750721 \n", - "... ... ... \n", - "2904 -2.039466 -12.002493 \n", - "2903 -2.039466 -12.002493 \n", - "2902 -2.039466 -12.002493 \n", - "2901 -2.039466 -12.002493 \n", - "2900 -2.039466 -12.002493 \n", + " 399006.SZ_volatility 000852.SH_volume_change_rate \\\n", + "4 0.935322 5.203088 \n", + "3 0.935322 5.203088 \n", + "2 0.935322 5.203088 \n", + "1 0.935322 5.203088 \n", + "0 0.935322 5.203088 \n", + "... ... ... \n", + "2904 1.195355 -2.039466 \n", + "2903 1.195355 -2.039466 \n", + "2902 1.195355 -2.039466 \n", + "2901 1.195355 -2.039466 \n", + "2900 1.195355 -2.039466 \n", "\n", - " 399006.SZ_volume_change_rate score score_ranks \n", - "4 8.827360 0.391351 1496.0 \n", - "3 8.827360 0.410662 1497.0 \n", - "2 8.827360 0.470817 1498.0 \n", - "1 8.827360 0.567032 1499.0 \n", - "0 8.827360 0.596280 1500.0 \n", - "... ... ... ... \n", - "2904 5.078672 0.707379 1490.0 \n", - "2903 5.078672 0.769014 1491.0 \n", - "2902 5.078672 0.874366 1492.0 \n", - "2901 5.078672 0.975826 1493.0 \n", - "2900 5.078672 1.072646 1494.0 \n", + " 000905.SH_volume_change_rate 399006.SZ_volume_change_rate score \\\n", + "4 -0.750721 8.827360 0.042645 \n", + "3 -0.750721 8.827360 0.047474 \n", + "2 -0.750721 8.827360 0.055433 \n", + "1 -0.750721 8.827360 0.061053 \n", + "0 -0.750721 8.827360 0.068349 \n", + "... ... ... ... \n", + "2904 -12.002493 5.078672 0.064251 \n", + "2903 -12.002493 5.078672 0.064517 \n", + "2902 -12.002493 5.078672 0.065030 \n", + "2901 -12.002493 5.078672 0.068848 \n", + "2900 -12.002493 5.078672 0.074780 \n", "\n", - "[2905 rows x 241 columns]\n", + " score_ranks \n", + "4 996.0 \n", + "3 997.0 \n", + "2 998.0 \n", + "1 999.0 \n", + "0 1000.0 \n", + "... ... \n", + "2904 996.0 \n", + "2903 997.0 \n", + "2902 998.0 \n", + "2901 999.0 \n", + "2900 1000.0 \n", + "\n", + "[2905 rows x 248 columns]\n", "\n", "验证成功：原始 DataFrame 和从 Redis 加载的 DataFrame 一致。\n", "\n", @@ -2019,7 +2221,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "09b1799e", "metadata": {}, "outputs": [ @@ -2027,8 +2229,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "200\n", - "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'cat_senti_mom_vol_spike', 'cat_senti_pre_breakout', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'senti_strong_inflow', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n" + "205\n", + "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'holder_net_change_sum_10d', 'holder_increase_days_10d', 'holder_decrease_days_10d', 'holder_any_increase_flag_10d', 'holder_any_decrease_flag_10d', 'cat_senti_mom_vol_spike', 'cat_senti_pre_breakout', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'senti_strong_inflow', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n" ] } ], @@ -2039,7 +2241,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "bceabd1f", "metadata": {}, "outputs": [ @@ -2047,22 +2249,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "警告: DataFrame 中没有 'group_id' 列。假设整个 DataFrame 是一个需要排序的组。\n" - ] - }, - { - "ename": "AttributeError", - "evalue": "`np.asfarray` was removed in the NumPy 2.0 release. Use `np.asarray` with a proper dtype instead.", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[105]\u001b[39m\u001b[32m, line 52\u001b[39m\n\u001b[32m 48\u001b[39m avg_ndcg = {k: np.mean(v) \u001b[38;5;28;01mif\u001b[39;00m v \u001b[38;5;28;01melse\u001b[39;00m np.nan \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m ndcg_scores.items()}\n\u001b[32m 49\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m avg_ndcg\n\u001b[32m---> \u001b[39m\u001b[32m52\u001b[39m ndcg_results_single_group = \u001b[43mcalculate_ndcg\u001b[49m\u001b[43m(\u001b[49m\u001b[43mscore_df\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscore_col\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mscore\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabel_col\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mlabel\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mk_values\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m5\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroup_id\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[32m 53\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33mNDCG 结果\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 54\u001b[39m \u001b[38;5;28mprint\u001b[39m(ndcg_results_single_group)\n", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[105]\u001b[39m\u001b[32m, line 40\u001b[39m, in \u001b[36mcalculate_ndcg\u001b[39m\u001b[34m(df, score_col, label_col, group_id, k_values)\u001b[39m\n\u001b[32m 38\u001b[39m relevant_labels = group_df[label_col].values\n\u001b[32m 39\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m k_values:\n\u001b[32m---> \u001b[39m\u001b[32m40\u001b[39m ndcg_scores[\u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[33mndcg@\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mk\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m].append(\u001b[43mndcg_at_k\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrelevant_labels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[32m 41\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 42\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m _, group_df \u001b[38;5;129;01min\u001b[39;00m df.groupby(group_id):\n", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[105]\u001b[39m\u001b[32m, line 27\u001b[39m, in \u001b[36mcalculate_ndcg..ndcg_at_k\u001b[39m\u001b[34m(r, k)\u001b[39m\n\u001b[32m 26\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mndcg_at_k\u001b[39m(r, k):\n\u001b[32m---> \u001b[39m\u001b[32m27\u001b[39m dcg_max = \u001b[43mdcg_at_k\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43msorted\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreverse\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 28\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m dcg_max:\n\u001b[32m 29\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[32m0.\u001b[39m\n", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[105]\u001b[39m\u001b[32m, line 23\u001b[39m, in \u001b[36mcalculate_ndcg..dcg_at_k\u001b[39m\u001b[34m(r, k)\u001b[39m\n\u001b[32m 22\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdcg_at_k\u001b[39m(r, k):\n\u001b[32m---> \u001b[39m\u001b[32m23\u001b[39m r = \u001b[43mnp\u001b[49m\u001b[43m.\u001b[49m\u001b[43masfarray\u001b[49m(r)[:k] \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(r) > \u001b[32m0\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m np.zeros(k)\n\u001b[32m 24\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m np.sum(r / np.log2(np.arange(\u001b[32m2\u001b[39m, r.size + \u001b[32m2\u001b[39m)))\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/stock/lib/python3.13/site-packages/numpy/__init__.py:400\u001b[39m, in \u001b[36m__getattr__\u001b[39m\u001b[34m(attr)\u001b[39m\n\u001b[32m 397\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(__former_attrs__[attr], name=\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[32m 399\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m attr \u001b[38;5;129;01min\u001b[39;00m __expired_attributes__:\n\u001b[32m--> \u001b[39m\u001b[32m400\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[32m 401\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m`np.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mattr\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m` was removed in the NumPy 2.0 release. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 402\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m__expired_attributes__[attr]\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m,\n\u001b[32m 403\u001b[39m name=\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 404\u001b[39m )\n\u001b[32m 406\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m attr == \u001b[33m\"\u001b[39m\u001b[33mchararray\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m 407\u001b[39m warnings.warn(\n\u001b[32m 408\u001b[39m \u001b[33m\"\u001b[39m\u001b[33m`np.chararray` is deprecated and will be removed from \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 409\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mthe main namespace in the future. Use an array with a string \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 410\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mor bytes dtype instead.\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;167;01mDeprecationWarning\u001b[39;00m, stacklevel=\u001b[32m2\u001b[39m)\n", - "\u001b[31mAttributeError\u001b[39m: `np.asfarray` was removed in the NumPy 2.0 release. Use `np.asarray` with a proper dtype instead." + "警告: DataFrame 中没有 'group_id' 列。假设整个 DataFrame 是一个需要排序的组。\n", + "\n", + "NDCG 结果\n", + "{'ndcg@1': np.float64(0.4489795918367347), 'ndcg@3': np.float64(0.40668217598446815), 'ndcg@5': np.float64(0.45584495629735)}\n" ] } ], @@ -2089,7 +2279,7 @@ " ndcg_scores = {f'ndcg@{k}': [] for k in k_values}\n", "\n", " def dcg_at_k(r, k):\n", - " r = np.asfarray(r)[:k] if len(r) > 0 else np.zeros(k)\n", + " r = np.asarray(r)[:k] if len(r) > 0 else np.zeros(k)\n", " return np.sum(r / np.log2(np.arange(2, r.size + 2)))\n", "\n", " def ndcg_at_k(r, k):\n", @@ -2125,7 +2315,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "44f64679", "metadata": {}, "outputs": [ @@ -2134,84 +2324,84 @@ "output_type": "stream", "text": [ " ts_code trade_date open close high low vol pct_chg \\\n", - "1626778 002652.SZ 2019-01-02 19.59 19.64 19.89 19.28 20196.79 1.03 \n", - "1626779 002652.SZ 2019-01-03 19.74 19.44 19.84 19.33 15731.99 -1.02 \n", - "1626780 002652.SZ 2019-01-04 19.33 19.94 19.99 19.08 21099.93 2.57 \n", - "1626781 002652.SZ 2019-01-07 20.04 21.95 21.95 20.04 83534.19 10.08 \n", - "1626782 002652.SZ 2019-01-08 23.21 21.65 23.87 21.65 149377.97 -1.37 \n", + "1632028 002652.SZ 2019-01-02 19.59 19.64 19.89 19.28 20196.79 1.03 \n", + "1632029 002652.SZ 2019-01-03 19.74 19.44 19.84 19.33 15731.99 -1.02 \n", + "1632030 002652.SZ 2019-01-04 19.33 19.94 19.99 19.08 21099.93 2.57 \n", + "1632031 002652.SZ 2019-01-07 20.04 21.95 21.95 20.04 83534.19 10.08 \n", + "1632032 002652.SZ 2019-01-08 23.21 21.65 23.87 21.65 149377.97 -1.37 \n", "... ... ... ... ... ... ... ... ... \n", - "1628321 002652.SZ 2025-05-19 15.05 15.05 15.21 14.80 142648.00 1.69 \n", - "1628322 002652.SZ 2025-05-20 15.11 15.31 15.36 14.85 131075.23 1.73 \n", - "1628323 002652.SZ 2025-05-21 15.51 15.26 15.56 15.11 147109.00 -0.33 \n", - "1628324 002652.SZ 2025-05-22 15.11 14.95 15.46 14.75 129187.20 -2.03 \n", - "1628325 002652.SZ 2025-05-23 14.95 14.70 15.11 14.70 139145.40 -1.67 \n", + "1633576 002652.SZ 2025-05-26 14.75 14.85 15.11 14.55 99560.80 1.02 \n", + "1633577 002652.SZ 2025-05-27 14.90 15.00 15.11 14.70 101184.00 1.01 \n", + "1633578 002652.SZ 2025-05-28 15.11 14.85 15.16 14.80 75859.20 -1.00 \n", + "1633579 002652.SZ 2025-05-29 15.00 15.36 15.36 14.85 126044.40 3.43 \n", + "1633580 002652.SZ 2025-05-30 15.36 15.11 15.41 14.95 107732.00 -1.63 \n", "\n", " amount turnover_rate ... cs_rank_vol_x_profit_margin \\\n", - "1626778 7867.047 0.3964 ... 0.608839 \n", - "1626779 6121.460 0.3088 ... 0.586710 \n", - "1626780 8245.083 0.4141 ... 0.682847 \n", - "1626781 35514.117 1.6394 ... 0.987591 \n", - "1626782 67160.354 2.9317 ... 0.765693 \n", + "1632028 7867.047 0.3964 ... 0.608839 \n", + "1632029 6121.460 0.3088 ... 0.586710 \n", + "1632030 8245.083 0.4141 ... 0.682847 \n", + "1632031 35514.117 1.6394 ... 0.987591 \n", + "1632032 67160.354 2.9317 ... 0.765693 \n", "... ... ... ... ... \n", - "1628321 42651.655 2.7857 ... 0.758644 \n", - "1628322 39438.290 2.5597 ... 0.834661 \n", - "1628323 44703.816 2.8729 ... 0.365327 \n", - "1628324 38679.608 2.5229 ... 0.810362 \n", - "1628325 41151.946 2.7173 ... 0.738293 \n", + "1633576 29428.560 1.9443 ... 0.652159 \n", + "1633577 30112.801 1.9760 ... 0.657694 \n", + "1633578 22507.876 1.4814 ... 0.664673 \n", + "1633579 38068.857 2.4615 ... 0.921236 \n", + "1633580 32385.927 2.1039 ... 0.702990 \n", "\n", " cs_rank_lg_flow_price_concordance cs_rank_turnover_per_winner \\\n", - "1626778 0.203142 0.864865 \n", - "1626779 0.156684 0.763417 \n", - "1626780 0.184009 0.660949 \n", - "1626781 0.734940 0.700000 \n", - "1626782 0.874042 0.914234 \n", + "1632028 0.203142 0.864865 \n", + "1632029 0.156684 0.763417 \n", + "1632030 0.184009 0.660949 \n", + "1632031 0.734940 0.700000 \n", + "1632032 0.874042 0.914234 \n", "... ... ... \n", - "1628321 0.106051 0.544548 \n", - "1628322 0.202523 0.478420 \n", - "1628323 0.580870 0.520757 \n", - "1628324 0.808369 0.476918 \n", - "1628325 0.617735 0.404517 \n", + "1633576 0.122259 0.394684 \n", + "1633577 0.092722 0.414756 \n", + "1633578 0.684945 0.323363 \n", + "1633579 0.295779 0.390828 \n", + "1633580 0.705316 0.419934 \n", "\n", " cs_rank_ind_cap_neutral_pe cs_rank_volume_ratio \\\n", - "1626778 NaN 0.646930 \n", - "1626779 NaN 0.251279 \n", - "1626780 NaN 0.311724 \n", - "1626781 NaN 0.988313 \n", - "1626782 NaN 0.990142 \n", + "1632028 NaN 0.646930 \n", + "1632029 NaN 0.251279 \n", + "1632030 NaN 0.311724 \n", + "1632031 NaN 0.988313 \n", + "1632032 NaN 0.990142 \n", "... ... ... \n", - "1628321 NaN 0.695645 \n", - "1628322 NaN 0.542497 \n", - "1628323 NaN 0.678180 \n", - "1628324 NaN 0.524743 \n", - "1628325 NaN 0.585852 \n", + "1633576 NaN 0.400997 \n", + "1633577 NaN 0.450150 \n", + "1633578 NaN 0.199236 \n", + "1633579 NaN 0.640744 \n", + "1633580 NaN 0.537542 \n", "\n", " cs_rank_elg_buy_sell_sm_ratio cs_rank_cost_dist_vol_ratio \\\n", - "1626778 0.341855 0.678941 \n", - "1626779 0.318912 0.402916 \n", - "1626780 0.260036 0.460713 \n", - "1626781 0.796350 0.988501 \n", - "1626782 0.598905 0.991571 \n", + "1632028 0.341855 0.678941 \n", + "1632029 0.318912 0.402916 \n", + "1632030 0.260036 0.460713 \n", + "1632031 0.796350 0.988501 \n", + "1632032 0.598905 0.991571 \n", "... ... ... \n", - "1628321 0.287899 0.788896 \n", - "1628322 0.116534 0.705843 \n", - "1628323 0.492860 0.783793 \n", - "1628324 0.130521 0.696446 \n", - "1628325 0.134175 0.735636 \n", + "1633576 0.153987 0.620930 \n", + "1633577 0.156198 0.643403 \n", + "1633578 0.153373 0.484546 \n", + "1633579 0.623795 0.764374 \n", + "1633580 0.133056 0.703987 \n", "\n", " cs_rank_size future_return label \n", - "1626778 0.258948 0.092159 45.0 \n", - "1626779 0.258123 0.075103 41.0 \n", - "1626780 0.257664 0.058175 41.0 \n", - "1626781 0.290146 -0.034169 4.0 \n", - "1626782 0.282482 -0.023095 4.0 \n", + "1632028 0.258948 0.158859 40.0 \n", + "1632029 0.258123 0.136831 37.0 \n", + "1632030 0.257664 0.106319 39.0 \n", + "1632031 0.290146 -0.072893 4.0 \n", + "1632032 0.282482 -0.057737 5.0 \n", "... ... ... ... \n", - "1628321 0.032912 NaN NaN \n", - "1628322 0.034861 NaN NaN \n", - "1628323 0.035204 NaN NaN \n", - "1628324 0.034208 NaN NaN \n", - "1628325 0.032547 NaN NaN \n", + "1633576 0.032226 NaN NaN \n", + "1633577 0.032901 NaN NaN \n", + "1633578 0.032237 NaN NaN \n", + "1633579 0.034231 NaN NaN \n", + "1633580 0.033887 NaN NaN \n", "\n", - "[1548 rows x 190 columns]\n" + "[1553 rows x 196 columns]\n" ] } ], diff --git a/predictions_test.tsv b/predictions_test.tsv index 32ae634..cdc0bd4 100644 --- a/predictions_test.tsv +++ b/predictions_test.tsv @@ -1,1163 +1,2906 @@ trade_date,score,ts_code -2023-01-03,0.4684767428254628,600355.SH -2023-01-03,0.47384737888678313,002199.SZ -2023-01-04,0.479552808311038,600455.SH -2023-01-04,0.6357351396377157,000691.SZ -2023-01-05,0.45820197910998034,600455.SH -2023-01-05,0.47814801785119365,000691.SZ 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