多线程rank6.0，赚钱，回撤略微减小
This commit is contained in:
liaozhaorun
@@ -2,13 +2,15 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "initial_id",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-03-30T16:42:23.864275Z",
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"start_time": "2025-03-30T16:42:22.963221Z"
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"end_time": "2025-04-06T15:33:29.087509Z",
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"start_time": "2025-04-06T15:33:28.293879Z"
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}
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},
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"outputs": [],
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"source": [
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"from operator import index\n",
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"\n",
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@@ -18,19 +20,35 @@
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"\n",
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"ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n",
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"pro = ts.pro_api()"
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],
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"outputs": [],
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"execution_count": 1
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "f448da220816bf98",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-03-30T16:42:25.559047Z",
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"start_time": "2025-03-30T16:42:23.868783Z"
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"end_time": "2025-04-06T15:33:32.756495Z",
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"start_time": "2025-04-06T15:33:29.097180Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"数据已经成功存储到index_data.h5文件中\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_26824\\1832869062.py:13: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
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" final_df = pd.concat(all_data, ignore_index=True)\n"
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]
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}
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],
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"source": [
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"# 定义四个指数\n",
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"index_list = ['399300.SH', '000905.SH', '000852.SH', '399006.SZ']\n",
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@@ -50,78 +68,60 @@
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"final_df.to_hdf('../../data/index_data.h5', key='index_data', mode='w')\n",
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"\n",
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"print(\"数据已经成功存储到index_data.h5文件中\")"
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],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"数据已经成功存储到index_data.h5文件中\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_6192\\3209233630.py:13: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
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" final_df = pd.concat(all_data, ignore_index=True)\n"
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]
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}
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],
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"execution_count": 2
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "907f732d3c397bf",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-03-30T16:42:25.802535Z",
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"start_time": "2025-03-30T16:42:25.766399Z"
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"end_time": "2025-04-06T15:33:32.795003Z",
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"start_time": "2025-04-06T15:33:32.758127Z"
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}
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},
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"source": [
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"h5_filename = '../../data/index_data.h5'\n",
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"key = '/index_data'\n",
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"with pd.HDFStore(h5_filename, mode='r') as store:\n",
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" df = store[key]\n",
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" print(df)\n"
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],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" ts_code trade_date close open high low \\\n",
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"0 000905.SH 20250328 5916.0314 5954.7297 5973.8015 5904.9159 \n",
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"1 000905.SH 20250327 5957.6017 5932.5165 6000.6615 5891.7664 \n",
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"2 000905.SH 20250326 5948.4986 5935.8537 5983.4739 5935.8537 \n",
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"3 000905.SH 20250325 5946.9510 5969.4164 5993.9312 5929.6734 \n",
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"4 000905.SH 20250324 5969.0789 5973.0466 5987.0606 5882.8780 \n",
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"0 000905.SH 20250407 5287.0333 5523.9636 5587.8502 5212.6773 \n",
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"1 000905.SH 20250403 5845.5045 5842.6167 5906.7057 5817.9662 \n",
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"2 000905.SH 20250402 5899.0865 5884.8925 5936.6467 5884.1126 \n",
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"3 000905.SH 20250401 5892.8502 5870.9424 5931.5038 5867.8480 \n",
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"4 000905.SH 20250331 5857.7721 5886.9560 5908.3026 5802.4187 \n",
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"... ... ... ... ... ... ... \n",
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"13423 399006.SZ 20100607 1069.4680 1005.0280 1075.2250 1001.7020 \n",
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"13424 399006.SZ 20100604 1027.6810 989.6810 1027.6810 986.5040 \n",
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"13425 399006.SZ 20100603 998.3940 1002.3550 1026.7020 997.7750 \n",
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"13426 399006.SZ 20100602 997.1190 967.6090 997.1190 952.6110 \n",
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"13427 399006.SZ 20100601 973.2330 986.0150 994.7930 948.1180 \n",
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"13438 399006.SZ 20100607 1069.4680 1005.0280 1075.2250 1001.7020 \n",
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"13439 399006.SZ 20100604 1027.6810 989.6810 1027.6810 986.5040 \n",
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"13440 399006.SZ 20100603 998.3940 1002.3550 1026.7020 997.7750 \n",
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"13441 399006.SZ 20100602 997.1190 967.6090 997.1190 952.6110 \n",
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"13442 399006.SZ 20100601 973.2330 986.0150 994.7930 948.1180 \n",
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"\n",
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" pre_close change pct_chg vol amount \n",
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"0 5957.6017 -41.5703 -0.6978 1.342619e+08 1.688995e+08 \n",
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"1 5948.4986 9.1031 0.1530 1.347089e+08 1.765905e+08 \n",
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"2 5946.9510 1.5476 0.0260 1.367021e+08 1.716958e+08 \n",
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"3 5969.0789 -22.1279 -0.3707 1.474839e+08 1.922270e+08 \n",
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"4 5971.9302 -2.8513 -0.0477 1.691924e+08 2.200943e+08 \n",
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"... ... ... ... ... ... \n",
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"13423 1027.6810 41.7870 4.0661 2.655275e+06 9.106095e+06 \n",
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"13424 998.3940 29.2870 2.9334 1.500295e+06 5.269441e+06 \n",
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"13425 997.1190 1.2750 0.1279 1.616805e+06 6.240835e+06 \n",
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"13426 973.2330 23.8860 2.4543 1.074628e+06 4.001206e+06 \n",
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"13427 1000.0000 -26.7670 -2.6767 1.356285e+06 4.924177e+06 \n",
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" pre_close change pct_chg vol amount \n",
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"0 5845.5045 -558.4712 -9.5539 2.365227e+08 2.673974e+08 \n",
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"1 5899.0865 -53.5820 -0.9083 1.349386e+08 1.736621e+08 \n",
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"2 5892.8502 6.2363 0.1058 1.121600e+08 1.406421e+08 \n",
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"3 5857.7721 35.0781 0.5988 1.364486e+08 1.793280e+08 \n",
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"4 5916.0314 -58.2593 -0.9848 1.542561e+08 1.895634e+08 \n",
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"... ... ... ... ... ... \n",
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"13438 1027.6810 41.7870 4.0661 2.655275e+06 9.106095e+06 \n",
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"13439 998.3940 29.2870 2.9334 1.500295e+06 5.269441e+06 \n",
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"13440 997.1190 1.2750 0.1279 1.616805e+06 6.240835e+06 \n",
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"13441 973.2330 23.8860 2.4543 1.074628e+06 4.001206e+06 \n",
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"13442 1000.0000 -26.7670 -2.6767 1.356285e+06 4.924177e+06 \n",
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"\n",
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"[13428 rows x 11 columns]\n"
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"[13443 rows x 11 columns]\n"
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]
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}
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],
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"execution_count": 3
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"source": [
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"h5_filename = '../../data/index_data.h5'\n",
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"key = '/index_data'\n",
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"with pd.HDFStore(h5_filename, mode='r') as store:\n",
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" df = store[key]\n",
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" print(df)\n"
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]
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}
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],
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"metadata": {
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