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liaozhaorun
2025-02-15 23:33:34 +08:00
parent 18d0876d86
commit 49fa31ac63
13 changed files with 2028 additions and 2580 deletions
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343
code/data/update/update_daily_basic.ipynb
View File

@@ -2,6 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "18d1d622-b083-4cc4-a6f8-7c1ed2d0edd2",
"metadata": {
"ExecuteTime": {
@@ -9,22 +10,24 @@
"start_time": "2025-02-11T15:43:53.837662Z"
}
},
"outputs": [],
"source": [
"import tushare as ts\n",
"ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n",
"pro = ts.pro_api()"
],
"outputs": [],
"execution_count": 1
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "14671a7f72de2564",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-11T15:53:08.235573Z",
"start_time": "2025-02-11T15:53:07.753701Z"
}
},
"cell_type": "code",
"outputs": [],
"source": [
"from datetime import datetime\n",
"import pandas as pd\n",
@@ -56,19 +59,38 @@
" st_data = group[(group['change_reason'] == 'ST') | (group['change_reason'] == '*ST')]\n",
" if not st_data.empty:\n",
" name_change_dict[ts_code] = st_data"
],
"id": "14671a7f72de2564",
"outputs": [],
"execution_count": 31
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "e7f8cce2f80e2f20",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-11T15:53:19.812860Z",
"start_time": "2025-02-11T15:53:09.614377Z"
}
},
"cell_type": "code",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 8291970 entries, 0 to 8291969\n",
"Data columns (total 2 columns):\n",
" # Column Dtype \n",
"--- ------ ----- \n",
" 0 ts_code object\n",
" 1 trade_date object\n",
"dtypes: object(2)\n",
"memory usage: 189.8+ MB\n",
"None\n",
"20250211\n",
"20250212\n"
]
}
],
"source": [
"import time\n",
"from concurrent.futures import ThreadPoolExecutor, as_completed\n",
@@ -87,40 +109,34 @@
"trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n",
"start_date = min(trade_dates)\n",
"print(start_date)"
],
"id": "e7f8cce2f80e2f20",
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "553cfb36-f560-4cc4-b2bc-68323ccc5072",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-11T15:53:24.100612Z",
"start_time": "2025-02-11T15:53:22.361257Z"
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 8295494 entries, 0 to 8295493\n",
"Data columns (total 2 columns):\n",
" # Column Dtype \n",
"--- ------ ----- \n",
" 0 ts_code object\n",
" 1 trade_date object\n",
"dtypes: object(2)\n",
"memory usage: 189.9+ MB\n",
"None\n",
"20250210\n",
"20250211\n"
"任务 20250220 完成\n",
"任务 20250219 完成\n",
"任务 20250217 完成\n",
"任务 20250218 完成\n",
"任务 20250214 完成\n",
"任务 20250213 完成\n",
"任务 20250212 完成\n"
]
}
],
"execution_count": 32
},
{
"cell_type": "code",
"id": "553cfb36-f560-4cc4-b2bc-68323ccc5072",
"metadata": {
"scrolled": true,
"ExecuteTime": {
"end_time": "2025-02-11T15:53:24.100612Z",
"start_time": "2025-02-11T15:53:22.361257Z"
}
},
"source": [
"\n",
"\n",
@@ -170,189 +186,169 @@
" # 重置批次起始时间\n",
" batch_start_time = time.time()\n",
"\n"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"任务 20250220 完成\n",
"任务 20250219 完成\n",
"任务 20250218 完成\n",
"任务 20250217 完成\n",
"任务 20250214 完成\n",
"任务 20250213 完成\n",
"任务 20250212 完成\n",
"任务 20250211 完成\n"
]
}
],
"execution_count": 33
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "919023c693d7a47a",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-11T15:53:25.913933Z",
"start_time": "2025-02-11T15:53:25.902629Z"
}
},
"cell_type": "code",
"source": [
"all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n",
"print(all_daily_data_df)"
],
"id": "919023c693d7a47a",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" ts_code trade_date close turnover_rate turnover_rate_f \\\n",
"0 002512.SZ 20250211 5.03 5.9759 7.8713 \n",
"1 600966.SH 20250211 4.83 0.6904 1.3494 \n",
"2 600358.SH 20250211 3.68 8.5826 11.3780 \n",
"3 002893.SZ 20250211 9.73 1.9217 2.6415 \n",
"4 300648.SZ 20250211 22.90 1.7775 2.3188 \n",
"0 601162.SH 20250212 4.77 7.3760 9.7054 \n",
"1 603216.SH 20250212 11.42 8.8711 8.8711 \n",
"2 872808.BJ 20250212 74.36 4.1219 15.3296 \n",
"3 601881.SH 20250212 14.43 0.5617 1.9533 \n",
"4 002837.SZ 20250212 42.25 3.8199 5.7136 \n",
"... ... ... ... ... ... \n",
"5380 300886.SZ 20250211 21.80 8.9341 13.4176 \n",
"5381 600050.SH 20250211 5.48 2.3899 5.6722 \n",
"5382 300149.SZ 20250211 6.73 3.5271 5.3077 \n",
"5383 002197.SZ 20250211 4.42 4.0058 4.6595 \n",
"5384 688270.SH 20250211 37.34 2.9212 2.9212 \n",
"5380 603931.SH 20250212 23.83 1.4692 4.6843 \n",
"5381 688567.SH 20250212 12.35 1.3091 2.1970 \n",
"5382 688530.SH 20250212 19.30 6.6093 6.6093 \n",
"5383 301363.SZ 20250212 31.99 2.1990 2.1990 \n",
"5384 833533.BJ 20250212 46.02 27.7269 27.7597 \n",
"\n",
" volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n",
"0 0.87 NaN NaN 12.8888 2.9340 3.0625 0.0000 \n",
"1 1.16 35.5101 15.2315 0.9534 0.3454 0.3402 0.5633 \n",
"2 1.38 NaN NaN 15.2661 3.4220 4.2041 0.0000 \n",
"3 0.85 48.9883 41.5405 2.2074 2.3641 2.3637 0.8222 \n",
"4 0.69 NaN NaN 4.1442 3.7325 3.3186 0.0000 \n",
"0 2.00 134.5633 NaN 1.7935 12.0634 19.0461 0.0000 \n",
"1 2.09 26.5657 27.5224 1.4454 1.9304 1.9996 2.6270 \n",
"2 1.20 142.3485 196.0315 22.9124 22.8711 25.8281 NaN \n",
"3 0.84 20.0264 15.5707 1.4245 4.6898 4.4609 2.1067 \n",
"4 0.65 91.3544 64.5935 11.2259 8.9056 7.2600 0.3621 \n",
"... ... ... ... ... ... ... ... \n",
"5380 3.00 NaN 111.0678 2.9043 6.0326 4.9204 0.0000 \n",
"5381 1.15 21.3231 19.5079 1.0668 0.4677 0.4574 2.6625 \n",
"5382 1.34 NaN NaN 2.5009 2.9440 3.3158 0.0000 \n",
"5383 1.41 NaN NaN 1.1195 2.0851 2.5837 0.0000 \n",
"5384 0.75 110.2738 170.0477 3.7594 28.4642 27.3030 NaN \n",
"5380 1.16 27.1631 29.0662 3.0982 6.8392 6.9124 1.1120 \n",
"5381 1.01 NaN NaN 1.4955 0.9183 1.0469 NaN \n",
"5382 0.99 62.5995 198.4906 3.6879 6.4857 7.9319 NaN \n",
"5383 0.98 41.5226 47.9900 3.8396 9.7258 8.9664 0.4982 \n",
"5384 0.84 52.3997 62.1858 13.3582 6.6261 5.9638 NaN \n",
"\n",
" dv_ttm total_share float_share free_share total_mv \\\n",
"0 NaN 1.147095e+05 1.048455e+05 7.959795e+04 5.769885e+05 \n",
"1 0.5633 1.336844e+05 1.336844e+05 6.839785e+04 6.456958e+05 \n",
"2 NaN 5.049367e+04 5.049367e+04 3.808829e+04 1.858167e+05 \n",
"3 0.8222 2.636400e+04 2.027786e+04 1.475173e+04 2.565217e+05 \n",
"4 NaN 1.477839e+04 1.061894e+04 8.140048e+03 3.384251e+05 \n",
"... ... ... ... ... ... \n",
"5380 NaN 7.455500e+03 4.346405e+03 2.894040e+03 1.625299e+05 \n",
"5381 2.6625 3.180058e+06 3.128014e+06 1.317969e+06 1.742672e+07 \n",
"5382 NaN 4.979640e+04 4.970844e+04 3.303210e+04 3.351298e+05 \n",
"5383 NaN 6.143629e+04 5.340007e+04 4.590857e+04 2.715484e+05 \n",
"5384 NaN 2.140516e+04 1.442317e+04 1.442317e+04 7.992687e+05 \n",
" dv_ttm total_share float_share free_share total_mv \\\n",
"0 NaN 8.665757e+05 866575.7464 658594.7570 4.133566e+06 \n",
"1 2.6270 2.226900e+04 5669.0000 5669.0000 2.543120e+05 \n",
"2 NaN 2.000000e+04 19461.9464 5233.0650 1.487200e+06 \n",
"3 2.1067 1.093440e+06 724341.7623 208280.6759 1.577834e+07 \n",
"4 0.3621 7.438227e+04 64662.2002 43230.4691 3.142651e+06 \n",
"... ... ... ... ... ... \n",
"5380 1.1120 1.995584e+04 19955.8380 6258.8392 4.755476e+05 \n",
"5381 NaN 1.222104e+05 122210.3885 72818.9706 1.509298e+06 \n",
"5382 NaN 1.600448e+04 3200.8966 3200.8966 3.088865e+05 \n",
"5383 0.4982 4.066600e+04 11215.9100 11215.9100 1.300905e+06 \n",
"5384 NaN 1.005826e+04 3796.0235 3791.5280 4.628809e+05 \n",
"\n",
" circ_mv is_st \n",
"0 5.273728e+05 False \n",
"1 6.456958e+05 False \n",
"2 1.858167e+05 True \n",
"3 1.973036e+05 False \n",
"4 2.431738e+05 False \n",
"0 4.133566e+06 False \n",
"1 6.473998e+04 False \n",
"2 1.447190e+06 False \n",
"3 1.045225e+07 False \n",
"4 2.731978e+06 False \n",
"... ... ... \n",
"5380 9.475163e+04 False \n",
"5381 1.714152e+07 False \n",
"5382 3.345378e+05 False \n",
"5383 2.360283e+05 True \n",
"5384 5.385612e+05 False \n",
"5380 4.755476e+05 False \n",
"5381 1.509298e+06 False \n",
"5382 6.177730e+04 False \n",
"5383 3.587970e+05 False \n",
"5384 1.746930e+05 False \n",
"\n",
"[5385 rows x 19 columns]\n"
]
}
],
"execution_count": 34
"source": [
"all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n",
"print(all_daily_data_df)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "28cb78d032671b20",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-11T15:53:42.062142Z",
"start_time": "2025-02-11T15:53:42.044324Z"
}
},
"cell_type": "code",
"source": "print(all_daily_data_df[all_daily_data_df['is_st']])",
"id": "28cb78d032671b20",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" ts_code trade_date close turnover_rate turnover_rate_f \\\n",
"2 600358.SH 20250211 3.68 8.5826 11.3780 \n",
"20 000889.SZ 20250211 2.48 2.0846 2.8167 \n",
"50 603879.SH 20250211 3.58 1.7126 2.7285 \n",
"62 002024.SZ 20250211 1.99 0.2997 0.8575 \n",
"65 600078.SH 20250211 5.77 1.0536 1.8102 \n",
"10 002366.SZ 20250212 5.10 3.8029 4.1742 \n",
"48 002124.SZ 20250212 2.80 1.8388 1.9195 \n",
"57 000504.SZ 20250212 9.32 0.9666 1.5370 \n",
"63 603007.SH 20250212 10.03 2.0477 2.7581 \n",
"91 300201.SZ 20250212 5.33 2.3317 3.1604 \n",
"... ... ... ... ... ... \n",
"5327 688309.SH 20250211 13.80 0.5594 1.0928 \n",
"5328 002800.SZ 20250211 10.57 2.0449 3.9025 \n",
"5342 300368.SZ 20250211 4.50 1.5755 2.2505 \n",
"5375 600515.SH 20250211 3.64 0.4111 0.6804 \n",
"5383 002197.SZ 20250211 4.42 4.0058 4.6595 \n",
"5303 002316.SZ 20250212 3.52 3.1023 3.3580 \n",
"5335 600568.SH 20250212 1.30 0.3996 0.6514 \n",
"5364 002168.SZ 20250212 2.48 0.8869 1.0824 \n",
"5367 300600.SZ 20250212 7.19 0.7517 1.4024 \n",
"5369 000972.SZ 20250212 3.38 4.6979 7.2993 \n",
"\n",
" volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n",
"2 1.38 NaN NaN 15.2661 3.4220 4.2041 0.0000 \n",
"20 1.08 NaN NaN 20.6126 1.6250 1.6047 0.0000 \n",
"50 1.08 NaN NaN 3.4116 3.8093 3.5391 0.0000 \n",
"62 1.01 NaN NaN 1.5246 0.2944 0.3546 0.0000 \n",
"65 0.97 NaN NaN 2.1866 1.2329 1.2311 0.5373 \n",
"... ... ... ... ... ... ... ... \n",
"5327 0.73 60.8452 186.0174 1.5353 6.7361 13.4432 NaN \n",
"5328 0.72 NaN NaN 3.0468 1.6938 1.3629 0.0000 \n",
"5342 0.99 NaN NaN 7.1301 6.7544 11.8519 0.0000 \n",
"5375 0.91 43.6494 110.6536 1.7765 6.1506 7.8214 0.0000 \n",
"5383 1.41 NaN NaN 1.1195 2.0851 2.5837 0.0000 \n",
" volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n",
"10 0.92 52.0324 56.8856 2.2889 14.2486 11.9214 0.0000 \n",
"48 0.97 NaN 260.7218 1.7484 0.6080 0.6154 0.0000 \n",
"57 0.83 NaN NaN 12.3702 22.4855 24.7156 0.0000 \n",
"63 0.86 NaN NaN 24.6750 55.2244 76.4853 0.0000 \n",
"91 0.75 26.1255 26.1088 4.2311 3.9774 4.2028 0.6431 \n",
"... ... ... ... ... ... ... ... \n",
"5303 0.95 NaN NaN 19.4146 2.2930 2.3153 0.0000 \n",
"5335 0.76 NaN NaN 1.1378 4.0571 4.0379 0.0000 \n",
"5364 0.88 1024.9794 NaN NaN 7.6515 7.4299 0.0000 \n",
"5367 1.18 NaN NaN 2.2914 10.7845 8.9952 0.0000 \n",
"5369 0.77 24.0853 120.2360 16.2931 4.5277 4.9137 0.0000 \n",
"\n",
" dv_ttm total_share float_share free_share total_mv \\\n",
"2 NaN 5.049367e+04 50493.6660 38088.2934 1.858167e+05 \n",
"20 NaN 9.362911e+04 86984.9676 64375.7658 2.322002e+05 \n",
"50 NaN 3.593444e+04 35934.4440 22555.6496 1.286453e+05 \n",
"62 NaN 9.264768e+05 919834.5068 321453.1001 1.843689e+06 \n",
"65 0.5373 6.625729e+04 66257.2861 38563.8247 3.823045e+05 \n",
"... ... ... ... ... ... \n",
"5327 NaN 8.001073e+03 8001.0733 4095.6641 1.104148e+05 \n",
"5328 NaN 1.522531e+04 14165.4100 7422.5200 1.609315e+05 \n",
"5342 NaN 5.289435e+04 52894.3475 37030.2475 2.380246e+05 \n",
"5375 NaN 1.142531e+06 917601.2508 554411.0843 4.158813e+06 \n",
"5383 NaN 6.143629e+04 53400.0687 45908.5733 2.715484e+05 \n",
" dv_ttm total_share float_share free_share total_mv \\\n",
"10 NaN 208093.7640 125646.4390 114472.2056 1.061278e+06 \n",
"48 NaN 222193.3832 197428.3498 189130.4452 6.221415e+05 \n",
"57 NaN 33002.3098 31066.8701 19536.7046 3.075815e+05 \n",
"63 NaN 87689.6101 49983.0778 37108.5778 8.795268e+05 \n",
"91 0.6431 100904.3607 100450.7422 74110.3317 5.378202e+05 \n",
"... ... ... ... ... ... \n",
"5303 NaN 39312.0000 31500.7500 29101.6694 1.383782e+05 \n",
"5335 NaN 199286.9681 166906.7279 102374.4773 2.590731e+05 \n",
"5364 NaN 78416.3368 78416.3368 64258.0991 1.944725e+05 \n",
"5367 NaN 29423.4480 24616.3436 13195.4382 2.115546e+05 \n",
"5369 NaN 77128.3579 77128.3579 49641.0760 2.606938e+05 \n",
"\n",
" circ_mv is_st \n",
"2 1.858167e+05 True \n",
"20 2.157227e+05 True \n",
"50 1.286453e+05 True \n",
"62 1.830471e+06 True \n",
"65 3.823045e+05 True \n",
"... ... ... \n",
"5327 1.104148e+05 True \n",
"5328 1.497284e+05 True \n",
"5342 2.380246e+05 True \n",
"5375 3.340069e+06 True \n",
"5383 2.360283e+05 True \n",
" circ_mv is_st \n",
"10 640796.8389 True \n",
"48 552799.3794 True \n",
"57 289543.2293 True \n",
"63 501330.2703 True \n",
"91 535402.4559 True \n",
"... ... ... \n",
"5303 110882.6400 True \n",
"5335 216978.7463 True \n",
"5364 194472.5153 True \n",
"5367 176991.5105 True \n",
"5369 260693.8497 True \n",
"\n",
"[318 rows x 19 columns]\n"
]
}
],
"execution_count": 37
"source": [
"print(all_daily_data_df[all_daily_data_df['is_st']])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "692b58674b7462c9",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-11T15:53:33.693894Z",
"start_time": "2025-02-11T15:53:33.609884Z"
}
},
"cell_type": "code",
"source": [
"# 将数据保存为 HDF5 文件（table 格式）\n",
"all_daily_data_df.to_hdf(h5_filename, key='daily_basic', mode='a', format='table', append=True, data_columns=True)\n",
"\n",
"print(\"所有每日基础数据获取并保存完毕！\")\n"
],
"id": "692b58674b7462c9",
"outputs": [
{
"name": "stdout",
@@ -362,29 +358,30 @@
]
}
],
"execution_count": 36
"source": [
"# 将数据保存为 HDF5 文件（table 格式）\n",
"all_daily_data_df.to_hdf(h5_filename, key='daily_basic', mode='a', format='table', append=True, data_columns=True)\n",
"\n",
"print(\"所有每日基础数据获取并保存完毕！\")\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "d7a773fc20293477",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-11T15:54:27.868021Z",
"start_time": "2025-02-11T15:54:18.853803Z"
}
},
"cell_type": "code",
"source": [
"with pd.HDFStore(h5_filename, mode='r') as store:\n",
" df = store[key][['ts_code', 'trade_date', 'is_st']]\n",
" print(df.info())"
],
"id": "d7a773fc20293477",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 8300879 entries, 0 to 5384\n",
"Index: 8297355 entries, 0 to 5384\n",
"Data columns (total 3 columns):\n",
" # Column Dtype \n",
"--- ------ ----- \n",
@@ -392,12 +389,16 @@
" 1 trade_date object\n",
" 2 is_st bool \n",
"dtypes: bool(1), object(2)\n",
"memory usage: 197.9+ MB\n",
"memory usage: 197.8+ MB\n",
"None\n"
]
}
],
"execution_count": 39
"source": [
"with pd.HDFStore(h5_filename, mode='r') as store:\n",
" df = store[key][['ts_code', 'trade_date', 'is_st']]\n",
" print(df.info())"
]
}
],
"metadata": {

1170
code/data/update/update_daily_data.ipynb
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119
code/data/update/update_money_flow.ipynb
View File

@@ -2,6 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "b94bb1f2-5332-485e-ae1b-eea01f938106",
"metadata": {
"ExecuteTime": {
@@ -9,43 +10,24 @@
"start_time": "2025-02-11T15:21:54.050569Z"
}
},
"outputs": [],
"source": [
"import tushare as ts\n",
"\n",
"ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n",
"pro = ts.pro_api()"
],
"outputs": [],
"execution_count": 1
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "742c29d453b9bb38",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-11T15:22:32.726905Z",
"start_time": "2025-02-11T15:22:25.018135Z"
}
},
"cell_type": "code",
"source": [
"import pandas as pd\n",
"import time\n",
"\n",
"h5_filename = '../../../data/money_flow.h5'\n",
"key = '/money_flow'\n",
"max_date = None\n",
"with pd.HDFStore(h5_filename, mode='r') as store:\n",
" df = store[key][['ts_code', 'trade_date']]\n",
" print(df.info())\n",
" max_date = df['trade_date'].max()\n",
"\n",
"print(max_date)\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20250220')\n",
"trade_cal = trade_cal[trade_cal['is_open'] == 1] # 只保留交易日\n",
"trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n",
"start_date = min(trade_dates)\n",
"print(f'start_date: {start_date}')"
],
"id": "742c29d453b9bb38",
"outputs": [
{
"name": "stdout",
@@ -66,18 +48,52 @@
]
}
],
"execution_count": 6
"source": [
"import pandas as pd\n",
"import time\n",
"\n",
"h5_filename = '../../../data/money_flow.h5'\n",
"key = '/money_flow'\n",
"max_date = None\n",
"with pd.HDFStore(h5_filename, mode='r') as store:\n",
" df = store[key][['ts_code', 'trade_date']]\n",
" print(df.info())\n",
" max_date = df['trade_date'].max()\n",
"\n",
"print(max_date)\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20250220')\n",
"trade_cal = trade_cal[trade_cal['is_open'] == 1] # 只保留交易日\n",
"trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n",
"start_date = min(trade_dates)\n",
"print(f'start_date: {start_date}')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "679ce40e-8d62-4887-970c-e1d8cbdeee6b",
"metadata": {
"scrolled": true,
"ExecuteTime": {
"end_time": "2025-02-11T15:22:14.513527Z",
"start_time": "2025-02-11T15:22:12.973331Z"
}
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"任务 20250220 完成\n",
"任务 20250219 完成\n",
"任务 20250217 完成\n",
"任务 20250218 完成\n",
"任务 20250213 完成\n",
"任务 20250214 完成\n",
"任务 20250212 完成\n"
]
}
],
"source": [
"from concurrent.futures import ThreadPoolExecutor, as_completed\n",
"\n",
@@ -107,40 +123,26 @@
" except Exception as e:\n",
" print(f\"获取 {trade_date} 数据时出错: {e}\")\n",
"\n"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"任务 20250219 完成\n",
"任务 20250220 完成\n",
"任务 20250218 完成\n",
"任务 20250217 完成\n",
"任务 20250214 完成\n",
"任务 20250213 完成\n",
"任务 20250212 完成\n",
"任务 20250211 完成\n"
]
}
],
"execution_count": 3
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "9af80516849d4e80",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-11T15:22:16.656650Z",
"start_time": "2025-02-11T15:22:16.639271Z"
}
},
"cell_type": "code",
"source": "all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n",
"id": "9af80516849d4e80",
"outputs": [],
"execution_count": 4
"source": [
"all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "a2b05187-437f-4053-bc43-bd80d4cf8b0e",
"metadata": {
"ExecuteTime": {
@@ -148,15 +150,6 @@
"start_time": "2025-02-11T15:22:19.145561Z"
}
},
"source": [
"\n",
"# 将所有数据合并为一个 DataFrame\n",
"\n",
"# 将数据保存为 HDF5 文件（table 格式）\n",
"all_daily_data_df.to_hdf(h5_filename, key='money_flow', mode='a', format='table', append=True, data_columns=True)\n",
"\n",
"print(\"所有每日基础数据获取并保存完毕！\")"
],
"outputs": [
{
"name": "stdout",
@@ -166,7 +159,15 @@
]
}
],
"execution_count": 5
"source": [
"\n",
"# 将所有数据合并为一个 DataFrame\n",
"\n",
"# 将数据保存为 HDF5 文件（table 格式）\n",
"all_daily_data_df.to_hdf(h5_filename, key='money_flow', mode='a', format='table', append=True, data_columns=True)\n",
"\n",
"print(\"所有每日基础数据获取并保存完毕！\")"
]
}
],
"metadata": {

171
code/data/update/update_stk_limit.ipynb
View File

@@ -2,6 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "500802dc-7a20-48b7-a470-a4bae3ec534b",
"metadata": {
"ExecuteTime": {
@@ -9,17 +10,17 @@
"start_time": "2025-02-11T15:18:36.020822Z"
}
},
"outputs": [],
"source": [
"import tushare as ts\n",
"\n",
"ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n",
"pro = ts.pro_api()"
],
"outputs": [],
"execution_count": 1
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "5a84bc9da6d54868",
"metadata": {
"ExecuteTime": {
@@ -27,26 +28,6 @@
"start_time": "2025-02-11T15:20:00.110127Z"
}
},
"source": [
"import pandas as pd\n",
"import time\n",
"\n",
"h5_filename = '../../../data/stk_limit.h5'\n",
"key = '/stk_limit'\n",
"max_date = None\n",
"with pd.HDFStore(h5_filename, mode='r') as store:\n",
" df = store[key][['ts_code', 'trade_date']]\n",
" print(df.sort_values(by='trade_date', ascending=True).tail())\n",
" print(df.info())\n",
" max_date = df['trade_date'].max()\n",
"\n",
"print(max_date)\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20250220')\n",
"trade_cal = trade_cal[trade_cal['is_open'] == 1] # 只保留交易日\n",
"trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n",
"start_date = min(trade_dates)\n",
"print(start_date)"
],
"outputs": [
{
"name": "stdout",
@@ -73,18 +54,53 @@
]
}
],
"execution_count": 5
"source": [
"import pandas as pd\n",
"import time\n",
"\n",
"h5_filename = '../../../data/stk_limit.h5'\n",
"key = '/stk_limit'\n",
"max_date = None\n",
"with pd.HDFStore(h5_filename, mode='r') as store:\n",
" df = store[key][['ts_code', 'trade_date']]\n",
" print(df.sort_values(by='trade_date', ascending=True).tail())\n",
" print(df.info())\n",
" max_date = df['trade_date'].max()\n",
"\n",
"print(max_date)\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20250220')\n",
"trade_cal = trade_cal[trade_cal['is_open'] == 1] # 只保留交易日\n",
"trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n",
"start_date = min(trade_dates)\n",
"print(start_date)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "bb3191de-27a2-4c89-a3b5-32a0d7b9496f",
"metadata": {
"scrolled": true,
"ExecuteTime": {
"end_time": "2025-02-11T15:21:27.831699Z",
"start_time": "2025-02-11T15:21:26.665039Z"
}
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"任务 20250219 完成\n",
"任务 20250220 完成\n",
"任务 20250217 完成\n",
"任务 20250218 完成\n",
"任务 20250214 完成\n",
"任务 20250213 完成\n",
"任务 20250212 完成\n"
]
}
],
"source": [
"from concurrent.futures import ThreadPoolExecutor, as_completed\n",
"\n",
@@ -115,64 +131,62 @@
" except Exception as e:\n",
" print(f\"获取 {trade_date} 数据时出错: {e}\")\n",
"\n"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"任务 20250220 完成\n",
"任务 20250219 完成\n",
"任务 20250217 完成\n",
"任务 20250218 完成\n",
"任务 20250214 完成\n",
"任务 20250213 完成\n",
"任务 20250212 完成\n"
]
}
],
"execution_count": 10
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "96a81aa5890ea3c3",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-11T15:21:29.294283Z",
"start_time": "2025-02-11T15:21:29.247112Z"
}
},
"cell_type": "code",
"source": [
"print(all_daily_data)\n",
"# 将所有数据合并为一个 DataFrame\n",
"all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)"
],
"id": "96a81aa5890ea3c3",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[]\n"
]
},
{
"ename": "ValueError",
"evalue": "No objects to concatenate",
"output_type": "error",
"traceback": [
"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[1;31mValueError\u001B[0m Traceback (most recent call last)",
"Cell \u001B[1;32mIn[11], line 3\u001B[0m\n\u001B[0;32m 1\u001B[0m \u001B[38;5;28mprint\u001B[39m(all_daily_data)\n\u001B[0;32m 2\u001B[0m \u001B[38;5;66;03m# 将所有数据合并为一个 DataFrame\u001B[39;00m\n\u001B[1;32m----> 3\u001B[0m all_daily_data_df \u001B[38;5;241m=\u001B[39m \u001B[43mpd\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mconcat\u001B[49m\u001B[43m(\u001B[49m\u001B[43mall_daily_data\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mignore_index\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m)\u001B[49m\n",
"File \u001B[1;32mE:\\Python\\anaconda\\envs\\try_trader\\lib\\site-packages\\pandas\\core\\reshape\\concat.py:372\u001B[0m, in \u001B[0;36mconcat\u001B[1;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001B[0m\n\u001B[0;32m 369\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m copy \u001B[38;5;129;01mand\u001B[39;00m using_copy_on_write():\n\u001B[0;32m 370\u001B[0m copy \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mFalse\u001B[39;00m\n\u001B[1;32m--> 372\u001B[0m op \u001B[38;5;241m=\u001B[39m \u001B[43m_Concatenator\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 373\u001B[0m \u001B[43m \u001B[49m\u001B[43mobjs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 374\u001B[0m \u001B[43m \u001B[49m\u001B[43maxis\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43maxis\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 375\u001B[0m \u001B[43m \u001B[49m\u001B[43mignore_index\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mignore_index\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 376\u001B[0m \u001B[43m \u001B[49m\u001B[43mjoin\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mjoin\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 377\u001B[0m \u001B[43m \u001B[49m\u001B[43mkeys\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mkeys\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 378\u001B[0m \u001B[43m \u001B[49m\u001B[43mlevels\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mlevels\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 379\u001B[0m \u001B[43m \u001B[49m\u001B[43mnames\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mnames\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 380\u001B[0m \u001B[43m \u001B[49m\u001B[43mverify_integrity\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mverify_integrity\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 381\u001B[0m \u001B[43m \u001B[49m\u001B[43mcopy\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcopy\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 382\u001B[0m \u001B[43m \u001B[49m\u001B[43msort\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msort\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 383\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 385\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m op\u001B[38;5;241m.\u001B[39mget_result()\n",
"File \u001B[1;32mE:\\Python\\anaconda\\envs\\try_trader\\lib\\site-packages\\pandas\\core\\reshape\\concat.py:429\u001B[0m, in \u001B[0;36m_Concatenator.__init__\u001B[1;34m(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001B[0m\n\u001B[0;32m 426\u001B[0m objs \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlist\u001B[39m(objs)\n\u001B[0;32m 428\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(objs) \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m0\u001B[39m:\n\u001B[1;32m--> 429\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mNo objects to concatenate\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m 431\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m keys \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m 432\u001B[0m objs \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlist\u001B[39m(com\u001B[38;5;241m.\u001B[39mnot_none(\u001B[38;5;241m*\u001B[39mobjs))\n",
"\u001B[1;31mValueError\u001B[0m: No objects to concatenate"
"[ trade_date ts_code up_limit down_limit\n",
"0 20250213 000001.SZ 12.56 10.28\n",
"1 20250213 000002.SZ 8.76 7.16\n",
"2 20250213 000004.SZ 15.40 12.60\n",
"3 20250213 000006.SZ 7.92 6.48\n",
"4 20250213 000007.SZ 7.39 6.05\n",
"... ... ... ... ...\n",
"7014 20250213 920108.BJ 27.22 14.66\n",
"7015 20250213 920111.BJ 35.98 19.38\n",
"7016 20250213 920116.BJ 80.44 43.32\n",
"7017 20250213 920118.BJ 34.46 18.56\n",
"7018 20250213 920128.BJ 39.84 21.46\n",
"\n",
"[7019 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20250212 000001.SZ 12.56 10.28\n",
"1 20250212 000002.SZ 7.96 6.52\n",
"2 20250212 000004.SZ 15.07 12.33\n",
"3 20250212 000006.SZ 7.74 6.34\n",
"4 20250212 000007.SZ 7.40 6.06\n",
"... ... ... ... ...\n",
"7014 20250212 920108.BJ 27.41 14.77\n",
"7015 20250212 920111.BJ 34.51 18.59\n",
"7016 20250212 920116.BJ 79.66 42.90\n",
"7017 20250212 920118.BJ 34.81 18.75\n",
"7018 20250212 920128.BJ 38.98 21.00\n",
"\n",
"[7019 rows x 4 columns]]\n"
]
}
],
"execution_count": 11
"source": [
"print(all_daily_data)\n",
"# 将所有数据合并为一个 DataFrame\n",
"all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "ad9733a1-2f42-43ee-a98c-0bf699304c21",
"metadata": {
"ExecuteTime": {
@@ -180,6 +194,15 @@
"start_time": "2025-02-11T15:20:37.375220Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"所有每日基础数据获取并保存完毕！\n"
]
}
],
"source": [
"\n",
"\n",
@@ -187,23 +210,7 @@
"all_daily_data_df.to_hdf(h5_filename, key='stk_limit', mode='a', format='table', append=True, data_columns=True)\n",
"\n",
"print(\"所有每日基础数据获取并保存完毕！\")"
],
"outputs": [
{
"ename": "ValueError",
"evalue": "All objects passed were None",
"output_type": "error",
"traceback": [
"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[1;31mValueError\u001B[0m Traceback (most recent call last)",
"Cell \u001B[1;32mIn[7], line 2\u001B[0m\n\u001B[0;32m 1\u001B[0m \u001B[38;5;66;03m# 将所有数据合并为一个 DataFrame\u001B[39;00m\n\u001B[1;32m----> 2\u001B[0m all_daily_data_df \u001B[38;5;241m=\u001B[39m \u001B[43mpd\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mconcat\u001B[49m\u001B[43m(\u001B[49m\u001B[43mall_daily_data\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mignore_index\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[0;32m 4\u001B[0m \u001B[38;5;66;03m# 将数据保存为 HDF5 文件（table 格式）\u001B[39;00m\n\u001B[0;32m 5\u001B[0m all_daily_data_df\u001B[38;5;241m.\u001B[39mto_hdf(h5_filename, key\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mstk_limit\u001B[39m\u001B[38;5;124m'\u001B[39m, mode\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124ma\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;28mformat\u001B[39m\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mtable\u001B[39m\u001B[38;5;124m'\u001B[39m, append\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m, data_columns\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n",
"File \u001B[1;32mE:\\Python\\anaconda\\envs\\try_trader\\lib\\site-packages\\pandas\\core\\reshape\\concat.py:372\u001B[0m, in \u001B[0;36mconcat\u001B[1;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001B[0m\n\u001B[0;32m 369\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m copy \u001B[38;5;129;01mand\u001B[39;00m using_copy_on_write():\n\u001B[0;32m 370\u001B[0m copy \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mFalse\u001B[39;00m\n\u001B[1;32m--> 372\u001B[0m op \u001B[38;5;241m=\u001B[39m \u001B[43m_Concatenator\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 373\u001B[0m \u001B[43m \u001B[49m\u001B[43mobjs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 374\u001B[0m \u001B[43m \u001B[49m\u001B[43maxis\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43maxis\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 375\u001B[0m \u001B[43m \u001B[49m\u001B[43mignore_index\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mignore_index\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 376\u001B[0m \u001B[43m \u001B[49m\u001B[43mjoin\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mjoin\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 377\u001B[0m \u001B[43m \u001B[49m\u001B[43mkeys\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mkeys\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 378\u001B[0m \u001B[43m \u001B[49m\u001B[43mlevels\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mlevels\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 379\u001B[0m \u001B[43m \u001B[49m\u001B[43mnames\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mnames\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 380\u001B[0m \u001B[43m \u001B[49m\u001B[43mverify_integrity\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mverify_integrity\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 381\u001B[0m \u001B[43m \u001B[49m\u001B[43mcopy\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcopy\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 382\u001B[0m \u001B[43m \u001B[49m\u001B[43msort\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msort\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 383\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 385\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m op\u001B[38;5;241m.\u001B[39mget_result()\n",
"File \u001B[1;32mE:\\Python\\anaconda\\envs\\try_trader\\lib\\site-packages\\pandas\\core\\reshape\\concat.py:452\u001B[0m, in \u001B[0;36m_Concatenator.__init__\u001B[1;34m(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001B[0m\n\u001B[0;32m 449\u001B[0m keys \u001B[38;5;241m=\u001B[39m Index(clean_keys, name\u001B[38;5;241m=\u001B[39mname, dtype\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mgetattr\u001B[39m(keys, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdtype\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m))\n\u001B[0;32m 451\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(objs) \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m0\u001B[39m:\n\u001B[1;32m--> 452\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mAll objects passed were None\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m 454\u001B[0m \u001B[38;5;66;03m# figure out what our result ndim is going to be\u001B[39;00m\n\u001B[0;32m 455\u001B[0m ndims \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mset\u001B[39m()\n",
"\u001B[1;31mValueError\u001B[0m: All objects passed were None"
]
}
],
"execution_count": 7
]
},
{
"cell_type": "code",