RollingRank赚钱- Sharp-1.43
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liaozhaorun
195
main/data/update/update_money_flow.ipynb
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195
main/data/update/update_money_flow.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"id": "b94bb1f2-5332-485e-ae1b-eea01f938106",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-09T14:57:40.184418Z",
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"start_time": "2025-04-09T14:57:39.137312Z"
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}
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},
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"source": [
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"import tushare as ts\n",
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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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"cell_type": "code",
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"id": "742c29d453b9bb38",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-09T14:58:10.515830Z",
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"start_time": "2025-04-09T14:57:40.190466Z"
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}
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},
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"source": [
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"import pandas as pd\n",
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"import time\n",
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"\n",
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"h5_filename = '../../../data/money_flow.h5'\n",
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"key = '/money_flow'\n",
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"max_date = None\n",
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"with pd.HDFStore(h5_filename, mode='r') as store:\n",
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" df = store[key][['ts_code', 'trade_date']]\n",
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" print(df.info())\n",
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" max_date = df['trade_date'].max()\n",
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"\n",
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"print(max_date)\n",
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"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20250420')\n",
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"trade_cal = trade_cal[trade_cal['is_open'] == 1] # 只保留交易日\n",
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"trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n",
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"start_date = min(trade_dates)\n",
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"print(f'start_date: {start_date}')"
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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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"<class 'pandas.core.frame.DataFrame'>\n",
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"Index: 8353711 entries, 0 to 5126\n",
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"Data columns (total 2 columns):\n",
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" # Column Dtype \n",
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"--- ------ ----- \n",
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" 0 ts_code object\n",
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" 1 trade_date object\n",
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"dtypes: object(2)\n",
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"memory usage: 191.2+ MB\n",
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"None\n",
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"20250408\n",
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"start_date: 20250409\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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"cell_type": "code",
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"id": "679ce40e-8d62-4887-970c-e1d8cbdeee6b",
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"metadata": {
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"scrolled": true,
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"ExecuteTime": {
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"end_time": "2025-04-09T14:58:17.197319Z",
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"start_time": "2025-04-09T14:58:10.724923Z"
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}
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},
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"source": [
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"from concurrent.futures import ThreadPoolExecutor, as_completed\n",
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"\n",
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"all_daily_data = []\n",
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"\n",
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"# API 调用计数和时间控制变量\n",
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"api_call_count = 0\n",
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"batch_start_time = time.time()\n",
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"\n",
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"\n",
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"def get_data(trade_date):\n",
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" time.sleep(0.1)\n",
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" money_flow_data = pro.moneyflow(trade_date=trade_date)\n",
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" if money_flow_data is not None and not money_flow_data.empty:\n",
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" return money_flow_data\n",
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"\n",
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"\n",
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"with ThreadPoolExecutor(max_workers=2) as executor:\n",
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" future_to_date = {executor.submit(get_data, td): td for td in trade_dates}\n",
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"\n",
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" for future in as_completed(future_to_date):\n",
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" trade_date = future_to_date[future] # 获取对应的交易日期\n",
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" try:\n",
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" result = future.result() # 获取任务执行的结果\n",
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" all_daily_data.append(result)\n",
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" print(f\"任务 {trade_date} 完成\")\n",
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" except Exception as e:\n",
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" print(f\"获取 {trade_date} 数据时出错: {e}\")\n",
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"\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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"任务 20250417 完成\n",
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"任务 20250418 完成\n",
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"任务 20250416 完成\n",
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"任务 20250415 完成\n",
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"任务 20250411 完成\n",
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"任务 20250414 完成\n",
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"任务 20250410 完成\n",
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"任务 20250409 完成\n"
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]
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}
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],
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"execution_count": 3
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},
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{
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"cell_type": "code",
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"id": "9af80516849d4e80",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-09T14:58:17.214168Z",
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"start_time": "2025-04-09T14:58:17.210734Z"
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}
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},
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"source": [
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"all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n"
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],
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"outputs": [],
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"execution_count": 4
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},
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{
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"cell_type": "code",
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"id": "a2b05187-437f-4053-bc43-bd80d4cf8b0e",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-09T14:58:19.633456Z",
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"start_time": "2025-04-09T14:58:17.229837Z"
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}
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},
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"source": [
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"\n",
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"# 将所有数据合并为一个 DataFrame\n",
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"\n",
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"# 将数据保存为 HDF5 文件（table 格式）\n",
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"all_daily_data_df.to_hdf(h5_filename, key='money_flow', mode='a', format='table', append=True, data_columns=True)\n",
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"\n",
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"print(\"所有每日基础数据获取并保存完毕！\")"
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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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"所有每日基础数据获取并保存完毕！\n"
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]
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}
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],
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"execution_count": 5
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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