Classify2
This commit is contained in:
liaozhaorun
@@ -2,6 +2,7 @@
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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": "500802dc-7a20-48b7-a470-a4bae3ec534b",
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"metadata": {
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"ExecuteTime": {
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@@ -9,17 +10,17 @@
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"start_time": "2025-04-09T14:57:40.584930Z"
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}
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},
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"outputs": [],
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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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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "5a84bc9da6d54868",
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"metadata": {
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"ExecuteTime": {
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@@ -27,6 +28,32 @@
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"start_time": "2025-04-09T14:57:41.540345Z"
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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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" ts_code trade_date\n",
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"4745 600276.SH 20250506\n",
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"4746 600278.SH 20250506\n",
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"4747 600279.SH 20250506\n",
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"4736 600262.SH 20250506\n",
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"281 000791.SZ 20250506\n",
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"<class 'pandas.core.frame.DataFrame'>\n",
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"Index: 10436295 entries, 0 to 113592\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: 238.9+ MB\n",
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"None\n",
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"20250506\n",
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"20250507\n"
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]
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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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@@ -41,50 +68,84 @@
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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 = pro.trade_cal(exchange='', start_date='20170101', end_date='20250720')\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(start_date)"
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],
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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": "bb3191de-27a2-4c89-a3b5-32a0d7b9496f",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-09T14:58:09.342522Z",
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"start_time": "2025-04-09T14:58:05.259974Z"
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},
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"scrolled": true
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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\n",
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"4721 600284.SH 20250408\n",
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"4722 600285.SH 20250408\n",
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"4723 600287.SH 20250408\n",
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"4712 600272.SH 20250408\n",
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"5 000008.SZ 20250408\n",
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"<class 'pandas.core.frame.DataFrame'>\n",
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"Index: 10315620 entries, 0 to 14151\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: 236.1+ MB\n",
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"None\n",
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"20250408\n",
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"20250409\n"
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"任务 20250718 完成\n",
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"任务 20250717 完成\n",
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"任务 20250715 完成\n",
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"任务 20250716 完成\n",
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"任务 20250714 完成\n",
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"任务 20250711 完成\n",
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"任务 20250709 完成\n",
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"任务 20250710 完成\n",
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"任务 20250708 完成\n",
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"任务 20250707 完成\n",
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"任务 20250703 完成\n",
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"任务 20250704 完成\n",
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"任务 20250701 完成\n",
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"任务 20250702 完成\n",
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"任务 20250630 完成\n",
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"任务 20250627 完成\n",
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"任务 20250626 完成\n",
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"任务 20250625 完成\n",
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"任务 20250624 完成\n",
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"任务 20250623 完成\n",
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"任务 20250620 完成\n",
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"任务 20250619 完成\n",
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"任务 20250618 完成\n",
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"任务 20250617 完成\n",
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"任务 20250616 完成\n",
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"任务 20250613 完成\n",
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"任务 20250612 完成\n",
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"任务 20250611 完成\n",
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"任务 20250610 完成\n",
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"任务 20250609 完成\n",
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"任务 20250606 完成\n",
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"任务 20250605 完成\n",
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"任务 20250604 完成\n",
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"任务 20250603 完成\n",
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"任务 20250530 完成\n",
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"任务 20250529 完成\n",
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"任务 20250528 完成\n",
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"任务 20250527 完成\n",
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"任务 20250526 完成\n",
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"任务 20250523 完成\n",
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"任务 20250522 完成\n",
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"任务 20250521 完成\n",
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"任务 20250520 完成\n",
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"任务 20250519 完成\n",
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"任务 20250516 完成\n",
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"任务 20250515 完成\n",
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"任务 20250514 完成\n",
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"任务 20250513 完成\n",
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"任务 20250512 完成\n",
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"任务 20250509 完成\n",
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"任务 20250508 完成\n",
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"任务 20250507 完成\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": "bb3191de-27a2-4c89-a3b5-32a0d7b9496f",
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"metadata": {
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"scrolled": true,
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"ExecuteTime": {
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"end_time": "2025-04-09T14:58:09.342522Z",
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"start_time": "2025-04-09T14:58:05.259974Z"
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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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@@ -115,27 +176,11 @@
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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 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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"任务 20250411 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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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "96a81aa5890ea3c3",
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"metadata": {
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"ExecuteTime": {
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@@ -143,37 +188,38 @@
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"start_time": "2025-04-09T14:58:09.346528Z"
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}
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},
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"source": [
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"print(all_daily_data)\n",
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"# 将所有数据合并为一个 DataFrame\n",
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"all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)"
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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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"[ trade_date ts_code up_limit down_limit\n",
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"0 20250409 000001.SZ 11.90 9.74\n",
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"1 20250409 000002.SZ 7.48 6.12\n",
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"2 20250409 000004.SZ 9.53 7.79\n",
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"3 20250409 000006.SZ 6.28 5.14\n",
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"4 20250409 000007.SZ 5.91 4.83\n",
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"... ... ... ... ...\n",
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"7077 20250409 920108.BJ 26.55 14.31\n",
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"7078 20250409 920111.BJ 30.84 16.62\n",
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"7079 20250409 920116.BJ 100.29 54.01\n",
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"7080 20250409 920118.BJ 31.62 17.04\n",
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"7081 20250409 920128.BJ 35.26 19.00\n",
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"\n",
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"[7082 rows x 4 columns]]\n"
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"[]\n"
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]
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},
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{
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"ename": "ValueError",
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"evalue": "No objects to concatenate",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[1;32mIn[4], 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 pd\u001b[38;5;241m.\u001b[39mconcat(all_daily_data, ignore_index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
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"File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\reshape\\concat.py:382\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 379\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 380\u001b[0m copy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m--> 382\u001b[0m op \u001b[38;5;241m=\u001b[39m _Concatenator(\n\u001b[0;32m 383\u001b[0m objs,\n\u001b[0;32m 384\u001b[0m axis\u001b[38;5;241m=\u001b[39maxis,\n\u001b[0;32m 385\u001b[0m ignore_index\u001b[38;5;241m=\u001b[39mignore_index,\n\u001b[0;32m 386\u001b[0m join\u001b[38;5;241m=\u001b[39mjoin,\n\u001b[0;32m 387\u001b[0m keys\u001b[38;5;241m=\u001b[39mkeys,\n\u001b[0;32m 388\u001b[0m levels\u001b[38;5;241m=\u001b[39mlevels,\n\u001b[0;32m 389\u001b[0m names\u001b[38;5;241m=\u001b[39mnames,\n\u001b[0;32m 390\u001b[0m verify_integrity\u001b[38;5;241m=\u001b[39mverify_integrity,\n\u001b[0;32m 391\u001b[0m copy\u001b[38;5;241m=\u001b[39mcopy,\n\u001b[0;32m 392\u001b[0m sort\u001b[38;5;241m=\u001b[39msort,\n\u001b[0;32m 393\u001b[0m )\n\u001b[0;32m 395\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m op\u001b[38;5;241m.\u001b[39mget_result()\n",
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"File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\reshape\\concat.py:445\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 442\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverify_integrity \u001b[38;5;241m=\u001b[39m verify_integrity\n\u001b[0;32m 443\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcopy \u001b[38;5;241m=\u001b[39m copy\n\u001b[1;32m--> 445\u001b[0m objs, keys \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_clean_keys_and_objs(objs, keys)\n\u001b[0;32m 447\u001b[0m \u001b[38;5;66;03m# figure out what our result ndim is going to be\u001b[39;00m\n\u001b[0;32m 448\u001b[0m ndims \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_ndims(objs)\n",
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"File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\reshape\\concat.py:507\u001b[0m, in \u001b[0;36m_Concatenator._clean_keys_and_objs\u001b[1;34m(self, objs, keys)\u001b[0m\n\u001b[0;32m 504\u001b[0m objs_list \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(objs)\n\u001b[0;32m 506\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(objs_list) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m--> 507\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 509\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 510\u001b[0m objs_list \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_list))\n",
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"\u001b[1;31mValueError\u001b[0m: No objects to concatenate"
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]
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}
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],
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"execution_count": 4
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"source": [
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"print(all_daily_data)\n",
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"# 将所有数据合并为一个 DataFrame\n",
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"all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)"
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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": null,
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"id": "ad9733a1-2f42-43ee-a98c-0bf699304c21",
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"metadata": {
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"ExecuteTime": {
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@@ -181,14 +227,6 @@
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"start_time": "2025-04-09T14:58:09.366441Z"
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}
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},
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"source": [
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"\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='stk_limit', 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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@@ -198,10 +236,18 @@
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]
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}
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],
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"execution_count": 5
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"source": [
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"\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='stk_limit', 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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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7e777f1f-4d54-4a74-b916-691ede6af055",
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"metadata": {
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"ExecuteTime": {
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@@ -209,14 +255,13 @@
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"start_time": "2025-04-09T14:58:09.686524Z"
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}
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},
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"source": [],
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"outputs": [],
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"execution_count": null
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"source": []
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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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"display_name": "new_trader",
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"language": "python",
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"name": "python3"
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},
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Reference in New Issue
Block a user