网页network发现接口返回的是json数据,怎样通过python,通过分页方式爬取下载到excel里或者数据库里面
接口参数意义:
https://stock.xueqiu.com/v5/stock/chart/kline.json?symbol=SZ159915&begin=1589340438277&period=day&type=before&count=-142&indicator=kline,pe,pb,ps,pcf,market_capital,agt,ggt,balance
参数 意义
begin 起始日
period K线单位选择,日k,月k等
type 不知道什么意义
count 数据个数
indicator 其他指标参数
接口含义:从begin那天开始,向前记录count个交易日,并且得到indicator的指标。
图中一些变量的意义
变量 意义
timestamp 时间戳(以ms计)。
volume 成交量
open 开盘价
high 最高价
low 收盘价
close 收盘价
其他的一些参数自己可以对比K线查看。
在Preview页面可以更简单查看到:
使用接口
写代码的时候需要用到Request Hearders项下面的Cookie和User-Agent项
接下来可以写代码爬取了,代码直接贴上了,使用requests库。
import requests import json import pandas as pd import time number = 2000 # 需要获取的交易日的个数 begin = int(time.time() * 1000) url = 'https://stock.xueqiu.com/v5/stock/chart/kline.json?symbol=SZ159915&begin=' + str( begin) + '&period=day&type=before&count=-' + str(number) # Cookie参数根据每个人的设备来变动 headers = {'User-Agent': 'Mozilla/5.0', 'Cookie': 'xq_a_token=48575b79f8efa6d34166cc7bdc5abb09fd83ce63; xqat=48575b79f8efa6d34166cc7bdc5abb09fd83ce63; xq_r_token=7dcc6339975b01fbc2c14240ce55a3a20bdb7873; xq_id_token=eyJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiJ9.eyJ1aWQiOi0xLCJpc3MiOiJ1YyIsImV4cCI6MTU4OTY4MjczMCwiY3RtIjoxNTg4OTE5Njc4OTk4LCJjaWQiOiJkOWQwbjRBWnVwIn0.l6yOJc-qTWMNU8g6wXjew0X7TmWbi82cuGiYkVvWGnUoxYSGWIx3DtfIki0etjSbN8mG0r1Gwd_q-PGo6EHL4h-SreHzt7tnteLtmnFrJ5hdyNh1g_x2u4XMvTX-pIEZmVInhBIM_BGVFerYXHuIJ6lm1G-EPR4RlVG2PQ7PTvvsz9-VycQJVZuF1zguF936WiSbPTBmhG0wcXUdfziFC1RPrXgFNTrwNXqaIiWfT5WbRWckm8aFNM3krCGCaES494Jco0FBM3eB5GJlGeB5xS1if_de7T6__PSTCmzMHokG133gRqt4FvYHu9kIQg74CdGw8u7EDWSigw-kASVAzg; u=851588919733219; is_overseas=0; Hm_lvt_1db88642e346389874251b5a1eded6e3=1588919732; Hm_lpvt_1db88642e346389874251b5a1eded6e3=1588919732; device_id=fa23c8c5b1bd5f49c8c9ac7a657ccec3'} r = requests.get(url, headers=headers) # 爬取数据 text = r.text # 获得文本 data = json.loads(text) # str转成json item = data['data']['item'] # 从全部数据中取出item项 df = pd.DataFrame(item, columns=["timestamp", "volume", "open", "high", "low", "close", "chg", "percent", "turnoverrate", "amount", "volume_post", "amount_post"]) # list转为DataFrame数据格式,更方便以后的处理 print(df)
输出的数据如下:
timestamp volume open ... amount volume_post amount_post 0 1329408000000 67987778 0.726 ... NaN None None 1 1329667200000 39183956 0.725 ... NaN None None 2 1329753600000 77306937 0.721 ... NaN None None 3 1329840000000 193157652 0.738 ... NaN None None 4 1329926400000 124234294 0.765 ... NaN None None ... ... ... ... ... ... ... ... 1995 1588089600000 356095691 1.943 ... 696005741.0 None None 1996 1588176000000 411736129 1.964 ... 817442890.0 None None 1997 1588694400000 367767579 1.980 ... 737917205.0 None None 1998 1588780800000 265935124 2.030 ... 538456242.0 None None 1999 1588867200000 304340396 2.035 ... 622569015.0 None None [2000 rows x 12 columns]
至此,爬取工作完成,后面如何使用根据个人需求而定。
https://blog.csdn.net/qq_34769201/article/details/106072280?spm=1001.2101.3001.6650.1&utm_medium=distribute.pc_relevant.none-task-blog-2%7Edefault%7ECTRLIST%7Edefault-1.no_search_link&depth_1-utm_source=distribute.pc_relevant.none-task-blog-2%7Edefault%7ECTRLIST%7Edefault-1.no_search_link