f925dff46b
- Added dingdanliu_nb_mflow for improved order processing - Updated related scripts and configurations to support new functionality
86 lines
3.2 KiB
Python
86 lines
3.2 KiB
Python
import os
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import requests
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import time
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from datetime import datetime
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from requests.adapters import HTTPAdapter
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import pandas as pd
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pd.set_option('display.max_rows', 1000)
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pd.set_option('expand_frame_repr', False) # 当列太多时不换行
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# 设置命令行输出时的列对齐功能
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pd.set_option('display.unicode.ambiguous_as_wide', True)
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pd.set_option('display.unicode.east_asian_width', True)
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def requestForNew(url):
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session = requests.Session()
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session.mount('http://', HTTPAdapter(max_retries=3))
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session.mount('https://', HTTPAdapter(max_retries=3))
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session.keep_alive = False
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response = session.get(url, headers={'Connection': 'close'}, timeout=30)
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if response.content:
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return response
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else:
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print("链接失败", response)
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def getDate():
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url = 'http://hq.sinajs.cn/list=sh000001'
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response = requestForNew(url).text
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data_date = str(response.split(',')[-4])
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# 获取上证的指数日期
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return data_date
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# 通过新浪财经获取每日更新的股票代码
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def getStockCodeForEveryday():
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df = pd.DataFrame()
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for page in range(1, 100):
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# 1~100页，不用担心每天新增
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url = 'http://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/Market_Center.getHQNodeData?page=' \
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+ str(page) + '&num=80&sort=changepercent&asc=0&node=hs_a&symbol=&_s_r_a=page'
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# print(url)
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content = requestForNew(url).json()
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if not content:
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# if content =[]: 这个写法也可以
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print("股票信息，获取完毕。")
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break
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print("正在读取页面" + str(page))
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time.sleep(3)
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df = df.append(pd.DataFrame(content, dtype='float'), ignore_index=True)
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rename_dict = {'symbol': '股票代码', 'code': '交易日期', 'name': '股票名称', 'open': '开盘价',
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'settlement': '前收盘价', 'trade': '收盘价', 'high': '最高价', 'low': '最低价',
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'buy': '买一', 'sell': '卖一', 'volume': '成交量', 'amount': '成交额',
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'changepercent': '涨跌幅', 'pricechange': '涨跌额',
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'mktcap': '总市值', 'nmc': '流通市值', 'ticktime': '数据更新时间', 'per': 'per', 'pb': '市净率',
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'turnoverratio': '换手率'}
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df.rename(columns=rename_dict, inplace=True)
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tradeDate = getDate()
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df['交易日期'] = tradeDate
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df = df[['股票代码', '股票名称', '交易日期', '开盘价', '最高价', '最低价', '收盘价', '前收盘价', '成交量', '成交额', '流通市值', '总市值']]
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# 把转化成float的code替换成交易日期
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return df
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df = getStockCodeForEveryday()
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print(df)
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for i in df.index:
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t = df.iloc[i:i + 1, :]
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stock_code = t.iloc[0]['股票代码']
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# 构建存储文件路径
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path = './data/' \
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+ stock_code + '.csv'
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# 文件存在，不是新股
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if os.path.exists(path):
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t.to_csv(path, header=None, index=False, mode='a', encoding='gbk')
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# 文件不存在，说明是新股
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else:
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# 先将头文件输出
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pd.DataFrame(columns=['数据由邢不行整理']).to_csv(path, index=False, encoding='gbk')
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t.to_csv(path, index=False, mode='a', encoding='gbk')
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print(stock_code)
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