662 lines
23 KiB
Python
662 lines
23 KiB
Python
#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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所有 A 股的实时行情数据
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Created on 2023/12/06
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@author: Monday
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@group : waditu
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@contact:
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"""
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import time
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import pandas as pd
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import requests
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from tqdm import tqdm
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from typing import Optional
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import re
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from tushare.util.format_stock_code import symbol_verify
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from tushare.util.format_stock_code import format_stock_code, verify_stock_or_index
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from tushare.util.form_date import timestemp_to_time
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from tushare.stock import rtq_vars as rtqv
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from tushare.util.verify_token import require_permission
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from tushare.stock import cons as ct
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from tushare.stock.rtq_vars import (
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zh_sina_a_stock_payload,
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zh_sina_a_stock_url,
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zh_sina_a_stock_count_url,
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zh_sina_a_stock_headers,
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zh_sina_a_stock_cookies,
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)
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def _random(n=13):
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from random import randint
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start = 10 ** (n - 1)
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end = (10 ** n) - 1
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return str(randint(start, end))
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def _get_current_timestamp():
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return str(int(time.time() * 1000))
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@require_permission(event_name="realtime_quote", event_detail="个股实时交易数据")
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def realtime_quote(ts_code="688553.SH", src="sina", ):
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"""
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获取实时交易数据 getting real time quotes data
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用于跟踪交易情况（本次执行的结果-上一次执行的数据）
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Parameters
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------
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ts_code : string
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src : sina ，dc
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return
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-------
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DataFrame 实时交易数据
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属性:0：name，股票名字
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1：open，今日开盘价
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2：pre_close，昨日收盘价
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3：price，当前价格
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4：high，今日最高价
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5：low，今日最低价
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6：bid，竞买价，即“买一”报价
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7：ask，竞卖价，即“卖一”报价
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8：volumn，成交量 maybe you need do volumn/100
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9：amount，成交金额（元 CNY）
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10：b1_v，委买一（笔数 bid volume）
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11：b1_p，委买一（价格 bid price）
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12：b2_v，“买二”
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13：b2_p，“买二”
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14：b3_v，“买三”
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15：b3_p，“买三”
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16：b4_v，“买四”
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17：b4_p，“买四”
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18：b5_v，“买五”
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19：b5_p，“买五”
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20：a1_v，委卖一（笔数 ask volume）
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21：a1_p，委卖一（价格 ask price）
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...
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30：date，日期；
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31：time，时间；
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"""
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symbols = symbol_verify(ts_code)
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if src == "sina":
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return get_realtime_quotes_sina(symbols)
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else:
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return get_realtime_quotes_dc(symbols)
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sina_stock_code = {}
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def get_realtime_quotes_sina(symbol="688553"): # "688553"
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global sina_stock_code
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symbols = []
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syms = []
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for i in [j for j in symbol.split(",")]:
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s = i.split(".")
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sina_stock_code[s[0]] = s[1].upper()
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symbols.append(s[1].lower() + s[0])
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syms.append(s[0])
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# symbols = re.search(r"(\d+)", str(symbols), re.S | re.M).group(1)
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symbols_list = ''
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if isinstance(symbols, list) or isinstance(symbols, set) or \
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isinstance(symbols, tuple) or isinstance(symbols, pd.Series):
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for code in symbols:
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symbols_list += code + ','
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else:
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symbols_list = symbols
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symbols_list = symbols_list[:-1] if len(symbols_list) > 8 else symbols_list
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root_url = ct.LIVE_DATA_URL % (ct.P_TYPE['http'], ct.DOMAINS['sinahq'], _get_current_timestamp(), symbols_list)
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response = requests.get(root_url,
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headers={
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"user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36 Edg/119.0.0.0",
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'host': 'hq.sinajs.cn',
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'referer': 'https://finance.sina.com.cn/'
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})
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text = response.content.decode('GBK')
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reg = re.compile(r'\="(.*?)\";')
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data = reg.findall(text)
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data_list = []
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syms_list = []
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for index, row in enumerate(data):
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if len(row) > 1:
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data_list.append([astr for astr in row.split(',')[:33]])
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syms_list.append(syms[index])
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if len(syms_list) == 0:
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return None
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df = pd.DataFrame(data_list, columns=ct.LIVE_DATA_COLS)
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df = df.drop('s', axis=1)
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df['code'] = syms_list
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ls = [cls for cls in df.columns if '_v' in cls]
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for txt in ls:
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df[txt] = df[txt].map(lambda x: x[:-2])
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df.columns = rtqv.LIVE_DATA_COLS
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df["TS_CODE"] = df["TS_CODE"].apply(format_sina_stock_code)
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df["DATE"] = df["DATE"].apply(format_date_str)
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new_order = rtqv.LIVE_DATA_COLS_REINDEX
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df = df[new_order]
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# 指定要转换为 float 类型的列
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cols_to_convert = ['OPEN', 'PRE_CLOSE', 'PRICE', 'HIGH', 'LOW', 'BID', 'ASK',
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'VOLUME', 'AMOUNT', 'B1_V', 'B1_P',
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'B2_V', 'B2_P', 'B3_V', 'B3_P', 'B4_V', 'B4_P', 'B5_V', 'B5_P', 'A1_V', 'A1_P', 'A2_V',
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'A2_P',
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'A3_V', 'A3_P', 'A4_V', 'A4_P', 'A5_V', 'A5_P']
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# 使用 to_numeric() 方法将指定的列转换为 float 类型，并将非数值类型的数据转换为 NaN
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df[cols_to_convert] = df[cols_to_convert].apply(pd.to_numeric, errors='coerce')
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# 使用 fillna() 方法将 NaN 值替换为 0
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df_filled = df.fillna(0)
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return df_filled
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def format_sina_stock_code(x):
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ts_code = f"{x}.{sina_stock_code[x]}"
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return ts_code
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def format_date_str(date_str):
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return date_str.replace("-", "")
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@require_permission(event_name="realtime_list", event_detail="A股所有实时交易数据")
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def realtime_list(src: Optional[str] = None, interval: Optional[int] = 3,
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page_count: Optional[int] = None, proxies: Optional[dict] = {}) -> pd.DataFrame:
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"""
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沪深京 A 股 all -实时行情
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@param src: 数据源 新浪sina |东方财富 dc
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@param interval: 分页采集时间间隔（默认3秒翻译一夜）
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@param page_count: 限制抓取的页数（仅对新浪有效）
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@param proxies: 设置代理 防止被封禁
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@return: 按涨跌幅 倒序排序
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-------
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DataFrame 实时交易数据
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东方财富：
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2、代码:TS_CODE
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3、名称:NAME
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4、最新价:CLOSE
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5、涨跌幅:PCT_CHANGE
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6、涨跌额:CHANGE
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7、成交量:VOLUME
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8、成交额:AMOUNT
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9、振幅:SWING
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10、最高:HIGH
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11、最低:LOW
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12、今开:OPEN
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13、昨收:PRICE
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14、量比:VOL_RATIO
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15、换手率:TURNOVER_RATE
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16、市盈率-动态:PE
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17、市净率:PB
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18、总市值:TOTAL_MV
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19、流通市值:FLOAT_MV
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20、涨速:RISE
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21、5分钟涨跌:5MIN
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22、60日涨跌幅:60DAY
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23、年初至今涨跌幅:1YEAR
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新浪财经：
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1、代码:TS_CODE
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2、名称:NAME
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3、最新价:CLOSE
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4、涨跌额:CHANGE
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5、涨跌幅:PCT_CHANGE
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6、买入:BUY
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7、卖出:SALE
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8、昨收:CLOSE
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9、今开:OPEN
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10、最高:HIGH
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11、最低:LOW
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12、成交量:VOLUME
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13、成交额:AMOUNT
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14、时间戳:TIME
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"""
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if src == "dc":
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return get_stock_all_a_dc(page_count, proxies)
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elif src == "sina":
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return get_stock_all_a_sina(interval, page_count, proxies)
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else:
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return get_stock_all_a_dc(page_count, proxies)
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def get_stock_all_a_dc(page_count: Optional[int] = None,
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proxies: Optional[dict] = {}) -> pd.DataFrame:
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"""
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东方财富网-沪深京 A 股-实时行情
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https://quote.eastmoney.com/center/gridlist.html#hs_a_board
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:return: 实时行情
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:rtype: pandas.DataFrame
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1、序号:RANK
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2、代码:TS_CODE
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3、名称:NAME
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4、最新价:PRICE
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5、涨跌幅:PCT_CHANGE
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6、涨跌额:CHANGE
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7、成交量:VOLUME
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8、成交额:AMOUNT
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9、振幅:SWING
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10、最高:HIGH
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11、最低:LOW
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12、今开:OPEN
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13、昨收:CLOSE
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14、量比:VOL_RATIO
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15、换手率:TURNOVER_RATE
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16、市盈率-动态:PE
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17、市净率:PB
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18、总市值:TOTAL_MV
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19、流通市值:FLOAT_MV
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20、涨速:RISE
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21、5分钟涨跌:5MIN
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22、60日涨跌幅:60DAY
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23、年初至今涨跌幅:1YEAR
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"""
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dfs = []
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for page in range(1,500):
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url = "http://82.push2.eastmoney.com/api/qt/clist/get"
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params = {
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"pn": page,
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"pz": "200",
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"po": "1",
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"np": "1",
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"ut": "bd1d9ddb04089700cf9c27f6f7426281",
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"fltt": "2",
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"invt": "2",
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"fid": "f3",
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"fs": "m:0 t:6,m:0 t:80,m:1 t:2,m:1 t:23,m:0 t:81 s:2048",
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"fields": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,f21,f23,f24,f25,f22,f11,f62,f128,f136,f115,f152",
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"_": "1623833739532",
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}
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if page_count:
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params["pz"] = 20
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r = requests.get(url, params=params, proxies=proxies)
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data_json = r.json()
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if not data_json["data"]:
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break
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# return pd.DataFrame()
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temp_df = pd.DataFrame(data_json["data"]["diff"])
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temp_df.columns = [
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"_",
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"最新价",
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"涨跌幅",
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"涨跌额",
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"成交量",
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"成交额",
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"振幅",
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"换手率",
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"市盈率-动态",
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"量比",
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"5分钟涨跌",
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"代码",
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"_",
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"名称",
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"最高",
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"最低",
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"今开",
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"昨收",
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"总市值",
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"流通市值",
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"涨速",
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"市净率",
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"60日涨跌幅",
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"年初至今涨跌幅",
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"-",
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"-",
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"-",
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"-",
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"-",
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"-",
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"-",
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]
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temp_df.reset_index(inplace=True)
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# temp_df["index"] = temp_df.index + 1
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# temp_df.rename(columns={"index": "序号"}, inplace=True)
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temp_df = temp_df[
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[
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# "序号",
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"代码",
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"名称",
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"最新价",
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"涨跌幅",
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"涨跌额",
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"成交量",
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"成交额",
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"振幅",
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"最高",
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"最低",
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"今开",
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"昨收",
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"量比",
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"换手率",
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"市盈率-动态",
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"市净率",
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"总市值",
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"流通市值",
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"涨速",
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"5分钟涨跌",
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"60日涨跌幅",
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"年初至今涨跌幅",
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]
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]
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temp_df["代码"] = temp_df["代码"].apply(format_stock_code)
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temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce")
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temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
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temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce")
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temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
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temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
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temp_df["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce")
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temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce")
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temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce")
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temp_df["今开"] = pd.to_numeric(temp_df["今开"], errors="coerce")
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temp_df["昨收"] = pd.to_numeric(temp_df["昨收"], errors="coerce")
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temp_df["量比"] = pd.to_numeric(temp_df["量比"], errors="coerce")
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temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce")
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temp_df["市盈率-动态"] = pd.to_numeric(temp_df["市盈率-动态"], errors="coerce")
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temp_df["市净率"] = pd.to_numeric(temp_df["市净率"], errors="coerce")
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temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce")
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temp_df["流通市值"] = pd.to_numeric(temp_df["流通市值"], errors="coerce")
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temp_df["涨速"] = pd.to_numeric(temp_df["涨速"], errors="coerce")
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temp_df["5分钟涨跌"] = pd.to_numeric(temp_df["5分钟涨跌"], errors="coerce")
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temp_df["60日涨跌幅"] = pd.to_numeric(temp_df["60日涨跌幅"], errors="coerce")
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temp_df["年初至今涨跌幅"] = pd.to_numeric(temp_df["年初至今涨跌幅"], errors="coerce")
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temp_df.columns = [
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# "RANK",
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"TS_CODE",
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"NAME",
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"PRICE",
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"PCT_CHANGE",
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"CHANGE",
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"VOLUME",
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"AMOUNT",
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"SWING",
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"HIGH",
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"LOW",
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"OPEN",
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"CLOSE",
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"VOL_RATIO",
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"TURNOVER_RATE",
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"PE",
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"PB",
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"TOTAL_MV",
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"FLOAT_MV",
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"RISE",
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"5MIN",
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"60DAY",
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"1YEAR",
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]
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# 指定要转换为 float 类型的列
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cols_to_convert = ['PRICE', 'PCT_CHANGE', 'CHANGE', "VOLUME", "AMOUNT", "SWING",
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'HIGH', "LOW", "OPEN", "CLOSE", "VOL_RATIO", "TURNOVER_RATE", "PE", "PB", "TOTAL_MV", "FLOAT_MV",
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"RISE", "5MIN", "60DAY", "1YEAR"
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]
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# 使用 to_numeric() 方法将指定的列转换为 float 类型，并将非数值类型的数据转换为 NaN
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temp_df[cols_to_convert] = temp_df[cols_to_convert].apply(pd.to_numeric, errors='coerce')
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dfs.append(
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temp_df
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)
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if page_count:
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break
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result_df = pd.concat(dfs, ignore_index=True)
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# 使用 fillna() 方法将 NaN 值替换为 0
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df_filled = result_df.fillna(0)
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df_sorted = df_filled.sort_values(by='PCT_CHANGE', ascending=False).reset_index(drop=True)
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return df_sorted
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def _get_zh_a_page_count() -> int:
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"""
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所有股票的总页数
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https://vip.stock.finance.sina.com.cn/mkt/#hs_a
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:return: 需要采集的股票总页数
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:rtype: int
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"""
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res = requests.get(zh_sina_a_stock_count_url)
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page_count = int(re.findall(re.compile(r"\d+"), res.text)[0]) / 80
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if isinstance(page_count, int):
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return page_count
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else:
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return int(page_count) + 1
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def get_stock_all_a_sina(interval: Optional[int] = 3, page_count: Optional[int] = None,
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proxies: Optional[dict] = {}) -> pd.DataFrame:
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"""
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新浪财经-所有 A 股的实时行情数据; 重复运行本函数会被新浪暂时封 IP
|
||
https://vip.stock.finance.sina.com.cn/mkt/#hs_a
|
||
:param interval:请求间隔时间
|
||
:param page_count:限制抓取页数
|
||
:param proxies: 代理ip {}
|
||
:return: 所有股票的实时行情数据
|
||
:rtype: pandas.DataFrame
|
||
1、代码:TS_CODE
|
||
2、名称:NAME
|
||
3、最新价:PRICE
|
||
4、涨跌额:CHANGE
|
||
5、涨跌幅:PCT_CHANGE
|
||
6、买入:BUY
|
||
7、卖出:SALE
|
||
8、昨收:CLOSE
|
||
9、今开:OPEN
|
||
10、最高:HIGH
|
||
11、最低:LOW
|
||
12、成交量:VOLUME
|
||
13、成交额:AMOUNT
|
||
14、时间戳:TIME
|
||
|
||
"""
|
||
big_df = pd.DataFrame()
|
||
if not page_count:
|
||
page_count = _get_zh_a_page_count()
|
||
zh_sina_stock_payload_copy = zh_sina_a_stock_payload.copy()
|
||
for page in tqdm(
|
||
range(1, page_count + 1), leave=False, desc="Please wait for a moment"
|
||
):
|
||
zh_sina_stock_payload_copy.update({"page": page})
|
||
r = requests.get(
|
||
zh_sina_a_stock_url, headers=zh_sina_a_stock_headers,
|
||
cookies=zh_sina_a_stock_cookies,
|
||
params=zh_sina_stock_payload_copy,
|
||
proxies=proxies,
|
||
)
|
||
data_json = r.json()
|
||
big_df = pd.concat(
|
||
[big_df, pd.DataFrame(data_json)], ignore_index=True
|
||
)
|
||
|
||
time.sleep(interval)
|
||
|
||
big_df = big_df.astype(
|
||
{
|
||
"trade": "float",
|
||
"pricechange": "float",
|
||
"changepercent": "float",
|
||
"buy": "float",
|
||
"sell": "float",
|
||
"settlement": "float",
|
||
"open": "float",
|
||
"high": "float",
|
||
"low": "float",
|
||
"volume": "float",
|
||
"amount": "float",
|
||
"per": "float",
|
||
"pb": "float",
|
||
"mktcap": "float",
|
||
"nmc": "float",
|
||
"turnoverratio": "float",
|
||
}
|
||
)
|
||
big_df.columns = [
|
||
"代码",
|
||
"_",
|
||
"名称",
|
||
"最新价",
|
||
"涨跌额",
|
||
"涨跌幅",
|
||
"买入",
|
||
"卖出",
|
||
"昨收",
|
||
"今开",
|
||
"最高",
|
||
"最低",
|
||
"成交量",
|
||
"成交额",
|
||
"时间戳",
|
||
"_",
|
||
"_",
|
||
"_",
|
||
"_",
|
||
"_",
|
||
]
|
||
big_df = big_df[
|
||
[
|
||
"代码",
|
||
"名称",
|
||
"最新价",
|
||
"涨跌额",
|
||
"涨跌幅",
|
||
"买入",
|
||
"卖出",
|
||
"昨收",
|
||
"今开",
|
||
"最高",
|
||
"最低",
|
||
"成交量",
|
||
"成交额",
|
||
"时间戳",
|
||
]
|
||
]
|
||
big_df["代码"] = big_df["代码"].apply(format_stock_code)
|
||
big_df.columns = [
|
||
"TS_CODE",
|
||
"NAME",
|
||
"PRICE",
|
||
"CHANGE",
|
||
"PCT_CHANGE",
|
||
"BUY",
|
||
"SALE",
|
||
"CLOSE",
|
||
"OPEN",
|
||
"HIGH",
|
||
"LOW",
|
||
"VOLUME",
|
||
"AMOUNT",
|
||
"TIME",
|
||
|
||
]
|
||
df_sorted = big_df.sort_values(by='PCT_CHANGE', ascending=False).reset_index(drop=True)
|
||
return df_sorted
|
||
|
||
|
||
def get_realtime_quotes_dc(symbols="688553"):
|
||
"""
|
||
https://quote.eastmoney.com/sh601096.html
|
||
"""
|
||
symbols = str(symbols).split(",")[0]
|
||
url = "https://push2.eastmoney.com/api/qt/stock/get"
|
||
symbol = re.search(r"(\d+)", symbols, re.S | re.M).group(1)
|
||
# print(symbol)
|
||
params = {
|
||
"invt": "2",
|
||
"fltt": "1",
|
||
# "cb": "jQuery35108939078769986013_1701853424476",
|
||
"fields": "f58,f734,f107,f57,f43,f59,f169,f301,f60,f170,f152,f177,f111,f46,f44,f45,f47,f260,f48,f261,f279,f277,f278,f288,f19,f17,f531,f15,f13,f11,f20,f18,f16,f14,f12,f39,f37,f35,f33,f31,f40,f38,f36,f34,f32,f211,f212,f213,f214,f215,f210,f209,f208,f207,f206,f161,f49,f171,f50,f86,f84,f85,f168,f108,f116,f167,f164,f162,f163,f92,f71,f117,f292,f51,f52,f191,f192,f262,f294,f295,f748,f747",
|
||
"secid": f"0.{symbol}",
|
||
"ut": "fa5fd1943c7b386f172d6893dbfba10b",
|
||
"wbp2u": "|0|0|0|web",
|
||
"_": _get_current_timestamp()
|
||
}
|
||
if not verify_stock_or_index(symbols):
|
||
params["secid"] = f"1.{symbol}"
|
||
# print(params["secid"])
|
||
response = requests.get(url, headers=rtqv.dc_cookies, cookies=rtqv.dc_headers, params=params)
|
||
data_info = response.json()["data"]
|
||
if not data_info:
|
||
return pd.DataFrame()
|
||
name = data_info["f58"]
|
||
open = data_info["f46"] # / 100
|
||
high = data_info["f44"] # / 100
|
||
pre_close = data_info["f60"] # / 100
|
||
low = data_info["f45"] # / 100
|
||
price = data_info["f43"] # / 100 if data_info["f43"] != "-" else ""
|
||
b5_v = format_str_to_float(data_info["f12"])
|
||
b5_p = data_info["f11"] # / 100 if data_info["f11"] != "-" else ""
|
||
b4_v = format_str_to_float(data_info["f14"])
|
||
b4_p = data_info["f13"] # / 100 if data_info["f13"] != "-" else ""
|
||
b3_v = format_str_to_float(data_info["f16"])
|
||
b3_p = data_info["f15"] # / 100 if data_info["f15"] != "-" else ""
|
||
b2_v = format_str_to_float(data_info["f18"])
|
||
b2_p = data_info["f17"] # / 100 if data_info["f17"] != "-" else ""
|
||
b1_v = format_str_to_float(data_info["f20"])
|
||
b1_p = data_info["f19"] # / 100 if data_info["f19"] != "-" else ""
|
||
a5_v = format_str_to_float(data_info["f32"])
|
||
a5_p = data_info["f31"] # / 100 if data_info["f31"] != "-" else ""
|
||
a4_v = format_str_to_float(data_info["f34"])
|
||
a4_p = data_info["f33"] # / 100 if data_info["f33"] != "-" else ""
|
||
a3_v = format_str_to_float(data_info["f36"])
|
||
a3_p = data_info["f35"] # / 100 if data_info["f35"] != "-" else ""
|
||
a2_v = format_str_to_float(data_info["f38"])
|
||
a2_p = data_info["f37"] # / 100 if data_info["f38"] != "-" else ""
|
||
a1_v = format_str_to_float(data_info["f40"])
|
||
a1_p = data_info["f39"] # / 100 if data_info["f39"] != "-" else ""
|
||
date_time = timestemp_to_time(data_info["f86"])
|
||
date = date_time[0:10]
|
||
times = date_time[10:]
|
||
volume = format_str_to_float(data_info["f47"])
|
||
amount = format_str_to_float(data_info["f48"])
|
||
bid = format_str_to_float(data_info["f19"])
|
||
ask = format_str_to_float(data_info["f39"])
|
||
code = symbols
|
||
data_list = [[name, open, pre_close, price, high, low, bid, ask, volume, amount,
|
||
b1_v, b1_p, b2_v, b2_p, b3_v, b3_p, b4_v, b4_p, b5_v, b5_p,
|
||
a1_v, a1_p, a2_v, a2_p, a3_v, a3_p, a4_v, a4_p, a5_v, a5_p, date, times, code]]
|
||
df = pd.DataFrame(data_list, columns=rtqv.LIVE_DATA_COLS)
|
||
df["DATE"] = df["DATE"].apply(format_date_str)
|
||
df["ASK"] = df["ASK"].apply(format_dc_str)
|
||
df["OPEN"] = df["OPEN"].apply(format_dc_str)
|
||
df["HIGH"] = df["HIGH"].apply(format_dc_str)
|
||
df["LOW"] = df["LOW"].apply(format_dc_str)
|
||
df["PRE_CLOSE"] = df["PRE_CLOSE"].apply(format_dc_str)
|
||
df["BID"] = df["BID"].apply(format_dc_str)
|
||
df["A1_P"] = df["A1_P"].apply(format_dc_str)
|
||
df["A2_P"] = df["A2_P"].apply(format_dc_str)
|
||
df["A3_P"] = df["A3_P"].apply(format_dc_str)
|
||
df["A4_P"] = df["A4_P"].apply(format_dc_str)
|
||
df["A5_P"] = df["A5_P"].apply(format_dc_str)
|
||
df["PRICE"] = df["PRICE"].apply(format_dc_str)
|
||
df["B1_P"] = df["B1_P"].apply(format_dc_str)
|
||
df["B2_P"] = df["B2_P"].apply(format_dc_str)
|
||
df["B3_P"] = df["B3_P"].apply(format_dc_str)
|
||
df["B4_P"] = df["B4_P"].apply(format_dc_str)
|
||
df["B5_P"] = df["B5_P"].apply(format_dc_str)
|
||
new_order = rtqv.LIVE_DATA_COLS_REINDEX
|
||
df = df[new_order]
|
||
return df
|
||
|
||
|
||
def format_dc_str(x):
|
||
return float(x / 100) if x != "-" else 0
|
||
|
||
|
||
def format_str_to_float(x):
|
||
try:
|
||
return float(x) if x != "" else 0
|
||
except:
|
||
return 0
|
||
|
||
|
||
if __name__ == '__main__':
|
||
# df = realtime_quote(ts_code="000688.SH,000010.SH,000012.SH,399005.SZ", src="sina")
|
||
# df = realtime_list(src="dc", page_count=1)
|
||
df = realtime_list(src="sina")
|
||
print(df)
|
||
# ts_code = '399005.SZ'
|
||
# ts_code = '000001.SZ'
|
||
# # ts_code = '836149.BJ'
|
||
# # ts_code = '600000.SH'
|
||
# # ts_code = '000001.SH'
|
||
# # ts_code = '000010.SH'
|
||
# ts_code = '600148.SH'
|
||
# df = realtime_quote(src="dc", ts_code=ts_code)
|
||
# print(df)
|
||
|
||
|