知微
fa45d8aa5f
- health_checklist.json: 192.168.1.122→node122
- ocr_client.py: docstring IP→node122
- docs/market-data-requirements.md: IP→node122
- 所有API调用通过ProxyHandler({})绕过系统代理
Privoxy对node122:18003返回500，直连正常
256 lines
8.4 KiB
Python
256 lines
8.4 KiB
Python
#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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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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Desc: 腾讯-股票-实时行情-成交明细
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成交明细-每个交易日 16:00 提供当日数据
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港股报价延时 15 分钟
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"""
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import warnings
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import pandas as pd
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import requests
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from io import StringIO
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from tushare.util.verify_token import require_permission
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from tushare.util.format_stock_code import format_stock_code
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from tushare.stock.rtq_vars import zh_sina_a_stock_cookies, zh_sina_a_stock_headers
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import time
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import json
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from typing import Optional
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from tushare.util.form_date import get_current_date
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from tushare.stock import rtq_vars
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from tushare.util.format_stock_code import symbol_verify
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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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}
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@require_permission(event_name="realtime_tick", event_detail="个股历史分笔数据")
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def realtime_tick(ts_code: str = "000001.SZ", src: Optional[str] = "tx",
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page_count: Optional[int] = None) -> pd.DataFrame:
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"""
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历史分笔数据
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:param ts_code: 股票代码
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:type ts_code: str
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:param src: 来源 腾讯财经tx 新浪财经sina
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:type src: str
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:param page_count: 限制页数
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:type page_count: str
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:return: 历史分笔数据
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:rtype: pandas.DataFrame
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1、TIME : 成交时间
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2、PRICE : 成交价格
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3、PCHANGE : 涨跌幅
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4、CHANGE : 价格变动
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5、VOLUME : 成交量(手)
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6、AMOUNT : 成交额(元)
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7、TYPE : 性质
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"""
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symbol = symbol_verify(ts_code)
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if src == "sina":
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return get_stock_sina_a_divide_amount(symbol, page_count)
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elif src == 'dc':
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return get_stock_dc_a_divide_amount(symbol, page_count)
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else:
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return get_stock_tx_a_divide_amount(symbol, page_count)
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def get_stock_tx_a_divide_amount(symbol: str = "sz000001", page_count: Optional[int] = None) -> pd.DataFrame:
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"""
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腾讯财经-历史分笔数据
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https://gu.qq.com/sz300494/gp/detail
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:param symbol: 股票代码
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:type symbol: str
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:param page_count: 限制页数
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:type page_count: str
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:return: 历史分笔数据
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:rtype: pandas.DataFrame
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"""
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symbols = str(symbol).lower().split(".")
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symbol = f"{symbols[1]}{symbols[0]}"
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big_df = pd.DataFrame()
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page = 0
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warnings.warn("正在下载数据，请稍等")
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while True:
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try:
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url = "http://stock.gtimg.cn/data/index.php"
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params = {
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"appn": "detail",
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"action": "data",
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"c": symbol,
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"p": page,
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}
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r = requests.get(url, headers=headers, params=params)
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text_data = r.text
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temp_df = (
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pd.DataFrame(eval(text_data[text_data.find("["):])[1].split("|"))
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.iloc[:, 0]
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.str.split("/", expand=True)
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)
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page += 1
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big_df = pd.concat([big_df, temp_df], ignore_index=True)
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time.sleep(0.5)
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if page_count and page >= page_count:
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break
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except:
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break
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if not big_df.empty:
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big_df = big_df.iloc[:, 1:].copy()
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# big_df.columns = ["成交时间", "成交价格", "价格变动", "成交量", "成交金额", "性质"]
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big_df.columns = rtq_vars.TICK_COLUMNS
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big_df.reset_index(drop=True, inplace=True)
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property_map = {
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"S": "卖盘",
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"B": "买盘",
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"M": "中性盘",
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}
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# big_df["性质"] = big_df["性质"].map(property_map)
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big_df["TYPE"] = big_df["TYPE"].map(property_map)
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big_df = big_df.astype(
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{
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"TIME": str,
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"PRICE": float,
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"CHANGE": float,
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"VOLUME": int,
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"AMOUNT": int,
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"TYPE": str,
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}
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)
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return big_df
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def get_stock_sina_a_divide_amount(symbol: str = "sz000001", page_count: Optional[int] = None, ) -> pd.DataFrame:
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"""
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腾新浪财经-历史分笔数据
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https://vip.stock.finance.sina.com.cn/quotes_service/view/vMS_tradedetail.php?symbol=sh688553
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:param symbol: 股票代码
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:type symbol: str
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:param page_count: 限制页数
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:type page_count: str
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:return: 历史分笔数据
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:rtype: pandas.DataFrame
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"""
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warnings.warn("正在下载数据，请稍等")
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symbols = str(symbol).lower().split(".")
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symbol = f"{symbols[1]}{symbols[0]}"
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page = 0
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big_df = pd.DataFrame()
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while True:
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try:
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url = "https://vip.stock.finance.sina.com.cn/quotes_service/view/vMS_tradedetail.php"
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params = {
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"symbol": symbol,
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"date": get_current_date(date_format="%Y-%m-%d"),
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"page": page
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}
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response = requests.get(url, headers=zh_sina_a_stock_headers, cookies=zh_sina_a_stock_cookies,
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params=params)
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temp_df = (pd.read_html(StringIO(response.content.decode("GBK")))[3])
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big_df = pd.concat([big_df, temp_df], ignore_index=True)
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page += 1
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if page_count and page >= page_count:
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break
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except:
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break
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time.sleep(0.5)
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if not big_df.empty:
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big_df = big_df.iloc[:, 0:].copy()
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# big_df.columns = ["成交时间", "成交价格", "涨跌幅", "价格变动", "成交量(手)", "成交额(元)", "性质"]
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big_df.columns = rtq_vars.TODAY_TICK_COLUMNS
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big_df.reset_index(drop=True, inplace=True)
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# big_df = big_df.astype(
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# {
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# "成交时间": str,
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# "成交价格": float,
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# "涨跌幅": str,
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# "价格变动": str,
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# "成交量(手)": int,
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# "成交额(元)": int,
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# "性质": str,
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# }
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# )
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big_df = big_df.astype(
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{
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"TIME": str,
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"PRICE": float,
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"PCHANGE": str,
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"CHANGE": str,
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"VOLUME": int,
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"AMOUNT": int,
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"TYPE": str,
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}
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)
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return big_df
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def __event_stream(url, params, ):
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response = requests.get(url, params=params, stream=True)
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event_data = ""
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for line in response.iter_lines():
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# 过滤掉保持连接的空行
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if line:
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event_data += line.decode() + "\n"
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elif event_data:
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yield event_data
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event_data = ""
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def get_stock_dc_a_divide_amount(symbol: str = "000001", page_count: Optional[int] = None) -> pd.DataFrame:
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"""
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东方财富-分时数据
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https://quote.eastmoney.com/f1.html?newcode=0.000001
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:param symbol: 股票代码
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:type symbol: str
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:return: 分时数据
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:rtype: pandas.DataFrame
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"""
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symbols = str(symbol).lower().split(".")
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symbol = symbols[0]
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# print(symbol)
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market_code = 1 if symbol.startswith("6") else 0
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url = "https://70.push2.eastmoney.com/api/qt/stock/details/sse"
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params = {
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"fields1": "f1,f2,f3,f4",
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"fields2": "f51,f52,f53,f54,f55",
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"mpi": "2000",
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"ut": "bd1d9ddb04089700cf9c27f6f7426281",
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"fltt": "2",
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"pos": "-0",
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"secid": f"{market_code}.{symbol}",
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"wbp2u": "|0|0|0|web",
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}
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big_df = pd.DataFrame() # 创建一个空的 DataFrame
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for event in __event_stream(url, params):
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# 从每个事件的数据行中删除 "data: "，然后解析 JSON
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event_json = json.loads(event.replace("data: ", ""))
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# 将 JSON 数据转换为 DataFrame，然后添加到主 DataFrame 中
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temp_df = pd.DataFrame(
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[item.split(",") for item in event_json["data"]["details"]]
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)
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big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
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break
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big_df.columns = ["TIME", "PRICE", "VOLUME", "-", "TYPE"]
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big_df["TYPE"] = big_df["TYPE"].map(
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{"2": "买盘", "1": "卖盘", "4": "中性盘"}
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)
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big_df = big_df[["TIME", "PRICE", "VOLUME", "TYPE"]]
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big_df["PRICE"] = pd.to_numeric(big_df["PRICE"], errors="coerce")
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big_df["VOLUME"] = pd.to_numeric(big_df["VOLUME"], errors="coerce")
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return big_df
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if __name__ == '__main__':
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df = realtime_tick(ts_code="000001.SZ", src="dc", page_count=1)
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print(help(realtime_tick))
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print(df)
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# r = get_stock_dc_a_divide_amount()
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# print(r)
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