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
729 lines
26 KiB
Python
729 lines
26 KiB
Python
#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2024/3/20 16:00
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Desc: 新浪财经-国内期货-实时数据获取
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https://vip.stock.finance.sina.com.cn/quotes_service/view/qihuohangqing.html#titlePos_3
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P.S. 注意采集速度, 容易封禁 IP, 如果不能访问请稍后再试
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"""
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import json
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import time
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from functools import lru_cache
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import pandas as pd
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import requests
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import py_mini_racer
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from akshare.futures.cons import (
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zh_subscribe_exchange_symbol_url,
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zh_match_main_contract_url,
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zh_match_main_contract_payload,
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)
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from akshare.futures.futures_contract_detail import futures_contract_detail
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from akshare.utils import demjson
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@lru_cache()
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def futures_symbol_mark() -> pd.DataFrame:
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"""
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期货的品种和代码映射
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https://vip.stock.finance.sina.com.cn/quotes_service/view/js/qihuohangqing.js
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:return: 期货的品种和代码映射
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:rtype: pandas.DataFrame
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"""
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url = (
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"https://vip.stock.finance.sina.com.cn/quotes_service/view/js/qihuohangqing.js"
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)
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r = requests.get(url)
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r.encoding = "gb2312"
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data_text = r.text
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raw_json = data_text[data_text.find("{") : data_text.find("}") + 1]
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data_json = demjson.decode(raw_json)
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czce_mark_list = [item[1] for item in data_json["czce"][1:]]
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dce_mark_list = [item[1] for item in data_json["dce"][1:]]
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shfe_mark_list = [item[1] for item in data_json["shfe"][1:]]
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cffex_mark_list = [item[1] for item in data_json["cffex"][1:]]
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gfex_mark_list = [item[1] for item in data_json["gfex"][1:]]
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all_mark_list = (
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czce_mark_list
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+ dce_mark_list
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+ shfe_mark_list
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+ cffex_mark_list
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+ gfex_mark_list
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)
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czce_market_name_list = [data_json["czce"][0]] * len(czce_mark_list)
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dce_market_name_list = [data_json["dce"][0]] * len(dce_mark_list)
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shfe_market_name_list = [data_json["shfe"][0]] * len(shfe_mark_list)
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cffex_market_name_list = [data_json["cffex"][0]] * len(cffex_mark_list)
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gfex_market_name_list = [data_json["gfex"][0]] * len(gfex_mark_list)
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all_market_name_list = (
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czce_market_name_list
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+ dce_market_name_list
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+ shfe_market_name_list
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+ cffex_market_name_list
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+ gfex_market_name_list
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)
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czce_symbol_list = [item[0] for item in data_json["czce"][1:]]
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dce_symbol_list = [item[0] for item in data_json["dce"][1:]]
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shfe_symbol_list = [item[0] for item in data_json["shfe"][1:]]
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cffex_symbol_list = [item[0] for item in data_json["cffex"][1:]]
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gfex_symbol_list = [item[0] for item in data_json["gfex"][1:]]
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all_symbol_list = (
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czce_symbol_list
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+ dce_symbol_list
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+ shfe_symbol_list
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+ cffex_symbol_list
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+ gfex_symbol_list
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)
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temp_df = pd.DataFrame([all_market_name_list, all_symbol_list, all_mark_list]).T
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temp_df.columns = [
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"exchange",
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"symbol",
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"mark",
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]
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return temp_df
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def futures_zh_realtime(symbol: str = "PTA") -> pd.DataFrame:
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"""
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期货品种当前时刻所有可交易的合约实时数据
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https://vip.stock.finance.sina.com.cn/quotes_service/view/qihuohangqing.html#titlePos_1
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:param symbol: 品种名称；可以通过 ak.futures_symbol_mark() 获取所有品种命名表
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:type symbol: str
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:return: 期货品种当前时刻所有可交易的合约实时数据
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:rtype: pandas.DataFrame
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"""
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_futures_symbol_mark_df = futures_symbol_mark()
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symbol_mark_map = dict(
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zip(_futures_symbol_mark_df["symbol"], _futures_symbol_mark_df["mark"])
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)
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url = "https://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/Market_Center.getHQFuturesData"
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params = {
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"page": "1",
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"sort": "position",
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"asc": "0",
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"node": symbol_mark_map[symbol],
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"base": "futures",
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}
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r = requests.get(url, params=params)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json)
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temp_df["trade"] = pd.to_numeric(temp_df["trade"], errors="coerce")
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temp_df["settlement"] = pd.to_numeric(temp_df["settlement"], errors="coerce")
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temp_df["presettlement"] = pd.to_numeric(temp_df["presettlement"], errors="coerce")
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temp_df["open"] = pd.to_numeric(temp_df["open"], errors="coerce")
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temp_df["high"] = pd.to_numeric(temp_df["high"], errors="coerce")
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temp_df["low"] = pd.to_numeric(temp_df["low"], errors="coerce")
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temp_df["close"] = pd.to_numeric(temp_df["close"], errors="coerce")
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temp_df["bidprice1"] = pd.to_numeric(temp_df["bidprice1"], errors="coerce")
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temp_df["askprice1"] = pd.to_numeric(temp_df["askprice1"], errors="coerce")
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temp_df["bidvol1"] = pd.to_numeric(temp_df["bidvol1"], errors="coerce")
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temp_df["askvol1"] = pd.to_numeric(temp_df["askvol1"], errors="coerce")
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temp_df["volume"] = pd.to_numeric(temp_df["volume"], errors="coerce")
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temp_df["position"] = pd.to_numeric(temp_df["position"], errors="coerce")
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temp_df["preclose"] = pd.to_numeric(temp_df["preclose"], errors="coerce")
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temp_df["changepercent"] = pd.to_numeric(temp_df["changepercent"], errors="coerce")
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temp_df["bid"] = pd.to_numeric(temp_df["bid"], errors="coerce")
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temp_df["ask"] = pd.to_numeric(temp_df["ask"], errors="coerce")
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temp_df["prevsettlement"] = pd.to_numeric(
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temp_df["prevsettlement"], errors="coerce"
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)
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return temp_df
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def zh_subscribe_exchange_symbol(symbol: str = "cffex") -> pd.DataFrame:
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"""
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交易所具体的可交易品种
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https://vip.stock.finance.sina.com.cn/quotes_service/view/qihuohangqing.html#titlePos_1
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:param symbol: choice of {'czce', 'dce', 'shfe', 'cffex', 'gfex'}
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:type symbol: str
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:return: 交易所具体的可交易品种
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:rtype: dict
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"""
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r = requests.get(zh_subscribe_exchange_symbol_url)
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r.encoding = "gbk"
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data_text = r.text
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data_json = demjson.decode(
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data_text[data_text.find("{") : data_text.find("};") + 1]
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)
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if symbol == "czce":
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data_json["czce"].remove("郑州商品交易所")
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return pd.DataFrame(data_json["czce"])
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if symbol == "dce":
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data_json["dce"].remove("大连商品交易所")
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return pd.DataFrame(data_json["dce"])
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if symbol == "shfe":
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data_json["shfe"].remove("上海期货交易所")
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return pd.DataFrame(data_json["shfe"])
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if symbol == "cffex":
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data_json["cffex"].remove("中国金融期货交易所")
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return pd.DataFrame(data_json["cffex"])
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if symbol == "gfex":
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data_json["gfex"].remove("广州期货交易所")
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return pd.DataFrame(data_json["gfex"])
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def match_main_contract(symbol: str = "cffex") -> str:
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"""
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新浪财经-期货-主力合约
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https://vip.stock.finance.sina.com.cn/quotes_service/view/qihuohangqing.html#titlePos_1
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:param symbol: choice of {'czce', 'dce', 'shfe', 'cffex', 'gfex'}
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:type symbol: str
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:return: 主力合约的字符串
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:rtype: str
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"""
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subscribe_exchange_list = []
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exchange_symbol_list = zh_subscribe_exchange_symbol(symbol).iloc[:, 1].tolist()
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for item in exchange_symbol_list:
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# item = 'sngz_qh'
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zh_match_main_contract_payload.update({"node": item})
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res = requests.get(
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zh_match_main_contract_url, params=zh_match_main_contract_payload
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)
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data_json = demjson.decode(res.text)
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data_df = pd.DataFrame(data_json)
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try:
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main_contract = data_df[data_df.iloc[:, 3:].duplicated()]
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print(main_contract["symbol"].values[0])
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subscribe_exchange_list.append(main_contract["symbol"].values[0])
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except: # noqa: E722
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if len(data_df) == 1:
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subscribe_exchange_list.append(data_df["symbol"].values[0])
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print(data_df["symbol"].values[0])
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else:
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print(item, "无主力合约")
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continue
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print(f"{symbol}主力合约获取成功")
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return ",".join([item for item in subscribe_exchange_list])
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def futures_zh_spot(
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symbol: str = "V2309",
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market: str = "CF",
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adjust: str = "0",
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) -> pd.DataFrame:
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"""
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期货的实时行情数据
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https://vip.stock.finance.sina.com.cn/quotes_service/view/qihuohangqing.html#titlePos_1
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:param symbol: 合约名称的字符串组合
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:type symbol: str
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:param market: CF 为商品期货
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:type market: str
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:param adjust: '1' or '0'；字符串的 0 或 1；返回合约、交易所和最小变动单位的实时数据, 返回数据会变慢
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:type adjust: str
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:return: 期货的实时行情数据
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:rtype: pandas.DataFrame
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"""
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file_data = "Math.round(Math.random() * 2147483648).toString(16)"
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ctx = py_mini_racer.MiniRacer()
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rn_code = ctx.eval(file_data)
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subscribe_list = ",".join(["nf_" + item.strip() for item in symbol.split(",")])
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url = f"https://hq.sinajs.cn/rn={rn_code}&list={subscribe_list}"
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headers = {
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"Accept": "*/*",
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"Accept-Encoding": "gzip, deflate",
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"Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8",
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"Cache-Control": "no-cache",
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"Host": "hq.sinajs.cn",
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"Pragma": "no-cache",
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"Proxy-Connection": "keep-alive",
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"Referer": "https://vip.stock.finance.sina.com.cn/",
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
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"Chrome/97.0.4692.71 Safari/537.36",
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}
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r = requests.get(url, headers=headers)
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data_df = pd.DataFrame(
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[
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item.strip().split("=")[1].split(",")
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for item in r.text.split(";")
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if item.strip() != ""
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]
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)
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data_df.iloc[:, 0] = data_df.iloc[:, 0].str.replace('"', "")
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data_df.iloc[:, -1] = data_df.iloc[:, -1].str.replace('"', "")
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if adjust == "1":
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contract_name_list = [item.split("_")[1] for item in subscribe_list.split(",")]
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contract_min_list = []
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contract_exchange_list = []
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for contract_name in contract_name_list:
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temp_df = futures_contract_detail(symbol=contract_name)
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exchange_name = temp_df[temp_df["item"] == "上市交易所"]["value"].values[0]
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contract_exchange_list.append(exchange_name)
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contract_min = temp_df[temp_df["item"] == "最小变动价位"]["value"].values[0]
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contract_min_list.append(contract_min)
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if market == "CF":
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data_df.columns = [
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"symbol",
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"time",
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"open",
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"high",
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"low",
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"last_close",
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"bid_price",
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"ask_price",
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"current_price",
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"avg_price",
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"last_settle_price",
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"buy_vol",
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"sell_vol",
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"hold",
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"volume",
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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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"_",
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"_",
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"_",
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"_",
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"_",
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]
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data_df = data_df[
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[
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"symbol",
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"time",
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"open",
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"high",
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"low",
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"current_price",
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"bid_price",
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"ask_price",
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"buy_vol",
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"sell_vol",
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"hold",
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"volume",
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"avg_price",
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"last_close",
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"last_settle_price",
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]
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]
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data_df["exchange"] = contract_exchange_list
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data_df["contract"] = contract_name_list
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data_df["contract_min_change"] = contract_min_list
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data_df["open"] = pd.to_numeric(data_df["open"], errors="coerce")
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data_df["high"] = pd.to_numeric(data_df["high"], errors="coerce")
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data_df["low"] = pd.to_numeric(data_df["low"], errors="coerce")
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data_df["current_price"] = pd.to_numeric(
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data_df["current_price"], errors="coerce"
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)
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data_df["bid_price"] = pd.to_numeric(data_df["bid_price"], errors="coerce")
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data_df["ask_price"] = pd.to_numeric(data_df["ask_price"], errors="coerce")
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data_df["buy_vol"] = pd.to_numeric(data_df["buy_vol"], errors="coerce")
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data_df["sell_vol"] = pd.to_numeric(data_df["sell_vol"], errors="coerce")
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data_df["hold"] = pd.to_numeric(data_df["hold"], errors="coerce")
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data_df["volume"] = pd.to_numeric(data_df["volume"], errors="coerce")
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data_df["avg_price"] = pd.to_numeric(data_df["avg_price"], errors="coerce")
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data_df["last_close"] = pd.to_numeric(
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data_df["last_close"], errors="coerce"
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)
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data_df["last_settle_price"] = pd.to_numeric(
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data_df["last_settle_price"], errors="coerce"
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)
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data_df.dropna(subset=["current_price"], ignore_index=True, inplace=True)
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return data_df
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else:
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data_df.columns = [
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"open",
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"high",
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"low",
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"current_price",
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"volume",
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"amount",
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"hold",
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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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"_",
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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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"_",
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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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"_",
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"_",
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"_",
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"__",
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"time",
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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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"_",
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"_",
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"_",
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"symbol",
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]
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data_df = data_df[
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[
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"symbol",
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"time",
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"open",
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"high",
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"low",
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"current_price",
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"hold",
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"volume",
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"amount",
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]
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]
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data_df["exchange"] = contract_exchange_list
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data_df["contract"] = contract_name_list
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data_df["contract_min_change"] = contract_min_list
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data_df["open"] = pd.to_numeric(data_df["open"], errors="coerce")
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data_df["high"] = pd.to_numeric(data_df["high"], errors="coerce")
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data_df["low"] = pd.to_numeric(data_df["low"], errors="coerce")
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data_df["current_price"] = pd.to_numeric(
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data_df["current_price"], errors="coerce"
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)
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data_df["hold"] = pd.to_numeric(data_df["hold"], errors="coerce")
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data_df["volume"] = pd.to_numeric(data_df["volume"], errors="coerce")
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data_df["amount"] = pd.to_numeric(data_df["amount"], errors="coerce")
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data_df.dropna(subset=["current_price"], ignore_index=True, inplace=True)
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return data_df
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else:
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if market == "CF":
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# 此处由于 20220601 接口变动，增加了字段，此处增加异常判断，except 后为新代码
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try:
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data_df.columns = [
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"symbol",
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"time",
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"open",
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"high",
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"low",
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"last_close",
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"bid_price",
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"ask_price",
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"current_price",
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"avg_price",
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"last_settle_price",
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"buy_vol",
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"sell_vol",
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"hold",
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"volume",
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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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|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
]
|
|
except: # noqa: E722
|
|
data_df.columns = [
|
|
"symbol",
|
|
"time",
|
|
"open",
|
|
"high",
|
|
"low",
|
|
"last_close",
|
|
"bid_price",
|
|
"ask_price",
|
|
"current_price",
|
|
"avg_price",
|
|
"last_settle_price",
|
|
"buy_vol",
|
|
"sell_vol",
|
|
"hold",
|
|
"volume",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
]
|
|
data_df = data_df[
|
|
[
|
|
"symbol",
|
|
"time",
|
|
"open",
|
|
"high",
|
|
"low",
|
|
"current_price",
|
|
"bid_price",
|
|
"ask_price",
|
|
"buy_vol",
|
|
"sell_vol",
|
|
"hold",
|
|
"volume",
|
|
"avg_price",
|
|
"last_close",
|
|
"last_settle_price",
|
|
]
|
|
]
|
|
|
|
data_df["open"] = pd.to_numeric(data_df["open"], errors="coerce")
|
|
data_df["high"] = pd.to_numeric(data_df["high"], errors="coerce")
|
|
data_df["low"] = pd.to_numeric(data_df["low"], errors="coerce")
|
|
data_df["current_price"] = pd.to_numeric(
|
|
data_df["current_price"], errors="coerce"
|
|
)
|
|
data_df["bid_price"] = pd.to_numeric(data_df["bid_price"], errors="coerce")
|
|
data_df["ask_price"] = pd.to_numeric(data_df["ask_price"], errors="coerce")
|
|
data_df["buy_vol"] = pd.to_numeric(data_df["buy_vol"], errors="coerce")
|
|
data_df["sell_vol"] = pd.to_numeric(data_df["sell_vol"], errors="coerce")
|
|
data_df["hold"] = pd.to_numeric(data_df["hold"], errors="coerce")
|
|
data_df["volume"] = pd.to_numeric(data_df["volume"], errors="coerce")
|
|
data_df["avg_price"] = pd.to_numeric(data_df["avg_price"], errors="coerce")
|
|
data_df["last_close"] = pd.to_numeric(
|
|
data_df["last_close"], errors="coerce"
|
|
)
|
|
data_df["last_settle_price"] = pd.to_numeric(
|
|
data_df["last_settle_price"], errors="coerce"
|
|
)
|
|
data_df.dropna(subset=["current_price"], ignore_index=True, inplace=True)
|
|
return data_df
|
|
else:
|
|
data_df.columns = [
|
|
"open",
|
|
"high",
|
|
"low",
|
|
"current_price",
|
|
"volume",
|
|
"amount",
|
|
"hold",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"__",
|
|
"time",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"_",
|
|
"symbol",
|
|
]
|
|
data_df = data_df[
|
|
[
|
|
"symbol",
|
|
"time",
|
|
"open",
|
|
"high",
|
|
"low",
|
|
"current_price",
|
|
"hold",
|
|
"volume",
|
|
"amount",
|
|
]
|
|
]
|
|
data_df["open"] = pd.to_numeric(data_df["open"], errors="coerce")
|
|
data_df["high"] = pd.to_numeric(data_df["high"], errors="coerce")
|
|
data_df["low"] = pd.to_numeric(data_df["low"], errors="coerce")
|
|
data_df["current_price"] = pd.to_numeric(
|
|
data_df["current_price"], errors="coerce"
|
|
)
|
|
data_df["hold"] = pd.to_numeric(data_df["hold"], errors="coerce")
|
|
data_df["volume"] = pd.to_numeric(data_df["volume"], errors="coerce")
|
|
data_df["amount"] = pd.to_numeric(data_df["amount"], errors="coerce")
|
|
data_df.dropna(subset=["current_price"], inplace=True, ignore_index=True)
|
|
return data_df
|
|
|
|
|
|
def futures_zh_minute_sina(symbol: str = "IF2008", period: str = "1") -> pd.DataFrame:
|
|
"""
|
|
中国各品种期货分钟频率数据
|
|
https://vip.stock.finance.sina.com.cn/quotes_service/view/qihuohangqing.html#titlePos_3
|
|
:param symbol: 可以通过 match_main_contract(symbol="cffex") 获取, 或者访问网页获取
|
|
:type symbol: str
|
|
:param period: choice of {"1": "1分钟", "5": "5分钟", "15": "15分钟", "30": "30分钟", "60": "60分钟"}
|
|
:type period: str
|
|
:return: 指定 symbol 和 period 的数据
|
|
:rtype: pandas.DataFrame
|
|
"""
|
|
url = "https://stock2.finance.sina.com.cn/futures/api/jsonp.php/=/InnerFuturesNewService.getFewMinLine"
|
|
params = {
|
|
"symbol": symbol,
|
|
"type": period,
|
|
}
|
|
r = requests.get(url, params=params)
|
|
temp_df = pd.DataFrame(json.loads(r.text.split("=(")[1].split(");")[0]))
|
|
temp_df.columns = [
|
|
"datetime",
|
|
"open",
|
|
"high",
|
|
"low",
|
|
"close",
|
|
"volume",
|
|
"hold",
|
|
]
|
|
temp_df["open"] = pd.to_numeric(temp_df["open"], errors="coerce")
|
|
temp_df["high"] = pd.to_numeric(temp_df["high"], errors="coerce")
|
|
temp_df["low"] = pd.to_numeric(temp_df["low"], errors="coerce")
|
|
temp_df["close"] = pd.to_numeric(temp_df["close"], errors="coerce")
|
|
temp_df["volume"] = pd.to_numeric(temp_df["volume"], errors="coerce")
|
|
temp_df["hold"] = pd.to_numeric(temp_df["hold"], errors="coerce")
|
|
return temp_df
|
|
|
|
|
|
def futures_zh_daily_sina(symbol: str = "RB0") -> pd.DataFrame:
|
|
"""
|
|
中国各品种期货日频率数据
|
|
https://finance.sina.com.cn/futures/quotes/V2105.shtml
|
|
:param symbol: 可以通过 match_main_contract(symbol="cffex") 获取, 或者访问网页获取
|
|
:type symbol: str
|
|
:return: 指定 symbol 的数据
|
|
:rtype: pandas.DataFrame
|
|
"""
|
|
date = "20210412"
|
|
url = (
|
|
"https://stock2.finance.sina.com.cn/futures/api/jsonp.php/var%20_V21052021_4_12="
|
|
"/InnerFuturesNewService.getDailyKLine"
|
|
)
|
|
params = {
|
|
"symbol": symbol,
|
|
"type": "_".join([date[:4], date[4:6], date[6:]]),
|
|
}
|
|
r = requests.get(url, params=params)
|
|
temp_df = pd.DataFrame(json.loads(r.text.split("=(")[1].split(");")[0]))
|
|
temp_df.columns = [
|
|
"date",
|
|
"open",
|
|
"high",
|
|
"low",
|
|
"close",
|
|
"volume",
|
|
"hold",
|
|
"settle",
|
|
]
|
|
temp_df["open"] = pd.to_numeric(temp_df["open"], errors="coerce")
|
|
temp_df["high"] = pd.to_numeric(temp_df["high"], errors="coerce")
|
|
temp_df["low"] = pd.to_numeric(temp_df["low"], errors="coerce")
|
|
temp_df["close"] = pd.to_numeric(temp_df["close"], errors="coerce")
|
|
temp_df["volume"] = pd.to_numeric(temp_df["volume"], errors="coerce")
|
|
temp_df["hold"] = pd.to_numeric(temp_df["hold"], errors="coerce")
|
|
temp_df["settle"] = pd.to_numeric(temp_df["settle"], errors="coerce")
|
|
return temp_df
|
|
|
|
|
|
if __name__ == "__main__":
|
|
match_main_contract_df = match_main_contract(symbol="gfex")
|
|
print(match_main_contract_df)
|
|
|
|
futures_zh_spot_df = futures_zh_spot(symbol="V2405,V2409", market="CF", adjust="0")
|
|
print(futures_zh_spot_df)
|
|
|
|
futures_zh_spot_df = futures_zh_spot(symbol="V2405", market="CF", adjust="0")
|
|
print(futures_zh_spot_df)
|
|
|
|
futures_symbol_mark_df = futures_symbol_mark()
|
|
print(futures_symbol_mark_df)
|
|
|
|
futures_zh_realtime_df = futures_zh_realtime(symbol="工业硅")
|
|
print(futures_zh_realtime_df)
|
|
|
|
futures_zh_minute_sina_df = futures_zh_minute_sina(symbol="RB0", period="1")
|
|
print(futures_zh_minute_sina_df)
|
|
|
|
futures_zh_daily_sina_df = futures_zh_daily_sina(symbol="RB0")
|
|
print(futures_zh_daily_sina_df)
|
|
|
|
futures_zh_daily_sina_df = futures_zh_daily_sina(symbol="RB2410")
|
|
print(futures_zh_daily_sina_df)
|
|
|
|
dce_text = match_main_contract(symbol="dce")
|
|
czce_text = match_main_contract(symbol="czce")
|
|
shfe_text = match_main_contract(symbol="shfe")
|
|
gfex_text = match_main_contract(symbol="gfex")
|
|
|
|
while True:
|
|
time.sleep(3)
|
|
futures_zh_spot_df = futures_zh_spot(
|
|
symbol=",".join([dce_text, czce_text, shfe_text, gfex_text]),
|
|
market="CF",
|
|
adjust="0",
|
|
)
|
|
print(futures_zh_spot_df)
|