tushare实盘交易接口

# -*- coding:utf-8 -*-
"""
交易数据接口
Created on 2014/07/31
@author: Jimmy Liu
@group : waditu
@contact: jimmysoa@sina.cn
"""
from __future__ import divisionimport time
import json
import lxml.html
from lxml import etree
import pandas as pd
import numpy as np
import datetime
from tushare.stock import cons as ct
import re
from tushare.util import dateu as du
from tushare.util.formula import MA
import os
from tushare.util.conns import get_apis, close_apis
from tushare.stock.fundamental import get_stock_basics
try:from urllib.request import urlopen, Request
except ImportError:from urllib2 import urlopen, Request
v = pd.__version__
if int(v.split('.')[1])>=25 or int(v.split('.')[0])>0:from io import StringIO
else:    from pandas.compat import StringIOdef get_hist_data(code=None, start=None, end=None,ktype='D', retry_count=3,pause=0.001):"""获取个股历史交易记录Parameters------code:string股票代码 e.g. 600848start:string开始日期 format：YYYY-MM-DD 为空时取到API所提供的最早日期数据end:string结束日期 format：YYYY-MM-DD 为空时取到最近一个交易日数据ktype：string数据类型，D=日k线 W=周 M=月 5=5分钟 15=15分钟 30=30分钟 60=60分钟，默认为Dretry_count : int, 默认 3如遇网络等问题重复执行的次数 pause : int, 默认 0重复请求数据过程中暂停的秒数，防止请求间隔时间太短出现的问题return-------DataFrame属性:日期 ，开盘价， 最高价， 收盘价， 最低价， 成交量， 价格变动 ，涨跌幅，5日均价，10日均价，20日均价，5日均量，10日均量，20日均量，换手率"""print("本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2")symbol = ct._code_to_symbol(code)url = ''if ktype.upper() in ct.K_LABELS:url = ct.DAY_PRICE_URL%(ct.P_TYPE['http'], ct.DOMAINS['ifeng'],ct.K_TYPE[ktype.upper()], symbol)elif ktype in ct.K_MIN_LABELS:url = ct.DAY_PRICE_MIN_URL%(ct.P_TYPE['http'], ct.DOMAINS['ifeng'],symbol, ktype)else:raise TypeError('ktype input error.')for _ in range(retry_count):time.sleep(pause)try:request = Request(url)lines = urlopen(request, timeout = 10).read()if len(lines) < 15: #no datareturn Noneexcept Exception as e:print(e)else:js = json.loads(lines.decode('utf-8') if ct.PY3 else lines)cols = []if (code in ct.INDEX_LABELS) & (ktype.upper() in ct.K_LABELS):cols = ct.INX_DAY_PRICE_COLUMNSelse:cols = ct.DAY_PRICE_COLUMNSif len(js['record'][0]) == 14:cols = ct.INX_DAY_PRICE_COLUMNSdf = pd.DataFrame(js['record'], columns=cols)if ktype.upper() in ['D', 'W', 'M']:df = df.applymap(lambda x: x.replace(u',', u''))df[df==''] = 0for col in cols[1:]:df[col] = df[col].astype(float)if start is not None:df = df[df.date >= start]if end is not None:df = df[df.date <= end]if (code in ct.INDEX_LABELS) & (ktype in ct.K_MIN_LABELS):df = df.drop('turnover', axis=1)df = df.set_index('date')df = df.sort_index(ascending = False)return dfraise IOError(ct.NETWORK_URL_ERROR_MSG)def _parsing_dayprice_json(types=None, page=1):"""处理当日行情分页数据，格式为jsonParameters------pageNum:页码return-------DataFrame 当日所有股票交易数据(DataFrame)"""ct._write_console()request = Request(ct.SINA_DAY_PRICE_URL%(ct.P_TYPE['http'], ct.DOMAINS['vsf'],ct.PAGES['jv'], types, page))text = urlopen(request, timeout=10).read()if text == 'null':return Nonereg = re.compile(r'\,(.*?)\:') text = reg.sub(r',"\1":', text.decode('gbk') if ct.PY3 else text) text = text.replace('"{symbol', '{"symbol')text = text.replace('{symbol', '{"symbol"')text = text.replace('""', '"')text = text.replace('"{"', '{"')if ct.PY3:
#         text = text.decode('GBK')js = json.dumps(text)else:js = json.dumps(text, encoding='GBK')js = json.loads(js)df = pd.DataFrame(pd.read_json(js, dtype={'code':object}),columns=ct.DAY_TRADING_COLUMNS)df = df.drop('symbol', axis=1)
#     df = df.ix[df.volume > 0]return dfdef get_tick_data(code=None, date=None, retry_count=3, pause=0.001,src='sn'):"""获取分笔数据Parameters------code:string股票代码 e.g. 600848date:string日期 format: YYYY-MM-DDretry_count : int, 默认 3如遇网络等问题重复执行的次数pause : int, 默认 0重复请求数据过程中暂停的秒数，防止请求间隔时间太短出现的问题src : 数据源选择，可输入sn(新浪)、tt(腾讯)、nt(网易)，默认snreturn-------DataFrame 当日所有股票交易数据(DataFrame)属性:成交时间、成交价格、价格变动，成交手、成交金额(元)，买卖类型"""if (src.strip() not in ct.TICK_SRCS):print(ct.TICK_SRC_ERROR)return Nonesymbol = ct._code_to_symbol(code)symbol_dgt = ct._code_to_symbol_dgt(code)datestr = date.replace('-', '')url = {ct.TICK_SRCS[0] : ct.TICK_PRICE_URL % (ct.P_TYPE['http'], ct.DOMAINS['sf'], ct.PAGES['dl'],date, symbol),ct.TICK_SRCS[1] : ct.TICK_PRICE_URL_TT % (ct.P_TYPE['http'], ct.DOMAINS['tt'], ct.PAGES['idx'],symbol, datestr),ct.TICK_SRCS[2] : ct.TICK_PRICE_URL_NT % (ct.P_TYPE['http'], ct.DOMAINS['163'], date[0:4], datestr, symbol_dgt)}for _ in range(retry_count):time.sleep(pause)try:if src == ct.TICK_SRCS[2]:df = pd.read_excel(url[src])df.columns = ct.TICK_COLUMNSelse:re = Request(url[src])lines = urlopen(re, timeout=10).read()lines = lines.decode('GBK') if len(lines) < 20:return Nonedf = pd.read_table(StringIO(lines), names=ct.TICK_COLUMNS,skiprows=[0])      except Exception as e:print(e)else:return dfraise IOError(ct.NETWORK_URL_ERROR_MSG)def get_sina_dd(code=None, date=None, vol=400, retry_count=3, pause=0.001):"""获取sina大单数据Parameters------code:string股票代码 e.g. 600848date:string日期 format：YYYY-MM-DDretry_count : int, 默认 3如遇网络等问题重复执行的次数pause : int, 默认 0重复请求数据过程中暂停的秒数，防止请求间隔时间太短出现的问题return-------DataFrame 当日所有股票交易数据(DataFrame)属性:股票代码    股票名称    交易时间    价格    成交量    前一笔价格    类型（买、卖、中性盘）"""if code is None or len(code)!=6 or date is None:return Nonesymbol = ct._code_to_symbol(code)vol = vol*100for _ in range(retry_count):time.sleep(pause)try:re = Request(ct.SINA_DD % (ct.P_TYPE['http'], ct.DOMAINS['vsf'], ct.PAGES['sinadd'],symbol, vol, date))lines = urlopen(re, timeout=10).read()lines = lines.decode('GBK') if len(lines) < 100:return Nonedf = pd.read_csv(StringIO(lines), names=ct.SINA_DD_COLS,skiprows=[0])    if df is not None:df['code'] = df['code'].map(lambda x: x[2:])except Exception as e:print(e)else:return dfraise IOError(ct.NETWORK_URL_ERROR_MSG)def get_today_tickss(code=None, retry_count=3, pause=0.001):"""获取当日分笔明细数据Parameters------code:string股票代码 e.g. 600848retry_count : int, 默认 3如遇网络等问题重复执行的次数pause : int, 默认 0重复请求数据过程中暂停的秒数，防止请求间隔时间太短出现的问题return-------DataFrame 当日所有股票交易数据(DataFrame)属性:成交时间、成交价格、价格变动，成交手、成交金额(元)，买卖类型"""if code is None or len(code)!=6 :return Nonesymbol = ct._code_to_symbol(code)date = du.today()for _ in range(retry_count):time.sleep(pause)try:request = Request(ct.TODAY_TICKS_PAGE_URL % (ct.P_TYPE['http'], ct.DOMAINS['vsf'],ct.PAGES['jv'], date,symbol))data_str = urlopen(request, timeout=10).read()data_str = data_str.decode('GBK')data_str = data_str[1:-1]data_str = eval(data_str, type('Dummy', (dict,), dict(__getitem__ = lambda s, n:n))())data_str = json.dumps(data_str)data_str = json.loads(data_str)pages = len(data_str['detailPages'])data = pd.DataFrame()ct._write_head()for pNo in range(1, pages+1):data = data.append(_today_ticks(symbol, date, pNo,retry_count, pause), ignore_index=True)except Exception as er:print(str(er))else:return dataraise IOError(ct.NETWORK_URL_ERROR_MSG)def _today_ticks(symbol, tdate, pageNo, retry_count, pause):ct._write_console()for _ in range(retry_count):time.sleep(pause)try:html = lxml.html.parse(ct.TODAY_TICKS_URL % (ct.P_TYPE['http'],ct.DOMAINS['vsf'], ct.PAGES['t_ticks'],symbol, tdate, pageNo))  res = html.xpath('//table[@id=\"datatbl\"]/tbody/tr')if ct.PY3:sarr = [etree.tostring(node).decode('utf-8') for node in res]else:sarr = [etree.tostring(node) for node in res]sarr = ''.join(sarr)sarr = '<table>%s</table>'%sarrsarr = sarr.replace('--', '0')df = pd.read_html(StringIO(sarr), parse_dates=False)[0]df.columns = ct.TODAY_TICK_COLUMNSdf['pchange'] = df['pchange'].map(lambda x : x.replace('%', ''))except Exception as e:print(e)else:return dfraise IOError(ct.NETWORK_URL_ERROR_MSG)def get_today_all():"""一次性获取最近一个日交易日所有股票的交易数据return-------DataFrame属性：代码，名称，涨跌幅，现价，开盘价，最高价，最低价，最日收盘价，成交量，换手率，成交额，市盈率，市净率，总市值，流通市值"""ct._write_head()df = _parsing_dayprice_json('hs_a', 1)if df is not None:for i in range(2, ct.PAGE_NUM[1]):newdf = _parsing_dayprice_json('hs_a', i)if newdf.shape[0] > 0:df = df.append(newdf, ignore_index=True)else:breakdf = df.append(_parsing_dayprice_json('shfxjs', 1),ignore_index=True)return dfdef get_realtime_quotes(symbols=None):"""获取实时交易数据 getting real time quotes data用于跟踪交易情况（本次执行的结果-上一次执行的数据）Parameters------symbols : string, array-like object (list, tuple, Series).return-------DataFrame 实时交易数据属性:0：name，股票名字1：open，今日开盘价2：pre_close，昨日收盘价3：price，当前价格4：high，今日最高价5：low，今日最低价6：bid，竞买价，即“买一”报价7：ask，竞卖价，即“卖一”报价8：volumn，成交量 maybe you need do volumn/1009：amount，成交金额（元 CNY）10：b1_v，委买一（笔数 bid volume）11：b1_p，委买一（价格 bid price）12：b2_v，“买二”13：b2_p，“买二”14：b3_v，“买三”15：b3_p，“买三”16：b4_v，“买四”17：b4_p，“买四”18：b5_v，“买五”19：b5_p，“买五”20：a1_v，委卖一（笔数 ask volume）21：a1_p，委卖一（价格 ask price）...30：date，日期；31：time，时间；"""symbols_list = ''if isinstance(symbols, list) or isinstance(symbols, set) or isinstance(symbols, tuple) or isinstance(symbols, pd.Series):for code in symbols:symbols_list += ct._code_to_symbol(code) + ','else:symbols_list = ct._code_to_symbol(symbols)symbols_list = symbols_list[:-1] if len(symbols_list) > 8 else symbols_list request = Request(ct.LIVE_DATA_URL%(ct.P_TYPE['http'], ct.DOMAINS['sinahq'],_random(), symbols_list))text = urlopen(request,timeout=10).read()text = text.decode('GBK')reg = re.compile(r'\="(.*?)\";')data = reg.findall(text)regSym = re.compile(r'(?:sh|sz)(.*?)\=')syms = regSym.findall(text)data_list = []syms_list = []for index, row in enumerate(data):if len(row)>1:data_list.append([astr for astr in row.split(',')[:33]])syms_list.append(syms[index])if len(syms_list) == 0:return Nonedf = pd.DataFrame(data_list, columns=ct.LIVE_DATA_COLS)df = df.drop('s', axis=1)df['code'] = syms_listls = [cls for cls in df.columns if '_v' in cls]for txt in ls:df[txt] = df[txt].map(lambda x : x[:-2])return dfdef get_h_data(code, start=None, end=None, autype='qfq',index=False, retry_count=3, pause=0.001, drop_factor=True):'''获取历史复权数据Parameters------code:string股票代码 e.g. 600848start:string开始日期 format：YYYY-MM-DD 为空时取当前日期end:string结束日期 format：YYYY-MM-DD 为空时取去年今日autype:string复权类型，qfq-前复权 hfq-后复权 None-不复权，默认为qfqretry_count : int, 默认 3如遇网络等问题重复执行的次数 pause : int, 默认 0重复请求数据过程中暂停的秒数，防止请求间隔时间太短出现的问题drop_factor : bool, 默认 True是否移除复权因子，在分析过程中可能复权因子意义不大，但是如需要先储存到数据库之后再分析的话，有该项目会更加灵活return-------DataFramedate 交易日期 (index)open 开盘价high  最高价close 收盘价low 最低价volume 成交量amount 成交金额'''print("本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2")start = du.today_last_year() if start is None else startend = du.today() if end is None else endqs = du.get_quarts(start, end)qt = qs[0]ct._write_head()data = _parse_fq_data(_get_index_url(index, code, qt), index,retry_count, pause)if data is None:data = pd.DataFrame()if len(qs)>1:for d in range(1, len(qs)):qt = qs[d]ct._write_console()df = _parse_fq_data(_get_index_url(index, code, qt), index,retry_count, pause)if df is None:  # 可能df为空，退出循环breakelse:data = data.append(df, ignore_index = True)if len(data) == 0 or len(data[(data.date >= start) & (data.date <= end)]) == 0:return pd.DataFrame()data = data.drop_duplicates('date')if index:data = data[(data.date >= start) & (data.date <= end)]data = data.set_index('date')data = data.sort_index(ascending = False)return dataif autype == 'hfq':if drop_factor:data = data.drop('factor', axis=1)data = data[(data.date >= start) & (data.date <= end)]for label in ['open', 'high', 'close', 'low']:data[label] = data[label].map(ct.FORMAT)data[label] = data[label].astype(float)data = data.set_index('date')data = data.sort_index(ascending = False)return dataelse:if autype == 'qfq':if drop_factor:data = data.drop('factor', axis = 1)df = _parase_fq_factor(code, start, end)df = df.drop_duplicates('date')df = df.sort_values('date', ascending = False)firstDate = data.head(1)['date']frow = df[df.date == firstDate[0]]rt = get_realtime_quotes(code)if rt is None:return pd.DataFrame()if ((float(rt['high']) == 0) & (float(rt['low']) == 0)):preClose = float(rt['pre_close'])else:if du.is_holiday(du.today()):preClose = float(rt['price'])else:if (du.get_hour() > 9) & (du.get_hour() < 18):preClose = float(rt['pre_close'])else:preClose = float(rt['price'])rate = float(frow['factor']) / preClosedata = data[(data.date >= start) & (data.date <= end)]for label in ['open', 'high', 'low', 'close']:data[label] = data[label] / ratedata[label] = data[label].map(ct.FORMAT)data[label] = data[label].astype(float)data = data.set_index('date')data = data.sort_index(ascending = False)return dataelse:for label in ['open', 'high', 'close', 'low']:data[label] = data[label] / data['factor']if drop_factor:data = data.drop('factor', axis=1)data = data[(data.date >= start) & (data.date <= end)]for label in ['open', 'high', 'close', 'low']:data[label] = data[label].map(ct.FORMAT)data = data.set_index('date')data = data.sort_index(ascending = False)data = data.astype(float)return datadef _parase_fq_factor(code, start, end):symbol = ct._code_to_symbol(code)request = Request(ct.HIST_FQ_FACTOR_URL%(ct.P_TYPE['http'],ct.DOMAINS['vsf'], symbol))text = urlopen(request, timeout=10).read()text = text[1:len(text)-1]text = text.decode('utf-8') if ct.PY3 else texttext = text.replace('{_', '{"')text = text.replace('total', '"total"')text = text.replace('data', '"data"')text = text.replace(':"', '":"')text = text.replace('",_', '","')text = text.replace('_', '-')text = json.loads(text)df = pd.DataFrame({'date':list(text['data'].keys()), 'factor':list(text['data'].values())})df['date'] = df['date'].map(_fun_except) # for null caseif df['date'].dtypes == np.object:df['date'] = pd.to_datetime(df['date'])df = df.drop_duplicates('date')df['factor'] = df['factor'].astype(float)return dfdef _fun_except(x):if len(x) > 10:return x[-10:]else:return xdef _parse_fq_data(url, index, retry_count, pause):for _ in range(retry_count):time.sleep(pause)try:request = Request(url)text = urlopen(request, timeout=10).read()text = text.decode('GBK')html = lxml.html.parse(StringIO(text))res = html.xpath('//table[@id=\"FundHoldSharesTable\"]')if ct.PY3:sarr = [etree.tostring(node).decode('utf-8') for node in res]else:sarr = [etree.tostring(node) for node in res]sarr = ''.join(sarr)if sarr == '':return Nonedf = pd.read_html(sarr, skiprows = [0, 1])[0]if len(df) == 0:return pd.DataFrame()if index:df.columns = ct.HIST_FQ_COLS[0:7]else:df.columns = ct.HIST_FQ_COLSif df['date'].dtypes == np.object:df['date'] = pd.to_datetime(df['date'])df = df.drop_duplicates('date')except ValueError as e:# 时间较早，已经读不到数据return Noneexcept Exception as e:print(e)else:return dfraise IOError(ct.NETWORK_URL_ERROR_MSG)def get_index():"""获取大盘指数行情return-------DataFramecode:指数代码name:指数名称change:涨跌幅open:开盘价preclose:昨日收盘价close:收盘价high:最高价low:最低价volume:成交量(手)amount:成交金额（亿元）"""request = Request(ct.INDEX_HQ_URL%(ct.P_TYPE['http'],ct.DOMAINS['sinahq']))text = urlopen(request, timeout=10).read()text = text.decode('GBK')text = text.replace('var hq_str_sh', '').replace('var hq_str_sz', '')text = text.replace('";', '').replace('"', '').replace('=', ',')text = '%s%s'%(ct.INDEX_HEADER, text)df = pd.read_csv(StringIO(text), sep=',', thousands=',')df['change'] = (df['close'] / df['preclose'] - 1 ) * 100df['amount'] = df['amount'] / 100000000df['change'] = df['change'].map(ct.FORMAT)df['amount'] = df['amount'].map(ct.FORMAT4)df = df[ct.INDEX_COLS]df['code'] = df['code'].map(lambda x:str(x).zfill(6))df['change'] = df['change'].astype(float)df['amount'] = df['amount'].astype(float)return dfdef _get_index_url(index, code, qt):if index:url = ct.HIST_INDEX_URL%(ct.P_TYPE['http'], ct.DOMAINS['vsf'],code, qt[0], qt[1])else:url = ct.HIST_FQ_URL%(ct.P_TYPE['http'], ct.DOMAINS['vsf'],code, qt[0], qt[1])return urldef get_k_data(code=None, start='', end='',ktype='D', autype='qfq', index=False,retry_count=3,pause=0.001):"""获取k线数据---------Parameters:code:string股票代码 e.g. 600848start:string开始日期 format：YYYY-MM-DD 为空时取上市首日end:string结束日期 format：YYYY-MM-DD 为空时取最近一个交易日autype:string复权类型，qfq-前复权 hfq-后复权 None-不复权，默认为qfqktype：string数据类型，D=日k线 W=周 M=月 5=5分钟 15=15分钟 30=30分钟 60=60分钟，默认为Dretry_count : int, 默认 3如遇网络等问题重复执行的次数 pause : int, 默认 0重复请求数据过程中暂停的秒数，防止请求间隔时间太短出现的问题return-------DataFramedate 交易日期 (index)open 开盘价high  最高价close 收盘价low 最低价volume 成交量amount 成交额turnoverratio 换手率code 股票代码"""print("本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2")symbol = ct.INDEX_SYMBOL[code] if index else ct._code_to_symbol(code)url = ''dataflag = ''autype = '' if autype is None else autypeif (start is not None) & (start != ''):end = du.today() if end is None or end == '' else endif ktype.upper() in ct.K_LABELS:fq = autype if autype is not None else ''if code[:1] in ('1', '5') or index:fq = ''kline = '' if autype is None else 'fq'if (start is None or start == '') & (end is None or end == ''):urls = [ct.KLINE_TT_URL%(ct.P_TYPE['http'], ct.DOMAINS['tt'],kline, fq, symbol, ct.TT_K_TYPE[ktype.upper()], start, end,fq, _random(17))]else:years = du.tt_dates(start, end)urls = []for year in years:startdate = str(year) + '-01-01'enddate = str(year+1) + '-12-31'url = ct.KLINE_TT_URL%(ct.P_TYPE['http'], ct.DOMAINS['tt'],kline, fq+str(year), symbol, ct.TT_K_TYPE[ktype.upper()], startdate, enddate,fq, _random(17))urls.append(url)dataflag = '%s%s'%(fq, ct.TT_K_TYPE[ktype.upper()])elif ktype in ct.K_MIN_LABELS:urls = [ct.KLINE_TT_MIN_URL%(ct.P_TYPE['http'], ct.DOMAINS['tt'],symbol, ktype, ktype,_random(16))]dataflag = 'm%s'%ktypeelse:raise TypeError('ktype input error.')data = pd.DataFrame()for url in urls:data = data.append(_get_k_data(url, dataflag, symbol, code,index, ktype,retry_count, pause), ignore_index=True)if ktype not in ct.K_MIN_LABELS:if ((start is not None) & (start != '')) & ((end is not None) & (end != '')):if data.empty==False:       data = data[(data.date >= start) & (data.date <= end)]return dataraise IOError(ct.NETWORK_URL_ERROR_MSG)def _get_k_data(url, dataflag='',symbol='',code = '',index = False,ktype = '',retry_count=3,pause=0.001):for _ in range(retry_count):time.sleep(pause)try:request = Request(url)lines = urlopen(request, timeout = 10).read()if len(lines) < 100: #no datareturn Noneexcept Exception as e:print(e)else:lines = lines.decode('utf-8') if ct.PY3 else lineslines = lines.split('=')[1]reg = re.compile(r',{"nd.*?}') lines = re.subn(reg, '', lines) js = json.loads(lines[0])dataflag = dataflag if dataflag in list(js['data'][symbol].keys()) else ct.TT_K_TYPE[ktype.upper()]if len(js['data'][symbol][dataflag]) == 0:return Noneif len(js['data'][symbol][dataflag][0]) == 6:df = pd.DataFrame(js['data'][symbol][dataflag], columns = ct.KLINE_TT_COLS_MINS)else:df = pd.DataFrame(js['data'][symbol][dataflag], columns = ct.KLINE_TT_COLS)df['code'] = symbol if index else codeif ktype in ct.K_MIN_LABELS:df['date'] = df['date'].map(lambda x: '%s-%s-%s %s:%s'%(x[0:4], x[4:6], x[6:8], x[8:10], x[10:12]))for col in df.columns[1:6]:df[col] = df[col].astype(float)return dfdef get_hists(symbols, start=None, end=None,ktype='D', retry_count=3,pause=0.001):"""批量获取历史行情数据，具体参数和返回数据类型请参考get_hist_data接口"""df = pd.DataFrame()if isinstance(symbols, list) or isinstance(symbols, set) or isinstance(symbols, tuple) or isinstance(symbols, pd.Series):for symbol in symbols:data = get_hist_data(symbol, start=start, end=end,ktype=ktype, retry_count=retry_count,pause=pause)data['code'] = symboldf = df.append(data, ignore_index=True)return dfelse:return Nonedef get_day_all(date=None):"""获取每日收盘行情Parameters:-------------date:交易日期，格式:YYYY-MM-DDReturn:-------------DataFramecode 代码, name 名称, p_change 涨幅%,price 现价, change 涨跌, open 今开, high 最高,low 最低, preprice 昨收, pe 市盈(动),volratio 量比, turnover 换手%, range 振幅%%,volume 总量, selling 内盘, buying 外盘,amount 总金额, totals 总股本(万), industry 细分行业,area 地区, floats 流通股本(万), fvalues 流通市值,abvalues AB股总市值, avgprice 均价, strength 强弱度%,activity 活跃度, avgturnover 笔换手, attack 攻击波%,interval3 近3月涨幅 ，interval 近6月涨幅"""wdate = du.last_tddate() if date is None else datewdate = wdate.replace('-', '')if wdate < '20170614':return Nonedatepre = '' if date is None else wdate[0:4] + wdate[4:6] + '/'df = pd.read_csv(ct.ALL_DAY_FILE%(datepre, \'hq' if date is None else wdate), \dtype={'code':'object'})return dfdef get_dt_time(t):tstr = str(t)[:-2]tstr = tstr.replace('-', '').replace(':', '')return tstrdef bar2h5(market='', date='', freq='D', asset='E', filepath=''):cons = get_apis()stks = get_stock_basics()fname = "%s%s%sbar%s.h5"%(filepath, market, date, freq)store = pd.HDFStore(fname, "a")if market in ['SH', 'SZ']:if market == 'SH':stks = stks.loc[stks.index.str[0]=='6', :]elif market == 'SZ':stks = stks.loc[stks.index.str[0]!='6', :]else:stks = ''market = 1 if market == 'SH' else 0for stk in stks.index:symbol = '%s.SH'%stkif 'min' in freq:df = bar(stk, conn=cons, start_date=date, end_date=date, freq=freq, market=market, asset=asset)df['Time'] = df.indexdf['Time'] = df['Time'].apply(get_dt_time) df.index = df['Time']df.drop(['code','Time'], axis = 1, inplace=True)    df.rename(columns={'open':'OPEN'}, inplace=True) df.rename(columns={'close':'CLOSE'}, inplace=True)df.rename(columns={'low':'LOW'}, inplace=True)df.rename(columns={'high':'HIGH'}, inplace=True)df.rename(columns={'vol':'VOLUME'}, inplace=True) df.rename(columns={'amount':'TURNOVER'}, inplace=True) df.loc[:,'HIGH'] =  df.loc[:,'HIGH'].astype("int64")df.loc[:,'LOW'] =  df.loc[:,'LOW'].astype("int64")df.loc[:,'OPEN'] =  df.loc[:,'OPEN'].astype("int64")df.loc[:,'CLOSE'] =  df.loc[:,'CLOSE'].astype("int64")df.loc[:,'VOLUME'] =  df.loc[:,'VOLUME'].astype("int64")df.loc[:,'TURNOVER'] =  df.loc[:,'TURNOVER'].astype("int64")    df.loc[:,'OPEN'] *= 10000   df.loc[:,'CLOSE'] *= 10000    df.loc[:,'HIGH'] *= 10000    df.loc[:,'LOW'] *= 10000df.loc[:,'ASKPRICE1']  = 0df.loc[:,'ASKPRICE2']  = 0df.loc[:,'ASKPRICE3']  = 0df.loc[:,'ASKPRICE4']  = 0df.loc[:,'ASKPRICE5']  = 0df.loc[:,'ASKPRICE6']  = 0df.loc[:,'ASKPRICE7']  = 0df.loc[:,'ASKPRICE8']  = 0df.loc[:,'ASKPRICE9']  = 0df.loc[:,'ASKPRICE10'] = 0    df.loc[:,'BIDPRICE1']  = 0df.loc[:,'BIDPRICE2']  = 0df.loc[:,'BIDPRICE3']  = 0df.loc[:,'BIDPRICE4']  = 0df.loc[:,'BIDPRICE5']  = 0df.loc[:,'BIDPRICE6']  = 0df.loc[:,'BIDPRICE7']  = 0df.loc[:,'BIDPRICE8']  = 0df.loc[:,'BIDPRICE9']  = 0df.loc[:,'BIDPRICE10'] = 0    df.loc[:,'ASKVOL1']  = 0df.loc[:,'ASKVOL2']  = 0df.loc[:,'ASKVOL3']  = 0df.loc[:,'ASKVOL4']  = 0df.loc[:,'ASKVOL5']  = 0df.loc[:,'ASKVOL6']  = 0df.loc[:,'ASKVOL7']  = 0df.loc[:,'ASKVOL8']  = 0df.loc[:,'ASKVOL9']  = 0df.loc[:,'ASKVOL10'] = 0    df.loc[:,'BIDVOL1']  = 0df.loc[:,'BIDVOL2']  = 0df.loc[:,'BIDVOL3']  = 0df.loc[:,'BIDVOL4']  = 0df.loc[:,'BIDVOL5']  = 0df.loc[:,'BIDVOL6']  = 0df.loc[:,'BIDVOL7']  = 0df.loc[:,'BIDVOL8']  = 0df.loc[:,'BIDVOL9']  = 0df.loc[:,'BIDVOL10'] = 0    df.loc[:,'VWAP'] = 0.0df.loc[:,'VOL30']=0.0df.loc[:,'TOTAL_VOLUME']=0.0df.loc[:,'TOTAL_TURNOVER']=0.0df.loc[:,'INTEREST']=0.0print(df)
#             if market == 1 and stk[0] == '6':
#                 df = bar(stk, conn=cons, start_date=date, end_date=date, freq=freq, market=market, asset=asset)store[symbol] = dfstore.close()close_apis(cons)def bar(code, conn=None, start_date=None, end_date=None, freq='D', asset='E', market='',adj = None,ma = [],factors = [],retry_count = 3):"""BAR数据Parameters:------------code:证券代码，支持股票,ETF/LOF,期货/期权,港股con:服务器连接 ，通过ts.api()或者ts.xpi()获得start_date:开始日期  YYYY-MM-DD/YYYYMMDDend_date:结束日期 YYYY-MM-DD/YYYYMMDDfreq:支持1/5/15/30/60分钟,周/月/季/年asset:证券类型 E:股票和交易所基金，INDEX:沪深指数,X:期货/期权/港股/中概美国/中证指数/国际指数market:市场代码，通过ts.get_markets()获取adj:复权类型,None不复权,qfq:前复权,hfq:后复权ma:均线,支持自定义均线频度，如：ma5/ma10/ma20/ma60/maNfactors因子数据，目前支持以下两种：vr:量比,默认不返回，返回需指定：factor=['vr']tor:换手率，默认不返回，返回需指定：factor=['tor']以上两种都需要：factor=['vr', 'tor']retry_count:网络重试次数Return----------DataFramecode:代码open：开盘close/high/low/vol成交量/amount成交额/maN均价/vr量比/tor换手率期货(asset='X')code/open/close/high/low/avg_price：均价  position：持仓量  vol：成交总量"""print("本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2")code = code.strip().upper()for _ in range(retry_count):try:if conn is None:print(ct.MSG_NOT_CONNECTED)return Noneapi, xapi = connktype = freq.strip().upper()asset = asset.strip().upper()mkcode = _get_mkcode(code, asset=asset, xapi=xapi) if market == '' else marketif asset in['E', 'INDEX']:func = getattr(api, ct.ASSET[asset])else:ktype = 'XD' if ktype == 'D' else ktypefunc = getattr(xapi, ct.ASSET['X'])if ktype in ct.KTYPE_LOW_COLS:data = pd.DataFrame()for i in range(100): ds = func(ct.KTYPE[ktype], mkcode, code, i * 800, 800)df =  api.to_df(ds)data = data.append(df) if i == 0 else df.append(data,  ignore_index=True)if len(ds) < 800:breakdata['datetime'] = data['datetime'].apply(lambda x: str(x[0:10]))if ktype in ct.KTYPE_ARR:data = pd.DataFrame()for i in range(100): ds = func(ct.KTYPE[ktype], mkcode, code, i * 800, 800)df =  api.to_df(ds)data = data.append(df) if i == 0 else df.append(data,  ignore_index=True)if len(ds) < 800:breakdata['datetime'] = pd.to_datetime(data['datetime'])data = data.assign(code=str(code)) \.set_index('datetime', drop=True, inplace=False) \.drop(ct.T_DROP_COLS, axis=1)[ None if start_date == '' else start_date : None if end_date == '' else end_date]data = data.sort_index(ascending=False)if asset in['E', 'INDEX']:data = data[ct.BAR_E_COLS]if ktype in ct.KTYPE_ARR:data['vol'] = data['vol'] / 100else:data = data[ct.BAR_X_COLS]if mkcode in [28, 29, 30, 47, 60]:data.columns = ct.BAR_X_FUTURE_COLSdata = data[ct.BAR_X_FUTURE_RL_COLS]else:data = data.drop(['price', 'position'], axis=1)data.columns = ct.BAR_X_OTHER_COLSif asset == 'E':if adj is not None:df = factor_adj(code)if ktype in ct.KTYPE_LOW_COLS: data = data.merge(df, left_index=True, right_index=True)data['adj_factor'] = data['adj_factor'].fillna(method='bfill')else:def get_val(day):return df.loc[day]['adj_factor']data['adj_factor'] = data.index.map(lambda x: get_val(str(x)[0:10]))for col in ct.BAR_E_COLS[1:5]:if adj == 'hfq':data[col] = data[col] * data['adj_factor']else:data[col] = data[col] * data['adj_factor'] / float(df['adj_factor'][0])data[col] = data[col].map(ct.FORMAT)data = data.drop('adj_factor', axis=1)if factors is not None and len(factors) >0 :if 'tor' in factors:df = factor_shares(code)if ktype in ct.KTYPE_LOW_COLS: data = data.merge(df, left_index=True, right_index=True)data['floats'] = data['floats'].fillna(method='bfill')else:def get_val(day):return df.loc[day]['floats']data['floats'] = data.index.map(lambda x: get_val(str(x)[0:10]))data['tor'] = data['vol'] / data['floats'] data['tor'] = data['tor'].map(ct.FORMAT)data['tor'] = data['tor'].astype(float)data = data.drop('floats', axis=1)if 'vr' in factors:data['vol5'] = MA(data['vol'], 5)data['mean'] = data['vol5'].shift(-5)data['vr'] = (data['vol'] / data['mean']).map(ct.FORMAT)data['vr'] = data['vr'].astype(float)data = data.drop(['vol5', 'mean'], axis=1)if ma is not None and len(ma) > 0:for a in ma:if isinstance(a, int):data['ma%s'%a] = MA(data['close'], a).map(ct.FORMAT).shift(-(a-1))data['ma%s'%a] = data['ma%s'%a].astype(float)for col in ['open', 'high', 'low', 'close']:data[col] = data[col].astype(float)data['p_change'] = data['close'].pct_change(-1) * 100data['p_change'] = data['p_change'].map(ct.FORMAT).astype(float)return dataexcept:return Noneelse:data['p_change'] = data['close'].pct_change(-1) * 100data['p_change'] = data['p_change'].map(ct.FORMAT).astype(float)return dataraise IOError(ct.NETWORK_URL_ERROR_MSG)def _get_mkcode(code='', asset='E', xapi=None):mkcode = ''if asset == 'E':mkcode = ct._market_code(code)elif asset == 'INDEX':mkcode = ct._idx_market_code(code)else:if os.path.exists(ct.INST_PLK_F):mks = pd.read_pickle(ct.INST_PLK_F)else:mks = get_instrument(xapi)mks.to_pickle(ct.INST_PLK_F)mkcode = mks[mks.code == code]['market'].values[0]return mkcodedef tick(code, conn=None, date='', asset='E', market='', retry_count = 3):"""tick数据Parameters:------------code:证券代码，支持股票,ETF/LOF,期货/期权,港股conn:服务器连接 ，通过ts.api()或者ts.xpi()获得date:日期asset:证券品种，E:沪深交易所股票和基金, INDEX:沪深交易所指数， X:其他证券品种，大致如下：支持的扩展行情包括(asset='X')：郑州商品期权         OZ 大连商品期权         OD 上海商品期权         OS上海个股期权         QQ 香港指数         FH 郑州商品         QZ 大连商品         QD 上海期货         QS香港主板         KH 香港权证         KR 开放式基金         FU 货币型基金         FB招商理财产品         LC 招商货币产品         LB 国际指数         FW 国内宏观指标         HG 中国概念股         CH美股知名公司         MG B股转H股         HB 股份转让         SB 股指期货         CZ 香港创业板         KG 香港信托基金         KT国债预发行         GY 主力期货合约         MA中证指数         ZZ 港股通         GHmarket:市场代码，通过ts.get_markets()获取Return----------DataFramedate:日期time:时间price:成交价vol:成交量type:买卖方向，0-买入 1-卖出 2-集合竞价成交期货  0:开仓  1:多开   -1:空开期货多一列数据oi_change:增仓数据"""code = code.strip().upper()date = int(date.replace('-', ''))today = int(str(du.today()).replace('-', ''))for _ in range(retry_count):try:if conn is None:print(ct.MSG_NOT_CONNECTED)return Noneapi, xapi = conndata = pd.DataFrame()mkcode = _get_mkcode(code, asset=asset, xapi=xapi) if market == '' else marketcon = api if asset in['E', 'INDEX'] else xapifor i in range(200):if date == today:ds = con.get_transaction_data(market=mkcode, code=code, start=i * 300, count=300)else:ds = con.get_history_transaction_data(market=mkcode, code=code, date=date, start=i * 300, count=300)df =  api.to_df(ds)data = data.append(df) if i == 0 else df.append(data,  ignore_index=True)if len(ds) < 300:breakif asset in['E', 'INDEX']:data['date'] = datedata['date'] = data['date'].map(lambda x: '%s-%s-%s '%(str(x)[0:4], str(x)[4:6], str(x)[6:8]))data['datetime'] = data['date'] + data['time']data = data[['datetime', 'price', 'vol', 'buyorsell']]data.columns = ['datetime', 'price', 'vol', 'type']else:if mkcode in [31, 71]:if date == today:data = data.drop(['hour', 'minute', 'nature_name', 'zengcang', 'direction', 'second', 'nature_mark', 'nature_value'], axis=1)else:data = data.drop(['hour', 'minute', 'nature_name', 'zengcang', 'direction'], axis=1)data.loc[data.nature== 512, 'nature' ] = 2data.loc[data.nature== 256, 'nature' ] = 1data = data.sort_values('date')data.columns = ['date', 'price', 'vol', 'type']elif mkcode in [28, 29, 30, 47, 60]:if date == today:data = data.drop(['hour', 'minute', 'nature', 'direction', 'second', 'nature_mark', 'nature_value'], axis=1)else:data = data.drop(['hour', 'minute', 'nature', 'direction'], axis=1)data.columns = ['date', 'price', 'vol', 'oi_change', 'type']else:data = data.drop(['hour', 'minute', 'nature_name', 'zengcang', 'direction', 'nature'], axis=1)except Exception as e:print(e)else:return datadef quotes(symbols, conn=None, asset='E', market=[], retry_count = 3):"""获取实时快照Parameters------symbols : string, array-like object (list, tuple, Series).return-------DataFrame 实时快照，5档行情"""for _ in range(retry_count):try:if conn is None:print(ct.MSG_NOT_CONNECTED)return Noneapi, xapi = conndata = pd.DataFrame()if isinstance(symbols, list) or isinstance(symbols, set) or isinstance(symbols, tuple) or isinstance(symbols, pd.Series):for code in symbols:mkcode = _get_mkcode(code, asset=asset, xapi=xapi)if asset == 'E':df = api.to_df(api.get_security_quotes([(mkcode, code)]))elif asset == 'INDEX':df = api.to_df(api.get_security_quotes([(mkcode, code)]))else:df = xapi.to_df(xapi.get_instrument_quote(mkcode, code))data = data.append(df)else:mkcode = _get_mkcode(symbols, asset=asset, xapi=xapi)if asset == 'E':data = api.to_df(api.get_security_quotes([(mkcode, symbols)]))elif asset == 'INDEX':data = api.to_df(api.get_security_quotes([(mkcode, symbols)]))else:data = xapi.to_df(xapi.get_instrument_quote(mkcode, symbols))if asset in ['E', 'INDEX']:data = data.drop(['market', 'active1', 'active2', 'reversed_bytes0', 'reversed_bytes1', 'reversed_bytes2','reversed_bytes3','reversed_bytes4','reversed_bytes5','reversed_bytes6','reversed_bytes7','reversed_bytes8','reversed_bytes9'], axis=1)else:data = data.drop(['market'], axis=1)except Exception as e:print(e)else:return dataraise IOError(ct.NETWORK_URL_ERROR_MSG)def get_security(api):"""获取股票列表"""data = []for p in range(100):ds = api.get_security_list(0, p*1000)data += dsif len(ds) < 1000:breakdata = api.to_df(data)return datadef reset_instrument(xapi=None):"""重新设置本地证券列表"""import tushare.util.conns as cs xapi = cs.xapi_x() if xapi is None else xapidata=[]for i in range(200): ds = xapi.get_instrument_info(i * 300, 300)data += dsif len(ds) < 300:breakdata = xapi.to_df(data)data.to_pickle(ct.INST_PLK_F)return datadef get_instrument(xapi=None):"""获取证券列表"""print("本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2")import tushare.util.conns as cs xapi = cs.xapi_x() if xapi is None else xapiif xapi is None:print(ct.MSG_NOT_CONNECTED)return Nonedata=[]for i in range(200): # range for python2/3ds = xapi.get_instrument_info(i * 300, 300)data += dsif len(ds) < 300:breakdata = xapi.to_df(data)return datadef get_today_ticks(code=None, retry_count=3, pause=0.001):"""获取分笔数据Parameters------code:string股票代码 e.g. 600848如遇网络等问题重复执行的次数pause : int, 默认 0重复请求数据过程中暂停的秒数，防止请求间隔时间太短出现的问题src : 数据源选择，可输入sn(新浪)、tt(腾讯)、nt(网易)，默认snreturn-------DataFrame 当日所有股票交易数据(DataFrame)属性:成交时间、成交价格、价格变动，成交手、成交金额(元)，买卖类型"""url = 'http://push2ex.eastmoney.com/getStockFenShi?pagesize=6644&ut=7eea3edcaed734bea9cbfc24409ed989&dpt=wzfscj&pageindex=0&id=%s&sort=1&ft=1&code=%s&market=1&_=%s'for _ in range(retry_count):time.sleep(pause)try:re = Request(url%(code, code, _random()))lines = urlopen(re, timeout=10).read()
#             if ct.PY3:
#                 lines = lines.decode('GBK') lines = json.loads(lines)lines = lines['data']['data']df = pd.DataFrame(lines)   df = df.rename(columns={'t': 'time', 'p': 'price', 'v': 'vol', 'bs': 'type'})df = df[['time', 'price', 'vol', 'type']]df['price'] = df['price'].map(lambda x: x/1000)df['type'] = df['type'].map(lambda x: bs_type[str(x)])df['time'] = df['time'].map(lambda x: str(x).zfill(6))except Exception as e:print(e)else:return dfraise IOError(ct.NETWORK_URL_ERROR_MSG)bs_type = {'1':u'买入', '2': u'卖出', '4': u'-'}def get_markets(xapi=None):"""获取市场代码"""if xapi is None:print(ct.MSG_NOT_CONNECTED)return Nonedata = xapi.get_markets()data = xapi.to_df(data)return datadef factor_adj(code):df = pd.read_csv(ct.ADJ_FAC_URL%(ct.P_TYPE['http'],ct.DOMAINS['oss'], code))df = df.set_index('datetime')return dfdef factor_shares(code):df = pd.read_csv(ct.SHS_FAC_URL%(ct.P_TYPE['http'],ct.DOMAINS['oss'], code))[['datetime', 'floats']]df = df.set_index('datetime')return dfdef _random(n=13):from random import randintstart = 10**(n-1)end = (10**n)-1return str(randint(start, end))if __name__ == '__main__':
#     from tushare.util.conns import get_apis, close_apis
#     cons = get_apis()
#     df = bar('rb2001', conn=cons, asset='X', freq='1min')
#     print(df)
#     close_apis(cons)
#     df = get_tick_data('600848', src='sn', date='2020-04-10')
#     df = get_today_ticks(code='000001')
#     print(df)
#     df = get_today_ticks(code='600000')
#     print(df)df = get_realtime_quotes('123033')print(df)
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