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A humanize XueQiu API wrappers.

Project description

xueqiu

a humanize XueQiu API wrappers.

Quick start

Installation

pip install xueqiu

Example:

>>> news = xueqiu.news()  # watch the news
>>> news
{'list': [<xueqiu.Post 为何价值投资长期有效[https://xueqiu.com/8291461932/120351059]>,
  <xueqiu.Post 韬蕴资本CEO温晓东怒斥贾跃亭怎就一个[https://xueqiu.com/2095268812/120483699]>,
  <xueqiu.Post 增持与回购20190122-201901[https://xueqiu.com/9206540776/120458648]>,
  <xueqiu.Post 医药研发外包为什么这么红?()[https://xueqiu.com/1472391509/120481662]>,
  <xueqiu.Post 医药大赛道之大分子生物药[https://xueqiu.com/1472391509/120482094]>,
  <xueqiu.Post 增强型指数基金到底在哪里[https://xueqiu.com/8082119199/120480761]>,
  <xueqiu.Post 价值投资不需要概率思维吗?—与董宝珍先生[https://xueqiu.com/3555476733/120245234]>,
  <xueqiu.Post 邓晓峰的投资观[https://xueqiu.com/7649503965/120430145]>,
  <xueqiu.Post 复利无敌买入一只股票看这四点[https://xueqiu.com/1876906471/120479202]>,
  <xueqiu.Post 再论安全边际[https://xueqiu.com/4465952737/120453192]>],
 'next_max_id': 20323343}
>>> p = news['list'][0]
>>> "{} {}{} 评论{} 转发{} {}".format(p.title, p.user.name, p.like_count,
                                        p.reply_count, p.retweet_count, p.target)
'为何价值投资长期有效 房杨凯的投资世界 赞9 评论11 转发9 https://xueqiu.com/8291461932/120351059'
>>> p.user.get_posts()  # get user's article
>>> p.user.posts
{'count': 622,
 'page': 1,
 'maxpage': 63,
 'list': [<xueqiu.Post [https://xueqiu.com/8291461932/120497097]>,
  <xueqiu.Post [https://xueqiu.com/8291461932/120491351]>,
  <xueqiu.Post [https://xueqiu.com/8291461932/120487476]>,
  <xueqiu.Post [https://xueqiu.com/8291461932/120487448]>,
  <xueqiu.Post [https://xueqiu.com/8291461932/120486037]>,
  <xueqiu.Post 腾讯游戏帝国的护城河还在吗[https://xueqiu.com/8291461932/120485596]>,
  <xueqiu.Post [https://xueqiu.com/8291461932/120473933]>,
  <xueqiu.Post [https://xueqiu.com/8291461932/120434054]>,
  <xueqiu.Post [https://xueqiu.com/8291461932/120434037]>,
  <xueqiu.Post [https://xueqiu.com/8291461932/120434020]>]}
>>> p.user.posts['list'][0].text  # content
'回复@A8天道酬勤: 这个问题应该放在买之前。//@A8天道酬勤:回复@房杨凯的投资世界:假如花旗银行做假账,聂夫还会不会持有?'
>>> p.user.posts['list'][0].like()  # like this (need login)

API

User class

A user class that contains user-related methods.

User object attributes:

  • id - user id.
  • profile - user's profile url.
  • name - user name.
  • city - city, for example '上海'.
  • description - user description.
  • friends_count - the number of user's friends.
  • followers_count - the number of user's fans.
  • posts_count - the number of user's post.
  • stocks_count - the number of stocks.
  • friends - use to save User object for friends.
  • followers - use the save User object for fans.
  • posts - use the save Post object for post.
  • articles - use the save Post object for user's article.
  • favorites - use the save Post object for favorite articles.
  • stocks - use the save Stock object for favorite stocks.
  • hot_stocks - use the save Stock object for the current hot stocks.

User object methods:

  • get_friends(page: int = 1) - get your friends and save to self.friends.

  • get_followers(page: int = 1) - get your fans and save to self.followers.

  • get_posts(page: int = 1, count: int = 10) - get your posts and save to self.posts.

  • get_articles(page: int = 1, count: int = 10) - get your articles and save to self.articles.

  • get_favorites(page: int = 1, count: int = 20) - get your favorite posts and save to self.favorites.

  • get_stocks(mkt: int = 1, count: int = 1000) - get your stocks and save to self.stocks.

  • get_hot_stocks(mkt: int = 10, time_range: str = "hour", count: int = 10) - get hottest stocks.

    • :param mkt: (optional) market type, default is 10.
      • value: 全球10 沪深12 港股13 美股11
    • :param time_range: (optional) hottest stocks by time range, default is hour.
      • value: hour, day
    • :param count: (optional) the number of results, default is 10.
  • send_verification_code(phone: int) - send verification code to your phone. Note: only 5 times a day.

  • login(uid: str = '', passwd: str = '', login_type: str = 'phone') - user login by password or verification code. If the cookie cache exists, load it first and return. Otherwise, login and save the cookie to file (Linux ~/.xueqiu/cookie or Windows).

    • :param uid: your username or phone number.
    • :param passwd: your password or verification code.
    • :param login_type: (optional) login type, default is phone.
      • value: password, phone
  • load_cookie() - load cookies from local file or browser(chrome or firefox). You can login your account on the chrome browser, then execute load_cookie(), and now login successfully.

Example:

>>> u = User(2478797769)
>>> u.name
"红利基金"
>>> u.get_posts()
>>> u.posts['list'][0].title
'【你了解红利基金吗】红利基金(501029)热问快答!(12.31)'
>>> u.get_favorites()
>>> u.favorites['list'][0].title
'2018年A股大数据盘点:30张图尽览市场热点'

Post class

A post class that contains post-related methods.

Post object attributes:

  • id - post id.
  • user - post authors. a User class object.
  • created_at - created time. a Arrow class object.
  • target - post url.
  • view_count - view count.
  • reply_count - reply count.
  • retweet_count - retweet count.
  • fav_count - favorites count.
  • like_count - like count.
  • title - post title.
  • text - post content.
  • full_text - the full content of the article.
  • comments - use the save Comment object for post.

Post object methods:

  • get_content() - get article content and save to self.full_text.
  • get_comments(page: int = 1, count: int = 20, asc: str = 'false') - get article comments and save to self.comments.
  • like() - like the article. (require login)
  • unlike() - unlike the article. (require login)
  • favorite() - favorite the article. (require login)
  • unfavorite() - unfavorite the article. (require login)

Example:

>>> p = Post('2478797769/78869335')
>>> p.user.name
"红利基金"
>>> p.created_at.format("YYYY-MM-DD")
"2016-12-13"
>>> p.title
'【你了解红利基金吗】红利基金(501029)热问快答!(12.31)'
>>> p.target
"https://xueqiu.com/2478797769/78869335"
>>> p.get_content()
>>> p.full_text
'目录:\n一、\n华宝标普中国A股红利机会指数证券投资基金\n......'
>>> p.get_comments()
>>> p.comments['list'][-1].text
'为什么成份股中有很多次新股?百思不得其解'

Comment class

A comment class that contains comment-related methods.

Comment object attributes:

  • id - comment id.
  • user - comment authors. a User class object.
  • post - comment on an article. a Post class object.
  • created_at - created time. a Arrow class object.
  • like_count - like count.
  • text - comment content.

Comment object methods:

  • like() - like the comment. (require login)
  • unlike() - unlike the comment. (require login)

Example:

>>> p = Post('2478797769/78869335')
>>> p.get_comments()
>>> c = p.comments['list'][0]
>>> c.user.name
'红利基金'
>>> c.text
'回复@孙浩云: 怎么可能....2018年跌幅为24.54%,较主流指数跌幅较小。不知道您50%多是哪儿看来的呢'

Selector class

The Selector class implements a stock filter.

Selector object attributes:

  • market - market string, default is SH.
    • value: SH, HK, US
  • queries - include default parameters with selector.

Selector object methods:

  • url() - return a selector url string.
  • help(range: str = "base", show: str = "text") - show selector parameters.
    • :param range: (optional) parameters range, default is base. value:
      • SH: industries, areas, base, ball, quota, finan_rate, stock_data, profit_sheet, balance_sheet, cash_sheet
      • HK: industries, base, ball, quota
      • US: industries, base, ball, quota, grow, profit_sheet, balance_sheet, cash_sheet
    • :param show: (optional) output help or return generator, default is text.
      • value: text, keys
  • scope(exchange: str = '', indcode: str = '', areacode: str = '') - set stock selector scope.
    • :param exchange: (optional) set A-share exchange market, default is None.
      • value: SH, SZ or None
    • :param indcode: (optional) set industry code, default is None. please see self.help('industries')
    • :param areacode: (optional) set area code, default is None. please see self.help('areas')
  • param(*args, **kwargs) - set stock selector paramters.
    • :param *args: (optional) set parameters key, default value is ALL. for example, the self.param('pb', 'mc') will be set pb=ALL&mc=ALL params.
    • :param **kwargs: (optional) set parameters key and value. for example, the self.param('pettm'=0_30) will be set pettm=0_30 param.
  • orderby(key: str = 'symbol') - stock selector results are sorted by field.
    • :param key: the results are sorted by the key, default is symbol. the key parameters can be viewed through self.help('base').
  • order(ord: str = 'desc') - set stock selector results are sorted.
    • :param ord: the ascending and descending order, default is desc.
      • value: asc, desc
  • page(page: int = 1) - set stock selector results page number.
  • count(size: int = 10) - the number of stock selector results.
  • run() - sends a stock screener request and return [Stock class] list.

Example:

>>> s = Selector("SH")
# scope 深市,房地产,浙江地区
# param 筛选总市值,18年3季度ROE 0-30%
# orderby 按市值排序
# order 升序排列
# page 第2页
# count 每页2个
>>> result = s.scope('SZ','K70','CN330000').param('mc', roediluted_20180930='0_30').orderby('mc').order('asc').page(2).count(2).run()
>>> result['list']
[<xueqiu.Stock 荣安地产[SZ000517]>, <xueqiu.Stock 滨江集团[SZ002244]>]

Stock class

A stock class that contains stock-related methods.

Stock object attributes:

base

  • symbol - stock symbol.
  • code - stock code.
  • name - stock name.
  • current - current price.
  • current_year_percent - current year return.
  • percent - change rate.
  • chg - change amount.
  • open - price today.
  • last_close - last close.
  • high - highest.
  • low - lowest.
  • avg_price - average price.
  • volume - trading volume.
  • amount - amount.
  • turnover_rate - turnover rate.
  • amplitude - amplitude.
  • market_capital - market capital.
  • float_market_capital - float market capital.
  • total_shares - total shares.
  • float_shares - float shares.
  • currency - currency unit.
  • exchange - stock exchange.
  • issue_date - launch date. a Arrow class object.

extend

  • limit_up - stock limit up.
  • limit_down - stock limit down.
  • high52w - the highest of the fifty-two weeks.
  • low52w - the lowest of the fifty-two weeks.
  • volume_ratio - volume ratio.
  • pe_lyr - pe lyr.
  • pe_ttm - pe ttm.
  • pe_forecast - pe forecast.
  • pb - price/book value ratio.
  • eps - earnings per share.
  • bps - net asset value per share.
  • dividend - stock dividend.
  • dividend_yield - stock dividend yield.
  • profit - net profit.
  • profit_forecast - profit forecast.
  • profit_four - profit last four quarters.

others

  • time - current time. a Arrow class object.
  • posts - used to the save Post object for stock.
  • followers - used to the save User object for stock's fans.
  • prousers - used to the save User object for stock's professional users.
  • popstocks - pop stocks.
  • industries - industry stocks.
  • history - stock history.

Stock object methods:

  • refresh(dt: dict = {}) - get current stock data and update self.time.
  • get_posts(page: int = 1, count: int = 20, sort: str = "time", source: str = "all") - get stock posts and save to self.posts.
    • :param page: (optional) page number, default is 1.
    • :param count: (optional) the number of results, default is 20.
    • :param sort: (optional) order type, default is time.
      • value: time最新, reply评论, relevance默认
    • :param source: (optional) source of the results, default is all.
      • value: all, user讨论, news新闻, notice公告, trans交易
  • get_followers(page: int = 1, count: int = 15) - get stock fans and save to self.followers.
    • :param page: (optional) page number, default is 1.
    • :param count: (optional) the number of results, default is 15.
  • get_prousers(count: int = 5) - get stock professional users and save to self.prousers.
  • get_popstocks(count: int = 8) - get pop stocks and save to self.popstocks.
  • get_industry_stocks(count: int = 8) - get industry stocks and save to self.industries.
  • get_histories(begin: str = '-1m', end: str = arrow.now(), period: str = 'day') - get stock history data and save to self.history.
    • :param begin: the start date of the results.
      • value: -1w -2w -1m -3m -6m -1y -2y -3y -5y cyear issue or YYYY-MM-DD
    • :param end: (optional) the end date of the results, default is now.
    • :param period: (optional) set date period, default is day.
      • value: day week month quarter year 120m 60m 30m 15m 5m 1m

Example:

>>> s = Stock("SH601318")
>>> s.symbol
"SH601318"
>>> s.name
"中国平安"
>>> s.market_capital
1119664786363.0
>>> s.issue_date.format("YYYY-MM-DD")
"2007-02-28"
>>> s.refresh()  # update stock data
>>> s.get_posts()
{'count': 188745,
 'page': 1,
 'maxpage': 100,
 'list': [<xueqiu.Post [https://xueqiu.com/1566609429/120543602]>,
  <xueqiu.Post [https://xueqiu.com/1083048635/120542397]>,
  <xueqiu.Post [https://xueqiu.com/6376335219/120542355]>,
  <xueqiu.Post [https://xueqiu.com/8335420516/120542213]>,
  <xueqiu.Post [https://xueqiu.com/2706248223/120542082]>,
  <xueqiu.Post [https://xueqiu.com/4298761680/120542015]>,
  <xueqiu.Post [https://xueqiu.com/2856403580/120541995]>,
  <xueqiu.Post [https://xueqiu.com/6122867052/120541786]>,
  <xueqiu.Post [https://xueqiu.com/1083048635/120541288]>,
  <xueqiu.Post [https://xueqiu.com/9598902646/120541255]>]}
>>> s.get_popstocks()
>>> s.popstocks
[<xueqiu.Stock 招商银行[SH600036]>,
 <xueqiu.Stock 兴业银行[SH601166]>,
 <xueqiu.Stock 民生银行[SH600016]>,
 <xueqiu.Stock 贵州茅台[SH600519]>,
 <xueqiu.Stock 苏宁易购[SZ002024]>,
 <xueqiu.Stock 万科A[SZ000002]>,
 <xueqiu.Stock 腾讯控股[00700]>,
 <xueqiu.Stock 中绿[02988]>]
>>> s.get_industry_stocks()
>>> s.industries
{'industryname': '非银金融',
 'list': [<xueqiu.Stock 九鼎投资[SH600053]>,
  <xueqiu.Stock 华林证券[SZ002945]>,
  <xueqiu.Stock 爱建集团[SH600643]>,
  <xueqiu.Stock 中航资本[SH600705]>,
  <xueqiu.Stock 华铁科技[SH603300]>,
  <xueqiu.Stock 民生控股[SZ000416]>,
  <xueqiu.Stock 熊猫金控[SH600599]>,
  <xueqiu.Stock 宏源证券[SZ000562]>]}
>>> s.get_histories('2019-01-07','2019-01-11')
>>> s.history.iloc[:,0:8]
date           volume   open   high    low  close   chg  percent  turnoverrate
2019-01-07   76593007  57.09  57.17  55.90  56.30 -0.29    -0.51          0.70
2019-01-08   55992092  56.05  56.09  55.20  55.80 -0.50    -0.89          0.51
2019-01-09   81914613  56.20  57.60  55.96  56.95  1.15     2.06          0.75
2019-01-10   67328223  56.87  57.82  56.55  57.50  0.55     0.97          0.61
2019-01-11   45756973  58.00  58.29  57.50  58.07  0.57     0.99          0.42
>>> s.history.iloc[:,8:17]
date            ma5    ma10     ma20     ma30      pe     pb        ps       pcf  market_capital
2019-01-07   55.970  56.885  59.2520  60.7143  10.073  1.949  1.051972  3.536000    1.029178e+12
2019-01-08   55.910  56.631  58.8920  60.4863   9.984  1.932  1.042629  3.504597    1.020037e+12
2019-01-09   56.264  56.501  58.6305  60.2780  10.190  1.972  1.064117  3.576824    1.041060e+12
2019-01-10   56.628  56.430  58.3970  60.0910  10.288  1.991  1.074394  3.611368    1.051114e+12
2019-01-11   56.924  56.507  58.1775  59.9010  10.390  2.011  1.085044  3.647167    1.061534e+12
>>> s.get_histories('-1w')
>>> s.history.iloc[:,0:8]
date           volume   open   high    low  close   chg  percent  turnoverrate
2019-01-24   44940618  59.61  60.52  59.22  60.43  0.94     1.58          0.41
2019-01-25   67245911  60.50  61.78  60.43  61.29  0.86     1.42          0.62
2019-01-28   58164884  61.80  62.41  61.20  61.52  0.23     0.38          0.53
2019-01-29   39519294  61.38  61.90  60.98  61.65  0.13     0.21          0.36
2019-01-30   31000323  60.88  61.86  60.78  61.25 -0.40    -0.65          0.27
>>> s.get_histories('-1y')
>>> s.history[['pe','pb','ps']].describe()
               pe          pb          ps
count  243.000000  243.000000  243.000000
mean    11.840588    2.273996    1.217041
std      1.357489    0.215217    0.110052
min      9.728900    1.911000    1.031044
25%     10.849450    2.143200    1.150554
50%     11.504900    2.237300    1.197700
75%     12.628600    2.345200    1.251150
max     15.596700    2.935400    1.559700

Fund class

A fund class that contains fund-related methods.

Fund object attributes:

  • fund_nav - fund net value.
  • fund_nav_guess - estimate value.
  • fund_nav_premium - fund nav premium rate.
  • fund_history - fund history.
  • fund_stocks - component stocks.
  • fund_weight - stocks weight.

Fund object methods:

  • get_fund_stocks(year: str = "", mouth: str = "12") - get fund's stocks from 天天基金.
  • get_fund_nav() - get fund nav.
  • get_fund_histories(page: int = 1, size: int = 90) - get history fund nav.
  • calc_premium() - calculate fund premium.
  • refresh_all() - refresh all of the fund stock objects.

Example:

>>> f = Fund('501301')
>>> f.symbol
"SH501301"
>>> f.fund_nav
['2019-01-29', 1.1311, 1.1311, '-0.47%']
>>> f.get_fund_stocks()
>>> f.fund_stocks
       stocks          weight
0      中国移动[00941]  0.1082
1      工商银行[01398]  0.0975
2      腾讯控股[00700]  0.0970
3      建设银行[00939]  0.0932
4      中国平安[02318]  0.0922
5      中国银行[03988]  0.0642
6   中国海洋石油[00883]  0.0522
7      中国石化[00386]  0.0343
8      中国人寿[02628]  0.0297
9      招商银行[03968]  0.0267
>>> list(f.fund_stocks.weight)
[0.1082, 0.0975, 0.097, 0.0932, 0.0922, 0.0642, 0.0522, 0.0343, 0.0297, 0.0267]
>>> f.get_fund_histories('2019-01-07','2019-01-11')
>>> f.fund_history
date           nav    cnav percent
2019-01-07  1.0743  1.0743    0.70
2019-01-08  1.0679  1.0679   -0.60
2019-01-09  1.0949  1.0949    2.53
2019-01-10  1.0944  1.0944   -0.05
2019-01-11  1.0964  1.0964    0.18
>>> f.get_fund_histories('-1w')
date           nav    cnav percent
2019-01-25  1.1413  1.1413    2.02
2019-01-28  1.1364  1.1364   -0.43
2019-01-29  1.1311  1.1311   -0.47
2019-01-30  1.1379  1.1379    0.60
2019-01-31  1.1475  1.1475    0.84

get_all_funds function

Example:

>>> df = get_all_funds()
>>> df.groupby(by='type').count()
type       code  name
ETF-场内    171   171
QDII        171   171
QDII-ETF     10    10
QDII-指数    83    83
保本型       52    52
债券型     1613  1613
债券指数     69    69
其他创新      2     2
分级杠杆    132   132
固定收益    132   132
定开债券    657   657
混合-FOF     40    40
混合型     3167  3167
理财型      116   116
联接基金    194   194
股票型      373   373
股票指数    462   462
货币型      665   665
>>> df[df['code'].str.contains('^510')].head()
        code          name        type
7319  510010  交银上证180治理ETF  ETF-场内
7320  510020  博时上证超大盘ETF   ETF-场内
7321  510030  华宝上证180价值ETF  ETF-场内
7322  510050  华夏上证50ETF       ETF-场内
7323  510060  工银上证央企50ETF   ETF-场内
>>> df[df['name'].str.contains('恒生')].head()
        code          name            type
54    000071  华夏恒生ETF联接A        QDII-指数
58    000075  华夏恒生ETF联接现汇     QDII-指数
59    000076  华夏恒生ETF联接现钞     QDII-指数
761   000948  华夏沪港通恒生ETF联接A  QDII-指数
919   001149  汇丰晋信恒生龙头指数C   股票指数

get_all_funds_ranking function

Example:

>>> df = get_all_funds_ranking(fund_type='fof')  # 开放式基金排行
>>> df.head()[['code','name','issue_date','nav','current_year']]
   code    name                      issue_date  nav     current_year
0  005220  海富通聚优精选混合(FOF)   2017-11-06  0.8277  0.050781
1  006306  泰达宏利泰和平衡养老(FOF) 2018-10-25  1.0099  0.020513
2  006042  上投摩根尚睿混合(FOF)     2018-08-15  0.9931  0.011613
3  005222  泰达宏利全能混合(FOF)C    2017-11-02  0.9803  0.015644
4  005221  泰达宏利全能混合(FOF)A    2017-11-02  0.9850  0.015883
>>> df = get_all_funds_ranking(fund_type='ct')  # 场内基金排行
>>> df.tail()[['code','name','issue_date','nav','-1year','current_year']]
     code    name                      issue_date  nav    -1year     current_year
419  150197  国泰国证有色金属行业分级B 2015-03-30  0.3411 -0.715443  -0.038349
420  150294  南方中证高铁产业指数分级B 2015-06-10  0.4018 -0.543043  -0.057697
421  150308  富国中证体育产业指数分级B 2015-06-25  0.8470 -0.663614  -0.055753
422  150264  华宝中证1000指数分级B     2015-06-04  0.3436 -0.661696   0.031840
423  512590  浦银安盛中证高股息ETF     2019-01-29  1.0032       NaN        NaN

get_economic function

Example:

>>> get_economic()  # 获取经济指标
{'中国人民银行利率': '1083',
 '中国季度国内生产总值(GDP)年率': '461',
 '中国规模以上工业增加值年率': '462',
 '中国官方制造业采购经理人指数(PMI)': '594',
 '中国财新制造业采购经理人指数(PMI)': '753',
 '中国失业率': '1793',
 '中国贸易帐 (美元)': '466',
 '中国台湾利率决议': '1117',
......
>>> get_economic(search='美国')  # 获取经济指标 - 美国
{'美国失业率': '300',
 '美国总统选举': '371',
 '美国ADP就业人数': '1',
 '美国ISM制造业PMI': '173',
 '美国零售销售月率': '256',
 '美国营建许可总数': '25',
 '美国ISM非制造业PMI': '176',
 '美国核心零售销售月率': '63',
......
>>> df = get_economic('中国财新制造业采购经理人指数(PMI)')  # 获取财新PMI
>>> df.tail()
date        actual actual_state  forecast  revised
2018-09-30    50.0         down      50.5      NaN
2018-11-01    50.1           up      49.9      NaN
2018-12-03    50.2           up      50.1      NaN
2019-01-02    49.7         down      50.3      NaN
2019-02-01    48.3         down      49.5      NaN
>>> df.to_excel('output.xls')  # 导出excel

get_economic_of_china function

Example:

>>> get_economic_of_china(search='总人口')
[{'id': 'A01050201', 'name': '民族自治地方总人口数'},
 {'id': 'A030301', 'name': '年末总人口'},
 {'id': 'A030501', 'name': '人口普查总人口'},
 {'id': 'A030508', 'name': '人口普查0-14岁人口占总人口比重'},
......
>>> df = get_economic_of_china('A030101,A030102,A030103', time_period='1949-')
>>> df.to_period('A').tail()
   年末总人口  男性人口  女性人口
1953  58796.0  30468.0  28328.0
1952  57482.0  29833.0  27649.0
1951  56300.0  29231.0  27069.0
1950  55196.0  28669.0  26527.0
1949  54167.0  28145.0  26022.0
>>> get_economic_of_china(category='month', search='居民消费价格指数')
[{'id': 'A01010101', 'name': '居民消费价格指数(上年同月=100)'},
 {'id': 'A01010102', 'name': '食品烟酒类居民消费价格指数(上年同月=100)'},
 {'id': 'A01010103', 'name': '衣着类居民消费价格指数(上年同月=100)'},
 {'id': 'A01010104', 'name': '居住类居民消费价格指数(上年同月=100)'},
......
>>> get_economic_of_china("A01010101", category='month').to_period('M')
    居民消费价格指数(上年同月=100)
2018-12  101.860698
2018-11  102.175041
2018-10  102.543151
2018-09  102.472394
......
>>> get_economic_of_china(category='month_by_state', search='region')
[{'id': '110000', 'name': '北京市'},
 {'id': '120000', 'name': '天津市'},
 {'id': '130000', 'name': '河北省'},
 {'id': '140000', 'name': '山西省'},
......
>>> get_economic_of_china("A03010101", region='210000,130000', category='month_by_state').to_period('M')
        辽宁省  河北省
2018-12  333.6  381.0
2018-11  311.0  398.3
2018-10  274.3  429.2
2018-09  273.5  456.2
......

search function

  • search(query: str = "", query_type: str = "stock", symbol: str = "", count: int = 10, page: int = 1, sort: str = "time", source: str = "user") - Sends a search request.
    • :param query: query string.
    • :param query_type: (optional) type of the query request, default is stock.
      • value: stock, post, user
    • :param symbol: (optional) the stock symbol.
    • :param count: (optional) the number of results, default is 20.
    • :param page: (optional) page number, default is 1.
    • :param sort: (optional) order type, default is time.
      • value: time最新, reply评论, relevance默认
    • :param source: (optional) source of the results, default is user.
      • value: all, user讨论, news新闻, notice公告, trans交易
    • :return: a list of :class:Object <instance_id> objects. Object class: Stock, Post or User
    • :rtype: list([ins1, ins2, ...])

news function

  • news(category: int = -1, count: int = 10, max_id: int = -1) - Get news.
    • :param category: (optional) type of the news, default is -1.
      • value: 头条-1, 今日话题0, 直播6, 沪深105, 港股102, 美股101, 基金104, 私募113, 房产111, 汽车114, 保险110
    • :param count: (optional) the number of results, default is 10.
    • :param max_id: (optional) the max id of news, default is -1.
    • :return: a list of :class:Post <instance_id> objects.
    • :rtype: list([post1, post2, ...])

utils module

This module contains some utils.

  • get_cookies() - load cookies from local file, browser and selenium. return a LWPCookieJar class object.
  • get_session() - get the requests session.
  • clean_html(tree: str) - clean html.
  • check_symbol(code: str) - check stock symbol.
  • exrate(date: str = "", code: str = "USD") - get the monetary exchange rate by date.
    • code:
{'USD':'美元','EUR':'欧元','JPY':'日元','HKD':'港元','GBP':'英镑','AUD':'澳大利亚元',
 'NZD':'新西兰元','SGD':'新加坡元','CHF':'瑞士法郎','CAD':'加拿大元','MYR':'马来西亚林吉特',
 'RUB':'俄罗斯卢布','ZAR':'南非兰特','KRW':'韩元','AED':'阿联酋迪拉姆','SAR':'沙特里亚尔',
 'HUF':'匈牙利福林','PLN':'波兰兹罗提','DKK':'丹麦克朗','SEK':'瑞典克朗','NOK':'挪威克朗',
 'TRY':'土耳其里拉','MXN':'墨西哥比索','THB':'泰铢'}
  • exusd(date: str = "") - only for USD.
  • exhkd(date: str = "") - only for HKD.

Example:

>>> CJ = get_cookies()
>>> sess = get_session()
>>> clean_html("<span>hello.</span>")
hello.
>>> check_symbol(601318)
"SH601318"
>>> exrate("2019-01-10", "EUR")
[7.8765, 7.8443]
>>> exusd(date="2019-01-10")
[6.816, 6.8526]
>>> exhkd("2019-01-10")
[0.86959, 0.87419]

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