class Shibor(object):
def __init__(self):
super(Shibor, self).__init__()
def __call__(self, conns):
self.base = Base()
self.financial_data = conns['financial_data']
year = self.base.gettoday()[:4]
#Shibor拆放利率
shibor_data = ts.shibor_data(year)
print(shibor_data)
self.base.batchwri(shibor_data, 'shibor_data', self.financial_data)
#银行报价数据
shibor_quote_date = ts.shibor_quote_data(year)
self.base.batchwri(shibor_quote_date, 'shibor_quote_data',
self.financial_data)
#Shibor均值数据
shibor_ma_data = ts.shibor_ma_data(year)
self.base.batchwri(shibor_ma_data, 'shibor_ma_data',
self.financial_data)
#贷款基础利率(LPR)
lpr_data = ts.lpr_data(year)
self.base.batchwri(lpr_data, 'lpr_data', self.financial_data)
#LPR均值数据
lpr_ma_data = ts.lpr_ma_data(year)
self.base.batchwri(lpr_ma_data, 'lpr_ma_data', self.financial_data)
class GetDataTransaction(object):
def set_data(self):
self.code = '600848'
self.start = '2017-11-03'
self.end = '2017-11-07'
self.df=pd.DataFrame()
def __call__(self, conns):
self.set_data()
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.get_stock_basics()
self.base.batchwri(self.df, 'stock_basics1', self.financial_data)
def get_today_all(self, conns):
self.set_data()
self.base = Base()
self.financial_data = conns['financial_data']
#self.df=ts.get_hist_data(self.code,self.start,self.end)
#self.df=ts.get_stock_basics(self.code,self.start,self.end)
#self.df=ts.get_today_all()
#self.df=ts.get_tick_data(self.code,date='2017-01-09')
#self.df=ts.get_realtime_quotes(self.code)
#self.df=ts.get_today_ticks(self.code)
#self.df=ts.get_index()
#self.df=ts.get_sina_dd(self.code,date='2014-01-09')
self.base.batchwri(self.df, 'realtime_quotes', self.financial_data)
def dapanlishi(conns):
base = Base()
financial_data = conns['financial_data']
df = ts.get_index()
for stock in df['code']:
df1=ts.get_k_data(stock,index=True,start='2018-07-01', end='2018-07-30')
df1 = df1.sort_values(['date'], ascending=[0]).reset_index(drop=True) # 排序，0倒序，1正序 重置索引
df2 = ts.get_h_data(stock, index=True, start='2018-07-01', end='2018-07-30')
noise_df = pd.DataFrame(df2, columns=['amount']).reset_index(drop=True)
df3 =pd.merge(df1,noise_df, left_index=True, right_index=True)
base.batchwri(df3, 'dapanzhishulishi', financial_data)
def stocklishi(conns):
base = Base()
financial_data = conns['financial_data']
df = ts.get_today_all()['code']
# df = pd.DataFrame(df, columns=['code','name','open','high','low','volume','amount'])
for stock in df:
df1 = pd.DataFrame(ts.get_hist_data(stock) ,columns=['date','code','name','open','high','low','volume'])
df2 = ts.get_h_data(stock,start='2018-07-01', end='2018-07-30')
df3 = ts.get_h_data(stock,start='2018-07-01', end='2018-07-30',autype='hfq')
df2 = pd.merge(df1, df2, on='date')
df3 = pd.merge(df2, df3, on='date')
base.batchwri(df3, 'stocklishi', financial_data)
class LonghuBang(object):
def __init__(self):
super(LonghuBang,self).__init__()
def __call__(self,conns):
self.base=Base()
self.financial_data=conns['financial_data']
date=self.base.gettoday().replace('/','-')
# print(date)
# '''每日龙虎榜列表'''
# for day in self.base.datelist('20180702','20180705'):
# day=day.replace('/','-')
# top_list=ts.top_list(day)
# self.base.batchwri(top_list,'top_list',self.financial_data)
# '''
# 名称：个股上榜统计
# 参数说明：
# days：统计周期5、10、30和60日，默认为5日
# retry_count：当网络异常后重试次数，默认为3
# pause:重试时停顿秒数，默认为0'''
# cap_tops=ts.cap_tops()
# self.base.batchwri(cap_tops,'cap_tops',self.financial_data)
# '''
# 名称：营业部上榜统计
# 参数说明：
# days：统计周期5、10、30和60日，默认为5日
# retry_count：当网络异常后重试次数，默认为3
# pause:重试时停顿秒数，默认为0'''
# broker_tops=ts.broker_tops()
# self.base.batchwri(broker_tops,'broker_tops',self.financial_data)
# '''
# 名称：机构席位追踪
# 参数说明：
# days：统计周期5、10、30和60日，默认为5日
# retry_count：当网络异常后重试次数，默认为3
# pause:重试时停顿秒数，默认为0
# '''
# inst_tops=ts.inst_tops()
# self.base.batchwri(inst_tops,'inst_tops',self.financial_data)
'''机构成交明细'''
inst_detail=ts.inst_detail()
self.base.batchwri(inst_detail,'inst_detail',self.financial_data)
class DaPanHangQing(object):
def __init__(self):
super(DaPanHangQing, self).__init__()
def __call__(self, conns):
self.base = Base()
self.finacial_data = conns['financial_data']
#清空表
# self.finacial_data.dopost('TRUNCATE TABLE dapan_hangqing_date')
# self.finacial_data.dopost('TRUNCATE TABLE dapan_code_name')
# 实时行情
hangqing = ts.get_index()
today = self.base.gettoday()
hangqing['date'] = today.replace('/', '-')
#大盘指数每日行情数据
self.base.batchwri(hangqing, 'dapan_hangqing_date', self.finacial_data)
#大盘代码-名字对照表
self.base.batchwri(hangqing[['code', 'name']], 'dapan_code_name',
self.finacial_data)
#大盘代码-名字对照表去重
duizhao = self.finacial_data.getdata('dapan_code_name')
# print(duizhao.size)
duizhao_qc = duizhao.drop_duplicates().sort_values(
by='code').reset_index(drop=True)
# print(df)
self.finacial_data.dopost('TRUNCATE TABLE dapan_code_name')
self.base.batchwri(duizhao_qc, 'dapan_code_name', self.finacial_data)
class GeGuHangQing(object):
def __init__(self):
super(GeGuHangQing,self).__init__()
self.close_res = []
def __call__(self, conns, retry_num=3):
'''
抓取股票每日行情数据
:param conns: 数据库连接
:param retry_num: 重新获取数据的次数
:return: 数据存储在数据库
'''
global hangqing
self.base = Base()
self.finacial_data = conns['financial_data']
# self.finacial_data.dopost('TRUNCATE TABLE stock_hangqing_date')
# self.finacial_data.dopost('TRUNCATE TABLE stock_code_name')
#在股市收盘后，获取股票当日行情
#出现"urllib.error.HTTPError: HTTP Error 456"的问题
for i in range(retry_num+1):
try:
hangqing = ts.get_today_all()
break
except Exception as e:
logger.warning('Retry get today stock data , the [%d] times, err %s' % (i, e.message))
if i == retry_num:
logger.warning(traceback.format_exc())
raise e
time.sleep(300)
today = self.base.gettoday()
hangqing['date'] = today.replace('/','-')
#股市收盘后，trade现价就是股票的收盘价。
hangqing.rename(columns={'trade': 'close'}, inplace=True)
#去重
hangqing_qc = hangqing.drop_duplicates().sort_values(by='code').reset_index(drop=True)
# print(hangqing_qc)
self.base.batchwri(hangqing_qc, 'stock_hangqing_date',self.finacial_data)
self.base.batchwri(hangqing_qc[['code','name']], 'stock_code_name', self.finacial_data)
#股票代码-名称对照表去重
duizhao = self.finacial_data.getdata('stock_code_name')
print(duizhao.size)
duizhao_qc = duizhao.drop_duplicates().sort_values(by='code').reset_index(drop=True)
# print(df)
self.finacial_data.dopost('TRUNCATE TABLE stock_code_name')
self.base.batchwri(duizhao_qc, 'stock_code_name', self.finacial_data)
class XinwenShijian(object):
def __init__(self):
super(XinwenShijian, self).__init__()
def __call__(self, conns):
self.base = Base()
self.financial_data = conns['financial_data']
'''即时财经新闻'''
latest_news = ts.get_latest_news()
self.base.batchwri(latest_news, 'latest_news', self.financial_data)
'''
名称：个股信息地雷
功能：获得个股信息地雷数据
参数
code：股票代码
date：信息公布日期
'''
notices = ts.get_notices()
self.base.batchwri(notices, 'notices', self.financial_data)
'''新浪股吧新闻'''
guba_sina = ts.guba_sina()
self.base.batchwri(guba_sina, 'guba_sina', self.financial_data)
class GerDataTouzicankao(object):
def __init__(self):
#self.code = '002337'
self.df = pd.DataFrame()
def profit_data(self, conns, top):
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.profit_data(top)
self.base.batchwri(self.df, 'fenpeiyuan', self.financial_data)
def forecast_data(self, conns, year, quarter):
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.forecast_data(year, quarter)
self.base.batchwri(self.df, 'yejibaobiao', self.financial_data)
def xsg_data(self, conns):
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.xsg_data()
self.base.batchwri(self.df, 'xianshougujiejin', self.financial_data)
def fund_holdings(self, conns, year, quarter):
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.fund_holdings(year, quarter)
self.base.batchwri(self.df, 'jijinchigu', self.financial_data)
def new_stocks(self, conns):
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.new_stocks()
self.base.batchwri(self.df, 'xingushuju', self.financial_data)
def sh_margins(self, conns, start, end):
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.sh_margins(start, end)
self.base.batchwri(self.df, 'rongzirongquan_sh', self.financial_data)
def sz_margins(self, conns, start, end):
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.sz_margins(start, end)
self.base.batchwri(self.df, 'rongzirongquan_sz', self.financial_data)
class GetDataTransaction(object):
def __init__(self):
self.code = '000001'
self.start = '2018-07-01'
self.end = '2018-07-25'
self.df = pd.DataFrame()
def __call__(self, conns):
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.get_stock_basics()
self.base.batchwri(self.df, 'stock_basics1', self.financial_data)
#历史行情:获取个股历史交易数据（包括均线数据），可以通过参数设置获取日k线、周k线、月k线，以及5分钟、15分钟、30分钟和60分钟k线数据。
#本接口只能获取近3年的日线数据，适合搭配均线数据进行选股和分析，如果需要全部历史数据，请调用下一个接口get_h_data()
def get_hist_data(self, conns):
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.get_hist_data(self.code)
self.base.batchwri(self.df, 'history_data_0701-0725',
self.financial_data)
#复权数据:获取历史复权数据，分为前复权和后复权数据，接口提供股票上市以来所有历史数据，默认为前复权。如果不设定开始和结束日期，
#则返回近一年的复权数据，从性能上考虑，推荐设定开始日期和结束日期，而且最好不要超过三年以上，获取全部历史数据，请分年段分步获取。
def get_stock_basics(self, conns):
self.base = Base()
self.financial_data = conns['financial_data']
#self.df = ts.get_stock_basics(self.code)
# ts.get_h_data('002337') # 前复权
# ts.get_h_data('002337', autype='hfq') # 后复权
# ts.get_h_data('002337', autype=None) # 不复权
self.df = ts.get_h_data(self.code, self.start,
self.end) # 两个日期之间的前复权数据
#self.df = ts.get_k_data('000001', index=True, ktype='W', autype='hfq')
#
# ts.get_h_data('399106', index=True) # 深圳综合指数
self.base.batchwri(self.df, 'fuquan_data', self.financial_data)
#实时行情::一次性获取当前交易所有股票的行情数据（如果是节假日，即为上一交易日，结果显示速度取决于网速）
def get_today_all(self, conns):
self.base = Base()
self.financial_data = conns['financial_data']
#self.df=ts.get_hist_data(self.code,self.start,self.end)
#self.df=ts.get_stock_basics(self.code,self.start,self.end)
self.df = ts.get_today_all()
#self.df=ts.get_tick_data(self.code,date='2017-01-09')
#self.df=ts.get_realtime_quotes(self.code)
#self.df=ts.get_today_ticks(self.code)
#self.df=ts.get_index()
#self.df=ts.get_sina_dd(self.code,date='2014-01-09')
self.base.batchwri(self.df, 'shishihangqing', self.financial_data)
#历史分笔:获取个股以往交易历史的分笔数据明细，通过分析分笔数据，可以大致判断资金的进出情况。
def get_tick_data(self, conns):
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.get_tick_data(self.code, date='2018-06-01')
self.base.batchwri(self.df, 'lishifenbi', self.financial_data)
#实时分笔:获取实时分笔数据，可以实时取得股票当前报价和成交信息
def get_realtime_quotes(self, conns):
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.get_realtime_quotes(self.code)
#ts.get_realtime_quotes(['600848','000980','000981']) 请求多个股票
self.base.batchwri(self.df, 'shishifenbi', self.financial_data)
#当日历史分笔:获取当前交易日（交易进行中使用）已经产生的分笔明细数据。
def get_today_ticks(self, conns):
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.get_today_ticks(self.code)
self.base.batchwri(self.df, 'dangrilishifenbi', self.financial_data)
#大盘指数行情列表:获取大盘指数实时行情列表，以表格的形式展示大盘指数实时行情。
def get_index(self, conns):
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.get_index()
self.base.batchwri(self.df, 'dapanzhishu', self.financial_data)
#大单交易数据:获取大单交易数据，默认为大于等于400手，数据来源于新浪财经。
def get_sina_dd(self, conns):
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.get_sina_dd(self.code, date='2018-07-25') #默认400手
# self.df = ts.get_sina_dd('600848', date='2015-12-24', vol=500) #指定大于等于500手的数据
self.base.batchwri(self.df, 'dadanjiaoyi', self.financial_data)
class FinanceMacro(object):
def __init__(self):
super(FinanceMacro, self).__init__()
def __call__(self, conns):
self.base = Base()
self.financial_data = conns['financial_data']
'''存款利率'''
deposit_rate = ts.get_deposit_rate()
self.base.batchwri(deposit_rate, 'deposit_rate', self.financial_data)
'''贷款利率'''
loan_rate = ts.get_loan_rate()
self.base.batchwri(loan_rate, 'loan_rate', self.financial_data)
'''存款准备金率'''
rrr = ts.get_rrr()
self.base.batchwri(rrr, 'RatioOfDeposit', self.financial_data)
'''货币供应量'''
money_supply = ts.get_money_supply()
self.base.batchwri(money_supply, 'money_supply', self.financial_data)
'''货币供应量（年底余额)'''
money_supply_bal = ts.get_money_supply_bal()
self.base.batchwri(money_supply_bal, 'money_supply_bal',
self.financial_data)
'''国内生产总值(年度）'''
gdp_year = ts.get_gdp_year()
self.base.batchwri(gdp_year, 'gdp_year', self.financial_data)
'''国内生产总值（季度）'''
gdp_quarter = ts.get_gdp_quarter()
self.base.batchwri(gdp_quarter, 'gdp_quarter', self.financial_data)
'''三大需求对GDP贡献'''
gdp_for = ts.get_gdp_for()
self.base.batchwri(gdp_for, 'gdp_for', self.financial_data)
'''三大产业对GDP拉动'''
gdp_pull = ts.get_gdp_pull()
self.base.batchwri(gdp_pull, 'gdp_pull', self.financial_data)
'''三大产业贡献率'''
gdp_contrib = ts.get_gdp_contrib()
self.base.batchwri(gdp_contrib, 'gdp_contrib', self.financial_data)
'''居民消费价格指数'''
cpi = ts.get_cpi()
self.base.batchwri(cpi, 'cpi', self.financial_data)
'''工业品出场价格指数'''
ppi = ts.get_ppi()
self.base.batchwri(ppi, 'ppi', self.financial_data)
class GetDataGupiaofenlei(object):
def __init__(self):
self.code = '002337'
self.df = pd.DataFrame()
def get_industry_classified(self, conns):
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.get_industry_classified()
self.base.batchwri(self.df, 'industry_classified', self.financial_data)
def get_concept_classified(self, conns):
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.get_concept_classified()
self.base.batchwri(self.df, 'concept_classified', self.financial_data)
def get_area_classified(self, conns):
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.get_area_classified()
self.base.batchwri(self.df, 'area_classified', self.financial_data)
def get_sme_classified(self, conns):
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.get_sme_classified()
self.base.batchwri(self.df, 'sme_classified', self.financial_data)
def get_gem_classified(self, conns):
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.get_gem_classified()
self.base.batchwri(self.df, 'gem_classified', self.financial_data)
def get_st_classified(self, conns):
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.get_st_classified()
self.base.batchwri(self.df, 'st_classified', self.financial_data)
def get_hs300s(self, conns):
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.get_hs300s()
self.base.batchwri(self.df, 'hs300s', self.financial_data)
def get_sz50s(self, conns):
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.get_sz50s()
self.base.batchwri(self.df, 'sz50s', self.financial_data)
def get_zz500s(self, conns):
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.get_zz500s()
self.base.batchwri(self.df, 'zz500s', self.financial_data)
def get_terminated(self, conns):
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.get_terminated()
self.base.batchwri(self.df, 'terminated', self.financial_data)
def get_suspended(self, conns):
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.get_suspended()
self.base.batchwri(self.df, 'suspended', self.financial_data)
class GetDataJibenmian(object):
def __init__(self):
self.code = '002337'
self.df = pd.DataFrame()
def get_stock_basics(self, conns):
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.get_stock_basics()
self.base.batchwri(self.df, 'stock_basics', self.financial_data)
def get_report_data(self, conns, year, quarter):
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.get_report_data(year, quarter)
self.base.batchwri(self.df, 'report_data', self.financial_data)
def get_profit_data(self, conns, year, quarter):
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.get_profit_data(year, quarter)
self.base.batchwri(self.df, 'profit_data', self.financial_data)
def get_operation_data(self, conns, year, quarter):
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.get_operation_data(year, quarter)
self.base.batchwri(self.df, 'operation_data', self.financial_data)
def get_growth_data(self, conns, year, quarter):
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.get_growth_data(year, quarter)
self.base.batchwri(self.df, 'growth_data', self.financial_data)
def get_debtpaying_data(self, conns, year, quarter):
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.get_debtpaying_data(year, quarter)
self.base.batchwri(self.df, 'debtpaying_data', self.financial_data)
def get_cashflow_data(self, conns, year, quarter):
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.get_cashflow_data(year, quarter)
self.base.batchwri(self.df, 'cashflow_data', self.financial_data)
