def QA_fetch_get_stock_block(ip=None, port=None):
'板块数据'
global best_ip
if ip is None and port is None and best_ip['stock']['ip'] is None and best_ip['stock']['port'] is None:
best_ip = select_best_ip()
ip = best_ip['stock']['ip']
port = best_ip['stock']['port']
elif ip is None and port is None and best_ip['stock']['ip'] is not None and best_ip['stock']['port'] is not None:
ip = best_ip['stock']['ip']
port = best_ip['stock']['port']
else:
pass
api = TdxHq_API()
with api.connect(ip, port):
data = pd.concat([api.to_df(api.get_and_parse_block_info("block_gn.dat")).assign(type='gn'),
api.to_df(api.get_and_parse_block_info(
"block.dat")).assign(type='yb'),
api.to_df(api.get_and_parse_block_info(
"block_zs.dat")).assign(type='zs'),
api.to_df(api.get_and_parse_block_info("block_fg.dat")).assign(type='fg')])
if len(data) > 10:
return data.assign(source='tdx').drop(['block_type', 'code_index'], axis=1).set_index('code', drop=False, inplace=False).drop_duplicates()
else:
QA_util_log_info('Wrong with fetch block ')
def QA_fetch_get_stock_block(ip=best_ip, port=7709):
'板块数据'
api = TdxHq_API()
with api.connect(ip, port):
data = pd.concat([api.to_df(api.get_and_parse_block_info("block_gn.dat")),
api.to_df(api.get_and_parse_block_info("block.dat")),
api.to_df(api.get_and_parse_block_info(
"block_zs.dat")),
api.to_df(api.get_and_parse_block_info("block_fg.dat"))])
if len(data) > 10:
return data.assign(source='tdx').set_index('code', drop=False, inplace=False)
else:
QA_util_log_info('Wrong with fetch block ')
def QA_fetch_get_stock_block(ip=best_ip['stock'], port=7709):
'板块数据'
api = TdxHq_API()
with api.connect(ip, port):
data = pd.concat([api.to_df(api.get_and_parse_block_info("block_gn.dat")).assign(type='gn'),
api.to_df(api.get_and_parse_block_info(
"block.dat")).assign(type='yb'),
api.to_df(api.get_and_parse_block_info(
"block_zs.dat")).assign(type='zs'),
api.to_df(api.get_and_parse_block_info("block_fg.dat")).assign(type='fg')])
if len(data) > 10:
return data.assign(source='tdx').drop(['block_type', 'code_index'], axis=1).set_index('code', drop=False, inplace=False).drop_duplicates()
else:
QA_util_log_info('Wrong with fetch block ')
def _get_bankinfo_tdx(self):
'''
通过通达信获取板块信息
:return:
'''
# 创建TDX对象
api = TdxHq_API()
with api.connect('218.108.98.244', 7709):
# 概念
bank_gn = api.get_and_parse_block_info(TDXParams.BLOCK_GN)
# 风格
bank_fg = api.get_and_parse_block_info(TDXParams.BLOCK_FG)
# 指数
bank_zs = api.get_and_parse_block_info(TDXParams.BLOCK_SZ)
bank_gn_list = []
for bank in bank_gn:
bank_gn_list.append([bank['blockname'], '概念', bank['code']])
bank_fg_list = []
for bank in bank_fg:
bank_fg_list.append([bank['blockname'], '风格', bank['code']])
bank_zs_list = []
for bank in bank_zs:
bank_zs_list.append([bank['blockname'], '指数', bank['code']])
bank_gn_list.extend(bank_fg_list)
bank_gn_list.extend(bank_zs_list)
df_bank_stocks = pd.DataFrame(bank_gn_list, columns=['bank_name', 'bank_type', 'stock_code'])
df_bank = df_bank_stocks[['bank_name', 'bank_type']].drop_duplicates()
# 先删除原表数据
BankBasicInfo.objects.all().delete()
# 新增板块数据。每次更新板块数据，都要先删除旧数据，再重建新数据。因为板块内的个股是会变动的
for bank in df_bank.values:
# name stocks bank_type bank_desc
bankinfo = BankBasicInfo()
bankinfo.name = bank[0]
bankinfo.bank_type = bank[1]
bankinfo.bank_desc = bank[1]
bankinfo.save()
stocks = df_bank_stocks[df_bank_stocks['bank_name'] == bank[0]]
bankinfo.stocks.set(StockBasicInfo.objects.filter(code__in=list(stocks['stock_code'])))
def get_block_info():
api = TdxHq_API()
if api.connect('119.147.212.81', 7709):
data = api.get_and_parse_block_info("block.dat") #一般板块
#print(api.get_and_parse_block_info("block_zs.dat")) #指数板块
#print(api.get_and_parse_block_info("block_fg.dat")) #风格板块
#print(api.get_and_parse_block_info("block_gn.dat")) #概念板块
datadf = api.to_df(data)
print(datadf)
api.disconnect()
class TDX(object):
'''
This class is tong da xin data source.
We can use it to get down the stock datas.
Tushare can't get minter line and or year line.
TDX can search index of stock and funds.
'''
def __init__(self):
self.tdx_api = TdxHq_API()
self.__ip = '119.147.212.81' #输入IP
self.__port = 7709 #端口
self.__code = '600200'
self.__market = 1 #市场代码 0:深圳，1:上海
self._startdate = "2017-01-01"
self.today = datetime.date.today()
self._enddate = datetime.datetime.strftime(self.today, '%Y-%m-%d')
self.__mkt_segment = {
'sh': '60',
"sz": '00',
"cyb": "30",
} #segment 当前板块开始字符串
def __str__(self):
return 'TDX object (code : %s)' % self.code
@property
def IP(self): # self.IP
return self.__ip
@property
def PORT(self):
return self.__port
@property
def code(self): #定义stock code 属性
return self.__code
@code.setter #设定code
def code(self, code_input):
"""
The setter of the code property
"""
if not isinstance(code_input, str): #确定是否是字符串
raise ValueError('the code must string!')
if not len(code_input) == 6: #确定长度
raise ValueError('the code value error,the len must SIX !')
if code_input.startswith('60'): #确定表头
self.__market = 1
elif code_input.startswith('00'):
self.__market = 0
elif code_input.startswith('30'):
self.__market = 0
else:
raise ValueError('this code is not stock code')
self.__code = code_input
@property
def startdate(self): #开始日期
return self._startdate
@startdate.setter #设置日期
def startdate(self, date_input):
"""
The setter of the start date property
"""
if not isinstance(date_input, str):
raise ValueError('the date must string!')
if not len(date_input) == 8:
raise ValueError(
'the date value error,the date formet must xxxx-xx-xx !')
self._startdate = date_input
@property #结束日期
def enddate(self):
return self._enddate
@enddate.setter
def enddate(self, date_input):
"""
The setter of the start date property
"""
if not isinstance(date_input, str):
raise ValueError('the date must string!')
if not len(date_input) == 8:
raise ValueError(
'the date value error,the date formet must xxxx-xx-xx !')
self._enddate = date_input
def get_day_data_tdx(self): #获取K line
with self.tdx_api.connect(self.IP, self.PORT):
data = self.tdx_api.get_k_data(self.code, self.startdate,
self.enddate)
data = pandas.DataFrame(data)
data.date = data.date.apply(
lambda x: datetime.datetime.strptime(x, "%Y-%m-%d"))
return data
#TODO: 现在是用800点进行计数，以后会细化功能
def get_k_data_tdx(self, k_mode=9):
"""
获取k 线图，总计800 点
Parameters
----------
k_mode= 0-11
0 5分钟K线
1 15分钟K线
2 30分钟K线
3 1小时K线
4 日K线
5 周K线
6 月K线
7 1分钟
8 1分钟K线 9 日K线
10 季K线
11 年K线
Returns
-------
"""
with self.tdx_api.connect(self.self.IP, self.self.PORT):
data = self.tdx_api.get_security_bars(k_mode, self.__market,
self.code, 0, 800)
data = pandas.DataFrame(data)
#data.date = data.date.apply(
# lambda x: datetime.datetime.strptime(x, "%Y-%m-%d"))
return data
def len_market(self): #市场有多少只股票
with self.tdx_api.connect(self.IP, self.PORT):
_len = self.tdx_api.get_security_count(self.__market)
return _len
def get_page_tdx(self, block=None):
if block is None:
market = 1
page = [0]
elif block in ['sh', 'SH']:
market = 1
page = [13, 14]
elif block in ['sz', 'SZ']:
print('block for shenzhen')
market = 0
page = [0, 1]
elif block in ['cyb', 'CYB']:
print('block for chuang ye ban')
market = 0
page = [7, 8]
else:
pass
code_list_df = pandas.DataFrame()
with self.tdx_api.connect(self.IP, self.PORT):
for pn in page:
data = self.tdx_api.get_security_list(market, pn * 1000)
data = pandas.DataFrame(data)
print(data)
code_list_df = code_list_df.append(data, ignore_index=True)
return code_list_df
def get_base_finace_tdx(self):
with self.tdx_api.connect(self.IP, self.PORT):
data = self.tdx_api.get_finance_info(0, '000001')
data = pandas.Series(data)
print(data)
def get_min_data(self):
from pytdx.params import TDXParams
with self.tdx_api.connect(self.IP, self.PORT):
data = self.tdx_api.get_history_minute_time_data(
TDXParams.MARKET_SH, self.code, 20161209)
data = pandas.DataFrame(data)
print(data)
#TODO: 需要确定 0: buy 1 : sell
def get_tick_data(self):
"""
历史分笔交易：time 顺序； price ; vol ;buyorsell [1:] [0:];
sh 60 13000-14000
Parameters
----------
Returns
-------
"""
data = pandas.DataFrame()
with self.tdx_api.connect(self.IP, self.PORT):
for i in [2000, 0000]:
df = self.tdx_api.get_history_transaction_data(
TDXParams.MARKET_SH, "600547", i, 2000, 20160308)
df = pandas.DataFrame(df)
data = data.append(df, ignore_index=True)
return data
def get_tick_today(self):
"""
Get every time the each deal for today.每组数最大len 2 k 所以要确定的数据长度
Parameters
----------
self:
Returns
-------
"""
with self.tdx_api.connect(self.IP, self.PORT):
data = pandas.DataFrame()
for i in [0, 2000]:
df = self.tdx_api.get_transaction_data(self.__market,
self.code, i, 2000)
df = pandas.DataFrame(df)
data = data.append(df, ignore_index=True)
return data
def get_block(self):
with self.tdx_api.connect(self.IP, self.PORT):
data = self.tdx_api.get_and_parse_block_info("block.dat")
data = pandas.DataFrame(data)
print(data)
def get_market_segment_list(self, mkt):
data = self.get_page_tdx(mkt)
self.code_list = pandas.DataFrame()
pbar = tqdm(total=len(data.code))
mkt_hard = self.mkt_segment[mkt]
for idx, __code in enumerate(data.code):
pbar.update(1)
if __code.startswith(mkt_hard, 0, 2):
self.code_list = self.code_list.append(data.loc[idx],
ignore_index=True)
return self.code_list
def get_sh_list(self):
return self.get_market_segment_list('sh')
def get_sz_list(self):
return self.get_market_segment_list('sz')
def get_cyb_list(self):
return self.get_market_segment_list('cyb')
class TdxData:
def __init__(self):
self.api = TdxHq_API(heartbeat=True)
self.dbUtil = DBUtil.getInstance()
def get_security_quotes(self, code, type):
return self.api.get_security_quotes([(type, code)])
# 支持板块及个股
def days(self, code, type, bk=False, all=False, day=5):
category = int(config.getByKey('TDX_CATEGORY'))
try:
with self.api.connect(TDX_IP, TDX_PORT):
data = []
if all:
if bk:
for i in range(10):
data += self.api.get_index_bars(
category, type, code, (9 - i) * 800, 800)
else:
for i in range(10):
data += self.api.get_security_bars(
category, type, code, (9 - i) * 800, 800)
if len(data) > 0:
df = self.api.to_df(data).drop([
'amount', 'year', 'month', 'day', 'hour', 'minute'
],
axis=1)
df['trade_date'] = df['datetime'].apply(
lambda x: x[0:10].replace('-', ''))
df = df.drop(['datetime'], axis=1)
df = df.sort_values(by=['trade_date'],
axis=0,
ascending=False)
return df
else:
return self.api.to_df(data)
else:
if bk:
data = self.api.get_index_bars(category, type, code, 0,
day) # 返回DataFrame
else:
data = self.api.get_security_bars(
category, type, code, 0, day)
if len(data) > 0:
df = self.api.to_df(data).drop([
'amount', 'year', 'month', 'day', 'hour', 'minute'
],
axis=1)
df['trade_date'] = df['datetime'].apply(
lambda x: x[0:10].replace('-', ''))
df = df.drop(['datetime'], axis=1)
df = df.sort_values(by=['trade_date'],
axis=0,
ascending=False)
return df
else:
return self.api.to_df(data)
except Exception as e:
logging.info("暂不支持类型,代码：%s:%s" % (code, e))
return self.api.to_df([])
# F10 查询公司信息目录
def get_company_info_category(self, code, type):
with self.api.connect(TDX_IP, TDX_PORT):
df = pd.DataFrame(self.api.get_company_info_category(type, code))
df['txt'] = None
return df
return []
def get_company_info_content(self, code, type, df):
with self.api.connect(TDX_IP, TDX_PORT):
return self.api.get_company_info_content(type, code,
df['filename'].values[0],
df['start'].values[0],
df['length'].values[0])
return ""
# 查询财务数据
def get_finance_info(self, code, type):
with self.api.connect(TDX_IP, TDX_PORT):
return self.api.get_finance_info(type, code)
return ''
# 每年更新一次，板块个股关系
def updateBk(self):
with self.api.connect(TDX_IP, TDX_PORT):
"""
# 获取股票所属板块信息
# 板块相关参数
BLOCK_SZ = "block_zs.dat"
BLOCK_FG = "block_fg.dat"
BLOCK_GN = "block_gn.dat"
BLOCK_DEFAULT = "block.dat"
"""
bk_zs = self.api.to_df(
self.api.get_and_parse_block_info("block_zs.dat")) #指数板块
bk_fg = self.api.to_df(
self.api.get_and_parse_block_info("block_fg.dat")) #风格板块
bk_gn = self.api.to_df(
self.api.get_and_parse_block_info("block_gn.dat")) #概念板块
bk_default = self.api.to_df(
self.api.get_and_parse_block_info("block.dat")) # 默认
self.dbUtil.to_sql(bk_gn,
'stock_bk_gn',
if_exists='replace',
index=False,
dtype=STOCK_BK_DTYPE)
self.dbUtil.to_sql(bk_fg,
'stock_bk_fg',
if_exists='replace',
index=False,
dtype=STOCK_BK_DTYPE)
self.dbUtil.to_sql(bk_zs,
'stock_bk_zs',
if_exists='replace',
index=False,
dtype=STOCK_BK_DTYPE)
self.dbUtil.to_sql(bk_default,
'stock_bk_default',
if_exists='replace',
index=False,
dtype=STOCK_BK_DTYPE)
# 获取股票列表
tmp1 = self.api.to_df(self.api.get_security_list(0, 0)) # 深圳
tmp1['type'] = 0
tmp2 = self.api.to_df(self.api.get_security_list(1, 0)) # 上海
tmp2['type'] = 1
tmp = tmp1.append(tmp2)
self.dbUtil.to_sql(tmp,
'stock_bk',
if_exists='replace',
index=False,
dtype=STOCK_BK)
def updateGD(self, code, type):
url = 'http://emweb.securities.eastmoney.com/PC_HSF10/ShareholderResearch/ShareholderResearchAjax?code=%s%s' % (
type.lower(), code)
html = urllib.request.urlopen(url).read()
# 将字符串转换成字典
data = json.loads(html.decode('utf-8'))
# gdrs 股东人数,sdgd 十大股东 ，sdltgd 十大流通股东
df_gdrs = pd.DataFrame(data['gdrs'])
df_gdrs['code'] = code
try:
db_df_gdrs = self.dbUtil.read_sql(
"select * from stock_gdrs where code ='%s'" % code)
# 数据合并
df_gdrs = df_gdrs.append(db_df_gdrs).drop_duplicates(
subset=['code', 'rq', 'gdmc'], keep='last')
except Exception as e:
pass
self.dbUtil.to_sql(df_gdrs,
'stock_gdrs',
if_exists='append',
index=False,
dtype=STOCK_GDRS_DTYPE)
sdgd = []
for i in range(len(data['sdgd'])):
sdgd += data['sdgd'][i]['sdgd']
df_sdgd = pd.DataFrame(sdgd)
df_sdgd['code'] = code
try:
db_df_sdgd = self.dbUtil.read_sql(
"select * from stock_sdgd where code ='%s'" % code)
df_sdgd = df_sdgd.append(db_df_sdgd).drop_duplicates(
subset=['code', 'rq', 'gdmc'], keep='last')
except Exception as e:
pass
self.dbUtil.to_sql(df_sdgd,
'stock_sdgd',
if_exists='append',
index=False,
dtype=STOCK_SDGD_DTYPE)
sdltgd = []
for i in range(len(data['sdltgd'])):
sdltgd += data['sdltgd'][i]['sdltgd']
df_sdltgd = pd.DataFrame(sdltgd)
df_sdltgd['code'] = code
# 获取后与数据库中的数据进行merge,首次表不存在，会抛异常
try:
db_df_sdltgd = self.dbUtil.read_sql(
"select * from stock_sdltgd where code ='%s'" % code)
df_sdltgd = df_sdltgd.append(db_df_sdltgd).drop_duplicates(
subset=['code', 'rq', 'gdmc'], keep='last')
except Exception as e:
pass
self.dbUtil.to_sql(df_sdltgd,
'stock_sdltgd',
if_exists='append',
index=False,
dtype=STOCK_SDGD_DTYPE)
# 没季度更新一次
def updateGDs(self):
codes = self.dbUtil.read_sql("select ts_code from stock_basic")
tmp = codes['ts_code'].str.split('.', expand=True)
for index, row in tmp.iterrows():
try:
self.updateGD(row[0], row[1])
logging.info('%s更新结束,当前索引%s' % (row[0], index))
except Exception as e:
logging.info('%s更新失败,当前索引%s' % (row[0], index))
# 分红
# 分红地址http://data.eastmoney.com/yjfp/201812.html
def updateFh(self, rq):
url = 'http://data.eastmoney.com/DataCenter_V3/yjfp/getlist.ashx?filter=(ReportingPeriod=^%s^)' % rq
html = requests.get(url)
# 将字符串转换成字典
data = json.loads(html.text)['data']
if len(data) == 0:
return 0
df = pd.DataFrame(data)
df['ReportingPeriod'] = df['ReportingPeriod'].apply(lambda x: x[0:10])
# 首次需要将df_fh制空，因为表还不存在
if self.dbUtil.is_exist("stock_fh"):
db_fh = self.dbUtil.read_sql(
"select * from stock_fh where ReportingPeriod = '%s'" %
df['ReportingPeriod'][0])
if db_fh.empty: # 不存在当前日期的分红信息，进行拼接
self.dbUtil.to_sql(df,
'stock_fh',
if_exists='append',
index=False)
return 1
else:
pass
else:
self.dbUtil.to_sql(df, 'stock_fh', if_exists='append', index=False)
return 1
# 更新历年分红
def updateFhYears(self):
now = int(time.strftime("%Y", time.localtime())) + 1
lastYear = int(self._getFhMaxYear())
for i in range(lastYear, now): #初始化时开启
type = self.updateFh('%s-06-30' % i)
logging.info('%s-06-30%s' % (i, '成功' if type == 1 else '失败'))
self.updateFh('%s-12-31' % i)
logging.info('%s-12-31%s' % (i, '成功' if type == 1 else '失败'))
def _getFhMaxYear(self):
if self.dbUtil.is_exist('stock_fh'):
try:
df = self.dbUtil.read_sql(
'select substr(max(t.ReportingPeriod ),0,5) year from stock_fh t'
)
return df['year'][0].values
except Exception as e:
pass
return 1991
@classmethod
def getInstance(cls):
if not hasattr(TdxData, "_instance"):
TdxData._instance = TdxData()
return TdxData._instance
size = meta['size']
one_chunk = 0x7530
chuncks = size // one_chunk
if size % one_chunk != 0:
chuncks += 1
file_content = bytearray()
for seg in range(chuncks):
start = seg * one_chunk
piece_data = client.get_block_info(blockfile, start, size)
file_content.extend(piece_data)
return BlockReader().get_data(file_content, BlockReader_TYPE_FLAT)
if __name__ == '__main__':
from pytdx.hq import TdxHq_API
api = TdxHq_API()
with api.connect():
# ret = api.get_block_info("block_zs.dat", 0, 100)
# print(len(ret))
# ret = api.get_and_parse_block_info("block_fg.dat")
# ret = api.get_and_parse_block_info("block_zs.dat")
# ret = api.get_and_parse_block_info("block_gn.dat")
# ret = api.get_and_parse_block_info("block.dat")
ret = api.get_and_parse_block_info("block.dat")
print(api.to_df(ret))
class Engine:
def __init__(self, *args, **kwargs):
if kwargs.pop('best_ip', False):
self.ip = self.best_ip
else:
self.ip = '14.17.75.71'
self.ip = kwargs.pop('ip', '14.17.75.71')
self.thread_num = kwargs.pop('thread_num', 1)
if not PY2 and self.thread_num != 1:
self.use_concurrent = True
else:
self.use_concurrent = False
self.api = TdxHq_API(args, kwargs)
if self.use_concurrent:
self.apis = [
TdxHq_API(args, kwargs) for i in range(self.thread_num)
]
self.executor = ThreadPoolExecutor(self.thread_num)
def connect(self):
self.api.connect(self.ip)
if self.use_concurrent:
for api in self.apis:
api.connect(self.ip)
return self
def __enter__(self):
return self
def exit(self):
self.api.disconnect()
if self.use_concurrent:
for api in self.apis:
api.disconnect()
def __exit__(self, exc_type, exc_val, exc_tb):
self.api.disconnect()
if self.use_concurrent:
for api in self.apis:
api.disconnect()
def quotes(self, code):
code = [code] if not isinstance(code, list) else code
code = self.security_list[self.security_list.code.isin(
code)].index.tolist()
data = [
self.api.to_df(
self.api.get_security_quotes(code[80 * pos:80 * (pos + 1)]))
for pos in range(int(len(code) / 80) + 1)
]
return pd.concat(data)
# data = data[['code', 'open', 'high', 'low', 'price']]
# data['datetime'] = datetime.datetime.now()
# return data.set_index('code', drop=False, inplace=False)
def stock_quotes(self):
code = self.stock_list.index.tolist()
if self.use_concurrent:
res = {
self.executor.submit(self.apis[pos % self.thread_num].get_security_quotes,
code[80 * pos:80 * (pos + 1)]) \
for pos in range(int(len(code) / 80) + 1)}
return pd.concat([self.api.to_df(dic.result()) for dic in res])
else:
data = [
self.api.to_df(
self.api.get_security_quotes(code[80 * pos:80 *
(pos + 1)]))
for pos in range(int(len(code) / 80) + 1)
]
return pd.concat(data)
@lazyval
def security_list(self):
return pd.concat([
pd.concat([
self.api.to_df(self.api.get_security_list(
j, i * 1000)).assign(sse=0 if j == 0 else 1).set_index(
['sse', 'code'], drop=False)
for i in range(int(self.api.get_security_count(j) / 1000) + 1)
],
axis=0) for j in range(2)
],
axis=0)
@lazyval
def stock_list(self):
aa = map(stock_filter, self.security_list.index.tolist())
return self.security_list[list(aa)]
@lazyval
def best_ip(self):
return select_best_ip()
@lazyval
def concept(self):
return self.api.to_df(
self.api.get_and_parse_block_info(TDXParams.BLOCK_GN))
@lazyval
def index(self):
return self.api.to_df(
self.api.get_and_parse_block_info(TDXParams.BLOCK_SZ))
@lazyval
def fengge(self):
return self.api.to_df(
self.api.get_and_parse_block_info(TDXParams.BLOCK_FG))
@lazyval
def customer_block(self):
return CustomerBlockReader().get_df(CUSTOMER_BLOCK_PATH)
@lazyval
def gbbq(self):
df = GbbqReader().get_df(GBBQ_PATH).query('category == 1')
df['datetime'] = pd.to_datetime(df['datetime'], format='%Y%m%d')
return df
def get_security_type(self, code):
if code in self.security_list.code.values:
return self.security_list[self.security_list.code ==
code]['sse'].as_matrix()[0]
else:
raise SecurityNotExists()
def get_security_bars(self, code, freq, index=False):
if index:
exchange = self.get_security_type(code)
func = self.api.get_index_bars
else:
exchange = get_stock_type(code)
func = self.api.get_security_bars
df = pd.DataFrame()
if freq in ['1d', 'day']:
freq = 9
elif freq in ['1m', 'min']:
freq = 8
else:
raise Exception("1d and 1m frequency supported only")
res = []
start = 0
while True:
data = func(freq, exchange, code, start, 800)
if not data:
break
res = data + res
start += 800
df = self.api.to_df(res).drop(
['year', 'month', 'day', 'hour', 'minute'], axis=1)
df['datetime'] = pd.to_datetime(df.datetime)
df['code'] = code
return df.set_index('datetime')
def _get_transaction(self, code, date):
res = []
start = 0
while True:
data = self.api.get_history_transaction_data(
get_stock_type(code), code, start, 2000, date)
if not data:
break
start += 2000
res = data + res
if len(res) == 0:
return pd.DataFrame()
df = self.api.to_df(res).assign(date=date)
df.index = pd.to_datetime(str(date) + " " + df["time"])
df['code'] = code
return df.drop("time", axis=1)
def time_and_price(self, code):
start = 0
res = []
exchange = self.get_security_type(code)
while True:
data = self.api.get_transaction_data(exchange, code, start, 2000)
if not data:
break
res = data + res
start += 2000
df = self.api.to_df(res)
df.time = pd.to_datetime(
str(pd.to_datetime('today').date()) + " " + df['time'])
df.loc[0, 'time'] = df.time[1]
return df.set_index('time')
@classmethod
def minute_bars_from_transaction(cls, transaction, freq):
if transaction.empty:
return pd.DataFrame()
data = transaction['price'].resample(freq,
label='right',
closed='left').ohlc()
data['volume'] = transaction['vol'].resample(freq,
label='right',
closed='left').sum()
data['code'] = transaction['code'][0]
return fillna(data)
def get_k_data(self, code, start, end, freq):
if isinstance(start, str) or isinstance(end, str):
start = pd.Timestamp(start)
end = pd.Timestamp(end)
sessions = pd.date_range(start, end)
trade_days = map(int, sessions.strftime("%Y%m%d"))
if freq == '1m':
freq = '1 min'
if freq == '1d':
freq = '24 H'
res = []
for trade_day in trade_days:
df = Engine.minute_bars_from_transaction(
self._get_transaction(code, trade_day), freq)
if df.empty:
continue
res.append(df)
if len(res) != 0:
return pd.concat(res)
return pd.DataFrame()
class StdQuotes(object):
"""股票市场实时行情"""
bestip = ('47.103.48.45', 7709)
def __init__(self, **kwargs):
try:
default = settings.get('SERVER').get('HQ')[0]
self.bestip = config.get('BESTIP').get('HQ', default)
except ValueError:
self.config = None
self.client = TdxHq_API(**kwargs)
def traffic(self):
with self.client.connect(*self.bestip):
return self.client.get_traffic_stats()
def quotes(self, symbol=[]):
'''
获取实时日行情数据
:param symbol: 股票代码
:return: pd.dataFrame or None
'''
logger.debug(type(logger))
if type(symbol) is str:
symbol = [symbol]
with self.client.connect(*self.bestip):
symbol = get_stock_markets(symbol)
result = self.client.get_security_quotes(symbol)
return to_data(result)
def bars(self, symbol='000001', frequency='9', start='0', offset='100'):
'''
获取实时日K线数据
:param symbol: 股票代码
:param frequency: 数据类别
:param market: 证券市场
:param start: 开始位置
:param offset: 每次获取条数
:return: pd.dataFrame or None
'''
with self.client.connect(*self.bestip):
market = get_stock_market(symbol)
result = self.client.get_security_bars(int(frequency), int(market),
str(symbol), int(start),
int(offset))
return to_data(result)
def stock_count(self, market=MARKET_SH):
'''
获取市场股票数量
:param market: 股票市场代码 sh 上海， sz 深圳
:return: pd.dataFrame or None
'''
with self.client.connect(*self.bestip):
result = self.client.get_security_count(market=market)
return result
def stocks(self, market=MARKET_SH):
'''
获取股票列表
:param market:
:return:
'''
with self.client.connect(*self.bestip):
counts = self.client.get_security_count(market=market)
stocks = None
for start in tqdm(range(0, counts, 1000)):
result = self.client.get_security_list(market=market,
start=start)
stocks = pandas.concat(
[stocks, to_data(result)],
ignore_index=True) if start > 1 else to_data(result)
return stocks
def index_bars(self,
symbol='000001',
frequency='9',
start='0',
offset='100'):
'''
获取指数k线
:param symbol:
:param frequency:
:param start:
:param offset:
:return:
'''
with self.client.connect(*self.bestip):
market = get_stock_market(symbol)
result = self.client.get_index_bars(frequency=frequency,
market=market,
code=symbol,
start=start,
count=offset)
return to_data(result)
def minute(self, symbol=''):
'''
获取实时分时数据
:param market: 证券市场
:param symbol: 股票代码
:return: pd.DataFrame
'''
with self.client.connect(*self.bestip):
market = get_stock_market(symbol)
result = self.client.get_minute_time_data(market=market,
code=symbol)
return to_data(result)
def minutes(self, symbol='', date='20191023'):
'''
分时历史数据
:param market:
:param symbol:
:param date:
:return: pd.dataFrame or None
'''
with self.client.connect(*self.bestip):
market = get_stock_market(symbol)
result = self.client.get_history_minute_time_data(market=market,
code=symbol,
date=date)
return to_data(result)
def transaction(self, symbol='', start=0, offset=10):
'''
查询分笔成交
:param market: 市场代码
:param symbol: 股票代码
:param start: 起始位置
:param offset: 请求数量
:return: pd.dataFrame or None
'''
with self.client.connect(*self.bestip):
market = get_stock_market(symbol)
result = self.client.get_transaction_data(int(market), symbol,
int(start), int(offset))
return to_data(result)
def transactions(self, symbol='', start=0, offset=10, date='20170209'):
'''
查询历史分笔成交
参数：市场代码， 股票代码，起始位置，日期 数量 如： 0,000001,0,10,20170209
:param symbol: 股票代码
:param start: 起始位置
:param offset: 数量
:param date: 日期
:return: pd.dataFrame or None
'''
with self.client.connect(*self.bestip):
market = get_stock_market(symbol, string=False)
result = self.client.get_history_transaction_data(market=market,
code=symbol,
start=start,
count=offset,
date=date)
return to_data(result)
def F10C(self, symbol=''):
'''
查询公司信息目录
:param market: 市场代码
:param symbol: 股票代码
:return: pd.dataFrame or None
'''
with self.client.connect(*self.bestip):
market = get_stock_market(symbol)
result = self.client.get_company_info_category(int(market), symbol)
return result
def F10(self, symbol='', name=''):
'''
读取公司信息详情
:param name: 公司 F10 标题
:param symbol: 股票代码
:return: pd.dataFrame or None
'''
with self.client.connect(*self.bestip):
result = {}
market = get_stock_market(symbol, string=False)
frequency = self.client.get_company_info_category(
int(market), symbol)
if name:
for x in frequency:
if x['name'] == name:
return self.client.get_company_info_content(
market=market,
code=symbol,
filename=x['filename'],
start=x['start'],
length=x['length'])
for x in frequency:
result[x['name']] = self.client.get_company_info_content(
market=market,
code=symbol,
filename=x['filename'],
start=x['start'],
length=x['length'])
else:
pass
return result
def xdxr(self, symbol=''):
'''
读取除权除息信息
:param market: 市场代码
:param symbol: 股票代码
:return: pd.dataFrame or None
'''
with self.client.connect(*self.bestip):
market = get_stock_market(symbol)
result = self.client.get_xdxr_info(int(market), symbol)
return to_data(result)
def finance(self, symbol='000001'):
'''
读取财务信息
:param symbol:
:return:
'''
with self.client.connect(*self.bestip):
market = get_stock_market(symbol)
result = self.client.get_finance_info(market=market, code=symbol)
return to_data(result)
def k(self, symbol='', begin=None, end=None):
'''
读取k线信息
:param symbol:
:param begin: 开始日期
:param end: 截止日期
:return: pd.dataFrame or None
'''
with self.client.connect(*self.bestip):
result = self.client.get_k_data(symbol, begin, end)
return to_data(result)
def index(self,
symbol='000001',
market=MARKET_SH,
frequency='9',
start=1,
offset=2):
'''
获取指数k线
K线种类:
- 0 5分钟K线
- 1 15分钟K线
- 2 30分钟K线
- 3 1小时K线
- 4 日K线
- 5 周K线
- 6 月K线
- 7 1分钟
- 8 1分钟K线
- 9 日K线
- 10 季K线
- 11 年K线
:param symbol: 股票代码
:param frequency: 数据类别
:param market: 证券市场
:param start: 开始位置
:param offset: 每次获取条数
:return: pd.dataFrame or None
'''
with self.client.connect(*self.bestip):
result = self.client.get_index_bars(int(frequency), int(market),
str(symbol), int(start),
int(offset))
return to_data(result)
def block(self, tofile="block.dat"):
'''
获取证券板块信息
:param tofile:
:return: pd.dataFrame or None
'''
with self.client.connect(*self.bestip):
result = self.client.get_and_parse_block_info(tofile)
return to_data(result)
def test_all_functions(multithread, heartbeat, auto_retry, raise_exception):
api = TdxHq_API(multithread=multithread, heartbeat=heartbeat,
auto_retry=auto_retry, raise_exception=raise_exception)
with api.connect(time_out=30):
log.info("获取股票行情")
stocks = api.get_security_quotes([(0, "000001"), (1, "600300")])
assert stocks is not None
assert type(stocks) is list
# 方法2
stocks = api.get_security_quotes(0, "000001")
assert stocks is not None
assert type(stocks) is list
# 方法3
stocks = api.get_security_quotes((0, "000001"))
assert stocks is not None
assert type(stocks) is list
log.info("获取k线")
data = api.get_security_bars(9, 0, '000001', 4, 3)
assert data is not None
assert type(data) is list
assert len(data) == 3
log.info("获取 深市 股票数量")
assert api.get_security_count(0) > 0
log.info("获取股票列表")
stocks = api.get_security_list(1, 0)
assert stocks is not None
assert type(stocks) is list
assert len(stocks) > 0
log.info("获取指数k线")
data = api.get_index_bars(9, 1, '000001', 1, 2)
assert data is not None
assert type(data) is list
assert len(data) == 2
log.info("查询分时行情")
data = api.get_minute_time_data(TDXParams.MARKET_SH, '600300')
assert data is not None
log.info("查询历史分时行情")
data = api.get_history_minute_time_data(
TDXParams.MARKET_SH, '600300', 20161209)
assert data is not None
assert type(data) is list
assert len(data) > 0
log.info("查询分时成交")
data = api.get_transaction_data(TDXParams.MARKET_SZ, '000001', 0, 30)
assert data is not None
assert type(data) is list
log.info("查询历史分时成交")
data = api.get_history_transaction_data(
TDXParams.MARKET_SZ, '000001', 0, 10, 20170209)
assert data is not None
assert type(data) is list
assert len(data) == 10
log.info("查询公司信息目录")
data = api.get_company_info_category(TDXParams.MARKET_SZ, '000001')
assert data is not None
assert type(data) is list
assert len(data) > 0
start = data[0]['start']
length = data[0]['length']
log.info("读取公司信息-最新提示")
data = api.get_company_info_content(
0, '000001', '000001.txt', start, length)
assert data is not None
assert len(data) > 0
log.info("读取除权除息信息")
data = api.get_xdxr_info(1, '600300')
assert data is not None
assert type(data) is list
assert len(data) > 0
log.info("读取财务信息")
data = api.get_finance_info(0, '000001')
assert data is not None
assert type(data) is OrderedDict
assert len(data) > 0
log.info("日线级别k线获取函数")
data = api.get_k_data('000001', '2017-07-01', '2017-07-10')
assert type(data) is pd.DataFrame
assert len(data) == 6
log.info("获取板块信息")
data = api.get_and_parse_block_info(TDXParams.BLOCK_FG)
assert data is not None
assert type(data) is list
assert len(data) > 0
class StdQuotes(object):
"""股票市场实时行情"""
# __slots__ =
def __init__(self, **kwargs):
self.config = None
try:
self.config = json.loads(
os.path.join(os.environ['HOME'], '.mootdx/config.josn'))
except Exception as e:
self.config = None
self.client = TdxHq_API(**kwargs)
if not self.config:
self.bestip = os.environ.setdefault("MOOTDX_SERVER",
'202.108.253.131:7709')
self.bestip = self.bestip.split(':')
self.bestip[1] = int(self.bestip[1])
else:
self.bestip = self.config.get('SERVER')
# K线
def bars(self, symbol='000001', category='9', start='0', offset='100'):
'''
获取实时日K线数据
:param symbol: 股票代码
:param category: 数据类别
:param market: 证券市场
:param start: 开始位置
:param offset: 每次获取条数
:return: pd.dataFrame or None
'''
market = get_stock_market(symbol)
with self.client.connect(*self.bestip):
data = self.client.get_security_bars(int(category), int(market),
str(symbol), int(start),
int(offset))
return self.client.to_df(data)
# 分时数据
def minute(self, symbol=''):
'''
获取实时分时数据
:param market: 证券市场
:param symbol: 股票代码
:return: pd.DataFrame
'''
market = get_stock_market(symbol)
with self.client.connect(*self.bestip):
data = self.client.get_minute_time_data(int(market), symbol)
return self.client.to_df(data)
# 分时历史数据
def minute_his(self, symbol='', datetime='20161209'):
'''
分时历史数据
:param market:
:param symbol:
:param datetime:
:return: pd.dataFrame or None
'''
market = get_stock_market(symbol)
with self.client.connect(*self.bestip):
data = self.client.get_history_minute_time_data(
int(market), symbol, datetime)
return self.client.to_df(data)
def trans(self, symbol='', start=0, offset=10):
'''
查询分笔成交
:param market: 市场代码
:param symbol: 股票代码
:param start: 起始位置
:param offset: 请求数量
:return: pd.dataFrame or None
'''
market = get_stock_market(symbol)
with self.client.connect(*self.bestip):
data = self.client.get_transaction_data(int(market), symbol,
int(start), int(market))
return self.client.to_df(data)
def trans_his(self, symbol='', start=0, offset=10, date=''):
'''
查询历史分笔成交
:param market: 市场代码
:param symbol: 股票代码
:param start: 起始位置
:param offset: 数量
:param date: 日期
:return: pd.dataFrame or None
'''
market = get_stock_market(symbol)
with self.client.connect(*self.bestip):
data = self.client.get_history_transaction_data(
int(market), symbol, int(start), int(offset), date)
return self.client.to_df(data)
def company(self, symbol='', detail='category', *args, **kwargs):
'''
企业信息获取
:param symbol:
:param detail:
:param args:
:param kwargs:
:return:
'''
pass
def company_category(self, symbol=''):
'''
查询公司信息目录
:param market: 市场代码
:param symbol: 股票代码
:return: pd.dataFrame or None
'''
market = get_stock_market(symbol)
with self.client.connect(*self.bestip):
data = self.client.get_company_info_category(int(market), symbol)
return self.client.to_df(data)
def company_content(self, symbol='', file='', start=0, offset=10):
'''
读取公司信息详情
:param market: 市场代码
:param symbol: 股票代码
:param file: 文件名
:param start: 起始位置
:param offset: 数量
:return: pd.dataFrame or None
'''
market = get_stock_market(symbol)
with self.client.connect(*self.bestip):
data = self.client.get_company_info_content(
int(market), symbol, file, int(start), int(offset))
return self.client.to_df(data)
def xdxr(self, symbol=''):
'''
读取除权除息信息
:param market: 市场代码
:param symbol: 股票代码
:return: pd.dataFrame or None
'''
market = get_stock_market(symbol)
with self.client.connect(*self.bestip):
data = self.client.get_xdxr_info(int(market), symbol)
return self.client.to_df(data)
def k(self, symbol='', begin=None, end=None):
'''
读取k线信息
:param symbol:
:param begin:
:param end:
:return: pd.dataFrame or None
'''
with self.client.connect(*self.bestip):
data = self.client.get_k_data(symbol, begin, end)
return data
def index(self,
symbol='000001',
market='sh',
category='9',
start='0',
offset='100'):
'''
获取指数k线
K线种类:
- 0 5分钟K线
- 1 15分钟K线
- 2 30分钟K线
- 3 1小时K线
- 4 日K线
- 5 周K线
- 6 月K线
- 7 1分钟
- 8 1分钟K线
- 9 日K线
- 10 季K线
- 11 年K线
:param symbol: 股票代码
:param category: 数据类别
:param market: 证券市场
:param start: 开始位置
:param offset: 每次获取条数
:return: pd.dataFrame or None
'''
market = 1 if market == 'sh' else 0
with self.client.connect(*self.bestip):
data = self.client.get_index_bars(int(category), int(market),
str(symbol), int(start),
int(offset))
return self.client.to_df(data)
def block(self, tofile="block.dat"):
'''
获取证券板块信息
:param tofile:
:return: pd.dataFrame or None
'''
with self.client.connect(*self.bestip):
data = self.client.get_and_parse_block_info(tofile)
return self.client.to_df(data)
def batch(self, method='', offset=100, *args, **kwargs):
'''
批量下载相关数据
:param method:
:param offset:
:return:
'''
pass
class Engine:
concurrent_thread_count = 50
def __init__(self, *args, **kwargs):
if 'ip' in kwargs:
self.ip = kwargs.pop('ip')
else:
if kwargs.pop('best_ip', False):
self.ip = self.best_ip
else:
self.ip = '14.17.75.71'
if 'concurrent_thread_count' in kwargs:
self.concurrent_thread_count = kwargs.pop(
'concurrent_thread_count', 50)
self.thread_num = kwargs.pop('thread_num', 1)
self.api = TdxHq_API(args, kwargs, raise_exception=True)
def connect(self):
self.api.connect(self.ip)
return self
def __enter__(self):
return self
def exit(self):
self.api.disconnect()
def __exit__(self, exc_type, exc_val, exc_tb):
self.api.disconnect()
def quotes(self, code):
code = [code] if not isinstance(code, list) else code
code = self.security_list[self.security_list.code.isin(
code)].index.tolist()
data = [
self.api.to_df(
self.api.get_security_quotes(code[80 * pos:80 * (pos + 1)]))
for pos in range(int(len(code) / 80) + 1)
]
return pd.concat(data)
# data = data[['code', 'open', 'high', 'low', 'price']]
# data['datetime'] = datetime.datetime.now()
# return data.set_index('code', drop=False, inplace=False)
def stock_quotes(self):
code = self.stock_list.index.tolist()
data = [
self.api.to_df(
self.api.get_security_quotes(code[80 * pos:80 * (pos + 1)]))
for pos in range(int(len(code) / 80) + 1)
]
return pd.concat(data)
@lazyval
def security_list(self):
return pd.concat([
pd.concat([
self.api.to_df(self.api.get_security_list(
j, i * 1000)).assign(sse=0 if j == 0 else 1).set_index(
['sse', 'code'], drop=False)
for i in range(int(self.api.get_security_count(j) / 1000) + 1)
],
axis=0) for j in range(2)
],
axis=0)
@lazyval
def stock_list(self):
aa = map(stock_filter, self.security_list.index.tolist())
return self.security_list[list(aa)]
@lazyval
def best_ip(self):
return select_best_ip()
@lazyval
def concept(self):
return self.api.to_df(
self.api.get_and_parse_block_info(TDXParams.BLOCK_GN))
@lazyval
def index(self):
return self.api.to_df(
self.api.get_and_parse_block_info(TDXParams.BLOCK_SZ))
@lazyval
def fengge(self):
return self.api.to_df(
self.api.get_and_parse_block_info(TDXParams.BLOCK_FG))
@lazyval
def block(self):
return self.api.to_df(
self.api.get_and_parse_block_info(TDXParams.BLOCK_DEFAULT))
@lazyval
def customer_block(self):
return CustomerBlockReader().get_df(CUSTOMER_BLOCK_PATH)
def xdxr(self, code):
df = self.api.to_df(
self.api.get_xdxr_info(self.get_security_type(code), code))
if df.empty:
return df
df['datetime'] = pd.to_datetime((df.year * 10000 + df.month * 100 +
df.day).apply(lambda x: str(x)))
return df.drop(['year', 'month', 'day'], axis=1).set_index('datetime')
@lazyval
def gbbq(self):
df = GbbqReader().get_df(GBBQ_PATH).query('category == 1')
df['datetime'] = pd.to_datetime(df['datetime'], format='%Y%m%d')
return df
def get_security_type(self, code):
if code in self.security_list.code.values:
return self.security_list[self.security_list.code ==
code]['sse'].as_matrix()[0]
else:
raise SecurityNotExists()
@retry(3)
def get_security_bars(self, code, freq, start=None, end=None, index=False):
if index:
exchange = self.get_security_type(code)
func = self.api.get_index_bars
else:
exchange = get_stock_type(code)
func = self.api.get_security_bars
if start:
start = start.tz_localize(None)
if end:
end = end.tz_localize(None)
if freq in ['1d', 'day']:
freq = 9
elif freq in ['1m', 'min']:
freq = 8
else:
raise Exception("1d and 1m frequency supported only")
res = []
pos = 0
while True:
data = func(freq, exchange, code, pos, 800)
if not data:
break
res = data + res
pos += 800
if start and pd.to_datetime(data[0]['datetime']) < start:
break
try:
df = self.api.to_df(res).drop(
['year', 'month', 'day', 'hour', 'minute'], axis=1)
df['datetime'] = pd.to_datetime(df.datetime)
df.set_index('datetime', inplace=True)
if freq == 9:
df.index = df.index.normalize()
except ValueError: # 未上市股票，无数据
logger.warning("no k line data for {}".format(code))
# return pd.DataFrame({
# 'amount': [0],
# 'close': [0],
# 'open': [0],
# 'high': [0],
# 'low': [0],
# 'vol': [0],
# 'code': code
# },
# index=[start]
# )
return pd.DataFrame()
close = [df.close.values[-1]]
if start:
df = df.loc[lambda df: start <= df.index]
if end:
df = df.loc[lambda df: df.index.normalize() <= end]
if df.empty:
# return pd.DataFrame({
# 'amount': [0],
# 'close': close,
# 'open': close,
# 'high': close,
# 'low': close,
# 'vol': [0],
# 'code': code
# },
# index=[start]
# )
return df
else:
if int(df['vol'][-1]) <= 0 and end == df.index[-1] and len(
df) == 1: # 成交量为0，当天返回的是没开盘的数据
return pd.DataFrame()
df['code'] = code
return df
def _get_transaction(self, code, date):
res = []
start = 0
while True:
data = self.api.get_history_transaction_data(
get_stock_type(code), code, start, 2000, date)
if not data:
break
start += 2000
res = data + res
if len(res) == 0:
return pd.DataFrame()
df = self.api.to_df(res).assign(date=date)
df.loc[0, 'time'] = df.time[1]
df.index = pd.to_datetime(str(date) + " " + df["time"])
df['code'] = code
return df.drop("time", axis=1)
def time_and_price(self, code):
start = 0
res = []
exchange = self.get_security_type(code)
while True:
data = self.api.get_transaction_data(exchange, code, start, 2000)
if not data:
break
res = data + res
start += 2000
df = self.api.to_df(res)
df.time = pd.to_datetime(
str(pd.to_datetime('today').date()) + " " + df['time'])
df.loc[0, 'time'] = df.time[1]
return df.set_index('time')
@classmethod
def minute_bars_from_transaction(cls, transaction, freq):
if transaction.empty:
return pd.DataFrame()
mask = transaction.index < transaction.index[0].normalize(
) + pd.Timedelta('12 H')
def resample(transaction):
if transaction.empty:
return pd.DataFrame()
data = transaction['price'].resample(freq,
label='right',
closed='left').ohlc()
data['volume'] = transaction['vol'].resample(freq,
label='right',
closed='left').sum()
data['code'] = transaction['code'][0]
return data
morning = resample(transaction[mask])
afternoon = resample(transaction[~mask])
if morning.empty and afternoon.empty:
return pd.DataFrame()
if not afternoon.empty:
morning.index.values[-1] = afternoon.index[0] - pd.Timedelta(
'1 min')
df = pd.concat([morning, afternoon])
return fillna(df)
def _get_k_data(self, code, freq, sessions):
trade_days = map(int, sessions.strftime("%Y%m%d"))
if freq == '1m':
freq = '1 min'
if freq == '1d':
freq = '24 H'
res = []
concurrent_count = self.concurrent_thread_count
jobs = []
for trade_day in trade_days:
# df = Engine.minute_bars_from_transaction(self._get_transaction(code, trade_day), freq)
reqevent = gevent.spawn(Engine.minute_bars_from_transaction,
self._get_transaction(code, trade_day),
freq)
jobs.append(reqevent)
if len(jobs) >= concurrent_count:
gevent.joinall(jobs, timeout=30)
for j in jobs:
if j.value is not None and not j.value.empty:
res.append(j.value)
jobs.clear()
gevent.joinall(jobs, timeout=30)
for j in jobs:
if j.value is not None and not j.value.empty:
res.append(j.value)
jobs.clear()
if len(res) != 0:
return pd.concat(res)
return pd.DataFrame()
def get_k_data(self, code, start, end, freq, check=True):
if isinstance(start, str) or isinstance(end, str):
start = pd.Timestamp(start)
end = pd.Timestamp(end)
if check:
daily_bars = self.get_security_bars(code, '1d', start, end)
if daily_bars is None or daily_bars.empty:
return daily_bars
sessions = daily_bars.index
else:
sessions = pd.bdate_range(start,
end,
weekmask='Mon Tue Wed Thu Fri')
df = self._get_k_data(code, freq, sessions)
def check_df(freq, df, daily_bars):
if freq == '1m':
need_check = pd.DataFrame({
'open':
df['open'].resample('1D').first(),
'high':
df['high'].resample('1D').max(),
'low':
df['low'].resample('1D').min(),
'close':
df['close'].resample('1D').last(),
'volume':
df['volume'].resample('1D').sum()
}).dropna()
else:
need_check = df
if daily_bars.shape[0] != need_check.shape[0]:
logger.warning("{} merged {}, expected {}".format(
code, need_check.shape[0], daily_bars.shape[0]))
need_check = fillna(
need_check.reindex(daily_bars.index, copy=False))
diff = daily_bars[['open',
'close']] == need_check[['open', 'close']]
res = (diff.open) & (diff.close)
sessions = res[res == False].index
return sessions
if not df.empty:
if check:
sessions = check_df(freq, df, daily_bars)
if sessions.shape[0] != 0:
logger.info(
"fixing data for {}-{} with sessions: {}".format(
code, freq, sessions))
fix = self._get_k_data(code, freq, sessions)
df.loc[fix.index] = fix
return df
return df
one_chunk = 0x7530
chuncks = size // one_chunk
if size % one_chunk != 0:
chuncks += 1
file_content = bytearray()
for seg in range(chuncks):
start = seg * one_chunk
piece_data = client.get_block_info(blockfile, start, size)
file_content.extend(piece_data)
return BlockReader().get_data(file_content, BlockReader_TYPE_FLAT)
if __name__ == '__main__':
from pytdx.hq import TdxHq_API
api = TdxHq_API()
with api.connect():
# ret = api.get_block_info("block_zs.dat", 0, 100)
# print(len(ret))
# ret = api.get_and_parse_block_info("block_fg.dat")
# ret = api.get_and_parse_block_info("block_zs.dat")
# ret = api.get_and_parse_block_info("block_gn.dat")
# ret = api.get_and_parse_block_info("block.dat")
ret = api.get_and_parse_block_info("block.dat")
print(api.to_df(ret))
class TdxHelper:
ip_list = [{
'ip': '119.147.212.81',
'port': 7709
}, {
'ip': '60.12.136.250',
'port': 7709
}]
def __init__(self):
#连接tdx接口
self.api = TdxHq_API()
if not self.api.connect('60.12.136.250', 7709):
print("服务器连接失败！")
# pandas数据显示设置
pd.set_option('display.max_columns', None) # 显示所有列
#pd.set_option('display.max_rows', None) # 显示所有行
# mysql对象
self.mysql = mysqlHelper(config.mysql_host, config.mysql_username,
bluedothe.mysql_password, config.mysql_dbname)
# pandas的mysql对象
self.engine = create_engine(
f'mysql+pymysql://{config.mysql_username}:{bluedothe.mysql_password}@{config.mysql_host}/{config.mysql_dbname}?charset=utf8'
)
#断开tdx接口连接
def close_connect(self):
self.api.disconnect()
#获取k线，最后一个参数day,说明需要获取的数量，本接口只获取从最近交易日往前的数据
#输入参数：五个参数分别为：category（k线),市场代码(0:深圳，1:上海),股票代码,开始位置(从最近交易日向前取，0表示最近交易日),返回的记录条数
#K线种类: 0 5分钟K线; 1 15分钟K线; 2 30分钟K线; 3 1小时K线; 4 日K线;5 周K线;6 月K线;7 1分钟;8 1分钟K线; 9 日K线;10 季K线;11 年K线
#返回值：open,close,high,low,vol,amount,year,month,day,hour,minute,datetime
# csv格式：code,ts_code,trade_date(缩写）,trade_time,time_index,open,high,low,close,amount,volume
def get_security_bars(self, category, market, code, start=0, count=240):
dict = {0: 'SZ', 1: 'SH'}
ts_code = code + "." + dict[market]
order = [
'code', 'ts_code', 'trade_date', 'trade_time', 'time_index',
'open', 'high', 'low', 'close', 'amount', 'volume'
]
#df = self.api.get_security_bars(9, 0, '000001', 0, 10) # 返回普通list
df = self.api.to_df(
self.api.get_security_bars(category, market, code, start,
count)) # 返回DataFrame
if df.empty: return df
df.insert(0, 'ts_code', ts_code)
df.insert(0, 'code', code)
df['trade_time'] = df['datetime'].apply(lambda x: str(x)[11:19])
df['time_index'] = df['trade_time'].apply(
lambda x: datatime_util.stockTradeTime2Index(x))
df['trade_date'] = df['datetime'].apply(
lambda x: (str(x)[0:10]).replace('-', ''))
df.rename(columns={'vol': 'volume'}, inplace=True)
df.drop(['year', 'month', 'day', 'hour', 'minute', 'datetime'],
axis=1,
inplace=True)
df['volume'] = df['volume'].apply(lambda x: int(x)) #取整
df.loc[df['amount'] == 5.877471754111438e-39,
'amount'] = 0 #列值根据条件筛选后修改为0
df = df[order]
filename = config.tdx_csv_minline1_all + ts_code + ".csv"
if os.path.isfile(filename):
df.to_csv(filename,
index=False,
mode='a',
header=False,
sep=',',
encoding="utf_8_sig")
else:
df.to_csv(filename,
index=False,
mode='w',
header=True,
sep=',',
encoding="utf_8_sig")
print("新增加的一分钟all股票数据：", filename)
# 获取1分钟k线，最后一个参数说明需要获取的数量，本接口只获取从最近交易日往前的数据
# 输入参数：五个参数分别为：category（k线),市场代码(0:深圳，1:上海),股票代码,开始位置(从最近交易日向前取，0表示最近交易日),返回的记录条数
# K线种类: 0 5分钟K线; 1 15分钟K线; 2 30分钟K线; 3 1小时K线; 4 日K线;5 周K线;6 月K线;7 1分钟;8 1分钟K线; 9 日K线;10 季K线;11 年K线
# 返回值：open,close,high,low,vol,amount,year,month,day,hour,minute,datetime
# csv格式：code,ts_code,trade_date(缩写）,trade_time,time_index,open,high,low,close,amount,volume
def get_security_bars_minute1(self, category, market, code, start, count):
dict = {0: 'SZ', 1: 'SH'}
ts_code = code + "." + dict[market]
order = [
'code', 'ts_code', 'trade_date', 'trade_time', 'time_index',
'open', 'high', 'low', 'close', 'amount', 'volume'
]
# df = self.api.get_security_bars(9, 0, '000001', 0, 10) # 返回普通list
df = self.api.to_df(
self.api.get_security_bars(category, market, code, start,
count)) # 返回DataFrame
if df.empty: return
df.insert(0, 'ts_code', ts_code)
df.insert(0, 'code', code)
df['trade_time'] = df['datetime'].apply(lambda x: str(x)[11:19])
df['time_index'] = df['trade_time'].apply(
lambda x: datatime_util.stockTradeTime2Index(x))
df['trade_date'] = df['datetime'].apply(
lambda x: (str(x)[0:10]).replace('-', ''))
df.rename(columns={'vol': 'volume'}, inplace=True)
df.drop(['year', 'month', 'day', 'hour', 'minute', 'datetime'],
axis=1,
inplace=True)
df['volume'] = df['volume'].apply(lambda x: int(x)) # 取整
df.loc[df['amount'] == 5.877471754111438e-39,
'amount'] = 0 # 列值根据条件筛选后修改为0
df = df[order]
#过滤掉停牌的数据，在tdx中，停牌股票也能取到数据，价格是前一交易日的收盘价，所以只能用成交量或成交金额为0来判断
#1按日期分组后取出成交量为0的日期；2循环过滤掉成交量为0的日期的数据。
dfg = df.groupby(by='trade_date').mean() #分组
dfg['trade_date'] = dfg.index
dfg = dfg[dfg.volume == 0] #条件过滤，保留满足条件的数据
for trade_date in dfg['trade_date'].values:
df = df[(df['trade_date'] != trade_date)] # 每个条件要用括号()括起来
return df
#可以获取多只股票的行情信息
#返回值：market,code,active1,price,last_close,open,high,low,reversed_bytes0,reversed_bytes1,vol,cur_vol,amount,s_vol,
#reversed_bytes2,reversed_bytes3,bid1,ask1,bid_vol1,ask_vol1,bid2,ask2,bid_vol2,ask_vol2,bid3,ask3,bid_vol3,ask_vol3,bid4,
#ask4,bid_vol4,ask_vol4,bid5,ask5,bid_vol5,ask_vol5,reversed_bytes4,reversed_bytes5,reversed_bytes6,reversed_bytes7,
#reversed_bytes8,reversed_bytes9,active2
def get_security_quotes(self):
df = self.api.to_df(
self.api.get_security_quotes([(0, '000001'), (1, '600300')]))
print(df)
# 获取市场股票数量
#返回值：value
def get_security_count(self):
df = self.api.to_df(self.api.get_security_count(0)) #0 - 深圳， 1 - 上海
print(df)
# 获取股票列表，返回值里面除了股票，还有国债等
#返回值：code,volunit,decimal_point,name,pre_close
def get_security_list(self):
df = self.api.to_df(self.api.get_security_list(
0, 10000)) # 市场代码, 起始位置 如： 0,0 或 1,100
print(df)
# 获取指数k线
#输入参数同股票k线接口
# 返回值：open,close,high,low,vol,amount,year,month,day,hour,minute,datetime,up_count down_count
def get_index_bars(self):
index_dict_cn = {
"上证指数": "999999",
"深证成指": "399001",
"中小板指": "399005",
"创业板指": "399006",
"深证综指": "399106",
"上证50": "000016",
"沪深300": "000300"
}
index_dict = {
"sh": "999999",
"sz": "399001",
"zxb": "399005",
"cyb": "399006",
"szz": "399106",
"sz50": "000016",
"hs300": "000300"
}
for key in index_dict.keys():
df = self.api.to_df(
self.api.get_index_bars(9, 1, index_dict[key], 0, 2))
print(df)
# 查询分时行情，最近交易日的数据，一分钟一条记录
#返回值：price,vol
def get_minute_time_data(self):
df = self.api.to_df(self.api.get_minute_time_data(
1, '600300')) #市场代码， 股票代码
print(df)
# 查询历史分时行情
# 返回值：price,vol
def get_history_minute_time_data(self):
df = self.api.to_df(
self.api.get_history_minute_time_data(TDXParams.MARKET_SH,
'603887',
20200420)) #市场代码， 股票代码，时间
print(df)
# 查询分笔成交，最近交易日数据
#返回值：time，price，vol，num，buyorsell
def get_transaction_data(self):
df = self.api.to_df(
self.api.get_transaction_data(TDXParams.MARKET_SZ, '000001', 0,
30)) #市场代码， 股票代码，起始位置， 数量
print(df)
# 查询历史分笔成交
#返回值:time,price,vol,buyorsell
def get_history_transaction_data(self):
df = self.api.to_df(
self.api.get_history_transaction_data(
TDXParams.MARKET_SZ, '000001', 0, 10,
20170209)) #市场代码， 股票代码，起始位置，日期 数量
print(df)
# 查询公司信息目录,返回的不是具体数据
#返回值:name,filename,start,length
def get_company_info_category(self):
df = self.api.to_df(
self.api.get_company_info_category(TDXParams.MARKET_SZ,
'000001')) #市场代码， 股票代码
print(df)
# 读取公司信息详情
#返回值:value
def get_company_info_content(self):
df = self.api.to_df(
self.api.get_company_info_content(
0, '000001', '000001.txt', 0,
1000)) #市场代码， 股票代码, 文件名, 起始位置， 数量
print(df)
# 读取除权除息信息
#返回值:year,month,day,category,name,fenhong,peigujia,songzhuangu,peigu
def get_xdxr_info(self):
df = self.api.to_df(self.api.get_xdxr_info(1, '600300')) #市场代码， 股票代码
print(df)
# 读取财务信息
#返回值:market,code,liutongguben,province,industry,updated_date,ipo_date,zongguben,guojiagu,faqirenfarengu,farengu,bgu,hgu,zhigonggu,
#zongzichan,liudongzichan,gudingzichan,wuxingzichan,gudongrenshu,liudongfuzhai,changqifuzhai,zibengongjijin,jingzichan,zhuyingshouru,
#zhuyinglirun,yingshouzhangkuan,yingyelirun,touzishouyu,jingyingxianjinliu,zongxianjinliu,cunhuo,lirunzonghe,shuihoulirun,jinglirun,weifenlirun,baoliu1,baoliu2
def get_finance_info(self):
df = self.api.to_df(self.api.get_finance_info(1,
'600300')) #市场代码， 股票代码
print(df)
# 读取k线信息
# 返回值:value
def get_k_data(self):
df = self.api.to_df(
self.api.get_k_data('600300', '2017-07-03',
'2017-07-10')) #股票代码， 开始时间， 结束时间
print(df)
# 读取板块信息
#返回值：blockname, block_type, code_index, code
""" BLOCK_SZ = "block_zs.dat";BLOCK_FG = "block_fg.dat";BLOCK_GN = "block_gn.dat";BLOCK_DEFAULT = "block.dat" """
def get_and_parse_block_info(self):
##指数板块 风格板块 概念板块 一般板块
block_filename = [
"block_zs.dat", "block_fg.dat", "block_gn.dat", "block.dat"
]
for block in block_filename:
df = self.api.to_df(
self.api.get_and_parse_block_info(block)) #板块文件名称
filename = config.tdx_csv_block + block[0:-4] + ".csv"
if os.path.isfile(filename):
os.remove(filename)
df.to_csv(filename,
index=False,
mode='w',
header=True,
sep=',',
encoding="utf_8_sig")
else:
df.to_csv(filename,
index=False,
mode='w',
header=True,
sep=',',
encoding="utf_8_sig")
# 读取板块信息，多个类型封装到一个df对象中返回
# 返回值：data_source, block_category, block_type, block_name, block_code, ts_code, create_time
def update_block_member(self):
##指数板块 风格板块 概念板块 一般板块
#block_filename = ["block_zs.dat", "block_fg.dat", "block_gn.dat", "block.dat"]
block_filename = ["block_zs.dat", "block_fg.dat",
"block_gn.dat"] #block.dat中的数据都包含在其他版块里了，这个可以去掉
data_source = "tdx"
dfall = None
for block in block_filename:
df = self.api.to_df(
self.api.get_and_parse_block_info(block)) # 板块文件名称
df['data_source'] = data_source
if block == "block.dat":
df['block_category'] = data_source + ".yb"
else:
df['block_category'] = data_source + "." + block[6:8]
df['block_type'] = df['block_type'].map(lambda x: str(x))
df['block_type'] = df['block_category'].str.cat(
df['block_type'], sep=".") #, sep = "."
df['block_code'] = "" #使用pd直接插入到数据库时，字段不能是None值
df['ts_code'] = df['code'].apply(lambda x: x + ".SH"
if x[0:1] == "6" else x + ".SZ")
if (dfall is not None) and (not dfall.empty):
dfall = dfall.append(df, ignore_index=True)
else:
dfall = df
if (dfall is None) or (dfall.empty): return None
dfall.rename(columns={'blockname': 'block_name'}, inplace=True)
dfall['create_time'] = time.strftime('%Y-%m-%d %H:%M:%S',
time.localtime(time.time()))
dfall = dfall[[
'data_source', 'block_category', 'block_type', 'block_name',
'block_code', 'ts_code', 'create_time'
]] #列重排序
#分组统计
dfg = dfall.groupby(by=[
'data_source', 'block_category', 'block_type', 'block_name',
'block_code'
],
as_index=False).count() # 分组求每组数量
dfg.rename(columns={'ts_code': 'member_count'},
inplace=True) #ts_code列数值为汇总值，需要重命名
dfg['create_time'] = time.strftime(
'%Y-%m-%d %H:%M:%S',
time.localtime(time.time())) #create_time列数值为汇总值，需要重新赋值
delete_condition = f"data_source = '{data_source}'"
mysql_script.df2db_update(delete_condition=delete_condition,
block_basic_df=dfg,
block_member_df=dfall)
return (len(dfg), len(dfall))
#获取一段时间的1分钟数据，因为每次调用接口只能返回3天的分钟数据（240*3)，需要分多次调用
#返回值：0没有提取到数据；1提取到数据
def get_minute1_data(self, category, market, code, start_date, end_date):
init_start_date = start_date.replace('-', '')
init_end_date = end_date.replace('-', '')
day = datatime_util.diffrentPeriod(datatime_util.DAILY, start_date,
end_date)
df = self.get_security_bars_minute1(category, market, code, 0,
240 * 3) # 返回DataFrame
if df is None or df.empty:
print('{0}没有交易数据'.format(code))
return 0
print(market, '--', code, '--', start_date, '--', end_date)
#print("最大值：",df.groupby('datetime').max())
#print(df.describe()) #df数据统计
data_start_date = df.min()['trade_date']
data_end_date = df.max()['trade_date']
start_date = start_date.replace('-', '')
end_date = end_date.replace('-', '')
if data_end_date < start_date or end_date < data_start_date:
print("采集时间在数据范围之外，退出函数")
return 0
elif end_date > data_end_date:
end_date = data_end_date
if start_date < data_start_date:
#最近三天的数据中，去掉无用的数据后即是最终数据
#需要取的数据还有三天前的数据，需要继续向前取
n = (day - 3) // 3
m = (day - 3) % 3
for i in range(0, n):
dfn = self.get_security_bars_minute1(category, market, code,
240 * 3 * (i + 1),
240 * 3) # 返回DataFrame
if (dfn is not None) and (not dfn.empty):
df = dfn.append(df, ignore_index=True)
if m > 0:
dfn = self.get_security_bars_minute1(category, market, code,
240 * 3 * (n + 1),
240 * m)
if (dfn is not None) and (not dfn.empty):
df = dfn.append(df, ignore_index=True)
df = df.sort_values(by=['trade_date', 'time_index'],
axis=0,
ascending=True)
#过滤掉start_date, end_date之外的数据
df = df[(df['trade_date'] >= str(init_start_date)) &
(df['trade_date'] <= str(init_end_date))] #每个条件要用括号()括起来
dict = {0: 'SZ', 1: 'SH'}
ts_code = code + "." + dict[market]
filename = config.tdx_csv_minline1_all + ts_code + ".csv"
if os.path.isfile(filename):
df.to_csv(filename,
index=False,
mode='a',
header=False,
sep=',',
encoding="utf_8_sig")
print("更新一分钟all股票数据：", filename)
else:
df.to_csv(filename,
index=False,
mode='w',
header=True,
sep=',',
encoding="utf_8_sig")
print("新增加的一分钟all股票数据：", filename)
if (liutonggu < 100):
return None
if c5 < 5:
return None
chouma = str(get_chouma(code2))
return (code,name,close,float2(c1),float2(c5),float2(liutonggu),chouma)
#初始化 ,并获取概念板块名称
api.connect(serverip, 7709)
# 偶尔出现 gn加载不成功的情况
try:
b = api.get_and_parse_block_info('block_gn.dat')
except:
b = api.get_and_parse_block_info('block_gn.dat')
hy1 = pd.DataFrame(b)
# 获取板块
QA_fetch_get_tdx_industry()
hy = tdxblockdf
hydict = {}
hy1dict = {}
#个股对应板块名的表
for i in range(0,len(hy)):
sse = hy.sse.iloc[i]
code = hy.code.iloc[i]
