async def get_quotation(self,
code=None,
start=None,
end=None,
frequence=None,
market=MARKET_TYPE.STOCK_CN,
source=None,
output=None):
if market is MARKET_TYPE.STOCK_CN:
if frequence is FREQUENCE.DAY:
if source is DATASOURCE.MONGO:
res = await QAQueryAsync.QA_fetch_stock_day(
code, start, end)
elif source is DATASOURCE.TDX:
res = QATdx.QA_fetch_get_stock_day(code,
start,
end,
frequence=frequence)
elif frequence in [
FREQUENCE.ONE_MIN, FREQUENCE.FIVE_MIN,
FREQUENCE.FIFTEEN_MIN, FREQUENCE.THIRTY_MIN,
FREQUENCE.SIXTY_MIN
]:
if source is DATASOURCE.MONGO:
res = await QAQueryAsync.QA_fetch_stock_min(
code, start, end, frequence=frequence)
elif source is DATASOURCE.TDX:
res = QATdx.QA_fetch_get_stock_min(code,
start,
end,
frequence=frequence)
return res
def QA_get_tick(code, start, end, market):
"""
统一的获取期货/股票tick的接口
"""
res = None
if market == MARKET_TYPE.STOCK_CN:
res = QATdx.QA_fetch_get_stock_transaction(code, start, end)
elif market == MARKET_TYPE.FUTURE_CN:
res = QATdx.QA_fetch_get_future_transaction(code, start, end)
return res
def QA_get_realtime(code, market):
"""
统一的获取期货/股票实时行情的接口
"""
res = None
if market == MARKET_TYPE.STOCK_CN:
res = QATdx.QA_fetch_get_stock_realtime(code)
elif market == MARKET_TYPE.FUTURE_CN:
res = QATdx.QA_fetch_get_future_realtime(code)
return res
def QA_quotation(code, start, end, frequence, market, source, output):
"""一个统一的fetch
Arguments:
code {str/list} -- 证券/股票的代码
start {str} -- 开始日期
end {str} -- 结束日期
frequence {enum} -- 频率 QA.FREQUENCE
market {enum} -- 市场 QA.MARKET_TYPE
source {enum} -- 来源 QA.DATASOURCE
output {enum} -- 输出类型 QA.OUTPUT_FORMAT
"""
if market is MARKET_TYPE.STOCK_CN:
if frequence is FREQUENCE.DAY:
if source is DATASOURCE.MONGO:
res = QAQueryAdv.QA_fetch_stock_day_adv(code, start, end)
elif source is DATASOURCE.TDX:
res = QATdx.QA_fetch_get_stock_day(code, start, end, '00')
elif source is DATASOURCE.TUSHARE:
res = QATushare.QA_fetch_get_stock_day(code, start, end, '00')
elif frequence in [
FREQUENCE.ONE_MIN, FREQUENCE.FIVE_MIN, FREQUENCE.FIFTEEN_MIN,
FREQUENCE.THIRTY_MIN, FREQUENCE.SIXTY_MIN
]:
if source is DATASOURCE.MONGO:
res = QAQueryAdv.QA_fetch_stock_min_adv(code,
start,
end,
frequence=frequence)
elif source is DATASOURCE.TDX:
res = QATdx.QA_fetch_get_stock_min(code,
start,
end,
frequence=frequence)
elif frequence is FREQUENCE.TICK:
if source is DATASOURCE.TDX:
res = QATdx.QA_fetch_get_stock_transaction(code, start, end)
# 指数代码和股票代码是冲突重复的， sh000001 上证指数 000001 是不同的
elif market is MARKET_TYPE.INDEX_CN:
if frequence is FREQUENCE.DAY:
if source is DATASOURCE.MONGO:
res = QAQueryAdv.QA_fetch_index_day_adv(code, start, end)
elif market is MARKET_TYPE.OPTION_CN:
if source is DATASOURCE.MONGO:
#res = QAQueryAdv.QA_fetch_option_day_adv(code, start, end)
raise NotImplementedError('CURRENT NOT FINISH THIS METHOD')
# print(type(res))
return res
def test_cache(self):
# 测试内存缓存变量
from QUANTAXIS.QAUtil.QASetting import DATABASE, stock_ip_list, \
future_ip_list
best_ip = select_best_ip()
stockips = QATdx.get_ip_list_by_multi_process_ping(stock_ip_list,
_type='stock')
stockip = QATdx.get_ip_list_by_ping(stock_ip_list, _type='stock')
self.assertTrue(stockip == stockips[0],
'没有使用缓存： {} {}'.format(stockip, stockips[0]))
futurips = QATdx.get_ip_list_by_multi_process_ping(future_ip_list,
_type='future')
futurip = QATdx.get_ip_list_by_ping(future_ip_list, _type='future')
self.assertTrue(futurip == futurips[0],
'没有使用缓存： {} {}'.format(futurip, futurips[0]))
stockips = QATdx.get_ip_list_by_multi_process_ping(stock_ip_list,
_type='stock')
futurips = QATdx.get_ip_list_by_multi_process_ping(future_ip_list,
_type='future')
stockips = QATdx.get_ip_list_by_multi_process_ping(stock_ip_list,
_type='stock')
futurips = QATdx.get_ip_list_by_multi_process_ping(future_ip_list,
_type='future')
def get_info(self, code, frequence, market, source, output):
if source is DATASOURCE.TDX:
res = QATdx.QA_fetch_get_stock_info(code, self.best_ip)
return res
elif source is DATASOURCE.MONGO:
res = QAQuery.QA_fetch_stock_info(
code, format=output, collections=self.database.stock_info)
return res
def __init__(self, uri='mongodb://127.0.0.1:27017/quantaxis', username='', password=''):
"""
初始化的时候 会初始化
"""
self.database = QA_util_sql_mongo_setting(uri).quantaxis
self.history = {}
self.best_ip = QATdx.select_best_ip()
def __init__(self, ip='127.0.0.1', port=27017, username='',password=''):
"""
初始化的时候 会初始化
"""
self.ip = ip
self.port = port
self.database = QA_util_sql_mongo_setting(ip, port).quantaxis
self.history = {}
self.best_ip=QATdx.select_best_ip()
def __init__(self, ip='127.0.0.1', port=27017, username='', password=''):
"""
初始化的时候 会初始化
"""
self.ip = ip
self.port = port
self.database = QA_util_sql_mongo_setting(ip, port).quantaxis
self.history = {}
self.best_ip = QATdx.select_best_ip()
def QA_SU_save_future_min(engine, client=DATABASE):
err_timeout_product_list = []
err_unknown_product_list = []
succ_product_list = []
no_data_list = []
QA_util_log_info('Updating future min, total %d products' %
len(future_product_list))
for index, product_code in enumerate(future_product_list):
col = QASETTING.client.future_1min[product_code]
col.create_index([('datetime', pymongo.DESCENDING)], unique=True)
ref = col.find({
"code": product_code
}).sort('datetime', pymongo.DESCENDING)
start = '%s %s' % (DEFAULT_START_DATE, DEFAULT_START_TIME)
if ref.count() > 0:
start = ref.next()['datetime']
col.delete_one({'datetime': start})
end = time.strftime('%Y-%m-%d %H:%M:%S')
if start != end:
try:
print('UPDATE %s from %s to %s' % (product_code, start, end),
end="")
future_df = QATdx.QA_fetch_get_future_min(product_code,
start=start,
end=end)
if len(future_df) > 0:
future_json = future_df.to_json(orient='records')
col.insert_many(json.loads(future_json))
print(' OK!')
else:
no_data_list.append(product_code)
print(' NO DATA FROM TDX!')
succ_product_list.append(product_code)
except socket.timeout as ex:
err_timeout_product_list.append('%s: error.socket.timeout' %
product_code)
print(' TIMEOUT!')
except Exception as ex:
err_unknown_product_list.append('%s: %s\n %s' %
(product_code, ex, future_df))
print(' UNKNOWN_ERROR!')
else:
print('%s is up to date %s' % (product_code, end))
QA_util_log_info(
'Update future_min completed!\nTotal succeed: %d\n Total error:%d' %
(len(succ_product_list),
len(err_timeout_product_list) + len(err_unknown_product_list)))
if len(err_timeout_product_list) > 0:
for each_err_log in err_timeout_product_list:
QA_util_log_info(each_err_log)
if len(err_unknown_product_list) > 0:
for each_err_log in err_unknown_product_list:
QA_util_log_info(each_err_log)
if len(no_data_list) > 0:
QA_util_log_info('No data from TDX: %s' % no_data_list)
def get_stock_day(code,
start_date,
end_date,
if_fq='00',
frequence='day',
ip=None,
port=None):
# print(code, ip, port)
return tdx.QA_fetch_get_stock_day(code, start_date, end_date, if_fq,
frequence, ip, port)
def QA_quotation(code, start, end, frequence, market, source, output):
"""一个统一的fetch
Arguments:
code {str/list} -- 证券/股票的代码
start {str} -- 开始日期
end {str} -- 结束日期
frequence {enum} -- 频率 QA.FREQUENCE
market {enum} -- 市场 QA.MARKET_TYPE
source {enum} -- 来源 QA.DATASOURCE
output {enum} -- 输出类型 QA.OUTPUT_FORMAT
"""
if market is MARKET_TYPE.STOCK_CN:
if frequence is FREQUENCE.DAY:
if source is DATASOURCE.MONGO:
res = QAQueryAdv.QA_fetch_stock_day_adv(code, start, end)
elif source is DATASOURCE.TDX:
res = QATdx.QA_fetch_get_stock_day(code, start, end, '00')
elif source is DATASOURCE.TUSHARE:
res = QATushare.QA_fetch_get_stock_day(code, start, end, '00')
elif frequence in [
FREQUENCE.ONE_MIN, FREQUENCE.FIVE_MIN, FREQUENCE.FIFTEEN_MIN,
FREQUENCE.THIRTY_MIN, FREQUENCE.SIXTY_MIN
]:
if source is DATASOURCE.MONGO:
res = QAQueryAdv.QA_fetch_stock_min_adv(code,
start,
end,
frequence=frequence)
elif source is DATASOURCE.TDX:
res = QATdx.QA_fetch_get_stock_min(code,
start,
end,
frequence=frequence)
elif frequence is FREQUENCE.TICK:
if source is DATASOURCE.TDX:
res = QATdx.QA_fetch_get_stock_transaction(code, start, end)
print(type(res))
return res
def QA_fetch_get_security_bars(code, _type, lens):
return QATdx.QA_fetch_get_security_bars(code, _type, lens)
def get_stock_day_map(x):
return tdx.QA_fetch_get_stock_day(x[0], x[1], x[2], x[3], x[4], x[5], x[6])
def QA_quotation(code, start, end, frequence, market, source=DATASOURCE.TDX, output=OUTPUT_FORMAT.DATAFRAME):
"""一个统一的获取k线的方法
如果使用mongo,从本地数据库获取,失败则在线获取
Arguments:
code {str/list} -- 期货/股票的代码
start {str} -- 开始日期
end {str} -- 结束日期
frequence {enum} -- 频率 QA.FREQUENCE
market {enum} -- 市场 QA.MARKET_TYPE
source {enum} -- 来源 QA.DATASOURCE
output {enum} -- 输出类型 QA.OUTPUT_FORMAT
"""
res = None
if market == MARKET_TYPE.STOCK_CN:
if frequence == FREQUENCE.DAY:
if source == DATASOURCE.MONGO:
try:
res = QAQueryAdv.QA_fetch_stock_day_adv(code, start, end)
except:
res = None
if source == DATASOURCE.TDX or res == None:
res = QATdx.QA_fetch_get_stock_day(code, start, end, '00')
res = QA_DataStruct_Stock_day(res.set_index(['date', 'code']))
elif source == DATASOURCE.TUSHARE:
res = QATushare.QA_fetch_get_stock_day(code, start, end, '00')
elif frequence in [FREQUENCE.ONE_MIN, FREQUENCE.FIVE_MIN, FREQUENCE.FIFTEEN_MIN, FREQUENCE.THIRTY_MIN, FREQUENCE.SIXTY_MIN]:
if source == DATASOURCE.MONGO:
try:
res = QAQueryAdv.QA_fetch_stock_min_adv(
code, start, end, frequence=frequence)
except:
res = None
if source == DATASOURCE.TDX or res == None:
res = QATdx.QA_fetch_get_stock_min(
code, start, end, frequence=frequence)
res = QA_DataStruct_Stock_min(
res.set_index(['datetime', 'code']))
elif market == MARKET_TYPE.FUTURE_CN:
if frequence == FREQUENCE.DAY:
if source == DATASOURCE.MONGO:
try:
res = QAQueryAdv.QA_fetch_future_day_adv(code, start, end)
except:
res = None
if source == DATASOURCE.TDX or res is None:
res = QATdx.QA_fetch_get_future_day(code, start, end)
res = QA_DataStruct_Future_day(res.set_index(['date', 'code']))
elif frequence in [FREQUENCE.ONE_MIN, FREQUENCE.FIVE_MIN, FREQUENCE.FIFTEEN_MIN, FREQUENCE.THIRTY_MIN, FREQUENCE.SIXTY_MIN]:
if source == DATASOURCE.MONGO:
try:
res = QAQueryAdv.QA_fetch_future_min_adv(
code, start, end, frequence=frequence)
except:
res = None
if source == DATASOURCE.TDX or res is None:
res = QATdx.QA_fetch_get_future_min(
code, start, end, frequence=frequence)
res = QA_DataStruct_Future_min(
res.set_index(['datetime', 'code']))
elif market == MARKET_TYPE.INDEX_CN:
if frequence == FREQUENCE.DAY:
if source == DATASOURCE.MONGO:
try:
res = QAQueryAdv.QA_fetch_index_day_adv(code, start, end)
except:
return None
if source == DATASOURCE.TDX or res == None:
res = QATdx.QA_fetch_get_index_day(code, start, end)
res = QA_DataStruct_Index_day(res.set_index(['date', 'code']))
elif frequence in [FREQUENCE.ONE_MIN, FREQUENCE.FIVE_MIN, FREQUENCE.FIFTEEN_MIN, FREQUENCE.THIRTY_MIN, FREQUENCE.SIXTY_MIN]:
if source == DATASOURCE.MONGO:
try:
res = QAQueryAdv.QA_fetch_index_min_adv(
code, start, end, frequence=frequence)
except:
res = None
if source == DATASOURCE.TDX or res == None:
res = QATdx.QA_fetch_get_index_min(
code, start, end, frequence=frequence)
res = QA_DataStruct_Index_min(
res.set_index(['datetime', 'code']))
elif market == MARKET_TYPE.OPTION_CN:
if source == DATASOURCE.MONGO:
#res = QAQueryAdv.QA_fetch_option_day_adv(code, start, end)
raise NotImplementedError('CURRENT NOT FINISH THIS METHOD')
# print(type(res))
if output is OUTPUT_FORMAT.DATAFRAME:
return res.data
elif output is OUTPUT_FORMAT.DATASTRUCT:
return res
elif output is OUTPUT_FORMAT.NDARRAY:
return res.to_numpy()
elif output is OUTPUT_FORMAT.JSON:
return res.to_json()
elif output is OUTPUT_FORMAT.LIST:
return res.to_list()
def QA_quotation_adv(code, start, end=save_tdx.now_time(), frequence='1min',
market=MARKET_TYPE.STOCK_CN, source=DATASOURCE.AUTO, output=OUTPUT_FORMAT.DATAFRAME):
"""一个统一的获取k线的方法
如果source=DATASOURCE.AUTO,优先mongo,从本地数据库获取,mongo中未下载的数据从TDX中在线补全。(仅限股票)
Arguments:
code {str/list} -- 期货/股票的代码
start {str} -- 开始日期
end {str} -- 结束日期
frequence {enum} -- 频率 QA.FREQUENCE
market {enum} -- 市场 QA.MARKET_TYPE
source {enum} -- 来源 QA.DATASOURCE
output {enum} -- 输出类型 QA.OUTPUT_FORMAT
"""
if pd.Timestamp(end) > pd.Timestamp(save_tdx.now_time()):
end = save_tdx.now_time()
res = None
if market == MARKET_TYPE.STOCK_CN:
if frequence == FREQUENCE.DAY or frequence == FREQUENCE.WEEK:
if source == DATASOURCE.AUTO:
try:
# 返回的是QA_DataStruct_Stock_day对象，为了与在线获取的数据格式保持统一，转成单索引
res = QAQueryAdv.QA_fetch_stock_day_adv(
code, start, end).data.reset_index(level='code')
# res = QAQueryAdv.QA_fetch_stock_day_adv(
# code, start, end).data.reset_index(level='code')[:14]
start_date = res.index[-1]
end_date = pd.Timestamp(end)
if end_date-start_date > datetime.timedelta(hours=17):
# 从TDX补充数据，由于仅考虑个股，在这里不做入库操作，入库还是需要save
data_tdx = QATdx.QA_fetch_get_stock_day(
code, QA_util_get_next_period(start_date, frequence), end_date, '00')
# data_tdx与从数据库获取的数据格式上做一些统一。
data_tdx = data_tdx.rename(columns={"vol": "volume"}).drop([
'date', 'date_stamp'], axis=1)
data_tdx.index = pd.to_datetime(data_tdx.index)
res = pd.concat([res, data_tdx], sort=True)
res = QA_DataStruct_Stock_day(
res.reset_index().set_index(['date', 'code']))
except:
res = None
if source == DATASOURCE.MONGO:
try:
res = QAQueryAdv.QA_fetch_stock_day_adv(code, start, end)
except:
res = None
if source == DATASOURCE.TDX or res == None:
res = QATdx.QA_fetch_get_stock_day(code, start, end, '00')
res = QA_DataStruct_Stock_day(res.set_index(['date', 'code']))
elif source == DATASOURCE.TUSHARE:
res = QATushare.QA_fetch_get_stock_day(code, start, end, '00')
if frequence == FREQUENCE.WEEK:
res = QA_DataStruct_Stock_day(
QA_data_day_resample(res.data))
elif frequence in [FREQUENCE.ONE_MIN, FREQUENCE.FIVE_MIN, FREQUENCE.FIFTEEN_MIN, FREQUENCE.THIRTY_MIN, FREQUENCE.SIXTY_MIN]:
if source == DATASOURCE.AUTO:
try:
# 返回的是QA_DataStruct_Stock_day对象，为了与在线获取的数据格式保持统一，转成单索引
res = QAQueryAdv.QA_fetch_stock_min_adv(
code, start, end, frequence=frequence).data.reset_index(level='code')
# res = QAQueryAdv.QA_fetch_stock_min_adv(
# code, start, end, frequence=frequence).data.reset_index(level='code')[:710]
start_date = res.index[-1]
end_date = pd.Timestamp(end)
if end_date > start_date:
# 从TDX补充数据，由于仅考虑个股，在这里不做入库操作，入库还是需要save
data_tdx = QATdx.QA_fetch_get_stock_min(code, QA_util_get_next_period(
start_date, frequence), end_date, frequence=frequence)
# data_tdx与从数据库获取的数据格式上做一些统一。
data_tdx = data_tdx.rename(columns={"vol": "volume"}).drop(
['date', 'datetime', 'date_stamp', 'time_stamp'], axis=1)
data_tdx.index = pd.to_datetime(data_tdx.index)
res = pd.concat([res, data_tdx], sort=True)
res = QA_DataStruct_Stock_day(
res.reset_index().set_index(['datetime', 'code']))
except:
res = None
if source == DATASOURCE.MONGO:
try:
res = QAQueryAdv.QA_fetch_stock_min_adv(
code,
start,
end,
frequence=frequence
)
except:
res = None
if source == DATASOURCE.TDX or res == None:
res = QATdx.QA_fetch_get_stock_min(
code,
start,
end,
frequence=frequence
)
res = QA_DataStruct_Stock_min(
res.set_index(['datetime',
'code'])
)
elif market == MARKET_TYPE.FUTURE_CN:
if frequence == FREQUENCE.DAY:
if source == DATASOURCE.MONGO:
try:
res = QAQueryAdv.QA_fetch_future_day_adv(code, start, end)
except:
res = None
if source == DATASOURCE.TDX or res is None:
res = QATdx.QA_fetch_get_future_day(code, start, end)
res = QA_DataStruct_Future_day(res.set_index(['date', 'code']))
elif frequence in [FREQUENCE.ONE_MIN,
FREQUENCE.FIVE_MIN,
FREQUENCE.FIFTEEN_MIN,
FREQUENCE.THIRTY_MIN,
FREQUENCE.SIXTY_MIN]:
if source == DATASOURCE.MONGO:
try:
res = QAQueryAdv.QA_fetch_future_min_adv(
code,
start,
end,
frequence=frequence
)
except:
res = None
if source == DATASOURCE.TDX or res is None:
res = QATdx.QA_fetch_get_future_min(
code,
start,
end,
frequence=frequence
)
res = QA_DataStruct_Future_min(
res.set_index(['datetime',
'code'])
)
elif market == MARKET_TYPE.INDEX_CN:
if frequence == FREQUENCE.DAY:
if source == DATASOURCE.MONGO:
try:
res = QAQueryAdv.QA_fetch_index_day_adv(code, start, end)
except:
return None
if source == DATASOURCE.TDX or res == None:
res = QATdx.QA_fetch_get_index_day(code, start, end)
res = QA_DataStruct_Index_day(res.set_index(['date', 'code']))
elif frequence in [FREQUENCE.ONE_MIN,
FREQUENCE.FIVE_MIN,
FREQUENCE.FIFTEEN_MIN,
FREQUENCE.THIRTY_MIN,
FREQUENCE.SIXTY_MIN]:
if source == DATASOURCE.MONGO:
try:
res = QAQueryAdv.QA_fetch_index_min_adv(
code,
start,
end,
frequence=frequence
)
except:
res = None
if source == DATASOURCE.TDX or res == None:
res = QATdx.QA_fetch_get_index_min(
code,
start,
end,
frequence=frequence
)
res = QA_DataStruct_Index_min(
res.set_index(['datetime',
'code'])
)
elif market == MARKET_TYPE.OPTION_CN:
if source == DATASOURCE.MONGO:
#res = QAQueryAdv.QA_fetch_option_day_adv(code, start, end)
raise NotImplementedError('CURRENT NOT FINISH THIS METHOD')
# print(type(res))
if output is OUTPUT_FORMAT.DATAFRAME:
return res.data
elif output is OUTPUT_FORMAT.DATASTRUCT:
return res
elif output is OUTPUT_FORMAT.NDARRAY:
return res.to_numpy()
elif output is OUTPUT_FORMAT.JSON:
return res.to_json()
elif output is OUTPUT_FORMAT.LIST:
return res.to_list()
import QUANTAXIS as QA
from QUANTAXIS.QAUtil import (QASETTING, QA_util_log_info)
from QUANTAXIS.QAFetch import QATdx
import pymongo, time, json
import socket
from QUANTAXIS.QAUtil.QASetting import DATABASE
DEFAULT_START_DATE = '1990-01-01'
DEFAULT_START_TIME = '00:00:00'
future_product_list = QATdx.QA_fetch_get_future_list().code
def QA_SU_save_future_day(engine, client=DATABASE):
err_timeout_product_list = []
err_unknown_product_list = []
no_data_list = []
succ_product_list = []
QA_util_log_info('Updating future day, total %d products' %
len(future_product_list))
for index, product_code in enumerate(future_product_list):
col = QASETTING.client.future_day[product_code]
col.create_index([('date', pymongo.DESCENDING)], unique=True)
ref = col.find({"code": product_code}).sort('date', pymongo.DESCENDING)
start_date = DEFAULT_START_DATE
if ref.count() > 0:
start_date = ref.next()['date']
col.delete_one({'date': start_date})
end_date = time.strftime('%Y-%m-%d')
if start_date != end_date:
import time
import QUANTAXIS.QAFetch.QATdx as tdx
import QUANTAXIS as QA
code = ['600831','000755']
hold = {'600831': [100, 12.16, 13.23],'000755':[100,5.63,6.32]}
columns = ['datetime', 'last_close', 'price', 'open','high','low']
names={}
for c in code:
names[c]=QA.QA_fetch_stock_list_adv().loc[c]['name']
while True:
try:
df = tdx.QA_fetch_get_stock_realtime(code=code)[columns]
df['buy'] = 0.0
df['count'] = 0
df['profit'] = 0.0
df['target'] = 0.0
df['name']=''
for c, value in hold.items():
df.at[c, 'name']=names[c]
df.at[c, 'buy'] = value[1]
df.at[c, 'count'] = value[0]
df.at[c, 'target'] = value[2]
df['profit'] = (df['price'] - df['buy']) * df['count']
print(df)
print(''.join(['-' for i in range(115)]))
except:
continue
def test_QA_fetch_get_stock_day(self):
#读取 dad 日线数据
df_from_dad = self.extraDataFromDAD(
self.test_day_k_line_dad_file_name[0])
df_from_tdx = QATdx.QA_fetch_get_stock_day(
code="600000",
start_date=self.test_day_k_line_dates[0],
end_date=self.test_day_k_line_dates[0])
for idx_from_df_tdx in df_from_tdx.index:
print(idx_from_df_tdx)
#for idx_from_df_dad in df_from_dad.index:
open_price1 = df_from_tdx.loc[idx_from_df_tdx, "open"]
close_price1 = df_from_tdx.loc[idx_from_df_tdx, "close"]
high_price1 = df_from_tdx.loc[idx_from_df_tdx, "high"]
low_price1 = df_from_tdx.loc[idx_from_df_tdx, "low"]
volume1 = df_from_tdx.loc[idx_from_df_tdx, "vol"]
amount1 = df_from_tdx.loc[idx_from_df_tdx, "amount"]
date1 = df_from_tdx.loc[idx_from_df_tdx, "date"]
#fix here 构造和 QA_fetch_get_stock_day 返回一样类型的 dataframe 直接比较
open_price2 = df_from_dad.loc["SH600000", "open"]
close_price2 = df_from_dad.loc["SH600000", "close"]
high_price2 = df_from_dad.loc["SH600000", "high"]
low_price2 = df_from_dad.loc["SH600000", "low"]
volume2 = df_from_dad.loc["SH600000", "volumn"]
amount2 = df_from_dad.loc["SH600000", "turn"]
date2 = df_from_dad.loc["SH600000", "date"]
self.assertEqual(open_price1, round(open_price2, 2))
self.assertEqual(close_price1, round(close_price2, 2))
self.assertEqual(high_price1, round(high_price2, 2))
self.assertEqual(low_price1, round(low_price2, 2))
# 成交量和成交额 总是有误差 ？？
#self.assertEqual( volume1 , volume2 )
#self.assertEqual( amount1 , amount2)
self.assertEqual(date1, date2)
df_from_tdx = QATdx.QA_fetch_get_stock_day(
code="300439",
start_date=self.test_day_k_line_dates[0],
end_date=self.test_day_k_line_dates[0])
for idx_from_df_tdx in df_from_tdx.index:
print(idx_from_df_tdx)
# for idx_from_df_dad in df_from_dad.index:
open_price1 = df_from_tdx.loc[idx_from_df_tdx, "open"]
close_price1 = df_from_tdx.loc[idx_from_df_tdx, "close"]
high_price1 = df_from_tdx.loc[idx_from_df_tdx, "high"]
low_price1 = df_from_tdx.loc[idx_from_df_tdx, "low"]
volume1 = df_from_tdx.loc[idx_from_df_tdx, "vol"]
amount1 = df_from_tdx.loc[idx_from_df_tdx, "amount"]
date1 = df_from_tdx.loc[idx_from_df_tdx, "date"]
# fix here 构造和 QA_fetch_get_stock_day 返回一样类型的 dataframe 直接比较
open_price2 = df_from_dad.loc["SZ300439", "open"]
close_price2 = df_from_dad.loc["SZ300439", "close"]
high_price2 = df_from_dad.loc["SZ300439", "high"]
low_price2 = df_from_dad.loc["SZ300439", "low"]
volume2 = df_from_dad.loc["SZ300439", "volumn"]
amount2 = df_from_dad.loc["SZ300439", "turn"]
date2 = df_from_dad.loc["SZ300439", "date"]
self.assertEqual(open_price1, round(open_price2, 2))
self.assertEqual(close_price1, round(close_price2, 2))
self.assertEqual(high_price1, round(high_price2, 2))
self.assertEqual(low_price1, round(low_price2, 2))
# 成交量和成交额 总是有误差 ？？
# self.assertEqual(volume1, volume2)
# self.assertEqual( amount1 , amount2)
self.assertEqual(date1, date2)
#print(idx_from_df_dad)
#print(type(open_price1), type(close_price1) ,type(high_price1), type(low_price1), type(volume1), type(amount1), type(date1))
pass
def get_price(self, order_book_id, start, end, freq):
"""
:param order_book_id: e.g. 000002.XSHE
:param start: 20160101
:param end: 20160201
:param freq: 1m 1d 5m 15m ...
:returns:
:rtype: numpy.rec.array
"""
s = get_str_date_from_int(start)
e = get_str_date_from_int(end)
if freq != '1d':
raise NotImplementedError
is_index = False
if ((order_book_id.startswith("0") and order_book_id.endswith(".XSHG"))
or (order_book_id.startswith("3")
and order_book_id.endswith(".XSHE"))):
is_index = True
if order_book_id.endswith(".XSHG") or order_book_id.endswith(".XSHE"):
order_book_id = order_book_id[:6]
L = list(self.db[is_index and 'index_day' or 'stock_day'].find(
{
'code': order_book_id,
'date': {
'$gte': s,
'$lte': e
}
}, {
'_id': 0,
'date_stamp': 0
}).sort('date', 1))
df = pd.DataFrame(L)
df.rename(columns={'vol': 'volume'}, inplace=True)
del df['code']
df['volume'] *= 100
if freq == '1d':
df["datetime"] = df["date"].apply(
lambda x: int(x.replace("-", "")) * 1000000)
df = df[['date', 'open', 'close', 'high', 'low', 'volume', 'datetime']]
if freq == '1d' and (not is_index):
E = datetime.datetime.strptime(e, "%Y-%m-%d")
if not (e in df['date'].values):
N = datetime.datetime.today()
if (N.date() - E.date()
).days == 0 and N.weekday() != 5 and N.weekday() != 6:
# get realtime
T = QATdx.QA_fetch_get_stock_realtime(
(order_book_id)).reset_index()
T.rename(columns={
'price': 'close',
'vol': 'volume'
},
inplace=True)
T['date'] = T['datetime'].dt.date.apply(
lambda x: x.strftime('%Y-%m-%d'))
T["datetime"] = T["date"].apply(
lambda x: int(x.replace("-", "")) * 1000000)
T["volume"] = T["volume"].apply(lambda x: x * 100.0)
T = T[[
'date', 'open', 'close', 'high', 'low', 'volume',
'datetime'
]]
df = pd.concat(objs=[df, T])
df = df.reset_index(drop=True)
arr = df.to_records()
return arr
