def get_kdata(entity_id, level=IntervalLevel.LEVEL_1DAY, adjust_type=AdjustType.qfq, limit=10000):
entity_type, exchange, code = decode_entity_id(entity_id)
level = IntervalLevel(level)
sec_id = to_em_sec_id(entity_id)
fq_flag = to_em_fq_flag(adjust_type)
level_flag = to_em_level_flag(level)
# f131 结算价
# f133 持仓
# 目前未获取
url = f"https://push2his.eastmoney.com/api/qt/stock/kline/get?secid={sec_id}&klt={level_flag}&fqt={fq_flag}&lmt={limit}&end=20500000&iscca=1&fields1=f1,f2,f3,f4,f5,f6,f7,f8&fields2=f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61,f62,f63,f64&ut=f057cbcbce2a86e2866ab8877db1d059&forcect=1"
resp = requests.get(url, headers=DEFAULT_HEADER)
resp.raise_for_status()
results = resp.json()
data = results["data"]
kdatas = []
if data:
klines = data["klines"]
name = data["name"]
for result in klines:
# "2000-01-28,1005.26,1012.56,1173.12,982.13,3023326,3075552000.00"
# "2021-08-27,19.39,20.30,20.30,19.25,1688497,3370240912.00,5.48,6.01,1.15,3.98,0,0,0"
# time,open,close,high,low,volume,turnover
# "2022-04-13,10708,10664,10790,10638,402712,43124771328,1.43,0.57,60,0.00,4667112399583576064,4690067230254170112,1169270784"
fields = result.split(",")
the_timestamp = to_pd_timestamp(fields[0])
the_id = generate_kdata_id(entity_id=entity_id, timestamp=the_timestamp, level=level)
open = to_float(fields[1])
close = to_float(fields[2])
high = to_float(fields[3])
low = to_float(fields[4])
volume = to_float(fields[5])
turnover = to_float(fields[6])
# 7 振幅
change_pct = value_to_pct(to_float(fields[8]))
# 9 变动
turnover_rate = value_to_pct(to_float(fields[10]))
kdatas.append(
dict(
id=the_id,
timestamp=the_timestamp,
entity_id=entity_id,
provider="em",
code=code,
name=name,
level=level.value,
open=open,
close=close,
high=high,
low=low,
volume=volume,
turnover=turnover,
turnover_rate=turnover_rate,
change_pct=change_pct,
)
)
if kdatas:
df = pd.DataFrame.from_records(kdatas)
return df
def generate_domain_id(self, entity, original_data):
return generate_kdata_id(entity_id=entity.id,
timestamp=original_data['timestamp'],
level=self.level)
def get_kdata(entity_id,
level=IntervalLevel.LEVEL_1DAY,
adjust_type=AdjustType.qfq,
limit=10000):
entity_type, exchange, code = decode_entity_id(entity_id)
level = IntervalLevel(level)
sec_id = to_em_sec_id(entity_id)
fq_flag = to_em_fq_flag(adjust_type)
level_flag = to_em_level_flag(level)
url = f'https://push2his.eastmoney.com/api/qt/stock/kline/get?secid={sec_id}&klt={level_flag}&fqt={fq_flag}&lmt={limit}&end=20500000&iscca=1&fields1=f1,f2,f3,f4,f5,f6,f7,f8&fields2=f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61,f62,f63,f64&ut=f057cbcbce2a86e2866ab8877db1d059&forcect=1'
resp = requests.get(url, headers=DEFAULT_HEADER)
resp.raise_for_status()
results = resp.json()
data = results['data']
kdatas = []
if data:
klines = data['klines']
name = data['name']
# TODO: ignore the last unfinished kdata now,could control it better if need
for result in klines[:-1]:
# "2000-01-28,1005.26,1012.56,1173.12,982.13,3023326,3075552000.00"
# "2021-08-27,19.39,20.30,20.30,19.25,1688497,3370240912.00,5.48,6.01,1.15,3.98,0,0,0"
# time,open,close,high,low,volume,turnover
fields = result.split(',')
the_timestamp = to_pd_timestamp(fields[0])
the_id = generate_kdata_id(entity_id=entity_id,
timestamp=the_timestamp,
level=level)
open = to_float(fields[1])
close = to_float(fields[2])
high = to_float(fields[3])
low = to_float(fields[4])
volume = to_float(fields[5])
turnover = to_float(fields[6])
# 7 振幅
change_pct = value_to_pct(to_float(fields[8]))
# 9 变动
turnover_rate = value_to_pct(to_float(fields[10]))
kdatas.append(
dict(id=the_id,
timestamp=the_timestamp,
entity_id=entity_id,
provider='em',
code=code,
name=name,
level=level.value,
open=open,
close=close,
high=high,
low=low,
volume=volume,
turnover=turnover,
turnover_rate=turnover_rate,
change_pct=change_pct))
if kdatas:
df = pd.DataFrame.from_records(kdatas)
return df