def kline_res_handle(self, kline_acquired):
if isinstance(kline_acquired, list):
if not kline_acquired:
return kline_restful(self.kline_info, 2001, data=kline_acquired)
parameters_list = ['_id', 'open', 'high', 'low', 'close', 'vol', 'close_time',
'quote_asset_volume', 'trades_num', 'taker_buy_base', 'taker_buy_quote']
df = pandas.DataFrame(kline_acquired, columns=parameters_list).iloc[:, 0:6]
if self.period == '1min':
df['ts'] = df['_id']
df['ts'] = df['ts'].astype('int64')
for column in parameters_list[0:6]:
if column == '_id':
df[column] = (df[column] / 1000).astype('int32')
continue
data = df[column].astype('double')
df[column] = round(data, 8)
kline_list = df.to_dict('records')
return kline_restful(self.kline_info, 2000, data=kline_list)
if isinstance(kline_acquired, dict):
# {"code": -10009,"msg": "Invalid period!"}
return kline_restful(self.kline_info, 4000, data=kline_acquired)
def kline_res_handle(self, kline_acquired):
if kline_acquired['status'] == 200:
kline_data = kline_acquired['data']
if not kline_data:
# {"status":200,"msg":"","data":{},"time":1571123247,"microtime":"0.46184200 1571123247","source":"api"}
return kline_restful(self.kline_info,
2001,
data=kline_acquired)
kline_bars = kline_data['bars']
df = pandas.DataFrame(kline_bars)
df = df.rename(columns={'volume': 'vol', 'time': '_id'})
df = df.drop(columns=['datetime'])
if self.period == '1min':
df['ts'] = df['_id']
df['ts'] = df['ts'].astype('int64')
columns = [column for column in df]
for column in columns:
if column not in ['_id', 'ts']:
df[column] = round(df[column].astype('double'), 8)
df['_id'] = (df['_id'].astype('double') / 1000).astype('int32')
kline_list = df.to_dict('records')
return kline_restful(self.kline_info, 2000, data=kline_list)
else:
# {"status":-102,"msg":"","data":"","time":1571123137,"microtime":"0.69178400 1571123137","source":"api"}
return kline_restful(self.kline_info, 4000, data=kline_acquired)
def kline_res_handle(self, kline_acquired):
if isinstance(kline_acquired, list):
if kline_acquired:
# okex 返回值的存储规则处理
parameters_list = [
'_id', 'open', 'high', 'low', 'close', 'vol'
]
df = pandas.DataFrame(kline_acquired, columns=parameters_list)
df['_id'] = df['_id'].apply(lambda x: time.mktime(
time.strptime(x, "%Y-%m-%dT%H:%M:%S.000Z")))
if self.period == '1min':
df['ts'] = (df['_id'] * 1000).astype('int64')
for column in parameters_list:
if column == '_id':
df['_id'] = df['_id'].astype('int32')
continue
data = df[column].astype('double')
df[column] = round(data, 8)
final_kline = df.to_dict('records')
return kline_restful(self.kline_info, 2000, data=final_kline)
return kline_restful(self.kline_info, 2001, data=kline_acquired)
if isinstance(kline_acquired, dict):
# {'code': 30032, 'message': 'The currency pair does not exist'}
return kline_restful(self.kline_info, 4000, data=kline_acquired)
def kline_res_handle(self, kline_acquired):
if isinstance(kline_acquired, list):
if kline_acquired:
parameters_list = [
'_id', 'open', 'high', 'low', 'close', 'vol', 'close_time',
'quote_asset_volume', 'trades_num', 'taker_buy_base',
'taker_buy_quote', 'ignore'
]
df = pandas.DataFrame(kline_acquired,
columns=parameters_list).iloc[:, 0:6]
if self.period == '1min':
df['ts'] = df['_id']
df['ts'] = df['ts'].astype('int64')
for column in parameters_list[0:6]:
if column == '_id':
df[column] = (df[column] / 1000).astype('int32')
continue
data = df[column].astype('double')
df[column] = round(data, 8)
kline_list = df.to_dict('records')
return kline_restful(self.kline_info, 2000, data=kline_list)
return kline_restful(self.kline_info, 2001, data=kline_acquired)
if isinstance(kline_acquired, dict):
# {'code': -1104, 'msg': "Not all sent parameters were read; read '4' parameter(s) but was sent '5'."}
return kline_restful(self.kline_info, 4000, data=kline_acquired)
def kline_res_handle(self, kline_acquired):
if 'data' not in kline_acquired:
# {"error": "市场错误"}
# {"code": 3005,"message": "无效的参数"}
return kline_restful(self.kline_info, 4000, data=kline_acquired)
kline_data = kline_acquired['data']
if kline_data[0][0] > (self.kline_info['from_time'] * 1000) \
and kline_data[-1][0] > (self.kline_info['end_time'] * 1000):
return kline_restful(self.kline_info, 2001, data=kline_acquired)
# 存在数据时间重合，即处理数据存库
parameters_list = ['_id', 'open', 'high', 'low', 'close', 'vol']
df = pandas.DataFrame(kline_data, columns=parameters_list)
if self.period == '1min':
df['ts'] = df['_id'].astype('int64')
for column in parameters_list[0:6]:
if column == '_id':
df[column] = (df[column] / 1000).astype('int32')
continue
df[column] = round(df[column].astype('double'), 8)
kline_list = df.to_dict('records')
return kline_restful(self.kline_info, 2000, data=kline_list)
def kline_res_handle(self, kline_acquired):
if isinstance(kline_acquired, list):
if not kline_acquired:
return kline_restful(self.kline_info,
2001,
data=kline_acquired)
df = pandas.DataFrame(kline_acquired)
df = df.drop(columns=['volumeQuote'])
rename_map = {
'volume': 'vol',
'timestamp': '_id',
'min': 'low',
'max': 'high'
}
df = df.rename(columns=rename_map)
df['_id'] = df['_id'].apply(lambda x: time.mktime(
time.strptime(x, "%Y-%m-%dT%H:%M:%S.000Z")))
if self.period == '1min':
df['ts'] = df['_id'] * 1000
df['ts'] = df['ts'].astype('int64')
columns = [column for column in df]
for column in columns:
if column not in ['_id', 'ts']:
df[column] = round(df[column].astype('double'), 8)
df['_id'] = df['_id'].astype('int32')
kline_list = df.to_dict('records')
return kline_restful(self.kline_info, 2000, data=kline_list)
if isinstance(kline_acquired, dict):
'''
{
"error": {
"code": 10001,
"message": "Validation error",
"description": "Parameter period with value 1mon unsupported"
}
}
'''
return kline_restful(self.kline_info, 4000, data=kline_acquired)
def kline_res_handle(self, kline_acquired):
if isinstance(kline_acquired, list):
if not kline_acquired:
return kline_restful(self.kline_info,
2001,
data=kline_acquired)
if 'error' in kline_acquired:
# ['error', 10020, 'time_interval: invalid']
return kline_restful(self.kline_info,
4000,
data=kline_acquired[2])
df = self.df_handle(kline_acquired)
if len(kline_acquired) < self.per_count:
# 返回数据存在丢失，使用pandas获取丢失时间戳，并进行补全操作。
miss_info = self.missing_data_handle(df)
miss_data = self.get_miss_data(miss_info)
miss_df = self.df_handle(miss_data)
df = df.append(miss_df, ignore_index=True)
kline_list = df.to_dict('records')
return kline_restful(self.kline_info, 2000, data=kline_list)
def kline_res_handle(self, kline_acquired):
if isinstance(kline_acquired, list):
if not kline_acquired:
return kline_restful(self.kline_info,
2001,
data=kline_acquired)
df = pandas.DataFrame(kline_acquired)
drop_column = [
'foreignNotional', 'homeNotional', 'lastSize', 'symbol',
'trades', 'turnover', 'vwap'
]
df = df.drop(columns=drop_column)
df = df.rename(columns={'volume': 'vol', 'timestamp': '_id'})
df['_id'] = df['_id'].apply(lambda x: time.mktime(
time.strptime(x, "%Y-%m-%dT%H:%M:%S.000Z")))
if self.period == '1min':
df['ts'] = df['_id'] * 1000
df['ts'] = df['ts'].astype('int64')
df['_id'] = df['_id'].astype('int32')
kline_list = df.to_dict('records')
return kline_restful(self.kline_info, 2000, data=kline_list)
if isinstance(kline_acquired, dict):
# {'error': {'message': 'binSize is invalid.', 'name': 'HTTPError'}}
return kline_restful(self.kline_info, 4000, data=kline_acquired)
