partition stringclasses 3 values | func_name stringlengths 1 134 | docstring stringlengths 1 46.9k | path stringlengths 4 223 | original_string stringlengths 75 104k | code stringlengths 75 104k | docstring_tokens listlengths 1 1.97k | repo stringlengths 7 55 | language stringclasses 1 value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
|---|---|---|---|---|---|---|---|---|---|---|---|
train | QA_SU_save_stock_day | save stock_day
保存日线数据
:param client:
:param ui_log: 给GUI qt 界面使用
:param ui_progress: 给GUI qt 界面使用
:param ui_progress_int_value: 给GUI qt 界面使用 | QUANTAXIS/QASU/save_tdx.py | def QA_SU_save_stock_day(client=DATABASE, ui_log=None, ui_progress=None):
'''
save stock_day
保存日线数据
:param client:
:param ui_log: 给GUI qt 界面使用
:param ui_progress: 给GUI qt 界面使用
:param ui_progress_int_value: 给GUI qt 界面使用
'''
stock_list = QA_fetch_get_stock_list().code.unique().tolist()
coll_stock_day = client.stock_day
coll_stock_day.create_index(
[("code",
pymongo.ASCENDING),
("date_stamp",
pymongo.ASCENDING)]
)
err = []
def __saving_work(code, coll_stock_day):
try:
QA_util_log_info(
'##JOB01 Now Saving STOCK_DAY==== {}'.format(str(code)),
ui_log
)
# 首选查找数据库 是否 有 这个代码的数据
ref = coll_stock_day.find({'code': str(code)[0:6]})
end_date = str(now_time())[0:10]
# 当前数据库已经包含了这个代码的数据， 继续增量更新
# 加入这个判断的原因是因为如果股票是刚上市的 数据库会没有数据 所以会有负索引问题出现
if ref.count() > 0:
# 接着上次获取的日期继续更新
start_date = ref[ref.count() - 1]['date']
QA_util_log_info(
'UPDATE_STOCK_DAY \n Trying updating {} from {} to {}'
.format(code,
start_date,
end_date),
ui_log
)
if start_date != end_date:
coll_stock_day.insert_many(
QA_util_to_json_from_pandas(
QA_fetch_get_stock_day(
str(code),
QA_util_get_next_day(start_date),
end_date,
'00'
)
)
)
# 当前数据库中没有这个代码的股票数据， 从1990-01-01 开始下载所有的数据
else:
start_date = '1990-01-01'
QA_util_log_info(
'UPDATE_STOCK_DAY \n Trying updating {} from {} to {}'
.format(code,
start_date,
end_date),
ui_log
)
if start_date != end_date:
coll_stock_day.insert_many(
QA_util_to_json_from_pandas(
QA_fetch_get_stock_day(
str(code),
start_date,
end_date,
'00'
)
)
)
except Exception as error0:
print(error0)
err.append(str(code))
for item in range(len(stock_list)):
QA_util_log_info('The {} of Total {}'.format(item, len(stock_list)))
strProgressToLog = 'DOWNLOAD PROGRESS {} {}'.format(
str(float(item / len(stock_list) * 100))[0:4] + '%',
ui_log
)
intProgressToLog = int(float(item / len(stock_list) * 100))
QA_util_log_info(
strProgressToLog,
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intProgressToLog
)
__saving_work(stock_list[item], coll_stock_day)
if len(err) < 1:
QA_util_log_info('SUCCESS save stock day ^_^', ui_log)
else:
QA_util_log_info('ERROR CODE \n ', ui_log)
QA_util_log_info(err, ui_log) | def QA_SU_save_stock_day(client=DATABASE, ui_log=None, ui_progress=None):
'''
save stock_day
保存日线数据
:param client:
:param ui_log: 给GUI qt 界面使用
:param ui_progress: 给GUI qt 界面使用
:param ui_progress_int_value: 给GUI qt 界面使用
'''
stock_list = QA_fetch_get_stock_list().code.unique().tolist()
coll_stock_day = client.stock_day
coll_stock_day.create_index(
[("code",
pymongo.ASCENDING),
("date_stamp",
pymongo.ASCENDING)]
)
err = []
def __saving_work(code, coll_stock_day):
try:
QA_util_log_info(
'##JOB01 Now Saving STOCK_DAY==== {}'.format(str(code)),
ui_log
)
# 首选查找数据库 是否 有 这个代码的数据
ref = coll_stock_day.find({'code': str(code)[0:6]})
end_date = str(now_time())[0:10]
# 当前数据库已经包含了这个代码的数据， 继续增量更新
# 加入这个判断的原因是因为如果股票是刚上市的 数据库会没有数据 所以会有负索引问题出现
if ref.count() > 0:
# 接着上次获取的日期继续更新
start_date = ref[ref.count() - 1]['date']
QA_util_log_info(
'UPDATE_STOCK_DAY \n Trying updating {} from {} to {}'
.format(code,
start_date,
end_date),
ui_log
)
if start_date != end_date:
coll_stock_day.insert_many(
QA_util_to_json_from_pandas(
QA_fetch_get_stock_day(
str(code),
QA_util_get_next_day(start_date),
end_date,
'00'
)
)
)
# 当前数据库中没有这个代码的股票数据， 从1990-01-01 开始下载所有的数据
else:
start_date = '1990-01-01'
QA_util_log_info(
'UPDATE_STOCK_DAY \n Trying updating {} from {} to {}'
.format(code,
start_date,
end_date),
ui_log
)
if start_date != end_date:
coll_stock_day.insert_many(
QA_util_to_json_from_pandas(
QA_fetch_get_stock_day(
str(code),
start_date,
end_date,
'00'
)
)
)
except Exception as error0:
print(error0)
err.append(str(code))
for item in range(len(stock_list)):
QA_util_log_info('The {} of Total {}'.format(item, len(stock_list)))
strProgressToLog = 'DOWNLOAD PROGRESS {} {}'.format(
str(float(item / len(stock_list) * 100))[0:4] + '%',
ui_log
)
intProgressToLog = int(float(item / len(stock_list) * 100))
QA_util_log_info(
strProgressToLog,
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intProgressToLog
)
__saving_work(stock_list[item], coll_stock_day)
if len(err) < 1:
QA_util_log_info('SUCCESS save stock day ^_^', ui_log)
else:
QA_util_log_info('ERROR CODE \n ', ui_log)
QA_util_log_info(err, ui_log) | [
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] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QASU/save_tdx.py#L82-L184 | [
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"... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_SU_save_stock_week | save stock_week
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE}) | QUANTAXIS/QASU/save_tdx.py | def QA_SU_save_stock_week(client=DATABASE, ui_log=None, ui_progress=None):
"""save stock_week
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
stock_list = QA_fetch_get_stock_list().code.unique().tolist()
coll_stock_week = client.stock_week
coll_stock_week.create_index(
[("code",
pymongo.ASCENDING),
("date_stamp",
pymongo.ASCENDING)]
)
err = []
def __saving_work(code, coll_stock_week):
try:
QA_util_log_info(
'##JOB01 Now Saving STOCK_WEEK==== {}'.format(str(code)),
ui_log=ui_log
)
ref = coll_stock_week.find({'code': str(code)[0:6]})
end_date = str(now_time())[0:10]
if ref.count() > 0:
# 加入这个判断的原因是因为如果股票是刚上市的 数据库会没有数据 所以会有负索引问题出现
start_date = ref[ref.count() - 1]['date']
QA_util_log_info(
'UPDATE_STOCK_WEEK \n Trying updating {} from {} to {}'
.format(code,
start_date,
end_date),
ui_log=ui_log
)
if start_date != end_date:
coll_stock_week.insert_many(
QA_util_to_json_from_pandas(
QA_fetch_get_stock_day(
str(code),
QA_util_get_next_day(start_date),
end_date,
'00',
frequence='week'
)
)
)
else:
start_date = '1990-01-01'
QA_util_log_info(
'UPDATE_STOCK_WEEK \n Trying updating {} from {} to {}'
.format(code,
start_date,
end_date),
ui_log=ui_log
)
if start_date != end_date:
coll_stock_week.insert_many(
QA_util_to_json_from_pandas(
QA_fetch_get_stock_day(
str(code),
start_date,
end_date,
'00',
frequence='week'
)
)
)
except:
err.append(str(code))
for item in range(len(stock_list)):
QA_util_log_info(
'The {} of Total {}'.format(item,
len(stock_list)),
ui_log=ui_log
)
strProgress = 'DOWNLOAD PROGRESS {} '.format(
str(float(item / len(stock_list) * 100))[0:4] + '%'
)
intProgress = int(float(item / len(stock_list) * 100))
QA_util_log_info(
strProgress,
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intProgress
)
__saving_work(stock_list[item], coll_stock_week)
if len(err) < 1:
QA_util_log_info('SUCCESS', ui_log=ui_log)
else:
QA_util_log_info(' ERROR CODE \n ', ui_log=ui_log)
QA_util_log_info(err, ui_log=ui_log) | def QA_SU_save_stock_week(client=DATABASE, ui_log=None, ui_progress=None):
"""save stock_week
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
stock_list = QA_fetch_get_stock_list().code.unique().tolist()
coll_stock_week = client.stock_week
coll_stock_week.create_index(
[("code",
pymongo.ASCENDING),
("date_stamp",
pymongo.ASCENDING)]
)
err = []
def __saving_work(code, coll_stock_week):
try:
QA_util_log_info(
'##JOB01 Now Saving STOCK_WEEK==== {}'.format(str(code)),
ui_log=ui_log
)
ref = coll_stock_week.find({'code': str(code)[0:6]})
end_date = str(now_time())[0:10]
if ref.count() > 0:
# 加入这个判断的原因是因为如果股票是刚上市的 数据库会没有数据 所以会有负索引问题出现
start_date = ref[ref.count() - 1]['date']
QA_util_log_info(
'UPDATE_STOCK_WEEK \n Trying updating {} from {} to {}'
.format(code,
start_date,
end_date),
ui_log=ui_log
)
if start_date != end_date:
coll_stock_week.insert_many(
QA_util_to_json_from_pandas(
QA_fetch_get_stock_day(
str(code),
QA_util_get_next_day(start_date),
end_date,
'00',
frequence='week'
)
)
)
else:
start_date = '1990-01-01'
QA_util_log_info(
'UPDATE_STOCK_WEEK \n Trying updating {} from {} to {}'
.format(code,
start_date,
end_date),
ui_log=ui_log
)
if start_date != end_date:
coll_stock_week.insert_many(
QA_util_to_json_from_pandas(
QA_fetch_get_stock_day(
str(code),
start_date,
end_date,
'00',
frequence='week'
)
)
)
except:
err.append(str(code))
for item in range(len(stock_list)):
QA_util_log_info(
'The {} of Total {}'.format(item,
len(stock_list)),
ui_log=ui_log
)
strProgress = 'DOWNLOAD PROGRESS {} '.format(
str(float(item / len(stock_list) * 100))[0:4] + '%'
)
intProgress = int(float(item / len(stock_list) * 100))
QA_util_log_info(
strProgress,
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intProgress
)
__saving_work(stock_list[item], coll_stock_week)
if len(err) < 1:
QA_util_log_info('SUCCESS', ui_log=ui_log)
else:
QA_util_log_info(' ERROR CODE \n ', ui_log=ui_log)
QA_util_log_info(err, ui_log=ui_log) | [
"save",
"stock_week"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QASU/save_tdx.py#L196-L292 | [
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... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_SU_save_stock_xdxr | [summary]
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE}) | QUANTAXIS/QASU/save_tdx.py | def QA_SU_save_stock_xdxr(client=DATABASE, ui_log=None, ui_progress=None):
"""[summary]
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
stock_list = QA_fetch_get_stock_list().code.unique().tolist()
# client.drop_collection('stock_xdxr')
try:
coll = client.stock_xdxr
coll.create_index(
[('code',
pymongo.ASCENDING),
('date',
pymongo.ASCENDING)],
unique=True
)
except:
client.drop_collection('stock_xdxr')
coll = client.stock_xdxr
coll.create_index(
[('code',
pymongo.ASCENDING),
('date',
pymongo.ASCENDING)],
unique=True
)
err = []
def __saving_work(code, coll):
QA_util_log_info(
'##JOB02 Now Saving XDXR INFO ==== {}'.format(str(code)),
ui_log=ui_log
)
try:
coll.insert_many(
QA_util_to_json_from_pandas(QA_fetch_get_stock_xdxr(str(code))),
ordered=False
)
except:
err.append(str(code))
for i_ in range(len(stock_list)):
QA_util_log_info(
'The {} of Total {}'.format(i_,
len(stock_list)),
ui_log=ui_log
)
strLogInfo = 'DOWNLOAD PROGRESS {} '.format(
str(float(i_ / len(stock_list) * 100))[0:4] + '%'
)
intLogProgress = int(float(i_ / len(stock_list) * 100))
QA_util_log_info(
strLogInfo,
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intLogProgress
)
__saving_work(stock_list[i_], coll) | def QA_SU_save_stock_xdxr(client=DATABASE, ui_log=None, ui_progress=None):
"""[summary]
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
stock_list = QA_fetch_get_stock_list().code.unique().tolist()
# client.drop_collection('stock_xdxr')
try:
coll = client.stock_xdxr
coll.create_index(
[('code',
pymongo.ASCENDING),
('date',
pymongo.ASCENDING)],
unique=True
)
except:
client.drop_collection('stock_xdxr')
coll = client.stock_xdxr
coll.create_index(
[('code',
pymongo.ASCENDING),
('date',
pymongo.ASCENDING)],
unique=True
)
err = []
def __saving_work(code, coll):
QA_util_log_info(
'##JOB02 Now Saving XDXR INFO ==== {}'.format(str(code)),
ui_log=ui_log
)
try:
coll.insert_many(
QA_util_to_json_from_pandas(QA_fetch_get_stock_xdxr(str(code))),
ordered=False
)
except:
err.append(str(code))
for i_ in range(len(stock_list)):
QA_util_log_info(
'The {} of Total {}'.format(i_,
len(stock_list)),
ui_log=ui_log
)
strLogInfo = 'DOWNLOAD PROGRESS {} '.format(
str(float(i_ / len(stock_list) * 100))[0:4] + '%'
)
intLogProgress = int(float(i_ / len(stock_list) * 100))
QA_util_log_info(
strLogInfo,
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intLogProgress
)
__saving_work(stock_list[i_], coll) | [
"[",
"summary",
"]"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QASU/save_tdx.py#L494-L555 | [
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... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_SU_save_stock_min | save stock_min
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE}) | QUANTAXIS/QASU/save_tdx.py | def QA_SU_save_stock_min(client=DATABASE, ui_log=None, ui_progress=None):
"""save stock_min
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
stock_list = QA_fetch_get_stock_list().code.unique().tolist()
coll = client.stock_min
coll.create_index(
[
('code',
pymongo.ASCENDING),
('time_stamp',
pymongo.ASCENDING),
('date_stamp',
pymongo.ASCENDING)
]
)
err = []
def __saving_work(code, coll):
QA_util_log_info(
'##JOB03 Now Saving STOCK_MIN ==== {}'.format(str(code)),
ui_log=ui_log
)
try:
for type in ['1min', '5min', '15min', '30min', '60min']:
ref_ = coll.find({'code': str(code)[0:6], 'type': type})
end_time = str(now_time())[0:19]
if ref_.count() > 0:
start_time = ref_[ref_.count() - 1]['datetime']
QA_util_log_info(
'##JOB03.{} Now Saving {} from {} to {} =={} '.format(
['1min',
'5min',
'15min',
'30min',
'60min'].index(type),
str(code),
start_time,
end_time,
type
),
ui_log=ui_log
)
if start_time != end_time:
__data = QA_fetch_get_stock_min(
str(code),
start_time,
end_time,
type
)
if len(__data) > 1:
coll.insert_many(
QA_util_to_json_from_pandas(__data)[1::]
)
else:
start_time = '2015-01-01'
QA_util_log_info(
'##JOB03.{} Now Saving {} from {} to {} =={} '.format(
['1min',
'5min',
'15min',
'30min',
'60min'].index(type),
str(code),
start_time,
end_time,
type
),
ui_log=ui_log
)
if start_time != end_time:
__data = QA_fetch_get_stock_min(
str(code),
start_time,
end_time,
type
)
if len(__data) > 1:
coll.insert_many(
QA_util_to_json_from_pandas(__data)
)
except Exception as e:
QA_util_log_info(e, ui_log=ui_log)
err.append(code)
QA_util_log_info(err, ui_log=ui_log)
executor = ThreadPoolExecutor(max_workers=4)
# executor.map((__saving_work, stock_list[i_], coll),URLS)
res = {
executor.submit(__saving_work,
stock_list[i_],
coll)
for i_ in range(len(stock_list))
}
count = 0
for i_ in concurrent.futures.as_completed(res):
QA_util_log_info(
'The {} of Total {}'.format(count,
len(stock_list)),
ui_log=ui_log
)
strProgress = 'DOWNLOAD PROGRESS {} '.format(
str(float(count / len(stock_list) * 100))[0:4] + '%'
)
intProgress = int(count / len(stock_list) * 10000.0)
QA_util_log_info(
strProgress,
ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intProgress
)
count = count + 1
if len(err) < 1:
QA_util_log_info('SUCCESS', ui_log=ui_log)
else:
QA_util_log_info(' ERROR CODE \n ', ui_log=ui_log)
QA_util_log_info(err, ui_log=ui_log) | def QA_SU_save_stock_min(client=DATABASE, ui_log=None, ui_progress=None):
"""save stock_min
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
stock_list = QA_fetch_get_stock_list().code.unique().tolist()
coll = client.stock_min
coll.create_index(
[
('code',
pymongo.ASCENDING),
('time_stamp',
pymongo.ASCENDING),
('date_stamp',
pymongo.ASCENDING)
]
)
err = []
def __saving_work(code, coll):
QA_util_log_info(
'##JOB03 Now Saving STOCK_MIN ==== {}'.format(str(code)),
ui_log=ui_log
)
try:
for type in ['1min', '5min', '15min', '30min', '60min']:
ref_ = coll.find({'code': str(code)[0:6], 'type': type})
end_time = str(now_time())[0:19]
if ref_.count() > 0:
start_time = ref_[ref_.count() - 1]['datetime']
QA_util_log_info(
'##JOB03.{} Now Saving {} from {} to {} =={} '.format(
['1min',
'5min',
'15min',
'30min',
'60min'].index(type),
str(code),
start_time,
end_time,
type
),
ui_log=ui_log
)
if start_time != end_time:
__data = QA_fetch_get_stock_min(
str(code),
start_time,
end_time,
type
)
if len(__data) > 1:
coll.insert_many(
QA_util_to_json_from_pandas(__data)[1::]
)
else:
start_time = '2015-01-01'
QA_util_log_info(
'##JOB03.{} Now Saving {} from {} to {} =={} '.format(
['1min',
'5min',
'15min',
'30min',
'60min'].index(type),
str(code),
start_time,
end_time,
type
),
ui_log=ui_log
)
if start_time != end_time:
__data = QA_fetch_get_stock_min(
str(code),
start_time,
end_time,
type
)
if len(__data) > 1:
coll.insert_many(
QA_util_to_json_from_pandas(__data)
)
except Exception as e:
QA_util_log_info(e, ui_log=ui_log)
err.append(code)
QA_util_log_info(err, ui_log=ui_log)
executor = ThreadPoolExecutor(max_workers=4)
# executor.map((__saving_work, stock_list[i_], coll),URLS)
res = {
executor.submit(__saving_work,
stock_list[i_],
coll)
for i_ in range(len(stock_list))
}
count = 0
for i_ in concurrent.futures.as_completed(res):
QA_util_log_info(
'The {} of Total {}'.format(count,
len(stock_list)),
ui_log=ui_log
)
strProgress = 'DOWNLOAD PROGRESS {} '.format(
str(float(count / len(stock_list) * 100))[0:4] + '%'
)
intProgress = int(count / len(stock_list) * 10000.0)
QA_util_log_info(
strProgress,
ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intProgress
)
count = count + 1
if len(err) < 1:
QA_util_log_info('SUCCESS', ui_log=ui_log)
else:
QA_util_log_info(' ERROR CODE \n ', ui_log=ui_log)
QA_util_log_info(err, ui_log=ui_log) | [
"save",
"stock_min"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QASU/save_tdx.py#L558-L679 | [
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"... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_SU_save_index_day | save index_day
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE}) | QUANTAXIS/QASU/save_tdx.py | def QA_SU_save_index_day(client=DATABASE, ui_log=None, ui_progress=None):
"""save index_day
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
__index_list = QA_fetch_get_stock_list('index')
coll = client.index_day
coll.create_index(
[('code',
pymongo.ASCENDING),
('date_stamp',
pymongo.ASCENDING)]
)
err = []
def __saving_work(code, coll):
try:
ref_ = coll.find({'code': str(code)[0:6]})
end_time = str(now_time())[0:10]
if ref_.count() > 0:
start_time = ref_[ref_.count() - 1]['date']
QA_util_log_info(
'##JOB04 Now Saving INDEX_DAY==== \n Trying updating {} from {} to {}'
.format(code,
start_time,
end_time),
ui_log=ui_log
)
if start_time != end_time:
coll.insert_many(
QA_util_to_json_from_pandas(
QA_fetch_get_index_day(
str(code),
QA_util_get_next_day(start_time),
end_time
)
)
)
else:
try:
start_time = '1990-01-01'
QA_util_log_info(
'##JOB04 Now Saving INDEX_DAY==== \n Trying updating {} from {} to {}'
.format(code,
start_time,
end_time),
ui_log=ui_log
)
coll.insert_many(
QA_util_to_json_from_pandas(
QA_fetch_get_index_day(
str(code),
start_time,
end_time
)
)
)
except:
start_time = '2009-01-01'
QA_util_log_info(
'##JOB04 Now Saving INDEX_DAY==== \n Trying updating {} from {} to {}'
.format(code,
start_time,
end_time),
ui_log=ui_log
)
coll.insert_many(
QA_util_to_json_from_pandas(
QA_fetch_get_index_day(
str(code),
start_time,
end_time
)
)
)
except Exception as e:
QA_util_log_info(e, ui_log=ui_log)
err.append(str(code))
QA_util_log_info(err, ui_log=ui_log)
for i_ in range(len(__index_list)):
# __saving_work('000001')
QA_util_log_info(
'The {} of Total {}'.format(i_,
len(__index_list)),
ui_log=ui_log
)
strLogProgress = 'DOWNLOAD PROGRESS {} '.format(
str(float(i_ / len(__index_list) * 100))[0:4] + '%'
)
intLogProgress = int(float(i_ / len(__index_list) * 10000.0))
QA_util_log_info(
strLogProgress,
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intLogProgress
)
__saving_work(__index_list.index[i_][0], coll)
if len(err) < 1:
QA_util_log_info('SUCCESS', ui_log=ui_log)
else:
QA_util_log_info(' ERROR CODE \n ', ui_log=ui_log)
QA_util_log_info(err, ui_log=ui_log) | def QA_SU_save_index_day(client=DATABASE, ui_log=None, ui_progress=None):
"""save index_day
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
__index_list = QA_fetch_get_stock_list('index')
coll = client.index_day
coll.create_index(
[('code',
pymongo.ASCENDING),
('date_stamp',
pymongo.ASCENDING)]
)
err = []
def __saving_work(code, coll):
try:
ref_ = coll.find({'code': str(code)[0:6]})
end_time = str(now_time())[0:10]
if ref_.count() > 0:
start_time = ref_[ref_.count() - 1]['date']
QA_util_log_info(
'##JOB04 Now Saving INDEX_DAY==== \n Trying updating {} from {} to {}'
.format(code,
start_time,
end_time),
ui_log=ui_log
)
if start_time != end_time:
coll.insert_many(
QA_util_to_json_from_pandas(
QA_fetch_get_index_day(
str(code),
QA_util_get_next_day(start_time),
end_time
)
)
)
else:
try:
start_time = '1990-01-01'
QA_util_log_info(
'##JOB04 Now Saving INDEX_DAY==== \n Trying updating {} from {} to {}'
.format(code,
start_time,
end_time),
ui_log=ui_log
)
coll.insert_many(
QA_util_to_json_from_pandas(
QA_fetch_get_index_day(
str(code),
start_time,
end_time
)
)
)
except:
start_time = '2009-01-01'
QA_util_log_info(
'##JOB04 Now Saving INDEX_DAY==== \n Trying updating {} from {} to {}'
.format(code,
start_time,
end_time),
ui_log=ui_log
)
coll.insert_many(
QA_util_to_json_from_pandas(
QA_fetch_get_index_day(
str(code),
start_time,
end_time
)
)
)
except Exception as e:
QA_util_log_info(e, ui_log=ui_log)
err.append(str(code))
QA_util_log_info(err, ui_log=ui_log)
for i_ in range(len(__index_list)):
# __saving_work('000001')
QA_util_log_info(
'The {} of Total {}'.format(i_,
len(__index_list)),
ui_log=ui_log
)
strLogProgress = 'DOWNLOAD PROGRESS {} '.format(
str(float(i_ / len(__index_list) * 100))[0:4] + '%'
)
intLogProgress = int(float(i_ / len(__index_list) * 10000.0))
QA_util_log_info(
strLogProgress,
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intLogProgress
)
__saving_work(__index_list.index[i_][0], coll)
if len(err) < 1:
QA_util_log_info('SUCCESS', ui_log=ui_log)
else:
QA_util_log_info(' ERROR CODE \n ', ui_log=ui_log)
QA_util_log_info(err, ui_log=ui_log) | [
"save",
"index_day"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QASU/save_tdx.py#L682-L790 | [
"def",
"QA_SU_save_index_day",
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"coll",
".",
"... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_SU_save_index_min | save index_min
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE}) | QUANTAXIS/QASU/save_tdx.py | def QA_SU_save_index_min(client=DATABASE, ui_log=None, ui_progress=None):
"""save index_min
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
__index_list = QA_fetch_get_stock_list('index')
coll = client.index_min
coll.create_index(
[
('code',
pymongo.ASCENDING),
('time_stamp',
pymongo.ASCENDING),
('date_stamp',
pymongo.ASCENDING)
]
)
err = []
def __saving_work(code, coll):
QA_util_log_info(
'##JOB05 Now Saving Index_MIN ==== {}'.format(str(code)),
ui_log=ui_log
)
try:
for type in ['1min', '5min', '15min', '30min', '60min']:
ref_ = coll.find({'code': str(code)[0:6], 'type': type})
end_time = str(now_time())[0:19]
if ref_.count() > 0:
start_time = ref_[ref_.count() - 1]['datetime']
QA_util_log_info(
'##JOB05.{} Now Saving {} from {} to {} =={} '.format(
['1min',
'5min',
'15min',
'30min',
'60min'].index(type),
str(code),
start_time,
end_time,
type
),
ui_log=ui_log
)
if start_time != end_time:
__data = QA_fetch_get_index_min(
str(code),
start_time,
end_time,
type
)
if len(__data) > 1:
coll.insert_many(
QA_util_to_json_from_pandas(__data[1::])
)
else:
start_time = '2015-01-01'
QA_util_log_info(
'##JOB05.{} Now Saving {} from {} to {} =={} '.format(
['1min',
'5min',
'15min',
'30min',
'60min'].index(type),
str(code),
start_time,
end_time,
type
),
ui_log=ui_log
)
if start_time != end_time:
__data = QA_fetch_get_index_min(
str(code),
start_time,
end_time,
type
)
if len(__data) > 1:
coll.insert_many(
QA_util_to_json_from_pandas(__data)
)
except:
err.append(code)
executor = ThreadPoolExecutor(max_workers=4)
res = {
executor.submit(__saving_work,
__index_list.index[i_][0],
coll)
for i_ in range(len(__index_list))
} # multi index ./.
count = 0
for i_ in concurrent.futures.as_completed(res):
strLogProgress = 'DOWNLOAD PROGRESS {} '.format(
str(float(count / len(__index_list) * 100))[0:4] + '%'
)
intLogProgress = int(float(count / len(__index_list) * 10000.0))
QA_util_log_info(
'The {} of Total {}'.format(count,
len(__index_list)),
ui_log=ui_log
)
QA_util_log_info(
strLogProgress,
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intLogProgress
)
count = count + 1
if len(err) < 1:
QA_util_log_info('SUCCESS', ui_log=ui_log)
else:
QA_util_log_info(' ERROR CODE \n ', ui_log=ui_log)
QA_util_log_info(err, ui_log=ui_log) | def QA_SU_save_index_min(client=DATABASE, ui_log=None, ui_progress=None):
"""save index_min
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
__index_list = QA_fetch_get_stock_list('index')
coll = client.index_min
coll.create_index(
[
('code',
pymongo.ASCENDING),
('time_stamp',
pymongo.ASCENDING),
('date_stamp',
pymongo.ASCENDING)
]
)
err = []
def __saving_work(code, coll):
QA_util_log_info(
'##JOB05 Now Saving Index_MIN ==== {}'.format(str(code)),
ui_log=ui_log
)
try:
for type in ['1min', '5min', '15min', '30min', '60min']:
ref_ = coll.find({'code': str(code)[0:6], 'type': type})
end_time = str(now_time())[0:19]
if ref_.count() > 0:
start_time = ref_[ref_.count() - 1]['datetime']
QA_util_log_info(
'##JOB05.{} Now Saving {} from {} to {} =={} '.format(
['1min',
'5min',
'15min',
'30min',
'60min'].index(type),
str(code),
start_time,
end_time,
type
),
ui_log=ui_log
)
if start_time != end_time:
__data = QA_fetch_get_index_min(
str(code),
start_time,
end_time,
type
)
if len(__data) > 1:
coll.insert_many(
QA_util_to_json_from_pandas(__data[1::])
)
else:
start_time = '2015-01-01'
QA_util_log_info(
'##JOB05.{} Now Saving {} from {} to {} =={} '.format(
['1min',
'5min',
'15min',
'30min',
'60min'].index(type),
str(code),
start_time,
end_time,
type
),
ui_log=ui_log
)
if start_time != end_time:
__data = QA_fetch_get_index_min(
str(code),
start_time,
end_time,
type
)
if len(__data) > 1:
coll.insert_many(
QA_util_to_json_from_pandas(__data)
)
except:
err.append(code)
executor = ThreadPoolExecutor(max_workers=4)
res = {
executor.submit(__saving_work,
__index_list.index[i_][0],
coll)
for i_ in range(len(__index_list))
} # multi index ./.
count = 0
for i_ in concurrent.futures.as_completed(res):
strLogProgress = 'DOWNLOAD PROGRESS {} '.format(
str(float(count / len(__index_list) * 100))[0:4] + '%'
)
intLogProgress = int(float(count / len(__index_list) * 10000.0))
QA_util_log_info(
'The {} of Total {}'.format(count,
len(__index_list)),
ui_log=ui_log
)
QA_util_log_info(
strLogProgress,
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intLogProgress
)
count = count + 1
if len(err) < 1:
QA_util_log_info('SUCCESS', ui_log=ui_log)
else:
QA_util_log_info(' ERROR CODE \n ', ui_log=ui_log)
QA_util_log_info(err, ui_log=ui_log) | [
"save",
"index_min"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QASU/save_tdx.py#L793-L916 | [
"def",
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"... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_SU_save_stock_list | save stock_list
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE}) | QUANTAXIS/QASU/save_tdx.py | def QA_SU_save_stock_list(client=DATABASE, ui_log=None, ui_progress=None):
"""save stock_list
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
client.drop_collection('stock_list')
coll = client.stock_list
coll.create_index('code')
try:
# 🛠todo 这个应该是第一个任务 JOB01， 先更新股票列表！！
QA_util_log_info(
'##JOB08 Now Saving STOCK_LIST ====',
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=5000
)
stock_list_from_tdx = QA_fetch_get_stock_list()
pandas_data = QA_util_to_json_from_pandas(stock_list_from_tdx)
coll.insert_many(pandas_data)
QA_util_log_info(
"完成股票列表获取",
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=10000
)
except Exception as e:
QA_util_log_info(e, ui_log=ui_log)
print(" Error save_tdx.QA_SU_save_stock_list exception!")
pass | def QA_SU_save_stock_list(client=DATABASE, ui_log=None, ui_progress=None):
"""save stock_list
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
client.drop_collection('stock_list')
coll = client.stock_list
coll.create_index('code')
try:
# 🛠todo 这个应该是第一个任务 JOB01， 先更新股票列表！！
QA_util_log_info(
'##JOB08 Now Saving STOCK_LIST ====',
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=5000
)
stock_list_from_tdx = QA_fetch_get_stock_list()
pandas_data = QA_util_to_json_from_pandas(stock_list_from_tdx)
coll.insert_many(pandas_data)
QA_util_log_info(
"完成股票列表获取",
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=10000
)
except Exception as e:
QA_util_log_info(e, ui_log=ui_log)
print(" Error save_tdx.QA_SU_save_stock_list exception!")
pass | [
"save",
"stock_list"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QASU/save_tdx.py#L1140-L1171 | [
"def",
"QA_SU_save_stock_list",
"(",
"client",
"=",
"DATABASE",
",",
"ui_log",
"=",
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",",
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"=",
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")",
":",
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"=",
"client",
".",
"stock_list",
"coll",
".",
"create_... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_SU_save_etf_list | save etf_list
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE}) | QUANTAXIS/QASU/save_tdx.py | def QA_SU_save_etf_list(client=DATABASE, ui_log=None, ui_progress=None):
"""save etf_list
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
try:
QA_util_log_info(
'##JOB16 Now Saving ETF_LIST ====',
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=5000
)
etf_list_from_tdx = QA_fetch_get_stock_list(type_="etf")
pandas_data = QA_util_to_json_from_pandas(etf_list_from_tdx)
if len(pandas_data) > 0:
# 获取到数据后才进行drop collection 操作
client.drop_collection('etf_list')
coll = client.etf_list
coll.create_index('code')
coll.insert_many(pandas_data)
QA_util_log_info(
"完成ETF列表获取",
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=10000
)
except Exception as e:
QA_util_log_info(e, ui_log=ui_log)
print(" Error save_tdx.QA_SU_save_etf_list exception!")
pass | def QA_SU_save_etf_list(client=DATABASE, ui_log=None, ui_progress=None):
"""save etf_list
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
try:
QA_util_log_info(
'##JOB16 Now Saving ETF_LIST ====',
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=5000
)
etf_list_from_tdx = QA_fetch_get_stock_list(type_="etf")
pandas_data = QA_util_to_json_from_pandas(etf_list_from_tdx)
if len(pandas_data) > 0:
# 获取到数据后才进行drop collection 操作
client.drop_collection('etf_list')
coll = client.etf_list
coll.create_index('code')
coll.insert_many(pandas_data)
QA_util_log_info(
"完成ETF列表获取",
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=10000
)
except Exception as e:
QA_util_log_info(e, ui_log=ui_log)
print(" Error save_tdx.QA_SU_save_etf_list exception!")
pass | [
"save",
"etf_list"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QASU/save_tdx.py#L1174-L1205 | [
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",",
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"ui_progress",
"=",... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_SU_save_stock_block | save stock_block
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE}) | QUANTAXIS/QASU/save_tdx.py | def QA_SU_save_stock_block(client=DATABASE, ui_log=None, ui_progress=None):
"""save stock_block
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
client.drop_collection('stock_block')
coll = client.stock_block
coll.create_index('code')
try:
QA_util_log_info(
'##JOB09 Now Saving STOCK_BlOCK ====',
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=5000
)
coll.insert_many(
QA_util_to_json_from_pandas(QA_fetch_get_stock_block('tdx'))
)
QA_util_log_info(
'tdx Block ====',
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=5000
)
# 🛠todo fixhere here 获取同花顺板块， 还是调用tdx的
coll.insert_many(
QA_util_to_json_from_pandas(QA_fetch_get_stock_block('ths'))
)
QA_util_log_info(
'ths Block ====',
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=8000
)
QA_util_log_info(
'完成股票板块获取=',
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=10000
)
except Exception as e:
QA_util_log_info(e, ui_log=ui_log)
print(" Error save_tdx.QA_SU_save_stock_block exception!")
pass | def QA_SU_save_stock_block(client=DATABASE, ui_log=None, ui_progress=None):
"""save stock_block
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
client.drop_collection('stock_block')
coll = client.stock_block
coll.create_index('code')
try:
QA_util_log_info(
'##JOB09 Now Saving STOCK_BlOCK ====',
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=5000
)
coll.insert_many(
QA_util_to_json_from_pandas(QA_fetch_get_stock_block('tdx'))
)
QA_util_log_info(
'tdx Block ====',
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=5000
)
# 🛠todo fixhere here 获取同花顺板块， 还是调用tdx的
coll.insert_many(
QA_util_to_json_from_pandas(QA_fetch_get_stock_block('ths'))
)
QA_util_log_info(
'ths Block ====',
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=8000
)
QA_util_log_info(
'完成股票板块获取=',
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=10000
)
except Exception as e:
QA_util_log_info(e, ui_log=ui_log)
print(" Error save_tdx.QA_SU_save_stock_block exception!")
pass | [
"save",
"stock_block"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QASU/save_tdx.py#L1208-L1257 | [
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"=",
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".",
"stock_block",
"coll",
".",
"crea... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_SU_save_stock_info | save stock_info
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE}) | QUANTAXIS/QASU/save_tdx.py | def QA_SU_save_stock_info(client=DATABASE, ui_log=None, ui_progress=None):
"""save stock_info
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
client.drop_collection('stock_info')
stock_list = QA_fetch_get_stock_list().code.unique().tolist()
coll = client.stock_info
coll.create_index('code')
err = []
def __saving_work(code, coll):
QA_util_log_info(
'##JOB10 Now Saving STOCK INFO ==== {}'.format(str(code)),
ui_log=ui_log
)
try:
coll.insert_many(
QA_util_to_json_from_pandas(QA_fetch_get_stock_info(str(code)))
)
except:
err.append(str(code))
for i_ in range(len(stock_list)):
# __saving_work('000001')
strLogProgress = 'DOWNLOAD PROGRESS {} '.format(
str(float(i_ / len(stock_list) * 100))[0:4] + '%'
)
intLogProgress = int(float(i_ / len(stock_list) * 10000.0))
QA_util_log_info('The {} of Total {}'.format(i_, len(stock_list)))
QA_util_log_info(
strLogProgress,
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intLogProgress
)
__saving_work(stock_list[i_], coll)
if len(err) < 1:
QA_util_log_info('SUCCESS', ui_log=ui_log)
else:
QA_util_log_info(' ERROR CODE \n ', ui_log=ui_log)
QA_util_log_info(err, ui_log=ui_log) | def QA_SU_save_stock_info(client=DATABASE, ui_log=None, ui_progress=None):
"""save stock_info
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
client.drop_collection('stock_info')
stock_list = QA_fetch_get_stock_list().code.unique().tolist()
coll = client.stock_info
coll.create_index('code')
err = []
def __saving_work(code, coll):
QA_util_log_info(
'##JOB10 Now Saving STOCK INFO ==== {}'.format(str(code)),
ui_log=ui_log
)
try:
coll.insert_many(
QA_util_to_json_from_pandas(QA_fetch_get_stock_info(str(code)))
)
except:
err.append(str(code))
for i_ in range(len(stock_list)):
# __saving_work('000001')
strLogProgress = 'DOWNLOAD PROGRESS {} '.format(
str(float(i_ / len(stock_list) * 100))[0:4] + '%'
)
intLogProgress = int(float(i_ / len(stock_list) * 10000.0))
QA_util_log_info('The {} of Total {}'.format(i_, len(stock_list)))
QA_util_log_info(
strLogProgress,
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intLogProgress
)
__saving_work(stock_list[i_], coll)
if len(err) < 1:
QA_util_log_info('SUCCESS', ui_log=ui_log)
else:
QA_util_log_info(' ERROR CODE \n ', ui_log=ui_log)
QA_util_log_info(err, ui_log=ui_log) | [
"save",
"stock_info"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QASU/save_tdx.py#L1260-L1306 | [
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"=",
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"(",
")",
".",
"cod... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_SU_save_stock_transaction | save stock_transaction
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE}) | QUANTAXIS/QASU/save_tdx.py | def QA_SU_save_stock_transaction(
client=DATABASE,
ui_log=None,
ui_progress=None
):
"""save stock_transaction
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
stock_list = QA_fetch_get_stock_list().code.unique().tolist()
coll = client.stock_transaction
coll.create_index('code')
err = []
def __saving_work(code):
QA_util_log_info(
'##JOB11 Now Saving STOCK_TRANSACTION ==== {}'.format(str(code)),
ui_log=ui_log
)
try:
coll.insert_many(
QA_util_to_json_from_pandas(
# 🛠todo str(stock_list[code]) 参数不对？
QA_fetch_get_stock_transaction(
str(code),
'1990-01-01',
str(now_time())[0:10]
)
)
)
except:
err.append(str(code))
for i_ in range(len(stock_list)):
# __saving_work('000001')
QA_util_log_info(
'The {} of Total {}'.format(i_,
len(stock_list)),
ui_log=ui_log
)
strLogProgress = 'DOWNLOAD PROGRESS {} '.format(
str(float(i_ / len(stock_list) * 100))[0:4] + '%'
)
intLogProgress = int(float(i_ / len(stock_list) * 10000.0))
QA_util_log_info(
strLogProgress,
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intLogProgress
)
__saving_work(stock_list[i_])
if len(err) < 1:
QA_util_log_info('SUCCESS', ui_log=ui_log)
else:
QA_util_log_info(' ERROR CODE \n ', ui_log=ui_log)
QA_util_log_info(err, ui_log=ui_log) | def QA_SU_save_stock_transaction(
client=DATABASE,
ui_log=None,
ui_progress=None
):
"""save stock_transaction
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
stock_list = QA_fetch_get_stock_list().code.unique().tolist()
coll = client.stock_transaction
coll.create_index('code')
err = []
def __saving_work(code):
QA_util_log_info(
'##JOB11 Now Saving STOCK_TRANSACTION ==== {}'.format(str(code)),
ui_log=ui_log
)
try:
coll.insert_many(
QA_util_to_json_from_pandas(
# 🛠todo str(stock_list[code]) 参数不对？
QA_fetch_get_stock_transaction(
str(code),
'1990-01-01',
str(now_time())[0:10]
)
)
)
except:
err.append(str(code))
for i_ in range(len(stock_list)):
# __saving_work('000001')
QA_util_log_info(
'The {} of Total {}'.format(i_,
len(stock_list)),
ui_log=ui_log
)
strLogProgress = 'DOWNLOAD PROGRESS {} '.format(
str(float(i_ / len(stock_list) * 100))[0:4] + '%'
)
intLogProgress = int(float(i_ / len(stock_list) * 10000.0))
QA_util_log_info(
strLogProgress,
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intLogProgress
)
__saving_work(stock_list[i_])
if len(err) < 1:
QA_util_log_info('SUCCESS', ui_log=ui_log)
else:
QA_util_log_info(' ERROR CODE \n ', ui_log=ui_log)
QA_util_log_info(err, ui_log=ui_log) | [
"save",
"stock_transaction"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QASU/save_tdx.py#L1309-L1368 | [
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"(",
")",
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"tolist",
"(",
... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_SU_save_option_commodity_day | :param client:
:return: | QUANTAXIS/QASU/save_tdx.py | def QA_SU_save_option_commodity_day(
client=DATABASE,
ui_log=None,
ui_progress=None
):
'''
:param client:
:return:
'''
_save_option_commodity_cu_day(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
)
_save_option_commodity_m_day(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
)
_save_option_commodity_sr_day(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
)
_save_option_commodity_ru_day(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
)
_save_option_commodity_cf_day(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
)
_save_option_commodity_c_day(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
) | def QA_SU_save_option_commodity_day(
client=DATABASE,
ui_log=None,
ui_progress=None
):
'''
:param client:
:return:
'''
_save_option_commodity_cu_day(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
)
_save_option_commodity_m_day(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
)
_save_option_commodity_sr_day(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
)
_save_option_commodity_ru_day(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
)
_save_option_commodity_cf_day(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
)
_save_option_commodity_c_day(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
) | [
":",
"param",
"client",
":",
":",
"return",
":"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QASU/save_tdx.py#L2292-L2331 | [
"def",
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"(",
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"=",
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",",
"ui_log",
"=",
"ui_log",
",",
"ui_progress",
"=",... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_SU_save_option_commodity_min | :param client:
:return: | QUANTAXIS/QASU/save_tdx.py | def QA_SU_save_option_commodity_min(
client=DATABASE,
ui_log=None,
ui_progress=None
):
'''
:param client:
:return:
'''
# 测试中发现， 一起回去，容易出现错误，每次获取一个品种后 ，更换服务ip继续获取 ？
_save_option_commodity_cu_min(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
)
_save_option_commodity_sr_min(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
)
_save_option_commodity_m_min(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
)
_save_option_commodity_ru_min(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
)
_save_option_commodity_cf_min(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
)
_save_option_commodity_c_min(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
) | def QA_SU_save_option_commodity_min(
client=DATABASE,
ui_log=None,
ui_progress=None
):
'''
:param client:
:return:
'''
# 测试中发现， 一起回去，容易出现错误，每次获取一个品种后 ，更换服务ip继续获取 ？
_save_option_commodity_cu_min(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
)
_save_option_commodity_sr_min(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
)
_save_option_commodity_m_min(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
)
_save_option_commodity_ru_min(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
)
_save_option_commodity_cf_min(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
)
_save_option_commodity_c_min(
client=client,
ui_log=ui_log,
ui_progress=ui_progress
) | [
":",
"param",
"client",
":",
":",
"return",
":"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QASU/save_tdx.py#L3384-L3427 | [
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":",
"# 测试中发现， 一起回去，容易出现错误，每次获取一个品种后 ，更换服务ip继续获取 ？",
"_save_option_commodity_cu_min",
"(",
"client",
"=",
"client",
",",
"ui_log",... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_SU_save_option_min | :param client:
:return: | QUANTAXIS/QASU/save_tdx.py | def QA_SU_save_option_min(client=DATABASE, ui_log=None, ui_progress=None):
'''
:param client:
:return:
'''
option_contract_list = QA_fetch_get_option_contract_time_to_market()
coll_option_min = client.option_day_min
coll_option_min.create_index(
[("code",
pymongo.ASCENDING),
("date_stamp",
pymongo.ASCENDING)]
)
err = []
# 索引 code
err = []
def __saving_work(code, coll):
QA_util_log_info(
'##JOB13 Now Saving Option 50ETF MIN ==== {}'.format(str(code)),
ui_log=ui_log
)
try:
for type in ['1min', '5min', '15min', '30min', '60min']:
ref_ = coll.find({'code': str(code)[0:8], 'type': type})
end_time = str(now_time())[0:19]
if ref_.count() > 0:
start_time = ref_[ref_.count() - 1]['datetime']
QA_util_log_info(
'##JOB13.{} Now Saving Option 50ETF {} from {} to {} =={} '
.format(
['1min',
'5min',
'15min',
'30min',
'60min'].index(type),
str(code),
start_time,
end_time,
type
),
ui_log=ui_log
)
if start_time != end_time:
__data = QA_fetch_get_future_min(
str(code),
start_time,
end_time,
type
)
if len(__data) > 1:
QA_util_log_info(
" 写入 新增历史合约记录数 {} ".format(len(__data))
)
coll.insert_many(
QA_util_to_json_from_pandas(__data[1::])
)
else:
start_time = '2015-01-01'
QA_util_log_info(
'##JOB13.{} Now Option 50ETF {} from {} to {} =={} '
.format(
['1min',
'5min',
'15min',
'30min',
'60min'].index(type),
str(code),
start_time,
end_time,
type
),
ui_log=ui_log
)
if start_time != end_time:
__data = QA_fetch_get_future_min(
str(code),
start_time,
end_time,
type
)
if len(__data) > 1:
QA_util_log_info(
" 写入 新增合约记录数 {} ".format(len(__data))
)
coll.insert_many(
QA_util_to_json_from_pandas(__data)
)
except:
err.append(code)
executor = ThreadPoolExecutor(max_workers=4)
res = {
executor.submit(
__saving_work,
option_contract_list[i_]["code"],
coll_option_min
)
for i_ in range(len(option_contract_list))
} # multi index ./.
count = 0
for i_ in concurrent.futures.as_completed(res):
QA_util_log_info(
'The {} of Total {}'.format(count,
len(option_contract_list)),
ui_log=ui_log
)
strLogProgress = 'DOWNLOAD PROGRESS {} '.format(
str(float(count / len(option_contract_list) * 100))[0:4] + '%'
)
intLogProgress = int(float(count / len(option_contract_list) * 10000.0))
QA_util_log_info(
strLogProgress,
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intLogProgress
)
count = count + 1
if len(err) < 1:
QA_util_log_info('SUCCESS', ui_log=ui_log)
else:
QA_util_log_info(' ERROR CODE \n ', ui_log=ui_log)
QA_util_log_info(err, ui_log=ui_log) | def QA_SU_save_option_min(client=DATABASE, ui_log=None, ui_progress=None):
'''
:param client:
:return:
'''
option_contract_list = QA_fetch_get_option_contract_time_to_market()
coll_option_min = client.option_day_min
coll_option_min.create_index(
[("code",
pymongo.ASCENDING),
("date_stamp",
pymongo.ASCENDING)]
)
err = []
# 索引 code
err = []
def __saving_work(code, coll):
QA_util_log_info(
'##JOB13 Now Saving Option 50ETF MIN ==== {}'.format(str(code)),
ui_log=ui_log
)
try:
for type in ['1min', '5min', '15min', '30min', '60min']:
ref_ = coll.find({'code': str(code)[0:8], 'type': type})
end_time = str(now_time())[0:19]
if ref_.count() > 0:
start_time = ref_[ref_.count() - 1]['datetime']
QA_util_log_info(
'##JOB13.{} Now Saving Option 50ETF {} from {} to {} =={} '
.format(
['1min',
'5min',
'15min',
'30min',
'60min'].index(type),
str(code),
start_time,
end_time,
type
),
ui_log=ui_log
)
if start_time != end_time:
__data = QA_fetch_get_future_min(
str(code),
start_time,
end_time,
type
)
if len(__data) > 1:
QA_util_log_info(
" 写入 新增历史合约记录数 {} ".format(len(__data))
)
coll.insert_many(
QA_util_to_json_from_pandas(__data[1::])
)
else:
start_time = '2015-01-01'
QA_util_log_info(
'##JOB13.{} Now Option 50ETF {} from {} to {} =={} '
.format(
['1min',
'5min',
'15min',
'30min',
'60min'].index(type),
str(code),
start_time,
end_time,
type
),
ui_log=ui_log
)
if start_time != end_time:
__data = QA_fetch_get_future_min(
str(code),
start_time,
end_time,
type
)
if len(__data) > 1:
QA_util_log_info(
" 写入 新增合约记录数 {} ".format(len(__data))
)
coll.insert_many(
QA_util_to_json_from_pandas(__data)
)
except:
err.append(code)
executor = ThreadPoolExecutor(max_workers=4)
res = {
executor.submit(
__saving_work,
option_contract_list[i_]["code"],
coll_option_min
)
for i_ in range(len(option_contract_list))
} # multi index ./.
count = 0
for i_ in concurrent.futures.as_completed(res):
QA_util_log_info(
'The {} of Total {}'.format(count,
len(option_contract_list)),
ui_log=ui_log
)
strLogProgress = 'DOWNLOAD PROGRESS {} '.format(
str(float(count / len(option_contract_list) * 100))[0:4] + '%'
)
intLogProgress = int(float(count / len(option_contract_list) * 10000.0))
QA_util_log_info(
strLogProgress,
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intLogProgress
)
count = count + 1
if len(err) < 1:
QA_util_log_info('SUCCESS', ui_log=ui_log)
else:
QA_util_log_info(' ERROR CODE \n ', ui_log=ui_log)
QA_util_log_info(err, ui_log=ui_log) | [
":",
"param",
"client",
":",
":",
"return",
":"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QASU/save_tdx.py#L3430-L3563 | [
"def",
"QA_SU_save_option_min",
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"(",
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"optio... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_SU_save_option_day | :param client:
:return: | QUANTAXIS/QASU/save_tdx.py | def QA_SU_save_option_day(client=DATABASE, ui_log=None, ui_progress=None):
'''
:param client:
:return:
'''
option_contract_list = QA_fetch_get_option_50etf_contract_time_to_market()
coll_option_day = client.option_day
coll_option_day.create_index(
[("code",
pymongo.ASCENDING),
("date_stamp",
pymongo.ASCENDING)]
)
err = []
# 索引 code
def __saving_work(code, coll_option_day):
try:
QA_util_log_info(
'##JOB12 Now Saving OPTION_DAY==== {}'.format(str(code)),
ui_log=ui_log
)
# 首选查找数据库 是否 有 这个代码的数据
# 期权代码 从 10000001 开始编码 10001228
ref = coll_option_day.find({'code': str(code)[0:8]})
end_date = str(now_time())[0:10]
# 当前数据库已经包含了这个代码的数据， 继续增量更新
# 加入这个判断的原因是因为如果是刚上市的 数据库会没有数据 所以会有负索引问题出现
if ref.count() > 0:
# 接着上次获取的日期继续更新
start_date = ref[ref.count() - 1]['date']
QA_util_log_info(
' 上次获取期权日线数据的最后日期是 {}'.format(start_date),
ui_log=ui_log
)
QA_util_log_info(
'UPDATE_OPTION_DAY \n 从上一次下载数据开始继续 Trying update {} from {} to {}'
.format(code,
start_date,
end_date),
ui_log=ui_log
)
if start_date != end_date:
start_date0 = QA_util_get_next_day(start_date)
df0 = QA_fetch_get_option_day(
code=code,
start_date=start_date0,
end_date=end_date,
frequence='day',
ip=None,
port=None
)
retCount = df0.iloc[:, 0].size
QA_util_log_info(
"日期从开始{}-结束{} , 合约代码{} , 返回了{}条记录 , 准备写入数据库".format(
start_date0,
end_date,
code,
retCount
),
ui_log=ui_log
)
coll_option_day.insert_many(
QA_util_to_json_from_pandas(df0)
)
else:
QA_util_log_info(
"^已经获取过这天的数据了^ {}".format(start_date),
ui_log=ui_log
)
else:
start_date = '1990-01-01'
QA_util_log_info(
'UPDATE_OPTION_DAY \n 从新开始下载数据 Trying update {} from {} to {}'
.format(code,
start_date,
end_date),
ui_log=ui_log
)
if start_date != end_date:
df0 = QA_fetch_get_option_day(
code=code,
start_date=start_date,
end_date=end_date,
frequence='day',
ip=None,
port=None
)
retCount = df0.iloc[:, 0].size
QA_util_log_info(
"日期从开始{}-结束{} , 合约代码{} , 获取了{}条记录 , 准备写入数据库^_^ ".format(
start_date,
end_date,
code,
retCount
),
ui_log=ui_log
)
coll_option_day.insert_many(
QA_util_to_json_from_pandas(df0)
)
else:
QA_util_log_info(
"*已经获取过这天的数据了* {}".format(start_date),
ui_log=ui_log
)
except Exception as error0:
print(error0)
err.append(str(code))
for item in range(len(option_contract_list)):
QA_util_log_info(
'The {} of Total {}'.format(item,
len(option_contract_list)),
ui_log=ui_log
)
strLogProgress = 'DOWNLOAD PROGRESS {} '.format(
str(float(item / len(option_contract_list) * 100))[0:4] + '%'
)
intLogProgress = int(float(item / len(option_contract_list) * 10000.0))
QA_util_log_info(
strLogProgress,
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intLogProgress
)
__saving_work(option_contract_list[item].code, coll_option_day)
if len(err) < 1:
QA_util_log_info('SUCCESS save option day ^_^ ', ui_log=ui_log)
else:
QA_util_log_info(' ERROR CODE \n ', ui_log=ui_log)
QA_util_log_info(err, ui_log=ui_log) | def QA_SU_save_option_day(client=DATABASE, ui_log=None, ui_progress=None):
'''
:param client:
:return:
'''
option_contract_list = QA_fetch_get_option_50etf_contract_time_to_market()
coll_option_day = client.option_day
coll_option_day.create_index(
[("code",
pymongo.ASCENDING),
("date_stamp",
pymongo.ASCENDING)]
)
err = []
# 索引 code
def __saving_work(code, coll_option_day):
try:
QA_util_log_info(
'##JOB12 Now Saving OPTION_DAY==== {}'.format(str(code)),
ui_log=ui_log
)
# 首选查找数据库 是否 有 这个代码的数据
# 期权代码 从 10000001 开始编码 10001228
ref = coll_option_day.find({'code': str(code)[0:8]})
end_date = str(now_time())[0:10]
# 当前数据库已经包含了这个代码的数据， 继续增量更新
# 加入这个判断的原因是因为如果是刚上市的 数据库会没有数据 所以会有负索引问题出现
if ref.count() > 0:
# 接着上次获取的日期继续更新
start_date = ref[ref.count() - 1]['date']
QA_util_log_info(
' 上次获取期权日线数据的最后日期是 {}'.format(start_date),
ui_log=ui_log
)
QA_util_log_info(
'UPDATE_OPTION_DAY \n 从上一次下载数据开始继续 Trying update {} from {} to {}'
.format(code,
start_date,
end_date),
ui_log=ui_log
)
if start_date != end_date:
start_date0 = QA_util_get_next_day(start_date)
df0 = QA_fetch_get_option_day(
code=code,
start_date=start_date0,
end_date=end_date,
frequence='day',
ip=None,
port=None
)
retCount = df0.iloc[:, 0].size
QA_util_log_info(
"日期从开始{}-结束{} , 合约代码{} , 返回了{}条记录 , 准备写入数据库".format(
start_date0,
end_date,
code,
retCount
),
ui_log=ui_log
)
coll_option_day.insert_many(
QA_util_to_json_from_pandas(df0)
)
else:
QA_util_log_info(
"^已经获取过这天的数据了^ {}".format(start_date),
ui_log=ui_log
)
else:
start_date = '1990-01-01'
QA_util_log_info(
'UPDATE_OPTION_DAY \n 从新开始下载数据 Trying update {} from {} to {}'
.format(code,
start_date,
end_date),
ui_log=ui_log
)
if start_date != end_date:
df0 = QA_fetch_get_option_day(
code=code,
start_date=start_date,
end_date=end_date,
frequence='day',
ip=None,
port=None
)
retCount = df0.iloc[:, 0].size
QA_util_log_info(
"日期从开始{}-结束{} , 合约代码{} , 获取了{}条记录 , 准备写入数据库^_^ ".format(
start_date,
end_date,
code,
retCount
),
ui_log=ui_log
)
coll_option_day.insert_many(
QA_util_to_json_from_pandas(df0)
)
else:
QA_util_log_info(
"*已经获取过这天的数据了* {}".format(start_date),
ui_log=ui_log
)
except Exception as error0:
print(error0)
err.append(str(code))
for item in range(len(option_contract_list)):
QA_util_log_info(
'The {} of Total {}'.format(item,
len(option_contract_list)),
ui_log=ui_log
)
strLogProgress = 'DOWNLOAD PROGRESS {} '.format(
str(float(item / len(option_contract_list) * 100))[0:4] + '%'
)
intLogProgress = int(float(item / len(option_contract_list) * 10000.0))
QA_util_log_info(
strLogProgress,
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intLogProgress
)
__saving_work(option_contract_list[item].code, coll_option_day)
if len(err) < 1:
QA_util_log_info('SUCCESS save option day ^_^ ', ui_log=ui_log)
else:
QA_util_log_info(' ERROR CODE \n ', ui_log=ui_log)
QA_util_log_info(err, ui_log=ui_log) | [
":",
"param",
"client",
":",
":",
"return",
":"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QASU/save_tdx.py#L3566-L3710 | [
"def",
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"(",
")",
"coll_option_day",
"=",
"client",
".",
... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_SU_save_future_day | save future_day
保存日线数据
:param client:
:param ui_log: 给GUI qt 界面使用
:param ui_progress: 给GUI qt 界面使用
:param ui_progress_int_value: 给GUI qt 界面使用
:return: | QUANTAXIS/QASU/save_tdx.py | def QA_SU_save_future_day(client=DATABASE, ui_log=None, ui_progress=None):
'''
save future_day
保存日线数据
:param client:
:param ui_log: 给GUI qt 界面使用
:param ui_progress: 给GUI qt 界面使用
:param ui_progress_int_value: 给GUI qt 界面使用
:return:
'''
future_list = [
item for item in QA_fetch_get_future_list().code.unique().tolist()
if str(item)[-2:] in ['L8',
'L9']
]
coll_future_day = client.future_day
coll_future_day.create_index(
[("code",
pymongo.ASCENDING),
("date_stamp",
pymongo.ASCENDING)]
)
err = []
def __saving_work(code, coll_future_day):
try:
QA_util_log_info(
'##JOB12 Now Saving Future_DAY==== {}'.format(str(code)),
ui_log
)
# 首选查找数据库 是否 有 这个代码的数据
ref = coll_future_day.find({'code': str(code)[0:4]})
end_date = str(now_time())[0:10]
# 当前数据库已经包含了这个代码的数据， 继续增量更新
# 加入这个判断的原因是因为如果股票是刚上市的 数据库会没有数据 所以会有负索引问题出现
if ref.count() > 0:
# 接着上次获取的日期继续更新
start_date = ref[ref.count() - 1]['date']
QA_util_log_info(
'UPDATE_Future_DAY \n Trying updating {} from {} to {}'
.format(code,
start_date,
end_date),
ui_log
)
if start_date != end_date:
coll_future_day.insert_many(
QA_util_to_json_from_pandas(
QA_fetch_get_future_day(
str(code),
QA_util_get_next_day(start_date),
end_date
)
)
)
# 当前数据库中没有这个代码的股票数据， 从1990-01-01 开始下载所有的数据
else:
start_date = '2001-01-01'
QA_util_log_info(
'UPDATE_Future_DAY \n Trying updating {} from {} to {}'
.format(code,
start_date,
end_date),
ui_log
)
if start_date != end_date:
coll_future_day.insert_many(
QA_util_to_json_from_pandas(
QA_fetch_get_future_day(
str(code),
start_date,
end_date
)
)
)
except Exception as error0:
print(error0)
err.append(str(code))
for item in range(len(future_list)):
QA_util_log_info('The {} of Total {}'.format(item, len(future_list)))
strProgressToLog = 'DOWNLOAD PROGRESS {} {}'.format(
str(float(item / len(future_list) * 100))[0:4] + '%',
ui_log
)
intProgressToLog = int(float(item / len(future_list) * 100))
QA_util_log_info(
strProgressToLog,
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intProgressToLog
)
__saving_work(future_list[item], coll_future_day)
if len(err) < 1:
QA_util_log_info('SUCCESS save future day ^_^', ui_log)
else:
QA_util_log_info(' ERROR CODE \n ', ui_log)
QA_util_log_info(err, ui_log) | def QA_SU_save_future_day(client=DATABASE, ui_log=None, ui_progress=None):
'''
save future_day
保存日线数据
:param client:
:param ui_log: 给GUI qt 界面使用
:param ui_progress: 给GUI qt 界面使用
:param ui_progress_int_value: 给GUI qt 界面使用
:return:
'''
future_list = [
item for item in QA_fetch_get_future_list().code.unique().tolist()
if str(item)[-2:] in ['L8',
'L9']
]
coll_future_day = client.future_day
coll_future_day.create_index(
[("code",
pymongo.ASCENDING),
("date_stamp",
pymongo.ASCENDING)]
)
err = []
def __saving_work(code, coll_future_day):
try:
QA_util_log_info(
'##JOB12 Now Saving Future_DAY==== {}'.format(str(code)),
ui_log
)
# 首选查找数据库 是否 有 这个代码的数据
ref = coll_future_day.find({'code': str(code)[0:4]})
end_date = str(now_time())[0:10]
# 当前数据库已经包含了这个代码的数据， 继续增量更新
# 加入这个判断的原因是因为如果股票是刚上市的 数据库会没有数据 所以会有负索引问题出现
if ref.count() > 0:
# 接着上次获取的日期继续更新
start_date = ref[ref.count() - 1]['date']
QA_util_log_info(
'UPDATE_Future_DAY \n Trying updating {} from {} to {}'
.format(code,
start_date,
end_date),
ui_log
)
if start_date != end_date:
coll_future_day.insert_many(
QA_util_to_json_from_pandas(
QA_fetch_get_future_day(
str(code),
QA_util_get_next_day(start_date),
end_date
)
)
)
# 当前数据库中没有这个代码的股票数据， 从1990-01-01 开始下载所有的数据
else:
start_date = '2001-01-01'
QA_util_log_info(
'UPDATE_Future_DAY \n Trying updating {} from {} to {}'
.format(code,
start_date,
end_date),
ui_log
)
if start_date != end_date:
coll_future_day.insert_many(
QA_util_to_json_from_pandas(
QA_fetch_get_future_day(
str(code),
start_date,
end_date
)
)
)
except Exception as error0:
print(error0)
err.append(str(code))
for item in range(len(future_list)):
QA_util_log_info('The {} of Total {}'.format(item, len(future_list)))
strProgressToLog = 'DOWNLOAD PROGRESS {} {}'.format(
str(float(item / len(future_list) * 100))[0:4] + '%',
ui_log
)
intProgressToLog = int(float(item / len(future_list) * 100))
QA_util_log_info(
strProgressToLog,
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intProgressToLog
)
__saving_work(future_list[item], coll_future_day)
if len(err) < 1:
QA_util_log_info('SUCCESS save future day ^_^', ui_log)
else:
QA_util_log_info(' ERROR CODE \n ', ui_log)
QA_util_log_info(err, ui_log) | [
"save",
"future_day",
"保存日线数据",
":",
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"client",
":",
":",
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] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QASU/save_tdx.py#L3804-L3909 | [
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"... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_SU_save_future_min | save future_min
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE}) | QUANTAXIS/QASU/save_tdx.py | def QA_SU_save_future_min(client=DATABASE, ui_log=None, ui_progress=None):
"""save future_min
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
future_list = [
item for item in QA_fetch_get_future_list().code.unique().tolist()
if str(item)[-2:] in ['L8',
'L9']
]
coll = client.future_min
coll.create_index(
[
('code',
pymongo.ASCENDING),
('time_stamp',
pymongo.ASCENDING),
('date_stamp',
pymongo.ASCENDING)
]
)
err = []
def __saving_work(code, coll):
QA_util_log_info(
'##JOB13 Now Saving Future_MIN ==== {}'.format(str(code)),
ui_log=ui_log
)
try:
for type in ['1min', '5min', '15min', '30min', '60min']:
ref_ = coll.find({'code': str(code)[0:6], 'type': type})
end_time = str(now_time())[0:19]
if ref_.count() > 0:
start_time = ref_[ref_.count() - 1]['datetime']
QA_util_log_info(
'##JOB13.{} Now Saving Future {} from {} to {} =={} '
.format(
['1min',
'5min',
'15min',
'30min',
'60min'].index(type),
str(code),
start_time,
end_time,
type
),
ui_log=ui_log
)
if start_time != end_time:
__data = QA_fetch_get_future_min(
str(code),
start_time,
end_time,
type
)
if len(__data) > 1:
coll.insert_many(
QA_util_to_json_from_pandas(__data[1::])
)
else:
start_time = '2015-01-01'
QA_util_log_info(
'##JOB13.{} Now Saving Future {} from {} to {} =={} '
.format(
['1min',
'5min',
'15min',
'30min',
'60min'].index(type),
str(code),
start_time,
end_time,
type
),
ui_log=ui_log
)
if start_time != end_time:
__data = QA_fetch_get_future_min(
str(code),
start_time,
end_time,
type
)
if len(__data) > 1:
coll.insert_many(
QA_util_to_json_from_pandas(__data)
)
except:
err.append(code)
executor = ThreadPoolExecutor(max_workers=4)
res = {
executor.submit(__saving_work,
future_list[i_],
coll)
for i_ in range(len(future_list))
} # multi index ./.
count = 0
for i_ in concurrent.futures.as_completed(res):
QA_util_log_info(
'The {} of Total {}'.format(count,
len(future_list)),
ui_log=ui_log
)
strLogProgress = 'DOWNLOAD PROGRESS {} '.format(
str(float(count / len(future_list) * 100))[0:4] + '%'
)
intLogProgress = int(float(count / len(future_list) * 10000.0))
QA_util_log_info(
strLogProgress,
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intLogProgress
)
count = count + 1
if len(err) < 1:
QA_util_log_info('SUCCESS', ui_log=ui_log)
else:
QA_util_log_info(' ERROR CODE \n ', ui_log=ui_log)
QA_util_log_info(err, ui_log=ui_log) | def QA_SU_save_future_min(client=DATABASE, ui_log=None, ui_progress=None):
"""save future_min
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
future_list = [
item for item in QA_fetch_get_future_list().code.unique().tolist()
if str(item)[-2:] in ['L8',
'L9']
]
coll = client.future_min
coll.create_index(
[
('code',
pymongo.ASCENDING),
('time_stamp',
pymongo.ASCENDING),
('date_stamp',
pymongo.ASCENDING)
]
)
err = []
def __saving_work(code, coll):
QA_util_log_info(
'##JOB13 Now Saving Future_MIN ==== {}'.format(str(code)),
ui_log=ui_log
)
try:
for type in ['1min', '5min', '15min', '30min', '60min']:
ref_ = coll.find({'code': str(code)[0:6], 'type': type})
end_time = str(now_time())[0:19]
if ref_.count() > 0:
start_time = ref_[ref_.count() - 1]['datetime']
QA_util_log_info(
'##JOB13.{} Now Saving Future {} from {} to {} =={} '
.format(
['1min',
'5min',
'15min',
'30min',
'60min'].index(type),
str(code),
start_time,
end_time,
type
),
ui_log=ui_log
)
if start_time != end_time:
__data = QA_fetch_get_future_min(
str(code),
start_time,
end_time,
type
)
if len(__data) > 1:
coll.insert_many(
QA_util_to_json_from_pandas(__data[1::])
)
else:
start_time = '2015-01-01'
QA_util_log_info(
'##JOB13.{} Now Saving Future {} from {} to {} =={} '
.format(
['1min',
'5min',
'15min',
'30min',
'60min'].index(type),
str(code),
start_time,
end_time,
type
),
ui_log=ui_log
)
if start_time != end_time:
__data = QA_fetch_get_future_min(
str(code),
start_time,
end_time,
type
)
if len(__data) > 1:
coll.insert_many(
QA_util_to_json_from_pandas(__data)
)
except:
err.append(code)
executor = ThreadPoolExecutor(max_workers=4)
res = {
executor.submit(__saving_work,
future_list[i_],
coll)
for i_ in range(len(future_list))
} # multi index ./.
count = 0
for i_ in concurrent.futures.as_completed(res):
QA_util_log_info(
'The {} of Total {}'.format(count,
len(future_list)),
ui_log=ui_log
)
strLogProgress = 'DOWNLOAD PROGRESS {} '.format(
str(float(count / len(future_list) * 100))[0:4] + '%'
)
intLogProgress = int(float(count / len(future_list) * 10000.0))
QA_util_log_info(
strLogProgress,
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intLogProgress
)
count = count + 1
if len(err) < 1:
QA_util_log_info('SUCCESS', ui_log=ui_log)
else:
QA_util_log_info(' ERROR CODE \n ', ui_log=ui_log)
QA_util_log_info(err, ui_log=ui_log) | [
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"future_min"
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"... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | CLI.do_shell | run a shell commad | QUANTAXIS/QACmd/__init__.py | def do_shell(self, arg):
"run a shell commad"
print(">", arg)
sub_cmd = subprocess.Popen(arg, shell=True, stdout=subprocess.PIPE)
print(sub_cmd.communicate()[0]) | def do_shell(self, arg):
"run a shell commad"
print(">", arg)
sub_cmd = subprocess.Popen(arg, shell=True, stdout=subprocess.PIPE)
print(sub_cmd.communicate()[0]) | [
"run",
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"commad"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QACmd/__init__.py#L84-L88 | [
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train | QA_fetch_stock_min_adv | '获取股票分钟线'
:param code: 字符串str eg 600085
:param start: 字符串str 开始日期 eg 2011-01-01
:param end: 字符串str 结束日期 eg 2011-05-01
:param frequence: 字符串str 分钟线的类型 支持 1min 1m 5min 5m 15min 15m 30min 30m 60min 60m 类型
:param if_drop_index: Ture False ， dataframe drop index or not
:param collections: mongodb 数据库
:return: QA_DataStruct_Stock_min 类型 | QUANTAXIS/QAFetch/QAQuery_Advance.py | def QA_fetch_stock_min_adv(
code,
start, end=None,
frequence='1min',
if_drop_index=True,
# 🛠 todo collections 参数没有用到， 且数据库是固定的， 这个变量后期去掉
collections=DATABASE.stock_min):
'''
'获取股票分钟线'
:param code: 字符串str eg 600085
:param start: 字符串str 开始日期 eg 2011-01-01
:param end: 字符串str 结束日期 eg 2011-05-01
:param frequence: 字符串str 分钟线的类型 支持 1min 1m 5min 5m 15min 15m 30min 30m 60min 60m 类型
:param if_drop_index: Ture False ， dataframe drop index or not
:param collections: mongodb 数据库
:return: QA_DataStruct_Stock_min 类型
'''
if frequence in ['1min', '1m']:
frequence = '1min'
elif frequence in ['5min', '5m']:
frequence = '5min'
elif frequence in ['15min', '15m']:
frequence = '15min'
elif frequence in ['30min', '30m']:
frequence = '30min'
elif frequence in ['60min', '60m']:
frequence = '60min'
else:
print("QA Error QA_fetch_stock_min_adv parameter frequence=%s is none of 1min 1m 5min 5m 15min 15m 30min 30m 60min 60m" % frequence)
return None
# __data = [] 未使用
end = start if end is None else end
if len(start) == 10:
start = '{} 09:30:00'.format(start)
if len(end) == 10:
end = '{} 15:00:00'.format(end)
if start == end:
# 🛠 todo 如果相等，根据 frequence 获取开始时间的 时间段 QA_fetch_stock_min， 不支持start end是相等的
print("QA Error QA_fetch_stock_min_adv parameter code=%s , start=%s, end=%s is equal, should have time span! " % (
code, start, end))
return None
# 🛠 todo 报告错误 如果开始时间 在 结束时间之后
res = QA_fetch_stock_min(
code, start, end, format='pd', frequence=frequence)
if res is None:
print("QA Error QA_fetch_stock_min_adv parameter code=%s , start=%s, end=%s frequence=%s call QA_fetch_stock_min return None" % (
code, start, end, frequence))
return None
else:
res_set_index = res.set_index(['datetime', 'code'], drop=if_drop_index)
# if res_set_index is None:
# print("QA Error QA_fetch_stock_min_adv set index 'datetime, code' return None")
# return None
return QA_DataStruct_Stock_min(res_set_index) | def QA_fetch_stock_min_adv(
code,
start, end=None,
frequence='1min',
if_drop_index=True,
# 🛠 todo collections 参数没有用到， 且数据库是固定的， 这个变量后期去掉
collections=DATABASE.stock_min):
'''
'获取股票分钟线'
:param code: 字符串str eg 600085
:param start: 字符串str 开始日期 eg 2011-01-01
:param end: 字符串str 结束日期 eg 2011-05-01
:param frequence: 字符串str 分钟线的类型 支持 1min 1m 5min 5m 15min 15m 30min 30m 60min 60m 类型
:param if_drop_index: Ture False ， dataframe drop index or not
:param collections: mongodb 数据库
:return: QA_DataStruct_Stock_min 类型
'''
if frequence in ['1min', '1m']:
frequence = '1min'
elif frequence in ['5min', '5m']:
frequence = '5min'
elif frequence in ['15min', '15m']:
frequence = '15min'
elif frequence in ['30min', '30m']:
frequence = '30min'
elif frequence in ['60min', '60m']:
frequence = '60min'
else:
print("QA Error QA_fetch_stock_min_adv parameter frequence=%s is none of 1min 1m 5min 5m 15min 15m 30min 30m 60min 60m" % frequence)
return None
# __data = [] 未使用
end = start if end is None else end
if len(start) == 10:
start = '{} 09:30:00'.format(start)
if len(end) == 10:
end = '{} 15:00:00'.format(end)
if start == end:
# 🛠 todo 如果相等，根据 frequence 获取开始时间的 时间段 QA_fetch_stock_min， 不支持start end是相等的
print("QA Error QA_fetch_stock_min_adv parameter code=%s , start=%s, end=%s is equal, should have time span! " % (
code, start, end))
return None
# 🛠 todo 报告错误 如果开始时间 在 结束时间之后
res = QA_fetch_stock_min(
code, start, end, format='pd', frequence=frequence)
if res is None:
print("QA Error QA_fetch_stock_min_adv parameter code=%s , start=%s, end=%s frequence=%s call QA_fetch_stock_min return None" % (
code, start, end, frequence))
return None
else:
res_set_index = res.set_index(['datetime', 'code'], drop=if_drop_index)
# if res_set_index is None:
# print("QA Error QA_fetch_stock_min_adv set index 'datetime, code' return None")
# return None
return QA_DataStruct_Stock_min(res_set_index) | [
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train | QA_fetch_stock_day_full_adv | '返回全市场某一天的数据'
:param date:
:return: QA_DataStruct_Stock_day类 型数据 | QUANTAXIS/QAFetch/QAQuery_Advance.py | def QA_fetch_stock_day_full_adv(date):
'''
'返回全市场某一天的数据'
:param date:
:return: QA_DataStruct_Stock_day类 型数据
'''
# 🛠 todo 检查日期data参数
res = QA_fetch_stock_full(date, 'pd')
if res is None:
print("QA Error QA_fetch_stock_day_full_adv parameter date=%s call QA_fetch_stock_full return None" % (date))
return None
else:
res_set_index = res.set_index(['date', 'code'])
# if res_set_index is None:
# print("QA Error QA_fetch_stock_day_full set index 'date, code' return None")
return QA_DataStruct_Stock_day(res_set_index) | def QA_fetch_stock_day_full_adv(date):
'''
'返回全市场某一天的数据'
:param date:
:return: QA_DataStruct_Stock_day类 型数据
'''
# 🛠 todo 检查日期data参数
res = QA_fetch_stock_full(date, 'pd')
if res is None:
print("QA Error QA_fetch_stock_day_full_adv parameter date=%s call QA_fetch_stock_full return None" % (date))
return None
else:
res_set_index = res.set_index(['date', 'code'])
# if res_set_index is None:
# print("QA Error QA_fetch_stock_day_full set index 'date, code' return None")
return QA_DataStruct_Stock_day(res_set_index) | [
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train | QA_fetch_index_day_adv | :param code: code: 字符串str eg 600085
:param start: 字符串str 开始日期 eg 2011-01-01
:param end: 字符串str 结束日期 eg 2011-05-01
:param if_drop_index: Ture False ， dataframe drop index or not
:param collections: mongodb 数据库
:return: | QUANTAXIS/QAFetch/QAQuery_Advance.py | def QA_fetch_index_day_adv(
code,
start, end=None,
if_drop_index=True,
# 🛠 todo collections 参数没有用到， 且数据库是固定的， 这个变量后期去掉
collections=DATABASE.index_day):
'''
:param code: code: 字符串str eg 600085
:param start: 字符串str 开始日期 eg 2011-01-01
:param end: 字符串str 结束日期 eg 2011-05-01
:param if_drop_index: Ture False ， dataframe drop index or not
:param collections: mongodb 数据库
:return:
'''
'获取指数日线'
end = start if end is None else end
start = str(start)[0:10]
end = str(end)[0:10]
# 🛠 todo 报告错误 如果开始时间 在 结束时间之后
# 🛠 todo 如果相等
res = QA_fetch_index_day(code, start, end, format='pd')
if res is None:
print("QA Error QA_fetch_index_day_adv parameter code=%s start=%s end=%s call QA_fetch_index_day return None" % (
code, start, end))
return None
else:
res_set_index = res.set_index(['date', 'code'], drop=if_drop_index)
# if res_set_index is None:
# print("QA Error QA_fetch_index_day_adv set index 'date, code' return None")
# return None
return QA_DataStruct_Index_day(res_set_index) | def QA_fetch_index_day_adv(
code,
start, end=None,
if_drop_index=True,
# 🛠 todo collections 参数没有用到， 且数据库是固定的， 这个变量后期去掉
collections=DATABASE.index_day):
'''
:param code: code: 字符串str eg 600085
:param start: 字符串str 开始日期 eg 2011-01-01
:param end: 字符串str 结束日期 eg 2011-05-01
:param if_drop_index: Ture False ， dataframe drop index or not
:param collections: mongodb 数据库
:return:
'''
'获取指数日线'
end = start if end is None else end
start = str(start)[0:10]
end = str(end)[0:10]
# 🛠 todo 报告错误 如果开始时间 在 结束时间之后
# 🛠 todo 如果相等
res = QA_fetch_index_day(code, start, end, format='pd')
if res is None:
print("QA Error QA_fetch_index_day_adv parameter code=%s start=%s end=%s call QA_fetch_index_day return None" % (
code, start, end))
return None
else:
res_set_index = res.set_index(['date', 'code'], drop=if_drop_index)
# if res_set_index is None:
# print("QA Error QA_fetch_index_day_adv set index 'date, code' return None")
# return None
return QA_DataStruct_Index_day(res_set_index) | [
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train | QA_fetch_index_min_adv | '获取股票分钟线'
:param code:
:param start:
:param end:
:param frequence:
:param if_drop_index:
:param collections:
:return: | QUANTAXIS/QAFetch/QAQuery_Advance.py | def QA_fetch_index_min_adv(
code,
start, end=None,
frequence='1min',
if_drop_index=True,
collections=DATABASE.index_min):
'''
'获取股票分钟线'
:param code:
:param start:
:param end:
:param frequence:
:param if_drop_index:
:param collections:
:return:
'''
if frequence in ['1min', '1m']:
frequence = '1min'
elif frequence in ['5min', '5m']:
frequence = '5min'
elif frequence in ['15min', '15m']:
frequence = '15min'
elif frequence in ['30min', '30m']:
frequence = '30min'
elif frequence in ['60min', '60m']:
frequence = '60min'
# __data = [] 没有使用
end = start if end is None else end
if len(start) == 10:
start = '{} 09:30:00'.format(start)
if len(end) == 10:
end = '{} 15:00:00'.format(end)
# 🛠 todo 报告错误 如果开始时间 在 结束时间之后
# if start == end:
# 🛠 todo 如果相等，根据 frequence 获取开始时间的 时间段 QA_fetch_index_min_adv， 不支持start end是相等的
#print("QA Error QA_fetch_index_min_adv parameter code=%s , start=%s, end=%s is equal, should have time span! " % (code, start, end))
# return None
res = QA_fetch_index_min(
code, start, end, format='pd', frequence=frequence)
if res is None:
print("QA Error QA_fetch_index_min_adv parameter code=%s start=%s end=%s frequence=%s call QA_fetch_index_min return None" % (
code, start, end, frequence))
else:
res_reset_index = res.set_index(
['datetime', 'code'], drop=if_drop_index)
# if res_reset_index is None:
# print("QA Error QA_fetch_index_min_adv set index 'date, code' return None")
return QA_DataStruct_Index_min(res_reset_index) | def QA_fetch_index_min_adv(
code,
start, end=None,
frequence='1min',
if_drop_index=True,
collections=DATABASE.index_min):
'''
'获取股票分钟线'
:param code:
:param start:
:param end:
:param frequence:
:param if_drop_index:
:param collections:
:return:
'''
if frequence in ['1min', '1m']:
frequence = '1min'
elif frequence in ['5min', '5m']:
frequence = '5min'
elif frequence in ['15min', '15m']:
frequence = '15min'
elif frequence in ['30min', '30m']:
frequence = '30min'
elif frequence in ['60min', '60m']:
frequence = '60min'
# __data = [] 没有使用
end = start if end is None else end
if len(start) == 10:
start = '{} 09:30:00'.format(start)
if len(end) == 10:
end = '{} 15:00:00'.format(end)
# 🛠 todo 报告错误 如果开始时间 在 结束时间之后
# if start == end:
# 🛠 todo 如果相等，根据 frequence 获取开始时间的 时间段 QA_fetch_index_min_adv， 不支持start end是相等的
#print("QA Error QA_fetch_index_min_adv parameter code=%s , start=%s, end=%s is equal, should have time span! " % (code, start, end))
# return None
res = QA_fetch_index_min(
code, start, end, format='pd', frequence=frequence)
if res is None:
print("QA Error QA_fetch_index_min_adv parameter code=%s start=%s end=%s frequence=%s call QA_fetch_index_min return None" % (
code, start, end, frequence))
else:
res_reset_index = res.set_index(
['datetime', 'code'], drop=if_drop_index)
# if res_reset_index is None:
# print("QA Error QA_fetch_index_min_adv set index 'date, code' return None")
return QA_DataStruct_Index_min(res_reset_index) | [
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"'1min'"... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_fetch_stock_list_adv | '获取股票列表'
:param collections: mongodb 数据库
:return: DataFrame | QUANTAXIS/QAFetch/QAQuery_Advance.py | def QA_fetch_stock_list_adv(collections=DATABASE.stock_list):
'''
'获取股票列表'
:param collections: mongodb 数据库
:return: DataFrame
'''
stock_list_items = QA_fetch_stock_list(collections)
if len(stock_list_items) == 0:
print("QA Error QA_fetch_stock_list_adv call item for item in collections.find() return 0 item, maybe the DATABASE.stock_list is empty!")
return None
return stock_list_items | def QA_fetch_stock_list_adv(collections=DATABASE.stock_list):
'''
'获取股票列表'
:param collections: mongodb 数据库
:return: DataFrame
'''
stock_list_items = QA_fetch_stock_list(collections)
if len(stock_list_items) == 0:
print("QA Error QA_fetch_stock_list_adv call item for item in collections.find() return 0 item, maybe the DATABASE.stock_list is empty!")
return None
return stock_list_items | [
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train | QA_fetch_index_list_adv | '获取股票列表'
:param collections: mongodb 数据库
:return: DataFrame | QUANTAXIS/QAFetch/QAQuery_Advance.py | def QA_fetch_index_list_adv(collections=DATABASE.index_list):
'''
'获取股票列表'
:param collections: mongodb 数据库
:return: DataFrame
'''
index_list_items = QA_fetch_index_list(collections)
if len(index_list_items) == 0:
print("QA Error QA_fetch_index_list_adv call item for item in collections.find() return 0 item, maybe the DATABASE.index_list is empty!")
return None
return index_list_items | def QA_fetch_index_list_adv(collections=DATABASE.index_list):
'''
'获取股票列表'
:param collections: mongodb 数据库
:return: DataFrame
'''
index_list_items = QA_fetch_index_list(collections)
if len(index_list_items) == 0:
print("QA Error QA_fetch_index_list_adv call item for item in collections.find() return 0 item, maybe the DATABASE.index_list is empty!")
return None
return index_list_items | [
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train | QA_fetch_future_day_adv | :param code: code: 字符串str eg 600085
:param start: 字符串str 开始日期 eg 2011-01-01
:param end: 字符串str 结束日期 eg 2011-05-01
:param if_drop_index: Ture False ， dataframe drop index or not
:param collections: mongodb 数据库
:return: | QUANTAXIS/QAFetch/QAQuery_Advance.py | def QA_fetch_future_day_adv(
code,
start, end=None,
if_drop_index=True,
# 🛠 todo collections 参数没有用到， 且数据库是固定的， 这个变量后期去掉
collections=DATABASE.index_day):
'''
:param code: code: 字符串str eg 600085
:param start: 字符串str 开始日期 eg 2011-01-01
:param end: 字符串str 结束日期 eg 2011-05-01
:param if_drop_index: Ture False ， dataframe drop index or not
:param collections: mongodb 数据库
:return:
'''
'获取期货日线'
end = start if end is None else end
start = str(start)[0:10]
end = str(end)[0:10]
# 🛠 todo 报告错误 如果开始时间 在 结束时间之后
# 🛠 todo 如果相等
res = QA_fetch_future_day(code, start, end, format='pd')
if res is None:
print("QA Error QA_fetch_future_day_adv parameter code=%s start=%s end=%s call QA_fetch_future_day return None" % (
code, start, end))
else:
res_set_index = res.set_index(['date', 'code'])
# if res_set_index is None:
# print("QA Error QA_fetch_index_day_adv set index 'date, code' return None")
# return None
return QA_DataStruct_Future_day(res_set_index) | def QA_fetch_future_day_adv(
code,
start, end=None,
if_drop_index=True,
# 🛠 todo collections 参数没有用到， 且数据库是固定的， 这个变量后期去掉
collections=DATABASE.index_day):
'''
:param code: code: 字符串str eg 600085
:param start: 字符串str 开始日期 eg 2011-01-01
:param end: 字符串str 结束日期 eg 2011-05-01
:param if_drop_index: Ture False ， dataframe drop index or not
:param collections: mongodb 数据库
:return:
'''
'获取期货日线'
end = start if end is None else end
start = str(start)[0:10]
end = str(end)[0:10]
# 🛠 todo 报告错误 如果开始时间 在 结束时间之后
# 🛠 todo 如果相等
res = QA_fetch_future_day(code, start, end, format='pd')
if res is None:
print("QA Error QA_fetch_future_day_adv parameter code=%s start=%s end=%s call QA_fetch_future_day return None" % (
code, start, end))
else:
res_set_index = res.set_index(['date', 'code'])
# if res_set_index is None:
# print("QA Error QA_fetch_index_day_adv set index 'date, code' return None")
# return None
return QA_DataStruct_Future_day(res_set_index) | [
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train | QA_fetch_future_min_adv | '获取股票分钟线'
:param code:
:param start:
:param end:
:param frequence:
:param if_drop_index:
:param collections:
:return: | QUANTAXIS/QAFetch/QAQuery_Advance.py | def QA_fetch_future_min_adv(
code,
start, end=None,
frequence='1min',
if_drop_index=True,
collections=DATABASE.future_min):
'''
'获取股票分钟线'
:param code:
:param start:
:param end:
:param frequence:
:param if_drop_index:
:param collections:
:return:
'''
if frequence in ['1min', '1m']:
frequence = '1min'
elif frequence in ['5min', '5m']:
frequence = '5min'
elif frequence in ['15min', '15m']:
frequence = '15min'
elif frequence in ['30min', '30m']:
frequence = '30min'
elif frequence in ['60min', '60m']:
frequence = '60min'
# __data = [] 没有使用
end = start if end is None else end
if len(start) == 10:
start = '{} 00:00:00'.format(start)
if len(end) == 10:
end = '{} 15:00:00'.format(end)
# 🛠 todo 报告错误 如果开始时间 在 结束时间之后
# if start == end:
# 🛠 todo 如果相等，根据 frequence 获取开始时间的 时间段 QA_fetch_index_min_adv， 不支持start end是相等的
#print("QA Error QA_fetch_index_min_adv parameter code=%s , start=%s, end=%s is equal, should have time span! " % (code, start, end))
# return None
res = QA_fetch_future_min(
code, start, end, format='pd', frequence=frequence)
if res is None:
print("QA Error QA_fetch_future_min_adv parameter code=%s start=%s end=%s frequence=%s call QA_fetch_future_min return None" % (
code, start, end, frequence))
else:
res_reset_index = res.set_index(
['datetime', 'code'], drop=if_drop_index)
# if res_reset_index is None:
# print("QA Error QA_fetch_index_min_adv set index 'date, code' return None")
return QA_DataStruct_Future_min(res_reset_index) | def QA_fetch_future_min_adv(
code,
start, end=None,
frequence='1min',
if_drop_index=True,
collections=DATABASE.future_min):
'''
'获取股票分钟线'
:param code:
:param start:
:param end:
:param frequence:
:param if_drop_index:
:param collections:
:return:
'''
if frequence in ['1min', '1m']:
frequence = '1min'
elif frequence in ['5min', '5m']:
frequence = '5min'
elif frequence in ['15min', '15m']:
frequence = '15min'
elif frequence in ['30min', '30m']:
frequence = '30min'
elif frequence in ['60min', '60m']:
frequence = '60min'
# __data = [] 没有使用
end = start if end is None else end
if len(start) == 10:
start = '{} 00:00:00'.format(start)
if len(end) == 10:
end = '{} 15:00:00'.format(end)
# 🛠 todo 报告错误 如果开始时间 在 结束时间之后
# if start == end:
# 🛠 todo 如果相等，根据 frequence 获取开始时间的 时间段 QA_fetch_index_min_adv， 不支持start end是相等的
#print("QA Error QA_fetch_index_min_adv parameter code=%s , start=%s, end=%s is equal, should have time span! " % (code, start, end))
# return None
res = QA_fetch_future_min(
code, start, end, format='pd', frequence=frequence)
if res is None:
print("QA Error QA_fetch_future_min_adv parameter code=%s start=%s end=%s frequence=%s call QA_fetch_future_min return None" % (
code, start, end, frequence))
else:
res_reset_index = res.set_index(
['datetime', 'code'], drop=if_drop_index)
# if res_reset_index is None:
# print("QA Error QA_fetch_index_min_adv set index 'date, code' return None")
return QA_DataStruct_Future_min(res_reset_index) | [
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train | QA_fetch_future_list_adv | '获取股票列表'
:param collections: mongodb 数据库
:return: DataFrame | QUANTAXIS/QAFetch/QAQuery_Advance.py | def QA_fetch_future_list_adv(collections=DATABASE.future_list):
'''
'获取股票列表'
:param collections: mongodb 数据库
:return: DataFrame
'''
future_list_items = QA_fetch_future_list()
if len(future_list_items) == 0:
print("QA Error QA_fetch_future_list_adv call item for item in collections.find() return 0 item, maybe the DATABASE.future_list is empty!")
return None
return future_list_items | def QA_fetch_future_list_adv(collections=DATABASE.future_list):
'''
'获取股票列表'
:param collections: mongodb 数据库
:return: DataFrame
'''
future_list_items = QA_fetch_future_list()
if len(future_list_items) == 0:
print("QA Error QA_fetch_future_list_adv call item for item in collections.find() return 0 item, maybe the DATABASE.future_list is empty!")
return None
return future_list_items | [
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train | QA_fetch_stock_block_adv | 返回板块 ❌
:param code:
:param blockname:
:param collections: 默认数据库 stock_block
:return: QA_DataStruct_Stock_block | QUANTAXIS/QAFetch/QAQuery_Advance.py | def QA_fetch_stock_block_adv(code=None, blockname=None, collections=DATABASE.stock_block):
'''
返回板块 ❌
:param code:
:param blockname:
:param collections: 默认数据库 stock_block
:return: QA_DataStruct_Stock_block
'''
if code is not None and blockname is None:
# 返回这个股票代码所属的板块
data = pd.DataFrame([item for item in collections.find(
{'code': {'$in': code}})])
data = data.drop(['_id'], axis=1)
return QA_DataStruct_Stock_block(data.set_index(['blockname', 'code'], drop=True).drop_duplicates())
elif blockname is not None and code is None:
#
# 🛠 todo fnished 返回 这个板块所有的股票
# 返回该板块所属的股票
# print("QA Error blockname is Not none code none, return all code from its block name have not implemented yet !")
items_from_collections = [item for item in collections.find(
{'blockname': re.compile(blockname)})]
data = pd.DataFrame(items_from_collections).drop(['_id'], axis=1)
data_set_index = data.set_index(['blockname', 'code'], drop=True)
return QA_DataStruct_Stock_block(data_set_index)
else:
# 🛠 todo 返回 判断 这个股票是否和属于该板块
data = pd.DataFrame(
[item for item in collections.find()]).drop(['_id'], axis=1)
data_set_index = data.set_index(['blockname', 'code'], drop=True)
return QA_DataStruct_Stock_block(data_set_index) | def QA_fetch_stock_block_adv(code=None, blockname=None, collections=DATABASE.stock_block):
'''
返回板块 ❌
:param code:
:param blockname:
:param collections: 默认数据库 stock_block
:return: QA_DataStruct_Stock_block
'''
if code is not None and blockname is None:
# 返回这个股票代码所属的板块
data = pd.DataFrame([item for item in collections.find(
{'code': {'$in': code}})])
data = data.drop(['_id'], axis=1)
return QA_DataStruct_Stock_block(data.set_index(['blockname', 'code'], drop=True).drop_duplicates())
elif blockname is not None and code is None:
#
# 🛠 todo fnished 返回 这个板块所有的股票
# 返回该板块所属的股票
# print("QA Error blockname is Not none code none, return all code from its block name have not implemented yet !")
items_from_collections = [item for item in collections.find(
{'blockname': re.compile(blockname)})]
data = pd.DataFrame(items_from_collections).drop(['_id'], axis=1)
data_set_index = data.set_index(['blockname', 'code'], drop=True)
return QA_DataStruct_Stock_block(data_set_index)
else:
# 🛠 todo 返回 判断 这个股票是否和属于该板块
data = pd.DataFrame(
[item for item in collections.find()]).drop(['_id'], axis=1)
data_set_index = data.set_index(['blockname', 'code'], drop=True)
return QA_DataStruct_Stock_block(data_set_index) | [
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train | QA_fetch_stock_realtime_adv | 返回当日的上下五档, code可以是股票可以是list, num是每个股票获取的数量
:param code:
:param num:
:param collections: realtime_XXXX-XX-XX 每天实时时间
:return: DataFrame | QUANTAXIS/QAFetch/QAQuery_Advance.py | def QA_fetch_stock_realtime_adv(code=None,
num=1,
collections=DATABASE.get_collection('realtime_{}'.format(datetime.date.today()))):
'''
返回当日的上下五档, code可以是股票可以是list, num是每个股票获取的数量
:param code:
:param num:
:param collections: realtime_XXXX-XX-XX 每天实时时间
:return: DataFrame
'''
if code is not None:
# code 必须转换成list 去查询数据库
if isinstance(code, str):
code = [code]
elif isinstance(code, list):
pass
else:
print(
"QA Error QA_fetch_stock_realtime_adv parameter code is not List type or String type")
items_from_collections = [item for item in collections.find(
{'code': {'$in': code}}, limit=num*len(code), sort=[('datetime', pymongo.DESCENDING)])]
if items_from_collections is None:
print("QA Error QA_fetch_stock_realtime_adv find parameter code={} num={} collection={} return NOne".format(
code, num, collections))
return
data = pd.DataFrame(items_from_collections)
data_set_index = data.set_index(
['datetime', 'code'], drop=False).drop(['_id'], axis=1)
return data_set_index
else:
print("QA Error QA_fetch_stock_realtime_adv parameter code is None") | def QA_fetch_stock_realtime_adv(code=None,
num=1,
collections=DATABASE.get_collection('realtime_{}'.format(datetime.date.today()))):
'''
返回当日的上下五档, code可以是股票可以是list, num是每个股票获取的数量
:param code:
:param num:
:param collections: realtime_XXXX-XX-XX 每天实时时间
:return: DataFrame
'''
if code is not None:
# code 必须转换成list 去查询数据库
if isinstance(code, str):
code = [code]
elif isinstance(code, list):
pass
else:
print(
"QA Error QA_fetch_stock_realtime_adv parameter code is not List type or String type")
items_from_collections = [item for item in collections.find(
{'code': {'$in': code}}, limit=num*len(code), sort=[('datetime', pymongo.DESCENDING)])]
if items_from_collections is None:
print("QA Error QA_fetch_stock_realtime_adv find parameter code={} num={} collection={} return NOne".format(
code, num, collections))
return
data = pd.DataFrame(items_from_collections)
data_set_index = data.set_index(
['datetime', 'code'], drop=False).drop(['_id'], axis=1)
return data_set_index
else:
print("QA Error QA_fetch_stock_realtime_adv parameter code is None") | [
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"num是每个股票获取的数量",
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"DataFrame"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAFetch/QAQuery_Advance.py#L481-L513 | [
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":... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_fetch_financial_report_adv | 高级财务查询接口
Arguments:
code {[type]} -- [description]
start {[type]} -- [description]
Keyword Arguments:
end {[type]} -- [description] (default: {None}) | QUANTAXIS/QAFetch/QAQuery_Advance.py | def QA_fetch_financial_report_adv(code, start, end=None, ltype='EN'):
"""高级财务查询接口
Arguments:
code {[type]} -- [description]
start {[type]} -- [description]
Keyword Arguments:
end {[type]} -- [description] (default: {None})
"""
if end is None:
return QA_DataStruct_Financial(QA_fetch_financial_report(code, start, ltype=ltype))
else:
series = pd.Series(
data=month_data, index=pd.to_datetime(month_data), name='date')
timerange = series.loc[start:end].tolist()
return QA_DataStruct_Financial(QA_fetch_financial_report(code, timerange, ltype=ltype)) | def QA_fetch_financial_report_adv(code, start, end=None, ltype='EN'):
"""高级财务查询接口
Arguments:
code {[type]} -- [description]
start {[type]} -- [description]
Keyword Arguments:
end {[type]} -- [description] (default: {None})
"""
if end is None:
return QA_DataStruct_Financial(QA_fetch_financial_report(code, start, ltype=ltype))
else:
series = pd.Series(
data=month_data, index=pd.to_datetime(month_data), name='date')
timerange = series.loc[start:end].tolist()
return QA_DataStruct_Financial(QA_fetch_financial_report(code, timerange, ltype=ltype)) | [
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... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_fetch_stock_financial_calendar_adv | 获取股票日线 | QUANTAXIS/QAFetch/QAQuery_Advance.py | def QA_fetch_stock_financial_calendar_adv(code, start="all", end=None, format='pd', collections=DATABASE.report_calendar):
'获取股票日线'
#code= [code] if isinstance(code,str) else code
end = start if end is None else end
start = str(start)[0:10]
end = str(end)[0:10]
# code checking
if start == 'all':
start = '1990-01-01'
end = str(datetime.date.today())
if end is None:
return QA_DataStruct_Financial(QA_fetch_stock_financial_calendar(code, start, str(datetime.date.today())))
else:
series = pd.Series(
data=month_data, index=pd.to_datetime(month_data), name='date')
timerange = series.loc[start:end].tolist()
return QA_DataStruct_Financial(QA_fetch_stock_financial_calendar(code, start, end)) | def QA_fetch_stock_financial_calendar_adv(code, start="all", end=None, format='pd', collections=DATABASE.report_calendar):
'获取股票日线'
#code= [code] if isinstance(code,str) else code
end = start if end is None else end
start = str(start)[0:10]
end = str(end)[0:10]
# code checking
if start == 'all':
start = '1990-01-01'
end = str(datetime.date.today())
if end is None:
return QA_DataStruct_Financial(QA_fetch_stock_financial_calendar(code, start, str(datetime.date.today())))
else:
series = pd.Series(
data=month_data, index=pd.to_datetime(month_data), name='date')
timerange = series.loc[start:end].tolist()
return QA_DataStruct_Financial(QA_fetch_stock_financial_calendar(code, start, end)) | [
"获取股票日线"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAFetch/QAQuery_Advance.py#L572-L591 | [
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... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_fetch_get_tdxtraderecord | QUANTAXIS 读取历史交易记录 通达信 历史成交-输出-xlsfile--转换csvfile | QUANTAXIS/QAFetch/QATradeFile.py | def QA_fetch_get_tdxtraderecord(file):
"""
QUANTAXIS 读取历史交易记录 通达信 历史成交-输出-xlsfile--转换csvfile
"""
try:
with open('./20180606.csv', 'r') as f:
l = csv.reader(f)
data = [item for item in l]
res = pd.DataFrame(data[1:], columns=data[0])
return res
except:
raise IOError('QA CANNOT READ THIS RECORD') | def QA_fetch_get_tdxtraderecord(file):
"""
QUANTAXIS 读取历史交易记录 通达信 历史成交-输出-xlsfile--转换csvfile
"""
try:
with open('./20180606.csv', 'r') as f:
l = csv.reader(f)
data = [item for item in l]
res = pd.DataFrame(data[1:], columns=data[0])
return res
except:
raise IOError('QA CANNOT READ THIS RECORD') | [
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] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAFetch/QATradeFile.py#L39-L51 | [
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train | AROON | 阿隆指标
Arguments:
DataFrame {[type]} -- [description]
Keyword Arguments:
N {int} -- [description] (default: {14})
Returns:
[type] -- [description] | QUANTAXIS/QAIndicator/talib_indicators.py | def AROON(DataFrame, N=14):
"""阿隆指标
Arguments:
DataFrame {[type]} -- [description]
Keyword Arguments:
N {int} -- [description] (default: {14})
Returns:
[type] -- [description]
"""
ar_up, ar_down = talib.AROON(DataFrame.high.values, DataFrame.low.values, N)
return pd.DataFrame({'AROON_UP': ar_up,'AROON_DOWN': ar_down}, index=DataFrame.index) | def AROON(DataFrame, N=14):
"""阿隆指标
Arguments:
DataFrame {[type]} -- [description]
Keyword Arguments:
N {int} -- [description] (default: {14})
Returns:
[type] -- [description]
"""
ar_up, ar_down = talib.AROON(DataFrame.high.values, DataFrame.low.values, N)
return pd.DataFrame({'AROON_UP': ar_up,'AROON_DOWN': ar_down}, index=DataFrame.index) | [
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train | MARKET_PRESET.get_commission_coeff | 当前无法区分是百分比还是按手数收费,不过可以拿到以后自行判断 | QUANTAXIS/QAARP/market_preset.py | def get_commission_coeff(self, code):
"""
当前无法区分是百分比还是按手数收费,不过可以拿到以后自行判断
"""
return max(self.get_code(code).get('commission_coeff_peramount'),
self.get_code(code).get('commission_coeff_pervol')) | def get_commission_coeff(self, code):
"""
当前无法区分是百分比还是按手数收费,不过可以拿到以后自行判断
"""
return max(self.get_code(code).get('commission_coeff_peramount'),
self.get_code(code).get('commission_coeff_pervol')) | [
"当前无法区分是百分比还是按手数收费",
"不过可以拿到以后自行判断"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAARP/market_preset.py#L638-L643 | [
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train | QAAnalysis_trade.import_trade | trade是一个可迭代的list/generator | QUANTAXIS/QAAnalysis/QAAnalysis_trade.py | def import_trade(self, trade):
"""
trade是一个可迭代的list/generator
"""
for item in trade:
self.make_deal(item.code, item.datetime, item.amount,
item.towards, item.price.item.order_model, item.amount_model) | def import_trade(self, trade):
"""
trade是一个可迭代的list/generator
"""
for item in trade:
self.make_deal(item.code, item.datetime, item.amount,
item.towards, item.price.item.order_model, item.amount_model) | [
"trade是一个可迭代的list",
"/",
"generator"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAAnalysis/QAAnalysis_trade.py#L54-L60 | [
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train | QAAnalysis_trade.make_deal | 这是一个一定会成交,并且立刻结转(及t+0)的交易入口 | QUANTAXIS/QAAnalysis/QAAnalysis_trade.py | def make_deal(self, code, datetime, amount=100, towards=ORDER_DIRECTION.BUY, price=0, order_model=ORDER_MODEL.MARKET, amount_model=AMOUNT_MODEL.BY_AMOUNT):
"""
这是一个一定会成交,并且立刻结转(及t+0)的交易入口
"""
self.account.receive_deal(self.backtest_broker.receive_order(QA_Event(order=self.account.send_order(
code=code, time=datetime, amount=amount, towards=towards, price=price, order_model=order_model, amount_model=amount_model
))))
self.account.settle() | def make_deal(self, code, datetime, amount=100, towards=ORDER_DIRECTION.BUY, price=0, order_model=ORDER_MODEL.MARKET, amount_model=AMOUNT_MODEL.BY_AMOUNT):
"""
这是一个一定会成交,并且立刻结转(及t+0)的交易入口
"""
self.account.receive_deal(self.backtest_broker.receive_order(QA_Event(order=self.account.send_order(
code=code, time=datetime, amount=amount, towards=towards, price=price, order_model=order_model, amount_model=amount_model
))))
self.account.settle() | [
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] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAAnalysis/QAAnalysis_trade.py#L62-L69 | [
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train | QA_Order.cancel | 撤单
Arguments:
amount {int} -- 撤单数量 | QUANTAXIS/QAMarket/QAOrder.py | def cancel(self):
"""撤单
Arguments:
amount {int} -- 撤单数量
"""
self.cancel_amount = self.amount - self.trade_amount
if self.trade_amount == 0:
# 未交易 直接订单全撤
self._status = ORDER_STATUS.CANCEL_ALL
else:
# 部分交易 剩余订单全撤
self._status = ORDER_STATUS.CANCEL_PART | def cancel(self):
"""撤单
Arguments:
amount {int} -- 撤单数量
"""
self.cancel_amount = self.amount - self.trade_amount
if self.trade_amount == 0:
# 未交易 直接订单全撤
self._status = ORDER_STATUS.CANCEL_ALL
else:
# 部分交易 剩余订单全撤
self._status = ORDER_STATUS.CANCEL_PART | [
"撤单"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAMarket/QAOrder.py#L246-L259 | [
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train | QA_Order.failed | 失败订单(未成功创建入broker)
Arguments:
reason {str} -- 失败原因 | QUANTAXIS/QAMarket/QAOrder.py | def failed(self, reason=None):
"""失败订单(未成功创建入broker)
Arguments:
reason {str} -- 失败原因
"""
# 订单创建失败(如废单/场外废单/价格高于涨停价/价格低于跌停价/通讯失败)
self._status = ORDER_STATUS.FAILED
self.reason = str(reason) | def failed(self, reason=None):
"""失败订单(未成功创建入broker)
Arguments:
reason {str} -- 失败原因
"""
# 订单创建失败(如废单/场外废单/价格高于涨停价/价格低于跌停价/通讯失败)
self._status = ORDER_STATUS.FAILED
self.reason = str(reason) | [
"失败订单",
"(",
"未成功创建入broker",
")"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAMarket/QAOrder.py#L261-L269 | [
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train | QA_Order.trade | trade 状态
Arguments:
amount {[type]} -- [description] | QUANTAXIS/QAMarket/QAOrder.py | def trade(self, trade_id, trade_price, trade_amount, trade_time):
"""trade 状态
Arguments:
amount {[type]} -- [description]
"""
if self.status in [ORDER_STATUS.SUCCESS_PART, ORDER_STATUS.QUEUED]:
trade_amount = int(trade_amount)
trade_id = str(trade_id)
if trade_amount < 1:
self._status = ORDER_STATUS.NEXT
else:
if trade_id not in self.trade_id:
trade_price = float(trade_price)
trade_time = str(trade_time)
self.trade_id.append(trade_id)
self.trade_price = (
self.trade_price * self.trade_amount +
trade_price * trade_amount
) / (
self.trade_amount + trade_amount
)
self.trade_amount += trade_amount
self.trade_time.append(trade_time)
self.callback(
self.code,
trade_id,
self.order_id,
self.realorder_id,
trade_price,
trade_amount,
self.towards,
trade_time
)
else:
pass
else:
raise RuntimeError(
'ORDER STATUS {} CANNNOT TRADE'.format(self.status)
) | def trade(self, trade_id, trade_price, trade_amount, trade_time):
"""trade 状态
Arguments:
amount {[type]} -- [description]
"""
if self.status in [ORDER_STATUS.SUCCESS_PART, ORDER_STATUS.QUEUED]:
trade_amount = int(trade_amount)
trade_id = str(trade_id)
if trade_amount < 1:
self._status = ORDER_STATUS.NEXT
else:
if trade_id not in self.trade_id:
trade_price = float(trade_price)
trade_time = str(trade_time)
self.trade_id.append(trade_id)
self.trade_price = (
self.trade_price * self.trade_amount +
trade_price * trade_amount
) / (
self.trade_amount + trade_amount
)
self.trade_amount += trade_amount
self.trade_time.append(trade_time)
self.callback(
self.code,
trade_id,
self.order_id,
self.realorder_id,
trade_price,
trade_amount,
self.towards,
trade_time
)
else:
pass
else:
raise RuntimeError(
'ORDER STATUS {} CANNNOT TRADE'.format(self.status)
) | [
"trade",
"状态"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAMarket/QAOrder.py#L271-L314 | [
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"... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_Order.to_otgdict | {
"aid": "insert_order", # //必填, 下单请求
# //必填, 需要与登录用户名一致, 或为登录用户的子账户(例如登录用户为user1, 则报单 user_id 应当为 user1 或 user1.some_unit)
"user_id": account_cookie,
# //必填, 委托单号, 需确保在一个账号中不重复, 限长512字节
"order_id": order_id if order_id else QA.QA_util_random_with_topic('QAOTG'),
"exchange_id": exchange_id, # //必填, 下单到哪个交易所
"instrument_id": code, # //必填, 下单合约代码
"direction": order_direction, # //必填, 下单买卖方向
# //必填, 下单开平方向, 仅当指令相关对象不支持开平机制(例如股票)时可不填写此字段
"offset": order_offset,
"volume": volume, # //必填, 下单手数
"price_type": "LIMIT", # //必填, 报单价格类型
"limit_price": price, # //当 price_type == LIMIT 时需要填写此字段, 报单价格
"volume_condition": "ANY",
"time_condition": "GFD",
} | QUANTAXIS/QAMarket/QAOrder.py | def to_otgdict(self):
"""{
"aid": "insert_order", # //必填, 下单请求
# //必填, 需要与登录用户名一致, 或为登录用户的子账户(例如登录用户为user1, 则报单 user_id 应当为 user1 或 user1.some_unit)
"user_id": account_cookie,
# //必填, 委托单号, 需确保在一个账号中不重复, 限长512字节
"order_id": order_id if order_id else QA.QA_util_random_with_topic('QAOTG'),
"exchange_id": exchange_id, # //必填, 下单到哪个交易所
"instrument_id": code, # //必填, 下单合约代码
"direction": order_direction, # //必填, 下单买卖方向
# //必填, 下单开平方向, 仅当指令相关对象不支持开平机制(例如股票)时可不填写此字段
"offset": order_offset,
"volume": volume, # //必填, 下单手数
"price_type": "LIMIT", # //必填, 报单价格类型
"limit_price": price, # //当 price_type == LIMIT 时需要填写此字段, 报单价格
"volume_condition": "ANY",
"time_condition": "GFD",
}
"""
return {
"aid": "insert_order", # //必填, 下单请求
# //必填, 需要与登录用户名一致, 或为登录用户的子账户(例如登录用户为user1, 则报单 user_id 应当为 user1 或 user1.some_unit)
"user_id": self.account_cookie,
# //必填, 委托单号, 需确保在一个账号中不重复, 限长512字节
"order_id": self.order_id,
"exchange_id": self.exchange_id, # //必填, 下单到哪个交易所
"instrument_id": self.code, # //必填, 下单合约代码
"direction": self.direction, # //必填, 下单买卖方向
# //必填, 下单开平方向, 仅当指令相关对象不支持开平机制(例如股票)时可不填写此字段
"offset": self.offset,
"volume": self.amount, # //必填, 下单手数
"price_type": self.order_model, # //必填, 报单价格类型
"limit_price": self.price, # //当 price_type == LIMIT 时需要填写此字段, 报单价格
"volume_condition": "ANY",
"time_condition": "GFD",
} | def to_otgdict(self):
"""{
"aid": "insert_order", # //必填, 下单请求
# //必填, 需要与登录用户名一致, 或为登录用户的子账户(例如登录用户为user1, 则报单 user_id 应当为 user1 或 user1.some_unit)
"user_id": account_cookie,
# //必填, 委托单号, 需确保在一个账号中不重复, 限长512字节
"order_id": order_id if order_id else QA.QA_util_random_with_topic('QAOTG'),
"exchange_id": exchange_id, # //必填, 下单到哪个交易所
"instrument_id": code, # //必填, 下单合约代码
"direction": order_direction, # //必填, 下单买卖方向
# //必填, 下单开平方向, 仅当指令相关对象不支持开平机制(例如股票)时可不填写此字段
"offset": order_offset,
"volume": volume, # //必填, 下单手数
"price_type": "LIMIT", # //必填, 报单价格类型
"limit_price": price, # //当 price_type == LIMIT 时需要填写此字段, 报单价格
"volume_condition": "ANY",
"time_condition": "GFD",
}
"""
return {
"aid": "insert_order", # //必填, 下单请求
# //必填, 需要与登录用户名一致, 或为登录用户的子账户(例如登录用户为user1, 则报单 user_id 应当为 user1 或 user1.some_unit)
"user_id": self.account_cookie,
# //必填, 委托单号, 需确保在一个账号中不重复, 限长512字节
"order_id": self.order_id,
"exchange_id": self.exchange_id, # //必填, 下单到哪个交易所
"instrument_id": self.code, # //必填, 下单合约代码
"direction": self.direction, # //必填, 下单买卖方向
# //必填, 下单开平方向, 仅当指令相关对象不支持开平机制(例如股票)时可不填写此字段
"offset": self.offset,
"volume": self.amount, # //必填, 下单手数
"price_type": self.order_model, # //必填, 报单价格类型
"limit_price": self.price, # //当 price_type == LIMIT 时需要填写此字段, 报单价格
"volume_condition": "ANY",
"time_condition": "GFD",
} | [
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train | QA_Order.from_otgformat | [summary]
Arguments:
otgOrder {[type]} -- [description]
{'seqno': 6,
'user_id': '106184',
'order_id': 'WDRB_QA01_FtNlyBem',
'exchange_id': 'SHFE',
'instrument_id': 'rb1905',
'direction': 'SELL',
'offset': 'OPEN',
'volume_orign': 50, #(总报单手数)
'price_type': 'LIMIT', # "LIMIT" (价格类型, ANY=市价, LIMIT=限价)
'limit_price': 3432.0, # 4500.0 (委托价格, 仅当 price_type = LIMIT 时有效)
'time_condition': 'GFD',# "GFD" (时间条件, IOC=立即完成，否则撤销, GFS=本节有效, GFD=当日有效, GTC=撤销前有效, GFA=集合竞价有效)
'volume_condition': 'ANY', # "ANY" (手数条件, ANY=任何数量, MIN=最小数量, ALL=全部数量)
'insert_date_time': 1545656460000000000,# 1501074872000000000 (下单时间(按北京时间)，自unix epoch(1970-01-01 00:00:00 GMT)以来的纳秒数)
'exchange_order_id': ' 3738',
'status': 'FINISHED', # "ALIVE" (委托单状态, ALIVE=有效, FINISHED=已完)
'volume_left': 0,
'last_msg': '全部成交报单已提交'} # "报单成功" (委托单状态信息) | QUANTAXIS/QAMarket/QAOrder.py | def from_otgformat(self, otgOrder):
"""[summary]
Arguments:
otgOrder {[type]} -- [description]
{'seqno': 6,
'user_id': '106184',
'order_id': 'WDRB_QA01_FtNlyBem',
'exchange_id': 'SHFE',
'instrument_id': 'rb1905',
'direction': 'SELL',
'offset': 'OPEN',
'volume_orign': 50, #(总报单手数)
'price_type': 'LIMIT', # "LIMIT" (价格类型, ANY=市价, LIMIT=限价)
'limit_price': 3432.0, # 4500.0 (委托价格, 仅当 price_type = LIMIT 时有效)
'time_condition': 'GFD',# "GFD" (时间条件, IOC=立即完成，否则撤销, GFS=本节有效, GFD=当日有效, GTC=撤销前有效, GFA=集合竞价有效)
'volume_condition': 'ANY', # "ANY" (手数条件, ANY=任何数量, MIN=最小数量, ALL=全部数量)
'insert_date_time': 1545656460000000000,# 1501074872000000000 (下单时间(按北京时间)，自unix epoch(1970-01-01 00:00:00 GMT)以来的纳秒数)
'exchange_order_id': ' 3738',
'status': 'FINISHED', # "ALIVE" (委托单状态, ALIVE=有效, FINISHED=已完)
'volume_left': 0,
'last_msg': '全部成交报单已提交'} # "报单成功" (委托单状态信息)
"""
self.order_id = otgOrder.get('order_id')
self.account_cookie = otgOrder.get('user_id')
self.exchange_id = otgOrder.get('exchange_id')
self.code = str(otgOrder.get('instrument_id')).upper()
self.offset = otgOrder.get('offset')
self.direction = otgOrder.get('direction')
self.towards = eval('ORDER_DIRECTION.{}_{}'.format(
self.direction,
self.offset
))
self.amount = otgOrder.get('volume_orign')
self.trade_amount = self.amount - otgOrder.get('volume_left')
self.price = otgOrder.get('limit_price')
self.order_model = eval(
'ORDER_MODEL.{}'.format(otgOrder.get('price_type'))
)
self.time_condition = otgOrder.get('time_condition')
if otgOrder.get('insert_date_time') == 0:
self.datetime = 0
else:
self.datetime = QA_util_stamp2datetime(
int(otgOrder.get('insert_date_time'))
)
self.sending_time = self.datetime
self.volume_condition = otgOrder.get('volume_condition')
self.message = otgOrder.get('last_msg')
self._status = ORDER_STATUS.NEW
if '已撤单' in self.message or '拒绝' in self.message or '仓位不足' in self.message:
# 仓位不足: 一般是平今/平昨仓位不足
self._status = ORDER_STATUS.FAILED
self.realorder_id = otgOrder.get('exchange_order_id')
return self | def from_otgformat(self, otgOrder):
"""[summary]
Arguments:
otgOrder {[type]} -- [description]
{'seqno': 6,
'user_id': '106184',
'order_id': 'WDRB_QA01_FtNlyBem',
'exchange_id': 'SHFE',
'instrument_id': 'rb1905',
'direction': 'SELL',
'offset': 'OPEN',
'volume_orign': 50, #(总报单手数)
'price_type': 'LIMIT', # "LIMIT" (价格类型, ANY=市价, LIMIT=限价)
'limit_price': 3432.0, # 4500.0 (委托价格, 仅当 price_type = LIMIT 时有效)
'time_condition': 'GFD',# "GFD" (时间条件, IOC=立即完成，否则撤销, GFS=本节有效, GFD=当日有效, GTC=撤销前有效, GFA=集合竞价有效)
'volume_condition': 'ANY', # "ANY" (手数条件, ANY=任何数量, MIN=最小数量, ALL=全部数量)
'insert_date_time': 1545656460000000000,# 1501074872000000000 (下单时间(按北京时间)，自unix epoch(1970-01-01 00:00:00 GMT)以来的纳秒数)
'exchange_order_id': ' 3738',
'status': 'FINISHED', # "ALIVE" (委托单状态, ALIVE=有效, FINISHED=已完)
'volume_left': 0,
'last_msg': '全部成交报单已提交'} # "报单成功" (委托单状态信息)
"""
self.order_id = otgOrder.get('order_id')
self.account_cookie = otgOrder.get('user_id')
self.exchange_id = otgOrder.get('exchange_id')
self.code = str(otgOrder.get('instrument_id')).upper()
self.offset = otgOrder.get('offset')
self.direction = otgOrder.get('direction')
self.towards = eval('ORDER_DIRECTION.{}_{}'.format(
self.direction,
self.offset
))
self.amount = otgOrder.get('volume_orign')
self.trade_amount = self.amount - otgOrder.get('volume_left')
self.price = otgOrder.get('limit_price')
self.order_model = eval(
'ORDER_MODEL.{}'.format(otgOrder.get('price_type'))
)
self.time_condition = otgOrder.get('time_condition')
if otgOrder.get('insert_date_time') == 0:
self.datetime = 0
else:
self.datetime = QA_util_stamp2datetime(
int(otgOrder.get('insert_date_time'))
)
self.sending_time = self.datetime
self.volume_condition = otgOrder.get('volume_condition')
self.message = otgOrder.get('last_msg')
self._status = ORDER_STATUS.NEW
if '已撤单' in self.message or '拒绝' in self.message or '仓位不足' in self.message:
# 仓位不足: 一般是平今/平昨仓位不足
self._status = ORDER_STATUS.FAILED
self.realorder_id = otgOrder.get('exchange_order_id')
return self | [
"[",
"summary",
"]"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAMarket/QAOrder.py#L419-L476 | [
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train | QA_Order.from_dict | 从字段类型的字段 填充 对象的字段
:param order_dict: dict 类型
:return: self QA_Order | QUANTAXIS/QAMarket/QAOrder.py | def from_dict(self, order_dict):
'''
从字段类型的字段 填充 对象的字段
:param order_dict: dict 类型
:return: self QA_Order
'''
try:
# QA_util_log_info('QA_ORDER CHANGE: from {} change to {}'.format(
# self.order_id, order['order_id']))
self.price = order_dict['price']
self.date = order_dict['date']
self.datetime = order_dict['datetime']
self.sending_time = order_dict['sending_time'] # 下单时间
self.trade_time = order_dict['trade_time']
self.amount = order_dict['amount']
self.frequence = order_dict['frequence']
self.market_type = order_dict['market_type']
self.towards = order_dict['towards']
self.code = order_dict['code']
self.user = order_dict['user']
self.account_cookie = order_dict['account_cookie']
self.strategy = order_dict['strategy']
self.type = order_dict['type']
self.order_model = order_dict['order_model']
self.amount_model = order_dict['amount_model']
self.order_id = order_dict['order_id']
self.realorder_id = order_dict['realorder_id']
self.trade_id = order_dict['trade_id']
self.callback = order_dict['callback']
self.commission_coeff = order_dict['commission_coeff']
self.tax_coeff = order_dict['tax_coeff']
self.money = order_dict['money']
self._status = order_dict['_status']
self.cancel_amount = order_dict['cancel_amount']
self.trade_amount = order_dict['trade_amount']
self.trade_price = order_dict['trade_price']
self.reason = order_dict['reason']
return self
except Exception as e:
QA_util_log_info('Failed to tran from dict {}'.format(e)) | def from_dict(self, order_dict):
'''
从字段类型的字段 填充 对象的字段
:param order_dict: dict 类型
:return: self QA_Order
'''
try:
# QA_util_log_info('QA_ORDER CHANGE: from {} change to {}'.format(
# self.order_id, order['order_id']))
self.price = order_dict['price']
self.date = order_dict['date']
self.datetime = order_dict['datetime']
self.sending_time = order_dict['sending_time'] # 下单时间
self.trade_time = order_dict['trade_time']
self.amount = order_dict['amount']
self.frequence = order_dict['frequence']
self.market_type = order_dict['market_type']
self.towards = order_dict['towards']
self.code = order_dict['code']
self.user = order_dict['user']
self.account_cookie = order_dict['account_cookie']
self.strategy = order_dict['strategy']
self.type = order_dict['type']
self.order_model = order_dict['order_model']
self.amount_model = order_dict['amount_model']
self.order_id = order_dict['order_id']
self.realorder_id = order_dict['realorder_id']
self.trade_id = order_dict['trade_id']
self.callback = order_dict['callback']
self.commission_coeff = order_dict['commission_coeff']
self.tax_coeff = order_dict['tax_coeff']
self.money = order_dict['money']
self._status = order_dict['_status']
self.cancel_amount = order_dict['cancel_amount']
self.trade_amount = order_dict['trade_amount']
self.trade_price = order_dict['trade_price']
self.reason = order_dict['reason']
return self
except Exception as e:
QA_util_log_info('Failed to tran from dict {}'.format(e)) | [
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train | QA_OrderQueue.insert_order | :param order: QA_Order类型
:return: | QUANTAXIS/QAMarket/QAOrder.py | def insert_order(self, order):
'''
:param order: QA_Order类型
:return:
'''
#print(" *>> QAOrder!insert_order {}".format(order))
# QUEUED = 300 # queued 用于表示在order_queue中 实际表达的意思是订单存活 待成交
#order.status = ORDER_STATUS.QUEUED
# 🛠 todo 是为了速度快把order对象转换成 df 对象的吗？
#self.queue_df = self.queue_df.append(order.to_df(), ignore_index=True)
#self.queue_df.set_index('order_id', drop=True, inplace=True)
if order is not None:
self.order_list[order.order_id] = order
return order
else:
print('QAERROR Wrong for get None type while insert order to Queue') | def insert_order(self, order):
'''
:param order: QA_Order类型
:return:
'''
#print(" *>> QAOrder!insert_order {}".format(order))
# QUEUED = 300 # queued 用于表示在order_queue中 实际表达的意思是订单存活 待成交
#order.status = ORDER_STATUS.QUEUED
# 🛠 todo 是为了速度快把order对象转换成 df 对象的吗？
#self.queue_df = self.queue_df.append(order.to_df(), ignore_index=True)
#self.queue_df.set_index('order_id', drop=True, inplace=True)
if order is not None:
self.order_list[order.order_id] = order
return order
else:
print('QAERROR Wrong for get None type while insert order to Queue') | [
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] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAMarket/QAOrder.py#L557-L572 | [
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train | QA_OrderQueue.pending | 600 废单 未委托成功
200 委托成功,完全交易
203 委托成功,未完全成功
300 委托队列 待成交
400 已撤单
500 服务器撤单/每日结算
订单生成(100) -- 废单(600)
订单生成(100) -- 进入待成交队列(300) -- 完全成交(200) -- 每日结算(500)-- 死亡
订单生成(100) -- 进入待成交队列(300) -- 部分成交(203) -- 未成交(300) -- 每日结算(500) -- 死亡
订单生成(100) -- 进入待成交队列(300) -- 主动撤单(400) -- 每日结算(500) -- 死亡
选择待成交列表
:return: dataframe | QUANTAXIS/QAMarket/QAOrder.py | def pending(self):
'''
600 废单 未委托成功
200 委托成功,完全交易
203 委托成功,未完全成功
300 委托队列 待成交
400 已撤单
500 服务器撤单/每日结算
订单生成(100) -- 废单(600)
订单生成(100) -- 进入待成交队列(300) -- 完全成交(200) -- 每日结算(500)-- 死亡
订单生成(100) -- 进入待成交队列(300) -- 部分成交(203) -- 未成交(300) -- 每日结算(500) -- 死亡
订单生成(100) -- 进入待成交队列(300) -- 主动撤单(400) -- 每日结算(500) -- 死亡
选择待成交列表
:return: dataframe
'''
try:
return [
item for item in self.order_list.values() if item.status in [
ORDER_STATUS.QUEUED,
ORDER_STATUS.NEXT,
ORDER_STATUS.SUCCESS_PART
]
]
except:
return [] | def pending(self):
'''
600 废单 未委托成功
200 委托成功,完全交易
203 委托成功,未完全成功
300 委托队列 待成交
400 已撤单
500 服务器撤单/每日结算
订单生成(100) -- 废单(600)
订单生成(100) -- 进入待成交队列(300) -- 完全成交(200) -- 每日结算(500)-- 死亡
订单生成(100) -- 进入待成交队列(300) -- 部分成交(203) -- 未成交(300) -- 每日结算(500) -- 死亡
订单生成(100) -- 进入待成交队列(300) -- 主动撤单(400) -- 每日结算(500) -- 死亡
选择待成交列表
:return: dataframe
'''
try:
return [
item for item in self.order_list.values() if item.status in [
ORDER_STATUS.QUEUED,
ORDER_STATUS.NEXT,
ORDER_STATUS.SUCCESS_PART
]
]
except:
return [] | [
"600",
"废单",
"未委托成功",
"200",
"委托成功",
"完全交易",
"203",
"委托成功",
"未完全成功",
"300",
"委托队列",
"待成交",
"400",
"已撤单",
"500",
"服务器撤单",
"/",
"每日结算"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAMarket/QAOrder.py#L593-L619 | [
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train | _QA_data_stock_to_fq | 使用数据库数据进行复权 | QUANTAXIS/QAData/data_fq.py | def _QA_data_stock_to_fq(bfq_data, xdxr_data, fqtype):
'使用数据库数据进行复权'
info = xdxr_data.query('category==1')
bfq_data = bfq_data.assign(if_trade=1)
if len(info) > 0:
data = pd.concat(
[
bfq_data,
info.loc[bfq_data.index[0]:bfq_data.index[-1],
['category']]
],
axis=1
)
data['if_trade'].fillna(value=0, inplace=True)
data = data.fillna(method='ffill')
data = pd.concat(
[
data,
info.loc[bfq_data.index[0]:bfq_data.index[-1],
['fenhong',
'peigu',
'peigujia',
'songzhuangu']]
],
axis=1
)
else:
data = pd.concat(
[
bfq_data,
info.
loc[:,
['category',
'fenhong',
'peigu',
'peigujia',
'songzhuangu']]
],
axis=1
)
data = data.fillna(0)
data['preclose'] = (
data['close'].shift(1) * 10 - data['fenhong'] +
data['peigu'] * data['peigujia']
) / (10 + data['peigu'] + data['songzhuangu'])
if fqtype in ['01', 'qfq']:
data['adj'] = (data['preclose'].shift(-1) /
data['close']).fillna(1)[::-1].cumprod()
else:
data['adj'] = (data['close'] /
data['preclose'].shift(-1)).cumprod().shift(1).fillna(1)
for col in ['open', 'high', 'low', 'close', 'preclose']:
data[col] = data[col] * data['adj']
data['volume'] = data['volume'] / \
data['adj'] if 'volume' in data.columns else data['vol']/data['adj']
try:
data['high_limit'] = data['high_limit'] * data['adj']
data['low_limit'] = data['high_limit'] * data['adj']
except:
pass
return data.query('if_trade==1 and open != 0').drop(
['fenhong',
'peigu',
'peigujia',
'songzhuangu',
'if_trade',
'category'],
axis=1,
errors='ignore'
) | def _QA_data_stock_to_fq(bfq_data, xdxr_data, fqtype):
'使用数据库数据进行复权'
info = xdxr_data.query('category==1')
bfq_data = bfq_data.assign(if_trade=1)
if len(info) > 0:
data = pd.concat(
[
bfq_data,
info.loc[bfq_data.index[0]:bfq_data.index[-1],
['category']]
],
axis=1
)
data['if_trade'].fillna(value=0, inplace=True)
data = data.fillna(method='ffill')
data = pd.concat(
[
data,
info.loc[bfq_data.index[0]:bfq_data.index[-1],
['fenhong',
'peigu',
'peigujia',
'songzhuangu']]
],
axis=1
)
else:
data = pd.concat(
[
bfq_data,
info.
loc[:,
['category',
'fenhong',
'peigu',
'peigujia',
'songzhuangu']]
],
axis=1
)
data = data.fillna(0)
data['preclose'] = (
data['close'].shift(1) * 10 - data['fenhong'] +
data['peigu'] * data['peigujia']
) / (10 + data['peigu'] + data['songzhuangu'])
if fqtype in ['01', 'qfq']:
data['adj'] = (data['preclose'].shift(-1) /
data['close']).fillna(1)[::-1].cumprod()
else:
data['adj'] = (data['close'] /
data['preclose'].shift(-1)).cumprod().shift(1).fillna(1)
for col in ['open', 'high', 'low', 'close', 'preclose']:
data[col] = data[col] * data['adj']
data['volume'] = data['volume'] / \
data['adj'] if 'volume' in data.columns else data['vol']/data['adj']
try:
data['high_limit'] = data['high_limit'] * data['adj']
data['low_limit'] = data['high_limit'] * data['adj']
except:
pass
return data.query('if_trade==1 and open != 0').drop(
['fenhong',
'peigu',
'peigujia',
'songzhuangu',
'if_trade',
'category'],
axis=1,
errors='ignore'
) | [
"使用数据库数据进行复权"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAData/data_fq.py#L102-L176 | [
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"(",
"i... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_data_stock_to_fq | 股票 日线/分钟线 动态复权接口 | QUANTAXIS/QAData/data_fq.py | def QA_data_stock_to_fq(__data, type_='01'):
def __QA_fetch_stock_xdxr(
code,
format_='pd',
collections=DATABASE.stock_xdxr
):
'获取股票除权信息/数据库'
try:
data = pd.DataFrame(
[item for item in collections.find({'code': code})]
).drop(['_id'],
axis=1)
data['date'] = pd.to_datetime(data['date'])
return data.set_index(['date', 'code'], drop=False)
except:
return pd.DataFrame(
data=[],
columns=[
'category',
'category_meaning',
'code',
'date',
'fenhong',
'fenshu',
'liquidity_after',
'liquidity_before',
'name',
'peigu',
'peigujia',
'shares_after',
'shares_before',
'songzhuangu',
'suogu',
'xingquanjia'
]
)
'股票 日线/分钟线 动态复权接口'
code = __data.index.remove_unused_levels().levels[1][0] if isinstance(
__data.index,
pd.core.indexes.multi.MultiIndex
) else __data['code'][0]
return _QA_data_stock_to_fq(
bfq_data=__data,
xdxr_data=__QA_fetch_stock_xdxr(code),
fqtype=type_
) | def QA_data_stock_to_fq(__data, type_='01'):
def __QA_fetch_stock_xdxr(
code,
format_='pd',
collections=DATABASE.stock_xdxr
):
'获取股票除权信息/数据库'
try:
data = pd.DataFrame(
[item for item in collections.find({'code': code})]
).drop(['_id'],
axis=1)
data['date'] = pd.to_datetime(data['date'])
return data.set_index(['date', 'code'], drop=False)
except:
return pd.DataFrame(
data=[],
columns=[
'category',
'category_meaning',
'code',
'date',
'fenhong',
'fenshu',
'liquidity_after',
'liquidity_before',
'name',
'peigu',
'peigujia',
'shares_after',
'shares_before',
'songzhuangu',
'suogu',
'xingquanjia'
]
)
'股票 日线/分钟线 动态复权接口'
code = __data.index.remove_unused_levels().levels[1][0] if isinstance(
__data.index,
pd.core.indexes.multi.MultiIndex
) else __data['code'][0]
return _QA_data_stock_to_fq(
bfq_data=__data,
xdxr_data=__QA_fetch_stock_xdxr(code),
fqtype=type_
) | [
"股票",
"日线",
"/",
"分钟线",
"动态复权接口"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAData/data_fq.py#L179-L228 | [
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"DATABASE",
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"stock_xdxr",
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":",
"'获取股票除权信息/数据库'",
"try",
":",
"data"... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | ChatBot.get_response | Return the bot's response based on the input.
:param statement: An statement object or string.
:returns: A response to the input.
:rtype: Statement
:param additional_response_selection_parameters: Parameters to pass to the
chat bot's logic adapters to control response selection.
:type additional_response_selection_parameters: dict
:param persist_values_to_response: Values that should be saved to the response
that the chat bot generates.
:type persist_values_to_response: dict | chatterbot/chatterbot.py | def get_response(self, statement=None, **kwargs):
"""
Return the bot's response based on the input.
:param statement: An statement object or string.
:returns: A response to the input.
:rtype: Statement
:param additional_response_selection_parameters: Parameters to pass to the
chat bot's logic adapters to control response selection.
:type additional_response_selection_parameters: dict
:param persist_values_to_response: Values that should be saved to the response
that the chat bot generates.
:type persist_values_to_response: dict
"""
Statement = self.storage.get_object('statement')
additional_response_selection_parameters = kwargs.pop('additional_response_selection_parameters', {})
persist_values_to_response = kwargs.pop('persist_values_to_response', {})
if isinstance(statement, str):
kwargs['text'] = statement
if isinstance(statement, dict):
kwargs.update(statement)
if statement is None and 'text' not in kwargs:
raise self.ChatBotException(
'Either a statement object or a "text" keyword '
'argument is required. Neither was provided.'
)
if hasattr(statement, 'serialize'):
kwargs.update(**statement.serialize())
tags = kwargs.pop('tags', [])
text = kwargs.pop('text')
input_statement = Statement(text=text, **kwargs)
input_statement.add_tags(*tags)
# Preprocess the input statement
for preprocessor in self.preprocessors:
input_statement = preprocessor(input_statement)
# Make sure the input statement has its search text saved
if not input_statement.search_text:
input_statement.search_text = self.storage.tagger.get_bigram_pair_string(input_statement.text)
if not input_statement.search_in_response_to and input_statement.in_response_to:
input_statement.search_in_response_to = self.storage.tagger.get_bigram_pair_string(input_statement.in_response_to)
response = self.generate_response(input_statement, additional_response_selection_parameters)
# Update any response data that needs to be changed
if persist_values_to_response:
for response_key in persist_values_to_response:
response_value = persist_values_to_response[response_key]
if response_key == 'tags':
input_statement.add_tags(*response_value)
response.add_tags(*response_value)
else:
setattr(input_statement, response_key, response_value)
setattr(response, response_key, response_value)
if not self.read_only:
self.learn_response(input_statement)
# Save the response generated for the input
self.storage.create(**response.serialize())
return response | def get_response(self, statement=None, **kwargs):
"""
Return the bot's response based on the input.
:param statement: An statement object or string.
:returns: A response to the input.
:rtype: Statement
:param additional_response_selection_parameters: Parameters to pass to the
chat bot's logic adapters to control response selection.
:type additional_response_selection_parameters: dict
:param persist_values_to_response: Values that should be saved to the response
that the chat bot generates.
:type persist_values_to_response: dict
"""
Statement = self.storage.get_object('statement')
additional_response_selection_parameters = kwargs.pop('additional_response_selection_parameters', {})
persist_values_to_response = kwargs.pop('persist_values_to_response', {})
if isinstance(statement, str):
kwargs['text'] = statement
if isinstance(statement, dict):
kwargs.update(statement)
if statement is None and 'text' not in kwargs:
raise self.ChatBotException(
'Either a statement object or a "text" keyword '
'argument is required. Neither was provided.'
)
if hasattr(statement, 'serialize'):
kwargs.update(**statement.serialize())
tags = kwargs.pop('tags', [])
text = kwargs.pop('text')
input_statement = Statement(text=text, **kwargs)
input_statement.add_tags(*tags)
# Preprocess the input statement
for preprocessor in self.preprocessors:
input_statement = preprocessor(input_statement)
# Make sure the input statement has its search text saved
if not input_statement.search_text:
input_statement.search_text = self.storage.tagger.get_bigram_pair_string(input_statement.text)
if not input_statement.search_in_response_to and input_statement.in_response_to:
input_statement.search_in_response_to = self.storage.tagger.get_bigram_pair_string(input_statement.in_response_to)
response = self.generate_response(input_statement, additional_response_selection_parameters)
# Update any response data that needs to be changed
if persist_values_to_response:
for response_key in persist_values_to_response:
response_value = persist_values_to_response[response_key]
if response_key == 'tags':
input_statement.add_tags(*response_value)
response.add_tags(*response_value)
else:
setattr(input_statement, response_key, response_value)
setattr(response, response_key, response_value)
if not self.read_only:
self.learn_response(input_statement)
# Save the response generated for the input
self.storage.create(**response.serialize())
return response | [
"Return",
"the",
"bot",
"s",
"response",
"based",
"on",
"the",
"input",
"."
] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/chatterbot.py#L57-L133 | [
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"=",
"kwargs",
".",
"pop",
... | 1a03dcb45cba7bdc24d3db5e750582e0cb1518e2 |
train | ChatBot.generate_response | Return a response based on a given input statement.
:param input_statement: The input statement to be processed. | chatterbot/chatterbot.py | def generate_response(self, input_statement, additional_response_selection_parameters=None):
"""
Return a response based on a given input statement.
:param input_statement: The input statement to be processed.
"""
Statement = self.storage.get_object('statement')
results = []
result = None
max_confidence = -1
for adapter in self.logic_adapters:
if adapter.can_process(input_statement):
output = adapter.process(input_statement, additional_response_selection_parameters)
results.append(output)
self.logger.info(
'{} selected "{}" as a response with a confidence of {}'.format(
adapter.class_name, output.text, output.confidence
)
)
if output.confidence > max_confidence:
result = output
max_confidence = output.confidence
else:
self.logger.info(
'Not processing the statement using {}'.format(adapter.class_name)
)
class ResultOption:
def __init__(self, statement, count=1):
self.statement = statement
self.count = count
# If multiple adapters agree on the same statement,
# then that statement is more likely to be the correct response
if len(results) >= 3:
result_options = {}
for result_option in results:
result_string = result_option.text + ':' + (result_option.in_response_to or '')
if result_string in result_options:
result_options[result_string].count += 1
if result_options[result_string].statement.confidence < result_option.confidence:
result_options[result_string].statement = result_option
else:
result_options[result_string] = ResultOption(
result_option
)
most_common = list(result_options.values())[0]
for result_option in result_options.values():
if result_option.count > most_common.count:
most_common = result_option
if most_common.count > 1:
result = most_common.statement
response = Statement(
text=result.text,
in_response_to=input_statement.text,
conversation=input_statement.conversation,
persona='bot:' + self.name
)
response.confidence = result.confidence
return response | def generate_response(self, input_statement, additional_response_selection_parameters=None):
"""
Return a response based on a given input statement.
:param input_statement: The input statement to be processed.
"""
Statement = self.storage.get_object('statement')
results = []
result = None
max_confidence = -1
for adapter in self.logic_adapters:
if adapter.can_process(input_statement):
output = adapter.process(input_statement, additional_response_selection_parameters)
results.append(output)
self.logger.info(
'{} selected "{}" as a response with a confidence of {}'.format(
adapter.class_name, output.text, output.confidence
)
)
if output.confidence > max_confidence:
result = output
max_confidence = output.confidence
else:
self.logger.info(
'Not processing the statement using {}'.format(adapter.class_name)
)
class ResultOption:
def __init__(self, statement, count=1):
self.statement = statement
self.count = count
# If multiple adapters agree on the same statement,
# then that statement is more likely to be the correct response
if len(results) >= 3:
result_options = {}
for result_option in results:
result_string = result_option.text + ':' + (result_option.in_response_to or '')
if result_string in result_options:
result_options[result_string].count += 1
if result_options[result_string].statement.confidence < result_option.confidence:
result_options[result_string].statement = result_option
else:
result_options[result_string] = ResultOption(
result_option
)
most_common = list(result_options.values())[0]
for result_option in result_options.values():
if result_option.count > most_common.count:
most_common = result_option
if most_common.count > 1:
result = most_common.statement
response = Statement(
text=result.text,
in_response_to=input_statement.text,
conversation=input_statement.conversation,
persona='bot:' + self.name
)
response.confidence = result.confidence
return response | [
"Return",
"a",
"response",
"based",
"on",
"a",
"given",
"input",
"statement",
"."
] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/chatterbot.py#L135-L206 | [
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"=",
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"]",
"result",
"="... | 1a03dcb45cba7bdc24d3db5e750582e0cb1518e2 |
train | ChatBot.learn_response | Learn that the statement provided is a valid response. | chatterbot/chatterbot.py | def learn_response(self, statement, previous_statement=None):
"""
Learn that the statement provided is a valid response.
"""
if not previous_statement:
previous_statement = statement.in_response_to
if not previous_statement:
previous_statement = self.get_latest_response(statement.conversation)
if previous_statement:
previous_statement = previous_statement.text
previous_statement_text = previous_statement
if not isinstance(previous_statement, (str, type(None), )):
statement.in_response_to = previous_statement.text
elif isinstance(previous_statement, str):
statement.in_response_to = previous_statement
self.logger.info('Adding "{}" as a response to "{}"'.format(
statement.text,
previous_statement_text
))
# Save the input statement
return self.storage.create(**statement.serialize()) | def learn_response(self, statement, previous_statement=None):
"""
Learn that the statement provided is a valid response.
"""
if not previous_statement:
previous_statement = statement.in_response_to
if not previous_statement:
previous_statement = self.get_latest_response(statement.conversation)
if previous_statement:
previous_statement = previous_statement.text
previous_statement_text = previous_statement
if not isinstance(previous_statement, (str, type(None), )):
statement.in_response_to = previous_statement.text
elif isinstance(previous_statement, str):
statement.in_response_to = previous_statement
self.logger.info('Adding "{}" as a response to "{}"'.format(
statement.text,
previous_statement_text
))
# Save the input statement
return self.storage.create(**statement.serialize()) | [
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train | StatementMixin.serialize | :returns: A dictionary representation of the statement object.
:rtype: dict | chatterbot/conversation.py | def serialize(self):
"""
:returns: A dictionary representation of the statement object.
:rtype: dict
"""
data = {}
for field_name in self.get_statement_field_names():
format_method = getattr(self, 'get_{}'.format(
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if format_method:
data[field_name] = format_method()
else:
data[field_name] = getattr(self, field_name)
return data | def serialize(self):
"""
:returns: A dictionary representation of the statement object.
:rtype: dict
"""
data = {}
for field_name in self.get_statement_field_names():
format_method = getattr(self, 'get_{}'.format(
field_name
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if format_method:
data[field_name] = format_method()
else:
data[field_name] = getattr(self, field_name)
return data | [
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train | import_module | Imports the specified module based on the
dot notated import path for the module. | chatterbot/utils.py | def import_module(dotted_path):
"""
Imports the specified module based on the
dot notated import path for the module.
"""
import importlib
module_parts = dotted_path.split('.')
module_path = '.'.join(module_parts[:-1])
module = importlib.import_module(module_path)
return getattr(module, module_parts[-1]) | def import_module(dotted_path):
"""
Imports the specified module based on the
dot notated import path for the module.
"""
import importlib
module_parts = dotted_path.split('.')
module_path = '.'.join(module_parts[:-1])
module = importlib.import_module(module_path)
return getattr(module, module_parts[-1]) | [
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train | initialize_class | :param data: A string or dictionary containing a import_path attribute. | chatterbot/utils.py | def initialize_class(data, *args, **kwargs):
"""
:param data: A string or dictionary containing a import_path attribute.
"""
if isinstance(data, dict):
import_path = data.get('import_path')
data.update(kwargs)
Class = import_module(import_path)
return Class(*args, **data)
else:
Class = import_module(data)
return Class(*args, **kwargs) | def initialize_class(data, *args, **kwargs):
"""
:param data: A string or dictionary containing a import_path attribute.
"""
if isinstance(data, dict):
import_path = data.get('import_path')
data.update(kwargs)
Class = import_module(import_path)
return Class(*args, **data)
else:
Class = import_module(data)
return Class(*args, **kwargs) | [
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train | validate_adapter_class | Raises an exception if validate_class is not a
subclass of adapter_class.
:param validate_class: The class to be validated.
:type validate_class: class
:param adapter_class: The class type to check against.
:type adapter_class: class
:raises: Adapter.InvalidAdapterTypeException | chatterbot/utils.py | def validate_adapter_class(validate_class, adapter_class):
"""
Raises an exception if validate_class is not a
subclass of adapter_class.
:param validate_class: The class to be validated.
:type validate_class: class
:param adapter_class: The class type to check against.
:type adapter_class: class
:raises: Adapter.InvalidAdapterTypeException
"""
from chatterbot.adapters import Adapter
# If a dictionary was passed in, check if it has an import_path attribute
if isinstance(validate_class, dict):
if 'import_path' not in validate_class:
raise Adapter.InvalidAdapterTypeException(
'The dictionary {} must contain a value for "import_path"'.format(
str(validate_class)
)
)
# Set the class to the import path for the next check
validate_class = validate_class.get('import_path')
if not issubclass(import_module(validate_class), adapter_class):
raise Adapter.InvalidAdapterTypeException(
'{} must be a subclass of {}'.format(
validate_class,
adapter_class.__name__
)
) | def validate_adapter_class(validate_class, adapter_class):
"""
Raises an exception if validate_class is not a
subclass of adapter_class.
:param validate_class: The class to be validated.
:type validate_class: class
:param adapter_class: The class type to check against.
:type adapter_class: class
:raises: Adapter.InvalidAdapterTypeException
"""
from chatterbot.adapters import Adapter
# If a dictionary was passed in, check if it has an import_path attribute
if isinstance(validate_class, dict):
if 'import_path' not in validate_class:
raise Adapter.InvalidAdapterTypeException(
'The dictionary {} must contain a value for "import_path"'.format(
str(validate_class)
)
)
# Set the class to the import path for the next check
validate_class = validate_class.get('import_path')
if not issubclass(import_module(validate_class), adapter_class):
raise Adapter.InvalidAdapterTypeException(
'{} must be a subclass of {}'.format(
validate_class,
adapter_class.__name__
)
) | [
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train | get_response_time | Returns the amount of time taken for a given
chat bot to return a response.
:param chatbot: A chat bot instance.
:type chatbot: ChatBot
:returns: The response time in seconds.
:rtype: float | chatterbot/utils.py | def get_response_time(chatbot, statement='Hello'):
"""
Returns the amount of time taken for a given
chat bot to return a response.
:param chatbot: A chat bot instance.
:type chatbot: ChatBot
:returns: The response time in seconds.
:rtype: float
"""
import time
start_time = time.time()
chatbot.get_response(statement)
return time.time() - start_time | def get_response_time(chatbot, statement='Hello'):
"""
Returns the amount of time taken for a given
chat bot to return a response.
:param chatbot: A chat bot instance.
:type chatbot: ChatBot
:returns: The response time in seconds.
:rtype: float
"""
import time
start_time = time.time()
chatbot.get_response(statement)
return time.time() - start_time | [
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train | print_progress_bar | Print progress bar
:param description: Training description
:type description: str
:param iteration_counter: Incremental counter
:type iteration_counter: int
:param total_items: total number items
:type total_items: int
:param progress_bar_length: Progress bar length
:type progress_bar_length: int
:returns: void
:rtype: void | chatterbot/utils.py | def print_progress_bar(description, iteration_counter, total_items, progress_bar_length=20):
"""
Print progress bar
:param description: Training description
:type description: str
:param iteration_counter: Incremental counter
:type iteration_counter: int
:param total_items: total number items
:type total_items: int
:param progress_bar_length: Progress bar length
:type progress_bar_length: int
:returns: void
:rtype: void
"""
import sys
percent = float(iteration_counter) / total_items
hashes = '#' * int(round(percent * progress_bar_length))
spaces = ' ' * (progress_bar_length - len(hashes))
sys.stdout.write('\r{0}: [{1}] {2}%'.format(description, hashes + spaces, int(round(percent * 100))))
sys.stdout.flush()
if total_items == iteration_counter:
print('\r') | def print_progress_bar(description, iteration_counter, total_items, progress_bar_length=20):
"""
Print progress bar
:param description: Training description
:type description: str
:param iteration_counter: Incremental counter
:type iteration_counter: int
:param total_items: total number items
:type total_items: int
:param progress_bar_length: Progress bar length
:type progress_bar_length: int
:returns: void
:rtype: void
"""
import sys
percent = float(iteration_counter) / total_items
hashes = '#' * int(round(percent * progress_bar_length))
spaces = ' ' * (progress_bar_length - len(hashes))
sys.stdout.write('\r{0}: [{1}] {2}%'.format(description, hashes + spaces, int(round(percent * 100))))
sys.stdout.flush()
if total_items == iteration_counter:
print('\r') | [
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train | UnitConversion.get_unit | Get the first match unit metric object supported by pint library
given a variation of unit metric names (Ex:['HOUR', 'hour']).
:param ureg: unit registry which units are defined and handled
:type ureg: pint.registry.UnitRegistry object
:param unit_variations: A list of strings with names of units
:type unit_variations: str | chatterbot/logic/unit_conversion.py | def get_unit(self, ureg, unit_variations):
"""
Get the first match unit metric object supported by pint library
given a variation of unit metric names (Ex:['HOUR', 'hour']).
:param ureg: unit registry which units are defined and handled
:type ureg: pint.registry.UnitRegistry object
:param unit_variations: A list of strings with names of units
:type unit_variations: str
"""
for unit in unit_variations:
try:
return getattr(ureg, unit)
except Exception:
continue
return None | def get_unit(self, ureg, unit_variations):
"""
Get the first match unit metric object supported by pint library
given a variation of unit metric names (Ex:['HOUR', 'hour']).
:param ureg: unit registry which units are defined and handled
:type ureg: pint.registry.UnitRegistry object
:param unit_variations: A list of strings with names of units
:type unit_variations: str
"""
for unit in unit_variations:
try:
return getattr(ureg, unit)
except Exception:
continue
return None | [
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train | UnitConversion.get_valid_units | Returns the firt match `pint.unit.Unit` object for from_unit and
target_unit strings from a possible variation of metric unit names
supported by pint library.
:param ureg: unit registry which units are defined and handled
:type ureg: `pint.registry.UnitRegistry`
:param from_unit: source metric unit
:type from_unit: str
:param from_unit: target metric unit
:type from_unit: str | chatterbot/logic/unit_conversion.py | def get_valid_units(self, ureg, from_unit, target_unit):
"""
Returns the firt match `pint.unit.Unit` object for from_unit and
target_unit strings from a possible variation of metric unit names
supported by pint library.
:param ureg: unit registry which units are defined and handled
:type ureg: `pint.registry.UnitRegistry`
:param from_unit: source metric unit
:type from_unit: str
:param from_unit: target metric unit
:type from_unit: str
"""
from_unit_variations = [from_unit.lower(), from_unit.upper()]
target_unit_variations = [target_unit.lower(), target_unit.upper()]
from_unit = self.get_unit(ureg, from_unit_variations)
target_unit = self.get_unit(ureg, target_unit_variations)
return from_unit, target_unit | def get_valid_units(self, ureg, from_unit, target_unit):
"""
Returns the firt match `pint.unit.Unit` object for from_unit and
target_unit strings from a possible variation of metric unit names
supported by pint library.
:param ureg: unit registry which units are defined and handled
:type ureg: `pint.registry.UnitRegistry`
:param from_unit: source metric unit
:type from_unit: str
:param from_unit: target metric unit
:type from_unit: str
"""
from_unit_variations = [from_unit.lower(), from_unit.upper()]
target_unit_variations = [target_unit.lower(), target_unit.upper()]
from_unit = self.get_unit(ureg, from_unit_variations)
target_unit = self.get_unit(ureg, target_unit_variations)
return from_unit, target_unit | [
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train | UnitConversion.handle_matches | Returns a response statement from a matched input statement.
:param match: It is a valid matched pattern from the input statement
:type: `_sre.SRE_Match` | chatterbot/logic/unit_conversion.py | def handle_matches(self, match):
"""
Returns a response statement from a matched input statement.
:param match: It is a valid matched pattern from the input statement
:type: `_sre.SRE_Match`
"""
response = Statement(text='')
from_parsed = match.group("from")
target_parsed = match.group("target")
n_statement = match.group("number")
if n_statement == 'a' or n_statement == 'an':
n_statement = '1.0'
n = mathparse.parse(n_statement, self.language.ISO_639.upper())
ureg = UnitRegistry()
from_parsed, target_parsed = self.get_valid_units(ureg, from_parsed, target_parsed)
if from_parsed is None or target_parsed is None:
response.confidence = 0.0
else:
from_value = ureg.Quantity(float(n), from_parsed)
target_value = from_value.to(target_parsed)
response.confidence = 1.0
response.text = str(target_value.magnitude)
return response | def handle_matches(self, match):
"""
Returns a response statement from a matched input statement.
:param match: It is a valid matched pattern from the input statement
:type: `_sre.SRE_Match`
"""
response = Statement(text='')
from_parsed = match.group("from")
target_parsed = match.group("target")
n_statement = match.group("number")
if n_statement == 'a' or n_statement == 'an':
n_statement = '1.0'
n = mathparse.parse(n_statement, self.language.ISO_639.upper())
ureg = UnitRegistry()
from_parsed, target_parsed = self.get_valid_units(ureg, from_parsed, target_parsed)
if from_parsed is None or target_parsed is None:
response.confidence = 0.0
else:
from_value = ureg.Quantity(float(n), from_parsed)
target_value = from_value.to(target_parsed)
response.confidence = 1.0
response.text = str(target_value.magnitude)
return response | [
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train | LogicAdapter.get_default_response | This method is called when a logic adapter is unable to generate any
other meaningful response. | chatterbot/logic/logic_adapter.py | def get_default_response(self, input_statement):
"""
This method is called when a logic adapter is unable to generate any
other meaningful response.
"""
from random import choice
if self.default_responses:
response = choice(self.default_responses)
else:
try:
response = self.chatbot.storage.get_random()
except StorageAdapter.EmptyDatabaseException:
response = input_statement
self.chatbot.logger.info(
'No known response to the input was found. Selecting a random response.'
)
# Set confidence to zero because a random response is selected
response.confidence = 0
return response | def get_default_response(self, input_statement):
"""
This method is called when a logic adapter is unable to generate any
other meaningful response.
"""
from random import choice
if self.default_responses:
response = choice(self.default_responses)
else:
try:
response = self.chatbot.storage.get_random()
except StorageAdapter.EmptyDatabaseException:
response = input_statement
self.chatbot.logger.info(
'No known response to the input was found. Selecting a random response.'
)
# Set confidence to zero because a random response is selected
response.confidence = 0
return response | [
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train | TimeLogicAdapter.time_question_features | Provide an analysis of significant features in the string. | chatterbot/logic/time_adapter.py | def time_question_features(self, text):
"""
Provide an analysis of significant features in the string.
"""
features = {}
# A list of all words from the known sentences
all_words = " ".join(self.positive + self.negative).split()
# A list of the first word in each of the known sentence
all_first_words = []
for sentence in self.positive + self.negative:
all_first_words.append(
sentence.split(' ', 1)[0]
)
for word in text.split():
features['first_word({})'.format(word)] = (word in all_first_words)
for word in text.split():
features['contains({})'.format(word)] = (word in all_words)
for letter in 'abcdefghijklmnopqrstuvwxyz':
features['count({})'.format(letter)] = text.lower().count(letter)
features['has({})'.format(letter)] = (letter in text.lower())
return features | def time_question_features(self, text):
"""
Provide an analysis of significant features in the string.
"""
features = {}
# A list of all words from the known sentences
all_words = " ".join(self.positive + self.negative).split()
# A list of the first word in each of the known sentence
all_first_words = []
for sentence in self.positive + self.negative:
all_first_words.append(
sentence.split(' ', 1)[0]
)
for word in text.split():
features['first_word({})'.format(word)] = (word in all_first_words)
for word in text.split():
features['contains({})'.format(word)] = (word in all_words)
for letter in 'abcdefghijklmnopqrstuvwxyz':
features['count({})'.format(letter)] = text.lower().count(letter)
features['has({})'.format(letter)] = (letter in text.lower())
return features | [
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train | MathematicalEvaluation.can_process | Determines whether it is appropriate for this
adapter to respond to the user input. | chatterbot/logic/mathematical_evaluation.py | def can_process(self, statement):
"""
Determines whether it is appropriate for this
adapter to respond to the user input.
"""
response = self.process(statement)
self.cache[statement.text] = response
return response.confidence == 1 | def can_process(self, statement):
"""
Determines whether it is appropriate for this
adapter to respond to the user input.
"""
response = self.process(statement)
self.cache[statement.text] = response
return response.confidence == 1 | [
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train | MathematicalEvaluation.process | Takes a statement string.
Returns the equation from the statement with the mathematical terms solved. | chatterbot/logic/mathematical_evaluation.py | def process(self, statement, additional_response_selection_parameters=None):
"""
Takes a statement string.
Returns the equation from the statement with the mathematical terms solved.
"""
from mathparse import mathparse
input_text = statement.text
# Use the result cached by the process method if it exists
if input_text in self.cache:
cached_result = self.cache[input_text]
self.cache = {}
return cached_result
# Getting the mathematical terms within the input statement
expression = mathparse.extract_expression(input_text, language=self.language.ISO_639.upper())
response = Statement(text=expression)
try:
response.text += ' = ' + str(
mathparse.parse(expression, language=self.language.ISO_639.upper())
)
# The confidence is 1 if the expression could be evaluated
response.confidence = 1
except mathparse.PostfixTokenEvaluationException:
response.confidence = 0
return response | def process(self, statement, additional_response_selection_parameters=None):
"""
Takes a statement string.
Returns the equation from the statement with the mathematical terms solved.
"""
from mathparse import mathparse
input_text = statement.text
# Use the result cached by the process method if it exists
if input_text in self.cache:
cached_result = self.cache[input_text]
self.cache = {}
return cached_result
# Getting the mathematical terms within the input statement
expression = mathparse.extract_expression(input_text, language=self.language.ISO_639.upper())
response = Statement(text=expression)
try:
response.text += ' = ' + str(
mathparse.parse(expression, language=self.language.ISO_639.upper())
)
# The confidence is 1 if the expression could be evaluated
response.confidence = 1
except mathparse.PostfixTokenEvaluationException:
response.confidence = 0
return response | [
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train | get_recent_repeated_responses | A filter that eliminates possibly repetitive responses to prevent
a chat bot from repeating statements that it has recently said. | chatterbot/filters.py | def get_recent_repeated_responses(chatbot, conversation, sample=10, threshold=3, quantity=3):
"""
A filter that eliminates possibly repetitive responses to prevent
a chat bot from repeating statements that it has recently said.
"""
from collections import Counter
# Get the most recent statements from the conversation
conversation_statements = list(chatbot.storage.filter(
conversation=conversation,
order_by=['id']
))[sample * -1:]
text_of_recent_responses = [
statement.text for statement in conversation_statements
]
counter = Counter(text_of_recent_responses)
# Find the n most common responses from the conversation
most_common = counter.most_common(quantity)
return [
counted[0] for counted in most_common
if counted[1] >= threshold
] | def get_recent_repeated_responses(chatbot, conversation, sample=10, threshold=3, quantity=3):
"""
A filter that eliminates possibly repetitive responses to prevent
a chat bot from repeating statements that it has recently said.
"""
from collections import Counter
# Get the most recent statements from the conversation
conversation_statements = list(chatbot.storage.filter(
conversation=conversation,
order_by=['id']
))[sample * -1:]
text_of_recent_responses = [
statement.text for statement in conversation_statements
]
counter = Counter(text_of_recent_responses)
# Find the n most common responses from the conversation
most_common = counter.most_common(quantity)
return [
counted[0] for counted in most_common
if counted[1] >= threshold
] | [
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train | get_most_frequent_response | :param input_statement: A statement, that closely matches an input to the chat bot.
:type input_statement: Statement
:param response_list: A list of statement options to choose a response from.
:type response_list: list
:param storage: An instance of a storage adapter to allow the response selection
method to access other statements if needed.
:type storage: StorageAdapter
:return: The response statement with the greatest number of occurrences.
:rtype: Statement | chatterbot/response_selection.py | def get_most_frequent_response(input_statement, response_list, storage=None):
"""
:param input_statement: A statement, that closely matches an input to the chat bot.
:type input_statement: Statement
:param response_list: A list of statement options to choose a response from.
:type response_list: list
:param storage: An instance of a storage adapter to allow the response selection
method to access other statements if needed.
:type storage: StorageAdapter
:return: The response statement with the greatest number of occurrences.
:rtype: Statement
"""
matching_response = None
occurrence_count = -1
logger = logging.getLogger(__name__)
logger.info('Selecting response with greatest number of occurrences.')
for statement in response_list:
count = len(list(storage.filter(
text=statement.text,
in_response_to=input_statement.text)
))
# Keep the more common statement
if count >= occurrence_count:
matching_response = statement
occurrence_count = count
# Choose the most commonly occuring matching response
return matching_response | def get_most_frequent_response(input_statement, response_list, storage=None):
"""
:param input_statement: A statement, that closely matches an input to the chat bot.
:type input_statement: Statement
:param response_list: A list of statement options to choose a response from.
:type response_list: list
:param storage: An instance of a storage adapter to allow the response selection
method to access other statements if needed.
:type storage: StorageAdapter
:return: The response statement with the greatest number of occurrences.
:rtype: Statement
"""
matching_response = None
occurrence_count = -1
logger = logging.getLogger(__name__)
logger.info('Selecting response with greatest number of occurrences.')
for statement in response_list:
count = len(list(storage.filter(
text=statement.text,
in_response_to=input_statement.text)
))
# Keep the more common statement
if count >= occurrence_count:
matching_response = statement
occurrence_count = count
# Choose the most commonly occuring matching response
return matching_response | [
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train | get_first_response | :param input_statement: A statement, that closely matches an input to the chat bot.
:type input_statement: Statement
:param response_list: A list of statement options to choose a response from.
:type response_list: list
:param storage: An instance of a storage adapter to allow the response selection
method to access other statements if needed.
:type storage: StorageAdapter
:return: Return the first statement in the response list.
:rtype: Statement | chatterbot/response_selection.py | def get_first_response(input_statement, response_list, storage=None):
"""
:param input_statement: A statement, that closely matches an input to the chat bot.
:type input_statement: Statement
:param response_list: A list of statement options to choose a response from.
:type response_list: list
:param storage: An instance of a storage adapter to allow the response selection
method to access other statements if needed.
:type storage: StorageAdapter
:return: Return the first statement in the response list.
:rtype: Statement
"""
logger = logging.getLogger(__name__)
logger.info('Selecting first response from list of {} options.'.format(
len(response_list)
))
return response_list[0] | def get_first_response(input_statement, response_list, storage=None):
"""
:param input_statement: A statement, that closely matches an input to the chat bot.
:type input_statement: Statement
:param response_list: A list of statement options to choose a response from.
:type response_list: list
:param storage: An instance of a storage adapter to allow the response selection
method to access other statements if needed.
:type storage: StorageAdapter
:return: Return the first statement in the response list.
:rtype: Statement
"""
logger = logging.getLogger(__name__)
logger.info('Selecting first response from list of {} options.'.format(
len(response_list)
))
return response_list[0] | [
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train | get_random_response | :param input_statement: A statement, that closely matches an input to the chat bot.
:type input_statement: Statement
:param response_list: A list of statement options to choose a response from.
:type response_list: list
:param storage: An instance of a storage adapter to allow the response selection
method to access other statements if needed.
:type storage: StorageAdapter
:return: Choose a random response from the selection.
:rtype: Statement | chatterbot/response_selection.py | def get_random_response(input_statement, response_list, storage=None):
"""
:param input_statement: A statement, that closely matches an input to the chat bot.
:type input_statement: Statement
:param response_list: A list of statement options to choose a response from.
:type response_list: list
:param storage: An instance of a storage adapter to allow the response selection
method to access other statements if needed.
:type storage: StorageAdapter
:return: Choose a random response from the selection.
:rtype: Statement
"""
from random import choice
logger = logging.getLogger(__name__)
logger.info('Selecting a response from list of {} options.'.format(
len(response_list)
))
return choice(response_list) | def get_random_response(input_statement, response_list, storage=None):
"""
:param input_statement: A statement, that closely matches an input to the chat bot.
:type input_statement: Statement
:param response_list: A list of statement options to choose a response from.
:type response_list: list
:param storage: An instance of a storage adapter to allow the response selection
method to access other statements if needed.
:type storage: StorageAdapter
:return: Choose a random response from the selection.
:rtype: Statement
"""
from random import choice
logger = logging.getLogger(__name__)
logger.info('Selecting a response from list of {} options.'.format(
len(response_list)
))
return choice(response_list) | [
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train | LevenshteinDistance.compare | Compare the two input statements.
:return: The percent of similarity between the text of the statements.
:rtype: float | chatterbot/comparisons.py | def compare(self, statement_a, statement_b):
"""
Compare the two input statements.
:return: The percent of similarity between the text of the statements.
:rtype: float
"""
# Return 0 if either statement has a falsy text value
if not statement_a.text or not statement_b.text:
return 0
# Get the lowercase version of both strings
statement_a_text = str(statement_a.text.lower())
statement_b_text = str(statement_b.text.lower())
similarity = SequenceMatcher(
None,
statement_a_text,
statement_b_text
)
# Calculate a decimal percent of the similarity
percent = round(similarity.ratio(), 2)
return percent | def compare(self, statement_a, statement_b):
"""
Compare the two input statements.
:return: The percent of similarity between the text of the statements.
:rtype: float
"""
# Return 0 if either statement has a falsy text value
if not statement_a.text or not statement_b.text:
return 0
# Get the lowercase version of both strings
statement_a_text = str(statement_a.text.lower())
statement_b_text = str(statement_b.text.lower())
similarity = SequenceMatcher(
None,
statement_a_text,
statement_b_text
)
# Calculate a decimal percent of the similarity
percent = round(similarity.ratio(), 2)
return percent | [
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train | SpacySimilarity.compare | Compare the two input statements.
:return: The percent of similarity between the closest synset distance.
:rtype: float | chatterbot/comparisons.py | def compare(self, statement_a, statement_b):
"""
Compare the two input statements.
:return: The percent of similarity between the closest synset distance.
:rtype: float
"""
document_a = self.nlp(statement_a.text)
document_b = self.nlp(statement_b.text)
return document_a.similarity(document_b) | def compare(self, statement_a, statement_b):
"""
Compare the two input statements.
:return: The percent of similarity between the closest synset distance.
:rtype: float
"""
document_a = self.nlp(statement_a.text)
document_b = self.nlp(statement_b.text)
return document_a.similarity(document_b) | [
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train | JaccardSimilarity.compare | Return the calculated similarity of two
statements based on the Jaccard index. | chatterbot/comparisons.py | def compare(self, statement_a, statement_b):
"""
Return the calculated similarity of two
statements based on the Jaccard index.
"""
# Make both strings lowercase
document_a = self.nlp(statement_a.text.lower())
document_b = self.nlp(statement_b.text.lower())
statement_a_lemmas = set([
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statement_b_lemmas = set([
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# Calculate Jaccard similarity
numerator = len(statement_a_lemmas.intersection(statement_b_lemmas))
denominator = float(len(statement_a_lemmas.union(statement_b_lemmas)))
ratio = numerator / denominator
return ratio | def compare(self, statement_a, statement_b):
"""
Return the calculated similarity of two
statements based on the Jaccard index.
"""
# Make both strings lowercase
document_a = self.nlp(statement_a.text.lower())
document_b = self.nlp(statement_b.text.lower())
statement_a_lemmas = set([
token.lemma_ for token in document_a if not token.is_stop
])
statement_b_lemmas = set([
token.lemma_ for token in document_b if not token.is_stop
])
# Calculate Jaccard similarity
numerator = len(statement_a_lemmas.intersection(statement_b_lemmas))
denominator = float(len(statement_a_lemmas.union(statement_b_lemmas)))
ratio = numerator / denominator
return ratio | [
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train | MongoDatabaseAdapter.get_statement_model | Return the class for the statement model. | chatterbot/storage/mongodb.py | def get_statement_model(self):
"""
Return the class for the statement model.
"""
from chatterbot.conversation import Statement
# Create a storage-aware statement
statement = Statement
statement.storage = self
return statement | def get_statement_model(self):
"""
Return the class for the statement model.
"""
from chatterbot.conversation import Statement
# Create a storage-aware statement
statement = Statement
statement.storage = self
return statement | [
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train | MongoDatabaseAdapter.mongo_to_object | Return Statement object when given data
returned from Mongo DB. | chatterbot/storage/mongodb.py | def mongo_to_object(self, statement_data):
"""
Return Statement object when given data
returned from Mongo DB.
"""
Statement = self.get_model('statement')
statement_data['id'] = statement_data['_id']
return Statement(**statement_data) | def mongo_to_object(self, statement_data):
"""
Return Statement object when given data
returned from Mongo DB.
"""
Statement = self.get_model('statement')
statement_data['id'] = statement_data['_id']
return Statement(**statement_data) | [
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train | MongoDatabaseAdapter.filter | Returns a list of statements in the database
that match the parameters specified. | chatterbot/storage/mongodb.py | def filter(self, **kwargs):
"""
Returns a list of statements in the database
that match the parameters specified.
"""
import pymongo
page_size = kwargs.pop('page_size', 1000)
order_by = kwargs.pop('order_by', None)
tags = kwargs.pop('tags', [])
exclude_text = kwargs.pop('exclude_text', None)
exclude_text_words = kwargs.pop('exclude_text_words', [])
persona_not_startswith = kwargs.pop('persona_not_startswith', None)
search_text_contains = kwargs.pop('search_text_contains', None)
if tags:
kwargs['tags'] = {
'$in': tags
}
if exclude_text:
if 'text' not in kwargs:
kwargs['text'] = {}
elif 'text' in kwargs and isinstance(kwargs['text'], str):
text = kwargs.pop('text')
kwargs['text'] = {
'$eq': text
}
kwargs['text']['$nin'] = exclude_text
if exclude_text_words:
if 'text' not in kwargs:
kwargs['text'] = {}
elif 'text' in kwargs and isinstance(kwargs['text'], str):
text = kwargs.pop('text')
kwargs['text'] = {
'$eq': text
}
exclude_word_regex = '|'.join([
'.*{}.*'.format(word) for word in exclude_text_words
])
kwargs['text']['$not'] = re.compile(exclude_word_regex)
if persona_not_startswith:
if 'persona' not in kwargs:
kwargs['persona'] = {}
elif 'persona' in kwargs and isinstance(kwargs['persona'], str):
persona = kwargs.pop('persona')
kwargs['persona'] = {
'$eq': persona
}
kwargs['persona']['$not'] = re.compile('^bot:*')
if search_text_contains:
or_regex = '|'.join([
'{}'.format(word) for word in search_text_contains.split(' ')
])
kwargs['search_text'] = re.compile(or_regex)
mongo_ordering = []
if order_by:
# Sort so that newer datetimes appear first
if 'created_at' in order_by:
order_by.remove('created_at')
mongo_ordering.append(('created_at', pymongo.DESCENDING, ))
for order in order_by:
mongo_ordering.append((order, pymongo.ASCENDING))
total_statements = self.statements.find(kwargs).count()
for start_index in range(0, total_statements, page_size):
if mongo_ordering:
for match in self.statements.find(kwargs).sort(mongo_ordering).skip(start_index).limit(page_size):
yield self.mongo_to_object(match)
else:
for match in self.statements.find(kwargs).skip(start_index).limit(page_size):
yield self.mongo_to_object(match) | def filter(self, **kwargs):
"""
Returns a list of statements in the database
that match the parameters specified.
"""
import pymongo
page_size = kwargs.pop('page_size', 1000)
order_by = kwargs.pop('order_by', None)
tags = kwargs.pop('tags', [])
exclude_text = kwargs.pop('exclude_text', None)
exclude_text_words = kwargs.pop('exclude_text_words', [])
persona_not_startswith = kwargs.pop('persona_not_startswith', None)
search_text_contains = kwargs.pop('search_text_contains', None)
if tags:
kwargs['tags'] = {
'$in': tags
}
if exclude_text:
if 'text' not in kwargs:
kwargs['text'] = {}
elif 'text' in kwargs and isinstance(kwargs['text'], str):
text = kwargs.pop('text')
kwargs['text'] = {
'$eq': text
}
kwargs['text']['$nin'] = exclude_text
if exclude_text_words:
if 'text' not in kwargs:
kwargs['text'] = {}
elif 'text' in kwargs and isinstance(kwargs['text'], str):
text = kwargs.pop('text')
kwargs['text'] = {
'$eq': text
}
exclude_word_regex = '|'.join([
'.*{}.*'.format(word) for word in exclude_text_words
])
kwargs['text']['$not'] = re.compile(exclude_word_regex)
if persona_not_startswith:
if 'persona' not in kwargs:
kwargs['persona'] = {}
elif 'persona' in kwargs and isinstance(kwargs['persona'], str):
persona = kwargs.pop('persona')
kwargs['persona'] = {
'$eq': persona
}
kwargs['persona']['$not'] = re.compile('^bot:*')
if search_text_contains:
or_regex = '|'.join([
'{}'.format(word) for word in search_text_contains.split(' ')
])
kwargs['search_text'] = re.compile(or_regex)
mongo_ordering = []
if order_by:
# Sort so that newer datetimes appear first
if 'created_at' in order_by:
order_by.remove('created_at')
mongo_ordering.append(('created_at', pymongo.DESCENDING, ))
for order in order_by:
mongo_ordering.append((order, pymongo.ASCENDING))
total_statements = self.statements.find(kwargs).count()
for start_index in range(0, total_statements, page_size):
if mongo_ordering:
for match in self.statements.find(kwargs).sort(mongo_ordering).skip(start_index).limit(page_size):
yield self.mongo_to_object(match)
else:
for match in self.statements.find(kwargs).skip(start_index).limit(page_size):
yield self.mongo_to_object(match) | [
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train | MongoDatabaseAdapter.create | Creates a new statement matching the keyword arguments specified.
Returns the created statement. | chatterbot/storage/mongodb.py | def create(self, **kwargs):
"""
Creates a new statement matching the keyword arguments specified.
Returns the created statement.
"""
Statement = self.get_model('statement')
if 'tags' in kwargs:
kwargs['tags'] = list(set(kwargs['tags']))
if 'search_text' not in kwargs:
kwargs['search_text'] = self.tagger.get_bigram_pair_string(kwargs['text'])
if 'search_in_response_to' not in kwargs:
if kwargs.get('in_response_to'):
kwargs['search_in_response_to'] = self.tagger.get_bigram_pair_string(kwargs['in_response_to'])
inserted = self.statements.insert_one(kwargs)
kwargs['id'] = inserted.inserted_id
return Statement(**kwargs) | def create(self, **kwargs):
"""
Creates a new statement matching the keyword arguments specified.
Returns the created statement.
"""
Statement = self.get_model('statement')
if 'tags' in kwargs:
kwargs['tags'] = list(set(kwargs['tags']))
if 'search_text' not in kwargs:
kwargs['search_text'] = self.tagger.get_bigram_pair_string(kwargs['text'])
if 'search_in_response_to' not in kwargs:
if kwargs.get('in_response_to'):
kwargs['search_in_response_to'] = self.tagger.get_bigram_pair_string(kwargs['in_response_to'])
inserted = self.statements.insert_one(kwargs)
kwargs['id'] = inserted.inserted_id
return Statement(**kwargs) | [
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train | MongoDatabaseAdapter.create_many | Creates multiple statement entries. | chatterbot/storage/mongodb.py | def create_many(self, statements):
"""
Creates multiple statement entries.
"""
create_statements = []
for statement in statements:
statement_data = statement.serialize()
tag_data = list(set(statement_data.pop('tags', [])))
statement_data['tags'] = tag_data
if not statement.search_text:
statement_data['search_text'] = self.tagger.get_bigram_pair_string(statement.text)
if not statement.search_in_response_to and statement.in_response_to:
statement_data['search_in_response_to'] = self.tagger.get_bigram_pair_string(statement.in_response_to)
create_statements.append(statement_data)
self.statements.insert_many(create_statements) | def create_many(self, statements):
"""
Creates multiple statement entries.
"""
create_statements = []
for statement in statements:
statement_data = statement.serialize()
tag_data = list(set(statement_data.pop('tags', [])))
statement_data['tags'] = tag_data
if not statement.search_text:
statement_data['search_text'] = self.tagger.get_bigram_pair_string(statement.text)
if not statement.search_in_response_to and statement.in_response_to:
statement_data['search_in_response_to'] = self.tagger.get_bigram_pair_string(statement.in_response_to)
create_statements.append(statement_data)
self.statements.insert_many(create_statements) | [
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train | MongoDatabaseAdapter.get_random | Returns a random statement from the database | chatterbot/storage/mongodb.py | def get_random(self):
"""
Returns a random statement from the database
"""
from random import randint
count = self.count()
if count < 1:
raise self.EmptyDatabaseException()
random_integer = randint(0, count - 1)
statements = self.statements.find().limit(1).skip(random_integer)
return self.mongo_to_object(list(statements)[0]) | def get_random(self):
"""
Returns a random statement from the database
"""
from random import randint
count = self.count()
if count < 1:
raise self.EmptyDatabaseException()
random_integer = randint(0, count - 1)
statements = self.statements.find().limit(1).skip(random_integer)
return self.mongo_to_object(list(statements)[0]) | [
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train | Statement.add_tags | Add a list of strings to the statement as tags. | chatterbot/ext/sqlalchemy_app/models.py | def add_tags(self, *tags):
"""
Add a list of strings to the statement as tags.
"""
self.tags.extend([
Tag(name=tag) for tag in tags
]) | def add_tags(self, *tags):
"""
Add a list of strings to the statement as tags.
"""
self.tags.extend([
Tag(name=tag) for tag in tags
]) | [
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train | Trainer.get_preprocessed_statement | Preprocess the input statement. | chatterbot/trainers.py | def get_preprocessed_statement(self, input_statement):
"""
Preprocess the input statement.
"""
for preprocessor in self.chatbot.preprocessors:
input_statement = preprocessor(input_statement)
return input_statement | def get_preprocessed_statement(self, input_statement):
"""
Preprocess the input statement.
"""
for preprocessor in self.chatbot.preprocessors:
input_statement = preprocessor(input_statement)
return input_statement | [
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train | Trainer.export_for_training | Create a file from the database that can be used to
train other chat bots. | chatterbot/trainers.py | def export_for_training(self, file_path='./export.json'):
"""
Create a file from the database that can be used to
train other chat bots.
"""
import json
export = {'conversations': self._generate_export_data()}
with open(file_path, 'w+') as jsonfile:
json.dump(export, jsonfile, ensure_ascii=False) | def export_for_training(self, file_path='./export.json'):
"""
Create a file from the database that can be used to
train other chat bots.
"""
import json
export = {'conversations': self._generate_export_data()}
with open(file_path, 'w+') as jsonfile:
json.dump(export, jsonfile, ensure_ascii=False) | [
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train | ListTrainer.train | Train the chat bot based on the provided list of
statements that represents a single conversation. | chatterbot/trainers.py | def train(self, conversation):
"""
Train the chat bot based on the provided list of
statements that represents a single conversation.
"""
previous_statement_text = None
previous_statement_search_text = ''
statements_to_create = []
for conversation_count, text in enumerate(conversation):
if self.show_training_progress:
utils.print_progress_bar(
'List Trainer',
conversation_count + 1, len(conversation)
)
statement_search_text = self.chatbot.storage.tagger.get_bigram_pair_string(text)
statement = self.get_preprocessed_statement(
Statement(
text=text,
search_text=statement_search_text,
in_response_to=previous_statement_text,
search_in_response_to=previous_statement_search_text,
conversation='training'
)
)
previous_statement_text = statement.text
previous_statement_search_text = statement_search_text
statements_to_create.append(statement)
self.chatbot.storage.create_many(statements_to_create) | def train(self, conversation):
"""
Train the chat bot based on the provided list of
statements that represents a single conversation.
"""
previous_statement_text = None
previous_statement_search_text = ''
statements_to_create = []
for conversation_count, text in enumerate(conversation):
if self.show_training_progress:
utils.print_progress_bar(
'List Trainer',
conversation_count + 1, len(conversation)
)
statement_search_text = self.chatbot.storage.tagger.get_bigram_pair_string(text)
statement = self.get_preprocessed_statement(
Statement(
text=text,
search_text=statement_search_text,
in_response_to=previous_statement_text,
search_in_response_to=previous_statement_search_text,
conversation='training'
)
)
previous_statement_text = statement.text
previous_statement_search_text = statement_search_text
statements_to_create.append(statement)
self.chatbot.storage.create_many(statements_to_create) | [
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train | UbuntuCorpusTrainer.is_downloaded | Check if the data file is already downloaded. | chatterbot/trainers.py | def is_downloaded(self, file_path):
"""
Check if the data file is already downloaded.
"""
if os.path.exists(file_path):
self.chatbot.logger.info('File is already downloaded')
return True
return False | def is_downloaded(self, file_path):
"""
Check if the data file is already downloaded.
"""
if os.path.exists(file_path):
self.chatbot.logger.info('File is already downloaded')
return True
return False | [
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train | UbuntuCorpusTrainer.is_extracted | Check if the data file is already extracted. | chatterbot/trainers.py | def is_extracted(self, file_path):
"""
Check if the data file is already extracted.
"""
if os.path.isdir(file_path):
self.chatbot.logger.info('File is already extracted')
return True
return False | def is_extracted(self, file_path):
"""
Check if the data file is already extracted.
"""
if os.path.isdir(file_path):
self.chatbot.logger.info('File is already extracted')
return True
return False | [
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train | UbuntuCorpusTrainer.download | Download a file from the given url.
Show a progress indicator for the download status.
Based on: http://stackoverflow.com/a/15645088/1547223 | chatterbot/trainers.py | def download(self, url, show_status=True):
"""
Download a file from the given url.
Show a progress indicator for the download status.
Based on: http://stackoverflow.com/a/15645088/1547223
"""
import requests
file_name = url.split('/')[-1]
file_path = os.path.join(self.data_directory, file_name)
# Do not download the data if it already exists
if self.is_downloaded(file_path):
return file_path
with open(file_path, 'wb') as open_file:
print('Downloading %s' % url)
response = requests.get(url, stream=True)
total_length = response.headers.get('content-length')
if total_length is None:
# No content length header
open_file.write(response.content)
else:
download = 0
total_length = int(total_length)
for data in response.iter_content(chunk_size=4096):
download += len(data)
open_file.write(data)
if show_status:
done = int(50 * download / total_length)
sys.stdout.write('\r[%s%s]' % ('=' * done, ' ' * (50 - done)))
sys.stdout.flush()
# Add a new line after the download bar
sys.stdout.write('\n')
print('Download location: %s' % file_path)
return file_path | def download(self, url, show_status=True):
"""
Download a file from the given url.
Show a progress indicator for the download status.
Based on: http://stackoverflow.com/a/15645088/1547223
"""
import requests
file_name = url.split('/')[-1]
file_path = os.path.join(self.data_directory, file_name)
# Do not download the data if it already exists
if self.is_downloaded(file_path):
return file_path
with open(file_path, 'wb') as open_file:
print('Downloading %s' % url)
response = requests.get(url, stream=True)
total_length = response.headers.get('content-length')
if total_length is None:
# No content length header
open_file.write(response.content)
else:
download = 0
total_length = int(total_length)
for data in response.iter_content(chunk_size=4096):
download += len(data)
open_file.write(data)
if show_status:
done = int(50 * download / total_length)
sys.stdout.write('\r[%s%s]' % ('=' * done, ' ' * (50 - done)))
sys.stdout.flush()
# Add a new line after the download bar
sys.stdout.write('\n')
print('Download location: %s' % file_path)
return file_path | [
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train | UbuntuCorpusTrainer.extract | Extract a tar file at the specified file path. | chatterbot/trainers.py | def extract(self, file_path):
"""
Extract a tar file at the specified file path.
"""
import tarfile
print('Extracting {}'.format(file_path))
if not os.path.exists(self.extracted_data_directory):
os.makedirs(self.extracted_data_directory)
def track_progress(members):
sys.stdout.write('.')
for member in members:
# This will be the current file being extracted
yield member
with tarfile.open(file_path) as tar:
tar.extractall(path=self.extracted_data_directory, members=track_progress(tar))
self.chatbot.logger.info('File extracted to {}'.format(self.extracted_data_directory))
return True | def extract(self, file_path):
"""
Extract a tar file at the specified file path.
"""
import tarfile
print('Extracting {}'.format(file_path))
if not os.path.exists(self.extracted_data_directory):
os.makedirs(self.extracted_data_directory)
def track_progress(members):
sys.stdout.write('.')
for member in members:
# This will be the current file being extracted
yield member
with tarfile.open(file_path) as tar:
tar.extractall(path=self.extracted_data_directory, members=track_progress(tar))
self.chatbot.logger.info('File extracted to {}'.format(self.extracted_data_directory))
return True | [
"Extract",
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] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/trainers.py#L263-L285 | [
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train | SQLStorageAdapter.count | Return the number of entries in the database. | chatterbot/storage/sql_storage.py | def count(self):
"""
Return the number of entries in the database.
"""
Statement = self.get_model('statement')
session = self.Session()
statement_count = session.query(Statement).count()
session.close()
return statement_count | def count(self):
"""
Return the number of entries in the database.
"""
Statement = self.get_model('statement')
session = self.Session()
statement_count = session.query(Statement).count()
session.close()
return statement_count | [
"Return",
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] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/storage/sql_storage.py#L70-L79 | [
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train | SQLStorageAdapter.remove | Removes the statement that matches the input text.
Removes any responses from statements where the response text matches
the input text. | chatterbot/storage/sql_storage.py | def remove(self, statement_text):
"""
Removes the statement that matches the input text.
Removes any responses from statements where the response text matches
the input text.
"""
Statement = self.get_model('statement')
session = self.Session()
query = session.query(Statement).filter_by(text=statement_text)
record = query.first()
session.delete(record)
self._session_finish(session) | def remove(self, statement_text):
"""
Removes the statement that matches the input text.
Removes any responses from statements where the response text matches
the input text.
"""
Statement = self.get_model('statement')
session = self.Session()
query = session.query(Statement).filter_by(text=statement_text)
record = query.first()
session.delete(record)
self._session_finish(session) | [
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] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/storage/sql_storage.py#L81-L95 | [
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train | SQLStorageAdapter.filter | Returns a list of objects from the database.
The kwargs parameter can contain any number
of attributes. Only objects which contain all
listed attributes and in which all values match
for all listed attributes will be returned. | chatterbot/storage/sql_storage.py | def filter(self, **kwargs):
"""
Returns a list of objects from the database.
The kwargs parameter can contain any number
of attributes. Only objects which contain all
listed attributes and in which all values match
for all listed attributes will be returned.
"""
from sqlalchemy import or_
Statement = self.get_model('statement')
Tag = self.get_model('tag')
session = self.Session()
page_size = kwargs.pop('page_size', 1000)
order_by = kwargs.pop('order_by', None)
tags = kwargs.pop('tags', [])
exclude_text = kwargs.pop('exclude_text', None)
exclude_text_words = kwargs.pop('exclude_text_words', [])
persona_not_startswith = kwargs.pop('persona_not_startswith', None)
search_text_contains = kwargs.pop('search_text_contains', None)
# Convert a single sting into a list if only one tag is provided
if type(tags) == str:
tags = [tags]
if len(kwargs) == 0:
statements = session.query(Statement).filter()
else:
statements = session.query(Statement).filter_by(**kwargs)
if tags:
statements = statements.join(Statement.tags).filter(
Tag.name.in_(tags)
)
if exclude_text:
statements = statements.filter(
~Statement.text.in_(exclude_text)
)
if exclude_text_words:
or_word_query = [
Statement.text.ilike('%' + word + '%') for word in exclude_text_words
]
statements = statements.filter(
~or_(*or_word_query)
)
if persona_not_startswith:
statements = statements.filter(
~Statement.persona.startswith('bot:')
)
if search_text_contains:
or_query = [
Statement.search_text.contains(word) for word in search_text_contains.split(' ')
]
statements = statements.filter(
or_(*or_query)
)
if order_by:
if 'created_at' in order_by:
index = order_by.index('created_at')
order_by[index] = Statement.created_at.asc()
statements = statements.order_by(*order_by)
total_statements = statements.count()
for start_index in range(0, total_statements, page_size):
for statement in statements.slice(start_index, start_index + page_size):
yield self.model_to_object(statement)
session.close() | def filter(self, **kwargs):
"""
Returns a list of objects from the database.
The kwargs parameter can contain any number
of attributes. Only objects which contain all
listed attributes and in which all values match
for all listed attributes will be returned.
"""
from sqlalchemy import or_
Statement = self.get_model('statement')
Tag = self.get_model('tag')
session = self.Session()
page_size = kwargs.pop('page_size', 1000)
order_by = kwargs.pop('order_by', None)
tags = kwargs.pop('tags', [])
exclude_text = kwargs.pop('exclude_text', None)
exclude_text_words = kwargs.pop('exclude_text_words', [])
persona_not_startswith = kwargs.pop('persona_not_startswith', None)
search_text_contains = kwargs.pop('search_text_contains', None)
# Convert a single sting into a list if only one tag is provided
if type(tags) == str:
tags = [tags]
if len(kwargs) == 0:
statements = session.query(Statement).filter()
else:
statements = session.query(Statement).filter_by(**kwargs)
if tags:
statements = statements.join(Statement.tags).filter(
Tag.name.in_(tags)
)
if exclude_text:
statements = statements.filter(
~Statement.text.in_(exclude_text)
)
if exclude_text_words:
or_word_query = [
Statement.text.ilike('%' + word + '%') for word in exclude_text_words
]
statements = statements.filter(
~or_(*or_word_query)
)
if persona_not_startswith:
statements = statements.filter(
~Statement.persona.startswith('bot:')
)
if search_text_contains:
or_query = [
Statement.search_text.contains(word) for word in search_text_contains.split(' ')
]
statements = statements.filter(
or_(*or_query)
)
if order_by:
if 'created_at' in order_by:
index = order_by.index('created_at')
order_by[index] = Statement.created_at.asc()
statements = statements.order_by(*order_by)
total_statements = statements.count()
for start_index in range(0, total_statements, page_size):
for statement in statements.slice(start_index, start_index + page_size):
yield self.model_to_object(statement)
session.close() | [
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"al... | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/storage/sql_storage.py#L97-L174 | [
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train | SQLStorageAdapter.create | Creates a new statement matching the keyword arguments specified.
Returns the created statement. | chatterbot/storage/sql_storage.py | def create(self, **kwargs):
"""
Creates a new statement matching the keyword arguments specified.
Returns the created statement.
"""
Statement = self.get_model('statement')
Tag = self.get_model('tag')
session = self.Session()
tags = set(kwargs.pop('tags', []))
if 'search_text' not in kwargs:
kwargs['search_text'] = self.tagger.get_bigram_pair_string(kwargs['text'])
if 'search_in_response_to' not in kwargs:
in_response_to = kwargs.get('in_response_to')
if in_response_to:
kwargs['search_in_response_to'] = self.tagger.get_bigram_pair_string(in_response_to)
statement = Statement(**kwargs)
for tag_name in tags:
tag = session.query(Tag).filter_by(name=tag_name).first()
if not tag:
# Create the tag
tag = Tag(name=tag_name)
statement.tags.append(tag)
session.add(statement)
session.flush()
session.refresh(statement)
statement_object = self.model_to_object(statement)
self._session_finish(session)
return statement_object | def create(self, **kwargs):
"""
Creates a new statement matching the keyword arguments specified.
Returns the created statement.
"""
Statement = self.get_model('statement')
Tag = self.get_model('tag')
session = self.Session()
tags = set(kwargs.pop('tags', []))
if 'search_text' not in kwargs:
kwargs['search_text'] = self.tagger.get_bigram_pair_string(kwargs['text'])
if 'search_in_response_to' not in kwargs:
in_response_to = kwargs.get('in_response_to')
if in_response_to:
kwargs['search_in_response_to'] = self.tagger.get_bigram_pair_string(in_response_to)
statement = Statement(**kwargs)
for tag_name in tags:
tag = session.query(Tag).filter_by(name=tag_name).first()
if not tag:
# Create the tag
tag = Tag(name=tag_name)
statement.tags.append(tag)
session.add(statement)
session.flush()
session.refresh(statement)
statement_object = self.model_to_object(statement)
self._session_finish(session)
return statement_object | [
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] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/storage/sql_storage.py#L176-L217 | [
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train | SQLStorageAdapter.create_many | Creates multiple statement entries. | chatterbot/storage/sql_storage.py | def create_many(self, statements):
"""
Creates multiple statement entries.
"""
Statement = self.get_model('statement')
Tag = self.get_model('tag')
session = self.Session()
create_statements = []
create_tags = {}
for statement in statements:
statement_data = statement.serialize()
tag_data = statement_data.pop('tags', [])
statement_model_object = Statement(**statement_data)
if not statement.search_text:
statement_model_object.search_text = self.tagger.get_bigram_pair_string(statement.text)
if not statement.search_in_response_to and statement.in_response_to:
statement_model_object.search_in_response_to = self.tagger.get_bigram_pair_string(statement.in_response_to)
new_tags = set(tag_data) - set(create_tags.keys())
if new_tags:
existing_tags = session.query(Tag).filter(
Tag.name.in_(new_tags)
)
for existing_tag in existing_tags:
create_tags[existing_tag.name] = existing_tag
for tag_name in tag_data:
if tag_name in create_tags:
tag = create_tags[tag_name]
else:
# Create the tag if it does not exist
tag = Tag(name=tag_name)
create_tags[tag_name] = tag
statement_model_object.tags.append(tag)
create_statements.append(statement_model_object)
session.add_all(create_statements)
session.commit() | def create_many(self, statements):
"""
Creates multiple statement entries.
"""
Statement = self.get_model('statement')
Tag = self.get_model('tag')
session = self.Session()
create_statements = []
create_tags = {}
for statement in statements:
statement_data = statement.serialize()
tag_data = statement_data.pop('tags', [])
statement_model_object = Statement(**statement_data)
if not statement.search_text:
statement_model_object.search_text = self.tagger.get_bigram_pair_string(statement.text)
if not statement.search_in_response_to and statement.in_response_to:
statement_model_object.search_in_response_to = self.tagger.get_bigram_pair_string(statement.in_response_to)
new_tags = set(tag_data) - set(create_tags.keys())
if new_tags:
existing_tags = session.query(Tag).filter(
Tag.name.in_(new_tags)
)
for existing_tag in existing_tags:
create_tags[existing_tag.name] = existing_tag
for tag_name in tag_data:
if tag_name in create_tags:
tag = create_tags[tag_name]
else:
# Create the tag if it does not exist
tag = Tag(name=tag_name)
create_tags[tag_name] = tag
statement_model_object.tags.append(tag)
create_statements.append(statement_model_object)
session.add_all(create_statements)
session.commit() | [
"Creates",
"multiple",
"statement",
"entries",
"."
] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/storage/sql_storage.py#L219-L267 | [
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train | SQLStorageAdapter.update | Modifies an entry in the database.
Creates an entry if one does not exist. | chatterbot/storage/sql_storage.py | def update(self, statement):
"""
Modifies an entry in the database.
Creates an entry if one does not exist.
"""
Statement = self.get_model('statement')
Tag = self.get_model('tag')
if statement is not None:
session = self.Session()
record = None
if hasattr(statement, 'id') and statement.id is not None:
record = session.query(Statement).get(statement.id)
else:
record = session.query(Statement).filter(
Statement.text == statement.text,
Statement.conversation == statement.conversation,
).first()
# Create a new statement entry if one does not already exist
if not record:
record = Statement(
text=statement.text,
conversation=statement.conversation,
persona=statement.persona
)
# Update the response value
record.in_response_to = statement.in_response_to
record.created_at = statement.created_at
record.search_text = self.tagger.get_bigram_pair_string(statement.text)
if statement.in_response_to:
record.search_in_response_to = self.tagger.get_bigram_pair_string(statement.in_response_to)
for tag_name in statement.get_tags():
tag = session.query(Tag).filter_by(name=tag_name).first()
if not tag:
# Create the record
tag = Tag(name=tag_name)
record.tags.append(tag)
session.add(record)
self._session_finish(session) | def update(self, statement):
"""
Modifies an entry in the database.
Creates an entry if one does not exist.
"""
Statement = self.get_model('statement')
Tag = self.get_model('tag')
if statement is not None:
session = self.Session()
record = None
if hasattr(statement, 'id') and statement.id is not None:
record = session.query(Statement).get(statement.id)
else:
record = session.query(Statement).filter(
Statement.text == statement.text,
Statement.conversation == statement.conversation,
).first()
# Create a new statement entry if one does not already exist
if not record:
record = Statement(
text=statement.text,
conversation=statement.conversation,
persona=statement.persona
)
# Update the response value
record.in_response_to = statement.in_response_to
record.created_at = statement.created_at
record.search_text = self.tagger.get_bigram_pair_string(statement.text)
if statement.in_response_to:
record.search_in_response_to = self.tagger.get_bigram_pair_string(statement.in_response_to)
for tag_name in statement.get_tags():
tag = session.query(Tag).filter_by(name=tag_name).first()
if not tag:
# Create the record
tag = Tag(name=tag_name)
record.tags.append(tag)
session.add(record)
self._session_finish(session) | [
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] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/storage/sql_storage.py#L269-L318 | [
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train | SQLStorageAdapter.get_random | Returns a random statement from the database. | chatterbot/storage/sql_storage.py | def get_random(self):
"""
Returns a random statement from the database.
"""
import random
Statement = self.get_model('statement')
session = self.Session()
count = self.count()
if count < 1:
raise self.EmptyDatabaseException()
random_index = random.randrange(0, count)
random_statement = session.query(Statement)[random_index]
statement = self.model_to_object(random_statement)
session.close()
return statement | def get_random(self):
"""
Returns a random statement from the database.
"""
import random
Statement = self.get_model('statement')
session = self.Session()
count = self.count()
if count < 1:
raise self.EmptyDatabaseException()
random_index = random.randrange(0, count)
random_statement = session.query(Statement)[random_index]
statement = self.model_to_object(random_statement)
session.close()
return statement | [
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train | SQLStorageAdapter.drop | Drop the database. | chatterbot/storage/sql_storage.py | def drop(self):
"""
Drop the database.
"""
Statement = self.get_model('statement')
Tag = self.get_model('tag')
session = self.Session()
session.query(Statement).delete()
session.query(Tag).delete()
session.commit()
session.close() | def drop(self):
"""
Drop the database.
"""
Statement = self.get_model('statement')
Tag = self.get_model('tag')
session = self.Session()
session.query(Statement).delete()
session.query(Tag).delete()
session.commit()
session.close() | [
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] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/storage/sql_storage.py#L341-L354 | [
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train | SQLStorageAdapter.create_database | Populate the database with the tables. | chatterbot/storage/sql_storage.py | def create_database(self):
"""
Populate the database with the tables.
"""
from chatterbot.ext.sqlalchemy_app.models import Base
Base.metadata.create_all(self.engine) | def create_database(self):
"""
Populate the database with the tables.
"""
from chatterbot.ext.sqlalchemy_app.models import Base
Base.metadata.create_all(self.engine) | [
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train | ChatterBotApiView.post | Return a response to the statement in the posted data.
* The JSON data should contain a 'text' attribute. | examples/django_app/example_app/views.py | def post(self, request, *args, **kwargs):
"""
Return a response to the statement in the posted data.
* The JSON data should contain a 'text' attribute.
"""
input_data = json.loads(request.body.decode('utf-8'))
if 'text' not in input_data:
return JsonResponse({
'text': [
'The attribute "text" is required.'
]
}, status=400)
response = self.chatterbot.get_response(input_data)
response_data = response.serialize()
return JsonResponse(response_data, status=200) | def post(self, request, *args, **kwargs):
"""
Return a response to the statement in the posted data.
* The JSON data should contain a 'text' attribute.
"""
input_data = json.loads(request.body.decode('utf-8'))
if 'text' not in input_data:
return JsonResponse({
'text': [
'The attribute "text" is required.'
]
}, status=400)
response = self.chatterbot.get_response(input_data)
response_data = response.serialize()
return JsonResponse(response_data, status=200) | [
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] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/examples/django_app/example_app/views.py#L20-L39 | [
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train | get_file_path | Reads a dotted file path and returns the file path. | chatterbot/corpus.py | def get_file_path(dotted_path, extension='json'):
"""
Reads a dotted file path and returns the file path.
"""
# If the operating system's file path seperator character is in the string
if os.sep in dotted_path or '/' in dotted_path:
# Assume the path is a valid file path
return dotted_path
parts = dotted_path.split('.')
if parts[0] == 'chatterbot':
parts.pop(0)
parts[0] = DATA_DIRECTORY
corpus_path = os.path.join(*parts)
if os.path.exists(corpus_path + '.{}'.format(extension)):
corpus_path += '.{}'.format(extension)
return corpus_path | def get_file_path(dotted_path, extension='json'):
"""
Reads a dotted file path and returns the file path.
"""
# If the operating system's file path seperator character is in the string
if os.sep in dotted_path or '/' in dotted_path:
# Assume the path is a valid file path
return dotted_path
parts = dotted_path.split('.')
if parts[0] == 'chatterbot':
parts.pop(0)
parts[0] = DATA_DIRECTORY
corpus_path = os.path.join(*parts)
if os.path.exists(corpus_path + '.{}'.format(extension)):
corpus_path += '.{}'.format(extension)
return corpus_path | [
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"."
] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/corpus.py#L11-L30 | [
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train | read_corpus | Read and return the data from a corpus json file. | chatterbot/corpus.py | def read_corpus(file_name):
"""
Read and return the data from a corpus json file.
"""
with io.open(file_name, encoding='utf-8') as data_file:
return yaml.load(data_file) | def read_corpus(file_name):
"""
Read and return the data from a corpus json file.
"""
with io.open(file_name, encoding='utf-8') as data_file:
return yaml.load(data_file) | [
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] | 1a03dcb45cba7bdc24d3db5e750582e0cb1518e2 |
train | list_corpus_files | Return a list of file paths to each data file in the specified corpus. | chatterbot/corpus.py | def list_corpus_files(dotted_path):
"""
Return a list of file paths to each data file in the specified corpus.
"""
corpus_path = get_file_path(dotted_path, extension=CORPUS_EXTENSION)
paths = []
if os.path.isdir(corpus_path):
paths = glob.glob(corpus_path + '/**/*.' + CORPUS_EXTENSION, recursive=True)
else:
paths.append(corpus_path)
paths.sort()
return paths | def list_corpus_files(dotted_path):
"""
Return a list of file paths to each data file in the specified corpus.
"""
corpus_path = get_file_path(dotted_path, extension=CORPUS_EXTENSION)
paths = []
if os.path.isdir(corpus_path):
paths = glob.glob(corpus_path + '/**/*.' + CORPUS_EXTENSION, recursive=True)
else:
paths.append(corpus_path)
paths.sort()
return paths | [
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"."
] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/corpus.py#L41-L54 | [
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... | 1a03dcb45cba7bdc24d3db5e750582e0cb1518e2 |
train | load_corpus | Return the data contained within a specified corpus. | chatterbot/corpus.py | def load_corpus(*data_file_paths):
"""
Return the data contained within a specified corpus.
"""
for file_path in data_file_paths:
corpus = []
corpus_data = read_corpus(file_path)
conversations = corpus_data.get('conversations', [])
corpus.extend(conversations)
categories = corpus_data.get('categories', [])
yield corpus, categories, file_path | def load_corpus(*data_file_paths):
"""
Return the data contained within a specified corpus.
"""
for file_path in data_file_paths:
corpus = []
corpus_data = read_corpus(file_path)
conversations = corpus_data.get('conversations', [])
corpus.extend(conversations)
categories = corpus_data.get('categories', [])
yield corpus, categories, file_path | [
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] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/corpus.py#L57-L70 | [
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train | PosLemmaTagger.get_bigram_pair_string | Return a string of text containing part-of-speech, lemma pairs. | chatterbot/tagging.py | def get_bigram_pair_string(self, text):
"""
Return a string of text containing part-of-speech, lemma pairs.
"""
bigram_pairs = []
if len(text) <= 2:
text_without_punctuation = text.translate(self.punctuation_table)
if len(text_without_punctuation) >= 1:
text = text_without_punctuation
document = self.nlp(text)
if len(text) <= 2:
bigram_pairs = [
token.lemma_.lower() for token in document
]
else:
tokens = [
token for token in document if token.is_alpha and not token.is_stop
]
if len(tokens) < 2:
tokens = [
token for token in document if token.is_alpha
]
for index in range(1, len(tokens)):
bigram_pairs.append('{}:{}'.format(
tokens[index - 1].pos_,
tokens[index].lemma_.lower()
))
if not bigram_pairs:
bigram_pairs = [
token.lemma_.lower() for token in document
]
return ' '.join(bigram_pairs) | def get_bigram_pair_string(self, text):
"""
Return a string of text containing part-of-speech, lemma pairs.
"""
bigram_pairs = []
if len(text) <= 2:
text_without_punctuation = text.translate(self.punctuation_table)
if len(text_without_punctuation) >= 1:
text = text_without_punctuation
document = self.nlp(text)
if len(text) <= 2:
bigram_pairs = [
token.lemma_.lower() for token in document
]
else:
tokens = [
token for token in document if token.is_alpha and not token.is_stop
]
if len(tokens) < 2:
tokens = [
token for token in document if token.is_alpha
]
for index in range(1, len(tokens)):
bigram_pairs.append('{}:{}'.format(
tokens[index - 1].pos_,
tokens[index].lemma_.lower()
))
if not bigram_pairs:
bigram_pairs = [
token.lemma_.lower() for token in document
]
return ' '.join(bigram_pairs) | [
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"-",
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] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/tagging.py#L15-L53 | [
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train | DjangoStorageAdapter.filter | Returns a list of statements in the database
that match the parameters specified. | chatterbot/storage/django_storage.py | def filter(self, **kwargs):
"""
Returns a list of statements in the database
that match the parameters specified.
"""
from django.db.models import Q
Statement = self.get_model('statement')
kwargs.pop('page_size', 1000)
order_by = kwargs.pop('order_by', None)
tags = kwargs.pop('tags', [])
exclude_text = kwargs.pop('exclude_text', None)
exclude_text_words = kwargs.pop('exclude_text_words', [])
persona_not_startswith = kwargs.pop('persona_not_startswith', None)
search_text_contains = kwargs.pop('search_text_contains', None)
# Convert a single sting into a list if only one tag is provided
if type(tags) == str:
tags = [tags]
if tags:
kwargs['tags__name__in'] = tags
statements = Statement.objects.filter(**kwargs)
if exclude_text:
statements = statements.exclude(
text__in=exclude_text
)
if exclude_text_words:
or_query = [
~Q(text__icontains=word) for word in exclude_text_words
]
statements = statements.filter(
*or_query
)
if persona_not_startswith:
statements = statements.exclude(
persona__startswith='bot:'
)
if search_text_contains:
or_query = Q()
for word in search_text_contains.split(' '):
or_query |= Q(search_text__contains=word)
statements = statements.filter(
or_query
)
if order_by:
statements = statements.order_by(*order_by)
for statement in statements.iterator():
yield statement | def filter(self, **kwargs):
"""
Returns a list of statements in the database
that match the parameters specified.
"""
from django.db.models import Q
Statement = self.get_model('statement')
kwargs.pop('page_size', 1000)
order_by = kwargs.pop('order_by', None)
tags = kwargs.pop('tags', [])
exclude_text = kwargs.pop('exclude_text', None)
exclude_text_words = kwargs.pop('exclude_text_words', [])
persona_not_startswith = kwargs.pop('persona_not_startswith', None)
search_text_contains = kwargs.pop('search_text_contains', None)
# Convert a single sting into a list if only one tag is provided
if type(tags) == str:
tags = [tags]
if tags:
kwargs['tags__name__in'] = tags
statements = Statement.objects.filter(**kwargs)
if exclude_text:
statements = statements.exclude(
text__in=exclude_text
)
if exclude_text_words:
or_query = [
~Q(text__icontains=word) for word in exclude_text_words
]
statements = statements.filter(
*or_query
)
if persona_not_startswith:
statements = statements.exclude(
persona__startswith='bot:'
)
if search_text_contains:
or_query = Q()
for word in search_text_contains.split(' '):
or_query |= Q(search_text__contains=word)
statements = statements.filter(
or_query
)
if order_by:
statements = statements.order_by(*order_by)
for statement in statements.iterator():
yield statement | [
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"."
] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/storage/django_storage.py#L31-L90 | [
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train | DjangoStorageAdapter.create | Creates a new statement matching the keyword arguments specified.
Returns the created statement. | chatterbot/storage/django_storage.py | def create(self, **kwargs):
"""
Creates a new statement matching the keyword arguments specified.
Returns the created statement.
"""
Statement = self.get_model('statement')
Tag = self.get_model('tag')
tags = kwargs.pop('tags', [])
if 'search_text' not in kwargs:
kwargs['search_text'] = self.tagger.get_bigram_pair_string(kwargs['text'])
if 'search_in_response_to' not in kwargs:
if kwargs.get('in_response_to'):
kwargs['search_in_response_to'] = self.tagger.get_bigram_pair_string(kwargs['in_response_to'])
statement = Statement(**kwargs)
statement.save()
tags_to_add = []
for _tag in tags:
tag, _ = Tag.objects.get_or_create(name=_tag)
tags_to_add.append(tag)
statement.tags.add(*tags_to_add)
return statement | def create(self, **kwargs):
"""
Creates a new statement matching the keyword arguments specified.
Returns the created statement.
"""
Statement = self.get_model('statement')
Tag = self.get_model('tag')
tags = kwargs.pop('tags', [])
if 'search_text' not in kwargs:
kwargs['search_text'] = self.tagger.get_bigram_pair_string(kwargs['text'])
if 'search_in_response_to' not in kwargs:
if kwargs.get('in_response_to'):
kwargs['search_in_response_to'] = self.tagger.get_bigram_pair_string(kwargs['in_response_to'])
statement = Statement(**kwargs)
statement.save()
tags_to_add = []
for _tag in tags:
tag, _ = Tag.objects.get_or_create(name=_tag)
tags_to_add.append(tag)
statement.tags.add(*tags_to_add)
return statement | [
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] | gunthercox/ChatterBot | python | https://github.com/gunthercox/ChatterBot/blob/1a03dcb45cba7bdc24d3db5e750582e0cb1518e2/chatterbot/storage/django_storage.py#L92-L121 | [
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"[... | 1a03dcb45cba7bdc24d3db5e750582e0cb1518e2 |
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