def job_delete(self, job_name):
job_data = self.db.read('job', job_name)
if not job_data:
return False
repo = Repository(job_data['repo'], db=self.db)
scenario = Scenario(job_data['scenario'], repo=repo, db=self.db)
job = Job(job_name, repo, scenario, db=self.db)
job.delete()
return job
async def job_run(self, repo, scenario):
job = Job(None, repo, scenario, db=self.db)
await job.execute(scenario.data)
repo.latest_job = job.name
repo.save()
scenario.latest_job = job.name
scenario.save()
return job
def test_case_5(self):
job = Job(source_filename=self.file1)
job.material = self.material
assert job.data==job.source
def test_case_3(self):
"""job source is an etree element"""
job = Job(source_filename=self.file1)
assert job.load()
log.info(type(job.source))
assert type(job.source) == etree._ElementTree
def test_case_2(self):
"""job loaded source file"""
filename = 'box.svg'
job = Job(source_filename=self.file1)
log.info(job.messages)
assert job.load()
def test_case_1(self):
"""material width and length"""
job = Job(source_filename=self.file1)
job.material = self.material
assert job.material.width==38.1
assert job.material.length==4572
'open': _sym+'_Open',
'high': _sym+'_High',
'low': _sym+'_Low',
'close': _sym,
'volume': _sym+'_Volume'
})
#bchn = s.get_dataset(DatasetType.BLOCKCHAIN)
# correlation(bchn.corr(), 'data/result/blockchain-corr.png', figsize=(32,18))
# pat = s.get_dataset(DatasetType.OHLCV_PATTERN)
# bchn.to_csv('data/result/block1chain-dataset.csv', sep=',', encoding='utf-8', index=True, index_label='Date')
# pat.to_csv('data/result/ohlcv_pattern.csv', sep=',', encoding='utf-8', index=True, index_label='Date')
# fourier_transform(bchn['CapMVRVCur'])
m = MNBModel()
s = s.time_slice('2018-01-01', '2018-02-27', format='%Y-%m-%d')
j = Job(symbol=s, model=m)
reports = j.grid_search(x_type=DatasetType.DISCRETE_TA,
y_type=DatasetType.DISCRETE_TA,
multiprocessing=False,
discretize=False,
variance_threshold=0.01,
params={'fit_prior': False, 'alpha': 0.01})
# Common
if isinstance(reports, list):
c = ReportCollection(reports)
df = c.to_dataframe()
print(df.head())
br = min(reports)
print('Best config:\n\t{} accuracy: {} mse: {} profit: {}%'.format(str(br), str(br.accuracy()), str(br.mse()), br.profit()))
else:
def test_delete():
job = Job('present_name', REPO, SCENARIO, db=DB)
result = job.delete()
assert_equals(result, True)
def test_dump():
job = Job('present_name', REPO, SCENARIO, db=DB)
result = job.dump()
assert_equals(type(result), dict)
def test_load_present():
job = Job('present_name', REPO, SCENARIO, db=DB)
job.load()
assert_equals(job.attribute, 51)
def test_save():
job = Job('present_name', REPO, SCENARIO, db=DB)
job.attribute = 51
result = job.save()
assert_equals(type(result), dict)
def test_load_absent():
job = Job('missing_name', REPO, SCENARIO, db=DB)
job.load()
assert_equals(job.exists, False)
log_level=logging.DEBUG,
logger='job_test')
df = pd.read_csv("./data/result/ohlcv.csv",
sep=',',
encoding='utf-8',
index_col='Date',
parse_dates=True)
btc = pd.read_csv("./data/coinmetrics.io/btc.csv",
sep=',',
encoding='utf-8',
index_col='date',
parse_dates=True)
s = Symbol('BTC',
ohlcv=df,
blockchain=btc,
column_map={
'open': 'BTC_Open',
'high': 'BTC_High',
'low': 'BTC_Low',
'close': 'BTC',
'volume': 'BTC_Volume'
})
m = ARIMAModel()
s = s.time_slice('2016-12-01', '2016-12-31', format='%Y-%m-%d')
j = Job(symbol=s, model=m)
r = j.holdout(x_type=DatasetType.OHLCV_PCT,
y_type=DatasetType.OHLCV_PCT,
univariate_column='close')
#r = min(reports)
print('Best config: {} mse: {}'.format(str(r), str(r.mse())))
'high': _sym + '_High',
'low': _sym + '_Low',
'close': _sym,
'volume': _sym + '_Volume'
})
# bchn = s.get_dataset(DatasetType.BLOCKCHAIN)
# correlation(bchn.corr(), 'data/result/blockchain-corr.png', figsize=(32,18))
# pat = s.get_dataset(DatasetType.OHLCV_PATTERN)
# bchn.to_csv('data/result/block1chain-dataset.csv', sep=',', encoding='utf-8', index=True, index_label='Date')
# pat.to_csv('data/result/ohlcv_pattern.csv', sep=',', encoding='utf-8', index=True, index_label='Date')
# fourier_transform(bchn['CapMVRVCur'])
m = ModelFactory.create_model('mlp')
#s = s.add_lag(7)
s = s.time_slice('2018-01-01', '2018-02-27', format='%Y-%m-%d')
j = Job(symbol=s, model=m)
reports = j.grid_search(
x_type=DatasetType.CONTINUOUS_TA,
y_type=DatasetType.DISCRETE_TA,
#undersample=True,
#multiprocessing=False,
#discretize=False,
#variance_threshold=0.01,
)
# Common
if isinstance(reports, list):
c = ReportCollection(reports)
df = c.to_dataframe()
print(df.head())
br = min(reports)
