'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:
print('{} accuracy: {} mse: {} profit: {}%'.format(str(reports), str(reports.accuracy()), str(reports.mse()), reports.profit()))
br = reports
#signal_plot(ohlcv, result)
})
# 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)
print('Best config:\n\t{} accuracy: {} mse: {} profit: {}%'.format(
str(br), str(br.accuracy()), str(br.mse()), br.profit()))
else:
print('{} accuracy: {} mse: {} profit: {}%'.format(str(reports),
str(reports.accuracy()),