def fetchcfg(cfg):
# config = configobj.ConfigObj(cfg, encoding='UTF8')
module = cfg['General']['module']
fetch = cfg['PreProcess']['fetch']
pre_analyze = cfg['PreProcess']['pre_analyze']
post_analyze = cfg['PreProcess']['post_analyze']
label = cfg['Analyze']['label']
indicators = cfg['Analyze']['indicators']
if label is None:
label = 'close'
action_fetch = get_attribute('.'.join([module, fetch]))
action_pre_analyze = get_attribute('.'.join([module, pre_analyze]))
action_post_analyze = get_attribute('.'.join([module, post_analyze]))
return action_fetch, action_pre_analyze, indicators, action_post_analyze, label
def get_active_strategy(strategy):
real_strategy = active_stragery if strategy is None else strategy
module = real_strategy.get('module')
fetch = real_strategy.get('fetch')
pre_analyze = real_strategy.get('pre_analyze')
analyze = real_strategy.get('analyze')
post_analyze = real_strategy.get('post_analyze')
label = real_strategy.get('label')
if label is None:
label = 'close'
action_fetch = get_attribute('.'.join([module, fetch]))
action_pre_analyze = get_attribute('.'.join([module, pre_analyze]))
# analyze = get_attribute('.'.join([module, active_stragery.get('analyze')]))
indicators = analyze
action_post_analyze = get_attribute('.'.join([module, post_analyze]))
return action_fetch, action_pre_analyze, indicators, action_post_analyze, label
def analyze(self):
df_indicators = pd.DataFrame()
# instance = self.get_instance()
for indicator in self._indicators:
indicator = indicator.lower()
meta_info = indicator.split('_')
method_name = meta_info[1]
method = getattr(self, method_name, None)
if method is not None:
del meta_info[1]
if df_indicators.empty:
df_indicators = (method(meta_info))
else:
df_indicators = df_indicators.join(method(meta_info))
else:
# try to get the method from talib
method = get_attribute('.'.join(['talib',
method_name.upper()]))
args = list(map(
int,
meta_info[2:len(meta_info)])) # convert from string to int
result = method(self._origin_frame[meta_info[0]], *args)
if isinstance(result, pd.core.series.Series):
df_result = pd.DataFrame(result, columns=[method_name])
else:
for idx, res in enumerate(result):
if idx == 0:
df_result = pd.DataFrame(
res, columns=[method_name + '_' + str(idx)])
else:
df_result = df_result.join(
pd.DataFrame(
res,
columns=[method_name + '_' + str(idx)]))
# df_result = pd.DataFrame(result[0], columns=[method_name])
if df_indicators.empty:
df_indicators = df_result
else:
df_indicators = df_indicators.join(df_result)
df_result = None # clean the data frame
return df_indicators
def get_instance(self):
""" initialize a instance """
ds_cls = get_attribute(
inspect.__package__ + inspect.getmodulename(__file__) + '.{}'.format(self.__class__.__name__))
return ds_cls
def analyze(self):
df_indicators = pd.DataFrame()
# instance = self.get_instance()
for indicator in self._indicators:
indicator = indicator.lower()
meta_info = indicator.split('|')
# if len(meta_info) == 4 and meta_info[3] == "label":
# self._label = "_".join([meta_info[0],meta_info[2]])
method_name = meta_info[0]
input_col = meta_info[1].split('_')
# method_name = meta_info[0]
# del meta_info[0]
# input_col = [val for val in paras if val.startswith(input_selector)]
# remove the columns, only arguments remained
# args = list((arg for arg in paras if not any(col == arg for col in input_col)))
args = []
if len(meta_info) == 3:
args = meta_info[2].split('_')
args = list(map(int, args)) # convert from string to int
# input_col_final = [col.replace(input_selector, '') for col in input_col]
input = self._origin_frame[input_col].transpose().values
method = getattr(self, method_name, None)
if method is not None:
result = method(*input, *args, input_col)
if df_indicators.empty:
df_indicators = result if result is not None else df_indicators
else:
df_indicators = df_indicators.join(result) if result is not None else df_indicators
else:
# try to get the method from talib
method = get_attribute('.'.join(['talib', method_name.upper()]))
# # get input columns
# input_col = [val for val in meta_info if val.startswith(source_selector)]
#
# # remove the columns, only arguments remained
# args = list((arg for arg in meta_info if not any(col == arg for col in input_col)))
# args = list(map(int, args)) # convert from string to int
# input_col_final = [col.replace(source_selector, '') for col in input_col]
# input = self._origin_frame[input_col_final].transpose().values
result = method(*input, *args)
args_str = "_".join([str(v) for v in args])
# if isinstance(result, pd.core.series.Series):
if not isinstance(result, tuple):
df_result = pd.DataFrame(data=result, index=self._index.flatten(), columns=["{}|{}|{}".format(method_name, input_col[0], args_str)])
else:
for idx, res in enumerate(result):
if idx == 0:
df_result = pd.DataFrame(data=res, index=self._index.flatten(),
columns=["{}_{}_{}".format(method_name,args_str, str(idx))])
else:
df_result = df_result.join(pd.DataFrame(data=res, index=self._index.flatten(),
columns=["{}_{}_{}".format(method_name,args_str, str(idx))]))
# df_result = pd.DataFrame(result[0], columns=[method_name])
if df_indicators.empty:
df_indicators = df_result
else:
df_indicators = df_indicators.join(df_result)
df_result = None # clean the data frame
return df_indicators
def get_instance(cls, data_source, state_codes, start_date, end_date, indicators, scaler, **kwargs):
cls = get_attribute(inspect.getmodule(cls).__name__ + '.Process' + data_source)
return cls(data_source, state_codes, start_date, end_date, indicators, scaler, **kwargs)
def test_get_strategy_analyze(self):
self.assertIsNotNone(
get_strategy_analyze(get_attribute(active_stragery)))