def __init__(self, tables_dict={}, scalar=1, round_figures_by=None):
self._tables_dict = tables_dict
self._scalar = scalar
self._round_figures_by = round_figures_by
self._time_series_ops = Mediator.get_time_series_ops()
self._util_func = Mediator.get_util_func()
def keys(self):
"""Returns the names of all the market or trades/orders stored internally.
Returns
-------
str (list)
"""
return Mediator.get_util_func().dict_key_list(self._df_dict.keys())
def __init__(self, trade_order_list=None, metric_name=None, aggregate_by_field=None, aggregation_metric='mean',
tag_value_combinations={}):
if not(isinstance(trade_order_list, list)) and trade_order_list is not None:
trade_order_list = [trade_order_list]
self._trade_order_list = trade_order_list
self._metric_name = metric_name
self._aggregate_by_field = aggregate_by_field
self._aggregation_metric = aggregation_metric
self._results_summary = ResultsSummary()
self._tag_value_combinations = tag_value_combinations
self._trade_order_filter_tag = TradeOrderFilterTag()
self._util_func = Mediator.get_util_func()
self._time_series_ops = Mediator.get_time_series_ops()
def __init__(self):
self._time_series_ops = Mediator.get_time_series_ops()
self._util_func = Mediator.get_util_func()
def get_dataframe_by_key(self,
key,
combined=True,
start_date=None,
finish_date=None):
"""Gets a specific trade/order and combine it into a single DataFrame.
Parameters
----------
key : str
Which market data ticker or trades/order to return
combined : True
Should we combine all the market data for a specific ticker or trades (or orders) into a single DataFrame before returning?
Returns
-------
DataFrame
"""
if key in self._df_dict.keys():
dataframe_key_list = self._df_dict[key]
if 'df' in key:
try:
df = Mediator.get_volatile_cache(
version=tcapy_version).get_dataframe_handle(
Mediator.get_util_func().flatten_list_of_lists(
dataframe_key_list),
burn_after_reading=True)
except Exception as e:
# print("DATAFRAMEHOLDER ERROR" + str(e))
df = dataframe_key_list
if combined:
df = Mediator.get_time_series_ops().concat_dataframe_list(
df)
if df is not None:
if not (df.empty):
df = df.sort_index()
if start_date is not None and finish_date is not None:
df = Mediator.get_time_series_ops(
).filter_start_finish_dataframe(df, start_date,
finish_date)
return df
# elif 'fig' in key:
# try:
# df = self._volatile_cache.get_dataframe_handle(
# self._util_func.flatten_list_of_lists(dataframe_key_list), burn_after_reading=True)
# except:
# df = dataframe_key_list
#
# if combined:
#
# xy_dict = {}
#
# for fig in df:
# for trace in fig['data']:
# name = trace['name']
#
# xy_dict[name + '_x'] = []
# xy_dict[name + '_y'] = []
# xy_dict['trace_name_list'] = []
#
# for fig in df:
# for trace in fig['data']:
# name = trace['name']
#
# xy_dict[name + '_x'].append(trace['x'])
# xy_dict[name + '_y'].append(trace['y'])
#
# if name not in xy_dict['trace_name_list']:
# xy_dict['trace_name_list'].append(name)
#
# fig = df[0]
#
# # aggregate all the x & y values
# for i in range(0, len(fig['data'])):
# name = fig['data'][i]['name']
#
# for j in range(1, len(xy_dict[name + '_x'])):
# fig['data'][i]['x'].extend(xy_dict[name + '_x'])
# fig['data'][i]['y'].extend(xy_dict[name + '_y'])
#
# return fig
else:
# otherwise different type of metadata (don't attempt to combine it)
try:
df = Mediator.get_volatile_cache(
version=tcapy_version).get_dataframe_handle(
Mediator.get_util_func().flatten_list_of_lists(
dataframe_key_list),
burn_after_reading=True)
except Exception as e:
print(e)
df = dataframe_key_list
if isinstance(df, list):
return df[0]
return df
return None
