def save_results(self, folder_path='.'):
import os
import pandas as pd
folder_path = os.path.abspath(folder_path)
trades = self.ctx.pm.trades
type_map = {'task_id': str,
'entrust_no': str,
'entrust_action': str,
'symbol': str,
'fill_price': float,
'fill_size': float,
'fill_date': int,
'fill_time': int,
'fill_no': str,
'commission': float}
# keys = trades[0].__dict__.keys()
ser_list = dict()
for key in type_map.keys():
v = [t.__getattribute__(key) for t in trades]
ser = pd.Series(data=v, index=None, dtype=type_map[key], name=key)
ser_list[key] = ser
df_trades = pd.DataFrame(ser_list)
df_trades.index.name = 'index'
trades_fn = os.path.join(folder_path, 'trades.csv')
configs_fn = os.path.join(folder_path, 'configs.json')
jutil.create_dir(trades_fn)
df_trades.to_csv(trades_fn)
jutil.save_json(self.props, configs_fn)
print ("Backtest results has been successfully saved to:\n" + folder_path)
def save_results(self, folder_path='.'):
import os
import pandas as pd
folder_path = os.path.abspath(folder_path)
trades = self.ctx.pm.trades
type_map = {'task_id': str,
'entrust_no': str,
'entrust_action': str,
'symbol': str,
'fill_price': float,
'fill_size': float,
'fill_date': np.integer,
'fill_time': np.integer,
'fill_no': str,
'commission': float}
# keys = trades[0].__dict__.keys()
ser_list = dict()
for key in type_map.keys():
v = [t.__getattribute__(key) for t in trades]
ser = pd.Series(data=v, index=None, dtype=type_map[key], name=key)
ser_list[key] = ser
df_trades = pd.DataFrame(ser_list)
df_trades.index.name = 'index'
trades_fn = os.path.join(folder_path, 'trades.csv')
configs_fn = os.path.join(folder_path, 'configs.json')
jutil.create_dir(trades_fn)
df_trades.to_csv(trades_fn)
jutil.save_json(self.props, configs_fn)
print ("Backtest results has been successfully saved to:\n" + folder_path)
def save_dataview(self, folder_path):
"""
Save data and meta_data_to_store to a single hd5 file.
Store at output/sub_folder
Parameters
----------
folder_path : str or unicode
Path to store your data.
"""
abs_folder = os.path.abspath(folder_path)
meta_path = os.path.join(folder_path, 'meta_data.json')
data_path = os.path.join(folder_path, 'data.hd5')
data_to_store = {'data': self.data,
'data_benchmark': self.data_benchmark, 'data_inst': self._data_inst}
data_to_store = {k: v for k, v in data_to_store.items() if v is not None}
meta_data_to_store = {key: self.__dict__[key] for key in self.meta_data_list}
print("\nStore data...")
jutil.save_json(meta_data_to_store, meta_path)
self._save_h5(data_path, data_to_store)
print("Dataview has been successfully saved to:\n"
+ abs_folder + "\n\n"
+ "You can load it with load_dataview('{:s}')".format(abs_folder))
def test_read_save_json():
fp = '../../output/tests/test_read_save_pickle.pic'
d = {'a': 1.0, 'b': 2, 'c': True, 'd': list()}
jutil.save_json(d, fp)
d2 = jutil.read_json(fp)
assert d2['b'] == 2
d3 = jutil.read_json('a_non_exits_file_blabla.pic')
assert d3 == dict()
def store_ic_weight():
"""
Calculate IC weight and save it to file
"""
dv = DataView()
dv.load_dataview(folder_path=dataview_dir_path)
factorList = ['TO', 'BP', 'REVS20', 'float_mv_factor']
orthFactor_dic = {}
for factor in factorList:
orthFactor_dic[factor] = {}
# add the orthogonalized factor to dataview
for trade_date in dv.dates:
snapshot = dv.get_snapshot(trade_date)
factorPanel = snapshot[factorList]
factorPanel = factorPanel.dropna()
if len(factorPanel) != 0:
orthfactorPanel = Schmidt(factorPanel)
orthfactorPanel.columns = [x + '_adj' for x in factorList]
snapshot = pd.merge(left=snapshot,
right=orthfactorPanel,
left_index=True,
right_index=True,
how='left')
for factor in factorList:
orthFactor_dic[factor][trade_date] = snapshot[factor]
for factor in factorList:
dv.append_df(pd.DataFrame(orthFactor_dic[factor]).T,
field_name=factor + '_adj',
is_quarterly=False)
dv.save_dataview(dataview_dir_path)
factorList_adj = [x + '_adj' for x in factorList]
jutil.save_json(factorList_adj, custom_data_path)
w = get_ic_weight(dv)
store = pd.HDFStore(ic_weight_hd5_path)
store['ic_weight'] = w
store.close()
def store_ic_weight():
"""
Calculate IC weight and save it to file
"""
dv = DataView()
dv.load_dataview(folder_path=dataview_dir_path)
factorList = ['TO', 'BP', 'REVS20', 'float_mv_factor']
orthFactor_dic = {}
for factor in factorList:
orthFactor_dic[factor] = {}
# add the orthogonalized factor to dataview
for trade_date in dv.dates:
snapshot = dv.get_snapshot(trade_date)
factorPanel = snapshot[factorList]
factorPanel = factorPanel.dropna()
if len(factorPanel) != 0:
orthfactorPanel = Schmidt(factorPanel)
orthfactorPanel.columns = [x + '_adj' for x in factorList]
snapshot = pd.merge(left=snapshot, right=orthfactorPanel,
left_index=True, right_index=True, how='left')
for factor in factorList:
orthFactor_dic[factor][trade_date] = snapshot[factor]
for factor in factorList:
dv.append_df(pd.DataFrame(orthFactor_dic[factor]).T, field_name=factor + '_adj', is_quarterly=False)
dv.save_dataview(dataview_dir_path)
factorList_adj = [x + '_adj' for x in factorList]
jutil.save_json(factorList_adj, custom_data_path)
w = get_ic_weight(dv)
store = pd.HDFStore(ic_weight_hd5_path)
store['ic_weight'] = w
store.close()