# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from AlgorithmImports import *

### <summary>
### Regression algorithm demonstrating use of map files with custom data
### </summary>
### <meta name="tag" content="using data" />
### <meta name="tag" content="custom data" />
### <meta name="tag" content="regression test" />
### <meta name="tag" content="rename event" />
### <meta name="tag" content="map" />
### <meta name="tag" content="mapping" />
### <meta name="tag" content="map files" />
class CustomDataUsingMapFileRegressionAlgorithm(QCAlgorithm):

 def initialize(self):
 # Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
 self.set_start_date(2013, 6, 27)
 self.set_end_date(2013, 7, 2)

 self.initial_mapping = False
 self.execution_mapping = False
 self.foxa = Symbol.create("FOXA", SecurityType.EQUITY, Market.USA)
 self._symbol = self.add_data(CustomDataUsingMapping, self.foxa).symbol

 for config in self.subscription_manager.subscription_data_config_service.get_subscription_data_configs(self._symbol):
 if config.resolution != Resolution.MINUTE:
 raise ValueError("Expected resolution to be set to Minute")

 def on_data(self, slice):
 date = self.time.date()
 if slice.symbol_changed_events.contains_key(self._symbol):
 mapping_event = slice.symbol_changed_events[self._symbol]
 self.log("{0} - Ticker changed from: {1} to {2}".format(str(self.time), mapping_event.old_symbol, mapping_event.new_symbol))

 if date == datetime(2013, 6, 27).date():
 # we should Not receive the initial mapping event
 if mapping_event.new_symbol != "NWSA" or mapping_event.old_symbol != "FOXA":
 raise AssertionError("Unexpected mapping event mapping_event")
 self.initial_mapping = True

 if date == datetime(2013, 6, 29).date():
 if mapping_event.new_symbol != "FOXA" or mapping_event.old_symbol != "NWSA":
 raise AssertionError("Unexpected mapping event mapping_event")
 self.set_holdings(self._symbol, 1)
 self.execution_mapping = True

 def on_end_of_algorithm(self):
 if self.initial_mapping:
 raise AssertionError("The ticker generated the initial rename event")
 if not self.execution_mapping:
 raise AssertionError("The ticker did not rename throughout the course of its life even though it should have")

class CustomDataUsingMapping(PythonData):
 '''Test example custom data showing how to enable the use of mapping.
 Implemented as a wrapper of existing NWSA->FOXA equity'''

 def get_source(self, config, date, is_live_mode):
 return TradeBar().get_source(SubscriptionDataConfig(config, CustomDataUsingMapping,
 # create a new symbol as equity so we find the existing data files
 Symbol.create(config.mapped_symbol, SecurityType.EQUITY, config.market)),
 date,
 is_live_mode)

 def reader(self, config, line, date, is_live_mode):
 return TradeBar.parse_equity(config, line, date)

 def requires_mapping(self):
 '''True indicates mapping should be done'''
 return True

 def is_sparse_data(self):
 '''Indicates that the data set is expected to be sparse'''
 return True

 def default_resolution(self):
 '''Gets the default resolution for this data and security type'''
 return Resolution.MINUTE

 def supported_resolutions(self):
 '''Gets the supported resolution for this data and security type'''
 return [ Resolution.MINUTE ]