# 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>
### Custom data universe selection regression algorithm asserting it's behavior. See GH issue #6396
### </summary>
class CustomDataUniverseRegressionAlgorithm(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(2014, 3, 24)
 self.set_end_date(2014, 3, 31)

 self.current_underlying_symbols = set()
 self.universe_settings.resolution = Resolution.DAILY
 self.add_universe(CoarseFundamental, "custom-data-universe", self.selection)

 self._selection_time = [datetime(2014, 3, 24), datetime(2014, 3, 25), datetime(2014, 3, 26),
 datetime(2014, 3, 27), datetime(2014, 3, 28), datetime(2014, 3, 29)]

 def selection(self, coarse):
 self.debug(f"Universe selection called: {self.time} Count: {len(coarse)}")

 expected_time = self._selection_time.pop(0)
 if expected_time != self.time:
 raise ValueError(f"Unexpected selection time {self.time} expected {expected_time}")

 # sort descending by daily dollar volume
 sorted_by_dollar_volume = sorted(coarse, key=lambda x: x.dollar_volume, reverse=True)

 # return the symbol objects of the top entries from our sorted collection
 underlying_symbols = [ x.symbol for x in sorted_by_dollar_volume[:10] ]
 custom_symbols = []
 for symbol in underlying_symbols:
 custom_symbols.append(Symbol.create_base(MyPyCustomData, symbol))
 return underlying_symbols + custom_symbols

 def on_data(self, data):
 '''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.

 Arguments:
 data: Slice object keyed by symbol containing the stock data
 '''
 if not self.portfolio.invested:
 custom_data = data.get(MyPyCustomData)
 if len(custom_data) > 0:
 for symbol in sorted(self.current_underlying_symbols, key=lambda x: x.id.symbol):
 if not self.securities[symbol].has_data:
 continue
 self.set_holdings(symbol, 1 / len(self.current_underlying_symbols))

 if len([x for x in custom_data.keys() if x.underlying == symbol]) == 0:
 raise ValueError(f"Custom data was not found for symbol {symbol}")

 def on_end_of_algorithm(self):
 if len(self._selection_time) != 0:
 raise ValueError(f"Unexpected selection times, missing {len(self._selection_time)}")

 def on_securities_changed(self, changes):
 for security in changes.added_securities:
 if security.symbol.security_type == SecurityType.BASE:
 continue
 self.current_underlying_symbols.add(security.symbol)

 for security in changes.removed_securities:
 if (security.symbol.security_type == SecurityType.BASE or
 # This check can be removed after GH issue #9055 is resolved
 not security.symbol in self.current_underlying_symbols):
 continue
 self.current_underlying_symbols.remove(security.symbol)

class MyPyCustomData(PythonData):

 def get_source(self, config, date, is_live_mode):
 source = f"{Globals.data_folder}/equity/usa/daily/{LeanData.generate_zip_file_name(config.symbol, date, config.resolution, config.tick_type)}"
 return SubscriptionDataSource(source)

 def reader(self, config, line, date, is_live_mode):
 csv = line.split(',')
 _scaleFactor = 1 / 10000

 custom = MyPyCustomData()
 custom.symbol = config.symbol
 custom.time = datetime.strptime(csv[0], '%Y%m%d %H:%M')
 custom.open = float(csv[1]) * _scaleFactor
 custom.high = float(csv[2]) * _scaleFactor
 custom.low = float(csv[3]) * _scaleFactor
 custom.close = float(csv[4]) * _scaleFactor
 custom.value = float(csv[4]) * _scaleFactor
 custom.period = Time.ONE_DAY
 custom.end_time = custom.time + custom.period

 return custom