# 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>
### This regression algorithm tests that we receive the expected data when
### we add future option contracts individually using <see cref="AddFutureOptionContract"/>
### </summary>
class AddFutureOptionContractDataStreamingRegressionAlgorithm(QCAlgorithm):
 def initialize(self):
 self.on_data_reached = False
 self.invested = False
 self.symbols_received = []
 self.expected_symbols_received = []
 self.data_received = {}

 self.set_start_date(2020, 1, 4)
 self.set_end_date(2020, 1, 8)

 self.es20h20 = self.add_future_contract(
 Symbol.create_future(Futures.Indices.SP_500_E_MINI, Market.CME, datetime(2020, 3, 20)),
 Resolution.MINUTE).symbol

 self.es19m20 = self.add_future_contract(
 Symbol.create_future(Futures.Indices.SP_500_E_MINI, Market.CME, datetime(2020, 6, 19)),
 Resolution.MINUTE).symbol

 # Get option contract lists for 2020年01月05日 (timedelta(days=1)) because Lean has local data for that date
 option_chains = list(self.option_chain_provider.get_option_contract_list(self.es20h20, self.time + timedelta(days=1)))
 option_chains += self.option_chain_provider.get_option_contract_list(self.es19m20, self.time + timedelta(days=1))

 for option_contract in option_chains:
 self.expected_symbols_received.append(self.add_future_option_contract(option_contract, Resolution.MINUTE).symbol)

 def on_data(self, data: Slice):
 if not data.has_data:
 return

 self.on_data_reached = True
 has_option_quote_bars = False

 for qb in data.quote_bars.values():
 if qb.symbol.security_type != SecurityType.FUTURE_OPTION:
 continue

 has_option_quote_bars = True

 self.symbols_received.append(qb.symbol)
 if qb.symbol not in self.data_received:
 self.data_received[qb.symbol] = []

 self.data_received[qb.symbol].append(qb)

 if self.invested or not has_option_quote_bars:
 return

 if data.contains_key(self.es20h20) and data.contains_key(self.es19m20):
 self.set_holdings(self.es20h20, 0.2)
 self.set_holdings(self.es19m20, 0.2)

 self.invested = True

 def on_end_of_algorithm(self):
 self.symbols_received = list(set(self.symbols_received))
 self.expected_symbols_received = list(set(self.expected_symbols_received))

 if not self.on_data_reached:
 raise AssertionError("OnData() was never called.")
 if len(self.symbols_received) != len(self.expected_symbols_received):
 raise AssertionError(f"Expected {len(self.expected_symbols_received)} option contracts Symbols, found {len(self.symbols_received)}")

 missing_symbols = [expected_symbol.value for expected_symbol in self.expected_symbols_received if expected_symbol not in self.symbols_received]
 if any(missing_symbols):
 raise AssertionError(f'Symbols: "{", ".join(missing_symbols)}" were not found in OnData')

 for expected_symbol in self.expected_symbols_received:
 data = self.data_received[expected_symbol]
 for data_point in data:
 data_point.end_time = datetime(1970, 1, 1)

 non_dupe_data_count = len(set(data))
 if non_dupe_data_count < 1000:
 raise AssertionError(f"Received too few data points. Expected >=1000, found {non_dupe_data_count} for {expected_symbol}")