# 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 *
from QuantConnect.Securities.Positions import IPositionGroup

### <summary>
### This base algorithm demonstrates how to use OptionStrategies helper class to batch send orders for common strategies.
### </summary>
class OptionStrategyFactoryMethodsBaseAlgorithm(QCAlgorithm):

    def initialize(self):
        self.set_start_date(2015, 12, 24)
        self.set_end_date(2015, 12, 24)
        self.set_cash(1000000)

        option = self.add_option("GOOG")
        self._option_symbol = option.symbol

        option.set_filter(-2, +2, 0, 180)

        self.set_benchmark("GOOG")

    def on_data(self, slice):
        if not self.portfolio.invested:
            chain = slice.option_chains.get(self._option_symbol)
            if chain is not None:
                self.trade_strategy(chain, self._option_symbol)
        else:
            # Verify that the strategy was traded
            position_group = list(self.portfolio.positions.groups)[0]

            buying_power_model = position_group.buying_power_model
            if not isinstance(buying_power_model, OptionStrategyPositionGroupBuyingPowerModel):
                raise AssertionError("Expected position group buying power model type: OptionStrategyPositionGroupBuyingPowerModel. "
                                f"Actual: {type(position_group.buying_power_model).__name__}")

            self.assert_strategy_position_group(position_group, self._option_symbol)

            # Now we should be able to close the position
            self.liquidate_strategy()

            # We can quit now, no more testing required
            self.quit()

    def on_end_of_algorithm(self):
        if self.portfolio.invested:
            raise AssertionError("Expected no holdings at end of algorithm")

        orders_count = len(list(self.transactions.get_orders(lambda order: order.status == OrderStatus.FILLED)))
        if orders_count != self.expected_orders_count():
            raise AssertionError(f"Expected {self.expected_orders_count()} orders to have been submitted and filled, "
                            f"half for buying the strategy and the other half for the liquidation. Actual {orders_count}")

    def expected_orders_count(self) -> int:
        raise NotImplementedError("ExpectedOrdersCount method is not implemented")

    def trade_strategy(self, chain: OptionChain, option_symbol: Symbol) -> None:
        raise NotImplementedError("TradeStrategy method is not implemented")

    def assert_strategy_position_group(self, position_group: IPositionGroup, option_symbol: Symbol) -> None:
        raise NotImplementedError("AssertStrategyPositionGroup method is not implemented")

    def liquidate_strategy(self) -> None:
        raise NotImplementedError("LiquidateStrategy method is not implemented")