# 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 datetime import timedelta

### <summary>
### Regression algorithm illustrating the usage of the <see cref="QCAlgorithm.OptionChains(IEnumerable{Symbol})"/> method
### to get multiple option chains, which contains additional data besides the symbols, including prices, implied volatility and greeks.
### It also shows how this data can be used to filter the contracts based on certain criteria.
### </summary>
class OptionChainsMultipleFullDataRegressionAlgorithm(QCAlgorithm):

 def initialize(self):
 self.set_start_date(2015, 12, 24)
 self.set_end_date(2015, 12, 24)
 self.set_cash(100000)

 goog = self.add_equity("GOOG").symbol
 spx = self.add_index("SPX").symbol

 chains = self.option_chains([goog, spx], flatten=True)

 self._goog_option_contract = self.get_contract(chains, goog, timedelta(days=10))
 self._spx_option_contract = self.get_contract(chains, spx, timedelta(days=60))

 self.add_option_contract(self._goog_option_contract)
 self.add_index_option_contract(self._spx_option_contract)

 def get_contract(self, chains: OptionChains, underlying: Symbol, expiry_span: timedelta) -> Symbol:
 df = chains.data_frame

 # Index by the requested underlying, by getting all data with canonicals which underlying is the requested underlying symbol:
 canonicals = df.index.get_level_values('canonical')
 condition = [canonical for canonical in canonicals if canonical.underlying == underlying]
 df = df.loc[condition]

 # Get contracts expiring in the next 10 days with an implied volatility greater than 0.5 and a delta less than 0.5
 contracts = df.loc[(df.expiry <= self.time + expiry_span) & (df.impliedvolatility > 0.5) & (df.delta < 0.5)]

 # Select the contract with the latest expiry date
 contracts.sort_values(by='expiry', ascending=False, inplace=True)

 # Get the symbol: the resulting series name is a tuple (canonical symbol, contract symbol)
 return contracts.iloc[0].name[1]

 def on_data(self, data):
 # Do some trading with the selected contract for sample purposes
 if not self.portfolio.invested:
 self.market_order(self._goog_option_contract, 1)
 else:
 self.liquidate()