# 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>
### Example demonstrating how to access to options history for a given underlying equity security.
### </summary>
### <meta name="tag" content="using data" />
### <meta name="tag" content="options" />
### <meta name="tag" content="filter selection" />
### <meta name="tag" content="history" />
class BasicTemplateOptionsHistoryAlgorithm(QCAlgorithm):
 ''' This example demonstrates how to get access to options history for a given underlying equity security.'''

 def initialize(self):
 # this test opens position in the first day of trading, lives through stock split (7 for 1), and closes adjusted position on the second day
 self.set_start_date(2015, 12, 24)
 self.set_end_date(2015, 12, 24)
 self.set_cash(1000000)

 option = self.add_option("GOOG")
 # add the initial contract filter 
 # SetFilter method accepts timedelta objects or integer for days.
 # The following statements yield the same filtering criteria
 option.set_filter(-2, +2, 0, 180)
 # option.set_filter(-2,2, timedelta(0), timedelta(180))

 # set the pricing model for Greeks and volatility
 # find more pricing models https://www.quantconnect.com/lean/documentation/topic27704.html
 option.price_model = OptionPriceModels.black_scholes()
 # set the warm-up period for the pricing model
 self.set_warm_up(TimeSpan.from_days(4))
 # set the benchmark to be the initial cash
 self.set_benchmark(lambda x: 1000000)

 def on_data(self,slice):
 if self.is_warming_up: return
 if not self.portfolio.invested:
 for chain in slice.option_chains:
 volatility = self.securities[chain.key.underlying].volatility_model.volatility
 for contract in chain.value:
 self.log("{0},Bid={1} Ask={2} Last={3} OI={4} sigma={5:.3f} NPV={6:.3f} \
 delta={7:.3f} gamma={8:.3f} vega={9:.3f} beta={10:.2f} theta={11:.2f} IV={12:.2f}".format(
 contract.symbol.value,
 contract.bid_price,
 contract.ask_price,
 contract.last_price,
 contract.open_interest,
 volatility,
 contract.theoretical_price,
 contract.greeks.delta,
 contract.greeks.gamma,
 contract.greeks.vega,
 contract.greeks.rho,
 contract.greeks.theta / 365,
 contract.implied_volatility))

 def on_securities_changed(self, changes):
 for change in changes.added_securities:
 # only print options price
 if change.symbol.value == "GOOG": return
 history = self.history(change.symbol, 10, Resolution.MINUTE).sort_index(level='time', ascending=False)[:3]
 for index, row in history.iterrows():
 self.log("History: " + str(index[3])
 + ": " + index[4].strftime("%m/%d/%Y %I:%M:%S %p")
 + " > " + str(row.close))