# 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>
### The demonstration algorithm shows some of the most common order methods when working with FutureOption assets.
### </summary>
### <meta name="tag" content="using data" />
### <meta name="tag" content="using quantconnect" />
### <meta name="tag" content="trading and orders" />

class BasicTemplateFutureOptionAlgorithm(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(2022, 1, 1)
 self.set_end_date(2022, 2, 1)
 self.set_cash(100000)

 gold_futures = self.add_future(Futures.Metals.GOLD, Resolution.MINUTE)
 gold_futures.set_filter(0, 180)
 self._symbol = gold_futures.symbol
 self.add_future_option(self._symbol, lambda universe: universe.strikes(-5, +5)
 .calls_only()
 .back_month()
 .only_apply_filter_at_market_open())

 # Historical Data
 history = self.history(self._symbol, 60, Resolution.DAILY)
 self.log(f"Received {len(history)} bars from {self._symbol} FutureOption historical data call.")

 def on_data(self, data):
 '''on_data event is the primary entry point for your algorithm. Each new data point will be pumped in here.
 Arguments:
 slice: Slice object keyed by symbol containing the stock data
 '''
 # Access Data
 for kvp in data.option_chains:
 underlying_future_contract = kvp.key.underlying
 chain = kvp.value

 if not chain: continue

 for contract in chain:
 self.log(f"""Canonical Symbol: {kvp.key}; 
 Contract: {contract}; 
 Right: {contract.right}; 
 Expiry: {contract.expiry}; 
 Bid price: {contract.bid_price}; 
 Ask price: {contract.ask_price}; 
 Implied Volatility: {contract.implied_volatility}""")

 if not self.portfolio.invested:
 atm_strike = sorted(chain, key = lambda x: abs(chain.underlying.price - x.strike))[0].strike
 selected_contract = sorted([contract for contract in chain if contract.strike == atm_strike], \
 key = lambda x: x.expiry, reverse=True)[0]
 self.market_order(selected_contract.symbol, 1)

 def on_order_event(self, order_event):
 self.debug("{} {}".format(self.time, order_event.to_string()))