# 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>
### Regression algorithm illustrating the usage of the <see cref="QCAlgorithm.FuturesChain(Symbol, bool)"/>
### method to get a future chain.
### </summary>
class FuturesChainFullDataRegressionAlgorithm(QCAlgorithm):

 def initialize(self):
 self.set_start_date(2013, 10, 7)
 self.set_end_date(2013, 10, 7)

 future = self.add_future(Futures.Indices.SP_500_E_MINI, Resolution.MINUTE).symbol

 chain = self.futures_chain(future, flatten=True)

 # Demonstration using data frame:
 df = chain.data_frame

 for index, row in df.iterrows():
 if row['bidprice'] == 0 and row['askprice'] == 0 and row['volume'] == 0:
 raise AssertionError("FuturesChain() returned contract with no data.");

 # Get contracts expiring within 6 months, with the latest expiration date, and lowest price
 contracts = df.loc[(df.expiry <= self.time + timedelta(days=180))]
 contracts = contracts.sort_values(['expiry', 'lastprice'], ascending=[False, True])
 self._future_contract = contracts.index[0]

 self.add_future_contract(self._future_contract)

 def on_data(self, data):
 # Do some trading with the selected contract for sample purposes
 if not self.portfolio.invested:
 self.set_holdings(self._future_contract, 0.5)
 else:
 self.liquidate()