# 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 * ### ### Regression algorithm illustrating the usage of the ### method to get a future chain. ### 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()