chore: import upstream snapshot with attribution

2026-07-13 13:02:50 +08:00
commit 0fc60fdcb1
5008 changed files with 910633 additions and 0 deletions
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+# 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.OptionChains(IEnumerable{Symbol})"/> method
+### to get multiple future option chains.
+### </summary>
+class FutureOptionChainsMultipleFullDataRegressionAlgorithm(QCAlgorithm):
+
+    def initialize(self):
+        self.set_start_date(2020, 1, 6)
+        self.set_end_date(2020, 1, 6)
+
+        es_future_contract = self.add_future_contract(
+            Symbol.create_future(Futures.Indices.SP_500_E_MINI, Market.CME, datetime(2020, 3, 20)),
+            Resolution.MINUTE).symbol
+
+        gc_future_contract = self.add_future_contract(
+            Symbol.create_future(Futures.Metals.GOLD, Market.COMEX, datetime(2020, 4, 28)),
+            Resolution.MINUTE).symbol
+
+        chains = self.option_chains([es_future_contract, gc_future_contract], flatten=True)
+
+        self._es_option_contract = self.get_contract(chains, es_future_contract)
+        self._gc_option_contract = self.get_contract(chains, gc_future_contract)
+
+        self.add_future_option_contract(self._es_option_contract)
+        self.add_future_option_contract(self._gc_option_contract)
+
+    def get_contract(self, chains: OptionChains, underlying: Symbol) -> 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]
+        contracts = df.loc[condition]
+
+        # Get contracts expiring within 4 months, with the latest expiration date, highest strike and lowest price
+        contracts = contracts.loc[(df.expiry <= self.time + timedelta(days=120))]
+        contracts = contracts.sort_values(['expiry', 'strike', 'lastprice'], ascending=[False, False, True])
+
+        return contracts.index[0][1]
+
+    def on_data(self, data):
+        # Do some trading with the selected contract for sample purposes
+        if not self.portfolio.invested:
+            self.set_holdings(self._es_option_contract, 0.25)
+            self.set_holdings(self._gc_option_contract, 0.25)
+        else:
+            self.liquidate()