Add regression algorithms

Algorithm.Python/FutureOptionChainFullDataRegressionAlgorithm.py

@@ -26,7 +26,7 @@ class FutureOptionChainFullDataRegressionAlgorithm(QCAlgorithm):
 
         future_contract = self.add_future_contract(
             Symbol.create_future(Futures.Indices.SP_500_E_MINI, Market.CME, datetime(2020, 3, 20)),
-            Resolution.MINUTE).symbol;
+            Resolution.MINUTE).symbol
 
         option_chain = self.option_chain(future_contract, flatten=True)
 
@@ -34,7 +34,7 @@ class FutureOptionChainFullDataRegressionAlgorithm(QCAlgorithm):
         df = option_chain.data_frame
         # Get contracts expiring within 4 months, with the latest expiration date, highest strike and lowest price
         contracts = df.loc[(df.expiry <= self.time + timedelta(days=120))]
-        contracts = df.sort_values(['expiry', 'strike', 'lastprice'], ascending=[False, False, True])
+        contracts = contracts.sort_values(['expiry', 'strike', 'lastprice'], ascending=[False, False, True])
         self._option_contract = contracts.index[0]
 
         self.add_future_option_contract(self._option_contract)

Algorithm.Python/FutureOptionChainsMultipleFullDataRegressionAlgorithm.py

@@ -26,11 +26,11 @@ class FutureOptionChainsMultipleFullDataRegressionAlgorithm(QCAlgorithm):
 
         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;
+            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;
+            Resolution.MINUTE).symbol
 
         chains = self.option_chains([es_future_contract, gc_future_contract], flatten=True)
 
@@ -46,17 +46,18 @@ class FutureOptionChainsMultipleFullDataRegressionAlgorithm(QCAlgorithm):
         # 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]
-        df = df.loc[condition]
+        contracts = df.loc[condition]
 
         # Get contracts expiring within 4 months, with the latest expiration date, highest strike and lowest price
-        contracts = df.loc[(df.expiry <= self.time + timedelta(days=120))]
-        contracts = df.sort_values(['expiry', 'strike', 'lastprice'], ascending=[False, False, True])
+        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, 0.5)
+            self.set_holdings(self._es_option_contract, 0.25)
+            self.set_holdings(self._gc_option_contract, 0.25)
         else:
             self.liquidate()

Algorithm.Python/FutureUniverseHistoryRegressionAlgorithm.py

@@ -0,0 +1,50 @@
+# 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 testing history requests for <see cref="FutureUniverse"/> type work as expected
+### and return the same data as the futures chain provider.
+### </summary>
+class OptionUniverseHistoryRegressionAlgorithm(QCAlgorithm):
+
+    def initialize(self):
+        self.set_start_date(2013, 10, 11)
+        self.set_end_date(2013, 10, 11)
+
+        future = self.add_future(Futures.Indices.SP_500_E_MINI).symbol
+
+        historical_futures_data_df = self.history(FutureUniverse, future, 3, flatten=True)
+
+        # Level 0 of the multi-index is the date, we expect 3 dates, 3 future chains
+        if historical_futures_data_df.index.levshape[0] != 3:
+            raise RegressionTestException(f"Expected 3 futures chains from history request, "
+                                          f"but got {historical_futures_data_df.index.levshape[1]}")
+
+        for date in historical_futures_data_df.index.levels[0]:
+            expected_chain = list(self.future_chain_provider.get_future_contract_list(future, date))
+            expected_chain_count = len(expected_chain)
+
+            actual_chain = historical_futures_data_df.loc[date]
+            actual_chain_count = len(actual_chain)
+
+            if expected_chain_count != actual_chain_count:
+                raise RegressionTestException(f"Expected {expected_chain_count} futures in chain on {date}, "
+                                              f"but got {actual_chain_count}")
+
+            for i, symbol in enumerate(actual_chain.index):
+                expected_symbol = expected_chain[i]
+                if symbol != expected_symbol:
+                    raise RegressionTestException(f"Expected symbol {expected_symbol} at index "
+                                                  f" {i} on {date}, but got {symbol}")

Algorithm.Python/FuturesChainFullDataRegressionAlgorithm.py

@@ -0,0 +1,45 @@
+# 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 *
+from datetime import timedelta
+
+### <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
+        # 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()

Algorithm.Python/FuturesChainsMultipleFullDataRegressionAlgorithm.py

@@ -0,0 +1,58 @@
+# 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 *
+from datetime import timedelta
+
+### <summary>
+### Regression algorithm illustrating the usage of the <see cref="QCAlgorithm.FuturesChains(IEnumerable{Symbol}, bool)"/>
+### method to get multiple futures chains.
+### </summary>
+class FuturesChainsMultipleFullDataRegressionAlgorithm(QCAlgorithm):
+
+    def initialize(self):
+        self.set_start_date(2013, 10, 7)
+        self.set_end_date(2013, 10, 7)
+
+        es_future = self.add_future(Futures.Indices.SP_500_E_MINI).symbol
+        gc_future = self.add_future(Futures.Metals.GOLD).symbol
+
+        chains = self.futures_chains([es_future, gc_future], flatten=True)
+
+        self._es_contract = self.get_contract(chains, es_future)
+        self._gc_contract = self.get_contract(chains, gc_future)
+
+        self.add_future_contract(self._es_contract)
+        self.add_future_contract(self._gc_contract)
+
+    def get_contract(self, chains: FuturesChains, canonical: 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 = [symbol for symbol in canonicals if symbol == canonical]
+        contracts = df.loc[condition]
+
+        # Get contracts expiring within 6 months, with the latest expiration date, and lowest price
+        contracts = contracts.loc[(df.expiry <= self.time + timedelta(days=180))]
+        contracts = contracts.sort_values(['expiry', 'lastprice'], ascending=[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_contract, 0.25)
+            self.set_holdings(self._gc_contract, 0.25)
+        else:
+            self.liquidate()

Algorithm.Python/QuantConnect.Algorithm.Python.csproj

@@ -43,6 +43,7 @@
     <Content Include="OptionUniverseFilterGreeksShortcutsRegressionAlgorithm.py" />
     <Content Include="OptionUniverseFilterOptionsDataRegressionAlgorithm.py" />
     <Content Include="OptionUniverseFilterGreeksRegressionAlgorithm.py" />
+    <Content Include="FutureUniverseHistoryRegressionAlgorithm.py" />
     <Content Include="OptionUniverseHistoryRegressionAlgorithm.py" />
     <Content Include="FundamentalUniverseSelectionAlgorithm.py" />
     <Content Include="AccumulativeInsightPortfolioRegressionAlgorithm.py" />