One-time warning for mismatching canonical/contracts security models (Q…

…uantConnect#7452)

* Send one-time warning about mismatching canonicals/contracts models

* Add regression algorithms

Algorithm.CSharp/ContinuousFutureModelsConsistencyRegressionAlgorithm.cs

@@ -0,0 +1,93 @@
+/*
+ * 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.
+*/
+
+using System.Collections.Generic;
+
+using QuantConnect.Securities;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Regression algorithm asserting that when setting custom models for canonical future, a one-time warning is sent
+    /// informing the user that the contracts models are different (not the custom ones).
+    /// </summary>
+    public class ContinuousFutureModelsConsistencyRegressionAlgorithm : OptionModelsConsistencyRegressionAlgorithm
+    {
+        protected override Security InitializeAlgorithm()
+        {
+            SetStartDate(2013, 7, 1);
+            SetEndDate(2014, 1, 1);
+
+            var continuousContract = AddFuture(Futures.Indices.SP500EMini,
+                dataNormalizationMode: DataNormalizationMode.BackwardsPanamaCanal,
+                dataMappingMode: DataMappingMode.OpenInterest,
+                contractDepthOffset: 1
+            );
+
+            return continuousContract;
+        }
+
+        /// <summary>
+        /// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
+        /// </summary>
+        public override bool CanRunLocally { get; } = true;
+
+        /// <summary>
+        /// This is used by the regression test system to indicate which languages this algorithm is written in.
+        /// </summary>
+        public override Language[] Languages { get; } = { Language.CSharp, Language.Python };
+
+        /// <summary>
+        /// Data Points count of all timeslices of algorithm
+        /// </summary>
+        public override long DataPoints => 703062;
+
+        /// <summary>
+        /// Data Points count of the algorithm history
+        /// </summary>
+        public override int AlgorithmHistoryDataPoints => 0;
+
+        /// <summary>
+        /// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
+        /// </summary>
+        public override Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
+        {
+            {"Total Trades", "0"},
+            {"Average Win", "0%"},
+            {"Average Loss", "0%"},
+            {"Compounding Annual Return", "0%"},
+            {"Drawdown", "0%"},
+            {"Expectancy", "0"},
+            {"Net Profit", "0%"},
+            {"Sharpe Ratio", "0"},
+            {"Probabilistic Sharpe Ratio", "0%"},
+            {"Loss Rate", "0%"},
+            {"Win Rate", "0%"},
+            {"Profit-Loss Ratio", "0"},
+            {"Alpha", "0"},
+            {"Beta", "0"},
+            {"Annual Standard Deviation", "0"},
+            {"Annual Variance", "0"},
+            {"Information Ratio", "0"},
+            {"Tracking Error", "0"},
+            {"Treynor Ratio", "0"},
+            {"Total Fees", "$0.00"},
+            {"Estimated Strategy Capacity", "$0"},
+            {"Lowest Capacity Asset", ""},
+            {"Portfolio Turnover", "0%"},
+            {"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
+        };
+    }
+}

Algorithm.CSharp/IndexOptionModelsConsistencyRegressionAlgorithm.cs

@@ -0,0 +1,91 @@
+/*
+ * 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.
+*/
+
+using QuantConnect.Securities;
+
+using System.Collections.Generic;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Regression algorithm asserting that when setting custom models for canonical index options, a one-time warning is sent
+    /// informing the user that the contracts models are different (not the custom ones).
+    /// </summary>
+    public class IndexOptionModelsConsistencyRegressionAlgorithm : OptionModelsConsistencyRegressionAlgorithm
+    {
+        protected override Security InitializeAlgorithm()
+        {
+            SetStartDate(2021, 1, 4);
+            SetEndDate(2021, 1, 5);
+
+            var index = AddIndex("SPX", Resolution.Minute);
+            var option = AddIndexOption(index.Symbol, "SPX", Resolution.Minute);
+            option.SetFilter((x) => x.Strikes(-5, 5).Expiration(0, 360));
+
+            return option;
+        }
+
+        /// <summary>
+        /// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
+        /// </summary>
+        public override bool CanRunLocally => true;
+
+        /// <summary>
+        /// This is used by the regression test system to indicate which languages this algorithm is written in.
+        /// </summary>
+        public override Language[] Languages { get; } = { Language.CSharp, Language.Python };
+
+        /// <summary>
+        /// Data Points count of all timeslices of algorithm
+        /// </summary>
+        public override long DataPoints => 19224;
+
+        /// <summary>
+        /// Data Points count of the algorithm history
+        /// </summary>
+        public override int AlgorithmHistoryDataPoints => 0;
+
+        /// <summary>
+        /// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
+        /// </summary>
+        public override Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
+        {
+            {"Total Trades", "0"},
+            {"Average Win", "0%"},
+            {"Average Loss", "0%"},
+            {"Compounding Annual Return", "0%"},
+            {"Drawdown", "0%"},
+            {"Expectancy", "0"},
+            {"Net Profit", "0%"},
+            {"Sharpe Ratio", "0"},
+            {"Probabilistic Sharpe Ratio", "0%"},
+            {"Loss Rate", "0%"},
+            {"Win Rate", "0%"},
+            {"Profit-Loss Ratio", "0"},
+            {"Alpha", "0"},
+            {"Beta", "0"},
+            {"Annual Standard Deviation", "0"},
+            {"Annual Variance", "0"},
+            {"Information Ratio", "0"},
+            {"Tracking Error", "0"},
+            {"Treynor Ratio", "0"},
+            {"Total Fees", "$0.00"},
+            {"Estimated Strategy Capacity", "$0"},
+            {"Lowest Capacity Asset", ""},
+            {"Portfolio Turnover", "0%"},
+            {"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
+        };
+    }
+}

Algorithm.CSharp/OptionModelsConsistencyRegressionAlgorithm.cs

@@ -0,0 +1,174 @@
+/*
+ * 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.
+*/
+
+using QuantConnect.Interfaces;
+using System.Collections.Generic;
+using QuantConnect.Orders.Fills;
+using System;
+using QuantConnect.Orders.Fees;
+using QuantConnect.Securities;
+using QuantConnect.Orders.Slippage;
+using QuantConnect.Securities.Volatility;
+using QuantConnect.Logging;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Regression algorithm asserting that when setting custom models for canonical options, a one-time warning is sent
+    /// informing the user that the contracts models are different (not the custom ones).
+    /// </summary>
+    public class OptionModelsConsistencyRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private ILogHandler _originalLogHandler;
+
+        protected bool WarningSent { get; set; }
+
+        public override void Initialize()
+        {
+            // Set a functional log handler in order to be able to assert on the warning message
+            _originalLogHandler = Logging.Log.LogHandler;
+            Logging.Log.LogHandler = new CompositeLogHandler(new ILogHandler[]
+            {
+                Logging.Log.LogHandler,
+                new FunctionalLogHandler(
+                    (debugMessage) => { },
+                    (traceMessage) =>
+                    {
+                        if (traceMessage.Contains("Debug: Warning: Security ") &&
+                            traceMessage.EndsWith("To avoid this, consider using a security initializer to set the right models to each security type according to your algorithm's requirements."))
+                        {
+                            WarningSent = true;
+                        }
+                    },
+                    (errorMessage) => { })
+            });
+
+            var security = InitializeAlgorithm();
+            SetModels(security);
+
+            SetBenchmark(x => 0);
+        }
+
+        protected virtual Security InitializeAlgorithm()
+        {
+            SetStartDate(2015, 12, 24);
+            SetEndDate(2015, 12, 24);
+
+            var equity = AddEquity("GOOG", leverage: 4);
+            var option = AddOption(equity.Symbol);
+            option.SetFilter(u => u.Strikes(-2, +2).Expiration(0, 180));
+
+            return option;
+        }
+
+        protected virtual void SetModels(Security security)
+        {
+            security.SetFillModel(new CustomFillModel());
+            security.SetFeeModel(new CustomFeeModel());
+            security.SetBuyingPowerModel(new CustomBuyingPowerModel());
+            security.SetSlippageModel(new CustomSlippageModel());
+            security.SetVolatilityModel(new CustomVolatilityModel());
+            security.SettlementModel = new CustomSettlementModel();
+        }
+
+        public override void OnEndOfAlgorithm()
+        {
+            Logging.Log.LogHandler = _originalLogHandler;
+
+            if (!WarningSent)
+            {
+                throw new Exception("On-time warning about canonical models mismatch was not sent.");
+            }
+        }
+
+        public class CustomFillModel : FillModel
+        {
+        }
+
+        public class CustomFeeModel : FeeModel
+        {
+        }
+
+        public class CustomBuyingPowerModel : BuyingPowerModel
+        {
+        }
+
+        public class CustomSlippageModel : ConstantSlippageModel
+        {
+            public CustomSlippageModel() : base(0)
+            {
+            }
+        }
+
+        public class CustomVolatilityModel : BaseVolatilityModel
+        {
+        }
+
+        public class CustomSettlementModel : ImmediateSettlementModel
+        {
+        }
+
+        /// <summary>
+        /// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
+        /// </summary>
+        public virtual bool CanRunLocally => true;
+
+        /// <summary>
+        /// This is used by the regression test system to indicate which languages this algorithm is written in.
+        /// </summary>
+        public virtual Language[] Languages { get; } = { Language.CSharp, Language.Python };
+
+        /// <summary>
+        /// Data Points count of all timeslices of algorithm
+        /// </summary>
+        public virtual long DataPoints => 475777;
+
+        /// <summary>
+        /// Data Points count of the algorithm history
+        /// </summary>
+        public virtual int AlgorithmHistoryDataPoints => 0;
+
+        /// <summary>
+        /// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
+        /// </summary>
+        public virtual Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
+        {
+            {"Total Trades", "0"},
+            {"Average Win", "0%"},
+            {"Average Loss", "0%"},
+            {"Compounding Annual Return", "0%"},
+            {"Drawdown", "0%"},
+            {"Expectancy", "0"},
+            {"Net Profit", "0%"},
+            {"Sharpe Ratio", "0"},
+            {"Probabilistic Sharpe Ratio", "0%"},
+            {"Loss Rate", "0%"},
+            {"Win Rate", "0%"},
+            {"Profit-Loss Ratio", "0"},
+            {"Alpha", "0"},
+            {"Beta", "0"},
+            {"Annual Standard Deviation", "0"},
+            {"Annual Variance", "0"},
+            {"Information Ratio", "0"},
+            {"Tracking Error", "0"},
+            {"Treynor Ratio", "0"},
+            {"Total Fees", "$0.00"},
+            {"Estimated Strategy Capacity", "$0"},
+            {"Lowest Capacity Asset", ""},
+            {"Portfolio Turnover", "0%"},
+            {"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
+        };
+    }
+}

Algorithm.Python/ContinuousFutureModelsConsistencyRegressionAlgorithm.py

@@ -0,0 +1,33 @@
+# 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 OptionModelsConsistencyRegressionAlgorithm import OptionModelsConsistencyRegressionAlgorithm
+
+### <summary>
+### Regression algorithm asserting that when setting custom models for canonical future, a one-time warning is sent
+### informing the user that the contracts models are different (not the custom ones).
+### </summary>
+class ContinuousFutureModelsConsistencyRegressionAlgorithm(OptionModelsConsistencyRegressionAlgorithm):
+
+    def InitializeAlgorithm(self) -> Security:
+        self.SetStartDate(2013, 7, 1)
+        self.SetEndDate(2014, 1, 1)
+
+        continuous_contract = self.AddFuture(Futures.Indices.SP500EMini,
+                                             dataNormalizationMode=DataNormalizationMode.BackwardsPanamaCanal,
+                                             dataMappingMode=DataMappingMode.OpenInterest,
+                                             contractDepthOffset=1)
+
+        return continuous_contract