Update package to include nPython.exe

* Adds strategy capacity estimation in report generator

* Style and license fixes

* Adds comments, constants, and new method accessibility

* Removes unneeded imports

.github/workflows/gh-actions.yml

@@ -0,0 +1,31 @@
+name: Build & Test Lean
+
+on:
+  push:
+    branches: ['*']
+  pull_request:
+    branches: [master]
+
+jobs:
+  build:
+    runs-on: ubuntu-16.04
+    container:
+        image: quantconnect/lean:foundation
+    steps:
+      - uses: actions/checkout@v2
+
+      - name: Restore nuget dependencies
+        run: |
+          nuget restore QuantConnect.Lean.sln -v quiet
+          nuget install NUnit.Runners -Version 3.11.1 -OutputDirectory testrunner
+
+      - name: Build
+        run: msbuild /p:Configuration=Release /p:VbcToolExe=vbnc.exe /v:quiet /p:WarningLevel=1 QuantConnect.Lean.sln
+
+      - name: Run Tests
+        run: mono ./testrunner/NUnit.ConsoleRunner.3.11.1/tools/nunit3-console.exe ./Tests/bin/Release/QuantConnect.Tests.dll --where "cat != TravisExclude" --labels=Off --params:log-handler=ConsoleErrorLogHandler
+
+      - name: Generate & Publish python stubs
+        run: |
+          chmod +x ci_build_stubs.sh
+          ./ci_build_stubs.sh -d -t -g #Ignore Publish as of since credentials are missing on CI

.nuget/NuGet.config

@@ -0,0 +1,10 @@
+<?xml version="1.0" encoding="utf-8"?>
+<configuration>
+    <packageRestore>
+        <add key="enabled" value="true" />
+        <add key="automatic" value="true" />
+    </packageRestore>
+    <packageSources>
+        <add key="PythonNetTesting" value="../localPackages" />
+    </packageSources>
+</configuration>

.travis.yml

@@ -1,10 +1,12 @@
 sudo: required
 language: csharp
+dotnet: 5.0
 mono:
   - 5.12.0
 solution: QuantConnect.Lean.sln
 before_install:
   - export PATH="$HOME/miniconda3/bin:$PATH"
+  - export PYTHONNET_PYDLL="$HOME/miniconda3/lib/libpython3.6m.so"
   - wget -q https://cdn.quantconnect.com/miniconda/Miniconda3-4.5.12-Linux-x86_64.sh
   - bash Miniconda3-4.5.12-Linux-x86_64.sh -b
   - rm -rf Miniconda3-4.5.12-Linux-x86_64.sh
@@ -17,10 +19,10 @@ before_install:
   - conda install -y scipy=1.4.1
   - conda install -y wrapt=1.12.1
 install:
-  - nuget restore QuantConnect.Lean.sln -v quiet
   - nuget install NUnit.Runners -Version 3.11.1 -OutputDirectory testrunner
 script:
-  - msbuild /p:Configuration=Release /p:VbcToolExe=vbnc.exe /v:quiet /p:WarningLevel=1 QuantConnect.Lean.sln
+  - dotnet nuget add source $TRAVIS_BUILD_DIR/localPackages
+  - dotnet build /p:Configuration=Release /p:VbcToolExe=vbnc.exe /v:quiet /p:WarningLevel=1 QuantConnect.Lean.sln
   - mono ./testrunner/NUnit.ConsoleRunner.3.11.1/tools/nunit3-console.exe ./Tests/bin/Release/QuantConnect.Tests.dll --where "cat != TravisExclude" --labels=Off --params:log-handler=ConsoleErrorLogHandler
   - chmod +x ci_build_stubs.sh
   - sudo -E ./ci_build_stubs.sh -d -t -g -p

Algorithm.CSharp/AccumulativeInsightPortfolioRegressionAlgorithm.cs

@@ -113,7 +113,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "54.5455%"},
             {"Rolling Averaged Population Direction", "59.8056%"},
             {"Rolling Averaged Population Magnitude", "59.8056%"},
-            {"OrderListHash", "1256341962"}
+            {"OrderListHash", "0a28eedf6304023f5002ef672b489b88"}
         };
     }
 }

Algorithm.CSharp/AddAlphaModelAlgorithm.cs

@@ -149,7 +149,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "100%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "-887015098"}
+            {"OrderListHash", "506e9fe18984ba6e569b2e327030de3a"}
         };
     }
 }

Algorithm.CSharp/AddFutureOptionContractDataStreamingRegressionAlgorithm.cs

@@ -204,7 +204,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "1074366800"}
+            {"OrderListHash", "35738733ff791eeeaf508faec804cab0"}
         };
     }
 }

Algorithm.CSharp/AddFutureOptionSingleOptionChainSelectedInUniverseFilterRegressionAlgorithm.cs

@@ -238,7 +238,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "1532330301"}
+            {"OrderListHash", "9347e3b610cfa21f7cbd968a0135c8af"}
         };
     }
 }

Algorithm.CSharp/AddOptionContractExpiresRegressionAlgorithm.cs

@@ -131,13 +131,13 @@ namespace QuantConnect.Algorithm.CSharp
             {"Loss Rate", "50%"},
             {"Win Rate", "50%"},
             {"Profit-Loss Ratio", "0.92"},
-            {"Alpha", "-0.023"},
-            {"Beta", "0.005"},
+            {"Alpha", "-0.021"},
+            {"Beta", "-0.01"},
             {"Annual Standard Deviation", "0.006"},
             {"Annual Variance", "0"},
-            {"Information Ratio", "-3.424"},
-            {"Tracking Error", "0.057"},
-            {"Treynor Ratio", "-4.775"},
+            {"Information Ratio", "-3.374"},
+            {"Tracking Error", "0.058"},
+            {"Treynor Ratio", "2.133"},
             {"Total Fees", "$2.00"},
             {"Fitness Score", "0"},
             {"Kelly Criterion Estimate", "0"},
@@ -158,7 +158,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "-1185639451"}
+            {"OrderListHash", "486118a60d78f74811fe8d927c2c6b43"}
         };
     }
 }

Algorithm.CSharp/AddOptionContractFromUniverseRegressionAlgorithm.cs

@@ -210,7 +210,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "721476625"}
+            {"OrderListHash", "ae0b430e9c728966e3736fb352a689c6"}
         };
     }
 }

Algorithm.CSharp/AddRemoveOptionUniverseRegressionAlgorithm.cs

@@ -249,7 +249,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "731140098"}
+            {"OrderListHash", "cf8f76fa441c2a5e3b2dbbabcab32cd2"}
         };
     }
 }

Algorithm.CSharp/AddRemoveSecurityRegressionAlgorithm.cs

@@ -150,7 +150,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "1779055144"}
+            {"OrderListHash", "5f7ba8b5defb310a2eaf98b11abd3b74"}
         };
     }
 }

Algorithm.CSharp/AddRiskManagementAlgorithm.cs

@@ -103,7 +103,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "54.5455%"},
             {"Rolling Averaged Population Direction", "59.8056%"},
             {"Rolling Averaged Population Magnitude", "59.8056%"},
-            {"OrderListHash", "-887190565"}
+            {"OrderListHash", "0b8cbbafdb77bae2f7abe3cf5e05ac5c"}
         };
     }
 }

Algorithm.CSharp/AddUniverseSelectionModelAlgorithm.cs

@@ -122,7 +122,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "1917702312"}
+            {"OrderListHash", "8971c92ba163cec8526379865d9b9ee4"}
         };
     }
 }

Algorithm.CSharp/AddUniverseSelectionModelCoarseAlgorithm.cs

@@ -129,7 +129,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "-1674230481"}
+            {"OrderListHash", "ce59e51c8e404b5dbbc02911473aed1c"}
         };
     }
 }

Algorithm.CSharp/AllShortableSymbolsCoarseSelectionRegressionAlgorithm.cs

@@ -0,0 +1,236 @@
+/*
+ * 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;
+using System.Collections.Generic;
+using System.Linq;
+using QuantConnect.Brokerages;
+using QuantConnect.Data;
+using QuantConnect.Data.Shortable;
+using QuantConnect.Data.UniverseSelection;
+using QuantConnect.Interfaces;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Tests filtering in coarse selection by shortable quantity
+    /// </summary>
+    public class AllShortableSymbolsCoarseSelectionRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private static readonly DateTime _20140325 = new DateTime(2014, 3, 25);
+        private static readonly DateTime _20140326 = new DateTime(2014, 3, 26);
+        private static readonly DateTime _20140327 = new DateTime(2014, 3, 27);
+        private static readonly DateTime _20140328 = new DateTime(2014, 3, 28);
+        private static readonly DateTime _20140329 = new DateTime(2014, 3, 29);
+
+        private static readonly Symbol _aapl = QuantConnect.Symbol.Create("AAPL", SecurityType.Equity, Market.USA);
+        private static readonly Symbol _bac = QuantConnect.Symbol.Create("BAC", SecurityType.Equity, Market.USA);
+        private static readonly Symbol _gme = QuantConnect.Symbol.Create("GME", SecurityType.Equity, Market.USA);
+        private static readonly Symbol _goog = QuantConnect.Symbol.Create("GOOG", SecurityType.Equity, Market.USA);
+        private static readonly Symbol _qqq = QuantConnect.Symbol.Create("QQQ", SecurityType.Equity, Market.USA);
+        private static readonly Symbol _spy = QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA);
+        private DateTime _lastTradeDate;
+
+        private static readonly Dictionary<DateTime, bool> _coarseSelected = new Dictionary<DateTime, bool>
+        {
+            { _20140325, false },
+            { _20140326, false },
+            { _20140327, false },
+            { _20140328, false },
+        };
+
+        private static readonly Dictionary<DateTime, Symbol[]> _expectedSymbols = new Dictionary<DateTime, Symbol[]>
+        {
+            { _20140325, new[]
+                {
+                    _bac,
+                    _qqq,
+                    _spy
+                }
+            },
+            { _20140326, new[]
+                {
+                    _spy
+                }
+            },
+            { _20140327, new[]
+                {
+                    _aapl,
+                    _bac,
+                    _gme,
+                    _qqq,
+                    _spy,
+                }
+            },
+            { _20140328, new[]
+                {
+                    _goog
+                }
+            },
+            { _20140329, new Symbol[0] }
+        };
+
+        public override void Initialize()
+        {
+            SetStartDate(2014, 3, 25);
+            SetEndDate(2014, 3, 29);
+            SetCash(10000000);
+
+            AddUniverse(CoarseSelection);
+            UniverseSettings.Resolution = Resolution.Daily;
+
+            SetBrokerageModel(new AllShortableSymbolsRegressionAlgorithmBrokerageModel());
+        }
+
+        public override void OnData(Slice data)
+        {
+            if (Time.Date == _lastTradeDate)
+            {
+                return;
+            }
+
+            foreach (var symbol in ActiveSecurities.Keys)
+            {
+                if (!Portfolio.ContainsKey(symbol) || !Portfolio[symbol].Invested)
+                {
+                    if (!Shortable(symbol))
+                    {
+                        throw new Exception($"Expected {symbol} to be shortable on {Time:yyyy-MM-dd}");
+                    }
+
+                    // Buy at least once into all Symbols. Since daily data will always use
+                    // MOO orders, it makes the testing of liquidating buying into Symbols difficult.
+                    MarketOrder(symbol, -(decimal)ShortableQuantity(symbol));
+                    _lastTradeDate = Time.Date;
+                }
+            }
+        }
+
+        private IEnumerable<Symbol> CoarseSelection(IEnumerable<CoarseFundamental> coarse)
+        {
+            var shortableSymbols = AllShortableSymbols();
+            var selectedSymbols = coarse
+                .Select(x => x.Symbol)
+                .Where(s => shortableSymbols.ContainsKey(s) && shortableSymbols[s] >= 500)
+                .OrderBy(s => s)
+                .ToList();
+
+            var expectedMissing = 0;
+            if (Time.Date == _20140327)
+            {
+                var gme = QuantConnect.Symbol.Create("GME", SecurityType.Equity, Market.USA);
+                if (!shortableSymbols.ContainsKey(gme))
+                {
+                    throw new Exception("Expected unmapped GME in shortable symbols list on 2014-03-27");
+                }
+                if (!coarse.Select(x => x.Symbol.Value).Contains("GME"))
+                {
+                    throw new Exception("Expected mapped GME in coarse symbols on 2014-03-27");
+                }
+
+                expectedMissing = 1;
+            }
+
+            var missing = _expectedSymbols[Time.Date].Except(selectedSymbols).ToList();
+            if (missing.Count != expectedMissing)
+            {
+                throw new Exception($"Expected Symbols selected on {Time.Date:yyyy-MM-dd} to match expected Symbols, but the following Symbols were missing: {string.Join(", ", missing.Select(s => s.ToString()))}");
+            }
+
+            _coarseSelected[Time.Date] = true;
+            return selectedSymbols;
+        }
+
+        public override void OnEndOfAlgorithm()
+        {
+            if (!_coarseSelected.Values.All(x => x))
+            {
+                throw new AggregateException($"Expected coarse selection on all dates, but didn't run on: {string.Join(", ", _coarseSelected.Where(kvp => !kvp.Value).Select(kvp => kvp.Key.ToStringInvariant("yyyy-MM-dd")))}");
+            }
+        }
+
+        private class AllShortableSymbolsRegressionAlgorithmBrokerageModel : DefaultBrokerageModel
+        {
+            public AllShortableSymbolsRegressionAlgorithmBrokerageModel() : base()
+            {
+                ShortableProvider = new RegressionTestShortableProvider();
+            }
+        }
+
+        private class RegressionTestShortableProvider : LocalDiskShortableProvider
+        {
+            public RegressionTestShortableProvider() : base(SecurityType.Equity, "testbrokerage", Market.USA)
+            {
+            }
+        }
+
+        /// <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 bool CanRunLocally { get; } = true;
+
+        /// <summary>
+        /// This is used by the regression test system to indicate which languages this algorithm is written in.
+        /// </summary>
+        public Language[] Languages { get; } = { Language.CSharp, Language.Python };
+
+        /// <summary>
+        /// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
+        /// </summary>
+        public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
+        {
+            {"Total Trades", "5"},
+            {"Average Win", "0%"},
+            {"Average Loss", "0%"},
+            {"Compounding Annual Return", "36.294%"},
+            {"Drawdown", "0%"},
+            {"Expectancy", "0"},
+            {"Net Profit", "0.340%"},
+            {"Sharpe Ratio", "21.2"},
+            {"Probabilistic Sharpe Ratio", "99.990%"},
+            {"Loss Rate", "0%"},
+            {"Win Rate", "0%"},
+            {"Profit-Loss Ratio", "0"},
+            {"Alpha", "0.274"},
+            {"Beta", "0.138"},
+            {"Annual Standard Deviation", "0.011"},
+            {"Annual Variance", "0"},
+            {"Information Ratio", "7.202"},
+            {"Tracking Error", "0.068"},
+            {"Treynor Ratio", "1.722"},
+            {"Total Fees", "$307.50"},
+            {"Fitness Score", "0.173"},
+            {"Kelly Criterion Estimate", "0"},
+            {"Kelly Criterion Probability Value", "0"},
+            {"Sortino Ratio", "79228162514264337593543950335"},
+            {"Return Over Maximum Drawdown", "79228162514264337593543950335"},
+            {"Portfolio Turnover", "0.173"},
+            {"Total Insights Generated", "0"},
+            {"Total Insights Closed", "0"},
+            {"Total Insights Analysis Completed", "0"},
+            {"Long Insight Count", "0"},
+            {"Short Insight Count", "0"},
+            {"Long/Short Ratio", "100%"},
+            {"Estimated Monthly Alpha Value", "$0"},
+            {"Total Accumulated Estimated Alpha Value", "$0"},
+            {"Mean Population Estimated Insight Value", "$0"},
+            {"Mean Population Direction", "0%"},
+            {"Mean Population Magnitude", "0%"},
+            {"Rolling Averaged Population Direction", "0%"},
+            {"Rolling Averaged Population Magnitude", "0%"},
+            {"OrderListHash", "6b1b205e5a6461ffd5bed645099714cd"}
+        };
+    }
+}

Algorithm.CSharp/Alphas/MortgageRateVolatilityAlpha.cs

@@ -19,28 +19,27 @@ using QuantConnect.Indicators;
 using QuantConnect.Orders.Fees;
 using QuantConnect.Data.Custom;
 using System.Collections.Generic;
-using QuantConnect.Algorithm.Framework;
 using QuantConnect.Algorithm.Framework.Alphas;
 using QuantConnect.Algorithm.Framework.Execution;
 using QuantConnect.Algorithm.Framework.Portfolio;
 using QuantConnect.Algorithm.Framework.Risk;
 using QuantConnect.Algorithm.Framework.Selection;
 
-namespace QuantConnect.Algorithm.CSharp
+namespace QuantConnect.Algorithm.CSharp.Alphas
 {
-	/// <summary>
-	/// This Alpha Model uses Wells Fargo 30-year Fixed Rate Mortgage data from Quandl to 
-	/// generate Insights about the movement of Real Estate ETFs. Mortgage rates can provide information 
-	/// regarding the general price trend of real estate, and ETFs provide good continuous-time instruments 
-	/// to measure the impact against. Volatility in mortgage rates tends to put downward pressure on real 
-	/// estate prices, whereas stable mortgage rates, regardless of true rate, lead to stable or higher real
-	/// estate prices. This Alpha model seeks to take advantage of this correlation by emitting insights
-	/// based on volatility and rate deviation from its historic mean.
-	
-	/// This alpha is part of the Benchmark Alpha Series created by QuantConnect which are open
+    ///<summary>
+    /// This Alpha Model uses Wells Fargo 30-year Fixed Rate Mortgage data from Quandl to
+    /// generate Insights about the movement of Real Estate ETFs. Mortgage rates can provide information
+    /// regarding the general price trend of real estate, and ETFs provide good continuous-time instruments
+    /// to measure the impact against. Volatility in mortgage rates tends to put downward pressure on real
+    /// estate prices, whereas stable mortgage rates, regardless of true rate, lead to stable or higher real
+    /// estate prices. This Alpha model seeks to take advantage of this correlation by emitting insights
+    /// based on volatility and rate deviation from its historic mean.
+    ///
+    /// This alpha is part of the Benchmark Alpha Series created by QuantConnect which are open
     /// sourced so the community and client funds can see an example of an alpha.
-	/// <summary>
-    public class MortgageRateVolatilityAlgorithm : QCAlgorithmFramework
+    ///</summary>
+    public class MortgageRateVolatilityAlgorithm : QCAlgorithm
     {
         public override void Initialize()
         {
@@ -51,8 +50,8 @@ namespace QuantConnect.Algorithm.CSharp
             SetSecurityInitializer(security => security.FeeModel = new ConstantFeeModel(0));
 
             // Basket of 6 liquid real estate ETFs
-            Func<string, Symbol> ToSymbol = x => QuantConnect.Symbol.Create(x, SecurityType.Equity, Market.USA);
-            var realEstateETFs = new[] { "VNQ", "REET", "TAO", "FREL", "SRET", "HIPS" }.Select(ToSymbol).ToArray();
+            Func<string, Symbol> toSymbol = x => QuantConnect.Symbol.Create(x, SecurityType.Equity, Market.USA);
+            var realEstateETFs = new[] { "VNQ", "REET", "TAO", "FREL", "SRET", "HIPS" }.Select(toSymbol).ToArray();
             SetUniverseSelection(new ManualUniverseSelectionModel(realEstateETFs));
 
             SetAlpha(new MortgageRateVolatilityAlphaModel(this));
@@ -64,8 +63,6 @@ namespace QuantConnect.Algorithm.CSharp
             SetRiskManagement(new NullRiskManagementModel());
 
         }
-        
-        public void OnData(QuandlMortgagePriceColumns data) {  }
 
         private class MortgageRateVolatilityAlphaModel : AlphaModel
         {
@@ -79,7 +76,7 @@ namespace QuantConnect.Algorithm.CSharp
             private readonly StandardDeviation _mortgageRateStd;
 
             public MortgageRateVolatilityAlphaModel(
-                QCAlgorithmFramework algorithm,
+                QCAlgorithm algorithm,
                 int indicatorPeriod = 15,
                 double insightMagnitude = 0.0005,
                 int deviations = 2,
@@ -102,7 +99,7 @@ namespace QuantConnect.Algorithm.CSharp
                 WarmUpIndicators(algorithm);
             }
             
-            public override IEnumerable<Insight> Update(QCAlgorithmFramework algorithm, Slice data)
+            public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice data)
             {
                 var insights = new List<Insight>();
                 
@@ -141,7 +138,7 @@ namespace QuantConnect.Algorithm.CSharp
                 return insights;
             }
 
-            private void WarmUpIndicators(QCAlgorithmFramework algorithm)
+            private void WarmUpIndicators(QCAlgorithm algorithm)
             {
             	// Make a history call and update the indicators
                 algorithm.History(new[] { _mortgageRate }, _indicatorPeriod, _resolution).PushThrough(bar =>

Algorithm.CSharp/AutoRegressiveIntegratedMovingAverageRegressionAlgorithm.cs

@@ -0,0 +1,123 @@
+/*
+ * 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;
+using System.Collections.Generic;
+using QuantConnect.Data;
+using QuantConnect.Indicators;
+using QuantConnect.Interfaces;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Regression algorithm to test the behaviour of ARMA versus AR models at the same order of differencing.
+    /// In particular, an ARIMA(1,1,1) and ARIMA(1,1,0) are instantiated while orders are placed if their difference
+    /// is sufficiently large (which would be due to the inclusion of the MA(1) term).
+    /// </summary>
+    public class AutoRegressiveIntegratedMovingAverageRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private AutoRegressiveIntegratedMovingAverage _arima;
+        private AutoRegressiveIntegratedMovingAverage _ar;
+        private decimal _last;
+
+        public override void Initialize()
+        {
+            SetStartDate(2013, 1, 07);
+            SetEndDate(2013, 12, 11);
+
+            EnableAutomaticIndicatorWarmUp = true;
+            AddEquity("SPY", Resolution.Daily);
+            _arima = ARIMA("SPY", 1, 1, 1, 50);
+            _ar = ARIMA("SPY", 1, 1, 0, 50);
+        }
+
+        public override void OnData(Slice slice)
+        {
+            if (_arima.IsReady)
+            {
+                if (Math.Abs(_ar.Current.Value - _arima.Current.Value) > 1) // Difference due to MA(1) being included.
+                {
+                    if (_arima.Current.Value > _last)
+                    {
+                        MarketOrder("SPY", 1);
+                    }
+                    else
+                    {
+                        MarketOrder("SPY", -1);
+                    }
+                }
+
+                _last = _arima.Current.Value;
+            }
+        }
+
+        /// <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 bool CanRunLocally { get; } = true;
+
+        /// <summary>
+        /// This is used by the regression test system to indicate which languages this algorithm is written in.
+        /// </summary>
+        public Language[] Languages { get; } = { Language.CSharp, Language.Python };
+
+        /// <summary>
+        /// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
+        /// </summary>
+        public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
+        {
+            {"Total Trades", "65"},
+            {"Average Win", "0.00%"},
+            {"Average Loss", "0.00%"},
+            {"Compounding Annual Return", "0.145%"},
+            {"Drawdown", "0.100%"},
+            {"Expectancy", "2.190"},
+            {"Net Profit", "0.134%"},
+            {"Sharpe Ratio", "0.993"},
+            {"Probabilistic Sharpe Ratio", "49.669%"},
+            {"Loss Rate", "29%"},
+            {"Win Rate", "71%"},
+            {"Profit-Loss Ratio", "3.50"},
+            {"Alpha", "0.001"},
+            {"Beta", "0"},
+            {"Annual Standard Deviation", "0.001"},
+            {"Annual Variance", "0"},
+            {"Information Ratio", "-2.168"},
+            {"Tracking Error", "0.099"},
+            {"Treynor Ratio", "-5.187"},
+            {"Total Fees", "$65.00"},
+            {"Fitness Score", "0"},
+            {"Kelly Criterion Estimate", "0"},
+            {"Kelly Criterion Probability Value", "0"},
+            {"Sortino Ratio", "1.51"},
+            {"Return Over Maximum Drawdown", "1.819"},
+            {"Portfolio Turnover", "0"},
+            {"Total Insights Generated", "0"},
+            {"Total Insights Closed", "0"},
+            {"Total Insights Analysis Completed", "0"},
+            {"Long Insight Count", "0"},
+            {"Short Insight Count", "0"},
+            {"Long/Short Ratio", "100%"},
+            {"Estimated Monthly Alpha Value", "$0"},
+            {"Total Accumulated Estimated Alpha Value", "$0"},
+            {"Mean Population Estimated Insight Value", "$0"},
+            {"Mean Population Direction", "0%"},
+            {"Mean Population Magnitude", "0%"},
+            {"Rolling Averaged Population Direction", "0%"},
+            {"Rolling Averaged Population Magnitude", "0%"},
+            {"OrderListHash", "c4c9c272037cfd8f6887052b8d739466"}
+        };
+    }
+}

Algorithm.CSharp/AutomaticIndicatorWarmupDataTypeRegressionAlgorithm.cs

@@ -187,7 +187,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "-1453269600"}
+            {"OrderListHash", "7c841ca58a4385f42236838e5bf0c382"}
         };
     }
 }

Algorithm.CSharp/AutomaticIndicatorWarmupRegressionAlgorithm.cs

@@ -149,7 +149,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "491919591"}
+            {"OrderListHash", "25885f979ca8c7b44f5d0f7daf00b241"}
         };
     }
 }

Algorithm.CSharp/BacktestingBrokerageRegressionAlgorithm.cs

@@ -1,4 +1,4 @@
-/*
+/*
  * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
  * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
  *
@@ -303,21 +303,39 @@ namespace QuantConnect.Algorithm.CSharp
             {"Drawdown", "0.400%"},
             {"Expectancy", "-1"},
             {"Net Profit", "-0.323%"},
-            {"Sharpe Ratio", "-0.888"},
+            {"Sharpe Ratio", "-11.098"},
             {"Probabilistic Sharpe Ratio", "0%"},
             {"Loss Rate", "100%"},
             {"Win Rate", "0%"},
             {"Profit-Loss Ratio", "0"},
-            {"Alpha", "0.035"},
-            {"Beta", "0.183"},
-            {"Annual Standard Deviation", "0.004"},
+            {"Alpha", "-0.002"},
+            {"Beta", "0.099"},
+            {"Annual Standard Deviation", "0.002"},
             {"Annual Variance", "0"},
-            {"Information Ratio", "12.058"},
-            {"Tracking Error", "0.017"},
-            {"Treynor Ratio", "-0.018"},
+            {"Information Ratio", "9.899"},
+            {"Tracking Error", "0.019"},
+            {"Treynor Ratio", "-0.23"},
             {"Total Fees", "$2.00"},
             {"Fitness Score", "0.213"},
-            {"OrderListHash", "904167951"}
+            {"Kelly Criterion Estimate", "0"},
+            {"Kelly Criterion Probability Value", "0"},
+            {"Sortino Ratio", "79228162514264337593543950335"},
+            {"Return Over Maximum Drawdown", "-73.456"},
+            {"Portfolio Turnover", "0.426"},
+            {"Total Insights Generated", "0"},
+            {"Total Insights Closed", "0"},
+            {"Total Insights Analysis Completed", "0"},
+            {"Long Insight Count", "0"},
+            {"Short Insight Count", "0"},
+            {"Long/Short Ratio", "100%"},
+            {"Estimated Monthly Alpha Value", "$0"},
+            {"Total Accumulated Estimated Alpha Value", "$0"},
+            {"Mean Population Estimated Insight Value", "$0"},
+            {"Mean Population Direction", "0%"},
+            {"Mean Population Magnitude", "0%"},
+            {"Rolling Averaged Population Direction", "0%"},
+            {"Rolling Averaged Population Magnitude", "0%"},
+            {"OrderListHash", "72a6ced0ed0c2da7136f3be652eb4744"}
         };
     }
 }

Algorithm.CSharp/BasicSetAccountCurrencyAlgorithm.cs

@@ -107,7 +107,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "-1575550889"}
+            {"OrderListHash", "18dc611407abec4ea47092e71f33f983"}
         };
     }
 }

Algorithm.CSharp/BasicTemplateAlgorithm.cs

@@ -116,7 +116,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "491919591"}
+            {"OrderListHash", "25885f979ca8c7b44f5d0f7daf00b241"}
         };
     }
-}
+}

Algorithm.CSharp/BasicTemplateCryptoAlgorithm.cs

@@ -242,7 +242,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "956597072"}
+            {"OrderListHash", "1bf1a6d9dd921982b72a6178f9e50e68"}
         };
     }
 }

Algorithm.CSharp/BasicTemplateDailyAlgorithm.cs

@@ -107,7 +107,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "1268340653"}
+            {"OrderListHash", "82fee25cd17100c53bb173834ab5f0b2"}
         };
     }
 }

Algorithm.CSharp/BasicTemplateFrameworkAlgorithm.cs

@@ -128,7 +128,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "54.5455%"},
             {"Rolling Averaged Population Direction", "59.8056%"},
             {"Rolling Averaged Population Magnitude", "59.8056%"},
-            {"OrderListHash", "951346025"}
+            {"OrderListHash", "17e29d58e5dabd93569da752c4552c70"}
         };
     }
 }

Algorithm.CSharp/BasicTemplateFuturesAlgorithm.cs

@@ -166,7 +166,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "-1197265007"}
+            {"OrderListHash", "35b3f4b7a225468d42ca085386a2383e"}
         };
     }
 }

Algorithm.CSharp/BasicTemplateFuturesFrameworkAlgorithm.cs

@@ -174,7 +174,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "-1624258832"}
+            {"OrderListHash", "18ffd3a774c68da83d867e3b09e3e05d"}
         };
     }
 }

Algorithm.CSharp/BasicTemplateFuturesHistoryAlgorithm.cs

@@ -1,4 +1,4 @@
-/*
+/*
  * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
  * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
  *
@@ -179,7 +179,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "371857150"}
+            {"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
         };
     }
-}
+}

Algorithm.CSharp/BasicTemplateOptionStrategyAlgorithm.cs

@@ -153,7 +153,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "-702975961"}
+            {"OrderListHash", "5484aef1443064c826e0071f757cb0f7"}
         };
     }
 }

Algorithm.CSharp/BasicTemplateOptionsAlgorithm.cs

@@ -150,7 +150,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "1130102123"}
+            {"OrderListHash", "9d9f9248ee8fe30d87ff0a6f6fea5112"}
         };
     }
 }

Algorithm.CSharp/BasicTemplateOptionsFilterUniverseAlgorithm.cs

@@ -1,4 +1,4 @@
-/*
+/*
  * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
  * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
  *
@@ -102,7 +102,7 @@ namespace QuantConnect.Algorithm.CSharp
         /// </summary>
         public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
         {
-            {"Total Trades", "1"},
+            {"Total Trades", "2"},
             {"Average Win", "0%"},
             {"Average Loss", "0%"},
             {"Compounding Annual Return", "0%"},
@@ -141,7 +141,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "687310345"}
+            {"OrderListHash", "92d8a50efe230524512404dab66b19dd"}
         };
     }
 }

Algorithm.CSharp/BasicTemplateOptionsFrameworkAlgorithm.cs

@@ -160,7 +160,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Information Ratio", "0"},
             {"Tracking Error", "0"},
             {"Treynor Ratio", "0"},
-            {"Total Fees", "$4.00"},
+            {"Total Fees", "$3.00"},
             {"Fitness Score", "0"},
             {"Kelly Criterion Estimate", "0.327"},
             {"Kelly Criterion Probability Value", "1"},
@@ -180,7 +180,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "50.0482%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "352959406"}
+            {"OrderListHash", "ce06ddfa4b2ffeb666a8910ac8836992"}
         };
     }
 }

Algorithm.CSharp/BlackLittermanPortfolioOptimizationFrameworkAlgorithm.cs

@@ -113,7 +113,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "50%"},
             {"Rolling Averaged Population Direction", "12.6429%"},
             {"Rolling Averaged Population Magnitude", "12.6429%"},
-            {"OrderListHash", "-2004493274"}
+            {"OrderListHash", "3edd51956c7c97af4863aa6059c11f1a"}
         };
     }
 }

Algorithm.CSharp/CBOECustomDataConsolidationRegressionAlgorithm.cs

@@ -0,0 +1,180 @@
+/*
+ * 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;
+using System.Collections.Generic;
+using System.IO;
+using QuantConnect.Data;
+using QuantConnect.Data.Custom.CBOE;
+using QuantConnect.Indicators;
+using QuantConnect.Interfaces;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Tests the consolidation of custom data with random data
+    /// </summary>
+    public class CBOECustomDataConsolidationRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private Symbol _vix;
+        private BollingerBands _bb;
+        private bool _invested;
+
+        /// <summary>
+        /// Initializes the algorithm with fake VIX data
+        /// </summary>
+        public override void Initialize()
+        {
+            SetStartDate(2013, 10, 7);
+            SetEndDate(2013, 10, 11);
+            SetCash(100000);
+
+            _vix = AddData<IncrementallyGeneratedCustomData>("VIX", Resolution.Daily).Symbol;
+            _bb = BB(_vix, 30, 2, MovingAverageType.Simple, Resolution.Daily);
+        }
+
+        /// <summary>
+        /// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
+        /// </summary>
+        /// <param name="data">Slice object keyed by symbol containing the stock data</param>
+        public override void OnData(Slice data)
+        {
+            if (_bb.Current.Value == 0)
+            {
+                throw new Exception("Bollinger Band value is zero when we expect non-zero value.");
+            }
+
+            if (!_invested && _bb.Current.Value > 0.05m)
+            {
+                MarketOrder(_vix, 1);
+                _invested = true;
+            }
+        }
+
+        /// <summary>
+        /// Incrementally updating data
+        /// </summary>
+        private class IncrementallyGeneratedCustomData : CBOE
+        {
+            private const decimal _start = 10.01m;
+            private static decimal _step;
+
+            /// <summary>
+            /// Gets the source of the subscription. In this case, we set it to existing
+            /// equity data so that we can pass fake data from Reader
+            /// </summary>
+            /// <param name="config">Subscription configuration</param>
+            /// <param name="date">Date we're making this request</param>
+            /// <param name="isLiveMode">Is live mode</param>
+            /// <returns>Source of subscription</returns>
+            public override SubscriptionDataSource GetSource(SubscriptionDataConfig config, DateTime date, bool isLiveMode)
+            {
+                return new SubscriptionDataSource(Path.Combine(Globals.DataFolder, "equity", "usa", "minute", "spy", $"{date:yyyyMMdd}_trade.zip#{date:yyyyMMdd}_spy_minute_trade.csv"), SubscriptionTransportMedium.LocalFile, FileFormat.Csv);
+            }
+
+            /// <summary>
+            /// Reads the data, which in this case is fake incremental data
+            /// </summary>
+            /// <param name="config">Subscription configuration</param>
+            /// <param name="line">Line of data</param>
+            /// <param name="date">Date of the request</param>
+            /// <param name="isLiveMode">Is live mode</param>
+            /// <returns>Incremental BaseData instance</returns>
+            public override BaseData Reader(SubscriptionDataConfig config, string line, DateTime date, bool isLiveMode)
+            {
+                var vix = new CBOE();
+                _step += 0.10m;
+                var open = _start + _step;
+                var close = _start + _step + 0.02m;
+                var high = close;
+                var low = open;
+
+                return new IncrementallyGeneratedCustomData
+                {
+                    Open = open,
+                    High = high,
+                    Low = low,
+                    Close = close,
+                    Time = date,
+                    Symbol = new Symbol(
+                        SecurityIdentifier.GenerateBase(typeof(IncrementallyGeneratedCustomData), "VIX", Market.USA, false),
+                        "VIX"),
+                    Period = vix.Period,
+                    DataType = vix.DataType
+                };
+            }
+        }
+
+        /// <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 bool CanRunLocally { get; } = true;
+
+        /// <summary>
+        /// This is used by the regression test system to indicate which languages this algorithm is written in.
+        /// </summary>
+        /// <remarks>
+        /// Unable to be tested in Python, due to pythonnet not supporting overriding of methods from Python
+        /// </remarks>
+        public Language[] Languages { get; } = { Language.CSharp };
+
+        /// <summary>
+        /// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
+        /// </summary>
+        public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
+        {
+            {"Total Trades", "1"},
+            {"Average Win", "0%"},
+            {"Average Loss", "0%"},
+            {"Compounding Annual Return", "0.029%"},
+            {"Drawdown", "0%"},
+            {"Expectancy", "0"},
+            {"Net Profit", "0.000%"},
+            {"Sharpe Ratio", "28.4"},
+            {"Probabilistic Sharpe Ratio", "88.597%"},
+            {"Loss Rate", "0%"},
+            {"Win Rate", "0%"},
+            {"Profit-Loss Ratio", "0"},
+            {"Alpha", "0"},
+            {"Beta", "0"},
+            {"Annual Standard Deviation", "0"},
+            {"Annual Variance", "0"},
+            {"Information Ratio", "-7.067"},
+            {"Tracking Error", "0.193"},
+            {"Treynor Ratio", "7.887"},
+            {"Total Fees", "$0.00"},
+            {"Fitness Score", "0"},
+            {"Kelly Criterion Estimate", "0"},
+            {"Kelly Criterion Probability Value", "0"},
+            {"Sortino Ratio", "79228162514264337593543950335"},
+            {"Return Over Maximum Drawdown", "79228162514264337593543950335"},
+            {"Portfolio Turnover", "0"},
+            {"Total Insights Generated", "0"},
+            {"Total Insights Closed", "0"},
+            {"Total Insights Analysis Completed", "0"},
+            {"Long Insight Count", "0"},
+            {"Short Insight Count", "0"},
+            {"Long/Short Ratio", "100%"},
+            {"Estimated Monthly Alpha Value", "$0"},
+            {"Total Accumulated Estimated Alpha Value", "$0"},
+            {"Mean Population Estimated Insight Value", "$0"},
+            {"Mean Population Direction", "0%"},
+            {"Mean Population Magnitude", "0%"},
+            {"Rolling Averaged Population Direction", "0%"},
+            {"Rolling Averaged Population Magnitude", "0%"},
+            {"OrderListHash", "918912ee4f64cd0290f3d58deca02713"}
+        };
+    }
+}

Algorithm.CSharp/CancelOpenOrdersRegressionAlgorithm.cs

@@ -166,7 +166,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "1349023435"}
+            {"OrderListHash", "aea2e321d17414c1f3c6fa2491f10c88"}
         };
     }
 }

Algorithm.CSharp/CfdTimeZonesRegressionAlgorithm.cs

@@ -114,7 +114,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "1456907343"}
+            {"OrderListHash", "6ea6184a2a8d0d69e552ad866933bfb6"}
         };
     }
 }

Algorithm.CSharp/CoarseFineFundamentalRegressionAlgorithm.cs

@@ -200,7 +200,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "-1465929889"}
+            {"OrderListHash", "edd9e9ffc8a1cdfb7a1e6ae601e61b12"}
         };
     }
 }

Algorithm.CSharp/CoarseFineOptionUniverseChainRegressionAlgorithm.cs

@@ -183,7 +183,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "1630141557"}
+            {"OrderListHash", "12470afd9a74ad9c9802361f6f092777"}
         };
     }
 }

Algorithm.CSharp/CoarseFundamentalTop3Algorithm.cs

@@ -155,7 +155,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "-1623759093"}
+            {"OrderListHash", "3d1ae61492b34c39115b76757510c058"}
         };
     }
 }

Algorithm.CSharp/CoarseNoLookAheadBiasAlgorithm.cs

@@ -133,7 +133,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "371857150"}
+            {"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
         };
     }
 }

Algorithm.CSharp/CoarseSelectionTimeRegressionAlgorithm.cs

@@ -139,7 +139,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "-1959413055"}
+            {"OrderListHash", "718d73fbddccb63aeacbf4659938b4b8"}
         };
     }
 }

Algorithm.CSharp/CompositeAlphaModelFrameworkAlgorithm.cs

@@ -110,7 +110,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "-28636839"}
+            {"OrderListHash", "ba44309886ea8ff515ef593a24456c47"}
         };
     }
 }

Algorithm.CSharp/CompositeRiskManagementModelFrameworkAlgorithm.cs

@@ -104,7 +104,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "54.5455%"},
             {"Rolling Averaged Population Direction", "59.8056%"},
             {"Rolling Averaged Population Magnitude", "59.8056%"},
-            {"OrderListHash", "-1225025518"}
+            {"OrderListHash", "e0f388bf9e88b34388c866150b292573"}
         };
     }
 }

Algorithm.CSharp/ConfidenceWeightedFrameworkAlgorithm.cs

@@ -113,7 +113,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "54.5455%"},
             {"Rolling Averaged Population Direction", "59.8056%"},
             {"Rolling Averaged Population Magnitude", "59.8056%"},
-            {"OrderListHash", "501060991"}
+            {"OrderListHash", "07eb3e2c199575b547459a534057eb5e"}
         };
     }
 }

Algorithm.CSharp/ConsolidateRegressionAlgorithm.cs

@@ -183,7 +183,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "-1453269600"}
+            {"OrderListHash", "7c841ca58a4385f42236838e5bf0c382"}
         };
     }
 }

Algorithm.CSharp/ConstituentsUniverseRegressionAlgorithm.cs

@@ -209,7 +209,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "-611289773"}
+            {"OrderListHash", "71984e154883ece4aef1d71bafbfccaf"}
         };
     }
 }

Algorithm.CSharp/ConvertToFrameworkAlgorithm.cs

@@ -144,7 +144,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Annual Variance", "0.027"},
             {"Information Ratio", "-0.391"},
             {"Tracking Error", "0.239"},
-            {"Treynor Ratio", "-1.416"},
+            {"Treynor Ratio", "-1.435"},
             {"Total Fees", "$755.29"},
             {"Fitness Score", "0.024"},
             {"Kelly Criterion Estimate", "-0.84"},
@@ -165,7 +165,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "48.2217%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "1370210213"}
+            {"OrderListHash", "95f34359f25a7a7a2725f0343a75a105"}
         };
     }
 }

Algorithm.CSharp/CustomBenchmarkAlgorithm.cs

@@ -38,6 +38,10 @@ namespace QuantConnect.Algorithm.CSharp
             // Find more symbols here: http://quantconnect.com/data
             AddSecurity(SecurityType.Equity, "SPY", Resolution.Second);
 
+            // Disabling the benchmark / setting to a fixed value
+            // SetBenchmark(time => 0);
+
+            // Set the benchmark to AAPL US Equity
             SetBenchmark("AAPL");
         }
 
@@ -114,7 +118,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "2069976135"}
+            {"OrderListHash", "9cd604d2c1e3c273697e2ff2cc7faef1"}
         };
     }
 }

Algorithm.CSharp/CustomBenchmarkRegressionAlgorithm.cs

@@ -115,7 +115,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "519536519"}
+            {"OrderListHash", "be3334e4aeb9dd7cca4ecc07419d0f95"}
         };
     }
 }

Algorithm.CSharp/CustomBuyingPowerModelAlgorithm.cs

@@ -132,7 +132,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "639761089"}
+            {"OrderListHash", "b88362c462e9ab2942cbcb8dfddc6ce0"}
         };
     }
 }

Algorithm.CSharp/CustomDataAddDataCoarseSelectionRegressionAlgorithm.cs

@@ -138,7 +138,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "559673838"}
+            {"OrderListHash", "6b05339bfcb5bd93bfd66e32a1d2181a"}
         };
     }
 }

Algorithm.CSharp/CustomDataAddDataOnSecuritiesChangedRegressionAlgorithm.cs

@@ -143,7 +143,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "559673838"}
+            {"OrderListHash", "6b05339bfcb5bd93bfd66e32a1d2181a"}
         };
     }
 }

Algorithm.CSharp/CustomDataAddDataRegressionAlgorithm.cs

@@ -150,7 +150,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "1531253381"}
+            {"OrderListHash", "214f38f9084bc350c93010aa2fb69822"}
         };
     }
 }

Algorithm.CSharp/CustomDataPropertiesRegressionAlgorithm.cs

@@ -0,0 +1,260 @@
+/*
+ * 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;
+using System.Collections.Generic;
+using System.Globalization;
+using Newtonsoft.Json;
+using QuantConnect.Data;
+using QuantConnect.Interfaces;
+using QuantConnect.Securities;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Regression test to demonstrate setting custom Symbol Properties and Market Hours for a custom data import
+    /// </summary>
+    /// <meta name="tag" content="using data" />
+    /// <meta name="tag" content="custom data" />
+    /// <meta name="tag" content="crypto" />
+    /// <meta name="tag" content="regression test" />
+    public class CustomDataPropertiesRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private string _ticker = "BTC";
+        private Security _bitcoin;
+
+        /// <summary>
+        /// Initialize the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
+        /// </summary>
+        public override void Initialize()
+        {
+            SetStartDate(2011, 9, 13);
+            SetEndDate(2015, 12, 01);
+
+            //Set the cash for the strategy:
+            SetCash(100000);
+
+            // Define our custom data properties and exchange hours
+            var properties = new SymbolProperties("Bitcoin", "USD", 1, 0.01m, 0.01m, _ticker);
+            var exchangeHours = SecurityExchangeHours.AlwaysOpen(TimeZones.NewYork);
+
+            // Add the custom data to our algorithm with our custom properties and exchange hours
+            _bitcoin = AddData<Bitcoin>(_ticker, properties, exchangeHours);
+
+            //Verify our symbol properties were changed and loaded into this security
+            if (_bitcoin.SymbolProperties != properties)
+            {
+                throw new Exception("Failed to set and retrieve custom SymbolProperties for BTC");
+            }
+
+            //Verify our exchange hours were changed and loaded into this security
+            if (_bitcoin.Exchange.Hours != exchangeHours)
+            {
+                throw new Exception("Failed to set and retrieve custom ExchangeHours for BTC");
+            }
+
+            // For regression purposes on AddData overloads, this call is simply to ensure Lean can accept this
+            // with default params and is not routed to a breaking function.
+            AddData<Bitcoin>("BTCUSD");
+        }
+
+        /// <summary>
+        /// Event Handler for Bitcoin Data Events: These Bitcoin objects are created from our
+        /// "Bitcoin" type below and fired into this event handler.
+        /// </summary>
+        /// <param name="data">One(1) Bitcoin Object, streamed into our algorithm synchronized in time with our other data streams</param>
+        public void OnData(Bitcoin data)
+        {
+            //If we don't have any bitcoin "SHARES" -- invest"
+            if (!Portfolio.Invested)
+            {
+                //Bitcoin used as a tradable asset, like stocks, futures etc.
+                if (data.Close != 0)
+                {
+                    //Access custom data symbols using <ticker>.<custom-type>
+                    Order("BTC.Bitcoin", Portfolio.MarginRemaining / Math.Abs(data.Close + 1));
+                }
+            }
+        }
+
+        public override void OnEndOfAlgorithm()
+        {
+            // Reset our Symbol property value, for testing purposes.
+            SymbolPropertiesDatabase.SetEntry(Market.USA, MarketHoursDatabase.GetDatabaseSymbolKey(_bitcoin.Symbol), SecurityType.Base,
+                SymbolProperties.GetDefault("USD"));
+        }
+
+        /// <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 bool CanRunLocally { get; } = true;
+
+        /// <summary>
+        /// This is used by the regression test system to indicate which languages this algorithm is written in.
+        /// </summary>
+        public Language[] Languages { get; } = { Language.CSharp, Language.Python };
+
+        /// <summary>
+        /// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
+        /// </summary>
+        public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
+        {
+            {"Total Trades", "1"},
+            {"Average Win", "0%"},
+            {"Average Loss", "0%"},
+            {"Compounding Annual Return", "155.262%"},
+            {"Drawdown", "84.800%"},
+            {"Expectancy", "0"},
+            {"Net Profit", "5123.242%"},
+            {"Sharpe Ratio", "2.067"},
+            {"Probabilistic Sharpe Ratio", "68.833%"},
+            {"Loss Rate", "0%"},
+            {"Win Rate", "0%"},
+            {"Profit-Loss Ratio", "0"},
+            {"Alpha", "1.732"},
+            {"Beta", "0.037"},
+            {"Annual Standard Deviation", "0.841"},
+            {"Annual Variance", "0.707"},
+            {"Information Ratio", "1.902"},
+            {"Tracking Error", "0.848"},
+            {"Treynor Ratio", "46.992"},
+            {"Total Fees", "$0.00"},
+            {"Fitness Score", "0"},
+            {"Kelly Criterion Estimate", "0"},
+            {"Kelly Criterion Probability Value", "0"},
+            {"Sortino Ratio", "2.238"},
+            {"Return Over Maximum Drawdown", "1.832"},
+            {"Portfolio Turnover", "0"},
+            {"Total Insights Generated", "0"},
+            {"Total Insights Closed", "0"},
+            {"Total Insights Analysis Completed", "0"},
+            {"Long Insight Count", "0"},
+            {"Short Insight Count", "0"},
+            {"Long/Short Ratio", "100%"},
+            {"Estimated Monthly Alpha Value", "$0"},
+            {"Total Accumulated Estimated Alpha Value", "$0"},
+            {"Mean Population Estimated Insight Value", "$0"},
+            {"Mean Population Direction", "0%"},
+            {"Mean Population Magnitude", "0%"},
+            {"Rolling Averaged Population Direction", "0%"},
+            {"Rolling Averaged Population Magnitude", "0%"},
+            {"OrderListHash", "1f54fb75ebcc0daafa5d45bfbaa4fbcb"}
+        };
+
+        /// <summary>
+        /// Custom Data Type: Bitcoin data from Quandl - http://www.quandl.com/help/api-for-bitcoin-data
+        /// </summary>
+        public class Bitcoin : BaseData
+        {
+            [JsonProperty("timestamp")]
+            public int Timestamp = 0;
+            [JsonProperty("open")]
+            public decimal Open = 0;
+            [JsonProperty("high")]
+            public decimal High = 0;
+            [JsonProperty("low")]
+            public decimal Low = 0;
+            [JsonProperty("last")]
+            public decimal Close = 0;
+            [JsonProperty("bid")]
+            public decimal Bid = 0;
+            [JsonProperty("ask")]
+            public decimal Ask = 0;
+            [JsonProperty("vwap")]
+            public decimal WeightedPrice = 0;
+            [JsonProperty("volume")]
+            public decimal VolumeBTC = 0;
+            public decimal VolumeUSD = 0;
+
+            /// <summary>
+            /// 1. DEFAULT CONSTRUCTOR: Custom data types need a default constructor.
+            /// We search for a default constructor so please provide one here. It won't be used for data, just to generate the "Factory".
+            /// </summary>
+            public Bitcoin()
+            {
+                Symbol = "BTC";
+            }
+
+            /// <summary>
+            /// 2. RETURN THE STRING URL SOURCE LOCATION FOR YOUR DATA:
+            /// This is a powerful and dynamic select source file method. If you have a large dataset, 10+mb we recommend you break it into smaller files. E.g. One zip per year.
+            /// We can accept raw text or ZIP files. We read the file extension to determine if it is a zip file.
+            /// </summary>
+            /// <param name="config">Configuration object</param>
+            /// <param name="date">Date of this source file</param>
+            /// <param name="isLiveMode">true if we're in live mode, false for backtesting mode</param>
+            /// <returns>String URL of source file.</returns>
+            public override SubscriptionDataSource GetSource(SubscriptionDataConfig config, DateTime date, bool isLiveMode)
+            {
+                if (isLiveMode)
+                {
+                    return new SubscriptionDataSource("https://www.bitstamp.net/api/ticker/", SubscriptionTransportMedium.Rest);
+                }
+
+                //return "http://my-ftp-server.com/futures-data-" + date.ToString("Ymd") + ".zip";
+                // OR simply return a fixed small data file. Large files will slow down your backtest
+                return new SubscriptionDataSource("https://www.quantconnect.com/api/v2/proxy/quandl/api/v3/datasets/BCHARTS/BITSTAMPUSD.csv?order=asc&api_key=WyAazVXnq7ATy_fefTqm", SubscriptionTransportMedium.RemoteFile);
+            }
+
+            /// <summary>
+            /// 3. READER METHOD: Read 1 line from data source and convert it into Object.
+            /// Each line of the CSV File is presented in here. The backend downloads your file, loads it into memory and then line by line
+            /// feeds it into your algorithm
+            /// </summary>
+            /// <param name="line">string line from the data source file submitted above</param>
+            /// <param name="config">Subscription data, symbol name, data type</param>
+            /// <param name="date">Current date we're requesting. This allows you to break up the data source into daily files.</param>
+            /// <param name="isLiveMode">true if we're in live mode, false for backtesting mode</param>
+            /// <returns>New Bitcoin Object which extends BaseData.</returns>
+            public override BaseData Reader(SubscriptionDataConfig config, string line, DateTime date, bool isLiveMode)
+            {
+                var coin = new Bitcoin();
+                if (isLiveMode)
+                {
+                    //Example Line Format:
+                    //{"high": "441.00", "last": "421.86", "timestamp": "1411606877", "bid": "421.96", "vwap": "428.58", "volume": "14120.40683975", "low": "418.83", "ask": "421.99"}
+                    try
+                    {
+                        coin = JsonConvert.DeserializeObject<Bitcoin>(line);
+                        coin.EndTime = DateTime.UtcNow.ConvertFromUtc(config.ExchangeTimeZone);
+                        coin.Value = coin.Close;
+                    }
+                    catch { /* Do nothing, possible error in json decoding */ }
+                    return coin;
+                }
+
+                //Example Line Format:
+                //Date      Open   High    Low     Close   Volume (BTC)    Volume (Currency)   Weighted Price
+                //2011-09-13 5.8    6.0     5.65    5.97    58.37138238,    346.0973893944      5.929230648356
+                try
+                {
+                    string[] data = line.Split(',');
+                    coin.Time = DateTime.Parse(data[0], CultureInfo.InvariantCulture);
+                    coin.Open = Convert.ToDecimal(data[1], CultureInfo.InvariantCulture);
+                    coin.High = Convert.ToDecimal(data[2], CultureInfo.InvariantCulture);
+                    coin.Low = Convert.ToDecimal(data[3], CultureInfo.InvariantCulture);
+                    coin.Close = Convert.ToDecimal(data[4], CultureInfo.InvariantCulture);
+                    coin.VolumeBTC = Convert.ToDecimal(data[5], CultureInfo.InvariantCulture);
+                    coin.VolumeUSD = Convert.ToDecimal(data[6], CultureInfo.InvariantCulture);
+                    coin.WeightedPrice = Convert.ToDecimal(data[7], CultureInfo.InvariantCulture);
+                    coin.Value = coin.Close;
+                }
+                catch { /* Do nothing, skip first title row */ }
+
+                return coin;
+            }
+        }
+    }
+}

Algorithm.CSharp/CustomDataRegressionAlgorithm.cs

@@ -120,7 +120,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "-541549381"}
+            {"OrderListHash", "0e7d560d0db2829adb19d3e403c30d97"}
         };
 
         /// <summary>

Algorithm.CSharp/CustomDataUsingMapFileRegressionAlgorithm.cs

@@ -160,7 +160,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "-1014157203"}
+            {"OrderListHash", "1c319ae4b15416184a247bb47b31aabc"}
         };
 
         /// <summary>

Algorithm.CSharp/CustomModelsAlgorithm.cs

@@ -231,7 +231,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "852026186"}
+            {"OrderListHash", "71c17655bd0731eb25433727526e95ba"}
         };
     }
 }

Algorithm.CSharp/CustomUniverseSelectionRegressionAlgorithm.cs

@@ -122,7 +122,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "1268340653"}
+            {"OrderListHash", "82fee25cd17100c53bb173834ab5f0b2"}
         };
     }
 }

Algorithm.CSharp/CustomUniverseWithBenchmarkRegressionAlgorithm.cs

@@ -212,7 +212,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "371857150"}
+            {"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
         };
     }
 }

Algorithm.CSharp/DailyHistoryForDailyResolutionRegressionAlgorithm.cs

@@ -137,7 +137,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "371857150"}
+            {"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
         };
     }
 }

Algorithm.CSharp/DailyHistoryForMinuteResolutionRegressionAlgorithm.cs

@@ -137,7 +137,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "371857150"}
+            {"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
         };
     }
 }

Algorithm.CSharp/DaylightSavingTimeHistoryRegressionAlgorithm.cs

@@ -1,4 +1,4 @@
-/*
+/*
  * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
  * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
  *
@@ -112,7 +112,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "371857150"}
+            {"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
         };
     }
 }

Algorithm.CSharp/DefaultResolutionRegressionAlgorithm.cs

@@ -155,7 +155,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "371857150"}
+            {"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
         };
     }
 }

Algorithm.CSharp/DelistedFutureLiquidateRegressionAlgorithm.cs

@@ -100,25 +100,25 @@ namespace QuantConnect.Algorithm.CSharp
             {"Drawdown", "1.300%"},
             {"Expectancy", "0"},
             {"Net Profit", "1.634%"},
-            {"Sharpe Ratio", "2.476"},
-            {"Probabilistic Sharpe Ratio", "92.194%"},
+            {"Sharpe Ratio", "2.495"},
+            {"Probabilistic Sharpe Ratio", "92.298%"},
             {"Loss Rate", "0%"},
             {"Win Rate", "100%"},
             {"Profit-Loss Ratio", "0"},
             {"Alpha", "0.006"},
             {"Beta", "0.158"},
-            {"Annual Standard Deviation", "0.032"},
+            {"Annual Standard Deviation", "0.033"},
             {"Annual Variance", "0.001"},
-            {"Information Ratio", "-4.89"},
+            {"Information Ratio", "-4.942"},
             {"Tracking Error", "0.08"},
-            {"Treynor Ratio", "0.509"},
+            {"Treynor Ratio", "0.517"},
             {"Total Fees", "$3.70"},
             {"Fitness Score", "0.019"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
             {"Sortino Ratio", "1.362"},
             {"Return Over Maximum Drawdown", "9.699"},
-            {"Portfolio Turnover", "0.022"},
+            {"Portfolio Turnover", "0.023"},
             {"Total Insights Generated", "0"},
             {"Total Insights Closed", "0"},
             {"Total Insights Analysis Completed", "0"},
@@ -132,7 +132,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "490786648"}
+            {"OrderListHash", "00d6dc8775da38f7f79defad06de240a"}
         };
     }
 }

Algorithm.CSharp/DelistingEventsAlgorithm.cs

@@ -45,7 +45,7 @@ namespace QuantConnect.Algorithm.CSharp
             SetEndDate(2007, 05, 25);    //Set End Date
             SetCash(100000);             //Set Strategy Cash
             // Find more symbols here: http://quantconnect.com/data
-            AddSecurity(SecurityType.Equity, "AAA", Resolution.Daily);
+            AddSecurity(SecurityType.Equity, "AAA.1", Resolution.Daily);
             AddSecurity(SecurityType.Equity, "SPY", Resolution.Daily);
         }
 
@@ -58,7 +58,7 @@ namespace QuantConnect.Algorithm.CSharp
             _dataCount += data.Bars.Count;
             if (Transactions.OrdersCount == 0)
             {
-                SetHoldings("AAA", 1);
+                SetHoldings("AAA.1", 1);
                 Debug("Purchased Stock");
             }
 
@@ -71,7 +71,7 @@ namespace QuantConnect.Algorithm.CSharp
 
             // the slice can also contain delisting data: data.Delistings in a dictionary string->Delisting
 
-            var aaa = Securities["AAA"];
+            var aaa = Securities["AAA.1"];
             if (aaa.IsDelisted && aaa.IsTradable)
             {
                 throw new Exception("Delisted security must NOT be tradable");
@@ -179,7 +179,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "-2022527947"}
+            {"OrderListHash", "61f4d3c109fc4b6b9eb14d2e4eec4843"}
         };
     }
 }

Algorithm.CSharp/DelistingFutureOptionRegressionAlgorithm.cs

@@ -0,0 +1,148 @@
+/*
+ * 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;
+using QuantConnect.Data;
+using QuantConnect.Interfaces;
+using QuantConnect.Securities;
+using System.Collections.Generic;
+using System.Linq;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Regression algorithm reproducing issue #5160 where delisting order would be cancelled because it was placed at the market close on the delisting day
+    /// </summary>
+    public class DelistingFutureOptionRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private bool _traded;
+        private int _lastMonth;
+
+        public override void Initialize()
+        {
+            SetStartDate(2012, 1, 1);
+            SetEndDate(2013, 1, 1);
+            SetCash(10000000);
+
+            var dc = AddFuture(Futures.Dairy.ClassIIIMilk, Resolution.Minute, Market.CME);
+            dc.SetFilter(1, 120);
+
+            AddFutureOption(dc.Symbol, universe => universe.Strikes(-2, 2));
+            _lastMonth = -1;
+        }
+
+        public override void OnData(Slice data)
+        {
+            if (Time.Month != _lastMonth)
+            {
+                _lastMonth = Time.Month;
+                var investedSymbols = Securities.Values
+                    .Where(security => security.Invested)
+                    .Select(security => security.Symbol)
+                    .ToList();
+
+                var delistedSecurity = investedSymbols.Where(symbol => symbol.ID.Date.AddDays(1) < Time).ToList();
+                if (delistedSecurity.Count > 0)
+                {
+                    throw new Exception($"[{UtcTime}] We hold a delisted securities: {string.Join(",", delistedSecurity)}");
+                }
+                Log($"Holdings({Time}): {string.Join(",", investedSymbols)}");
+            }
+
+            if (Portfolio.Invested)
+            {
+                return;
+            }
+
+            foreach (var chain in data.OptionChains.Values)
+            {
+                foreach (var contractsValue in chain.Contracts.Values)
+                {
+                    MarketOrder(contractsValue.Symbol, 1);
+                    _traded = true;
+                }
+            }
+        }
+
+        public override void OnEndOfAlgorithm()
+        {
+            if (!_traded)
+            {
+                throw new Exception("We expected some FOP trading to happen");
+            }
+            if (Portfolio.Invested)
+            {
+                throw new Exception("We shouldn't be invested anymore");
+            }
+        }
+
+        /// <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 bool CanRunLocally { get; } = true;
+
+        /// <summary>
+        /// This is used by the regression test system to indicate which languages this algorithm is written in.
+        /// </summary>
+        public Language[] Languages { get; } = { Language.CSharp };
+
+        /// <summary>
+        /// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
+        /// </summary>
+        public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
+        {
+            {"Total Trades", "16"},
+            {"Average Win", "0.01%"},
+            {"Average Loss", "-0.02%"},
+            {"Compounding Annual Return", "-0.111%"},
+            {"Drawdown", "0.100%"},
+            {"Expectancy", "-0.679"},
+            {"Net Profit", "-0.112%"},
+            {"Sharpe Ratio", "-1.052"},
+            {"Probabilistic Sharpe Ratio", "0.000%"},
+            {"Loss Rate", "80%"},
+            {"Win Rate", "20%"},
+            {"Profit-Loss Ratio", "0.61"},
+            {"Alpha", "-0.001"},
+            {"Beta", "-0.001"},
+            {"Annual Standard Deviation", "0.001"},
+            {"Annual Variance", "0"},
+            {"Information Ratio", "-1.187"},
+            {"Tracking Error", "0.115"},
+            {"Treynor Ratio", "1.545"},
+            {"Total Fees", "$37.00"},
+            {"Fitness Score", "0"},
+            {"Kelly Criterion Estimate", "0"},
+            {"Kelly Criterion Probability Value", "0"},
+            {"Sortino Ratio", "-0.128"},
+            {"Return Over Maximum Drawdown", "-0.995"},
+            {"Portfolio Turnover", "0"},
+            {"Total Insights Generated", "0"},
+            {"Total Insights Closed", "0"},
+            {"Total Insights Analysis Completed", "0"},
+            {"Long Insight Count", "0"},
+            {"Short Insight Count", "0"},
+            {"Long/Short Ratio", "100%"},
+            {"Estimated Monthly Alpha Value", "$0"},
+            {"Total Accumulated Estimated Alpha Value", "$0"},
+            {"Mean Population Estimated Insight Value", "$0"},
+            {"Mean Population Direction", "0%"},
+            {"Mean Population Magnitude", "0%"},
+            {"Rolling Averaged Population Direction", "0%"},
+            {"Rolling Averaged Population Magnitude", "0%"},
+            {"OrderListHash", "de309ab56d2fcd80ff03df2802d9feda"}
+        };
+    }
+}

Algorithm.CSharp/DisplacedMovingAverageRibbon.cs

@@ -205,7 +205,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "-807056289"}
+            {"OrderListHash", "7c4fcd79dd817a9cd3bf44525eaed96c"}
         };
     }
 }

Algorithm.CSharp/DropboxBaseDataUniverseSelectionAlgorithm.cs

@@ -223,7 +223,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "-75671425"}
+            {"OrderListHash", "0b6746b5759ecd45ab21360fd40858bb"}
         };
     }
 }

Algorithm.CSharp/DropboxUniverseSelectionAlgorithm.cs

@@ -196,7 +196,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "1142077166"}
+            {"OrderListHash", "a7a893a17a5afa7c2f73a444a7aea507"}
         };
     }
 }

Algorithm.CSharp/DuplicateSecurityWithBenchmarkRegressionAlgorithm.cs

@@ -129,7 +129,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "371857150"}
+            {"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
         };
     }
 }

Algorithm.CSharp/EmitInsightCryptoCashAccountType.cs

@@ -110,7 +110,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "1296183675"}
+            {"OrderListHash", "21ac8e4179b36d9658f0080868c0e552"}
         };
     }
 }

Algorithm.CSharp/EmitInsightNoAlphaModelAlgorithm.cs

@@ -150,7 +150,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "1300818910"}
+            {"OrderListHash", "17245c38f1b192d2041ca1f3e88250be"}
         };
     }
 }

Algorithm.CSharp/EmitInsightsAlgorithm.cs

@@ -146,7 +146,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "50%"},
             {"Rolling Averaged Population Direction", "50%"},
             {"Rolling Averaged Population Magnitude", "50%"},
-            {"OrderListHash", "-218498072"}
+            {"OrderListHash", "4178a84209934b1eb6d03c2267654f32"}
         };
     }
 }

Algorithm.CSharp/EqualWeightingPortfolioConstructionModelFutureRegressionAlgorithm.cs

@@ -167,7 +167,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "25.058%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "-1105779454"}
+            {"OrderListHash", "f4c4b763b5ade918cfb7932e276d069f"}
         };
     }
 }

Algorithm.CSharp/EquityTickQuoteAdjustedModeRegressionAlgorithm.cs

@@ -85,11 +85,11 @@ namespace QuantConnect.Algorithm.CSharp
         {
             {"Total Trades", "2"},
             {"Average Win", "0%"},
-            {"Average Loss", "-0.01%"},
-            {"Compounding Annual Return", "-0.500%"},
-            {"Drawdown", "0.000%"},
+            {"Average Loss", "-0.12%"},
+            {"Compounding Annual Return", "-9.062%"},
+            {"Drawdown", "0.100%"},
             {"Expectancy", "-1"},
-            {"Net Profit", "-0.006%"},
+            {"Net Profit", "-0.121%"},
             {"Sharpe Ratio", "0"},
             {"Probabilistic Sharpe Ratio", "0%"},
             {"Loss Rate", "100%"},
@@ -103,12 +103,12 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.22"},
             {"Treynor Ratio", "0"},
             {"Total Fees", "$6.41"},
-            {"Fitness Score", "0.248"},
+            {"Fitness Score", "0.249"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
             {"Sortino Ratio", "79228162514264337593543950335"},
-            {"Return Over Maximum Drawdown", "-82.815"},
-            {"Portfolio Turnover", "0.497"},
+            {"Return Over Maximum Drawdown", "-79.031"},
+            {"Portfolio Turnover", "0.498"},
             {"Total Insights Generated", "0"},
             {"Total Insights Closed", "0"},
             {"Total Insights Analysis Completed", "0"},
@@ -122,7 +122,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "1213851303"}
+            {"OrderListHash", "af92d7f4e0810bc4a95d5ccb5542b438"}
         };
     }
 }

Algorithm.CSharp/EquityTradeAndQuotesRegressionAlgorithm.cs

@@ -203,7 +203,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "1040964928"}
+            {"OrderListHash", "2cd87d138f8f9f5fcab28b6f983f68b1"}
         };
     }
 }

Algorithm.CSharp/ExtendedMarketHoursHistoryRegressionAlgorithm.cs

@@ -166,7 +166,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "-1961710414"}
+            {"OrderListHash", "9f381f81ea9939f285b432207fa0d024"}
         };
     }
 }

Algorithm.CSharp/ExtendedMarketTradingRegressionAlgorithm.cs

@@ -141,7 +141,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "1717552327"}
+            {"OrderListHash", "ee79a87e6a386f5ee620d77b7bfbd964"}
         };
     }
 }

Algorithm.CSharp/FeeModelNotUsingAccountCurrency.cs

@@ -193,7 +193,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "-1116140375"}
+            {"OrderListHash", "c84d1ceacb0da30c1c1c0314f9fc850c"}
         };
     }
 }

Algorithm.CSharp/FillForwardEnumeratorOutOfOrderBarRegressionAlgorithm.cs

@@ -117,7 +117,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "-850144190"}
+            {"OrderListHash", "5315f54b62b2eaf9d587602137d641a5"}
         };
     }
-}
+}

Algorithm.CSharp/FillForwardUntilExpiryRegressionAlgorithm.cs

@@ -0,0 +1,169 @@
+/*
+ * 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.Data;
+using QuantConnect.Data.UniverseSelection;
+using QuantConnect.Interfaces;
+using QuantConnect.Securities.Option;
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using QuantConnect.Data.Market;
+using QuantConnect.Securities;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Regression algorithm checks FillForwardEnumerator should FF the data until it reaches the delisting date
+    /// replicates GH issue https://github.com/QuantConnect/Lean/issues/4872
+    /// </summary>
+    public class FillForwardUntilExpiryRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private DateTime _realEndDate = new DateTime(2014, 06, 07);
+        private SecurityExchange _exchange;
+        private Dictionary<Symbol, HashSet<DateTime>> _options;
+
+        private string[] _contracts =
+        {
+            "TWX   140621P00067500",
+            "TWX   140621C00067500",
+            "TWX   140621C00070000",
+            "TWX   140621P00070000"
+        };
+
+        public override void Initialize()
+        {
+            SetStartDate(2014, 06, 05);
+            SetEndDate(2014, 06, 30);
+
+            _options = new Dictionary<Symbol, HashSet<DateTime>>();
+            var _twxOption = AddOption("TWX", Resolution.Minute);
+            _exchange = _twxOption.Exchange;
+            _twxOption.SetFilter((x) => x
+                .Contracts(c => c.Where(s => _contracts.Contains(s.Value))));
+            SetBenchmark(t => 1);
+        }
+
+        public override void OnData(Slice data)
+        {
+            foreach (var value in data.OptionChains.Values)
+            {
+                foreach (var contact in value.Contracts)
+                {
+                    BaseData bar = null;
+                    QuoteBar quoteBar;
+                    if (bar == null && value.QuoteBars.TryGetValue(contact.Key, out quoteBar))
+                    {
+                        bar = quoteBar;
+                    }
+                    TradeBar tradeBar;
+                    if (bar == null && value.TradeBars.TryGetValue(contact.Key, out tradeBar))
+                    {
+                        bar = tradeBar;
+                    }
+                    if (bar.IsFillForward)
+                    {
+                        _options[contact.Key].Add(value.Time.Date);
+                    }
+                }
+            }
+        }
+
+        public override void OnSecuritiesChanged(SecurityChanges changes)
+        {
+            foreach (var security in changes.AddedSecurities.OfType<Option>())
+            {
+                _options.Add(security.Symbol, new HashSet<DateTime>());
+            }
+        }
+
+        public override void OnEndOfAlgorithm()
+        {
+            if (_options.Count != _contracts.Length)
+            {
+                throw new Exception($"Options weren't setup properly. Expected: {_contracts.Length}");
+            }
+
+            foreach (var option in _options)
+            {
+                for (DateTime date = _realEndDate; date < option.Key.ID.Date; date = date.AddDays(1))
+                {
+                    if (_exchange.Hours.IsDateOpen(date) &&
+                        !option.Value.Contains(date))
+                    {
+                        throw new Exception("Delisted security should be FF until expiry date");
+                    }
+                }
+            }
+        }
+
+        /// <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 bool CanRunLocally { get; } = true;
+
+        /// <summary>
+        /// This is used by the regression test system to indicate which languages this algorithm is written in.
+        /// </summary>
+        public Language[] Languages { get; } = { Language.CSharp };
+
+        /// <summary>
+        /// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
+        /// </summary>
+        public 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"},
+            {"Fitness Score", "0"},
+            {"Kelly Criterion Estimate", "0"},
+            {"Kelly Criterion Probability Value", "0"},
+            {"Sortino Ratio", "79228162514264337593543950335"},
+            {"Return Over Maximum Drawdown", "79228162514264337593543950335"},
+            {"Portfolio Turnover", "0"},
+            {"Total Insights Generated", "0"},
+            {"Total Insights Closed", "0"},
+            {"Total Insights Analysis Completed", "0"},
+            {"Long Insight Count", "0"},
+            {"Short Insight Count", "0"},
+            {"Long/Short Ratio", "100%"},
+            {"Estimated Monthly Alpha Value", "$0"},
+            {"Total Accumulated Estimated Alpha Value", "$0"},
+            {"Mean Population Estimated Insight Value", "$0"},
+            {"Mean Population Direction", "0%"},
+            {"Mean Population Magnitude", "0%"},
+            {"Rolling Averaged Population Direction", "0%"},
+            {"Rolling Averaged Population Magnitude", "0%"},
+            {"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
+        };
+    }
+}

Algorithm.CSharp/FineFundamentalFilteredUniverseRegressionAlgorithm.cs

@@ -124,7 +124,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "-423205673"}
+            {"OrderListHash", "189eee984d524162b9676b83dab4d73c"}
         };
     }
 }

Algorithm.CSharp/ForexInternalFeedOnDataHigherResolutionRegressionAlgorithm.cs

@@ -172,7 +172,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "371857150"}
+            {"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
         };
     }
 }

Algorithm.CSharp/ForexInternalFeedOnDataSameResolutionRegressionAlgorithm.cs

@@ -176,7 +176,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "371857150"}
+            {"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
         };
     }
 }

Algorithm.CSharp/FractionalQuantityRegressionAlgorithm.cs

@@ -137,7 +137,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "-89452746"}
+            {"OrderListHash", "2b3ac55337ce5619fc0388ccdac72c54"}
         };
     }
 }

Algorithm.CSharp/FreePortfolioValueRegressionAlgorithm.cs

@@ -92,12 +92,12 @@ namespace QuantConnect.Algorithm.CSharp
             {"Win Rate", "0%"},
             {"Profit-Loss Ratio", "0"},
             {"Alpha", "0.093"},
-            {"Beta", "-0.1"},
+            {"Beta", "-0.099"},
             {"Annual Standard Deviation", "0.18"},
             {"Annual Variance", "0.032"},
             {"Information Ratio", "-0.001"},
             {"Tracking Error", "0.267"},
-            {"Treynor Ratio", "-0.846"},
+            {"Treynor Ratio", "-0.847"},
             {"Total Fees", "$41.17"},
             {"Fitness Score", "0"},
             {"Kelly Criterion Estimate", "38.884"},
@@ -118,7 +118,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "43.2962%"},
             {"Rolling Averaged Population Direction", "48.763%"},
             {"Rolling Averaged Population Magnitude", "48.763%"},
-            {"OrderListHash", "-1756838669"}
+            {"OrderListHash", "0ae9067022a716a31e7bc1cd6dbc3746"}
         };
     }
 }

Algorithm.CSharp/FutureOptionBuySellCallIntradayRegressionAlgorithm.cs

@@ -43,11 +43,6 @@ namespace QuantConnect.Algorithm.CSharp
             SetStartDate(2020, 1, 5);
             SetEndDate(2020, 6, 30);
 
-            // We add AAPL as a temporary workaround for https://github.com/QuantConnect/Lean/issues/4872
-            // which causes delisting events to never be processed, thus leading to options that might never
-            // be exercised until the next data point arrives.
-            AddEquity("AAPL", Resolution.Daily);
-
             var es20h20 = AddFutureContract(
                 QuantConnect.Symbol.CreateFuture(
                     Futures.Indices.SP500EMini,
@@ -126,29 +121,29 @@ namespace QuantConnect.Algorithm.CSharp
             {"Total Trades", "6"},
             {"Average Win", "2.93%"},
             {"Average Loss", "-4.15%"},
-            {"Compounding Annual Return", "-5.663%"},
+            {"Compounding Annual Return", "-6.023%"},
             {"Drawdown", "5.700%"},
             {"Expectancy", "-0.148"},
             {"Net Profit", "-2.802%"},
-            {"Sharpe Ratio", "-0.495"},
-            {"Probabilistic Sharpe Ratio", "10.470%"},
+            {"Sharpe Ratio", "-0.501"},
+            {"Probabilistic Sharpe Ratio", "10.679%"},
             {"Loss Rate", "50%"},
             {"Win Rate", "50%"},
             {"Profit-Loss Ratio", "0.70"},
-            {"Alpha", "-0.043"},
+            {"Alpha", "-0.045"},
             {"Beta", "-0.001"},
-            {"Annual Standard Deviation", "0.087"},
+            {"Annual Standard Deviation", "0.089"},
             {"Annual Variance", "0.008"},
-            {"Information Ratio", "0.957"},
-            {"Tracking Error", "0.192"},
-            {"Treynor Ratio", "57.633"},
+            {"Information Ratio", "0.966"},
+            {"Tracking Error", "0.195"},
+            {"Treynor Ratio", "55.977"},
             {"Total Fees", "$14.80"},
             {"Fitness Score", "0.018"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
-            {"Sortino Ratio", "-0.097"},
-            {"Return Over Maximum Drawdown", "-0.999"},
-            {"Portfolio Turnover", "0.043"},
+            {"Sortino Ratio", "-0.103"},
+            {"Return Over Maximum Drawdown", "-1.063"},
+            {"Portfolio Turnover", "0.045"},
             {"Total Insights Generated", "0"},
             {"Total Insights Closed", "0"},
             {"Total Insights Analysis Completed", "0"},
@@ -162,7 +157,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "-1863159170"}
+            {"OrderListHash", "fc9eb9b0a644e4890d5ec3d40367d0e1"}
         };
     }
 }

Algorithm.CSharp/FutureOptionCallITMExpiryRegressionAlgorithm.cs

@@ -16,7 +16,6 @@
 using System;
 using System.Collections.Generic;
 using System.Linq;
-using System.Reflection;
 using QuantConnect.Data;
 using QuantConnect.Interfaces;
 using QuantConnect.Orders;
@@ -46,11 +45,6 @@ namespace QuantConnect.Algorithm.CSharp
             SetStartDate(2020, 1, 5);
             SetEndDate(2020, 6, 30);
 
-            // We add AAPL as a temporary workaround for https://github.com/QuantConnect/Lean/issues/4872
-            // which causes delisting events to never be processed, thus leading to options that might never
-            // be exercised until the next data point arrives.
-            AddEquity("AAPL", Resolution.Daily);
-
             _es19m20 = AddFutureContract(
                 QuantConnect.Symbol.CreateFuture(
                     Futures.Indices.SP500EMini,
@@ -132,10 +126,7 @@ namespace QuantConnect.Algorithm.CSharp
 
         private void AssertFutureOptionOrderExercise(OrderEvent orderEvent, Security future, Security optionContract)
         {
-            // For unknown reasons, the delisting happens two minutes after the market open. Most likely
-            // stems from the placement of the ProcessDelistedSymbols and HandleDelistedSymbols methods in relation
-            // to the algorithm time update and the brokerage ProcessSynchronousEvents.
-            var expectedLiquidationTimeUtc = new DateTime(2020, 6, 19, 13, 32, 0);
+            var expectedLiquidationTimeUtc = new DateTime(2020, 6, 19, 20, 0, 0);
 
             if (orderEvent.Direction == OrderDirection.Sell && future.Holdings.Quantity != 0)
             {
@@ -214,29 +205,29 @@ namespace QuantConnect.Algorithm.CSharp
             {"Total Trades", "3"},
             {"Average Win", "1.22%"},
             {"Average Loss", "-7.42%"},
-            {"Compounding Annual Return", "-12.461%"},
+            {"Compounding Annual Return", "-13.222%"},
             {"Drawdown", "6.300%"},
             {"Expectancy", "-0.417"},
             {"Net Profit", "-6.282%"},
-            {"Sharpe Ratio", "-1.324"},
-            {"Probabilistic Sharpe Ratio", "0.004%"},
+            {"Sharpe Ratio", "-1.345"},
+            {"Probabilistic Sharpe Ratio", "0.005%"},
             {"Loss Rate", "50%"},
             {"Win Rate", "50%"},
             {"Profit-Loss Ratio", "0.17"},
-            {"Alpha", "-0.102"},
+            {"Alpha", "-0.105"},
             {"Beta", "-0.003"},
-            {"Annual Standard Deviation", "0.076"},
+            {"Annual Standard Deviation", "0.078"},
             {"Annual Variance", "0.006"},
-            {"Information Ratio", "0.671"},
-            {"Tracking Error", "0.188"},
-            {"Treynor Ratio", "33.52"},
+            {"Information Ratio", "0.678"},
+            {"Tracking Error", "0.191"},
+            {"Treynor Ratio", "33.18"},
             {"Total Fees", "$7.40"},
             {"Fitness Score", "0.008"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
-            {"Sortino Ratio", "-0.204"},
-            {"Return Over Maximum Drawdown", "-1.983"},
-            {"Portfolio Turnover", "0.023"},
+            {"Sortino Ratio", "-0.217"},
+            {"Return Over Maximum Drawdown", "-2.105"},
+            {"Portfolio Turnover", "0.024"},
             {"Total Insights Generated", "0"},
             {"Total Insights Closed", "0"},
             {"Total Insights Analysis Completed", "0"},
@@ -250,7 +241,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "1442219241"}
+            {"OrderListHash", "c59d790b89d76f1ad3bb7738b28567c9"}
         };
     }
 }

Algorithm.CSharp/FutureOptionCallITMGreeksExpiryRegressionAlgorithm.cs

@@ -42,11 +42,6 @@ namespace QuantConnect.Algorithm.CSharp
             SetStartDate(2020, 1, 5);
             SetEndDate(2020, 6, 30);
 
-            // We add AAPL as a temporary workaround for https://github.com/QuantConnect/Lean/issues/4872
-            // which causes delisting events to never be processed, thus leading to options that might never
-            // be exercised until the next data point arrives.
-            AddEquity("AAPL", Resolution.Daily);
-
             _es19m20 = AddFutureContract(
                 QuantConnect.Symbol.CreateFuture(
                     Futures.Indices.SP500EMini,
@@ -168,29 +163,29 @@ namespace QuantConnect.Algorithm.CSharp
             {"Total Trades", "3"},
             {"Average Win", "27.44%"},
             {"Average Loss", "-62.81%"},
-            {"Compounding Annual Return", "-78.376%"},
+            {"Compounding Annual Return", "-80.444%"},
             {"Drawdown", "52.600%"},
             {"Expectancy", "-0.282"},
             {"Net Profit", "-52.604%"},
-            {"Sharpe Ratio", "-0.864"},
-            {"Probabilistic Sharpe Ratio", "0.019%"},
+            {"Sharpe Ratio", "-0.867"},
+            {"Probabilistic Sharpe Ratio", "0.021%"},
             {"Loss Rate", "50%"},
             {"Win Rate", "50%"},
             {"Profit-Loss Ratio", "0.44"},
-            {"Alpha", "-0.598"},
-            {"Beta", "-0.032"},
-            {"Annual Standard Deviation", "0.684"},
-            {"Annual Variance", "0.467"},
-            {"Information Ratio", "-0.514"},
-            {"Tracking Error", "0.706"},
-            {"Treynor Ratio", "18.718"},
+            {"Alpha", "-0.611"},
+            {"Beta", "-0.033"},
+            {"Annual Standard Deviation", "0.695"},
+            {"Annual Variance", "0.484"},
+            {"Information Ratio", "-0.513"},
+            {"Tracking Error", "0.718"},
+            {"Treynor Ratio", "18.473"},
             {"Total Fees", "$66.60"},
-            {"Fitness Score", "0.158"},
+            {"Fitness Score", "0.162"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
-            {"Sortino Ratio", "-0.133"},
-            {"Return Over Maximum Drawdown", "-1.489"},
-            {"Portfolio Turnover", "0.413"},
+            {"Sortino Ratio", "-0.136"},
+            {"Return Over Maximum Drawdown", "-1.529"},
+            {"Portfolio Turnover", "0.427"},
             {"Total Insights Generated", "0"},
             {"Total Insights Closed", "0"},
             {"Total Insights Analysis Completed", "0"},
@@ -204,7 +199,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "891799117"}
+            {"OrderListHash", "a7f76d1e2d6f27651465217c92deea80"}
         };
     }
 }

Algorithm.CSharp/FutureOptionCallOTMExpiryRegressionAlgorithm.cs

@@ -51,11 +51,6 @@ namespace QuantConnect.Algorithm.CSharp
             SetStartDate(2020, 1, 5);
             SetEndDate(2020, 6, 30);
 
-            // We add AAPL as a temporary workaround for https://github.com/QuantConnect/Lean/issues/4872
-            // which causes delisting events to never be processed, thus leading to options that might never
-            // be exercised until the next data point arrives.
-            AddEquity("AAPL", Resolution.Daily);
-
             _es19m20 = AddFutureContract(
                 QuantConnect.Symbol.CreateFuture(
                     Futures.Indices.SP500EMini,
@@ -185,28 +180,28 @@ namespace QuantConnect.Algorithm.CSharp
             {"Total Trades", "2"},
             {"Average Win", "0%"},
             {"Average Loss", "-4.03%"},
-            {"Compounding Annual Return", "-8.088%"},
+            {"Compounding Annual Return", "-8.595%"},
             {"Drawdown", "4.000%"},
             {"Expectancy", "-1"},
             {"Net Profit", "-4.029%"},
-            {"Sharpe Ratio", "-1.274"},
-            {"Probabilistic Sharpe Ratio", "0.015%"},
+            {"Sharpe Ratio", "-1.294"},
+            {"Probabilistic Sharpe Ratio", "0.017%"},
             {"Loss Rate", "100%"},
             {"Win Rate", "0%"},
             {"Profit-Loss Ratio", "0"},
-            {"Alpha", "-0.066"},
+            {"Alpha", "-0.069"},
             {"Beta", "-0.002"},
-            {"Annual Standard Deviation", "0.052"},
+            {"Annual Standard Deviation", "0.053"},
             {"Annual Variance", "0.003"},
-            {"Information Ratio", "0.9"},
-            {"Tracking Error", "0.179"},
-            {"Treynor Ratio", "28.537"},
+            {"Information Ratio", "0.911"},
+            {"Tracking Error", "0.182"},
+            {"Treynor Ratio", "28.46"},
             {"Total Fees", "$3.70"},
             {"Fitness Score", "0"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
-            {"Sortino Ratio", "-0.183"},
-            {"Return Over Maximum Drawdown", "-2.007"},
+            {"Sortino Ratio", "-0.195"},
+            {"Return Over Maximum Drawdown", "-2.134"},
             {"Portfolio Turnover", "0"},
             {"Total Insights Generated", "0"},
             {"Total Insights Closed", "0"},
@@ -221,7 +216,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "1061918870"}
+            {"OrderListHash", "5dc2591837f882d173d2d4852b3b0626"}
         };
     }
 }

Algorithm.CSharp/FutureOptionMultipleContractsInDifferentContractMonthsWithSameUnderlyingFutureRegressionAlgorithm.cs

@@ -0,0 +1,147 @@
+/*
+ * 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;
+using System.Collections.Generic;
+using System.Linq;
+using QuantConnect.Data;
+using QuantConnect.Interfaces;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// This regression test tests for the loading of futures options contracts with a contract month of 2020-03 can live
+    /// and be loaded from the same ZIP file that the 2020-04 contract month Future Option contract lives in.
+    /// </summary>
+    public class FutureOptionMultipleContractsInDifferentContractMonthsWithSameUnderlyingFutureRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private readonly Dictionary<Symbol, bool> _expectedSymbols = new Dictionary<Symbol, bool>
+        {
+            { CreateOption(new DateTime(2020, 3, 26), OptionRight.Call, 1650), false },
+            { CreateOption(new DateTime(2020, 3, 26), OptionRight.Put, 1540), false },
+            { CreateOption(new DateTime(2020, 2, 25), OptionRight.Call, 1600), false },
+            { CreateOption(new DateTime(2020, 2, 25), OptionRight.Put, 1545), false }
+        };
+
+        public override void Initialize()
+        {
+            SetStartDate(2020, 1, 5);
+            SetEndDate(2020, 1, 6);
+
+            var goldFutures = AddFuture("GC", Resolution.Minute, Market.COMEX);
+            goldFutures.SetFilter(0, 365);
+
+            AddFutureOption(goldFutures.Symbol);
+        }
+
+        public override void OnData(Slice data)
+        {
+            foreach (var symbol in data.QuoteBars.Keys)
+            {
+                if (_expectedSymbols.ContainsKey(symbol))
+                {
+                    var invested = _expectedSymbols[symbol];
+                    if (!invested)
+                    {
+                        MarketOrder(symbol, 1);
+                    }
+
+                    _expectedSymbols[symbol] = true;
+                }
+            }
+        }
+
+        public override void OnEndOfAlgorithm()
+        {
+            var notEncountered = _expectedSymbols.Where(kvp => !kvp.Value).ToList();
+            if (notEncountered.Any())
+            {
+                throw new Exception($"Expected all Symbols encountered and invested in, but the following were not found: {string.Join(", ", notEncountered.Select(kvp => kvp.Value.ToStringInvariant()))}");
+            }
+            if (!Portfolio.Invested)
+            {
+                throw new Exception("Expected holdings at the end of algorithm, but none were found.");
+            }
+        }
+
+        private static Symbol CreateOption(DateTime expiry, OptionRight optionRight, decimal strikePrice)
+        {
+            return QuantConnect.Symbol.CreateOption(
+                QuantConnect.Symbol.CreateFuture("GC", Market.COMEX, new DateTime(2020, 4, 28)),
+                Market.COMEX,
+                OptionStyle.American,
+                optionRight,
+                strikePrice,
+                expiry);
+        }
+
+        /// <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 bool CanRunLocally { get; } = true;
+
+        /// <summary>
+        /// This is used by the regression test system to indicate which languages this algorithm is written in.
+        /// </summary>
+        public Language[] Languages { get; } = { Language.CSharp, Language.Python };
+
+        /// <summary>
+        /// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
+        /// </summary>
+        public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
+        {
+            {"Total Trades", "4"},
+            {"Average Win", "0%"},
+            {"Average Loss", "0%"},
+            {"Compounding Annual Return", "-8.289%"},
+            {"Drawdown", "3.500%"},
+            {"Expectancy", "0"},
+            {"Net Profit", "-0.047%"},
+            {"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", "-14.395"},
+            {"Tracking Error", "0.043"},
+            {"Treynor Ratio", "0"},
+            {"Total Fees", "$7.40"},
+            {"Fitness Score", "0.019"},
+            {"Kelly Criterion Estimate", "0"},
+            {"Kelly Criterion Probability Value", "0"},
+            {"Sortino Ratio", "79228162514264337593543950335"},
+            {"Return Over Maximum Drawdown", "-194.237"},
+            {"Portfolio Turnover", "0.038"},
+            {"Total Insights Generated", "0"},
+            {"Total Insights Closed", "0"},
+            {"Total Insights Analysis Completed", "0"},
+            {"Long Insight Count", "0"},
+            {"Short Insight Count", "0"},
+            {"Long/Short Ratio", "100%"},
+            {"Estimated Monthly Alpha Value", "$0"},
+            {"Total Accumulated Estimated Alpha Value", "$0"},
+            {"Mean Population Estimated Insight Value", "$0"},
+            {"Mean Population Direction", "0%"},
+            {"Mean Population Magnitude", "0%"},
+            {"Rolling Averaged Population Direction", "0%"},
+            {"Rolling Averaged Population Magnitude", "0%"},
+            {"OrderListHash", "979e3995c0dbedc46eaf3705e0438bf5"}
+        };
+    }
+}

Algorithm.CSharp/FutureOptionPutITMExpiryRegressionAlgorithm.cs

@@ -46,11 +46,6 @@ namespace QuantConnect.Algorithm.CSharp
             SetStartDate(2020, 1, 5);
             SetEndDate(2020, 6, 30);
 
-            // We add AAPL as a temporary workaround for https://github.com/QuantConnect/Lean/issues/4872
-            // which causes delisting events to never be processed, thus leading to options that might never
-            // be exercised until the next data point arrives.
-            AddEquity("AAPL", Resolution.Daily);
-
             _es19m20 = AddFutureContract(
                 QuantConnect.Symbol.CreateFuture(
                     Futures.Indices.SP500EMini,
@@ -132,10 +127,7 @@ namespace QuantConnect.Algorithm.CSharp
 
         private void AssertFutureOptionOrderExercise(OrderEvent orderEvent, Security future, Security optionContract)
         {
-            // For unknown reasons, the delisting happens two minutes after the market open. Most likely
-            // stems from the placement of the ProcessDelistedSymbols and HandleDelistedSymbols methods in relation
-            // to the algorithm time update and the brokerage ProcessSynchronousEvents.
-            var expectedLiquidationTimeUtc = new DateTime(2020, 6, 19, 13, 32, 0);
+            var expectedLiquidationTimeUtc = new DateTime(2020, 6, 19, 20, 0, 0);
 
             if (orderEvent.Direction == OrderDirection.Buy && future.Holdings.Quantity != 0)
             {
@@ -214,29 +206,29 @@ namespace QuantConnect.Algorithm.CSharp
             {"Total Trades", "3"},
             {"Average Win", "4.15%"},
             {"Average Loss", "-8.27%"},
-            {"Compounding Annual Return", "-8.928%"},
+            {"Compounding Annual Return", "-9.486%"},
             {"Drawdown", "4.500%"},
             {"Expectancy", "-0.249"},
             {"Net Profit", "-4.457%"},
-            {"Sharpe Ratio", "-1.389"},
+            {"Sharpe Ratio", "-1.412"},
             {"Probabilistic Sharpe Ratio", "0.002%"},
             {"Loss Rate", "50%"},
             {"Win Rate", "50%"},
             {"Profit-Loss Ratio", "0.50"},
-            {"Alpha", "-0.073"},
+            {"Alpha", "-0.076"},
             {"Beta", "-0.002"},
-            {"Annual Standard Deviation", "0.052"},
+            {"Annual Standard Deviation", "0.053"},
             {"Annual Variance", "0.003"},
-            {"Information Ratio", "0.861"},
-            {"Tracking Error", "0.179"},
-            {"Treynor Ratio", "38.365"},
+            {"Information Ratio", "0.871"},
+            {"Tracking Error", "0.183"},
+            {"Treynor Ratio", "37.798"},
             {"Total Fees", "$7.40"},
             {"Fitness Score", "0.008"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
-            {"Sortino Ratio", "-0.224"},
-            {"Return Over Maximum Drawdown", "-2.003"},
-            {"Portfolio Turnover", "0.023"},
+            {"Sortino Ratio", "-0.238"},
+            {"Return Over Maximum Drawdown", "-2.128"},
+            {"Portfolio Turnover", "0.024"},
             {"Total Insights Generated", "0"},
             {"Total Insights Closed", "0"},
             {"Total Insights Analysis Completed", "0"},
@@ -250,7 +242,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "-1705374528"}
+            {"OrderListHash", "d3fa88c3acadb9345ceac76a2dd3b520"}
         };
     }
 }

Algorithm.CSharp/FutureOptionPutOTMExpiryRegressionAlgorithm.cs

@@ -50,11 +50,6 @@ namespace QuantConnect.Algorithm.CSharp
             SetStartDate(2020, 1, 5);
             SetEndDate(2020, 6, 30);
 
-            // We add AAPL as a temporary workaround for https://github.com/QuantConnect/Lean/issues/4872
-            // which causes delisting events to never be processed, thus leading to options that might never
-            // be exercised until the next data point arrives.
-            AddEquity("AAPL", Resolution.Daily);
-
             _es19m20 = AddFutureContract(
                 QuantConnect.Symbol.CreateFuture(
                     Futures.Indices.SP500EMini,
@@ -184,28 +179,28 @@ namespace QuantConnect.Algorithm.CSharp
             {"Total Trades", "2"},
             {"Average Win", "0%"},
             {"Average Loss", "-5.12%"},
-            {"Compounding Annual Return", "-10.212%"},
+            {"Compounding Annual Return", "-10.844%"},
             {"Drawdown", "5.100%"},
             {"Expectancy", "-1"},
             {"Net Profit", "-5.116%"},
-            {"Sharpe Ratio", "-1.26"},
-            {"Probabilistic Sharpe Ratio", "0.016%"},
+            {"Sharpe Ratio", "-1.28"},
+            {"Probabilistic Sharpe Ratio", "0.017%"},
             {"Loss Rate", "100%"},
             {"Win Rate", "0%"},
             {"Profit-Loss Ratio", "0"},
-            {"Alpha", "-0.084"},
+            {"Alpha", "-0.086"},
             {"Beta", "-0.003"},
-            {"Annual Standard Deviation", "0.066"},
+            {"Annual Standard Deviation", "0.067"},
             {"Annual Variance", "0.004"},
-            {"Information Ratio", "0.785"},
-            {"Tracking Error", "0.184"},
-            {"Treynor Ratio", "28.158"},
+            {"Information Ratio", "0.794"},
+            {"Tracking Error", "0.187"},
+            {"Treynor Ratio", "28.078"},
             {"Total Fees", "$3.70"},
             {"Fitness Score", "0"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
-            {"Sortino Ratio", "-0.181"},
-            {"Return Over Maximum Drawdown", "-1.995"},
+            {"Sortino Ratio", "-0.193"},
+            {"Return Over Maximum Drawdown", "-2.12"},
             {"Portfolio Turnover", "0"},
             {"Total Insights Generated", "0"},
             {"Total Insights Closed", "0"},
@@ -220,7 +215,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "-312857564"}
+            {"OrderListHash", "a35054d03fd2caa0a96cbe12e427e928"}
         };
     }
 }

Algorithm.CSharp/FutureOptionShortCallITMExpiryRegressionAlgorithm.cs

@@ -46,11 +46,6 @@ namespace QuantConnect.Algorithm.CSharp
             SetStartDate(2020, 1, 5);
             SetEndDate(2020, 6, 30);
 
-            // We add AAPL as a temporary workaround for https://github.com/QuantConnect/Lean/issues/4872
-            // which causes delisting events to never be processed, thus leading to options that might never
-            // be exercised until the next data point arrives.
-            AddEquity("AAPL", Resolution.Daily);
-
             _es19m20 = AddFutureContract(
                 QuantConnect.Symbol.CreateFuture(
                     Futures.Indices.SP500EMini,
@@ -195,28 +190,28 @@ namespace QuantConnect.Algorithm.CSharp
             {"Total Trades", "3"},
             {"Average Win", "10.05%"},
             {"Average Loss", "-5.63%"},
-            {"Compounding Annual Return", "8.067%"},
+            {"Compounding Annual Return", "8.619%"},
             {"Drawdown", "0.500%"},
             {"Expectancy", "0.393"},
             {"Net Profit", "3.855%"},
-            {"Sharpe Ratio", "1.191"},
-            {"Probabilistic Sharpe Ratio", "58.149%"},
+            {"Sharpe Ratio", "1.212"},
+            {"Probabilistic Sharpe Ratio", "59.039%"},
             {"Loss Rate", "50%"},
             {"Win Rate", "50%"},
             {"Profit-Loss Ratio", "1.79"},
-            {"Alpha", "0.068"},
+            {"Alpha", "0.071"},
             {"Beta", "0.003"},
-            {"Annual Standard Deviation", "0.057"},
+            {"Annual Standard Deviation", "0.058"},
             {"Annual Variance", "0.003"},
-            {"Information Ratio", "1.64"},
-            {"Tracking Error", "0.18"},
-            {"Treynor Ratio", "22.061"},
+            {"Information Ratio", "1.663"},
+            {"Tracking Error", "0.183"},
+            {"Treynor Ratio", "22.266"},
             {"Total Fees", "$7.40"},
             {"Fitness Score", "0.021"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
             {"Sortino Ratio", "79228162514264337593543950335"},
-            {"Return Over Maximum Drawdown", "17.142"},
+            {"Return Over Maximum Drawdown", "18.319"},
             {"Portfolio Turnover", "0.021"},
             {"Total Insights Generated", "0"},
             {"Total Insights Closed", "0"},
@@ -231,7 +226,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "-991138464"}
+            {"OrderListHash", "8e380e4d5c5e3e145ba388f7853829bb"}
         };
     }
 }

Algorithm.CSharp/FutureOptionShortCallOTMExpiryRegressionAlgorithm.cs

@@ -47,11 +47,6 @@ namespace QuantConnect.Algorithm.CSharp
             SetStartDate(2020, 1, 5);
             SetEndDate(2020, 6, 30);
 
-            // We add AAPL as a temporary workaround for https://github.com/QuantConnect/Lean/issues/4872
-            // which causes delisting events to never be processed, thus leading to options that might never
-            // be exercised until the next data point arrives.
-            AddEquity("AAPL", Resolution.Daily);
-
             _es19m20 = AddFutureContract(
                 QuantConnect.Symbol.CreateFuture(
                     Futures.Indices.SP500EMini,
@@ -178,28 +173,28 @@ namespace QuantConnect.Algorithm.CSharp
             {"Total Trades", "2"},
             {"Average Win", "1.81%"},
             {"Average Loss", "0%"},
-            {"Compounding Annual Return", "3.745%"},
+            {"Compounding Annual Return", "3.996%"},
             {"Drawdown", "0.000%"},
             {"Expectancy", "0"},
             {"Net Profit", "1.809%"},
-            {"Sharpe Ratio", "1.292"},
-            {"Probabilistic Sharpe Ratio", "65.890%"},
+            {"Sharpe Ratio", "1.315"},
+            {"Probabilistic Sharpe Ratio", "66.818%"},
             {"Loss Rate", "0%"},
             {"Win Rate", "100%"},
             {"Profit-Loss Ratio", "0"},
-            {"Alpha", "0.031"},
+            {"Alpha", "0.032"},
             {"Beta", "0.001"},
             {"Annual Standard Deviation", "0.024"},
             {"Annual Variance", "0.001"},
-            {"Information Ratio", "1.496"},
-            {"Tracking Error", "0.173"},
-            {"Treynor Ratio", "27.281"},
+            {"Information Ratio", "1.516"},
+            {"Tracking Error", "0.176"},
+            {"Treynor Ratio", "27.339"},
             {"Total Fees", "$3.70"},
             {"Fitness Score", "0"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
             {"Sortino Ratio", "79228162514264337593543950335"},
-            {"Return Over Maximum Drawdown", "95.176"},
+            {"Return Over Maximum Drawdown", "101.571"},
             {"Portfolio Turnover", "0"},
             {"Total Insights Generated", "0"},
             {"Total Insights Closed", "0"},
@@ -214,7 +209,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "1847291350"}
+            {"OrderListHash", "35baadd70ec72c735eadbf55d702fe04"}
         };
     }
 }

Algorithm.CSharp/FutureOptionShortPutITMExpiryRegressionAlgorithm.cs

@@ -46,11 +46,6 @@ namespace QuantConnect.Algorithm.CSharp
             SetStartDate(2020, 1, 5);
             SetEndDate(2020, 6, 30);
 
-            // We add AAPL as a temporary workaround for https://github.com/QuantConnect/Lean/issues/4872
-            // which causes delisting events to never be processed, thus leading to options that might never
-            // be exercised until the next data point arrives.
-            AddEquity("AAPL", Resolution.Daily);
-
             _es19m20 = AddFutureContract(
                 QuantConnect.Symbol.CreateFuture(
                     Futures.Indices.SP500EMini,
@@ -192,29 +187,29 @@ namespace QuantConnect.Algorithm.CSharp
             {"Total Trades", "3"},
             {"Average Win", "10.18%"},
             {"Average Loss", "-8.05%"},
-            {"Compounding Annual Return", "2.721%"},
+            {"Compounding Annual Return", "2.902%"},
             {"Drawdown", "0.500%"},
             {"Expectancy", "0.133"},
             {"Net Profit", "1.318%"},
-            {"Sharpe Ratio", "0.934"},
-            {"Probabilistic Sharpe Ratio", "46.618%"},
+            {"Sharpe Ratio", "0.95"},
+            {"Probabilistic Sharpe Ratio", "47.360%"},
             {"Loss Rate", "50%"},
             {"Win Rate", "50%"},
             {"Profit-Loss Ratio", "1.27"},
-            {"Alpha", "0.023"},
+            {"Alpha", "0.024"},
             {"Beta", "0.002"},
-            {"Annual Standard Deviation", "0.024"},
+            {"Annual Standard Deviation", "0.025"},
             {"Annual Variance", "0.001"},
-            {"Information Ratio", "1.448"},
-            {"Tracking Error", "0.173"},
-            {"Treynor Ratio", "14.482"},
+            {"Information Ratio", "1.467"},
+            {"Tracking Error", "0.176"},
+            {"Treynor Ratio", "14.729"},
             {"Total Fees", "$7.40"},
-            {"Fitness Score", "0.021"},
+            {"Fitness Score", "0.022"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
             {"Sortino Ratio", "79228162514264337593543950335"},
-            {"Return Over Maximum Drawdown", "5.706"},
-            {"Portfolio Turnover", "0.022"},
+            {"Return Over Maximum Drawdown", "6.087"},
+            {"Portfolio Turnover", "0.023"},
             {"Total Insights Generated", "0"},
             {"Total Insights Closed", "0"},
             {"Total Insights Analysis Completed", "0"},
@@ -228,7 +223,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "777632049"}
+            {"OrderListHash", "519cc7c39703b6e6913dbe98468da872"}
         };
     }
 }

Algorithm.CSharp/FutureOptionShortPutOTMExpiryRegressionAlgorithm.cs

@@ -47,11 +47,6 @@ namespace QuantConnect.Algorithm.CSharp
             SetStartDate(2020, 1, 5);
             SetEndDate(2020, 6, 30);
 
-            // We add AAPL as a temporary workaround for https://github.com/QuantConnect/Lean/issues/4872
-            // which causes delisting events to never be processed, thus leading to options that might never
-            // be exercised until the next data point arrives.
-            AddEquity("AAPL", Resolution.Daily);
-
             _es19m20 = AddFutureContract(
                 QuantConnect.Symbol.CreateFuture(
                     Futures.Indices.SP500EMini,
@@ -177,28 +172,28 @@ namespace QuantConnect.Algorithm.CSharp
             {"Total Trades", "2"},
             {"Average Win", "3.28%"},
             {"Average Loss", "0%"},
-            {"Compounding Annual Return", "6.852%"},
+            {"Compounding Annual Return", "7.317%"},
             {"Drawdown", "0.000%"},
             {"Expectancy", "0"},
             {"Net Profit", "3.284%"},
-            {"Sharpe Ratio", "1.319"},
-            {"Probabilistic Sharpe Ratio", "66.574%"},
+            {"Sharpe Ratio", "1.343"},
+            {"Probabilistic Sharpe Ratio", "67.503%"},
             {"Loss Rate", "0%"},
             {"Win Rate", "100%"},
             {"Profit-Loss Ratio", "0"},
-            {"Alpha", "0.058"},
+            {"Alpha", "0.06"},
             {"Beta", "0.002"},
-            {"Annual Standard Deviation", "0.043"},
+            {"Annual Standard Deviation", "0.044"},
             {"Annual Variance", "0.002"},
-            {"Information Ratio", "1.614"},
-            {"Tracking Error", "0.176"},
-            {"Treynor Ratio", "28.2"},
+            {"Information Ratio", "1.636"},
+            {"Tracking Error", "0.179"},
+            {"Treynor Ratio", "28.253"},
             {"Total Fees", "$3.70"},
             {"Fitness Score", "0"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
             {"Sortino Ratio", "79228162514264337593543950335"},
-            {"Return Over Maximum Drawdown", "150.252"},
+            {"Return Over Maximum Drawdown", "160.505"},
             {"Portfolio Turnover", "0"},
             {"Total Insights Generated", "0"},
             {"Total Insights Closed", "0"},
@@ -213,7 +208,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "1003680014"}
+            {"OrderListHash", "5571280f1efb15dd3899896fb72385ff"}
         };
     }
 }

Algorithm.CSharp/FutureSharingTickerRegressionAlgorithm.cs

@@ -121,7 +121,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "378972600"}
+            {"OrderListHash", "c627a8c7156e3ab378f2ab13d21b5737"}
         };
     }
 }

Algorithm.CSharp/FuturesAndFuturesOptionsExpiryTimeAndLiquidationRegressionAlgorithm.cs

@@ -36,7 +36,7 @@ namespace QuantConnect.Algorithm.CSharp
 
         private readonly DateTime _expectedExpiryWarningTime = new DateTime(2020, 6, 19);
         private readonly DateTime _expectedExpiryDelistingTime = new DateTime(2020, 6, 20);
-        private readonly DateTime _expectedLiquidationTime = new DateTime(2020, 6, 19, 9, 32, 0);
+        private readonly DateTime _expectedLiquidationTime = new DateTime(2020, 6, 19, 16, 0, 0);
 
         public override void Initialize()
         {
@@ -44,11 +44,6 @@ namespace QuantConnect.Algorithm.CSharp
             SetEndDate(2020, 12, 1);
             SetCash(100000);
 
-            // To ensure that the expiry liquidations are ran for the Futures and FOPs, we
-            // add AAPL to pump a data point through on liquidation date so that the liquidation goes through
-            // at AAPL market open. See issue for more details: https://github.com/QuantConnect/Lean/issues/4872
-            AddEquity("AAPL", Resolution.Daily);
-
             var es = QuantConnect.Symbol.CreateFuture(
                 Futures.Indices.SP500EMini,
                 Market.CME,
@@ -114,7 +109,8 @@ namespace QuantConnect.Algorithm.CSharp
 
             // * Future Liquidation
             // * Future Option Exercise
-            // * Underlying Future Liquidation
+
+            // * We expect NO Underlying Future Liquidation because we already hold a Long future position so the FOP Put selling leaves us breakeven
             _liquidated++;
             if (orderEvent.Symbol.SecurityType == SecurityType.FutureOption && _expectedLiquidationTime != Time)
             {
@@ -136,7 +132,7 @@ namespace QuantConnect.Algorithm.CSharp
             {
                 throw new Exception($"Expected 4 delisting events received, found: {_delistingsReceived}");
             }
-            if (_liquidated != 3)
+            if (_liquidated != 2)
             {
                 throw new Exception($"Expected 3 liquidation events, found {_liquidated}");
             }
@@ -157,32 +153,32 @@ namespace QuantConnect.Algorithm.CSharp
         /// </summary>
         public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
         {
-            {"Total Trades", "4"},
-            {"Average Win", "0.13%"},
-            {"Average Loss", "-11.33%"},
-            {"Compounding Annual Return", "-2.634%"},
-            {"Drawdown", "2.400%"},
-            {"Expectancy", "-0.494"},
-            {"Net Profit", "-2.399%"},
-            {"Sharpe Ratio", "-0.933"},
-            {"Probabilistic Sharpe Ratio", "0.003%"},
+            {"Total Trades", "3"},
+            {"Average Win", "10.15%"},
+            {"Average Loss", "-11.34%"},
+            {"Compounding Annual Return", "-5.054%"},
+            {"Drawdown", "2.300%"},
+            {"Expectancy", "-0.053"},
+            {"Net Profit", "-2.345%"},
+            {"Sharpe Ratio", "-1.289"},
+            {"Probabilistic Sharpe Ratio", "0.028%"},
             {"Loss Rate", "50%"},
             {"Win Rate", "50%"},
-            {"Profit-Loss Ratio", "0.01"},
-            {"Alpha", "-0.017"},
+            {"Profit-Loss Ratio", "0.89"},
+            {"Alpha", "-0.031"},
             {"Beta", "-0.001"},
-            {"Annual Standard Deviation", "0.018"},
-            {"Annual Variance", "0"},
-            {"Information Ratio", "0.886"},
-            {"Tracking Error", "0.127"},
-            {"Treynor Ratio", "30.534"},
-            {"Total Fees", "$9.25"},
+            {"Annual Standard Deviation", "0.024"},
+            {"Annual Variance", "0.001"},
+            {"Information Ratio", "1.155"},
+            {"Tracking Error", "0.176"},
+            {"Treynor Ratio", "29.128"},
+            {"Total Fees", "$7.40"},
             {"Fitness Score", "0.007"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
-            {"Sortino Ratio", "-0.177"},
-            {"Return Over Maximum Drawdown", "-1.098"},
-            {"Portfolio Turnover", "0.018"},
+            {"Sortino Ratio", "-0.354"},
+            {"Return Over Maximum Drawdown", "-2.155"},
+            {"Portfolio Turnover", "0.024"},
             {"Total Insights Generated", "0"},
             {"Total Insights Closed", "0"},
             {"Total Insights Analysis Completed", "0"},
@@ -196,7 +192,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "542517089"}
+            {"OrderListHash", "0128b145984582f5eba7e95881d9b62d"}
         };
     }
 }

Algorithm.CSharp/HistoryAlgorithm.cs

@@ -336,7 +336,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "1268340653"}
+            {"OrderListHash", "82fee25cd17100c53bb173834ab5f0b2"}
         };
 
         /// <summary>