Fix typo (#5361)

This reverts commit caedddddf0.

.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

.gitignore

@@ -144,6 +144,7 @@ $tf/
 # ReSharper is a .NET coding add-in
 _ReSharper*/
 *.[Rr]e[Ss]harper
+*.DotSettings
 *.DotSettings.user
 
 # JustCode is a .NET coding addin-in

.idea/workspace.xml

@@ -3,7 +3,7 @@
   <component name="RunManager" selected="Python Debug Server.Debug in Container">
     <configuration name="Debug Local" type="PyRemoteDebugConfigurationType" factoryName="Python Remote Debug">
       <module name="LEAN" />
-      <option name="PORT" value="5678" />
+      <option name="PORT" value="6000" />
       <option name="HOST" value="localhost" />
       <PathMappingSettings>
         <option name="pathMappings">
@@ -16,7 +16,7 @@
     </configuration>
     <configuration name="Debug in Container" type="PyRemoteDebugConfigurationType" factoryName="Python Remote Debug">
       <module name="LEAN" />
-      <option name="PORT" value="5678" />
+      <option name="PORT" value="6000" />
       <option name="HOST" value="localhost" />
       <PathMappingSettings>
         <option name="pathMappings">

.travis.yml

@@ -1,11 +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"
-  - wget https://cdn.quantconnect.com/miniconda/Miniconda3-4.5.12-Linux-x86_64.sh
+  - 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
   - sudo ln -s $HOME/miniconda3/lib/libpython3.6m.so /usr/lib/libpython3.6m.so
@@ -17,10 +18,9 @@ before_install:
   - conda install -y scipy=1.4.1
   - conda install -y wrapt=1.12.1
 install:
-  - nuget restore QuantConnect.Lean.sln
   - nuget install NUnit.Runners -Version 3.11.1 -OutputDirectory testrunner
 script:
-  - msbuild /p:Configuration=Release /p:VbcToolExe=vbnc.exe 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
+  - 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

.vscode/readme.md

@@ -13,7 +13,7 @@ Before anything we need to ensure a few things have been done:
 
 
 1. Get [Visual Studio Code](https://code.visualstudio.com/download)
-    *   Get the Extension [Mono Debug](https://marketplace.visualstudio.com/items?itemName=ms-vscode.mono-debug) for C# Debugging
+    *   Get the Extension [Mono Debug **15.8**](https://marketplace.visualstudio.com/items?itemName=ms-vscode.mono-debug) for C# Debugging
     *   Get the Extension [Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python) for Python Debugging
 
 2. Get [Docker](https://docs.docker.com/get-docker/):
@@ -35,7 +35,8 @@ Before anything we need to ensure a few things have been done:
     *   Download the repo or clone it using: _git clone[ https://github.com/QuantConnect/Lean](https://github.com/QuantConnect/Lean)_
     *   Open the folder using VS Code
 
-
+**NOTES**: 
+- Mono Extension Version 16 and greater fails to debug the docker container remotely, please install **Version 15.8**. To install an older version from within VS Code go to the extensions tab, search "Mono Debug", and select "Install Another Version...".
 <br />
 
 <h1>Develop Algorithms Locally, Run in Container</h1>
@@ -66,7 +67,7 @@ Before we can use this method with Windows or Mac OS we need to share the Lean d
 
 Next we need to be sure that our Lean configuration at **.\Launcher\config.json** is properly set. Just like running lean locally the config must reflect what we want Lean to run.
 
-You configuration file should look something like this for the following languages:
+Your configuration file should look something like this for the following languages:
 
 <h3>Python:</h3>
 
@@ -112,6 +113,12 @@ In VS Code click on the debug/run icon on the left toolbar, at the top you shoul
 
 As defaults these are all great! Feel free to change them as needed for your setup.
 
+**NOTE:** VSCode may try and throw errors when launching this way regarding build on `QuantConnect.csx` and `Config.json` these errors can be ignored by selecting "*Debug Anyway*". To stop this error message in the future select "*Remember my choice in user settings*". 
+
+If using C# algorithms ensure that msbuild can build them successfully.  
+
+
+
 <br />
 
 <h3>Option 2</h3>
@@ -194,4 +201,6 @@ _Figure 2: Python Debugger Messages_
 <h1>Common Issues</h1>
 Here we will cover some common issues with setting this up. This section will expand as we get user feedback!
 
-*   Error messages about build in VSCode points to comments in JSON. Either select **ignore** or follow steps described [here](https://stackoverflow.com/questions/47834825/in-vs-code-disable-error-comments-are-not-permitted-in-json) to remove the errors entirely.
+*   Any error messages about building in VSCode that point to comments in JSON. Either select **ignore** or follow steps described [here](https://stackoverflow.com/questions/47834825/in-vs-code-disable-error-comments-are-not-permitted-in-json) to remove the errors entirely.
+*   `Errors exist after running preLaunchTask 'run-docker'`This VSCode error appears to warn you of CSharp errors when trying to use `Debug in Container` select "Debug Anyway" as the errors are false flags for JSON comments as well as `QuantConnect.csx` not finding references. Neither of these will impact your debugging. 
+*   `The container name "/LeanEngine" is already in use by container "****"` This Docker error implies that another instance of lean is already running under the container name /LeanEngine. If this error appears either use Docker Desktop to delete the container or use `docker kill LeanEngine` from the command line.

.vscode/tasks.json

@@ -20,6 +20,34 @@
             },
             "problemMatcher": "$msCompile"
         },
+        {
+            "label": "rebuild",
+            "type": "shell",
+            "command": "msbuild",
+            "args": [
+                "/p:Configuration=Debug",
+                "/p:DebugType=portable",
+                "/t:rebuild",
+            ],
+            "group": "build",
+            "presentation": {
+                "reveal": "silent"
+            },
+            "problemMatcher": "$msCompile"
+        },
+        {
+            "label": "clean",
+            "type": "shell",
+            "command": "msbuild",
+            "args": [
+                "/t:clean",
+            ],
+            "group": "build",
+            "presentation": {
+                "reveal": "silent"
+            },
+            "problemMatcher": "$msCompile"
+        },
         {
             "label": "force build linux",
             "type": "shell",

Algorithm.CSharp/AccumulativeInsightPortfolioRegressionAlgorithm.cs

@@ -94,6 +94,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.212"},
             {"Treynor Ratio", "-2.13"},
             {"Total Fees", "$199.00"},
+            {"Estimated Strategy Capacity", "$280000000.00"},
             {"Fitness Score", "0.002"},
             {"Kelly Criterion Estimate", "38.64"},
             {"Kelly Criterion Probability Value", "0.229"},
@@ -113,7 +114,7 @@ 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

@@ -130,6 +130,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.367"},
             {"Treynor Ratio", "-4.079"},
             {"Total Fees", "$14.33"},
+            {"Estimated Strategy Capacity", "$29000000.00"},
             {"Fitness Score", "0.408"},
             {"Kelly Criterion Estimate", "16.447"},
             {"Kelly Criterion Probability Value", "0.315"},
@@ -149,7 +150,7 @@ 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

@@ -185,6 +185,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.043"},
             {"Treynor Ratio", "0"},
             {"Total Fees", "$7.40"},
+            {"Estimated Strategy Capacity", "$28000000.00"},
             {"Fitness Score", "1"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -204,7 +205,7 @@ 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

@@ -219,6 +219,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.044"},
             {"Treynor Ratio", "0.479"},
             {"Total Fees", "$3.70"},
+            {"Estimated Strategy Capacity", "$12000.00"},
             {"Fitness Score", "0.41"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -238,7 +239,7 @@ 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

@@ -131,14 +131,15 @@ 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"},
+            {"Estimated Strategy Capacity", "$6400000.00"},
             {"Fitness Score", "0"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -158,7 +159,7 @@ 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

@@ -191,6 +191,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.042"},
             {"Treynor Ratio", "0.286"},
             {"Total Fees", "$2.00"},
+            {"Estimated Strategy Capacity", "$1400000.00"},
             {"Fitness Score", "0"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -210,7 +211,7 @@ 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

@@ -230,6 +230,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0"},
             {"Treynor Ratio", "0"},
             {"Total Fees", "$6.00"},
+            {"Estimated Strategy Capacity", "$3000.00"},
             {"Fitness Score", "0"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -249,7 +250,7 @@ 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

@@ -131,6 +131,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.072"},
             {"Treynor Ratio", "2.933"},
             {"Total Fees", "$26.39"},
+            {"Estimated Strategy Capacity", "$2200000.00"},
             {"Fitness Score", "0.374"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -150,7 +151,7 @@ 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

@@ -84,6 +84,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.006"},
             {"Treynor Ratio", "2.029"},
             {"Total Fees", "$9.77"},
+            {"Estimated Strategy Capacity", "$28000000.00"},
             {"Fitness Score", "0.747"},
             {"Kelly Criterion Estimate", "38.64"},
             {"Kelly Criterion Probability Value", "0.229"},
@@ -103,7 +104,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "54.5455%"},
             {"Rolling Averaged Population Direction", "59.8056%"},
             {"Rolling Averaged Population Magnitude", "59.8056%"},
-            {"OrderListHash", "-887190565"}
+            {"OrderListHash", "0b8cbbafdb77bae2f7abe3cf5e05ac5c"}
         };
     }
 }

Algorithm.CSharp/AddUniverseSelectionModelAlgorithm.cs

@@ -103,6 +103,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.376"},
             {"Treynor Ratio", "-0.084"},
             {"Total Fees", "$13.98"},
+            {"Estimated Strategy Capacity", "$31000000.00"},
             {"Fitness Score", "0.146"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "1"},
@@ -122,7 +123,7 @@ 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

@@ -110,6 +110,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.194"},
             {"Treynor Ratio", "-0.962"},
             {"Total Fees", "$25.92"},
+            {"Estimated Strategy Capacity", "$16000000.00"},
             {"Fitness Score", "0.004"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "1"},
@@ -129,7 +130,7 @@ 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 +1,237 @@
+/*
+ * 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"},
+            {"Estimated Strategy Capacity", "$710000.00"},
+            {"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 +1,124 @@
+/*
+ * 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"},
+            {"Estimated Strategy Capacity", "$2300000000.00"},
+            {"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

@@ -168,6 +168,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.441"},
             {"Treynor Ratio", "-0.008"},
             {"Total Fees", "$20.35"},
+            {"Estimated Strategy Capacity", "$19000000.00"},
             {"Fitness Score", "0.138"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -187,7 +188,7 @@ 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

@@ -130,6 +130,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.001"},
             {"Treynor Ratio", "1.922"},
             {"Total Fees", "$3.26"},
+            {"Estimated Strategy Capacity", "$15000000.00"},
             {"Fitness Score", "0.248"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -149,7 +150,7 @@ 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 +303,40 @@ 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"},
+            {"Estimated Strategy Capacity", "$0"},
             {"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

@@ -88,6 +88,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0"},
             {"Treynor Ratio", "0"},
             {"Total Fees", "$0.00"},
+            {"Estimated Strategy Capacity", "$85000.00"},
             {"Fitness Score", "0.506"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -107,7 +108,7 @@ 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

@@ -97,6 +97,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.001"},
             {"Treynor Ratio", "1.922"},
             {"Total Fees", "$3.26"},
+            {"Estimated Strategy Capacity", "$15000000.00"},
             {"Fitness Score", "0.248"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -116,7 +117,7 @@ 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

@@ -223,6 +223,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0"},
             {"Treynor Ratio", "0"},
             {"Total Fees", "$85.34"},
+            {"Estimated Strategy Capacity", "$0"},
             {"Fitness Score", "0.5"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -242,7 +243,7 @@ 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

@@ -88,6 +88,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.221"},
             {"Treynor Ratio", "-13.568"},
             {"Total Fees", "$3.26"},
+            {"Estimated Strategy Capacity", "$130000000.00"},
             {"Fitness Score", "0.111"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -107,7 +108,7 @@ 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

@@ -109,6 +109,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.002"},
             {"Treynor Ratio", "1.839"},
             {"Total Fees", "$9.77"},
+            {"Estimated Strategy Capacity", "$20000000.00"},
             {"Fitness Score", "0.747"},
             {"Kelly Criterion Estimate", "38.64"},
             {"Kelly Criterion Probability Value", "0.229"},
@@ -128,7 +129,7 @@ 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

@@ -147,6 +147,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.504"},
             {"Treynor Ratio", "1.011"},
             {"Total Fees", "$15207.00"},
+            {"Estimated Strategy Capacity", "$8800000.00"},
             {"Fitness Score", "0.033"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -166,7 +167,7 @@ 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

@@ -155,6 +155,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.188"},
             {"Treynor Ratio", "-3.318"},
             {"Total Fees", "$3.70"},
+            {"Estimated Strategy Capacity", "$13000000.00"},
             {"Fitness Score", "0.009"},
             {"Kelly Criterion Estimate", "-112.972"},
             {"Kelly Criterion Probability Value", "0.671"},
@@ -174,7 +175,7 @@ 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.
  *
@@ -160,6 +160,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0"},
             {"Treynor Ratio", "0"},
             {"Total Fees", "$0.00"},
+            {"Estimated Strategy Capacity", "$0"},
             {"Fitness Score", "0"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -179,7 +180,7 @@ 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

@@ -134,6 +134,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0"},
             {"Treynor Ratio", "0"},
             {"Total Fees", "$778.00"},
+            {"Estimated Strategy Capacity", "$180000.00"},
             {"Fitness Score", "0"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -153,7 +154,7 @@ 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

@@ -131,6 +131,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0"},
             {"Treynor Ratio", "0"},
             {"Total Fees", "$2.00"},
+            {"Estimated Strategy Capacity", "$2600000.00"},
             {"Fitness Score", "0"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -150,7 +151,7 @@ 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%"},
@@ -122,6 +122,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0"},
             {"Treynor Ratio", "0"},
             {"Total Fees", "$1.00"},
+            {"Estimated Strategy Capacity", "$0"},
             {"Fitness Score", "0"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -141,7 +142,7 @@ 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 +160,8 @@ namespace QuantConnect.Algorithm.CSharp
             {"Information Ratio", "0"},
             {"Tracking Error", "0"},
             {"Treynor Ratio", "0"},
-            {"Total Fees", "$4.00"},
+            {"Total Fees", "$3.00"},
+            {"Estimated Strategy Capacity", "$74000.00"},
             {"Fitness Score", "0"},
             {"Kelly Criterion Estimate", "0.327"},
             {"Kelly Criterion Probability Value", "1"},
@@ -180,7 +181,7 @@ 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

@@ -94,6 +94,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.09"},
             {"Treynor Ratio", "0.82"},
             {"Total Fees", "$41.70"},
+            {"Estimated Strategy Capacity", "$2000000.00"},
             {"Fitness Score", "0.634"},
             {"Kelly Criterion Estimate", "13.656"},
             {"Kelly Criterion Probability Value", "0.228"},
@@ -113,7 +114,7 @@ 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 +1,181 @@
+/*
+ * 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"},
+            {"Estimated Strategy Capacity", "$0"},
+            {"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

@@ -147,6 +147,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0"},
             {"Treynor Ratio", "0"},
             {"Total Fees", "$0.00"},
+            {"Estimated Strategy Capacity", "$370000.00"},
             {"Fitness Score", "0.501"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -166,7 +167,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "1349023435"}
+            {"OrderListHash", "aea2e321d17414c1f3c6fa2491f10c88"}
         };
     }
 }

Algorithm.CSharp/CapacityTests/BeastVsPenny.cs

@@ -0,0 +1,102 @@
+/*
+ * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
+ * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+*/
+
+using System.Collections.Generic;
+using QuantConnect.Interfaces;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Tests capacity by trading SPY (beast) alongside a small cap stock ABUS (penny)
+    /// </summary>
+    public class BeastVsPenny : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private Symbol _spy;
+
+        public override void Initialize()
+        {
+            SetStartDate(2020, 1, 1);
+            SetEndDate(2020, 3, 31);
+            SetCash(10000);
+
+            _spy = AddEquity("SPY", Resolution.Hour).Symbol;
+            var penny = AddEquity("ABUS", Resolution.Hour).Symbol;
+
+            Schedule.On(DateRules.EveryDay(_spy), TimeRules.AfterMarketOpen(_spy, 1, false), () =>
+            {
+                SetHoldings(_spy, 0.5m);
+                SetHoldings(penny, 0.5m);
+            });
+        }
+
+        /// <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; } = false;
+
+        /// <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", "70"},
+            {"Average Win", "0.07%"},
+            {"Average Loss", "-0.51%"},
+            {"Compounding Annual Return", "-89.548%"},
+            {"Drawdown", "49.900%"},
+            {"Expectancy", "-0.514"},
+            {"Net Profit", "-42.920%"},
+            {"Sharpe Ratio", "-0.797"},
+            {"Probabilistic Sharpe Ratio", "9.019%"},
+            {"Loss Rate", "57%"},
+            {"Win Rate", "43%"},
+            {"Profit-Loss Ratio", "0.13"},
+            {"Alpha", "-0.24"},
+            {"Beta", "1.101"},
+            {"Annual Standard Deviation", "1.031"},
+            {"Annual Variance", "1.063"},
+            {"Information Ratio", "-0.351"},
+            {"Tracking Error", "0.836"},
+            {"Treynor Ratio", "-0.747"},
+            {"Total Fees", "$81.45"},
+            {"Estimated Strategy Capacity", "$21000.00"},
+            {"Fitness Score", "0.01"},
+            {"Kelly Criterion Estimate", "0"},
+            {"Kelly Criterion Probability Value", "0"},
+            {"Sortino Ratio", "-1.284"},
+            {"Return Over Maximum Drawdown", "-1.789"},
+            {"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", "67c9083f604ed16fb68481e7c26878dc"}
+        };
+    }
+}

Algorithm.CSharp/CapacityTests/CheeseMilkHourlyRebalance.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.Indicators;
+using QuantConnect.Interfaces;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Tests an illiquid asset that has bursts of liquidity around 11:00 A.M. Central Time
+    /// with an hourly in and out strategy.
+    /// </summary>
+    public class CheeseMilkHourlyRebalance : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private ExponentialMovingAverage _fast;
+        private ExponentialMovingAverage _slow;
+        private Symbol _contract;
+        private DateTime _lastTrade;
+
+        public override void Initialize()
+        {
+            SetStartDate(2021, 1, 1);
+            SetEndDate(2021, 2, 17);
+            SetTimeZone(TimeZones.Chicago);
+            SetCash(100000);
+            SetWarmup(1000);
+
+            var dc = AddFuture("DC", Resolution.Minute, Market.CME);
+            dc.SetFilter(0, 10000);
+        }
+
+        public override void OnData(Slice data)
+        {
+            var contract = data.FutureChains.Values.SelectMany(c => c.Contracts.Values)
+                .OrderBy(c => c.Symbol.ID.Date)
+                .FirstOrDefault()?
+                .Symbol;
+
+            if (contract == null)
+            {
+                return;
+            }
+
+            if (_contract != contract || (_fast == null && _slow == null))
+            {
+                _fast = EMA(contract, 600);
+                _slow = EMA(contract, 1200);
+                _contract = contract;
+            }
+
+            if (!_fast.IsReady || !_slow.IsReady)
+            {
+                return;
+            }
+
+            if (Time - _lastTrade <= TimeSpan.FromHours(1) || Time.TimeOfDay <= new TimeSpan(10, 50, 0) || Time.TimeOfDay >= new TimeSpan(12, 30, 0))
+            {
+                return;
+            }
+
+            if (!Portfolio.ContainsKey(contract) || (Portfolio[contract].Quantity <= 0 && _fast > _slow))
+            {
+                SetHoldings(contract, 0.5);
+                _lastTrade = Time;
+            }
+            else if (Portfolio.ContainsKey(contract) && Portfolio[contract].Quantity >= 0 && _fast < _slow)
+            {
+                SetHoldings(contract, -0.5);
+                _lastTrade = Time;
+            }
+        }
+
+        /// <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; } = false;
+
+        /// <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", "19"},
+            {"Average Win", "39.16%"},
+            {"Average Loss", "-8.81%"},
+            {"Compounding Annual Return", "-99.857%"},
+            {"Drawdown", "82.900%"},
+            {"Expectancy", "-0.359"},
+            {"Net Profit", "-57.725%"},
+            {"Sharpe Ratio", "-0.555"},
+            {"Probabilistic Sharpe Ratio", "10.606%"},
+            {"Loss Rate", "88%"},
+            {"Win Rate", "12%"},
+            {"Profit-Loss Ratio", "4.45"},
+            {"Alpha", "-1.188"},
+            {"Beta", "0.603"},
+            {"Annual Standard Deviation", "1.754"},
+            {"Annual Variance", "3.075"},
+            {"Information Ratio", "-0.759"},
+            {"Tracking Error", "1.753"},
+            {"Treynor Ratio", "-1.612"},
+            {"Total Fees", "$2558.55"},
+            {"Estimated Strategy Capacity", "$20000.00"},
+            {"Fitness Score", "0.351"},
+            {"Kelly Criterion Estimate", "0"},
+            {"Kelly Criterion Probability Value", "0"},
+            {"Sortino Ratio", "-0.602"},
+            {"Return Over Maximum Drawdown", "-1.415"},
+            {"Portfolio Turnover", "14.226"},
+            {"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", "4f5fd2fb25e957bd0cb7cb6d275ddb97"}
+        };
+    }
+}

Algorithm.CSharp/CapacityTests/EmaPortfolioRebalance100.cs

@@ -0,0 +1,231 @@
+/*
+ * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
+ * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+*/
+
+using System.Collections.Generic;
+using QuantConnect.Data;
+using QuantConnect.Indicators;
+using QuantConnect.Interfaces;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Tests a wide variety of liquid and illiquid stocks together, with bins
+    /// of 20 ranging from micro-cap to mega-cap stocks.
+    /// </summary>
+    public class EmaPortfolioRebalance100 : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        public List<SymbolData> Data;
+
+        public override void Initialize()
+        {
+            SetStartDate(2020, 1, 1);
+            SetEndDate(2020, 2, 5);
+            SetWarmup(1000);
+            SetCash(100000);
+
+            Data = new List<SymbolData> {
+                new SymbolData(this, AddEquity("AADR", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("AAMC", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("AAU", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ABDC", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ABIO", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ABUS", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("AC", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ACER", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ACES", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ACGLO", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ACH", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ACHV", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ACIO", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ACIU", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ACNB", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ACRS", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ACSI", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ACT", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ACT", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ACTG", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ZYNE", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ZYME", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ZUO", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ZUMZ", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ZTR", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ZSL", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ZSAN", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ZROZ", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ZLAB", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ZIXI", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ZIV", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ZIOP", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ZGNX", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ZG", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ZEUS", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ZAGG", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("YYY", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("YRD", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("YRCW", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("YPF", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("AA", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("AAN", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("AAP", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("AAXN", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ABB", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ABC", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ACAD", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ACC", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ACGL", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ACIW", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ACM", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ACWV", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ACWX", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ADM", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ADPT", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ADS", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ADUS", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("AEM", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("AEO", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("AEP", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ZTS", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("YUM", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("XLY", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("XLV", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("XLRE", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("XLP", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("XLNX", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("XLF", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("XLC", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("XLB", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("XEL", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("XBI", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("X", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("WYNN", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("WW", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("WORK", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("WMB", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("WM", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("WELL", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("WEC", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("AAPL", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("ADBE", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("AGG", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("AMD", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("AMZN", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("BA", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("BABA", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("BAC", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("BMY", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("C", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("CMCSA", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("CRM", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("CSCO", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("DIS", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("EEM", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("EFA", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("FB", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("GDX", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("GE", Resolution.Minute).Symbol),
+                new SymbolData(this, AddEquity("SPY", Resolution.Minute).Symbol)
+            };
+        }
+
+        public override void OnData(Slice data)
+        {
+            var fastFactor = 0.005m;
+
+            foreach (var sd in Data)
+            {
+                if (!Portfolio.Invested && sd.Fast * (1 + fastFactor) > sd.Slow)
+                {
+                    SetHoldings(sd.Symbol, 0.01);
+                }
+                else if (Portfolio.Invested && sd.Fast * (1 - fastFactor) < sd.Slow)
+                {
+                    Liquidate(sd.Symbol);
+                }
+            }
+        }
+
+        public class SymbolData
+        {
+            public Symbol Symbol;
+            public ExponentialMovingAverage Fast;
+            public ExponentialMovingAverage Slow;
+            public bool IsCrossed => Fast > Slow;
+
+            public SymbolData(QCAlgorithm algorithm, Symbol symbol) {
+                Symbol = symbol;
+                Fast = algorithm.EMA(symbol, 20);
+                Slow = algorithm.EMA(symbol, 300);
+            }
+        }
+
+        /// <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; } = false;
+
+        /// <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", "1015"},
+            {"Average Win", "0.01%"},
+            {"Average Loss", "0.00%"},
+            {"Compounding Annual Return", "-12.674%"},
+            {"Drawdown", "1.400%"},
+            {"Expectancy", "-0.761"},
+            {"Net Profit", "-1.328%"},
+            {"Sharpe Ratio", "-12.258"},
+            {"Probabilistic Sharpe Ratio", "0.000%"},
+            {"Loss Rate", "95%"},
+            {"Win Rate", "5%"},
+            {"Profit-Loss Ratio", "3.67"},
+            {"Alpha", "-0.142"},
+            {"Beta", "0.038"},
+            {"Annual Standard Deviation", "0.01"},
+            {"Annual Variance", "0"},
+            {"Information Ratio", "-4.389"},
+            {"Tracking Error", "0.123"},
+            {"Treynor Ratio", "-3.359"},
+            {"Total Fees", "$1125.52"},
+            {"Estimated Strategy Capacity", "$300.00"},
+            {"Fitness Score", "0.007"},
+            {"Kelly Criterion Estimate", "0"},
+            {"Kelly Criterion Probability Value", "0"},
+            {"Sortino Ratio", "-14.315"},
+            {"Return Over Maximum Drawdown", "-9.589"},
+            {"Portfolio Turnover", "0.406"},
+            {"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", "4c165e8d648d54a85bb7b564050a6f85"}
+        };
+    }
+}

Algorithm.CSharp/CapacityTests/IntradayMinuteScalping.cs

@@ -0,0 +1,117 @@
+/*
+ * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
+ * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+*/
+
+using System.Collections.Generic;
+using QuantConnect.Data;
+using QuantConnect.Indicators;
+using QuantConnect.Interfaces;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Scalps SPY using an EMA cross strategy at minute resolution.
+    /// This tests equity strategies that trade at a higher frequency, which
+    /// should have a reduced capacity estimate as a result.
+    /// </summary>
+    public class IntradayMinuteScalping : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private Symbol _spy;
+        private ExponentialMovingAverage _fast;
+        private ExponentialMovingAverage _slow;
+
+
+        public override void Initialize()
+        {
+            SetStartDate(2020, 1, 1);
+            SetEndDate(2020, 1, 30);
+            SetCash(100000);
+            SetWarmup(100);
+
+            _spy = AddEquity("SPY", Resolution.Minute).Symbol;
+            _fast = EMA(_spy, 20);
+            _slow = EMA(_spy, 40);
+        }
+
+        public override void OnData(Slice data)
+        {
+            if (Portfolio[_spy].Quantity <= 0 && _fast > _slow)
+            {
+                SetHoldings(_spy, 1);
+            }
+            else if (Portfolio[_spy].Quantity >= 0 && _fast < _slow)
+            {
+                SetHoldings(_spy, -1);
+            }
+        }
+
+        /// <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; } = false;
+
+        /// <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", "150"},
+            {"Average Win", "0.16%"},
+            {"Average Loss", "-0.11%"},
+            {"Compounding Annual Return", "-19.320%"},
+            {"Drawdown", "3.900%"},
+            {"Expectancy", "-0.193"},
+            {"Net Profit", "-1.730%"},
+            {"Sharpe Ratio", "-1.606"},
+            {"Probabilistic Sharpe Ratio", "21.397%"},
+            {"Loss Rate", "67%"},
+            {"Win Rate", "33%"},
+            {"Profit-Loss Ratio", "1.45"},
+            {"Alpha", "-0.357"},
+            {"Beta", "0.635"},
+            {"Annual Standard Deviation", "0.119"},
+            {"Annual Variance", "0.014"},
+            {"Information Ratio", "-4.249"},
+            {"Tracking Error", "0.106"},
+            {"Treynor Ratio", "-0.302"},
+            {"Total Fees", "$449.14"},
+            {"Estimated Strategy Capacity", "$27000000.00"},
+            {"Fitness Score", "0.088"},
+            {"Kelly Criterion Estimate", "0"},
+            {"Kelly Criterion Probability Value", "0"},
+            {"Sortino Ratio", "-3.259"},
+            {"Return Over Maximum Drawdown", "-7.992"},
+            {"Portfolio Turnover", "14.605"},
+            {"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", "f5a0e9547f7455004fa6c3eb136534e9"}
+        };
+    }
+}

Algorithm.CSharp/CapacityTests/IntradayMinuteScalpingBTCETH.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.Collections.Generic;
+using QuantConnect.Data;
+using QuantConnect.Indicators;
+using QuantConnect.Interfaces;
+using QuantConnect.Securities;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Scalps BTCETH using an EMA cross strategy at minute resolution.
+    /// This tests crypto strategies that trade at a higher frequency, which
+    /// should have a reduced capacity estimate as a result. This also tests
+    /// that currency conversions are handled properly in the strategy capacity
+    /// calculation class.
+    /// </summary>
+    public class IntradayMinuteScalpingBTCETH : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private Symbol _ethbtc;
+        private ExponentialMovingAverage _fast;
+        private ExponentialMovingAverage _slow;
+
+
+        public override void Initialize()
+        {
+            SetStartDate(2021, 1, 1);
+            SetEndDate(2021, 1, 30);
+            SetCash(100000);
+            SetWarmup(100);
+
+            var ethbtc = AddCrypto("ETHBTC", Resolution.Minute, Market.GDAX);
+            ethbtc.BuyingPowerModel = new BuyingPowerModel();
+            _ethbtc = ethbtc.Symbol;
+
+            _fast = EMA(_ethbtc, 20);
+            _slow = EMA(_ethbtc, 40);
+        }
+
+        public override void OnData(Slice data)
+        {
+            if (Portfolio[_ethbtc].Quantity <= 0 && _fast > _slow)
+            {
+                SetHoldings(_ethbtc, 1);
+            }
+            else if (Portfolio[_ethbtc].Quantity >= 0 && _fast < _slow)
+            {
+                SetHoldings(_ethbtc, -1);
+            }
+        }
+
+        /// <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; } = false;
+
+        /// <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", "1005"},
+            {"Average Win", "0.96%"},
+            {"Average Loss", "-0.33%"},
+            {"Compounding Annual Return", "76.267%"},
+            {"Drawdown", "77.100%"},
+            {"Expectancy", "-0.012"},
+            {"Net Profit", "4.768%"},
+            {"Sharpe Ratio", "1.01909630017278E+24"},
+            {"Probabilistic Sharpe Ratio", "93.814%"},
+            {"Loss Rate", "75%"},
+            {"Win Rate", "25%"},
+            {"Profit-Loss Ratio", "2.95"},
+            {"Alpha", "1.3466330963256E+25"},
+            {"Beta", "25.59"},
+            {"Annual Standard Deviation", "13.214"},
+            {"Annual Variance", "174.61"},
+            {"Information Ratio", "1.02164274756513E+24"},
+            {"Tracking Error", "13.181"},
+            {"Treynor Ratio", "5.2622435344112E+23"},
+            {"Total Fees", "$0.00"},
+            {"Estimated Strategy Capacity", "$1300000.00"},
+            {"Fitness Score", "0.38"},
+            {"Kelly Criterion Estimate", "0"},
+            {"Kelly Criterion Probability Value", "0"},
+            {"Sortino Ratio", "-0.239"},
+            {"Return Over Maximum Drawdown", "-1.385"},
+            {"Portfolio Turnover", "81.433"},
+            {"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", "6a779e7a8d12b4808845c75b88d43b3a"}
+        };
+    }
+}

Algorithm.CSharp/CapacityTests/IntradayMinuteScalpingEURUSD.cs

@@ -0,0 +1,117 @@
+/*
+ * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
+ * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+*/
+
+using System.Collections.Generic;
+using QuantConnect.Data;
+using QuantConnect.Indicators;
+using QuantConnect.Interfaces;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Scalps EURUSD using an EMA cross strategy at minute resolution.
+    /// This tests FOREX strategies that trade at a higher frequency, which
+    /// should have a reduced capacity estimate as a result.
+    /// </summary>
+    public class IntradayMinuteScalpingEURUSD : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private Symbol _eurusd;
+        private ExponentialMovingAverage _fast;
+        private ExponentialMovingAverage _slow;
+
+
+        public override void Initialize()
+        {
+            SetStartDate(2021, 1, 1);
+            SetEndDate(2021, 1, 30);
+            SetCash(100000);
+            SetWarmup(100);
+
+            _eurusd = AddForex("EURUSD", Resolution.Minute, Market.Oanda).Symbol;
+            _fast = EMA(_eurusd, 20);
+            _slow = EMA(_eurusd, 40);
+        }
+
+        public override void OnData(Slice data)
+        {
+            if (Portfolio[_eurusd].Quantity <= 0 && _fast > _slow)
+            {
+                SetHoldings(_eurusd, 1);
+            }
+            else if (Portfolio[_eurusd].Quantity >= 0 && _fast < _slow)
+            {
+                SetHoldings(_eurusd, -1);
+            }
+        }
+
+        /// <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; } = false;
+
+        /// <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", "671"},
+            {"Average Win", "0.07%"},
+            {"Average Loss", "-0.04%"},
+            {"Compounding Annual Return", "-80.820%"},
+            {"Drawdown", "12.200%"},
+            {"Expectancy", "-0.447"},
+            {"Net Profit", "-12.180%"},
+            {"Sharpe Ratio", "-13.121"},
+            {"Probabilistic Sharpe Ratio", "0%"},
+            {"Loss Rate", "79%"},
+            {"Win Rate", "21%"},
+            {"Profit-Loss Ratio", "1.61"},
+            {"Alpha", "-0.746"},
+            {"Beta", "-0.02"},
+            {"Annual Standard Deviation", "0.057"},
+            {"Annual Variance", "0.003"},
+            {"Information Ratio", "-4.046"},
+            {"Tracking Error", "0.161"},
+            {"Treynor Ratio", "37.346"},
+            {"Total Fees", "$0.00"},
+            {"Estimated Strategy Capacity", "$44000000.00"},
+            {"Fitness Score", "0.025"},
+            {"Kelly Criterion Estimate", "0"},
+            {"Kelly Criterion Probability Value", "0"},
+            {"Sortino Ratio", "-16.609"},
+            {"Return Over Maximum Drawdown", "-7.115"},
+            {"Portfolio Turnover", "52.476"},
+            {"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", "74ee44736b9300c0262dc75c0cd140e1"}
+        };
+    }
+}

Algorithm.CSharp/CapacityTests/IntradayMinuteScalpingFuturesES.cs

@@ -0,0 +1,143 @@
+/*
+ * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
+ * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+*/
+
+using System.Collections.Generic;
+using System.Linq;
+using QuantConnect.Data;
+using QuantConnect.Indicators;
+using QuantConnect.Interfaces;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Scalps ES futures contracts (E-mini SP500) using an EMA cross strategy at minute resolution.
+    /// This tests futures strategies that trade at a higher frequency, which
+    /// should have a reduced capacity estimate as a result.
+    /// </summary>
+    /// <remarks>
+    /// The insanely high capacity estimate of this strategy is realistic.
+    /// ES notional contract value traded is around $600 Billion USD per day (!!!), which
+    /// is what the capacity is set to.
+    /// </remarks>
+    public class IntradayMinuteScalpingFuturesES : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private ExponentialMovingAverage _fast;
+        private ExponentialMovingAverage _slow;
+        private Symbol _contract;
+
+        public override void Initialize()
+        {
+            SetStartDate(2021, 1, 1);
+            SetEndDate(2021, 1, 31);
+            SetCash(100000);
+            SetWarmup(1000);
+
+            var a = AddFuture("ES", Resolution.Minute, Market.CME);
+            a.SetFilter(0, 10000);
+        }
+
+        public override void OnData(Slice data)
+        {
+            var contract = data.FutureChains.Values.SelectMany(c => c.Contracts.Values)
+                .OrderBy(c => c.Symbol.ID.Date)
+                .FirstOrDefault()?
+                .Symbol;
+
+            if (contract == null)
+            {
+                return;
+            }
+
+            if (_contract != contract || (_fast == null && _slow == null))
+            {
+                _fast = EMA(contract, 10);
+                _slow = EMA(contract, 20);
+                _contract = contract;
+            }
+
+            if (!_fast.IsReady || !_slow.IsReady)
+            {
+                return;
+            }
+
+            if (!Portfolio.ContainsKey(contract) || (Portfolio[contract].Quantity <= 0 && _fast > _slow))
+            {
+                SetHoldings(contract, 1);
+            }
+            else if (Portfolio.ContainsKey(contract) && Portfolio[contract].Quantity >= 0 && _fast < _slow)
+            {
+                SetHoldings(contract, -1);
+            }
+        }
+
+        /// <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; } = false;
+
+        /// <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", "1217"},
+            {"Average Win", "2.69%"},
+            {"Average Loss", "-0.93%"},
+            {"Compounding Annual Return", "-99.756%"},
+            {"Drawdown", "77.200%"},
+            {"Expectancy", "-0.047"},
+            {"Net Profit", "-40.013%"},
+            {"Sharpe Ratio", "-0.52"},
+            {"Probabilistic Sharpe Ratio", "19.865%"},
+            {"Loss Rate", "75%"},
+            {"Win Rate", "25%"},
+            {"Profit-Loss Ratio", "2.88"},
+            {"Alpha", "-1.279"},
+            {"Beta", "-3.686"},
+            {"Annual Standard Deviation", "1.85"},
+            {"Annual Variance", "3.422"},
+            {"Information Ratio", "-0.463"},
+            {"Tracking Error", "1.895"},
+            {"Treynor Ratio", "0.261"},
+            {"Total Fees", "$19843.10"},
+            {"Estimated Strategy Capacity", "$560000000.00"},
+            {"Fitness Score", "0.334"},
+            {"Kelly Criterion Estimate", "0"},
+            {"Kelly Criterion Probability Value", "0"},
+            {"Sortino Ratio", "-0.837"},
+            {"Return Over Maximum Drawdown", "-1.402"},
+            {"Portfolio Turnover", "1174.125"},
+            {"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", "f353843132df7b0604eff3a37b134ca2"}
+        };
+    }
+}

Algorithm.CSharp/CapacityTests/IntradayMinuteScalpingGBPJPY.cs

@@ -0,0 +1,118 @@
+/*
+ * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
+ * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+*/
+
+using System.Collections.Generic;
+using QuantConnect.Data;
+using QuantConnect.Indicators;
+using QuantConnect.Interfaces;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Scalps GBPJPY using an EMA cross strategy at minute resolution.
+    /// This tests FOREX strategies that trade at a higher frequency, which
+    /// should have a reduced capacity estimate as a result. This test also
+    /// tests that currency conversion rates are applied and calculated correctly.
+    /// </summary>
+    public class IntradayMinuteScalpingGBPJPY : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private Symbol _gbpjpy;
+        private ExponentialMovingAverage _fast;
+        private ExponentialMovingAverage _slow;
+
+
+        public override void Initialize()
+        {
+            SetStartDate(2021, 1, 1);
+            SetEndDate(2021, 1, 30);
+            SetCash(100000);
+            SetWarmup(100);
+
+            _gbpjpy = AddForex("GBPJPY", Resolution.Minute, Market.Oanda).Symbol;
+            _fast = EMA(_gbpjpy, 20);
+            _slow = EMA(_gbpjpy, 40);
+        }
+
+        public override void OnData(Slice data)
+        {
+            if (Portfolio[_gbpjpy].Quantity <= 0 && _fast > _slow)
+            {
+                SetHoldings(_gbpjpy, 1);
+            }
+            else if (Portfolio[_gbpjpy].Quantity >= 0 && _fast < _slow)
+            {
+                SetHoldings(_gbpjpy, -1);
+            }
+        }
+
+        /// <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; } = false;
+
+        /// <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", "735"},
+            {"Average Win", "0.08%"},
+            {"Average Loss", "-0.05%"},
+            {"Compounding Annual Return", "-93.946%"},
+            {"Drawdown", "19.900%"},
+            {"Expectancy", "-0.592"},
+            {"Net Profit", "-19.794%"},
+            {"Sharpe Ratio", "-10.054"},
+            {"Probabilistic Sharpe Ratio", "0%"},
+            {"Loss Rate", "84%"},
+            {"Win Rate", "16%"},
+            {"Profit-Loss Ratio", "1.56"},
+            {"Alpha", "-0.895"},
+            {"Beta", "0.068"},
+            {"Annual Standard Deviation", "0.09"},
+            {"Annual Variance", "0.008"},
+            {"Information Ratio", "-4.929"},
+            {"Tracking Error", "0.164"},
+            {"Treynor Ratio", "-13.276"},
+            {"Total Fees", "$0.00"},
+            {"Estimated Strategy Capacity", "$49000000.00"},
+            {"Fitness Score", "0.049"},
+            {"Kelly Criterion Estimate", "0"},
+            {"Kelly Criterion Probability Value", "0"},
+            {"Sortino Ratio", "-10.846"},
+            {"Return Over Maximum Drawdown", "-4.904"},
+            {"Portfolio Turnover", "58.921"},
+            {"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", "66f04c9622ab242993c8ce951418e6d9"}
+        };
+    }
+}

Algorithm.CSharp/CapacityTests/IntradayMinuteScalpingTRYJPY.cs

@@ -0,0 +1,118 @@
+/*
+ * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
+ * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+*/
+
+using System.Collections.Generic;
+using QuantConnect.Data;
+using QuantConnect.Indicators;
+using QuantConnect.Interfaces;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Scalps TRYJPY using an EMA cross strategy at minute resolution.
+    /// This tests FOREX strategies that trade at a higher frequency, which
+    /// should have a reduced capacity estimate as a result. This tests that
+    /// currency conversions are applied properly to the capacity estimate calculation.
+    /// </summary>
+    public class IntradayMinuteScalpingTRYJPY : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private Symbol _tryjpy;
+        private ExponentialMovingAverage _fast;
+        private ExponentialMovingAverage _slow;
+
+
+        public override void Initialize()
+        {
+            SetStartDate(2021, 1, 1);
+            SetEndDate(2021, 1, 30);
+            SetCash(100000);
+            SetWarmup(100);
+
+            _tryjpy = AddForex("TRYJPY", Resolution.Minute, Market.Oanda).Symbol;
+            _fast = EMA(_tryjpy, 20);
+            _slow = EMA(_tryjpy, 40);
+        }
+
+        public override void OnData(Slice data)
+        {
+            if (Portfolio[_tryjpy].Quantity <= 0 && _fast > _slow)
+            {
+                SetHoldings(_tryjpy, 1);
+            }
+            else if (Portfolio[_tryjpy].Quantity >= 0 && _fast < _slow)
+            {
+                SetHoldings(_tryjpy, -1);
+            }
+        }
+
+        /// <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; } = false;
+
+        /// <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", "603"},
+            {"Average Win", "0.20%"},
+            {"Average Loss", "-0.26%"},
+            {"Compounding Annual Return", "-100.000%"},
+            {"Drawdown", "73.200%"},
+            {"Expectancy", "-0.849"},
+            {"Net Profit", "-73.118%"},
+            {"Sharpe Ratio", "-2.046"},
+            {"Probabilistic Sharpe Ratio", "0%"},
+            {"Loss Rate", "91%"},
+            {"Win Rate", "9%"},
+            {"Profit-Loss Ratio", "0.75"},
+            {"Alpha", "-0.95"},
+            {"Beta", "0.541"},
+            {"Annual Standard Deviation", "0.489"},
+            {"Annual Variance", "0.239"},
+            {"Information Ratio", "-1.863"},
+            {"Tracking Error", "0.487"},
+            {"Treynor Ratio", "-1.849"},
+            {"Total Fees", "$0.00"},
+            {"Estimated Strategy Capacity", "$4400000.00"},
+            {"Fitness Score", "0.259"},
+            {"Kelly Criterion Estimate", "0"},
+            {"Kelly Criterion Probability Value", "0"},
+            {"Sortino Ratio", "-2.135"},
+            {"Return Over Maximum Drawdown", "-1.389"},
+            {"Portfolio Turnover", "49.501"},
+            {"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", "4eb4d703a9f200b6bb3d8b0ebbc9db7f"}
+        };
+    }
+}

Algorithm.CSharp/CapacityTests/MonthlyRebalanceDaily.cs

@@ -0,0 +1,111 @@
+/*
+ * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
+ * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+*/
+
+using System.Collections.Generic;
+using QuantConnect.Interfaces;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Rebalances ultra-liquid stocks monthly, testing
+    /// bursts of orders centered around the start of the month at Daily resolution
+    /// </summary>
+    public class MonthlyRebalanceDaily : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        public override void Initialize()
+        {
+            SetStartDate(2019, 12, 31);
+            SetEndDate(2020, 4, 5);
+            SetCash(100000);
+
+            var spy = AddEquity("SPY", Resolution.Daily).Symbol;
+            AddEquity("GE", Resolution.Daily);
+            AddEquity("FB", Resolution.Daily);
+            AddEquity("DIS", Resolution.Daily);
+            AddEquity("CSCO", Resolution.Daily);
+            AddEquity("CRM", Resolution.Daily);
+            AddEquity("C", Resolution.Daily);
+            AddEquity("BAC", Resolution.Daily);
+            AddEquity("BABA", Resolution.Daily);
+            AddEquity("AAPL", Resolution.Daily);
+
+            Schedule.On(DateRules.MonthStart(spy), TimeRules.Noon, () =>
+            {
+                foreach (var symbol in Securities.Keys)
+                {
+                    SetHoldings(symbol, 0.10);
+                }
+            });
+        }
+
+        /// <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; } = false;
+
+        /// <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", "35"},
+            {"Average Win", "0.07%"},
+            {"Average Loss", "-0.07%"},
+            {"Compounding Annual Return", "-68.407%"},
+            {"Drawdown", "32.400%"},
+            {"Expectancy", "-0.309"},
+            {"Net Profit", "-25.901%"},
+            {"Sharpe Ratio", "-1.503"},
+            {"Probabilistic Sharpe Ratio", "2.878%"},
+            {"Loss Rate", "64%"},
+            {"Win Rate", "36%"},
+            {"Profit-Loss Ratio", "0.90"},
+            {"Alpha", "-0.7"},
+            {"Beta", "-0.238"},
+            {"Annual Standard Deviation", "0.386"},
+            {"Annual Variance", "0.149"},
+            {"Information Ratio", "-0.11"},
+            {"Tracking Error", "0.712"},
+            {"Treynor Ratio", "2.442"},
+            {"Total Fees", "$38.99"},
+            {"Estimated Strategy Capacity", "$19000000.00"},
+            {"Fitness Score", "0.003"},
+            {"Kelly Criterion Estimate", "0"},
+            {"Kelly Criterion Probability Value", "0"},
+            {"Sortino Ratio", "-2.021"},
+            {"Return Over Maximum Drawdown", "-2.113"},
+            {"Portfolio Turnover", "0.014"},
+            {"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", "76d8164a3c0d4a7d45e94367c4ba5be1"}
+        };
+    }
+}

Algorithm.CSharp/CapacityTests/MonthlyRebalanceHourly.cs

@@ -0,0 +1,111 @@
+/*
+ * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
+ * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+*/
+
+using System.Collections.Generic;
+using QuantConnect.Interfaces;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Rebalances ultra-liquid stocks monthly, testing
+    /// bursts of orders centered around the start of the month at Hourly resolution
+    /// </summary>
+    public class MonthlyRebalanceHourly : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        public override void Initialize()
+        {
+            SetStartDate(2019, 12, 31);
+            SetEndDate(2020, 4, 5);
+            SetCash(100000);
+
+            var spy = AddEquity("SPY", Resolution.Hour).Symbol;
+            AddEquity("GE", Resolution.Hour);
+            AddEquity("FB", Resolution.Hour);
+            AddEquity("DIS", Resolution.Hour);
+            AddEquity("CSCO", Resolution.Hour);
+            AddEquity("CRM", Resolution.Hour);
+            AddEquity("C", Resolution.Hour);
+            AddEquity("BAC", Resolution.Hour);
+            AddEquity("BABA", Resolution.Hour);
+            AddEquity("AAPL", Resolution.Hour);
+
+            Schedule.On(DateRules.MonthStart(spy), TimeRules.Noon, () =>
+            {
+                foreach (var symbol in Securities.Keys)
+                {
+                    SetHoldings(symbol, 0.10);
+                }
+            });
+        }
+
+        /// <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; } = false;
+
+        /// <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", "35"},
+            {"Average Win", "0.05%"},
+            {"Average Loss", "-0.10%"},
+            {"Compounding Annual Return", "-72.444%"},
+            {"Drawdown", "36.500%"},
+            {"Expectancy", "-0.449"},
+            {"Net Profit", "-28.406%"},
+            {"Sharpe Ratio", "-1.369"},
+            {"Probabilistic Sharpe Ratio", "4.398%"},
+            {"Loss Rate", "64%"},
+            {"Win Rate", "36%"},
+            {"Profit-Loss Ratio", "0.51"},
+            {"Alpha", "-0.175"},
+            {"Beta", "0.892"},
+            {"Annual Standard Deviation", "0.503"},
+            {"Annual Variance", "0.253"},
+            {"Information Ratio", "-0.822"},
+            {"Tracking Error", "0.138"},
+            {"Treynor Ratio", "-0.772"},
+            {"Total Fees", "$38.83"},
+            {"Estimated Strategy Capacity", "$6000000.00"},
+            {"Fitness Score", "0.004"},
+            {"Kelly Criterion Estimate", "0"},
+            {"Kelly Criterion Probability Value", "0"},
+            {"Sortino Ratio", "-2.033"},
+            {"Return Over Maximum Drawdown", "-2.079"},
+            {"Portfolio Turnover", "0.018"},
+            {"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", "1de9bcf6cda0945af6ba1f74c4dcb22c"}
+        };
+    }
+}

Algorithm.CSharp/CapacityTests/SplitTestingStrategy.cs

@@ -0,0 +1,112 @@
+/*
+ * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
+ * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+*/
+
+using System.Collections.Generic;
+using QuantConnect.Data;
+using QuantConnect.Interfaces;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Tests that splits do not cause the algorithm to report capacity estimates
+    /// above or below the actual capacity due to splits. The stock HTGM is illiquid,
+    /// trading only $1.2 Million per day on average with sparse trade frequencies.
+    /// </summary>
+    public class SplitTestingStrategy : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private Symbol _htgm;
+
+        public override void Initialize()
+        {
+            SetStartDate(2020, 11, 1);
+            SetEndDate(2020, 12, 5);
+            SetCash(100000);
+
+            var htgm = AddEquity("HTGM", Resolution.Hour);
+            htgm.SetDataNormalizationMode(DataNormalizationMode.Raw);
+            _htgm = htgm.Symbol;
+        }
+
+        public override void OnData(Slice data)
+        {
+            if (!Portfolio.Invested)
+            {
+                SetHoldings(_htgm, 1);
+            }
+            else
+            {
+                SetHoldings(_htgm, -1);
+            }
+        }
+
+        /// <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; } = false;
+
+        /// <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", "162"},
+            {"Average Win", "0.10%"},
+            {"Average Loss", "-0.35%"},
+            {"Compounding Annual Return", "-94.432%"},
+            {"Drawdown", "30.400%"},
+            {"Expectancy", "-0.564"},
+            {"Net Profit", "-23.412%"},
+            {"Sharpe Ratio", "-1.041"},
+            {"Probabilistic Sharpe Ratio", "12.971%"},
+            {"Loss Rate", "66%"},
+            {"Win Rate", "34%"},
+            {"Profit-Loss Ratio", "0.29"},
+            {"Alpha", "-4.827"},
+            {"Beta", "1.43"},
+            {"Annual Standard Deviation", "0.876"},
+            {"Annual Variance", "0.767"},
+            {"Information Ratio", "-4.288"},
+            {"Tracking Error", "0.851"},
+            {"Treynor Ratio", "-0.637"},
+            {"Total Fees", "$2655.91"},
+            {"Estimated Strategy Capacity", "$11000.00"},
+            {"Fitness Score", "0.052"},
+            {"Kelly Criterion Estimate", "0"},
+            {"Kelly Criterion Probability Value", "0"},
+            {"Sortino Ratio", "-2.2"},
+            {"Return Over Maximum Drawdown", "-3.481"},
+            {"Portfolio Turnover", "0.307"},
+            {"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", "54f571c11525656e9b383e235e77002e"}
+        };
+    }
+}

Algorithm.CSharp/CapacityTests/SpyBondPortfolioRebalance.cs

@@ -0,0 +1,103 @@
+/*
+ * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
+ * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+*/
+
+using System.Collections.Generic;
+using QuantConnect.Interfaces;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Rebalances between SPY and BND. Tests capacity of the weakest link, which in this
+    /// case is BND, dragging down the capacity estimate.
+    /// </summary>
+    public class SpyBondPortfolioRebalance : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private Symbol _spy;
+
+        public override void Initialize()
+        {
+            SetStartDate(2020, 1, 1);
+            SetEndDate(2020, 3, 31);
+            SetCash(10000);
+
+            _spy = AddEquity("SPY", Resolution.Hour).Symbol;
+            var bnd = AddEquity("BND", Resolution.Hour).Symbol;
+
+            Schedule.On(DateRules.EveryDay(_spy), TimeRules.AfterMarketOpen(_spy, 1, false), () =>
+            {
+                SetHoldings(_spy, 0.5m);
+                SetHoldings(bnd, 0.5m);
+            });
+        }
+
+        /// <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; } = false;
+
+        /// <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", "21"},
+            {"Average Win", "0.02%"},
+            {"Average Loss", "-0.03%"},
+            {"Compounding Annual Return", "-33.564%"},
+            {"Drawdown", "19.700%"},
+            {"Expectancy", "-0.140"},
+            {"Net Profit", "-9.655%"},
+            {"Sharpe Ratio", "-0.99"},
+            {"Probabilistic Sharpe Ratio", "13.754%"},
+            {"Loss Rate", "50%"},
+            {"Win Rate", "50%"},
+            {"Profit-Loss Ratio", "0.72"},
+            {"Alpha", "-0.022"},
+            {"Beta", "0.538"},
+            {"Annual Standard Deviation", "0.309"},
+            {"Annual Variance", "0.096"},
+            {"Information Ratio", "0.826"},
+            {"Tracking Error", "0.269"},
+            {"Treynor Ratio", "-0.569"},
+            {"Total Fees", "$21.00"},
+            {"Estimated Strategy Capacity", "$1100000.00"},
+            {"Fitness Score", "0.005"},
+            {"Kelly Criterion Estimate", "0"},
+            {"Kelly Criterion Probability Value", "0"},
+            {"Sortino Ratio", "-1.524"},
+            {"Return Over Maximum Drawdown", "-1.688"},
+            {"Portfolio Turnover", "0.02"},
+            {"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", "95a130426900aaf227a08a5d1c617b2b"}
+        };
+    }
+}

Algorithm.CSharp/CfdTimeZonesRegressionAlgorithm.cs

@@ -95,6 +95,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0"},
             {"Treynor Ratio", "0"},
             {"Total Fees", "$0.00"},
+            {"Estimated Strategy Capacity", "$0"},
             {"Fitness Score", "0.5"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -114,7 +115,7 @@ 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

@@ -181,6 +181,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.149"},
             {"Treynor Ratio", "-1.405"},
             {"Total Fees", "$2.00"},
+            {"Estimated Strategy Capacity", "$6000000.00"},
             {"Fitness Score", "0.076"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -200,7 +201,7 @@ 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

@@ -164,6 +164,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "3.579"},
             {"Treynor Ratio", "383485597312030"},
             {"Total Fees", "$13.00"},
+            {"Estimated Strategy Capacity", "$3000000.00"},
             {"Fitness Score", "0.232"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -183,7 +184,7 @@ 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

@@ -136,6 +136,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.126"},
             {"Treynor Ratio", "-0.607"},
             {"Total Fees", "$11.63"},
+            {"Estimated Strategy Capacity", "$13000000.00"},
             {"Fitness Score", "0.013"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -155,7 +156,7 @@ 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

@@ -114,6 +114,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.096"},
             {"Treynor Ratio", "0"},
             {"Total Fees", "$0.00"},
+            {"Estimated Strategy Capacity", "$0"},
             {"Fitness Score", "0"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -133,7 +134,7 @@ 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

@@ -120,6 +120,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.105"},
             {"Treynor Ratio", "1.667"},
             {"Total Fees", "$2.91"},
+            {"Estimated Strategy Capacity", "$96000000.00"},
             {"Fitness Score", "0.141"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -139,7 +140,7 @@ 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

@@ -91,6 +91,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.101"},
             {"Treynor Ratio", "5.409"},
             {"Total Fees", "$67.00"},
+            {"Estimated Strategy Capacity", "$2400000.00"},
             {"Fitness Score", "0.501"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -110,7 +111,7 @@ 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

@@ -85,6 +85,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.009"},
             {"Treynor Ratio", "1.575"},
             {"Total Fees", "$22.77"},
+            {"Estimated Strategy Capacity", "$19000000.00"},
             {"Fitness Score", "0.999"},
             {"Kelly Criterion Estimate", "38.64"},
             {"Kelly Criterion Probability Value", "0.229"},
@@ -104,7 +105,7 @@ 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

@@ -94,6 +94,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.165"},
             {"Treynor Ratio", "1.212"},
             {"Total Fees", "$6.00"},
+            {"Estimated Strategy Capacity", "$32000000.00"},
             {"Fitness Score", "0.063"},
             {"Kelly Criterion Estimate", "38.64"},
             {"Kelly Criterion Probability Value", "0.229"},
@@ -113,7 +114,7 @@ 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

@@ -164,6 +164,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.441"},
             {"Treynor Ratio", "-0.008"},
             {"Total Fees", "$20.35"},
+            {"Estimated Strategy Capacity", "$19000000.00"},
             {"Fitness Score", "0.138"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -183,7 +184,7 @@ 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

@@ -190,6 +190,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.176"},
             {"Treynor Ratio", "-1.46"},
             {"Total Fees", "$7.82"},
+            {"Estimated Strategy Capacity", "$6000000.00"},
             {"Fitness Score", "0.1"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -209,7 +210,7 @@ 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

@@ -144,8 +144,9 @@ 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"},
+            {"Estimated Strategy Capacity", "$150000000.00"},
             {"Fitness Score", "0.024"},
             {"Kelly Criterion Estimate", "-0.84"},
             {"Kelly Criterion Probability Value", "0.53"},
@@ -165,7 +166,7 @@ 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");
         }
 
@@ -95,6 +99,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.168"},
             {"Treynor Ratio", "2.38"},
             {"Total Fees", "$3.26"},
+            {"Estimated Strategy Capacity", "$75000000.00"},
             {"Fitness Score", "0.245"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -114,7 +119,7 @@ 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

@@ -96,6 +96,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0"},
             {"Treynor Ratio", "0.988"},
             {"Total Fees", "$7.78"},
+            {"Estimated Strategy Capacity", "$2200000.00"},
             {"Fitness Score", "0.031"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -115,7 +116,7 @@ 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

@@ -113,6 +113,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "1.04"},
             {"Treynor Ratio", "5.073"},
             {"Total Fees", "$30.00"},
+            {"Estimated Strategy Capacity", "$2800000.00"},
             {"Fitness Score", "0.418"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -132,7 +133,7 @@ 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

@@ -119,6 +119,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.112"},
             {"Treynor Ratio", "-6.121"},
             {"Total Fees", "$3.50"},
+            {"Estimated Strategy Capacity", "$6900000.00"},
             {"Fitness Score", "0"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -138,7 +139,7 @@ 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

@@ -124,6 +124,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.112"},
             {"Treynor Ratio", "-6.121"},
             {"Total Fees", "$3.50"},
+            {"Estimated Strategy Capacity", "$6900000.00"},
             {"Fitness Score", "0"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -143,7 +144,7 @@ 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

@@ -131,6 +131,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.19"},
             {"Treynor Ratio", "2.159"},
             {"Total Fees", "$1.00"},
+            {"Estimated Strategy Capacity", "$14000000.00"},
             {"Fitness Score", "0.1"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -150,7 +151,7 @@ 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 +1,261 @@
+/*
+ * 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"},
+            {"Estimated Strategy Capacity", "$0"},
+            {"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

@@ -101,6 +101,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.848"},
             {"Treynor Ratio", "46.996"},
             {"Total Fees", "$0.00"},
+            {"Estimated Strategy Capacity", "$0"},
             {"Fitness Score", "0"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -120,7 +121,7 @@ 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

@@ -141,6 +141,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.726"},
             {"Treynor Ratio", "0.142"},
             {"Total Fees", "$0.00"},
+            {"Estimated Strategy Capacity", "$0"},
             {"Fitness Score", "0.127"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -160,7 +161,7 @@ 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

@@ -212,6 +212,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.118"},
             {"Treynor Ratio", "-0.591"},
             {"Total Fees", "$62.24"},
+            {"Estimated Strategy Capacity", "$100000000.00"},
             {"Fitness Score", "0.147"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -231,7 +232,7 @@ 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

@@ -103,6 +103,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.251"},
             {"Treynor Ratio", "9.323"},
             {"Total Fees", "$3.26"},
+            {"Estimated Strategy Capacity", "$220000000.00"},
             {"Fitness Score", "0.201"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -122,7 +123,7 @@ 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

@@ -193,6 +193,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.211"},
             {"Treynor Ratio", "0"},
             {"Total Fees", "$0.00"},
+            {"Estimated Strategy Capacity", "$0"},
             {"Fitness Score", "0"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -212,7 +213,7 @@ 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

@@ -118,6 +118,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.183"},
             {"Treynor Ratio", "0"},
             {"Total Fees", "$0.00"},
+            {"Estimated Strategy Capacity", "$0"},
             {"Fitness Score", "0"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -137,7 +138,7 @@ 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

@@ -118,6 +118,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.212"},
             {"Treynor Ratio", "0"},
             {"Total Fees", "$0.00"},
+            {"Estimated Strategy Capacity", "$0"},
             {"Fitness Score", "0"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -137,7 +138,7 @@ 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.
  *
@@ -16,7 +16,6 @@
 using System;
 using System.Collections.Generic;
 using System.Linq;
-using NodaTime;
 using QuantConnect.Data;
 using QuantConnect.Data.Market;
 using QuantConnect.Interfaces;
@@ -45,7 +44,26 @@ namespace QuantConnect.Algorithm.CSharp
             for (int i = 0; i < _symbols.Length; i++)
             {
                 var symbol = _symbols[i];
-                var history = History<QuoteBar>(symbol, 10, Resolution.Daily);
+                IEnumerable<BaseData> history;
+                if (symbol.SecurityType == SecurityType.Equity)
+                {
+                    try
+                    {
+
+                        history = History<QuoteBar>(symbol, 10, Resolution.Daily).Select(bar => bar as BaseData);
+                        throw new Exception("We were expecting an argument exception to be thrown. Equity does not have daily QuoteBars!");
+                    }
+                    catch (ArgumentException)
+                    {
+                        // expected
+                    }
+                    history = History<TradeBar>(symbol, 10, Resolution.Daily).Select(bar => bar as BaseData);
+                }
+                else
+                {
+                    history = History<QuoteBar>(symbol, 10, Resolution.Daily)
+                        .Select(bar => bar as BaseData);
+                }
 
                 var duplications = history
                     .GroupBy(k => k.Time)
@@ -93,6 +111,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0"},
             {"Treynor Ratio", "0"},
             {"Total Fees", "$0.00"},
+            {"Estimated Strategy Capacity", "$0"},
             {"Fitness Score", "0"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -112,7 +131,7 @@ 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

@@ -136,6 +136,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0"},
             {"Treynor Ratio", "0"},
             {"Total Fees", "$0.00"},
+            {"Estimated Strategy Capacity", "$0"},
             {"Fitness Score", "0"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -155,7 +156,7 @@ 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 +100,26 @@ 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"},
+            {"Estimated Strategy Capacity", "$38000000.00"},
             {"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 +133,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "-1252326142"}
+            {"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");
@@ -160,6 +160,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.155"},
             {"Treynor Ratio", "1.589"},
             {"Total Fees", "$55.05"},
+            {"Estimated Strategy Capacity", "$0"},
             {"Fitness Score", "0.002"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -179,7 +180,7 @@ 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 +1,149 @@
+/*
+ * 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"},
+            {"Estimated Strategy Capacity", "$58000.00"},
+            {"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

@@ -186,6 +186,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.204"},
             {"Treynor Ratio", "-8.165"},
             {"Total Fees", "$46.75"},
+            {"Estimated Strategy Capacity", "$96000000.00"},
             {"Fitness Score", "0.002"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -205,7 +206,7 @@ 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

@@ -204,6 +204,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.171"},
             {"Treynor Ratio", "-1.761"},
             {"Total Fees", "$8669.41"},
+            {"Estimated Strategy Capacity", "$170000.00"},
             {"Fitness Score", "0.675"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -223,7 +224,7 @@ 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

@@ -177,6 +177,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.171"},
             {"Treynor Ratio", "-1.971"},
             {"Total Fees", "$6806.67"},
+            {"Estimated Strategy Capacity", "$150000.00"},
             {"Fitness Score", "0.694"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -196,7 +197,7 @@ 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

@@ -110,6 +110,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.193"},
             {"Treynor Ratio", "0"},
             {"Total Fees", "$0.00"},
+            {"Estimated Strategy Capacity", "$0"},
             {"Fitness Score", "0"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -129,7 +130,7 @@ 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

@@ -91,6 +91,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0"},
             {"Treynor Ratio", "0"},
             {"Total Fees", "$14.92"},
+            {"Estimated Strategy Capacity", "$85000.00"},
             {"Fitness Score", "0.258"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -110,7 +111,7 @@ 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

@@ -131,6 +131,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.29"},
             {"Treynor Ratio", "4.005"},
             {"Total Fees", "$18.28"},
+            {"Estimated Strategy Capacity", "$200000000.00"},
             {"Fitness Score", "0.052"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -150,7 +151,7 @@ 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

@@ -127,6 +127,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.204"},
             {"Treynor Ratio", "-1.424"},
             {"Total Fees", "$16.26"},
+            {"Estimated Strategy Capacity", "$250000000.00"},
             {"Fitness Score", "0.003"},
             {"Kelly Criterion Estimate", "12.539"},
             {"Kelly Criterion Probability Value", "0.367"},
@@ -146,7 +147,7 @@ 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

@@ -148,6 +148,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "1.441"},
             {"Treynor Ratio", "-0.15"},
             {"Total Fees", "$33.30"},
+            {"Estimated Strategy Capacity", "$4200000.00"},
             {"Fitness Score", "0.079"},
             {"Kelly Criterion Estimate", "-9.366"},
             {"Kelly Criterion Probability Value", "0.607"},
@@ -167,7 +168,7 @@ 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 +103,13 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.22"},
             {"Treynor Ratio", "0"},
             {"Total Fees", "$6.41"},
-            {"Fitness Score", "0.248"},
+            {"Estimated Strategy Capacity", "$0"},
+            {"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 +123,7 @@ 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

@@ -184,6 +184,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.123"},
             {"Treynor Ratio", "-1.288"},
             {"Total Fees", "$669.76"},
+            {"Estimated Strategy Capacity", "$8100000.00"},
             {"Fitness Score", "0.021"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -203,7 +204,7 @@ 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

@@ -147,6 +147,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.049"},
             {"Treynor Ratio", "-0.934"},
             {"Total Fees", "$22.26"},
+            {"Estimated Strategy Capacity", "$3600000.00"},
             {"Fitness Score", "0.002"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -166,7 +167,7 @@ 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

@@ -122,6 +122,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.186"},
             {"Treynor Ratio", "1.557"},
             {"Total Fees", "$4.00"},
+            {"Estimated Strategy Capacity", "$1400000.00"},
             {"Fitness Score", "0.012"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -141,7 +142,7 @@ 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

@@ -174,6 +174,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0"},
             {"Treynor Ratio", "0"},
             {"Total Fees", "$804.33"},
+            {"Estimated Strategy Capacity", "$21000.00"},
             {"Fitness Score", "0.504"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -193,7 +194,7 @@ 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

@@ -0,0 +1,124 @@
+/*
+ * 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>
+    /// Regression test algorithm simply fetch and compare data of minute resolution around daylight saving period
+    /// reproduces issue reported in GB issue GH issue https://github.com/QuantConnect/Lean/issues/4925
+    /// related issues https://github.com/QuantConnect/Lean/issues/3707; https://github.com/QuantConnect/Lean/issues/4630
+    /// </summary>
+    public class FillForwardEnumeratorOutOfOrderBarRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private decimal _exptectedClose = 84.09m;
+        private DateTime _exptectedTime = new DateTime(2008, 3, 10, 9, 30, 0);
+        private Symbol _shy;
+
+        /// <summary>
+        /// Initialise 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(2008, 3, 7);
+            SetEndDate(2008, 3, 10);
+            _shy = AddEquity("SHY", Resolution.Minute).Symbol;
+            // just to make debugging easier, less subscriptions
+            SetBenchmark(time => 1);
+        }
+
+        public override void OnData(Slice slice)
+        {
+            var trackingBar = slice.Bars.Values.FirstOrDefault(s => s.Time.Equals(_exptectedTime));
+
+            if (trackingBar != null)
+            {
+                if (!Portfolio.Invested)
+                {
+                    SetHoldings(_shy, 1);
+                }
+
+                if (trackingBar.Close != _exptectedClose)
+                {
+                    throw new Exception(
+                        $"Bar at {_exptectedTime.ToStringInvariant()} closed at price {trackingBar.Close.ToStringInvariant()}; expected {_exptectedClose.ToStringInvariant()}");
+                }
+            }
+        }
+
+        /// <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", "1"},
+            {"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", "$5.93"},
+            {"Estimated Strategy Capacity", "$200000.00"},
+            {"Fitness Score", "0.499"},
+            {"Kelly Criterion Estimate", "0"},
+            {"Kelly Criterion Probability Value", "0"},
+            {"Sortino Ratio", "79228162514264337593543950335"},
+            {"Return Over Maximum Drawdown", "-105.726"},
+            {"Portfolio Turnover", "0.998"},
+            {"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", "5315f54b62b2eaf9d587602137d641a5"}
+        };
+    }
+}

Algorithm.CSharp/FillForwardUntilExpiryRegressionAlgorithm.cs

@@ -0,0 +1,170 @@
+/*
+ * 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"},
+            {"Estimated Strategy Capacity", "$0"},
+            {"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

@@ -105,6 +105,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.394"},
             {"Treynor Ratio", "-5.313"},
             {"Total Fees", "$5.40"},
+            {"Estimated Strategy Capacity", "$59000000.00"},
             {"Fitness Score", "0.244"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -124,7 +125,7 @@ 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

@@ -153,6 +153,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.107"},
             {"Treynor Ratio", "0"},
             {"Total Fees", "$0.00"},
+            {"Estimated Strategy Capacity", "$0"},
             {"Fitness Score", "0"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -172,7 +173,7 @@ 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

@@ -157,6 +157,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.107"},
             {"Treynor Ratio", "0"},
             {"Total Fees", "$0.00"},
+            {"Estimated Strategy Capacity", "$0"},
             {"Fitness Score", "0"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -176,7 +177,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "371857150"}
+            {"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
         };
     }
 }