Update readme.md

* Update projects to use .NET 5.0, the successor to .NET Core

* Fix ambiguous errors. Add IBAutomator net5

* Remove FXCM

* Upgrade IBAutomater to v1.0.51

ignored, and an empty message aborts the commit.

* Fix rebase

- Fix ambiguous Index
- Remove StrategyCapacity.cs
- Update System.Threading.Tasks.Extensionsy

* Remove unrequired references

* Fixes

- Travis will use dotnet, not nunit nor mono
- Remove mono from foundation image
- Fix python setup in research
- Fix unit tests

* Don't call ReadKey when input is redirected

* Fix ConsoleLeanOptimizer

* Research fixes

* Update comment

* Add vsdbg to Dockerfile

* Fixes

- Revert dockerfile FROM custom changes
- Adjust and fix regression algorithms
   - Option assignment will be deterministic in the order
   - 'Rolling Averaged Population' is calculated using doubles, updating
     expected values.
- Update readme, removing references to mono
- Add missing Py.Gil lock

* Replace ICSharp with .NET Interactive

* Fixes after rebase

* CSharp research fixes

- Adding new Initialize.csx that pre loads all assemblies
- Adjusting template research file
- Moving steps in dockerfilejupyter
- Fix unit tests and regression tests after rebase

Co-authored-by: Gerardo Salazar <gsalaz9800@gmail.com>
Co-authored-by: Stefano Raggi <stefano.raggi67@gmail.com>
Co-authored-by: Jasper van Merle <jaspervmerle@gmail.com>

.editorconfig

@@ -0,0 +1,13 @@
+root = true
+
+[*]
+charset = utf-8
+indent_size = 4
+indent_style = space
+insert_final_newline = true
+
+[*.{js,yml,json,config,csproj}]
+indent_size = 2
+
+[*.sh]
+end_of_line = lf

.github/workflows/gh-actions.yml

@@ -3,29 +3,28 @@ name: Build & Test Lean
 on:
   push:
     branches: ['*']
+    tags: ['*']
   pull_request:
     branches: [master]
 
 jobs:
   build:
-    runs-on: ubuntu-16.04
+    runs-on: ubuntu-20.04
     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
+        run: dotnet build /p:Configuration=Release /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
+        run: dotnet test ./Tests/bin/Release/QuantConnect.Tests.dll --filter TestCategory!=TravisExclude -- TestRunParameters.Parameter\(name=\"log-handler\", value=\"ConsoleErrorLogHandler\"\)
 
       - name: Generate & Publish python stubs
+        if: startsWith(github.ref, 'refs/tags/')
         run: |
           chmod +x ci_build_stubs.sh
-          ./ci_build_stubs.sh -d -t -g #Ignore Publish as of since credentials are missing on CI
+          ./ci_build_stubs.sh -t -g -p
+        env:
+          PYPI_API_TOKEN: ${{ secrets.PYPI_API_TOKEN }}

.gitignore

@@ -196,6 +196,7 @@ publish/
 
 # NuGet Packages
 *.nupkg
+!LocalPackages/*
 # The packages folder can be ignored because of Package Restore
 **/packages/*
 # except build/, which is used as an MSBuild target.
@@ -204,6 +205,7 @@ publish/
 #!**/packages/repositories.config
 # ignore sln level nuget
 .nuget/
+!.nuget/NuGet.config
 
 # Windows Azure Build Output
 csx/

.nuget/NuGet.config

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

.travis.yml

@@ -1,10 +1,11 @@
-sudo: required
 language: csharp
-mono:
-  - 5.12.0
-solution: QuantConnect.Lean.sln
+mono: none
+dotnet: 5.0
+os: linux
+dist: focal
 before_install:
   - export PATH="$HOME/miniconda3/bin:$PATH"
+  - export PYTHONNET_PYDLL="$HOME/miniconda3/lib/libpython3.6m.so"
   - wget -q https://cdn.quantconnect.com/miniconda/Miniconda3-4.5.12-Linux-x86_64.sh
   - bash Miniconda3-4.5.12-Linux-x86_64.sh -b
   - rm -rf Miniconda3-4.5.12-Linux-x86_64.sh
@@ -16,11 +17,7 @@ before_install:
   - conda install -y cython=0.29.15
   - conda install -y scipy=1.4.1
   - conda install -y wrapt=1.12.1
-install:
-  - nuget restore QuantConnect.Lean.sln -v quiet
-  - nuget install NUnit.Runners -Version 3.11.1 -OutputDirectory testrunner
 script:
-  - msbuild /p:Configuration=Release /p:VbcToolExe=vbnc.exe /v:quiet /p:WarningLevel=1 QuantConnect.Lean.sln
-  - 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
+  - dotnet nuget add source $TRAVIS_BUILD_DIR/LocalPackages
+  - dotnet build /p:Configuration=Release /v:quiet /p:WarningLevel=1 QuantConnect.Lean.sln
+  - dotnet test ./Tests/bin/Release/QuantConnect.Tests.dll --filter TestCategory!=TravisExclude -- TestRunParameters.Parameter\(name=\"log-handler\", value=\"ConsoleErrorLogHandler\"\)

.vscode/readme.md

@@ -67,7 +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>
 
@@ -203,4 +203,4 @@ Here we will cover some common issues with setting this up. This section will ex
 
 *   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.
+*   `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.

Algorithm.CSharp/AccumulativeInsightPortfolioRegressionAlgorithm.cs

@@ -77,43 +77,44 @@ namespace QuantConnect.Algorithm.CSharp
             {"Total Trades", "199"},
             {"Average Win", "0.00%"},
             {"Average Loss", "0.00%"},
-            {"Compounding Annual Return", "-12.472%"},
+            {"Compounding Annual Return", "-12.392%"},
             {"Drawdown", "0.200%"},
             {"Expectancy", "-0.586"},
-            {"Net Profit", "-0.170%"},
-            {"Sharpe Ratio", "-9.693"},
-            {"Probabilistic Sharpe Ratio", "12.704%"},
+            {"Net Profit", "-0.169%"},
+            {"Sharpe Ratio", "-9.597"},
+            {"Probabilistic Sharpe Ratio", "13.309%"},
             {"Loss Rate", "79%"},
             {"Win Rate", "21%"},
             {"Profit-Loss Ratio", "0.95"},
             {"Alpha", "-0.149"},
-            {"Beta", "0.037"},
+            {"Beta", "0.036"},
             {"Annual Standard Deviation", "0.008"},
             {"Annual Variance", "0"},
-            {"Information Ratio", "-9.471"},
-            {"Tracking Error", "0.212"},
-            {"Treynor Ratio", "-2.13"},
+            {"Information Ratio", "-9.605"},
+            {"Tracking Error", "0.214"},
+            {"Treynor Ratio", "-2.136"},
             {"Total Fees", "$199.00"},
+            {"Estimated Strategy Capacity", "$22000000.00"},
             {"Fitness Score", "0.002"},
-            {"Kelly Criterion Estimate", "38.64"},
-            {"Kelly Criterion Probability Value", "0.229"},
-            {"Sortino Ratio", "-21.545"},
-            {"Return Over Maximum Drawdown", "-77.972"},
-            {"Portfolio Turnover", "1.135"},
+            {"Kelly Criterion Estimate", "38.796"},
+            {"Kelly Criterion Probability Value", "0.228"},
+            {"Sortino Ratio", "-21.623"},
+            {"Return Over Maximum Drawdown", "-77.986"},
+            {"Portfolio Turnover", "1.154"},
             {"Total Insights Generated", "100"},
             {"Total Insights Closed", "99"},
             {"Total Insights Analysis Completed", "99"},
             {"Long Insight Count", "100"},
             {"Short Insight Count", "0"},
             {"Long/Short Ratio", "100%"},
-            {"Estimated Monthly Alpha Value", "$126657.6305"},
-            {"Total Accumulated Estimated Alpha Value", "$20405.9516"},
-            {"Mean Population Estimated Insight Value", "$206.1207"},
-            {"Mean Population Direction", "54.5455%"},
-            {"Mean Population Magnitude", "54.5455%"},
-            {"Rolling Averaged Population Direction", "59.8056%"},
-            {"Rolling Averaged Population Magnitude", "59.8056%"},
-            {"OrderListHash", "1256341962"}
+            {"Estimated Monthly Alpha Value", "$117277.2200"},
+            {"Total Accumulated Estimated Alpha Value", "$18894.6632"},
+            {"Mean Population Estimated Insight Value", "$190.8552"},
+            {"Mean Population Direction", "53.5354%"},
+            {"Mean Population Magnitude", "53.5354%"},
+            {"Rolling Averaged Population Direction", "58.2788%"},
+            {"Rolling Averaged Population Magnitude", "58.2788%"},
+            {"OrderListHash", "7baad0d75f652da1b801ec2fc368e710"}
         };
     }
 }

Algorithm.CSharp/AddAlphaModelAlgorithm.cs

@@ -113,28 +113,29 @@ namespace QuantConnect.Algorithm.CSharp
             {"Total Trades", "9"},
             {"Average Win", "0.89%"},
             {"Average Loss", "-0.27%"},
-            {"Compounding Annual Return", "196.104%"},
+            {"Compounding Annual Return", "196.304%"},
             {"Drawdown", "1.700%"},
-            {"Expectancy", "1.853"},
-            {"Net Profit", "1.498%"},
-            {"Sharpe Ratio", "4.275"},
-            {"Probabilistic Sharpe Ratio", "60.462%"},
+            {"Expectancy", "1.854"},
+            {"Net Profit", "1.499%"},
+            {"Sharpe Ratio", "4.265"},
+            {"Probabilistic Sharpe Ratio", "60.408%"},
             {"Loss Rate", "33%"},
             {"Win Rate", "67%"},
             {"Profit-Loss Ratio", "3.28"},
-            {"Alpha", "1.574"},
-            {"Beta", "-0.289"},
-            {"Annual Standard Deviation", "0.276"},
-            {"Annual Variance", "0.076"},
-            {"Information Ratio", "-0.495"},
-            {"Tracking Error", "0.367"},
-            {"Treynor Ratio", "-4.079"},
-            {"Total Fees", "$14.33"},
+            {"Alpha", "1.579"},
+            {"Beta", "-0.284"},
+            {"Annual Standard Deviation", "0.277"},
+            {"Annual Variance", "0.077"},
+            {"Information Ratio", "-0.586"},
+            {"Tracking Error", "0.369"},
+            {"Treynor Ratio", "-4.159"},
+            {"Total Fees", "$14.46"},
+            {"Estimated Strategy Capacity", "$38000000.00"},
             {"Fitness Score", "0.408"},
-            {"Kelly Criterion Estimate", "16.447"},
-            {"Kelly Criterion Probability Value", "0.315"},
-            {"Sortino Ratio", "13.611"},
-            {"Return Over Maximum Drawdown", "117.635"},
+            {"Kelly Criterion Estimate", "16.438"},
+            {"Kelly Criterion Probability Value", "0.314"},
+            {"Sortino Ratio", "13.495"},
+            {"Return Over Maximum Drawdown", "117.2"},
             {"Portfolio Turnover", "0.411"},
             {"Total Insights Generated", "3"},
             {"Total Insights Closed", "3"},
@@ -142,14 +143,14 @@ namespace QuantConnect.Algorithm.CSharp
             {"Long Insight Count", "0"},
             {"Short Insight Count", "3"},
             {"Long/Short Ratio", "0%"},
-            {"Estimated Monthly Alpha Value", "$19868365.6628"},
-            {"Total Accumulated Estimated Alpha Value", "$3421774.0864"},
-            {"Mean Population Estimated Insight Value", "$1140591.3621"},
+            {"Estimated Monthly Alpha Value", "$19348842.7070"},
+            {"Total Accumulated Estimated Alpha Value", "$3332300.6884"},
+            {"Mean Population Estimated Insight Value", "$1110766.8961"},
             {"Mean Population Direction", "100%"},
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "100%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "-887015098"}
+            {"OrderListHash", "4e0e07a4b92e6d23d681220125617e62"}
         };
     }
 }

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

@@ -132,13 +132,14 @@ namespace QuantConnect.Algorithm.CSharp
             {"Win Rate", "50%"},
             {"Profit-Loss Ratio", "0.92"},
             {"Alpha", "-0.021"},
-            {"Beta", "-0.01"},
+            {"Beta", "-0.011"},
             {"Annual Standard Deviation", "0.006"},
             {"Annual Variance", "0"},
-            {"Information Ratio", "-3.374"},
+            {"Information Ratio", "-3.385"},
             {"Tracking Error", "0.058"},
-            {"Treynor Ratio", "2.133"},
+            {"Treynor Ratio", "2.117"},
             {"Total Fees", "$2.00"},
+            {"Estimated Strategy Capacity", "$45000000.00"},
             {"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", "-262924291"}
+            {"OrderListHash", "486118a60d78f74811fe8d927c2c6b43"}
         };
     }
 }

Algorithm.CSharp/AddOptionContractFromUniverseRegressionAlgorithm.cs

@@ -183,14 +183,15 @@ namespace QuantConnect.Algorithm.CSharp
             {"Loss Rate", "100%"},
             {"Win Rate", "0%"},
             {"Profit-Loss Ratio", "0"},
-            {"Alpha", "0.027"},
-            {"Beta", "-0.174"},
+            {"Alpha", "0.024"},
+            {"Beta", "-0.171"},
             {"Annual Standard Deviation", "0.006"},
             {"Annual Variance", "0"},
-            {"Information Ratio", "-11.586"},
-            {"Tracking Error", "0.042"},
-            {"Treynor Ratio", "0.286"},
+            {"Information Ratio", "-11.082"},
+            {"Tracking Error", "0.043"},
+            {"Treynor Ratio", "0.291"},
             {"Total Fees", "$2.00"},
+            {"Estimated Strategy Capacity", "$2800000.00"},
             {"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

@@ -79,19 +79,19 @@ namespace QuantConnect.Algorithm.CSharp
                 // things like manually added, auto added, internal, and any other boolean state we need to track against a single security)
                 throw new Exception("The underlying equity data should NEVER be removed in this algorithm because it was manually added");
             }
-            if (_expectedSecurities.AreDifferent(LinqExtensions.ToHashSet(Securities.Keys)))
+            if (_expectedSecurities.AreDifferent(Securities.Keys.ToHashSet()))
             {
                 var expected = string.Join(Environment.NewLine, _expectedSecurities.OrderBy(s => s.ToString()));
                 var actual = string.Join(Environment.NewLine, Securities.Keys.OrderBy(s => s.ToString()));
                 throw new Exception($"{Time}:: Detected differences in expected and actual securities{Environment.NewLine}Expected:{Environment.NewLine}{expected}{Environment.NewLine}Actual:{Environment.NewLine}{actual}");
             }
-            if (_expectedUniverses.AreDifferent(LinqExtensions.ToHashSet(UniverseManager.Keys)))
+            if (_expectedUniverses.AreDifferent(UniverseManager.Keys.ToHashSet()))
             {
                 var expected = string.Join(Environment.NewLine, _expectedUniverses.OrderBy(s => s.ToString()));
                 var actual = string.Join(Environment.NewLine, UniverseManager.Keys.OrderBy(s => s.ToString()));
                 throw new Exception($"{Time}:: Detected differences in expected and actual universes{Environment.NewLine}Expected:{Environment.NewLine}{expected}{Environment.NewLine}Actual:{Environment.NewLine}{actual}");
             }
-            if (_expectedData.AreDifferent(LinqExtensions.ToHashSet(data.Keys)))
+            if (_expectedData.AreDifferent(data.Keys.ToHashSet()))
             {
                 var expected = string.Join(Environment.NewLine, _expectedData.OrderBy(s => s.ToString()));
                 var actual = string.Join(Environment.NewLine, data.Keys.OrderBy(s => s.ToString()));
@@ -183,7 +183,7 @@ namespace QuantConnect.Algorithm.CSharp
                 if (changes.RemovedSecurities
                     .Where(x => x.Symbol.SecurityType == SecurityType.Option)
                     .ToHashSet(s => s.Symbol)
-                    .AreDifferent(LinqExtensions.ToHashSet(_expectedContracts)))
+                    .AreDifferent(_expectedContracts.ToHashSet()))
                 {
                     throw new Exception("Expected removed securities to equal expected contracts added");
                 }
@@ -230,6 +230,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0"},
             {"Treynor Ratio", "0"},
             {"Total Fees", "$6.00"},
+            {"Estimated Strategy Capacity", "$1500.00"},
             {"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", "1e7b3e90918777b9dbf46353a96f3329"}
         };
     }
 }

Algorithm.CSharp/AddRemoveSecurityRegressionAlgorithm.cs

@@ -114,28 +114,29 @@ namespace QuantConnect.Algorithm.CSharp
             {"Total Trades", "5"},
             {"Average Win", "0.47%"},
             {"Average Loss", "0%"},
-            {"Compounding Annual Return", "293.067%"},
+            {"Compounding Annual Return", "297.013%"},
             {"Drawdown", "1.400%"},
             {"Expectancy", "0"},
-            {"Net Profit", "1.765%"},
-            {"Sharpe Ratio", "13.11"},
-            {"Probabilistic Sharpe Ratio", "80.231%"},
+            {"Net Profit", "1.778%"},
+            {"Sharpe Ratio", "13.156"},
+            {"Probabilistic Sharpe Ratio", "80.461%"},
             {"Loss Rate", "0%"},
             {"Win Rate", "100%"},
             {"Profit-Loss Ratio", "0"},
-            {"Alpha", "0.705"},
+            {"Alpha", "0.697"},
             {"Beta", "0.7"},
-            {"Annual Standard Deviation", "0.157"},
+            {"Annual Standard Deviation", "0.158"},
             {"Annual Variance", "0.025"},
-            {"Information Ratio", "1.76"},
-            {"Tracking Error", "0.072"},
-            {"Treynor Ratio", "2.933"},
-            {"Total Fees", "$26.39"},
+            {"Information Ratio", "1.405"},
+            {"Tracking Error", "0.073"},
+            {"Treynor Ratio", "2.978"},
+            {"Total Fees", "$26.48"},
+            {"Estimated Strategy Capacity", "$4400000.00"},
             {"Fitness Score", "0.374"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
             {"Sortino Ratio", "79228162514264337593543950335"},
-            {"Return Over Maximum Drawdown", "373.973"},
+            {"Return Over Maximum Drawdown", "373.688"},
             {"Portfolio Turnover", "0.374"},
             {"Total Insights Generated", "0"},
             {"Total Insights Closed", "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", "7d0e013e09d9d5f831d24720686fd724"}
         };
     }
 }

Algorithm.CSharp/AddRiskManagementAlgorithm.cs

@@ -67,28 +67,29 @@ namespace QuantConnect.Algorithm.CSharp
             {"Total Trades", "3"},
             {"Average Win", "1.02%"},
             {"Average Loss", "0%"},
-            {"Compounding Annual Return", "289.119%"},
+            {"Compounding Annual Return", "296.066%"},
             {"Drawdown", "2.200%"},
             {"Expectancy", "0"},
-            {"Net Profit", "1.752%"},
-            {"Sharpe Ratio", "9.235"},
-            {"Probabilistic Sharpe Ratio", "68.013%"},
+            {"Net Profit", "1.775%"},
+            {"Sharpe Ratio", "9.373"},
+            {"Probabilistic Sharpe Ratio", "68.302%"},
             {"Loss Rate", "0%"},
             {"Win Rate", "100%"},
             {"Profit-Loss Ratio", "0"},
             {"Alpha", "0.105"},
-            {"Beta", "1.022"},
-            {"Annual Standard Deviation", "0.224"},
-            {"Annual Variance", "0.05"},
-            {"Information Ratio", "24.59"},
+            {"Beta", "1.021"},
+            {"Annual Standard Deviation", "0.227"},
+            {"Annual Variance", "0.052"},
+            {"Information Ratio", "25.083"},
             {"Tracking Error", "0.006"},
-            {"Treynor Ratio", "2.029"},
-            {"Total Fees", "$9.77"},
+            {"Treynor Ratio", "2.086"},
+            {"Total Fees", "$10.33"},
+            {"Estimated Strategy Capacity", "$32000000.00"},
             {"Fitness Score", "0.747"},
-            {"Kelly Criterion Estimate", "38.64"},
-            {"Kelly Criterion Probability Value", "0.229"},
+            {"Kelly Criterion Estimate", "38.796"},
+            {"Kelly Criterion Probability Value", "0.228"},
             {"Sortino Ratio", "79228162514264337593543950335"},
-            {"Return Over Maximum Drawdown", "107.109"},
+            {"Return Over Maximum Drawdown", "107.013"},
             {"Portfolio Turnover", "0.747"},
             {"Total Insights Generated", "100"},
             {"Total Insights Closed", "99"},
@@ -96,14 +97,14 @@ namespace QuantConnect.Algorithm.CSharp
             {"Long Insight Count", "100"},
             {"Short Insight Count", "0"},
             {"Long/Short Ratio", "100%"},
-            {"Estimated Monthly Alpha Value", "$126657.6305"},
-            {"Total Accumulated Estimated Alpha Value", "$20405.9516"},
-            {"Mean Population Estimated Insight Value", "$206.1207"},
-            {"Mean Population Direction", "54.5455%"},
-            {"Mean Population Magnitude", "54.5455%"},
-            {"Rolling Averaged Population Direction", "59.8056%"},
-            {"Rolling Averaged Population Magnitude", "59.8056%"},
-            {"OrderListHash", "-887190565"}
+            {"Estimated Monthly Alpha Value", "$117277.2200"},
+            {"Total Accumulated Estimated Alpha Value", "$18894.6632"},
+            {"Mean Population Estimated Insight Value", "$190.8552"},
+            {"Mean Population Direction", "53.5354%"},
+            {"Mean Population Magnitude", "53.5354%"},
+            {"Rolling Averaged Population Direction", "58.2788%"},
+            {"Rolling Averaged Population Magnitude", "58.2788%"},
+            {"OrderListHash", "af3a9c98c190d1b6b36fad184e796b0b"}
         };
     }
 }

Algorithm.CSharp/AddUniverseSelectionModelAlgorithm.cs

@@ -86,28 +86,29 @@ namespace QuantConnect.Algorithm.CSharp
             {"Total Trades", "10"},
             {"Average Win", "0%"},
             {"Average Loss", "-0.01%"},
-            {"Compounding Annual Return", "-14.333%"},
+            {"Compounding Annual Return", "-14.502%"},
             {"Drawdown", "3.300%"},
             {"Expectancy", "-1"},
-            {"Net Profit", "-0.169%"},
-            {"Sharpe Ratio", "-0.131"},
-            {"Probabilistic Sharpe Ratio", "45.057%"},
+            {"Net Profit", "-0.172%"},
+            {"Sharpe Ratio", "-0.133"},
+            {"Probabilistic Sharpe Ratio", "45.048%"},
             {"Loss Rate", "100%"},
             {"Win Rate", "0%"},
             {"Profit-Loss Ratio", "0"},
-            {"Alpha", "-3.068"},
-            {"Beta", "0.595"},
-            {"Annual Standard Deviation", "0.382"},
-            {"Annual Variance", "0.146"},
-            {"Information Ratio", "-13.618"},
-            {"Tracking Error", "0.376"},
-            {"Treynor Ratio", "-0.084"},
-            {"Total Fees", "$13.98"},
+            {"Alpha", "-2.956"},
+            {"Beta", "0.563"},
+            {"Annual Standard Deviation", "0.384"},
+            {"Annual Variance", "0.147"},
+            {"Information Ratio", "-13.74"},
+            {"Tracking Error", "0.38"},
+            {"Treynor Ratio", "-0.091"},
+            {"Total Fees", "$14.04"},
+            {"Estimated Strategy Capacity", "$61000000.00"},
             {"Fitness Score", "0.146"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "1"},
             {"Sortino Ratio", "79228162514264337593543950335"},
-            {"Return Over Maximum Drawdown", "-4.398"},
+            {"Return Over Maximum Drawdown", "-4.436"},
             {"Portfolio Turnover", "0.268"},
             {"Total Insights Generated", "15"},
             {"Total Insights Closed", "12"},
@@ -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", "32f5f657f91216b1583e8ed89a511550"}
         };
     }
 }

Algorithm.CSharp/AddUniverseSelectionModelCoarseAlgorithm.cs

@@ -91,30 +91,31 @@ namespace QuantConnect.Algorithm.CSharp
         public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
         {
             {"Total Trades", "23"},
-            {"Average Win", "0.00%"},
+            {"Average Win", "0.01%"},
             {"Average Loss", "-0.01%"},
-            {"Compounding Annual Return", "-75.307%"},
+            {"Compounding Annual Return", "-75.293%"},
             {"Drawdown", "5.800%"},
-            {"Expectancy", "-0.859"},
-            {"Net Profit", "-5.586%"},
-            {"Sharpe Ratio", "-3.257"},
-            {"Probabilistic Sharpe Ratio", "5.931%"},
+            {"Expectancy", "-0.822"},
+            {"Net Profit", "-5.584%"},
+            {"Sharpe Ratio", "-3.264"},
+            {"Probabilistic Sharpe Ratio", "5.887%"},
             {"Loss Rate", "92%"},
             {"Win Rate", "8%"},
-            {"Profit-Loss Ratio", "0.70"},
+            {"Profit-Loss Ratio", "1.13"},
             {"Alpha", "-0.593"},
-            {"Beta", "0.692"},
+            {"Beta", "0.711"},
             {"Annual Standard Deviation", "0.204"},
             {"Annual Variance", "0.042"},
-            {"Information Ratio", "-2.884"},
-            {"Tracking Error", "0.194"},
-            {"Treynor Ratio", "-0.962"},
-            {"Total Fees", "$25.92"},
+            {"Information Ratio", "-2.924"},
+            {"Tracking Error", "0.193"},
+            {"Treynor Ratio", "-0.935"},
+            {"Total Fees", "$25.95"},
+            {"Estimated Strategy Capacity", "$69000000.00"},
             {"Fitness Score", "0.004"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "1"},
-            {"Sortino Ratio", "-4.462"},
-            {"Return Over Maximum Drawdown", "-13.032"},
+            {"Sortino Ratio", "-4.452"},
+            {"Return Over Maximum Drawdown", "-13.058"},
             {"Portfolio Turnover", "0.083"},
             {"Total Insights Generated", "33"},
             {"Total Insights Closed", "30"},
@@ -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", "be3b0d8b0e2cb312aae1b043e1bef9aa"}
         };
     }
 }

Algorithm.CSharp/AllShortableSymbolsCoarseSelectionRegressionAlgorithm.cs

@@ -101,7 +101,7 @@ namespace QuantConnect.Algorithm.CSharp
                 return;
             }
 
-            foreach (var symbol in ActiveSecurities.Keys)
+            foreach (var symbol in ActiveSecurities.Keys.OrderBy(symbol => symbol))
             {
                 if (!Portfolio.ContainsKey(symbol) || !Portfolio[symbol].Invested)
                 {
@@ -194,23 +194,24 @@ namespace QuantConnect.Algorithm.CSharp
             {"Total Trades", "5"},
             {"Average Win", "0%"},
             {"Average Loss", "0%"},
-            {"Compounding Annual Return", "36.294%"},
+            {"Compounding Annual Return", "36.239%"},
             {"Drawdown", "0%"},
             {"Expectancy", "0"},
-            {"Net Profit", "0.340%"},
-            {"Sharpe Ratio", "21.2"},
-            {"Probabilistic Sharpe Ratio", "99.990%"},
+            {"Net Profit", "0.339%"},
+            {"Sharpe Ratio", "21.173"},
+            {"Probabilistic Sharpe Ratio", "99.997%"},
             {"Loss Rate", "0%"},
             {"Win Rate", "0%"},
             {"Profit-Loss Ratio", "0"},
-            {"Alpha", "0.274"},
+            {"Alpha", "0.271"},
             {"Beta", "0.138"},
             {"Annual Standard Deviation", "0.011"},
             {"Annual Variance", "0"},
-            {"Information Ratio", "7.202"},
-            {"Tracking Error", "0.068"},
-            {"Treynor Ratio", "1.722"},
+            {"Information Ratio", "6.894"},
+            {"Tracking Error", "0.069"},
+            {"Treynor Ratio", "1.727"},
             {"Total Fees", "$307.50"},
+            {"Estimated Strategy Capacity", "$2800000.00"},
             {"Fitness Score", "0.173"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -230,7 +231,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "97613274"}
+            {"OrderListHash", "5ce14f87f21733ec686385da7404484c"}
         };
     }
 }

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", "52"},
+            {"Average Win", "0.00%"},
+            {"Average Loss", "0.00%"},
+            {"Compounding Annual Return", "0.096%"},
+            {"Drawdown", "0.100%"},
+            {"Expectancy", "3.321"},
+            {"Net Profit", "0.089%"},
+            {"Sharpe Ratio", "0.868"},
+            {"Probabilistic Sharpe Ratio", "44.482%"},
+            {"Loss Rate", "24%"},
+            {"Win Rate", "76%"},
+            {"Profit-Loss Ratio", "4.67"},
+            {"Alpha", "0.001"},
+            {"Beta", "0"},
+            {"Annual Standard Deviation", "0.001"},
+            {"Annual Variance", "0"},
+            {"Information Ratio", "-2.148"},
+            {"Tracking Error", "0.101"},
+            {"Treynor Ratio", "-4.168"},
+            {"Total Fees", "$52.00"},
+            {"Estimated Strategy Capacity", "$27000000000.00"},
+            {"Fitness Score", "0"},
+            {"Kelly Criterion Estimate", "0"},
+            {"Kelly Criterion Probability Value", "0"},
+            {"Sortino Ratio", "1.266"},
+            {"Return Over Maximum Drawdown", "1.622"},
+            {"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", "cf43585a8d1781f04b53a4f1ee3380cb"}
+        };
+    }
+}

Algorithm.CSharp/AutomaticIndicatorWarmupDataTypeRegressionAlgorithm.cs

@@ -160,14 +160,15 @@ namespace QuantConnect.Algorithm.CSharp
             {"Loss Rate", "0%"},
             {"Win Rate", "0%"},
             {"Profit-Loss Ratio", "0"},
-            {"Alpha", "5.579"},
-            {"Beta", "-63.972"},
+            {"Alpha", "5.56"},
+            {"Beta", "-71.105"},
             {"Annual Standard Deviation", "0.434"},
             {"Annual Variance", "0.188"},
-            {"Information Ratio", "0.996"},
-            {"Tracking Error", "0.441"},
-            {"Treynor Ratio", "-0.008"},
+            {"Information Ratio", "1.016"},
+            {"Tracking Error", "0.44"},
+            {"Treynor Ratio", "-0.007"},
             {"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

@@ -113,28 +113,29 @@ namespace QuantConnect.Algorithm.CSharp
             {"Total Trades", "1"},
             {"Average Win", "0%"},
             {"Average Loss", "0%"},
-            {"Compounding Annual Return", "264.819%"},
+            {"Compounding Annual Return", "271.453%"},
             {"Drawdown", "2.200%"},
             {"Expectancy", "0"},
-            {"Net Profit", "1.668%"},
-            {"Sharpe Ratio", "8.749"},
-            {"Probabilistic Sharpe Ratio", "67.311%"},
+            {"Net Profit", "1.692%"},
+            {"Sharpe Ratio", "8.888"},
+            {"Probabilistic Sharpe Ratio", "67.609%"},
             {"Loss Rate", "0%"},
             {"Win Rate", "0%"},
             {"Profit-Loss Ratio", "0"},
             {"Alpha", "-0.005"},
             {"Beta", "0.996"},
-            {"Annual Standard Deviation", "0.219"},
-            {"Annual Variance", "0.048"},
-            {"Information Ratio", "-14.189"},
+            {"Annual Standard Deviation", "0.222"},
+            {"Annual Variance", "0.049"},
+            {"Information Ratio", "-14.565"},
             {"Tracking Error", "0.001"},
-            {"Treynor Ratio", "1.922"},
-            {"Total Fees", "$3.26"},
+            {"Treynor Ratio", "1.978"},
+            {"Total Fees", "$3.44"},
+            {"Estimated Strategy Capacity", "$48000000.00"},
             {"Fitness Score", "0.248"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
             {"Sortino Ratio", "79228162514264337593543950335"},
-            {"Return Over Maximum Drawdown", "93.761"},
+            {"Return Over Maximum Drawdown", "93.728"},
             {"Portfolio Turnover", "0.248"},
             {"Total Insights Generated", "0"},
             {"Total Insights Closed", "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", "9e4bfd2eb0b81ee5bc1b197a87ccedbe"}
         };
     }
 }

Algorithm.CSharp/BacktestingBrokerageRegressionAlgorithm.cs

@@ -76,9 +76,11 @@ namespace QuantConnect.Algorithm.CSharp
         /// <param name="data">Slice object keyed by symbol containing the stock data</param>
         public override void OnData(Slice data)
         {
-            if (!_equityBought && data.ContainsKey(_spy)) {
-                //Buy our Equity
-                var quantity = CalculateOrderQuantity(_spy, .1m);
+            if (!_equityBought && data.ContainsKey(_spy))
+            {
+                //Buy our Equity.
+                //Quantity is rounded down to an even number since it will be split in two equal halves
+                var quantity = Math.Floor(CalculateOrderQuantity(_spy, .1m) / 2) * 2;
                 _equityBuy = MarketOrder(_spy, quantity, asynchronous: true);
                 _equityBought = true;
             }
@@ -119,7 +121,7 @@ namespace QuantConnect.Algorithm.CSharp
             var order = Transactions.GetOrderById(orderEvent.OrderId);
 
             // Based on the type verify the order
-            switch(order.Type)
+            switch (order.Type)
             {
                 case OrderType.Market:
                     VerifyMarketOrder(order, orderEvent);
@@ -140,7 +142,7 @@ namespace QuantConnect.Algorithm.CSharp
         /// <param name="order">Order object to analyze</param>
         public void VerifyMarketOrder(Order order, OrderEvent orderEvent)
         {
-            switch(order.Status)
+            switch (order.Status)
             {
                 case OrderStatus.Submitted:
                     break;
@@ -152,7 +154,7 @@ namespace QuantConnect.Algorithm.CSharp
                         throw new Exception("LastFillTime should not be null");
                     }
 
-                    if (order.Quantity/2 != orderEvent.FillQuantity)
+                    if (order.Quantity / 2 != orderEvent.FillQuantity)
                     {
                         throw new Exception("Order size should be half");
                     }
@@ -215,9 +217,9 @@ namespace QuantConnect.Algorithm.CSharp
             }
 
             //Check equity holding, should be invested, profit should be
-            //Quantity should be 50, AveragePrice should be ticket AverageFillPrice
+            //Quantity should be 52, AveragePrice should be ticket AverageFillPrice
             var equityHolding = Portfolio[_equityBuy.Symbol];
-            if (!equityHolding.Invested || equityHolding.Quantity != 50 || equityHolding.AveragePrice != _equityBuy.AverageFillPrice)
+            if (!equityHolding.Invested || equityHolding.Quantity != 52 || equityHolding.AveragePrice != _equityBuy.AverageFillPrice)
             {
                 throw new Exception("Equity holding does not match expected outcome");
             }
@@ -299,29 +301,30 @@ namespace QuantConnect.Algorithm.CSharp
             {"Total Trades", "3"},
             {"Average Win", "0%"},
             {"Average Loss", "-0.40%"},
-            {"Compounding Annual Return", "-22.335%"},
+            {"Compounding Annual Return", "-22.231%"},
             {"Drawdown", "0.400%"},
             {"Expectancy", "-1"},
-            {"Net Profit", "-0.323%"},
-            {"Sharpe Ratio", "-11.098"},
+            {"Net Profit", "-0.321%"},
+            {"Sharpe Ratio", "-11.083"},
             {"Probabilistic Sharpe Ratio", "0%"},
             {"Loss Rate", "100%"},
             {"Win Rate", "0%"},
             {"Profit-Loss Ratio", "0"},
-            {"Alpha", "-0.002"},
-            {"Beta", "0.099"},
+            {"Alpha", "-0.003"},
+            {"Beta", "0.097"},
             {"Annual Standard Deviation", "0.002"},
             {"Annual Variance", "0"},
-            {"Information Ratio", "9.899"},
-            {"Tracking Error", "0.019"},
-            {"Treynor Ratio", "-0.23"},
+            {"Information Ratio", "9.742"},
+            {"Tracking Error", "0.021"},
+            {"Treynor Ratio", "-0.26"},
             {"Total Fees", "$2.00"},
-            {"Fitness Score", "0.213"},
+            {"Estimated Strategy Capacity", "$0"},
+            {"Fitness Score", "0.212"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
             {"Sortino Ratio", "79228162514264337593543950335"},
-            {"Return Over Maximum Drawdown", "-73.456"},
-            {"Portfolio Turnover", "0.426"},
+            {"Return Over Maximum Drawdown", "-73.565"},
+            {"Portfolio Turnover", "0.425"},
             {"Total Insights Generated", "0"},
             {"Total Insights Closed", "0"},
             {"Total Insights Analysis Completed", "0"},
@@ -335,7 +338,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "-1990039314"}
+            {"OrderListHash", "7f99e1a8ce4675a1e8bbe1ba45967ccd"}
         };
     }
-}
+}

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

@@ -80,28 +80,29 @@ namespace QuantConnect.Algorithm.CSharp
             {"Total Trades", "1"},
             {"Average Win", "0%"},
             {"Average Loss", "0%"},
-            {"Compounding Annual Return", "264.819%"},
+            {"Compounding Annual Return", "271.453%"},
             {"Drawdown", "2.200%"},
             {"Expectancy", "0"},
-            {"Net Profit", "1.668%"},
-            {"Sharpe Ratio", "8.749"},
-            {"Probabilistic Sharpe Ratio", "67.311%"},
+            {"Net Profit", "1.692%"},
+            {"Sharpe Ratio", "8.888"},
+            {"Probabilistic Sharpe Ratio", "67.609%"},
             {"Loss Rate", "0%"},
             {"Win Rate", "0%"},
             {"Profit-Loss Ratio", "0"},
             {"Alpha", "-0.005"},
             {"Beta", "0.996"},
-            {"Annual Standard Deviation", "0.219"},
-            {"Annual Variance", "0.048"},
-            {"Information Ratio", "-14.189"},
+            {"Annual Standard Deviation", "0.222"},
+            {"Annual Variance", "0.049"},
+            {"Information Ratio", "-14.565"},
             {"Tracking Error", "0.001"},
-            {"Treynor Ratio", "1.922"},
-            {"Total Fees", "$3.26"},
+            {"Treynor Ratio", "1.978"},
+            {"Total Fees", "$3.44"},
+            {"Estimated Strategy Capacity", "$48000000.00"},
             {"Fitness Score", "0.248"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
             {"Sortino Ratio", "79228162514264337593543950335"},
-            {"Return Over Maximum Drawdown", "93.761"},
+            {"Return Over Maximum Drawdown", "93.728"},
             {"Portfolio Turnover", "0.248"},
             {"Total Insights Generated", "0"},
             {"Total Insights Closed", "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", "9e4bfd2eb0b81ee5bc1b197a87ccedbe"}
         };
     }
-}
+}

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

@@ -71,29 +71,30 @@ namespace QuantConnect.Algorithm.CSharp
             {"Total Trades", "1"},
             {"Average Win", "0%"},
             {"Average Loss", "0%"},
-            {"Compounding Annual Return", "246.000%"},
-            {"Drawdown", "1.100%"},
+            {"Compounding Annual Return", "246.546%"},
+            {"Drawdown", "1.200%"},
             {"Expectancy", "0"},
-            {"Net Profit", "3.459%"},
-            {"Sharpe Ratio", "10.11"},
-            {"Probabilistic Sharpe Ratio", "83.150%"},
+            {"Net Profit", "3.464%"},
+            {"Sharpe Ratio", "9.933"},
+            {"Probabilistic Sharpe Ratio", "82.470%"},
             {"Loss Rate", "0%"},
             {"Win Rate", "0%"},
             {"Profit-Loss Ratio", "0"},
-            {"Alpha", "1.935"},
-            {"Beta", "-0.119"},
-            {"Annual Standard Deviation", "0.16"},
-            {"Annual Variance", "0.026"},
-            {"Information Ratio", "-4.556"},
-            {"Tracking Error", "0.221"},
-            {"Treynor Ratio", "-13.568"},
-            {"Total Fees", "$3.26"},
-            {"Fitness Score", "0.111"},
+            {"Alpha", "1.957"},
+            {"Beta", "-0.125"},
+            {"Annual Standard Deviation", "0.164"},
+            {"Annual Variance", "0.027"},
+            {"Information Ratio", "-4.577"},
+            {"Tracking Error", "0.225"},
+            {"Treynor Ratio", "-13.006"},
+            {"Total Fees", "$3.45"},
+            {"Estimated Strategy Capacity", "$840000000.00"},
+            {"Fitness Score", "0.112"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
-            {"Sortino Ratio", "52.533"},
-            {"Return Over Maximum Drawdown", "214.75"},
-            {"Portfolio Turnover", "0.111"},
+            {"Sortino Ratio", "53.951"},
+            {"Return Over Maximum Drawdown", "209.464"},
+            {"Portfolio Turnover", "0.112"},
             {"Total Insights Generated", "0"},
             {"Total Insights Closed", "0"},
             {"Total Insights Analysis Completed", "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", "33d01821923c397f999cfb2e5b5928ad"}
         };
     }
 }

Algorithm.CSharp/BasicTemplateFrameworkAlgorithm.cs

@@ -92,28 +92,29 @@ namespace QuantConnect.Algorithm.CSharp
             {"Total Trades", "3"},
             {"Average Win", "0%"},
             {"Average Loss", "-1.01%"},
-            {"Compounding Annual Return", "254.782%"},
+            {"Compounding Annual Return", "261.134%"},
             {"Drawdown", "2.200%"},
             {"Expectancy", "-1"},
-            {"Net Profit", "1.632%"},
-            {"Sharpe Ratio", "8.371"},
-            {"Probabilistic Sharpe Ratio", "66.555%"},
+            {"Net Profit", "1.655%"},
+            {"Sharpe Ratio", "8.505"},
+            {"Probabilistic Sharpe Ratio", "66.840%"},
             {"Loss Rate", "100%"},
             {"Win Rate", "0%"},
             {"Profit-Loss Ratio", "0"},
-            {"Alpha", "-0.088"},
+            {"Alpha", "-0.091"},
             {"Beta", "1.006"},
-            {"Annual Standard Deviation", "0.221"},
-            {"Annual Variance", "0.049"},
-            {"Information Ratio", "-32.586"},
+            {"Annual Standard Deviation", "0.224"},
+            {"Annual Variance", "0.05"},
+            {"Information Ratio", "-33.445"},
             {"Tracking Error", "0.002"},
-            {"Treynor Ratio", "1.839"},
-            {"Total Fees", "$9.77"},
+            {"Treynor Ratio", "1.893"},
+            {"Total Fees", "$10.32"},
+            {"Estimated Strategy Capacity", "$23000000.00"},
             {"Fitness Score", "0.747"},
-            {"Kelly Criterion Estimate", "38.64"},
-            {"Kelly Criterion Probability Value", "0.229"},
+            {"Kelly Criterion Estimate", "38.796"},
+            {"Kelly Criterion Probability Value", "0.228"},
             {"Sortino Ratio", "79228162514264337593543950335"},
-            {"Return Over Maximum Drawdown", "85.209"},
+            {"Return Over Maximum Drawdown", "85.095"},
             {"Portfolio Turnover", "0.747"},
             {"Total Insights Generated", "100"},
             {"Total Insights Closed", "99"},
@@ -121,14 +122,14 @@ namespace QuantConnect.Algorithm.CSharp
             {"Long Insight Count", "100"},
             {"Short Insight Count", "0"},
             {"Long/Short Ratio", "100%"},
-            {"Estimated Monthly Alpha Value", "$126657.6305"},
-            {"Total Accumulated Estimated Alpha Value", "$20405.9516"},
-            {"Mean Population Estimated Insight Value", "$206.1207"},
-            {"Mean Population Direction", "54.5455%"},
-            {"Mean Population Magnitude", "54.5455%"},
-            {"Rolling Averaged Population Direction", "59.8056%"},
-            {"Rolling Averaged Population Magnitude", "59.8056%"},
-            {"OrderListHash", "951346025"}
+            {"Estimated Monthly Alpha Value", "$117277.2200"},
+            {"Total Accumulated Estimated Alpha Value", "$18894.6632"},
+            {"Mean Population Estimated Insight Value", "$190.8552"},
+            {"Mean Population Direction", "53.5354%"},
+            {"Mean Population Magnitude", "53.5354%"},
+            {"Rolling Averaged Population Direction", "58.2788%"},
+            {"Rolling Averaged Population Magnitude", "58.2788%"},
+            {"OrderListHash", "ad2216297c759d8e5aef48ff065f8919"}
         };
     }
 }

Algorithm.CSharp/BasicTemplateFuturesAlgorithm.cs

@@ -139,14 +139,15 @@ namespace QuantConnect.Algorithm.CSharp
             {"Loss Rate", "89%"},
             {"Win Rate", "11%"},
             {"Profit-Loss Ratio", "0.69"},
-            {"Alpha", "4.398"},
-            {"Beta", "-0.989"},
+            {"Alpha", "4.469"},
+            {"Beta", "-0.961"},
             {"Annual Standard Deviation", "0.373"},
             {"Annual Variance", "0.139"},
-            {"Information Ratio", "-12.816"},
-            {"Tracking Error", "0.504"},
-            {"Treynor Ratio", "1.011"},
+            {"Information Ratio", "-13.191"},
+            {"Tracking Error", "0.507"},
+            {"Treynor Ratio", "1.04"},
             {"Total Fees", "$15207.00"},
+            {"Estimated Strategy Capacity", "$7700.00"},
             {"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

@@ -147,14 +147,15 @@ namespace QuantConnect.Algorithm.CSharp
             {"Loss Rate", "0%"},
             {"Win Rate", "0%"},
             {"Profit-Loss Ratio", "0"},
-            {"Alpha", "-1.347"},
+            {"Alpha", "-1.362"},
             {"Beta", "0.257"},
             {"Annual Standard Deviation", "0.109"},
             {"Annual Variance", "0.012"},
-            {"Information Ratio", "-14.763"},
-            {"Tracking Error", "0.188"},
-            {"Treynor Ratio", "-3.318"},
+            {"Information Ratio", "-14.947"},
+            {"Tracking Error", "0.19"},
+            {"Treynor Ratio", "-3.309"},
             {"Total Fees", "$3.70"},
+            {"Estimated Strategy Capacity", "$52000000.00"},
             {"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/BasicTemplateIndexAlgorithm.cs

@@ -0,0 +1,157 @@
+/*
+ * 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 System.Collections.Generic;
+using QuantConnect.Indicators;
+using QuantConnect.Interfaces;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// This example demonstrates how to add index asset types.
+    /// </summary>
+    /// <meta name="tag" content="using data" />
+    /// <meta name="tag" content="benchmarks" />
+    /// <meta name="tag" content="indexes" />
+    public class BasicTemplateIndexAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private Symbol _spx;
+        private Symbol _spxOption;
+        private ExponentialMovingAverage _emaSlow;
+        private ExponentialMovingAverage _emaFast;
+
+        /// <summary>
+        /// Initialize your algorithm and add desired assets.
+        /// </summary>
+        public override void Initialize()
+        {
+            SetStartDate(2021, 1, 4);
+            SetEndDate(2021, 1, 15);
+            SetCash(1000000);
+
+            // Use indicator for signal; but it cannot be traded
+            _spx = AddIndex("SPX", Resolution.Minute).Symbol;
+
+            // Trade on SPX ITM calls
+            _spxOption = QuantConnect.Symbol.CreateOption(
+                _spx,
+                Market.USA,
+                OptionStyle.European,
+                OptionRight.Call,
+                3200m,
+                new DateTime(2021, 1, 15));
+
+            AddIndexOptionContract(_spxOption, Resolution.Minute);
+
+            _emaSlow = EMA(_spx, 80);
+            _emaFast = EMA(_spx, 200);
+        }
+
+        /// <summary>
+        /// Index EMA Cross trading underlying.
+        /// </summary>
+        public override void OnData(Slice slice)
+        {
+            if (!slice.Bars.ContainsKey(_spx) || !slice.Bars.ContainsKey(_spxOption))
+            {
+                return;
+            }
+
+            // Warm up indicators
+            if (!_emaSlow.IsReady)
+            {
+                return;
+            }
+
+            if (_emaFast > _emaSlow)
+            {
+                SetHoldings(_spxOption, 1);
+            }
+            else
+            {
+                Liquidate();
+            }
+        }
+
+        public override void OnEndOfAlgorithm()
+        {
+            if (Portfolio[_spx].TotalSaleVolume > 0)
+            {
+                throw new Exception("Index is not tradable.");
+            }
+        }
+
+        /// <summary>
+        /// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
+        /// </summary>
+        public bool CanRunLocally { get; } = true;
+
+        /// <summary>
+        /// This is used by the regression test system to indicate which languages this algorithm is written in.
+        /// </summary>
+        public Language[] Languages { get; } = { Language.CSharp, Language.Python };
+
+        /// <summary>
+        /// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
+        /// </summary>
+        public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
+        {
+            {"Total Trades", "4"},
+            {"Average Win", "0%"},
+            {"Average Loss", "-53.10%"},
+            {"Compounding Annual Return", "-96.172%"},
+            {"Drawdown", "10.100%"},
+            {"Expectancy", "-1"},
+            {"Net Profit", "-9.915%"},
+            {"Sharpe Ratio", "-4.217"},
+            {"Probabilistic Sharpe Ratio", "0.052%"},
+            {"Loss Rate", "100%"},
+            {"Win Rate", "0%"},
+            {"Profit-Loss Ratio", "0"},
+            {"Alpha", "-0.908"},
+            {"Beta", "0.468"},
+            {"Annual Standard Deviation", "0.139"},
+            {"Annual Variance", "0.019"},
+            {"Information Ratio", "-9.003"},
+            {"Tracking Error", "0.142"},
+            {"Treynor Ratio", "-1.251"},
+            {"Total Fees", "$0.00"},
+            {"Estimated Strategy Capacity", "$14000000.00"},
+            {"Fitness Score", "0.044"},
+            {"Kelly Criterion Estimate", "0"},
+            {"Kelly Criterion Probability Value", "0"},
+            {"Sortino Ratio", "-1.96"},
+            {"Return Over Maximum Drawdown", "-10.171"},
+            {"Portfolio Turnover", "0.34"},
+            {"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", "52521ab779446daf4d38a7c9bbbdd893"}
+        };
+    }
+}

Algorithm.CSharp/BasicTemplateIndexOptionsAlgorithm.cs

@@ -0,0 +1,180 @@
+/*
+ * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
+ * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+*/
+
+using System;
+using QuantConnect.Data;
+using System.Collections.Generic;
+using QuantConnect.Indicators;
+using QuantConnect.Interfaces;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// This example demonstrates how to add index asset types and trade index options on SPX.
+    /// </summary>
+    public class BasicTemplateIndexOptionsAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private Symbol _spx;
+        private ExponentialMovingAverage _emaSlow;
+        private ExponentialMovingAverage _emaFast;
+
+        /// <summary>
+        /// Initialize your algorithm and add desired assets.
+        /// </summary>
+        public override void Initialize()
+        {
+            SetStartDate(2021, 1, 4);
+            SetEndDate(2021, 2, 1);
+            SetCash(1000000);
+
+            // Use indicator for signal; but it cannot be traded.
+            // We will instead trade on SPX options
+            _spx = AddIndex("SPX", Resolution.Minute).Symbol;
+            var spxOptions = AddIndexOption(_spx, Resolution.Minute);
+            spxOptions.SetFilter(filterFunc => filterFunc.CallsOnly());
+
+            _emaSlow = EMA(_spx, 80);
+            _emaFast = EMA(_spx, 200);
+        }
+
+        /// <summary>
+        /// Index EMA Cross trading index options of the index.
+        /// </summary>
+        public override void OnData(Slice slice)
+        {
+            if (!slice.Bars.ContainsKey(_spx))
+            {
+                Debug($"No SPX on {Time}");
+                return;
+            }
+
+            // Warm up indicators
+            if (!_emaSlow.IsReady)
+            {
+                Debug($"EMA slow not ready on {Time}");
+                return;
+            }
+
+            foreach (var chain in slice.OptionChains.Values)
+            {
+                foreach (var contract in chain.Contracts.Values)
+                {
+                    if (contract.Expiry.Month == 3 && contract.Symbol.ID.StrikePrice == 3700m && contract.Right == OptionRight.Call && slice.QuoteBars.ContainsKey(contract.Symbol))
+                    {
+                        Log($"{Time} {contract.Strike}{(contract.Right == OptionRight.Call ? 'C' : 'P')} -- {slice.QuoteBars[contract.Symbol]}");
+                    }
+
+                    if (Portfolio.Invested)
+                    {
+                        continue;
+                    }
+
+                    if (_emaFast > _emaSlow && contract.Right == OptionRight.Call)
+                    {
+                        Liquidate(InvertOption(contract.Symbol));
+                        MarketOrder(contract.Symbol, 1);
+                    }
+                    else if (_emaFast < _emaSlow && contract.Right == OptionRight.Put)
+                    {
+                        Liquidate(InvertOption(contract.Symbol));
+                        MarketOrder(contract.Symbol, 1);
+                    }
+                }
+            }
+        }
+
+        public override void OnEndOfAlgorithm()
+        {
+            if (Portfolio[_spx].TotalSaleVolume > 0)
+            {
+                throw new Exception("Index is not tradable.");
+            }
+            if (Portfolio.TotalSaleVolume == 0)
+            {
+                throw new Exception("Trade volume should be greater than zero by the end of this algorithm");
+            }
+        }
+
+        public Symbol InvertOption(Symbol symbol)
+        {
+            return QuantConnect.Symbol.CreateOption(
+                symbol.Underlying,
+                symbol.ID.Market,
+                symbol.ID.OptionStyle,
+                symbol.ID.OptionRight == OptionRight.Call ? OptionRight.Put : OptionRight.Call,
+                symbol.ID.StrikePrice,
+                symbol.ID.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; } = 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, 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", "8220"},
+            {"Average Win", "0.00%"},
+            {"Average Loss", "0.00%"},
+            {"Compounding Annual Return", "-100.000%"},
+            {"Drawdown", "13.500%"},
+            {"Expectancy", "-0.818"},
+            {"Net Profit", "-13.517%"},
+            {"Sharpe Ratio", "-2.678"},
+            {"Probabilistic Sharpe Ratio", "0%"},
+            {"Loss Rate", "89%"},
+            {"Win Rate", "11%"},
+            {"Profit-Loss Ratio", "0.69"},
+            {"Alpha", "4.398"},
+            {"Beta", "-0.989"},
+            {"Annual Standard Deviation", "0.373"},
+            {"Annual Variance", "0.139"},
+            {"Information Ratio", "-12.816"},
+            {"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"},
+            {"Sortino Ratio", "-8.62"},
+            {"Return Over Maximum Drawdown", "-7.81"},
+            {"Portfolio Turnover", "302.321"},
+            {"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", "35b3f4b7a225468d42ca085386a2383e"}
+        };
+    }
+}

Algorithm.CSharp/BasicTemplateOptionEquityStrategyAlgorithm.cs

@@ -0,0 +1,150 @@
+/*
+ * 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.Linq;
+using QuantConnect.Data;
+using QuantConnect.Orders;
+using QuantConnect.Interfaces;
+using QuantConnect.Data.Market;
+using System.Collections.Generic;
+using QuantConnect.Securities.Option;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Basic template algorithm trading a Call Butterfly option equity strategy
+    /// </summary>
+    /// <meta name="tag" content="options" />
+    /// <meta name="tag" content="using data" />
+    /// <meta name="tag" content="using quantconnect" />
+    /// <meta name="tag" content="trading and orders" />
+    public class BasicTemplateOptionEquityStrategyAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        protected Symbol _optionSymbol;
+
+        public override void Initialize()
+        {
+            SetStartDate(2015, 12, 24);
+            SetEndDate(2015, 12, 24);
+
+            var equity = AddEquity("GOOG", leverage: 4);
+            var option = AddOption(equity.Symbol);
+            _optionSymbol = option.Symbol;
+
+            // set our strike/expiry filter for this option chain
+            option.SetFilter(u => u.Strikes(-2, +2)
+                // Expiration method accepts TimeSpan objects or integer for days.
+                // The following statements yield the same filtering criteria
+                .Expiration(0, 180));
+        }
+        /// <summary>
+        /// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
+        /// </summary>
+        /// <param name="slice">Slice object keyed by symbol containing the stock data</param>
+        public override void OnData(Slice slice)
+        {
+            if (!Portfolio.Invested)
+            {
+                OptionChain chain;
+                if (IsMarketOpen(_optionSymbol) && slice.OptionChains.TryGetValue(_optionSymbol, out chain))
+                {
+                    var callContracts = chain.Where(contract => contract.Right == OptionRight.Call)
+                        .GroupBy(x => x.Expiry)
+                        .OrderBy(grouping => grouping.Key)
+                        .First()
+                        .OrderBy(x => x.Strike)
+                        .ToList();
+
+                    var expiry = callContracts[0].Expiry;
+                    var lowerStrike = callContracts[0].Strike;
+                    var middleStrike = callContracts[1].Strike;
+                    var higherStrike = callContracts[2].Strike;
+
+                    var optionStrategy = OptionStrategies.CallButterfly(_optionSymbol, higherStrike, middleStrike, lowerStrike, expiry);
+                    
+                    Order(optionStrategy, 10);
+                }
+            }
+        }
+
+        /// <summary>
+        /// Order fill event handler. On an order fill update the resulting information is passed to this method.
+        /// </summary>
+        /// <param name="orderEvent">Order event details containing details of the evemts</param>
+        /// <remarks>This method can be called asynchronously and so should only be used by seasoned C# experts. Ensure you use proper locks on thread-unsafe objects</remarks>
+        public override void OnOrderEvent(OrderEvent orderEvent)
+        {
+            Log($"{orderEvent}");
+        }
+
+        /// <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 => 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", "3"},
+            {"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", "$10.00"},
+            {"Estimated Strategy Capacity", "$84000.00"},
+            {"Fitness Score", "0"},
+            {"Kelly Criterion Estimate", "0"},
+            {"Kelly Criterion Probability Value", "0"},
+            {"Sortino Ratio", "0"},
+            {"Return Over Maximum Drawdown", "0"},
+            {"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", "82c29cc9db9a300074d6ff136253f4ac"}
+        };
+    }
+}

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", "$720.00"},
             {"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", "$1300000.00"},
             {"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

@@ -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", "-91832511"}
+            {"OrderListHash", "92d8a50efe230524512404dab66b19dd"}
         };
     }
 }

Algorithm.CSharp/BasicTemplateOptionsFrameworkAlgorithm.cs

@@ -161,6 +161,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0"},
             {"Treynor Ratio", "0"},
             {"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", "-695205588"}
+            {"OrderListHash", "ce06ddfa4b2ffeb666a8910ac8836992"}
         };
     }
 }

Algorithm.CSharp/Benchmarks/HistoryRequestBenchmark.cs

@@ -28,11 +28,11 @@ namespace QuantConnect.Algorithm.CSharp.Benchmarks
             _symbol = AddEquity("SPY").Symbol;
         }
 
-        public override void OnEndOfDay()
+        public override void OnEndOfDay(Symbol symbol)
         {
-            var minuteHistory = History(_symbol, 60, Resolution.Minute);
+            var minuteHistory = History(symbol, 60, Resolution.Minute);
             var lastHourHigh = minuteHistory.Select(minuteBar => minuteBar.High).DefaultIfEmpty(0).Max();
-            var dailyHistory = History(_symbol, 1, Resolution.Daily).First();
+            var dailyHistory = History(symbol, 1, Resolution.Daily).First();
             var dailyHigh = dailyHistory.High;
             var dailyLow = dailyHistory.Low;
             var dailyOpen = dailyHistory.Open;

Algorithm.CSharp/BlackLittermanPortfolioOptimizationFrameworkAlgorithm.cs

@@ -74,31 +74,32 @@ namespace QuantConnect.Algorithm.CSharp
         /// </summary>
         public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
         {
-            {"Total Trades", "18"},
+            {"Total Trades", "19"},
             {"Average Win", "0%"},
             {"Average Loss", "-0.16%"},
-            {"Compounding Annual Return", "72.164%"},
+            {"Compounding Annual Return", "71.962%"},
             {"Drawdown", "1.100%"},
             {"Expectancy", "-1"},
-            {"Net Profit", "0.747%"},
-            {"Sharpe Ratio", "4.086"},
-            {"Probabilistic Sharpe Ratio", "61.091%"},
+            {"Net Profit", "0.745%"},
+            {"Sharpe Ratio", "4.072"},
+            {"Probabilistic Sharpe Ratio", "61.045%"},
             {"Loss Rate", "100%"},
             {"Win Rate", "0%"},
             {"Profit-Loss Ratio", "0"},
-            {"Alpha", "-0.305"},
-            {"Beta", "0.564"},
+            {"Alpha", "-0.314"},
+            {"Beta", "0.554"},
             {"Annual Standard Deviation", "0.113"},
             {"Annual Variance", "0.013"},
-            {"Information Ratio", "-10.007"},
-            {"Tracking Error", "0.09"},
-            {"Treynor Ratio", "0.82"},
-            {"Total Fees", "$41.70"},
+            {"Information Ratio", "-10.043"},
+            {"Tracking Error", "0.093"},
+            {"Treynor Ratio", "0.832"},
+            {"Total Fees", "$42.71"},
+            {"Estimated Strategy Capacity", "$3000000.00"},
             {"Fitness Score", "0.634"},
             {"Kelly Criterion Estimate", "13.656"},
             {"Kelly Criterion Probability Value", "0.228"},
             {"Sortino Ratio", "79228162514264337593543950335"},
-            {"Return Over Maximum Drawdown", "80.05"},
+            {"Return Over Maximum Drawdown", "79.683"},
             {"Portfolio Turnover", "0.634"},
             {"Total Insights Generated", "17"},
             {"Total Insights Closed", "14"},
@@ -106,14 +107,14 @@ namespace QuantConnect.Algorithm.CSharp
             {"Long Insight Count", "6"},
             {"Short Insight Count", "7"},
             {"Long/Short Ratio", "85.71%"},
-            {"Estimated Monthly Alpha Value", "$72447.6813"},
-            {"Total Accumulated Estimated Alpha Value", "$12477.1007"},
-            {"Mean Population Estimated Insight Value", "$891.2215"},
+            {"Estimated Monthly Alpha Value", "$44645.2887"},
+            {"Total Accumulated Estimated Alpha Value", "$7688.9108"},
+            {"Mean Population Estimated Insight Value", "$549.2079"},
             {"Mean Population Direction", "50%"},
             {"Mean Population Magnitude", "50%"},
             {"Rolling Averaged Population Direction", "12.6429%"},
             {"Rolling Averaged Population Magnitude", "12.6429%"},
-            {"OrderListHash", "-2004493274"}
+            {"OrderListHash", "b1dc004bd5163b865e17a429d402a9c5"}
         };
     }
 }

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", "-7.163"},
+            {"Tracking Error", "0.195"},
+            {"Treynor Ratio", "8.093"},
+            {"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

@@ -173,14 +173,15 @@ namespace QuantConnect.Algorithm.CSharp
             {"Loss Rate", "0%"},
             {"Win Rate", "100%"},
             {"Profit-Loss Ratio", "0"},
-            {"Alpha", "0.237"},
-            {"Beta", "-0.182"},
+            {"Alpha", "0.238"},
+            {"Beta", "-0.183"},
             {"Annual Standard Deviation", "0.09"},
             {"Annual Variance", "0.008"},
-            {"Information Ratio", "2.425"},
-            {"Tracking Error", "0.149"},
-            {"Treynor Ratio", "-1.405"},
+            {"Information Ratio", "2.41"},
+            {"Tracking Error", "0.148"},
+            {"Treynor Ratio", "-1.399"},
             {"Total Fees", "$2.00"},
+            {"Estimated Strategy Capacity", "$42000000.00"},
             {"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

@@ -151,19 +151,20 @@ namespace QuantConnect.Algorithm.CSharp
             {"Drawdown", "0.500%"},
             {"Expectancy", "1.393"},
             {"Net Profit", "32.840%"},
-            {"Sharpe Ratio", "7.14272222483913E+15"},
+            {"Sharpe Ratio", "7142722224839133"},
             {"Probabilistic Sharpe Ratio", "0%"},
             {"Loss Rate", "83%"},
             {"Win Rate", "17%"},
             {"Profit-Loss Ratio", "13.36"},
-            {"Alpha", "2.59468989671647E+16"},
-            {"Beta", "67.661"},
+            {"Alpha", "25946898967164744"},
+            {"Beta", "66.241"},
             {"Annual Standard Deviation", "3.633"},
             {"Annual Variance", "13.196"},
-            {"Information Ratio", "7.24987266907741E+15"},
-            {"Tracking Error", "3.579"},
-            {"Treynor Ratio", "383485597312030"},
+            {"Information Ratio", "7252204536250480"},
+            {"Tracking Error", "3.578"},
+            {"Treynor Ratio", "391705233723349.5"},
             {"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

@@ -119,28 +119,29 @@ namespace QuantConnect.Algorithm.CSharp
             {"Total Trades", "11"},
             {"Average Win", "0.51%"},
             {"Average Loss", "-0.33%"},
-            {"Compounding Annual Return", "-31.082%"},
-            {"Drawdown", "2.700%"},
+            {"Compounding Annual Return", "-31.050%"},
+            {"Drawdown", "2.600%"},
             {"Expectancy", "0.263"},
-            {"Net Profit", "-1.518%"},
-            {"Sharpe Ratio", "-2.118"},
-            {"Probabilistic Sharpe Ratio", "23.259%"},
+            {"Net Profit", "-1.516%"},
+            {"Sharpe Ratio", "-2.123"},
+            {"Probabilistic Sharpe Ratio", "23.232%"},
             {"Loss Rate", "50%"},
             {"Win Rate", "50%"},
             {"Profit-Loss Ratio", "1.53"},
-            {"Alpha", "-0.208"},
-            {"Beta", "0.415"},
-            {"Annual Standard Deviation", "0.119"},
+            {"Alpha", "-0.21"},
+            {"Beta", "0.416"},
+            {"Annual Standard Deviation", "0.118"},
             {"Annual Variance", "0.014"},
-            {"Information Ratio", "-1.167"},
-            {"Tracking Error", "0.126"},
-            {"Treynor Ratio", "-0.607"},
+            {"Information Ratio", "-1.2"},
+            {"Tracking Error", "0.125"},
+            {"Treynor Ratio", "-0.605"},
             {"Total Fees", "$11.63"},
-            {"Fitness Score", "0.013"},
+            {"Estimated Strategy Capacity", "$46000000.00"},
+            {"Fitness Score", "0.012"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
-            {"Sortino Ratio", "-5.1"},
-            {"Return Over Maximum Drawdown", "-11.717"},
+            {"Sortino Ratio", "-5.19"},
+            {"Return Over Maximum Drawdown", "-11.761"},
             {"Portfolio Turnover", "0.282"},
             {"Total Insights Generated", "0"},
             {"Total Insights Closed", "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", "d2412df9590523bc33e97ffa7683ce96"}
         };
     }
 }

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

@@ -103,28 +103,29 @@ namespace QuantConnect.Algorithm.CSharp
             {"Total Trades", "1"},
             {"Average Win", "0%"},
             {"Average Loss", "0%"},
-            {"Compounding Annual Return", "58.336%"},
-            {"Drawdown", "0.900%"},
+            {"Compounding Annual Return", "57.657%"},
+            {"Drawdown", "1.000%"},
             {"Expectancy", "0"},
-            {"Net Profit", "1.012%"},
-            {"Sharpe Ratio", "5.09"},
-            {"Probabilistic Sharpe Ratio", "68.472%"},
+            {"Net Profit", "1.003%"},
+            {"Sharpe Ratio", "5.024"},
+            {"Probabilistic Sharpe Ratio", "68.421%"},
             {"Loss Rate", "0%"},
             {"Win Rate", "0%"},
             {"Profit-Loss Ratio", "0"},
-            {"Alpha", "0.322"},
-            {"Beta", "0.265"},
+            {"Alpha", "0.312"},
+            {"Beta", "0.27"},
             {"Annual Standard Deviation", "0.087"},
             {"Annual Variance", "0.008"},
-            {"Information Ratio", "-0.088"},
+            {"Information Ratio", "-0.242"},
             {"Tracking Error", "0.105"},
-            {"Treynor Ratio", "1.667"},
-            {"Total Fees", "$2.91"},
+            {"Treynor Ratio", "1.616"},
+            {"Total Fees", "$3.08"},
+            {"Estimated Strategy Capacity", "$630000000.00"},
             {"Fitness Score", "0.141"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
-            {"Sortino Ratio", "9.731"},
-            {"Return Over Maximum Drawdown", "61.515"},
+            {"Sortino Ratio", "10.385"},
+            {"Return Over Maximum Drawdown", "58.709"},
             {"Portfolio Turnover", "0.143"},
             {"Total Insights Generated", "0"},
             {"Total Insights Closed", "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", "50145c3c1d58b09f38ec1b77cfe69eae"}
         };
     }
 }

Algorithm.CSharp/CompositeAlphaModelFrameworkAlgorithm.cs

@@ -83,14 +83,15 @@ namespace QuantConnect.Algorithm.CSharp
             {"Loss Rate", "33%"},
             {"Win Rate", "67%"},
             {"Profit-Loss Ratio", "0.02"},
-            {"Alpha", "4.314"},
-            {"Beta", "1.239"},
+            {"Alpha", "4.267"},
+            {"Beta", "1.227"},
             {"Annual Standard Deviation", "0.285"},
             {"Annual Variance", "0.081"},
-            {"Information Ratio", "47.452"},
-            {"Tracking Error", "0.101"},
-            {"Treynor Ratio", "5.409"},
+            {"Information Ratio", "48.639"},
+            {"Tracking Error", "0.097"},
+            {"Treynor Ratio", "5.459"},
             {"Total Fees", "$67.00"},
+            {"Estimated Strategy Capacity", "$3200000.00"},
             {"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

@@ -66,30 +66,31 @@ namespace QuantConnect.Algorithm.CSharp
         public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
         {
             {"Total Trades", "7"},
-            {"Average Win", "1.02%"},
+            {"Average Win", "1.01%"},
             {"Average Loss", "-1.01%"},
-            {"Compounding Annual Return", "205.606%"},
+            {"Compounding Annual Return", "210.936%"},
             {"Drawdown", "2.200%"},
             {"Expectancy", "0.339"},
-            {"Net Profit", "1.439%"},
-            {"Sharpe Ratio", "7.166"},
-            {"Probabilistic Sharpe Ratio", "64.794%"},
+            {"Net Profit", "1.461%"},
+            {"Sharpe Ratio", "7.289"},
+            {"Probabilistic Sharpe Ratio", "65.077%"},
             {"Loss Rate", "33%"},
             {"Win Rate", "67%"},
             {"Profit-Loss Ratio", "1.01"},
-            {"Alpha", "-0.341"},
+            {"Alpha", "-0.349"},
             {"Beta", "0.968"},
-            {"Annual Standard Deviation", "0.213"},
-            {"Annual Variance", "0.045"},
-            {"Information Ratio", "-46.719"},
+            {"Annual Standard Deviation", "0.216"},
+            {"Annual Variance", "0.046"},
+            {"Information Ratio", "-47.59"},
             {"Tracking Error", "0.009"},
-            {"Treynor Ratio", "1.575"},
-            {"Total Fees", "$22.77"},
+            {"Treynor Ratio", "1.623"},
+            {"Total Fees", "$24.07"},
+            {"Estimated Strategy Capacity", "$20000000.00"},
             {"Fitness Score", "0.999"},
-            {"Kelly Criterion Estimate", "38.64"},
-            {"Kelly Criterion Probability Value", "0.229"},
+            {"Kelly Criterion Estimate", "38.796"},
+            {"Kelly Criterion Probability Value", "0.228"},
             {"Sortino Ratio", "79228162514264337593543950335"},
-            {"Return Over Maximum Drawdown", "69.159"},
+            {"Return Over Maximum Drawdown", "69.017"},
             {"Portfolio Turnover", "1.242"},
             {"Total Insights Generated", "100"},
             {"Total Insights Closed", "99"},
@@ -97,14 +98,14 @@ namespace QuantConnect.Algorithm.CSharp
             {"Long Insight Count", "100"},
             {"Short Insight Count", "0"},
             {"Long/Short Ratio", "100%"},
-            {"Estimated Monthly Alpha Value", "$126657.6305"},
-            {"Total Accumulated Estimated Alpha Value", "$20405.9516"},
-            {"Mean Population Estimated Insight Value", "$206.1207"},
-            {"Mean Population Direction", "54.5455%"},
-            {"Mean Population Magnitude", "54.5455%"},
-            {"Rolling Averaged Population Direction", "59.8056%"},
-            {"Rolling Averaged Population Magnitude", "59.8056%"},
-            {"OrderListHash", "-1225025518"}
+            {"Estimated Monthly Alpha Value", "$117277.2200"},
+            {"Total Accumulated Estimated Alpha Value", "$18894.6632"},
+            {"Mean Population Estimated Insight Value", "$190.8552"},
+            {"Mean Population Direction", "53.5354%"},
+            {"Mean Population Magnitude", "53.5354%"},
+            {"Rolling Averaged Population Direction", "58.2788%"},
+            {"Rolling Averaged Population Magnitude", "58.2788%"},
+            {"OrderListHash", "d8d556bcf963ba50f85cea387c55922b"}
         };
     }
 }

Algorithm.CSharp/ConfidenceWeightedFrameworkAlgorithm.cs

@@ -75,30 +75,31 @@ namespace QuantConnect.Algorithm.CSharp
         public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
         {
             {"Total Trades", "6"},
-            {"Average Win", "0.00%"},
+            {"Average Win", "0%"},
             {"Average Loss", "0.00%"},
-            {"Compounding Annual Return", "38.059%"},
+            {"Compounding Annual Return", "38.832%"},
             {"Drawdown", "0.600%"},
-            {"Expectancy", "-0.502"},
-            {"Net Profit", "0.413%"},
-            {"Sharpe Ratio", "5.518"},
-            {"Probabilistic Sharpe Ratio", "66.933%"},
-            {"Loss Rate", "67%"},
-            {"Win Rate", "33%"},
-            {"Profit-Loss Ratio", "0.50"},
-            {"Alpha", "-0.178"},
-            {"Beta", "0.249"},
+            {"Expectancy", "-1"},
+            {"Net Profit", "0.420%"},
+            {"Sharpe Ratio", "5.579"},
+            {"Probabilistic Sharpe Ratio", "67.318%"},
+            {"Loss Rate", "100%"},
+            {"Win Rate", "0%"},
+            {"Profit-Loss Ratio", "0"},
+            {"Alpha", "-0.184"},
+            {"Beta", "0.248"},
             {"Annual Standard Deviation", "0.055"},
             {"Annual Variance", "0.003"},
-            {"Information Ratio", "-9.844"},
-            {"Tracking Error", "0.165"},
-            {"Treynor Ratio", "1.212"},
+            {"Information Ratio", "-10.012"},
+            {"Tracking Error", "0.167"},
+            {"Treynor Ratio", "1.241"},
             {"Total Fees", "$6.00"},
+            {"Estimated Strategy Capacity", "$33000000.00"},
             {"Fitness Score", "0.063"},
-            {"Kelly Criterion Estimate", "38.64"},
-            {"Kelly Criterion Probability Value", "0.229"},
+            {"Kelly Criterion Estimate", "38.796"},
+            {"Kelly Criterion Probability Value", "0.228"},
             {"Sortino Ratio", "79228162514264337593543950335"},
-            {"Return Over Maximum Drawdown", "70.188"},
+            {"Return Over Maximum Drawdown", "70.89"},
             {"Portfolio Turnover", "0.063"},
             {"Total Insights Generated", "100"},
             {"Total Insights Closed", "99"},
@@ -106,14 +107,14 @@ namespace QuantConnect.Algorithm.CSharp
             {"Long Insight Count", "100"},
             {"Short Insight Count", "0"},
             {"Long/Short Ratio", "100%"},
-            {"Estimated Monthly Alpha Value", "$126657.6305"},
-            {"Total Accumulated Estimated Alpha Value", "$20405.9516"},
-            {"Mean Population Estimated Insight Value", "$206.1207"},
-            {"Mean Population Direction", "54.5455%"},
-            {"Mean Population Magnitude", "54.5455%"},
-            {"Rolling Averaged Population Direction", "59.8056%"},
-            {"Rolling Averaged Population Magnitude", "59.8056%"},
-            {"OrderListHash", "501060991"}
+            {"Estimated Monthly Alpha Value", "$117277.2200"},
+            {"Total Accumulated Estimated Alpha Value", "$18894.6632"},
+            {"Mean Population Estimated Insight Value", "$190.8552"},
+            {"Mean Population Direction", "53.5354%"},
+            {"Mean Population Magnitude", "53.5354%"},
+            {"Rolling Averaged Population Direction", "58.2788%"},
+            {"Rolling Averaged Population Magnitude", "58.2788%"},
+            {"OrderListHash", "21e4704a124ba562d042e1e9962f4316"}
         };
     }
 }

Algorithm.CSharp/ConsolidateRegressionAlgorithm.cs

@@ -156,14 +156,15 @@ namespace QuantConnect.Algorithm.CSharp
             {"Loss Rate", "0%"},
             {"Win Rate", "0%"},
             {"Profit-Loss Ratio", "0"},
-            {"Alpha", "5.579"},
-            {"Beta", "-63.972"},
+            {"Alpha", "5.56"},
+            {"Beta", "-71.105"},
             {"Annual Standard Deviation", "0.434"},
             {"Annual Variance", "0.188"},
-            {"Information Ratio", "0.996"},
-            {"Tracking Error", "0.441"},
-            {"Treynor Ratio", "-0.008"},
+            {"Information Ratio", "1.016"},
+            {"Tracking Error", "0.44"},
+            {"Treynor Ratio", "-0.007"},
             {"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

@@ -182,14 +182,15 @@ namespace QuantConnect.Algorithm.CSharp
             {"Loss Rate", "100%"},
             {"Win Rate", "0%"},
             {"Profit-Loss Ratio", "0"},
-            {"Alpha", "-0.443"},
+            {"Alpha", "-0.449"},
             {"Beta", "0.157"},
             {"Annual Standard Deviation", "0.074"},
             {"Annual Variance", "0.005"},
-            {"Information Ratio", "-9.046"},
-            {"Tracking Error", "0.176"},
-            {"Treynor Ratio", "-1.46"},
+            {"Information Ratio", "-9.158"},
+            {"Tracking Error", "0.178"},
+            {"Treynor Ratio", "-1.456"},
             {"Total Fees", "$7.82"},
+            {"Estimated Strategy Capacity", "$12000000.00"},
             {"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

@@ -128,28 +128,29 @@ namespace QuantConnect.Algorithm.CSharp
         {
             {"Total Trades", "85"},
             {"Average Win", "4.85%"},
-            {"Average Loss", "-4.21%"},
-            {"Compounding Annual Return", "-3.100%"},
-            {"Drawdown", "52.900%"},
-            {"Expectancy", "-0.052"},
-            {"Net Profit", "-29.298%"},
-            {"Sharpe Ratio", "-0.076"},
+            {"Average Loss", "-4.22%"},
+            {"Compounding Annual Return", "-3.124%"},
+            {"Drawdown", "53.000%"},
+            {"Expectancy", "-0.053"},
+            {"Net Profit", "-29.486%"},
+            {"Sharpe Ratio", "-0.078"},
             {"Probabilistic Sharpe Ratio", "0.004%"},
             {"Loss Rate", "56%"},
             {"Win Rate", "44%"},
             {"Profit-Loss Ratio", "1.15"},
             {"Alpha", "-0.013"},
-            {"Beta", "0.009"},
-            {"Annual Standard Deviation", "0.164"},
+            {"Beta", "0.007"},
+            {"Annual Standard Deviation", "0.163"},
             {"Annual Variance", "0.027"},
-            {"Information Ratio", "-0.391"},
-            {"Tracking Error", "0.239"},
-            {"Treynor Ratio", "-1.435"},
-            {"Total Fees", "$755.29"},
+            {"Information Ratio", "-0.393"},
+            {"Tracking Error", "0.238"},
+            {"Treynor Ratio", "-1.72"},
+            {"Total Fees", "$796.82"},
+            {"Estimated Strategy Capacity", "$1000000000.00"},
             {"Fitness Score", "0.024"},
-            {"Kelly Criterion Estimate", "-0.84"},
-            {"Kelly Criterion Probability Value", "0.53"},
-            {"Sortino Ratio", "-0.224"},
+            {"Kelly Criterion Estimate", "-0.9"},
+            {"Kelly Criterion Probability Value", "0.532"},
+            {"Sortino Ratio", "-0.228"},
             {"Return Over Maximum Drawdown", "-0.058"},
             {"Portfolio Turnover", "0.05"},
             {"Total Insights Generated", "85"},
@@ -158,14 +159,14 @@ namespace QuantConnect.Algorithm.CSharp
             {"Long Insight Count", "42"},
             {"Short Insight Count", "43"},
             {"Long/Short Ratio", "97.67%"},
-            {"Estimated Monthly Alpha Value", "$-617339.2"},
-            {"Total Accumulated Estimated Alpha Value", "$-82686580"},
-            {"Mean Population Estimated Insight Value", "$-972783.3"},
+            {"Estimated Monthly Alpha Value", "$-579527.4"},
+            {"Total Accumulated Estimated Alpha Value", "$-77622060"},
+            {"Mean Population Estimated Insight Value", "$-913200.7"},
             {"Mean Population Direction", "51.7647%"},
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "48.2217%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "1370210213"}
+            {"OrderListHash", "177fb7f308a252864365442a30dd9eeb"}
         };
     }
 }

Algorithm.CSharp/CustomBenchmarkAlgorithm.cs

@@ -82,28 +82,29 @@ namespace QuantConnect.Algorithm.CSharp
             {"Total Trades", "1"},
             {"Average Win", "0%"},
             {"Average Loss", "0%"},
-            {"Compounding Annual Return", "264.819%"},
+            {"Compounding Annual Return", "272.157%"},
             {"Drawdown", "2.200%"},
             {"Expectancy", "0"},
-            {"Net Profit", "1.668%"},
-            {"Sharpe Ratio", "8.749"},
-            {"Probabilistic Sharpe Ratio", "67.311%"},
+            {"Net Profit", "1.694%"},
+            {"Sharpe Ratio", "8.897"},
+            {"Probabilistic Sharpe Ratio", "67.609%"},
             {"Loss Rate", "0%"},
             {"Win Rate", "0%"},
             {"Profit-Loss Ratio", "0"},
-            {"Alpha", "1.119"},
+            {"Alpha", "1.178"},
             {"Beta", "0.805"},
-            {"Annual Standard Deviation", "0.219"},
-            {"Annual Variance", "0.048"},
-            {"Information Ratio", "5.494"},
-            {"Tracking Error", "0.168"},
-            {"Treynor Ratio", "2.38"},
-            {"Total Fees", "$3.26"},
-            {"Fitness Score", "0.245"},
+            {"Annual Standard Deviation", "0.222"},
+            {"Annual Variance", "0.049"},
+            {"Information Ratio", "5.718"},
+            {"Tracking Error", "0.172"},
+            {"Treynor Ratio", "2.453"},
+            {"Total Fees", "$3.45"},
+            {"Estimated Strategy Capacity", "$270000000.00"},
+            {"Fitness Score", "0.246"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
-            {"Sortino Ratio", "9.606"},
-            {"Return Over Maximum Drawdown", "105.85"},
+            {"Sortino Ratio", "9.761"},
+            {"Return Over Maximum Drawdown", "107.509"},
             {"Portfolio Turnover", "0.249"},
             {"Total Insights Generated", "0"},
             {"Total Insights Closed", "0"},
@@ -118,7 +119,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "2069976135"}
+            {"OrderListHash", "e10039d74166b161f3ea2851a5e85843"}
         };
     }
 }

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", "$8700000.00"},
             {"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

@@ -43,7 +43,7 @@ namespace QuantConnect.Algorithm.CSharp
             security.SetBuyingPowerModel(new CustomBuyingPowerModel());
         }
 
-        public void OnData(Slice slice)
+        public override void OnData(Slice slice)
         {
             if (Portfolio.Invested)
             {
@@ -74,7 +74,11 @@ namespace QuantConnect.Algorithm.CSharp
             public override HasSufficientBuyingPowerForOrderResult HasSufficientBuyingPowerForOrder(
                 HasSufficientBuyingPowerForOrderParameters parameters)
             {
-                return new HasSufficientBuyingPowerForOrderResult(true);
+                // if portfolio doesn't have enough buying power:
+                //     parameters.Insufficient()
+
+                // this model never allows a lack of funds get in the way of buying securities
+                return parameters.Sufficient();
             }
         }
 
@@ -96,29 +100,30 @@ namespace QuantConnect.Algorithm.CSharp
             {"Total Trades", "1"},
             {"Average Win", "0%"},
             {"Average Loss", "0%"},
-            {"Compounding Annual Return", "5672.520%"},
-            {"Drawdown", "22.500%"},
+            {"Compounding Annual Return", "4775.196%"},
+            {"Drawdown", "21.600%"},
             {"Expectancy", "0"},
-            {"Net Profit", "40.601%"},
-            {"Sharpe Ratio", "40.201"},
-            {"Probabilistic Sharpe Ratio", "77.339%"},
+            {"Net Profit", "38.619%"},
+            {"Sharpe Ratio", "33.779"},
+            {"Probabilistic Sharpe Ratio", "77.029%"},
             {"Loss Rate", "0%"},
             {"Win Rate", "0%"},
             {"Profit-Loss Ratio", "0"},
-            {"Alpha", "41.848"},
-            {"Beta", "9.224"},
-            {"Annual Standard Deviation", "1.164"},
-            {"Annual Variance", "1.355"},
-            {"Information Ratio", "44.459"},
-            {"Tracking Error", "1.04"},
-            {"Treynor Ratio", "5.073"},
+            {"Alpha", "32.812"},
+            {"Beta", "8.756"},
+            {"Annual Standard Deviation", "1.11"},
+            {"Annual Variance", "1.231"},
+            {"Information Ratio", "37.501"},
+            {"Tracking Error", "0.985"},
+            {"Treynor Ratio", "4.281"},
             {"Total Fees", "$30.00"},
-            {"Fitness Score", "0.418"},
+            {"Estimated Strategy Capacity", "$19000000.00"},
+            {"Fitness Score", "0.395"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
-            {"Sortino Ratio", "113.05"},
-            {"Return Over Maximum Drawdown", "442.81"},
-            {"Portfolio Turnover", "0.418"},
+            {"Sortino Ratio", "98.148"},
+            {"Return Over Maximum Drawdown", "384.626"},
+            {"Portfolio Turnover", "0.395"},
             {"Total Insights Generated", "0"},
             {"Total Insights Closed", "0"},
             {"Total Insights Analysis Completed", "0"},
@@ -132,7 +137,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "639761089"}
+            {"OrderListHash", "3df007afa8125770e8f1a49263af90a2"}
         };
     }
 }

Algorithm.CSharp/CustomChartingAlgorithm.cs

@@ -80,7 +80,7 @@ namespace QuantConnect.Algorithm.CSharp
         /// OnEndOfDay Event Handler - At the end of each trading day we fire this code.
         /// To avoid flooding, we recommend running your plotting at the end of each day.
         /// </summary>
-        public override void OnEndOfDay()
+        public override void OnEndOfDay(Symbol symbol)
         {
             //Log the end of day prices:
             Plot("Trade Plot", "Price", _lastPrice);

Algorithm.CSharp/CustomDataAddDataCoarseSelectionRegressionAlgorithm.cs

@@ -112,13 +112,14 @@ namespace QuantConnect.Algorithm.CSharp
             {"Win Rate", "0%"},
             {"Profit-Loss Ratio", "0"},
             {"Alpha", "-0.273"},
-            {"Beta", "0.045"},
+            {"Beta", "0.047"},
             {"Annual Standard Deviation", "0.057"},
             {"Annual Variance", "0.003"},
-            {"Information Ratio", "-1.537"},
+            {"Information Ratio", "-1.581"},
             {"Tracking Error", "0.112"},
-            {"Treynor Ratio", "-6.121"},
+            {"Treynor Ratio", "-5.872"},
             {"Total Fees", "$3.50"},
+            {"Estimated Strategy Capacity", "$48000000.00"},
             {"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

@@ -117,13 +117,14 @@ namespace QuantConnect.Algorithm.CSharp
             {"Win Rate", "0%"},
             {"Profit-Loss Ratio", "0"},
             {"Alpha", "-0.273"},
-            {"Beta", "0.045"},
+            {"Beta", "0.047"},
             {"Annual Standard Deviation", "0.057"},
             {"Annual Variance", "0.003"},
-            {"Information Ratio", "-1.537"},
+            {"Information Ratio", "-1.581"},
             {"Tracking Error", "0.112"},
-            {"Treynor Ratio", "-6.121"},
+            {"Treynor Ratio", "-5.872"},
             {"Total Fees", "$3.50"},
+            {"Estimated Strategy Capacity", "$48000000.00"},
             {"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

@@ -124,13 +124,14 @@ namespace QuantConnect.Algorithm.CSharp
             {"Win Rate", "0%"},
             {"Profit-Loss Ratio", "0"},
             {"Alpha", "0.079"},
-            {"Beta", "0.099"},
+            {"Beta", "0.095"},
             {"Annual Standard Deviation", "0.079"},
             {"Annual Variance", "0.006"},
-            {"Information Ratio", "-6.058"},
-            {"Tracking Error", "0.19"},
-            {"Treynor Ratio", "2.159"},
+            {"Information Ratio", "-6.162"},
+            {"Tracking Error", "0.192"},
+            {"Treynor Ratio", "2.232"},
             {"Total Fees", "$1.00"},
+            {"Estimated Strategy Capacity", "$58000000.00"},
             {"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/CustomDataBitcoinAlgorithm.cs

@@ -58,7 +58,9 @@ namespace QuantConnect.Algorithm.CSharp
                 //Weather used as a tradable asset, like stocks, futures etc.
                 if (data.Close != 0)
                 {
-                    Order("BTC", (Portfolio.MarginRemaining / Math.Abs(data.Close + 1)));
+                    // It's only OK to use SetHoldings with crypto when using custom data. When trading with built-in crypto data, 
+                    // use the cashbook. Reference https://github.com/QuantConnect/Lean/blob/master/Algorithm.Python/BasicTemplateCryptoAlgorithm.py 
+                    SetHoldings("BTC", 1);
                 }
                 Console.WriteLine("Buying BTC 'Shares': BTC: " + data.Close);
             }
@@ -117,7 +119,7 @@ namespace QuantConnect.Algorithm.CSharp
 
                 //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.quandl.com/api/v3/datasets/BCHARTS/BITSTAMPUSD.csv?order=asc", SubscriptionTransportMedium.RemoteFile);
+                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>
@@ -154,6 +156,7 @@ namespace QuantConnect.Algorithm.CSharp
                 {
                     string[] data = line.Split(',');
                     coin.Time = DateTime.Parse(data[0], CultureInfo.InvariantCulture);
+                    coin.EndTime = coin.Time.AddDays(1);
                     coin.Open = Convert.ToDecimal(data[1], CultureInfo.InvariantCulture);
                     coin.High = Convert.ToDecimal(data[2], CultureInfo.InvariantCulture);
                     coin.Low = Convert.ToDecimal(data[3], CultureInfo.InvariantCulture);

Algorithm.CSharp/CustomDataNIFTYAlgorithm.cs

@@ -61,7 +61,7 @@ namespace QuantConnect.Algorithm.CSharp
         /// "Nifty" type below and fired into this event handler.
         /// </summary>
         /// <param name="data">One(1) Nifty Object, streamed into our algorithm synchronised in time with our other data streams</param>
-        public void OnData(Slice data)
+        public override void OnData(Slice data)
         {
             if (data.ContainsKey("USDINR"))
             {
@@ -77,7 +77,7 @@ namespace QuantConnect.Algorithm.CSharp
             {
 
                 _today.NiftyPrice = Convert.ToDouble(data["NIFTY"].Close);
-                if (_today.Date == data["NIFTY"].EndTime)
+                if (_today.Date == data["NIFTY"].Time)
                 {
                     _prices.Add(_today);
 
@@ -91,7 +91,7 @@ namespace QuantConnect.Algorithm.CSharp
                 var quantity = (int)(Portfolio.MarginRemaining * 0.9m / data["NIFTY"].Close);
                 var highestNifty = (from pair in _prices select pair.NiftyPrice).Max();
                 var lowestNifty = (from pair in _prices select pair.NiftyPrice).Min();
-                
+
                 if (Time.DayOfWeek == DayOfWeek.Wednesday) //prices.Count >= minimumCorrelationHistory &&
                 {
                     //List<double> niftyPrices = (from pair in prices select pair.NiftyPrice).ToList();
@@ -121,7 +121,7 @@ namespace QuantConnect.Algorithm.CSharp
         /// End of a trading day event handler. This method is called at the end of the algorithm day (or multiple times if trading multiple assets).
         /// </summary>
         /// <remarks>Method is called 10 minutes before closing to allow user to close out position.</remarks>
-        public override void OnEndOfDay()
+        public override void OnEndOfDay(Symbol symbol)
         {
             Plot("Nifty Closing Price", _today.NiftyPrice);
         }
@@ -181,6 +181,7 @@ namespace QuantConnect.Algorithm.CSharp
                 //2011-09-13  7792.9    7799.9     7722.65    7748.7    116534670    6107.78
                 var data = line.Split(',');
                 index.Time = DateTime.Parse(data[0], CultureInfo.InvariantCulture);
+                index.EndTime = index.Time.AddDays(1);
                 index.Open = Convert.ToDecimal(data[1], CultureInfo.InvariantCulture);
                 index.High = Convert.ToDecimal(data[2], CultureInfo.InvariantCulture);
                 index.Low = Convert.ToDecimal(data[3], CultureInfo.InvariantCulture);
@@ -247,6 +248,7 @@ namespace QuantConnect.Algorithm.CSharp
             {
                 var data = line.Split(',');
                 currency.Time = DateTime.Parse(data[0], CultureInfo.InvariantCulture);
+                currency.EndTime = currency.Time.AddDays(1);
                 currency.Close = Convert.ToDecimal(data[1], CultureInfo.InvariantCulture);
                 currency.Symbol = "USDINR";
                 currency.Value = currency.Close;

Algorithm.CSharp/CustomDataPropertiesRegressionAlgorithm.cs

@@ -1,4 +1,4 @@
-/*
+/*
  * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
  * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
  *
@@ -114,28 +114,29 @@ namespace QuantConnect.Algorithm.CSharp
             {"Total Trades", "1"},
             {"Average Win", "0%"},
             {"Average Loss", "0%"},
-            {"Compounding Annual Return", "155.262%"},
+            {"Compounding Annual Return", "157.498%"},
             {"Drawdown", "84.800%"},
             {"Expectancy", "0"},
-            {"Net Profit", "5123.242%"},
-            {"Sharpe Ratio", "2.067"},
-            {"Probabilistic Sharpe Ratio", "68.833%"},
+            {"Net Profit", "5319.081%"},
+            {"Sharpe Ratio", "2.086"},
+            {"Probabilistic Sharpe Ratio", "69.456%"},
             {"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"},
+            {"Alpha", "1.736"},
+            {"Beta", "0.142"},
+            {"Annual Standard Deviation", "0.84"},
+            {"Annual Variance", "0.706"},
+            {"Information Ratio", "1.925"},
+            {"Tracking Error", "0.846"},
+            {"Treynor Ratio", "12.334"},
             {"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"},
+            {"Sortino Ratio", "2.269"},
+            {"Return Over Maximum Drawdown", "1.858"},
             {"Portfolio Turnover", "0"},
             {"Total Insights Generated", "0"},
             {"Total Insights Closed", "0"},
@@ -150,7 +151,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "508036553"}
+            {"OrderListHash", "0d80bb47bd16b5bc6989a4c1c7aa8349"}
         };
 
         /// <summary>
@@ -242,6 +243,7 @@ namespace QuantConnect.Algorithm.CSharp
                 {
                     string[] data = line.Split(',');
                     coin.Time = DateTime.Parse(data[0], CultureInfo.InvariantCulture);
+                    coin.EndTime = coin.Time.AddDays(1);
                     coin.Open = Convert.ToDecimal(data[1], CultureInfo.InvariantCulture);
                     coin.High = Convert.ToDecimal(data[2], CultureInfo.InvariantCulture);
                     coin.Low = Convert.ToDecimal(data[3], CultureInfo.InvariantCulture);
@@ -257,4 +259,4 @@ namespace QuantConnect.Algorithm.CSharp
             }
         }
     }
-}
+}

Algorithm.CSharp/CustomDataRegressionAlgorithm.cs

@@ -84,28 +84,29 @@ namespace QuantConnect.Algorithm.CSharp
             {"Total Trades", "1"},
             {"Average Win", "0%"},
             {"Average Loss", "0%"},
-            {"Compounding Annual Return", "155.262%"},
+            {"Compounding Annual Return", "157.497%"},
             {"Drawdown", "84.800%"},
             {"Expectancy", "0"},
-            {"Net Profit", "5123.170%"},
-            {"Sharpe Ratio", "2.066"},
-            {"Probabilistic Sharpe Ratio", "68.832%"},
+            {"Net Profit", "5319.007%"},
+            {"Sharpe Ratio", "2.086"},
+            {"Probabilistic Sharpe Ratio", "69.456%"},
             {"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", "46.996"},
+            {"Alpha", "1.736"},
+            {"Beta", "0.142"},
+            {"Annual Standard Deviation", "0.84"},
+            {"Annual Variance", "0.706"},
+            {"Information Ratio", "1.925"},
+            {"Tracking Error", "0.846"},
+            {"Treynor Ratio", "12.333"},
             {"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"},
+            {"Sortino Ratio", "2.269"},
+            {"Return Over Maximum Drawdown", "1.858"},
             {"Portfolio Turnover", "0"},
             {"Total Insights Generated", "0"},
             {"Total Insights Closed", "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", "50faa37f15732bf5c24ad1eeaa335bc7"}
         };
 
         /// <summary>
@@ -212,6 +213,7 @@ namespace QuantConnect.Algorithm.CSharp
                 {
                     string[] data = line.Split(',');
                     coin.Time = DateTime.Parse(data[0], CultureInfo.InvariantCulture);
+                    coin.EndTime = coin.Time.AddDays(1);
                     coin.Open = Convert.ToDecimal(data[1], CultureInfo.InvariantCulture);
                     coin.High = Convert.ToDecimal(data[2], CultureInfo.InvariantCulture);
                     coin.Low = Convert.ToDecimal(data[3], CultureInfo.InvariantCulture);
@@ -227,4 +229,4 @@ namespace QuantConnect.Algorithm.CSharp
             }
         }
     }
-}
+}

Algorithm.CSharp/CustomDataUsingMapFileRegressionAlgorithm.cs

@@ -133,14 +133,15 @@ namespace QuantConnect.Algorithm.CSharp
             {"Loss Rate", "0%"},
             {"Win Rate", "0%"},
             {"Profit-Loss Ratio", "0"},
-            {"Alpha", "-0.898"},
-            {"Beta", "-7.027"},
+            {"Alpha", "-0.909"},
+            {"Beta", "-5.676"},
             {"Annual Standard Deviation", "0.651"},
             {"Annual Variance", "0.424"},
-            {"Information Ratio", "-1.396"},
-            {"Tracking Error", "0.726"},
-            {"Treynor Ratio", "0.142"},
+            {"Information Ratio", "-1.362"},
+            {"Tracking Error", "0.745"},
+            {"Treynor Ratio", "0.176"},
             {"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

@@ -193,30 +193,31 @@ namespace QuantConnect.Algorithm.CSharp
         public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
         {
             {"Total Trades", "62"},
-            {"Average Win", "0.10%"},
+            {"Average Win", "0.11%"},
             {"Average Loss", "-0.06%"},
-            {"Compounding Annual Return", "-7.727%"},
+            {"Compounding Annual Return", "-7.236%"},
             {"Drawdown", "2.400%"},
-            {"Expectancy", "-0.197"},
-            {"Net Profit", "-0.673%"},
-            {"Sharpe Ratio", "-1.565"},
-            {"Probabilistic Sharpe Ratio", "22.763%"},
+            {"Expectancy", "-0.187"},
+            {"Net Profit", "-0.629%"},
+            {"Sharpe Ratio", "-1.475"},
+            {"Probabilistic Sharpe Ratio", "23.597%"},
             {"Loss Rate", "70%"},
             {"Win Rate", "30%"},
-            {"Profit-Loss Ratio", "1.70"},
-            {"Alpha", "-0.14"},
-            {"Beta", "0.124"},
+            {"Profit-Loss Ratio", "1.73"},
+            {"Alpha", "-0.136"},
+            {"Beta", "0.126"},
             {"Annual Standard Deviation", "0.047"},
             {"Annual Variance", "0.002"},
-            {"Information Ratio", "-5.163"},
+            {"Information Ratio", "-5.094"},
             {"Tracking Error", "0.118"},
-            {"Treynor Ratio", "-0.591"},
-            {"Total Fees", "$62.24"},
-            {"Fitness Score", "0.147"},
+            {"Treynor Ratio", "-0.547"},
+            {"Total Fees", "$62.25"},
+            {"Estimated Strategy Capacity", "$45000000.00"},
+            {"Fitness Score", "0.16"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
-            {"Sortino Ratio", "-2.792"},
-            {"Return Over Maximum Drawdown", "-3.569"},
+            {"Sortino Ratio", "-2.59"},
+            {"Return Over Maximum Drawdown", "-3.337"},
             {"Portfolio Turnover", "2.562"},
             {"Total Insights Generated", "0"},
             {"Total Insights Closed", "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", "1118fb362bfe261323a6b496d50bddde"}
         };
     }
 }

Algorithm.CSharp/CustomUniverseSelectionRegressionAlgorithm.cs

@@ -86,28 +86,29 @@ namespace QuantConnect.Algorithm.CSharp
             {"Total Trades", "1"},
             {"Average Win", "0%"},
             {"Average Loss", "0%"},
-            {"Compounding Annual Return", "241.885%"},
-            {"Drawdown", "1.100%"},
+            {"Compounding Annual Return", "240.939%"},
+            {"Drawdown", "1.200%"},
             {"Expectancy", "0"},
-            {"Net Profit", "1.698%"},
-            {"Sharpe Ratio", "7.17"},
-            {"Probabilistic Sharpe Ratio", "68.718%"},
+            {"Net Profit", "1.694%"},
+            {"Sharpe Ratio", "6.988"},
+            {"Probabilistic Sharpe Ratio", "68.188%"},
             {"Loss Rate", "0%"},
             {"Win Rate", "0%"},
             {"Profit-Loss Ratio", "0"},
-            {"Alpha", "1.171"},
-            {"Beta", "0.147"},
-            {"Annual Standard Deviation", "0.191"},
-            {"Annual Variance", "0.037"},
-            {"Information Ratio", "0.035"},
-            {"Tracking Error", "0.251"},
-            {"Treynor Ratio", "9.323"},
-            {"Total Fees", "$3.26"},
+            {"Alpha", "1.172"},
+            {"Beta", "0.14"},
+            {"Annual Standard Deviation", "0.196"},
+            {"Annual Variance", "0.038"},
+            {"Information Ratio", "-0.118"},
+            {"Tracking Error", "0.256"},
+            {"Treynor Ratio", "9.783"},
+            {"Total Fees", "$3.45"},
+            {"Estimated Strategy Capacity", "$840000000.00"},
             {"Fitness Score", "0.201"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
             {"Sortino Ratio", "79228162514264337593543950335"},
-            {"Return Over Maximum Drawdown", "211.158"},
+            {"Return Over Maximum Drawdown", "204.701"},
             {"Portfolio Turnover", "0.201"},
             {"Total Insights Generated", "0"},
             {"Total Insights Closed", "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", "33d01821923c397f999cfb2e5b5928ad"}
         };
     }
 }

Algorithm.CSharp/CustomUniverseWithBenchmarkRegressionAlgorithm.cs

@@ -189,10 +189,11 @@ namespace QuantConnect.Algorithm.CSharp
             {"Beta", "0"},
             {"Annual Standard Deviation", "0"},
             {"Annual Variance", "0"},
-            {"Information Ratio", "-2.53"},
-            {"Tracking Error", "0.211"},
+            {"Information Ratio", "-2.564"},
+            {"Tracking Error", "0.214"},
             {"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

@@ -114,10 +114,11 @@ namespace QuantConnect.Algorithm.CSharp
             {"Beta", "0"},
             {"Annual Standard Deviation", "0"},
             {"Annual Variance", "0"},
-            {"Information Ratio", "-0.084"},
-            {"Tracking Error", "0.183"},
+            {"Information Ratio", "-0.098"},
+            {"Tracking Error", "0.179"},
             {"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

@@ -114,10 +114,11 @@ namespace QuantConnect.Algorithm.CSharp
             {"Beta", "0"},
             {"Annual Standard Deviation", "0"},
             {"Annual Variance", "0"},
-            {"Information Ratio", "-0.096"},
-            {"Tracking Error", "0.212"},
+            {"Information Ratio", "-0.111"},
+            {"Tracking Error", "0.207"},
             {"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

@@ -106,13 +106,14 @@ namespace QuantConnect.Algorithm.CSharp
             {"Win Rate", "100%"},
             {"Profit-Loss Ratio", "0"},
             {"Alpha", "0.006"},
-            {"Beta", "0.158"},
+            {"Beta", "0.157"},
             {"Annual Standard Deviation", "0.033"},
             {"Annual Variance", "0.001"},
-            {"Information Ratio", "-4.942"},
-            {"Tracking Error", "0.08"},
-            {"Treynor Ratio", "0.517"},
+            {"Information Ratio", "-4.901"},
+            {"Tracking Error", "0.081"},
+            {"Treynor Ratio", "0.519"},
             {"Total Fees", "$3.70"},
+            {"Estimated Strategy Capacity", "$270000000.00"},
             {"Fitness Score", "0.019"},
             {"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", "528208939"}
+            {"OrderListHash", "00d6dc8775da38f7f79defad06de240a"}
         };
     }
 }

Algorithm.CSharp/DelistingEventsAlgorithm.cs

@@ -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", "-335704027"}
+            {"OrderListHash", "61f4d3c109fc4b6b9eb14d2e4eec4843"}
         };
     }
 }

Algorithm.CSharp/DelistingFutureOptionRegressionAlgorithm.cs

@@ -119,10 +119,11 @@ namespace QuantConnect.Algorithm.CSharp
             {"Beta", "-0.001"},
             {"Annual Standard Deviation", "0.001"},
             {"Annual Variance", "0"},
-            {"Information Ratio", "-1.187"},
-            {"Tracking Error", "0.115"},
-            {"Treynor Ratio", "1.545"},
+            {"Information Ratio", "-1.182"},
+            {"Tracking Error", "0.117"},
+            {"Treynor Ratio", "1.617"},
             {"Total Fees", "$37.00"},
+            {"Estimated Strategy Capacity", "$400000.00"},
             {"Fitness Score", "0"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -142,7 +143,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "657651179"}
+            {"OrderListHash", "de309ab56d2fcd80ff03df2802d9feda"}
         };
     }
 }

Algorithm.CSharp/DisplacedMovingAverageRibbon.cs

@@ -167,30 +167,31 @@ namespace QuantConnect.Algorithm.CSharp
         public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
         {
             {"Total Trades", "7"},
-            {"Average Win", "19.16%"},
+            {"Average Win", "19.18%"},
             {"Average Loss", "0%"},
-            {"Compounding Annual Return", "16.727%"},
-            {"Drawdown", "12.200%"},
+            {"Compounding Annual Return", "16.740%"},
+            {"Drawdown", "12.400%"},
             {"Expectancy", "0"},
-            {"Net Profit", "153.058%"},
-            {"Sharpe Ratio", "1.239"},
-            {"Probabilistic Sharpe Ratio", "66.414%"},
+            {"Net Profit", "153.224%"},
+            {"Sharpe Ratio", "1.233"},
+            {"Probabilistic Sharpe Ratio", "65.906%"},
             {"Loss Rate", "0%"},
             {"Win Rate", "100%"},
             {"Profit-Loss Ratio", "0"},
             {"Alpha", "0.146"},
-            {"Beta", "-0.018"},
-            {"Annual Standard Deviation", "0.116"},
-            {"Annual Variance", "0.013"},
-            {"Information Ratio", "-0.053"},
+            {"Beta", "-0.016"},
+            {"Annual Standard Deviation", "0.117"},
+            {"Annual Variance", "0.014"},
+            {"Information Ratio", "-0.052"},
             {"Tracking Error", "0.204"},
-            {"Treynor Ratio", "-8.165"},
-            {"Total Fees", "$46.75"},
+            {"Treynor Ratio", "-8.847"},
+            {"Total Fees", "$49.43"},
+            {"Estimated Strategy Capacity", "$630000000.00"},
             {"Fitness Score", "0.002"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
-            {"Sortino Ratio", "1.607"},
-            {"Return Over Maximum Drawdown", "1.366"},
+            {"Sortino Ratio", "1.609"},
+            {"Return Over Maximum Drawdown", "1.351"},
             {"Portfolio Turnover", "0.003"},
             {"Total Insights Generated", "0"},
             {"Total Insights Closed", "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", "44481c3d7eeb5acd5e3bccfec501a132"}
         };
     }
 }

Algorithm.CSharp/DropboxBaseDataUniverseSelectionAlgorithm.cs

@@ -187,29 +187,30 @@ namespace QuantConnect.Algorithm.CSharp
             {"Total Trades", "6441"},
             {"Average Win", "0.07%"},
             {"Average Loss", "-0.07%"},
-            {"Compounding Annual Return", "13.331%"},
+            {"Compounding Annual Return", "13.366%"},
             {"Drawdown", "10.700%"},
-            {"Expectancy", "0.061"},
-            {"Net Profit", "13.331%"},
-            {"Sharpe Ratio", "0.963"},
-            {"Probabilistic Sharpe Ratio", "46.232%"},
+            {"Expectancy", "0.062"},
+            {"Net Profit", "13.366%"},
+            {"Sharpe Ratio", "0.966"},
+            {"Probabilistic Sharpe Ratio", "46.330%"},
             {"Loss Rate", "46%"},
             {"Win Rate", "54%"},
             {"Profit-Loss Ratio", "0.97"},
-            {"Alpha", "0.124"},
-            {"Beta", "-0.066"},
+            {"Alpha", "0.125"},
+            {"Beta", "-0.067"},
             {"Annual Standard Deviation", "0.121"},
             {"Annual Variance", "0.015"},
-            {"Information Ratio", "0.006"},
+            {"Information Ratio", "-0.021"},
             {"Tracking Error", "0.171"},
-            {"Treynor Ratio", "-1.761"},
-            {"Total Fees", "$8669.41"},
-            {"Fitness Score", "0.675"},
+            {"Treynor Ratio", "-1.747"},
+            {"Total Fees", "$8669.28"},
+            {"Estimated Strategy Capacity", "$320000.00"},
+            {"Fitness Score", "0.676"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
-            {"Sortino Ratio", "1.127"},
-            {"Return Over Maximum Drawdown", "1.246"},
-            {"Portfolio Turnover", "1.64"},
+            {"Sortino Ratio", "1.13"},
+            {"Return Over Maximum Drawdown", "1.251"},
+            {"Portfolio Turnover", "1.639"},
             {"Total Insights Generated", "0"},
             {"Total Insights Closed", "0"},
             {"Total Insights Analysis Completed", "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", "a5b4f8473c39a7e9d62659fa9f6a4e2f"}
         };
     }
 }

Algorithm.CSharp/DropboxUniverseSelectionAlgorithm.cs

@@ -160,29 +160,30 @@ namespace QuantConnect.Algorithm.CSharp
             {"Total Trades", "5059"},
             {"Average Win", "0.08%"},
             {"Average Loss", "-0.08%"},
-            {"Compounding Annual Return", "14.950%"},
+            {"Compounding Annual Return", "14.983%"},
             {"Drawdown", "10.600%"},
             {"Expectancy", "0.075"},
-            {"Net Profit", "14.950%"},
-            {"Sharpe Ratio", "1.072"},
-            {"Probabilistic Sharpe Ratio", "50.327%"},
-            {"Loss Rate", "45%"},
-            {"Win Rate", "55%"},
+            {"Net Profit", "14.983%"},
+            {"Sharpe Ratio", "1.075"},
+            {"Probabilistic Sharpe Ratio", "50.443%"},
+            {"Loss Rate", "46%"},
+            {"Win Rate", "54%"},
             {"Profit-Loss Ratio", "0.97"},
-            {"Alpha", "0.137"},
-            {"Beta", "-0.066"},
+            {"Alpha", "0.138"},
+            {"Beta", "-0.067"},
             {"Annual Standard Deviation", "0.121"},
             {"Annual Variance", "0.015"},
-            {"Information Ratio", "0.083"},
+            {"Information Ratio", "0.056"},
             {"Tracking Error", "0.171"},
-            {"Treynor Ratio", "-1.971"},
-            {"Total Fees", "$6806.67"},
+            {"Treynor Ratio", "-1.948"},
+            {"Total Fees", "$6806.62"},
+            {"Estimated Strategy Capacity", "$320000.00"},
             {"Fitness Score", "0.694"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
-            {"Sortino Ratio", "1.265"},
-            {"Return Over Maximum Drawdown", "1.409"},
-            {"Portfolio Turnover", "1.296"},
+            {"Sortino Ratio", "1.268"},
+            {"Return Over Maximum Drawdown", "1.411"},
+            {"Portfolio Turnover", "1.295"},
             {"Total Insights Generated", "0"},
             {"Total Insights Closed", "0"},
             {"Total Insights Analysis Completed", "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", "5e8e2426162c22b45935db2175c4bbfd"}
         };
     }
 }

Algorithm.CSharp/DuplicateSecurityWithBenchmarkRegressionAlgorithm.cs

@@ -106,10 +106,11 @@ namespace QuantConnect.Algorithm.CSharp
             {"Beta", "0"},
             {"Annual Standard Deviation", "0"},
             {"Annual Variance", "0"},
-            {"Information Ratio", "-7.068"},
-            {"Tracking Error", "0.193"},
+            {"Information Ratio", "-7.163"},
+            {"Tracking Error", "0.195"},
             {"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

@@ -114,29 +114,30 @@ namespace QuantConnect.Algorithm.CSharp
             {"Total Trades", "5"},
             {"Average Win", "0%"},
             {"Average Loss", "-0.02%"},
-            {"Compounding Annual Return", "-72.415%"},
-            {"Drawdown", "2.800%"},
+            {"Compounding Annual Return", "-72.266%"},
+            {"Drawdown", "2.900%"},
             {"Expectancy", "-1"},
-            {"Net Profit", "-1.749%"},
-            {"Sharpe Ratio", "-3.059"},
-            {"Probabilistic Sharpe Ratio", "21.811%"},
+            {"Net Profit", "-1.742%"},
+            {"Sharpe Ratio", "-2.983"},
+            {"Probabilistic Sharpe Ratio", "22.301%"},
             {"Loss Rate", "100%"},
             {"Win Rate", "0%"},
             {"Profit-Loss Ratio", "0"},
-            {"Alpha", "-0.385"},
-            {"Beta", "-0.146"},
-            {"Annual Standard Deviation", "0.191"},
-            {"Annual Variance", "0.036"},
-            {"Information Ratio", "-6.701"},
-            {"Tracking Error", "0.29"},
-            {"Treynor Ratio", "4.005"},
-            {"Total Fees", "$18.28"},
-            {"Fitness Score", "0.052"},
+            {"Alpha", "-0.388"},
+            {"Beta", "-0.138"},
+            {"Annual Standard Deviation", "0.195"},
+            {"Annual Variance", "0.038"},
+            {"Information Ratio", "-6.727"},
+            {"Tracking Error", "0.294"},
+            {"Treynor Ratio", "4.204"},
+            {"Total Fees", "$19.23"},
+            {"Estimated Strategy Capacity", "$470000000.00"},
+            {"Fitness Score", "0.054"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
-            {"Sortino Ratio", "-4.187"},
-            {"Return Over Maximum Drawdown", "-25.473"},
-            {"Portfolio Turnover", "0.998"},
+            {"Sortino Ratio", "-4.06"},
+            {"Return Over Maximum Drawdown", "-25.225"},
+            {"Portfolio Turnover", "0.999"},
             {"Total Insights Generated", "1"},
             {"Total Insights Closed", "0"},
             {"Total Insights Analysis Completed", "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", "39c512346541c1d40c04a514d605b723"}
         };
     }
 }

Algorithm.CSharp/EmitInsightsAlgorithm.cs

@@ -108,45 +108,46 @@ namespace QuantConnect.Algorithm.CSharp
         public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
         {
             {"Total Trades", "4"},
-            {"Average Win", "0.96%"},
-            {"Average Loss", "-0.95%"},
-            {"Compounding Annual Return", "-44.357%"},
-            {"Drawdown", "1.100%"},
-            {"Expectancy", "0.005"},
-            {"Net Profit", "-0.800%"},
-            {"Sharpe Ratio", "-2.218"},
-            {"Probabilistic Sharpe Ratio", "32.508%"},
+            {"Average Win", "0.94%"},
+            {"Average Loss", "-0.98%"},
+            {"Compounding Annual Return", "-47.257%"},
+            {"Drawdown", "1.200%"},
+            {"Expectancy", "-0.021"},
+            {"Net Profit", "-0.873%"},
+            {"Sharpe Ratio", "-2.308"},
+            {"Probabilistic Sharpe Ratio", "31.792%"},
             {"Loss Rate", "50%"},
             {"Win Rate", "50%"},
-            {"Profit-Loss Ratio", "1.01"},
-            {"Alpha", "-0.64"},
-            {"Beta", "0.23"},
-            {"Annual Standard Deviation", "0.147"},
-            {"Annual Variance", "0.022"},
-            {"Information Ratio", "-8.259"},
-            {"Tracking Error", "0.204"},
-            {"Treynor Ratio", "-1.424"},
-            {"Total Fees", "$16.26"},
-            {"Fitness Score", "0.003"},
-            {"Kelly Criterion Estimate", "12.539"},
-            {"Kelly Criterion Probability Value", "0.367"},
-            {"Sortino Ratio", "-22.336"},
-            {"Return Over Maximum Drawdown", "-38.722"},
-            {"Portfolio Turnover", "1.003"},
+            {"Profit-Loss Ratio", "0.96"},
+            {"Alpha", "-0.675"},
+            {"Beta", "0.232"},
+            {"Annual Standard Deviation", "0.152"},
+            {"Annual Variance", "0.023"},
+            {"Information Ratio", "-8.38"},
+            {"Tracking Error", "0.209"},
+            {"Treynor Ratio", "-1.514"},
+            {"Total Fees", "$17.19"},
+            {"Estimated Strategy Capacity", "$560000000.00"},
+            {"Fitness Score", "0.002"},
+            {"Kelly Criterion Estimate", "12.812"},
+            {"Kelly Criterion Probability Value", "0.363"},
+            {"Sortino Ratio", "-29.284"},
+            {"Return Over Maximum Drawdown", "-40.149"},
+            {"Portfolio Turnover", "1.004"},
             {"Total Insights Generated", "7"},
             {"Total Insights Closed", "4"},
             {"Total Insights Analysis Completed", "4"},
             {"Long Insight Count", "5"},
             {"Short Insight Count", "2"},
             {"Long/Short Ratio", "250.0%"},
-            {"Estimated Monthly Alpha Value", "$19016880.2887"},
-            {"Total Accumulated Estimated Alpha Value", "$3275129.3830"},
-            {"Mean Population Estimated Insight Value", "$818782.3458"},
+            {"Estimated Monthly Alpha Value", "$18576432.8867"},
+            {"Total Accumulated Estimated Alpha Value", "$3199274.5527"},
+            {"Mean Population Estimated Insight Value", "$799818.6382"},
             {"Mean Population Direction", "50%"},
             {"Mean Population Magnitude", "50%"},
             {"Rolling Averaged Population Direction", "50%"},
             {"Rolling Averaged Population Magnitude", "50%"},
-            {"OrderListHash", "-218498072"}
+            {"OrderListHash", "657b9cb12ee7097df61fff5661192ccf"}
         };
     }
 }

Algorithm.CSharp/EqualWeightingPortfolioConstructionModelFutureRegressionAlgorithm.cs

@@ -140,14 +140,15 @@ namespace QuantConnect.Algorithm.CSharp
             {"Loss Rate", "100%"},
             {"Win Rate", "0%"},
             {"Profit-Loss Ratio", "0"},
-            {"Alpha", "-13.685"},
-            {"Beta", "6.59"},
+            {"Alpha", "-13.915"},
+            {"Beta", "6.519"},
             {"Annual Standard Deviation", "1.632"},
             {"Annual Variance", "2.665"},
-            {"Information Ratio", "-2.023"},
-            {"Tracking Error", "1.441"},
-            {"Treynor Ratio", "-0.15"},
+            {"Information Ratio", "-2.066"},
+            {"Tracking Error", "1.438"},
+            {"Treynor Ratio", "-0.152"},
             {"Total Fees", "$33.30"},
+            {"Estimated Strategy Capacity", "$17000000.00"},
             {"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

@@ -99,10 +99,11 @@ namespace QuantConnect.Algorithm.CSharp
             {"Beta", "0"},
             {"Annual Standard Deviation", "0"},
             {"Annual Variance", "0"},
-            {"Information Ratio", "-8.769"},
-            {"Tracking Error", "0.22"},
+            {"Information Ratio", "-8.91"},
+            {"Tracking Error", "0.223"},
             {"Treynor Ratio", "0"},
             {"Total Fees", "$6.41"},
+            {"Estimated Strategy Capacity", "$0"},
             {"Fitness Score", "0.249"},
             {"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", "-1760998125"}
+            {"OrderListHash", "af92d7f4e0810bc4a95d5ccb5542b438"}
         };
     }
 }

Algorithm.CSharp/EquityTradeAndQuotesRegressionAlgorithm.cs

@@ -176,14 +176,15 @@ namespace QuantConnect.Algorithm.CSharp
             {"Loss Rate", "65%"},
             {"Win Rate", "35%"},
             {"Profit-Loss Ratio", "1.17"},
-            {"Alpha", "-1.423"},
-            {"Beta", "0.537"},
+            {"Alpha", "-1.43"},
+            {"Beta", "0.528"},
             {"Annual Standard Deviation", "0.134"},
             {"Annual Variance", "0.018"},
-            {"Information Ratio", "-16.652"},
-            {"Tracking Error", "0.123"},
-            {"Treynor Ratio", "-1.288"},
+            {"Information Ratio", "-16.594"},
+            {"Tracking Error", "0.126"},
+            {"Treynor Ratio", "-1.311"},
             {"Total Fees", "$669.76"},
+            {"Estimated Strategy Capacity", "$210000.00"},
             {"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

@@ -127,32 +127,33 @@ namespace QuantConnect.Algorithm.CSharp
         /// </summary>
         public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
         {
-            {"Total Trades", "20"},
+            {"Total Trades", "18"},
             {"Average Win", "0%"},
             {"Average Loss", "0.00%"},
-            {"Compounding Annual Return", "-74.182%"},
-            {"Drawdown", "2.200%"},
+            {"Compounding Annual Return", "-74.117%"},
+            {"Drawdown", "2.500%"},
             {"Expectancy", "-1"},
-            {"Net Profit", "-1.046%"},
+            {"Net Profit", "-1.044%"},
             {"Sharpe Ratio", "-8.269"},
             {"Probabilistic Sharpe Ratio", "0%"},
             {"Loss Rate", "100%"},
             {"Win Rate", "0%"},
             {"Profit-Loss Ratio", "0"},
-            {"Alpha", "-0.19"},
-            {"Beta", "0.579"},
+            {"Alpha", "-0.187"},
+            {"Beta", "0.584"},
             {"Annual Standard Deviation", "0.065"},
             {"Annual Variance", "0.004"},
-            {"Information Ratio", "1.326"},
-            {"Tracking Error", "0.049"},
-            {"Treynor Ratio", "-0.934"},
-            {"Total Fees", "$22.26"},
+            {"Information Ratio", "1.354"},
+            {"Tracking Error", "0.048"},
+            {"Treynor Ratio", "-0.926"},
+            {"Total Fees", "$20.45"},
+            {"Estimated Strategy Capacity", "$300000.00"},
             {"Fitness Score", "0.002"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
-            {"Sortino Ratio", "-11.855"},
-            {"Return Over Maximum Drawdown", "-70.945"},
-            {"Portfolio Turnover", "0.342"},
+            {"Sortino Ratio", "-11.829"},
+            {"Return Over Maximum Drawdown", "-71.014"},
+            {"Portfolio Turnover", "0.341"},
             {"Total Insights Generated", "0"},
             {"Total Insights Closed", "0"},
             {"Total Insights Analysis Completed", "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", "fbefbed1d94294a14bc563a71b336056"}
         };
     }
 }

Algorithm.CSharp/ExtendedMarketTradingRegressionAlgorithm.cs

@@ -105,29 +105,30 @@ namespace QuantConnect.Algorithm.CSharp
             {"Total Trades", "4"},
             {"Average Win", "0%"},
             {"Average Loss", "0%"},
-            {"Compounding Annual Return", "8.332%"},
+            {"Compounding Annual Return", "7.848%"},
             {"Drawdown", "0.100%"},
             {"Expectancy", "0"},
-            {"Net Profit", "0.106%"},
-            {"Sharpe Ratio", "7.474"},
-            {"Probabilistic Sharpe Ratio", "85.145%"},
+            {"Net Profit", "0.100%"},
+            {"Sharpe Ratio", "7.449"},
+            {"Probabilistic Sharpe Ratio", "85.066%"},
             {"Loss Rate", "0%"},
             {"Win Rate", "0%"},
             {"Profit-Loss Ratio", "0"},
-            {"Alpha", "0.007"},
-            {"Beta", "0.036"},
+            {"Alpha", "0.006"},
+            {"Beta", "0.033"},
             {"Annual Standard Deviation", "0.007"},
             {"Annual Variance", "0"},
-            {"Information Ratio", "-7.03"},
-            {"Tracking Error", "0.186"},
-            {"Treynor Ratio", "1.557"},
+            {"Information Ratio", "-7.131"},
+            {"Tracking Error", "0.189"},
+            {"Treynor Ratio", "1.576"},
             {"Total Fees", "$4.00"},
-            {"Fitness Score", "0.012"},
+            {"Estimated Strategy Capacity", "$5200000.00"},
+            {"Fitness Score", "0.011"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
-            {"Sortino Ratio", "38.663"},
-            {"Return Over Maximum Drawdown", "238.773"},
-            {"Portfolio Turnover", "0.012"},
+            {"Sortino Ratio", "38.493"},
+            {"Return Over Maximum Drawdown", "236.57"},
+            {"Portfolio Turnover", "0.011"},
             {"Total Insights Generated", "0"},
             {"Total Insights Closed", "0"},
             {"Total Insights Analysis Completed", "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", "65e705bf0e78139c376903d4ff241afc"}
         };
     }
 }