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>

.dockerignore

@@ -1,4 +1,6 @@
-*/packages/*
-*/.git/*
-*/.vs/*
-*/.nuget/*
+packages/*
+.git/*
+.github/*
+.vs/*
+.nuget/*
+Tests/*

.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

@@ -0,0 +1,30 @@
+name: Build & Test Lean
+
+on:
+  push:
+    branches: ['*']
+    tags: ['*']
+  pull_request:
+    branches: [master]
+
+jobs:
+  build:
+    runs-on: ubuntu-20.04
+    container:
+        image: quantconnect/lean:foundation
+    steps:
+      - uses: actions/checkout@v2
+
+      - name: Build
+        run: dotnet build /p:Configuration=Release /v:quiet /p:WarningLevel=1 QuantConnect.Lean.sln
+
+      - name: Run Tests
+        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 -t -g -p
+        env:
+          PYPI_API_TOKEN: ${{ secrets.PYPI_API_TOKEN }}

.gitignore

@@ -34,8 +34,9 @@
 
 # QC Cloud Setup Bash Files
 *.sh
-# Include docker build scripts for Mac/Linux
+# Include docker launch scripts for Mac/Linux
 !run_docker.sh
+!research/run_docker_notebook.sh
 
 # QC Config Files:
 # config.json
@@ -143,6 +144,7 @@ $tf/
 # ReSharper is a .NET coding add-in
 _ReSharper*/
 *.[Rr]e[Ss]harper
+*.DotSettings
 *.DotSettings.user
 
 # JustCode is a .NET coding addin-in
@@ -194,6 +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.
@@ -202,6 +205,7 @@ publish/
 #!**/packages/repositories.config
 # ignore sln level nuget
 .nuget/
+!.nuget/NuGet.config
 
 # Windows Azure Build Output
 csx/
@@ -267,3 +271,9 @@ Launcher/Plugins/*
 /ApiPython/quantconnect.egg-info/*
 
 QuantConnect.Lean.sln.DotSettings*
+
+#User notebook files
+Research/Notebooks
+
+#Docker result files
+Results/

.idea/readme.md

@@ -0,0 +1,144 @@
+<h1>Local Development & Docker Integration with Pycharm</h1>
+
+This document contains information regarding ways to use Lean’s Docker image in conjunction with local development in Pycharm.
+
+
+<br />
+
+<h1>Getting Setup</h1>
+
+
+Before anything we need to ensure a few things have been done:
+
+
+1. Get [Pycharm Professional](https://www.jetbrains.com/pycharm/)**
+
+2. Get [Docker](https://docs.docker.com/get-docker/):
+    *   Follow the instructions for your Operating System
+    *   New to Docker? Try docker getting-started
+
+
+3. Pull Lean’s latest image from a terminal
+    *   _docker pull quantconnect/lean_
+
+4. Get Lean into Pycharm
+    *   Download the repo or clone it using: _git clone[ https://github.com/QuantConnect/Lean](https://github.com/QuantConnect/Lean)_
+    *   Open the folder using Pycharm
+
+
+_**PyCharm’s remote debugger requires PyCharm Professional._
+
+<br />
+
+<h1>Develop Algorithms Locally, Run in Container</h1>
+
+
+We have set up a relatively easy way to develop algorithms in your local IDE and push them into the container to be run and debugged. 
+
+Before we can use this method with Windows or Mac OS we need to share the Lean directory with Docker.
+
+<br />
+
+<h2>Activate File Sharing for Docker:</h2>
+
+*   Windows: 
+    *   [Guide to sharing](https://docs.docker.com/docker-for-windows/#file-sharing)
+    *   Share the LEAN root directory with docker
+  
+*   Mac:
+    *   [Guide to sharing](https://docs.docker.com/docker-for-mac/#file-sharing)
+    *   Share the LEAN root directory with docker
+
+*   Linux:
+    *    (No setup required)
+
+<br />
+
+<h2>Lean Configuration</h2>
+
+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:
+
+<h3>Python:</h3>
+
+    "algorithm-type-name": "**AlgorithmName**",
+
+    "algorithm-language": "Python",
+
+    "algorithm-location": "../../../Algorithm.Python/**AlgorithmName**.py",
+
+<h4>Note About Python Algorithm Location</h4>
+
+
+Our specific configuration binds the Algorithm.Python directory to the container by default so any algorithm you would like to run should be in that directory. Please ensure your algorithm location looks just the same as the example above. If you want to use a different location refer to the section bellow on setting that argument for the container and make sure your config.json also reflects this.
+
+
+<br />
+
+<h2>Running Lean in the Container</h2>
+
+This section will cover how to actually launch Lean in the container with your desired configuration.
+
+From a terminal; Pycharm has a built in terminal on the bottom taskbar labeled **Terminal**; launch the run_docker.bat/.sh script; there are a few choices on how to launch this:
+ 1. Launch with no parameters and answer the questions regarding configuration (Press enter for defaults)
+   
+        *   Enter docker image [default: quantconnect/lean:latest]:
+        *   Enter absolute path to Lean config file [default: _~currentDir_\Launcher\config.json]:
+        *   Enter absolute path to Data folder [default: ~_currentDir_\Data\]:
+        *   Enter absolute path to store results [default: ~_currentDir_\]:
+        *   Would you like to debug C#? (Requires mono debugger attachment) [default: N]:
+
+ 2. Using the **run_docker.cfg** to store args for repeated use; any blank entries will resort to default values! example: **_./run_docker.bat run_docker.cfg_**
+  
+        IMAGE=quantconnect/lean:latest
+        CONFIG_FILE=
+        DATA_DIR=
+        RESULTS_DIR=
+        DEBUGGING=
+        PYTHON_DIR=
+
+ 3. Inline arguments; anything you don't enter will use the default args! example: **_./run_docker.bat DEBUGGING=y_** 
+      *    Accepted args for inline include all listed in the file in #2; must follow the **key=value** format
+
+<br />
+
+<h1>Debugging Python</h1>
+
+Debugging your Python algorithms requires an extra step within your configuration and inside of PyCharm. Thankfully we were able to configure the PyCharm launch configurations to take care of most of the work for you! 
+
+<br />
+
+<h2>Modifying the Configuration</h2>
+
+First in order to debug a Python algorithm in Pycharm we must make the following change to our configuration (Launcher\config.json) under the comment debugging configuration:
+
+    "debugging": true,
+    "debugging-method": "PyCharm",
+
+
+In setting this we are telling Lean to reach out and create a debugger connection using PyCharm’s PyDevd debugger server. Once this is set Lean will **always** attempt to connect to a debugger server on launch. **If you are no longer debugging set “debugging” to false.**
+
+<br />
+
+<h2>Using PyCharm Launch Options</h2>
+
+
+Now that Lean is configured for the debugger we can make use of the programmed launch options to connect. 
+
+
+
+**<h3>Container (Recommended)</h3>**
+
+
+To debug inside of the container we must first start the debugger server in Pycharm, to do this use the drop down configuration “Debug in Container” and launch the debugger. Be sure to set some breakpoints in your algorithms!
+
+Then we will need to launch the container, follow the steps described in the section “[Running Lean in the Container](#Running-Lean-in-the-Container)”. After launching the container the debugging configuration will take effect and it will connect to the debug server where you can begin debugging your algorithm.
+
+
+**<h3>Local</h3>**
+
+
+To debug locally we must run the program locally. First, just as the container setup, start the PyCharm debugger server by running the “Debug Local” configuration.
+
+Then start the program locally by whatever means you typically use, such as Mono, directly running the program at **QuantConnect.Lean.Launcher.exe**, etc. Once the program is running it will make the connection to your PyCharm debugger server where you can begin debugging your algorithm.

.idea/workspace.xml

@@ -0,0 +1,37 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="RunManager" selected="Python Debug Server.Debug in Container">
+    <configuration name="Debug Local" type="PyRemoteDebugConfigurationType" factoryName="Python Remote Debug">
+      <module name="LEAN" />
+      <option name="PORT" value="6000" />
+      <option name="HOST" value="localhost" />
+      <PathMappingSettings>
+        <option name="pathMappings">
+          <list />
+        </option>
+      </PathMappingSettings>
+      <option name="REDIRECT_OUTPUT" value="true" />
+      <option name="SUSPEND_AFTER_CONNECT" value="true" />
+      <method v="2" />
+    </configuration>
+    <configuration name="Debug in Container" type="PyRemoteDebugConfigurationType" factoryName="Python Remote Debug">
+      <module name="LEAN" />
+      <option name="PORT" value="6000" />
+      <option name="HOST" value="localhost" />
+      <PathMappingSettings>
+        <option name="pathMappings">
+          <list>
+            <mapping local-root="$PROJECT_DIR$" remote-root="/Lean" />
+          </list>
+        </option>
+      </PathMappingSettings>
+      <option name="REDIRECT_OUTPUT" value="true" />
+      <option name="SUSPEND_AFTER_CONNECT" value="true" />
+      <method v="2" />
+    </configuration>
+    <list>
+      <item itemvalue="Python Debug Server.Debug Local" />
+      <item itemvalue="Python Debug Server.Debug in Container" />
+    </list>
+  </component>
+</project>

.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

@@ -1,11 +1,12 @@
-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"
-  - wget https://cdn.quantconnect.com/miniconda/Miniconda3-4.5.12-Linux-x86_64.sh
+  - 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
   - sudo ln -s $HOME/miniconda3/lib/libpython3.6m.so /usr/lib/libpython3.6m.so
@@ -16,9 +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
-  - nuget install NUnit.Runners -Version 3.11.1 -OutputDirectory testrunner
 script:
-  - msbuild /p:Configuration=Release /p:VbcToolExe=vbnc.exe QuantConnect.Lean.sln
-  - mono ./testrunner/NUnit.ConsoleRunner.3.11.1/tools/nunit3-console.exe ./Tests/bin/Release/QuantConnect.Tests.dll --where "cat != TravisExclude" --labels=Off
+  - dotnet 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\(name=\"log-handler\", value=\"ConsoleErrorLogHandler\"\)

.vs/readme.md

@@ -0,0 +1,136 @@
+<h1>Local Development & Docker Integration with Visual Studio</h1>
+
+
+This document contains information regarding ways to use Visual Studio to work with the Lean's Docker image.
+
+
+<br />
+
+<h1>Getting Setup</h1>
+
+
+Before anything we need to ensure a few things have been done:
+
+
+1. Get [Visual Studio](https://code.visualstudio.com/download)
+    *   Get the Extension [VSMonoDebugger](https://marketplace.visualstudio.com/items?itemName=GordianDotNet.VSMonoDebugger0d62) for C# Debugging
+
+2. Get [Docker](https://docs.docker.com/get-docker/):
+    *   Follow the instructions for your Operating System
+    *   New to Docker? Try docker getting-started
+
+
+3. Pull Lean’s latest image from a terminal
+    *   _docker pull quantconnect/lean_
+
+4. Get Lean into Visual Studio
+    *   Download the repo or clone it using: _git clone[ https://github.com/QuantConnect/Lean](https://github.com/QuantConnect/Lean)_
+    *   Open the solution **QuantConnect.Lean.sln** using Visual Studio
+
+
+<br />
+
+<h1>Develop Algorithms Locally, Run in Container</h1>
+
+
+We have set up a relatively easy way to develop algorithms in your local IDE and push them into the container to be run and debugged. 
+
+Before we can use this method with Windows or Mac OS we need to share the Lean directory with Docker.
+
+<br />
+
+<h2>Activate File Sharing for Docker:</h2>
+
+*   Windows: 
+    *   [Guide to sharing](https://docs.docker.com/docker-for-windows/#file-sharing)
+    *   Share the LEAN root directory with docker
+  
+*   Mac:
+    *   [Guide to sharing](https://docs.docker.com/docker-for-mac/#file-sharing)
+    *   Share the LEAN root directory with docker
+
+*   Linux:
+    *    (No setup required)
+
+<br />
+
+<h2>Lean Configuration</h2>
+
+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:
+
+<h3>Python:</h3>
+
+    "algorithm-type-name": "**AlgorithmName**",
+
+    "algorithm-language": "Python",
+
+    "algorithm-location": "../../../Algorithm.Python/**AlgorithmName**.py",
+
+<h3>C#:</h3>
+
+    "algorithm-type-name": "**AlgorithmName**",
+
+    "algorithm-language": "CSharp",
+
+    "algorithm-location": "QuantConnect.Algorithm.CSharp.dll",
+
+<br />
+
+<h2>Important Note About C#</h2>
+
+In order to use a custom C# algorithm, the C# file must be compiled before running in the docker, as it is compiled into the file **"QuantConnect.Algorithm.CSharp.dll"**. Any new C# files will need to be added to the csproj compile list before it will compile, check **Algorithm.CSharp/QuantConnect.Algorithm.CSharp.csproj** for all algorithms that are compiled. Once there is an entry for your algorithm the project can be compiled by using  **Build > Build Solution**.
+
+If you would like to debug this file in the docker container one small change to the solutions target build is required.
+1. Right click on the solution **QuantConnect.Lean** in the _Solution Explorer_
+2. Select **Properties**
+3. For project entry **QuantConnect.Algorithm.CSharp** change the configuration to **DebugDocker**
+4. Select **Apply** and close out of the window.
+5. Build the project at least once before running the docker.
+
+<br />
+
+<h2>Running Lean in the Container</h2>
+
+This section will cover how to actually launch Lean in the container with your desired configuration.
+
+From a terminal launch the run_docker.bat/.sh script; there are a few choices on how to launch this:
+ 1. Launch with no parameters and answer the questions regarding configuration (Press enter for defaults)
+   
+        *   Enter docker image [default: quantconnect/lean:latest]:
+        *   Enter absolute path to Lean config file [default: _~currentDir_\Launcher\config.json]:
+        *   Enter absolute path to Data folder [default: ~_currentDir_\Data\]:
+        *   Enter absolute path to store results [default: ~_currentDir_\]:
+        *   Would you like to debug C#? (Requires mono debugger attachment) [default: N]:
+
+ 2. Using the **run_docker.cfg** to store args for repeated use; any blank entries will resort to default values! example: **_./run_docker.bat run_docker.cfg_**
+  
+        IMAGE=quantconnect/lean:latest
+        CONFIG_FILE=
+        DATA_DIR=
+        RESULTS_DIR=
+        DEBUGGING=
+        PYTHON_DIR=
+
+ 3. Inline arguments; anything you don't enter will use the default args! example: **_./run_docker.bat DEBUGGING=y_** 
+      *    Accepted args for inline include all listed in the file in #2
+
+<br />
+
+<h1>Connecting to Mono Debugger</h1>
+
+If you launch the script with debugging set to **yes** (y), then you will need to connect to the debugging server with the mono extension that you installed in the setup stage.
+
+To setup the extension do the following:
+   * Go to **Extensions > Mono > Settings...**
+   * Enter the following for the settings:
+     * Remote Host IP: 127.0.0.1
+     * Remote Host Port: 55555
+     * Mono Debug Port: 55555
+   * Click **Save** and then close the extension settings
+  
+Now that the extension is setup use it to connect to the Docker container by using:
+*  **Extensions > Mono > Attach to mono debugger**
+
+The program should then launch and trigger any breakpoints you have set in your C# Algorithm.

.vscode/launch.json

@@ -0,0 +1,82 @@
+{
+    /*
+        VS Code Launch configurations for the LEAN engine
+
+        Launch w/ Mono (Local):
+        Builds the project with MSBuild and then launches the program using mono locally;
+        supports debugging. In order to use this you need msbuild and mono on your system path.
+        As well as the Mono Debug extension from the marketplace.
+        
+        Debug in Container:
+        Launches our run_docker script to start the container and attaches to the debugger.
+        Requires that you have built the project at least once as it will transfer the compiled
+        csharp files.
+        Requires Mono Debug extension from the marketplace.
+
+        Attach to Python (Container):
+        Will attempt to attach to LEAN in the container using PTVSD. Requires that the container is
+        actively running and config is set: "debugging": true, "debugging-method": "PTVSD",
+        Requires Python extension from the marketplace.
+
+        Attach to Python (Local):
+        Will attempt to attach to LEAN running locally using PTVSD. Requires that the process is
+        actively running and config is set: "debugging": true, "debugging-method": "PTVSD",
+        Requires Python extension from the marketplace.
+
+    */
+
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Launch w/ Mono (Local)",
+            "type": "mono",
+            "request": "launch",
+            "preLaunchTask": "build",
+            "cwd": "${workspaceFolder}/Launcher/bin/Debug/",
+            "program": "${workspaceFolder}/Launcher/bin/Debug/QuantConnect.Lean.Launcher.exe",
+            "args": [
+                "--data-folder",
+                "${workspaceFolder}/Data",
+                "--config",
+                "${workspaceFolder}/Launcher/config.json"],
+            "console": "externalTerminal"
+        },
+        {
+            "name": "Debug in Container",
+            "type": "mono",
+            "preLaunchTask": "run-docker",
+            "postDebugTask": "close-docker",
+            "request": "attach",
+            "address": "localhost",
+            "port": 55555
+        },
+        {
+            "name": "Attach to Mono",
+            "type": "mono",
+            "request": "attach",
+            "address": "localhost",
+            "postDebugTask": "close-docker",
+            "port": 55555
+        },
+        {
+            "name": "Attach to Python (Container)",
+            "type": "python",
+            "request": "attach",
+            "port": 5678,
+            "pathMappings":[{ 
+                "localRoot": "${workspaceFolder}", 
+                "remoteRoot": "/Lean/"
+            }]
+        },
+        {
+            "name": "Attach to Python (Local)",
+            "type": "python",
+            "request": "attach",
+            "port": 5678,
+            "pathMappings":[{ 
+                "localRoot": "${workspaceFolder}", 
+                "remoteRoot": "${workspaceFolder}"
+            }]
+        }
+    ]
+}

.vscode/readme.md

@@ -0,0 +1,206 @@
+<h1>Local Development & Docker Integration with Visual Studio Code</h1>
+
+
+This document contains information regarding ways to use Visual Studio Code to work with the Lean engine, this includes using Lean’s Docker image in conjunction with local development as well as running Lean locally.
+
+
+<br />
+
+<h1>Getting Setup</h1>
+
+
+Before anything we need to ensure a few things have been done:
+
+
+1. Get [Visual Studio Code](https://code.visualstudio.com/download)
+    *   Get the Extension [Mono Debug **15.8**](https://marketplace.visualstudio.com/items?itemName=ms-vscode.mono-debug) for C# Debugging
+    *   Get the Extension [Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python) for Python Debugging
+
+2. Get [Docker](https://docs.docker.com/get-docker/):
+    *   Follow the instructions for your Operating System
+    *   New to Docker? Try docker getting-started
+
+3. Install a compiler for the project **(Only needed for C# Debugging or Running Locally)**
+   *   On Linux or Mac: 
+       *   Install [mono-complete](https://www.mono-project.com/docs/getting-started/install/linux/)
+       *   Test msbuild with command: _msbuild -version_
+   *   On Windows: 
+       *   Visual Studio comes packed with msbuild or download without VS [here](https://visualstudio.microsoft.com/downloads/?q=build+tools)
+       *   Put msbuild on your system path and test with command: _msbuild -version_
+
+4. Pull Lean’s latest image from a terminal
+    *   _docker pull quantconnect/lean_
+
+5. Get Lean into VS Code
+    *   Download the repo or clone it using: _git clone[ https://github.com/QuantConnect/Lean](https://github.com/QuantConnect/Lean)_
+    *   Open the folder using VS Code
+
+**NOTES**: 
+- Mono Extension Version 16 and greater fails to debug the docker container remotely, please install **Version 15.8**. To install an older version from within VS Code go to the extensions tab, search "Mono Debug", and select "Install Another Version...".
+<br />
+
+<h1>Develop Algorithms Locally, Run in Container</h1>
+
+
+We have set up a relatively easy way to develop algorithms in your local IDE and push them into the container to be run and debugged. 
+
+Before we can use this method with Windows or Mac OS we need to share the Lean directory with Docker.
+
+<br />
+
+<h2>Activate File Sharing for Docker:</h2>
+
+*   Windows: 
+    *   [Guide to sharing](https://docs.docker.com/docker-for-windows/#file-sharing)
+    *   Share the LEAN root directory with docker
+  
+*   Mac:
+    *   [Guide to sharing](https://docs.docker.com/docker-for-mac/#file-sharing)
+    *   Share the LEAN root directory with docker
+
+*   Linux:
+    *    (No setup required)
+
+<br />
+
+<h2>Lean Configuration</h2>
+
+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.
+
+Your configuration file should look something like this for the following languages:
+
+<h3>Python:</h3>
+
+    "algorithm-type-name": "**AlgorithmName**",
+
+    "algorithm-language": "Python",
+
+    "algorithm-location": "../../../Algorithm.Python/**AlgorithmName**.py",
+
+<h3>C#:</h3>
+
+    "algorithm-type-name": "**AlgorithmName**",
+
+    "algorithm-language": "CSharp",
+
+    "algorithm-location": "QuantConnect.Algorithm.CSharp.dll",
+
+
+<h3>Important Note About C#</h3>
+
+In order to use a custom C# algorithm, the C# file must be compiled before running in the docker, as it is compiled into the file "QuantConnect.Algorithm.CSharp.dll". Any new C# files will need to be added to the csproj compile list before it will compile, check Algorithm.CSharp/QuantConnect.Algorithm.CSharp.csproj for all algorithms that are compiled. Once there is an entry for your algorithm the project can be compiled by using the “build” task under _“Terminal” > “Run Build Task”._ 
+
+Python **does not** have this requirement as the engine will compile it on the fly.
+
+<br />
+
+<h2>Running Lean in the Container</h2>
+
+This section will cover how to actually launch Lean in the container with your desired configuration.
+
+<br />
+
+<h3>Option 1 (Recommended)</h3>
+
+In VS Code click on the debug/run icon on the left toolbar, at the top you should see a drop down menu with launch options, be sure to select **Debug in Container**. This option will kick off a launch script that will start the docker. With this specific launch option the parameters are already configured in VS Codes **tasks.json** under the **run-docker** task args. These set arguments are:
+
+    "IMAGE=quantconnect/lean:latest",
+    "CONFIG_FILE=${workspaceFolder}/Launcher/config.json",
+    "DATA_DIR=${workspaceFolder}/Data",
+    "RESULTS_DIR=${workspaceFolder}/Results",
+    "DEBUGGING=Y",
+    "PYHTON_DIR=${workspaceFolder}/Algorithm.Python"
+
+As defaults these are all great! Feel free to change them as needed for your setup.
+
+**NOTE:** VSCode may try and throw errors when launching this way regarding build on `QuantConnect.csx` and `Config.json` these errors can be ignored by selecting "*Debug Anyway*". To stop this error message in the future select "*Remember my choice in user settings*". 
+
+If using C# algorithms ensure that msbuild can build them successfully.  
+
+
+
+<br />
+
+<h3>Option 2</h3>
+
+From a terminal launch the run_docker.bat/.sh script; there are a few choices on how to launch this:
+ 1. Launch with no parameters and answer the questions regarding configuration (Press enter for defaults)
+   
+        *   Enter docker image [default: quantconnect/lean:latest]:
+        *   Enter absolute path to Lean config file [default: .\Launcher\config.json]:
+        *   Enter absolute path to Data folder [default: .\Data\]:
+        *   Enter absolute path to store results [default: .\Results]:
+        *   Would you like to debug C#? (Requires mono debugger attachment) [default: N]:
+
+ 2. Using the **run_docker.cfg** to store args for repeated use; any blank entries will resort to default values! example: **_./run_docker.bat run_docker.cfg_**
+  
+        IMAGE=quantconnect/lean:latest
+        CONFIG_FILE=
+        DATA_DIR=
+        RESULTS_DIR=
+        DEBUGGING=
+        PYTHON_DIR=
+
+ 3. Inline arguments; anything you don't enter will use the default args! example: **_./run_docker.bat DEBUGGING=y_** 
+      *    Accepted args for inline include all listed in the file in #2
+
+<br />
+
+<h1>Debugging Python</h1>
+
+Python algorithms require a little extra work in order to be able to debug them locally or in the container. Thankfully we were able to configure VS code tasks to take care of the work for you! Follow the steps below to get Python debugging working.
+
+<br />
+
+<h2>Modifying the Configuration</h2>
+
+First in order to debug a Python algorithm in VS Code we must make the following change to our configuration (Launcher\config.json) under the comment debugging configuration:
+
+    "debugging": true,
+    "debugging-method": "PTVSD",
+
+In setting this we are telling Lean to expect a debugger connection using ‘Python Tools for Visual Studio Debugger’. Once this is set Lean will stop upon initialization and await a connection to the debugger via port 5678.
+
+<br />
+
+<h2>Using VS Code Launch Options to Connect</h2>
+
+Now that Lean is configured for the python debugger we can make use of the programmed launch options to connect. 
+
+<br />
+
+<h3>Container</h3>
+
+
+To debug inside of the container we must first start the container, follow the steps described in the section “[Running Lean in the Container](#Running-Lean-in-the-Container)”. Once the container is started you should see the messages in Figure 2.
+
+If the message is displayed, use the same drop down for “Debug in Container” and select “Attach to Python (Container)”. Then press run, VS Code will now enter and debug any breakpoints you have set in your Python algorithm.
+
+<br />
+
+<h3>Local</h3>
+
+
+To debug locally we must run the program locally using the programmed task found under Terminal > Run Task > “Run Application”. Once Lean is started you should see the messages in Figure 2.
+
+If the message is displayed, use the launch option “Attach to Python (Local)”. Then press run, VS Code will now enter and debug any breakpoints you have set in your python algorithm.
+
+<br />
+
+_Figure 2: Python Debugger Messages_
+
+```
+20200715 17:12:06.546 Trace:: PythonInitializer.Initialize(): ended
+20200715 17:12:06.547 Trace:: DebuggerHelper.Initialize(): python initialization done
+20200715 17:12:06.547 Trace:: DebuggerHelper.Initialize(): starting...
+20200715 17:12:06.548 Trace:: DebuggerHelper.Initialize(): waiting for debugger to attach at localhost:5678...
+```
+
+<br />
+
+<h1>Common Issues</h1>
+Here we will cover some common issues with setting this up. This section will expand as we get user feedback!
+
+*   Any error messages about building in VSCode that point to comments in JSON. Either select **ignore** or follow steps described [here](https://stackoverflow.com/questions/47834825/in-vs-code-disable-error-comments-are-not-permitted-in-json) to remove the errors entirely.
+*   `Errors exist after running preLaunchTask 'run-docker'`This VSCode error appears to warn you of CSharp errors when trying to use `Debug in Container` select "Debug Anyway" as the errors are false flags for JSON comments as well as `QuantConnect.csx` not finding references. Neither of these will impact your debugging. 
+*   `The container name "/LeanEngine" is already in use by container "****"` This Docker error implies that another instance of lean is already running under the container name /LeanEngine. If this error appears either use Docker Desktop to delete the container or use `docker kill LeanEngine` from the command line.

.vscode/tasks.json

@@ -0,0 +1,139 @@
+{
+    /*
+        VS Code Tasks for the LEAN engine
+        In order to use the build tasks you need msbuild on your system path.
+    */
+    "version": "2.0.0",
+    "tasks": [
+        {
+            "label": "build",
+            "type": "shell",
+            "command": "msbuild",
+            "args": [
+                "/p:Configuration=Debug",
+                "/p:DebugType=portable",
+                "/t:build",
+            ],
+            "group": "build",
+            "presentation": {
+                "reveal": "silent"
+            },
+            "problemMatcher": "$msCompile"
+        },
+        {
+            "label": "rebuild",
+            "type": "shell",
+            "command": "msbuild",
+            "args": [
+                "/p:Configuration=Debug",
+                "/p:DebugType=portable",
+                "/t:rebuild",
+            ],
+            "group": "build",
+            "presentation": {
+                "reveal": "silent"
+            },
+            "problemMatcher": "$msCompile"
+        },
+        {
+            "label": "clean",
+            "type": "shell",
+            "command": "msbuild",
+            "args": [
+                "/t:clean",
+            ],
+            "group": "build",
+            "presentation": {
+                "reveal": "silent"
+            },
+            "problemMatcher": "$msCompile"
+        },
+        {
+            "label": "force build linux",
+            "type": "shell",
+            "command": "msbuild",
+            "args": [
+                "/property:GenerateFullPaths=true",
+                "/p:Configuration=Debug",
+                "/p:DebugType=portable",
+                "/t:build",
+                "/p:ForceLinuxBuild=true"
+            ],
+            "group": "build",
+            "presentation": {
+                "reveal": "silent"
+            },
+            "problemMatcher": "$msCompile"
+        },
+        {
+            "label": "run-docker",
+            "type": "shell",
+            "isBackground": true,
+            "windows": {
+                "command": "${workspaceFolder}/run_docker.bat",
+            },
+            "linux": {
+                "command": "${workspaceFolder}/run_docker.sh"
+            },
+            "osx": {
+                "command": "${workspaceFolder}/run_docker.sh"
+            },
+            "args": [
+                "IMAGE=quantconnect/lean:latest",
+                "CONFIG_FILE=${workspaceFolder}/Launcher/config.json",
+                "DATA_DIR=${workspaceFolder}/Data",
+                "RESULTS_DIR=${workspaceFolder}/Results",
+                "DEBUGGING=Y",
+                "PYTHON_DIR=${workspaceFolder}/Algorithm.Python",
+                "EXIT=Y"
+            ],
+            "problemMatcher": [
+                {
+                  "pattern": [
+                    {
+                      "regexp": ".",
+                      "file": 1,
+                      "location": 2,
+                      "message": 3
+                    }
+                  ],
+                  "background": {
+                    "activeOnStart": true,
+                    "beginsPattern": ".",
+                    "endsPattern": ".",
+                  }
+                }
+              ]
+        },
+        {
+            "label": "close-docker",
+            "type": "shell",
+            "command": "docker stop LeanEngine",
+            "presentation": {
+                "echo": false,
+                "reveal": "never",
+                "focus": false,
+                "panel": "shared",
+                "showReuseMessage": false,
+                "clear": true,
+            },
+            "linux":{
+                "command": "sudo docker stop LeanEngine"
+            }
+        },
+        {
+            "label": "Run Application",
+            "type": "process",
+            "command": "QuantConnect.Lean.Launcher.exe",
+            "args" : [
+                "--data-folder",
+                "${workspaceFolder}/Data",
+                "--config",
+                "${workspaceFolder}/Launcher/config.json"
+            ],
+            "options": {
+                "cwd": "${workspaceFolder}/Launcher/bin/Debug/"
+            }
+        }
+    ]
+}

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

@@ -0,0 +1,211 @@
+/*
+ * 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.Data.Market;
+using QuantConnect.Interfaces;
+using QuantConnect.Securities;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// This regression algorithm tests that we receive the expected data when
+    /// we add future option contracts individually using <see cref="AddFutureOptionContract"/>
+    /// </summary>
+    public class AddFutureOptionContractDataStreamingRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private bool _onDataReached;
+        private bool _invested;
+        private Symbol _es20h20;
+        private Symbol _es19m20;
+
+        private readonly HashSet<Symbol> _symbolsReceived = new HashSet<Symbol>();
+        private readonly HashSet<Symbol> _expectedSymbolsReceived = new HashSet<Symbol>();
+        private readonly Dictionary<Symbol, List<QuoteBar>> _dataReceived = new Dictionary<Symbol, List<QuoteBar>>();
+
+        public override void Initialize()
+        {
+            SetStartDate(2020, 1, 5);
+            SetEndDate(2020, 1, 6);
+
+            _es20h20 = AddFutureContract(
+                QuantConnect.Symbol.CreateFuture(Futures.Indices.SP500EMini, Market.CME, new DateTime(2020, 3, 20)),
+                Resolution.Minute).Symbol;
+
+            _es19m20 = AddFutureContract(
+                QuantConnect.Symbol.CreateFuture(Futures.Indices.SP500EMini, Market.CME, new DateTime(2020, 6, 19)),
+                Resolution.Minute).Symbol;
+
+            var optionChains = OptionChainProvider.GetOptionContractList(_es20h20, Time)
+                .Concat(OptionChainProvider.GetOptionContractList(_es19m20, Time));
+
+            foreach (var optionContract in optionChains)
+            {
+                _expectedSymbolsReceived.Add(AddFutureOptionContract(optionContract, Resolution.Minute).Symbol);
+            }
+
+            if (_expectedSymbolsReceived.Count == 0)
+            {
+                throw new InvalidOperationException("Expected Symbols receive count is 0, expected >0");
+            }
+        }
+
+        public override void OnData(Slice data)
+        {
+            if (!data.HasData)
+            {
+                return;
+            }
+
+            _onDataReached = true;
+
+            var hasOptionQuoteBars = false;
+            foreach (var qb in data.QuoteBars.Values)
+            {
+                if (qb.Symbol.SecurityType != SecurityType.FutureOption)
+                {
+                    continue;
+                }
+
+                hasOptionQuoteBars = true;
+
+                _symbolsReceived.Add(qb.Symbol);
+                if (!_dataReceived.ContainsKey(qb.Symbol))
+                {
+                    _dataReceived[qb.Symbol] = new List<QuoteBar>();
+                }
+
+                _dataReceived[qb.Symbol].Add(qb);
+            }
+
+            if (_invested || !hasOptionQuoteBars)
+            {
+                return;
+            }
+
+            if (data.ContainsKey(_es20h20) && data.ContainsKey(_es19m20))
+            {
+                SetHoldings(_es20h20, 0.2);
+                SetHoldings(_es19m20, 0.2);
+
+                _invested = true;
+            }
+        }
+
+        public override void OnEndOfAlgorithm()
+        {
+            base.OnEndOfAlgorithm();
+
+            if (!_onDataReached)
+            {
+                throw new Exception("OnData() was never called.");
+            }
+            if (_symbolsReceived.Count != _expectedSymbolsReceived.Count)
+            {
+                throw new AggregateException($"Expected {_expectedSymbolsReceived.Count} option contracts Symbols, found {_symbolsReceived.Count}");
+            }
+
+            var missingSymbols = new List<Symbol>();
+            foreach (var expectedSymbol in _expectedSymbolsReceived)
+            {
+                if (!_symbolsReceived.Contains(expectedSymbol))
+                {
+                    missingSymbols.Add(expectedSymbol);
+                }
+            }
+
+            if (missingSymbols.Count > 0)
+            {
+                throw new Exception($"Symbols: \"{string.Join(", ", missingSymbols)}\" were not found in OnData");
+            }
+
+            foreach (var expectedSymbol in _expectedSymbolsReceived)
+            {
+                var data = _dataReceived[expectedSymbol];
+                var nonDupeDataCount = data.Select(x =>
+                {
+                    x.EndTime = default(DateTime);
+                    return x;
+                }).Distinct().Count();
+
+                if (nonDupeDataCount < 1000)
+                {
+                    throw new Exception($"Received too few data points. Expected >=1000, found {nonDupeDataCount} for {expectedSymbol}");
+                }
+            }
+        }
+
+        /// <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", "2"},
+            {"Average Win", "0%"},
+            {"Average Loss", "0%"},
+            {"Compounding Annual Return", "217.585%"},
+            {"Drawdown", "0.600%"},
+            {"Expectancy", "0"},
+            {"Net Profit", "0.635%"},
+            {"Sharpe Ratio", "0"},
+            {"Probabilistic Sharpe Ratio", "0%"},
+            {"Loss Rate", "0%"},
+            {"Win Rate", "0%"},
+            {"Profit-Loss Ratio", "0"},
+            {"Alpha", "0"},
+            {"Beta", "0"},
+            {"Annual Standard Deviation", "0"},
+            {"Annual Variance", "0"},
+            {"Information Ratio", "-14.395"},
+            {"Tracking Error", "0.043"},
+            {"Treynor Ratio", "0"},
+            {"Total Fees", "$7.40"},
+            {"Estimated Strategy Capacity", "$28000000.00"},
+            {"Fitness Score", "1"},
+            {"Kelly Criterion Estimate", "0"},
+            {"Kelly Criterion Probability Value", "0"},
+            {"Sortino Ratio", "79228162514264337593543950335"},
+            {"Return Over Maximum Drawdown", "79228162514264337593543950335"},
+            {"Portfolio Turnover", "3.199"},
+            {"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", "35738733ff791eeeaf508faec804cab0"}
+        };
+    }
+}

Algorithm.CSharp/AddFutureOptionSingleOptionChainSelectedInUniverseFilterRegressionAlgorithm.cs

@@ -0,0 +1,245 @@
+/*
+ * 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.Data.Market;
+using QuantConnect.Interfaces;
+using QuantConnect.Securities;
+using QuantConnect.Securities.Future;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// This regression algorithm tests that we only receive the option chain for a single future contract
+    /// in the option universe filter.
+    /// </summary>
+    public class AddFutureOptionSingleOptionChainSelectedInUniverseFilterRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private bool _invested;
+        private bool _onDataReached;
+        private bool _optionFilterRan;
+        private readonly HashSet<Symbol> _symbolsReceived = new HashSet<Symbol>();
+        private readonly HashSet<Symbol> _expectedSymbolsReceived = new HashSet<Symbol>();
+        private readonly Dictionary<Symbol, List<QuoteBar>> _dataReceived = new Dictionary<Symbol, List<QuoteBar>>();
+
+        private Future _es;
+
+        public override void Initialize()
+        {
+            SetStartDate(2020, 1, 5);
+            SetEndDate(2020, 1, 6);
+
+            _es = AddFuture(Futures.Indices.SP500EMini, Resolution.Minute, Market.CME);
+            _es.SetFilter((futureFilter) =>
+            {
+                return futureFilter.Expiration(0, 365).ExpirationCycle(new[] { 3, 6 });
+            });
+
+            AddFutureOption(_es.Symbol, optionContracts =>
+            {
+                _optionFilterRan = true;
+
+                var expiry = new HashSet<DateTime>(optionContracts.Select(x => x.Underlying.ID.Date)).SingleOrDefault();
+                // Cast to IEnumerable<Symbol> because OptionFilterContract overrides some LINQ operators like `Select` and `Where`
+                // and cause it to mutate the underlying Symbol collection when using those operators.
+                var symbol = new HashSet<Symbol>(((IEnumerable<Symbol>)optionContracts).Select(x => x.Underlying)).SingleOrDefault();
+
+                if (expiry == null || symbol == null)
+                {
+                    throw new InvalidOperationException("Expected a single Option contract in the chain, found 0 contracts");
+                }
+
+                var enumerator = optionContracts.GetEnumerator();
+                while (enumerator.MoveNext())
+                {
+                    _expectedSymbolsReceived.Add(enumerator.Current);
+                }
+
+                return optionContracts;
+            });
+        }
+
+        public override void OnData(Slice data)
+        {
+            if (!data.HasData)
+            {
+                return;
+            }
+
+            _onDataReached = true;
+
+            var hasOptionQuoteBars = false;
+            foreach (var qb in data.QuoteBars.Values)
+            {
+                if (qb.Symbol.SecurityType != SecurityType.FutureOption)
+                {
+                    continue;
+                }
+
+                hasOptionQuoteBars = true;
+
+                _symbolsReceived.Add(qb.Symbol);
+                if (!_dataReceived.ContainsKey(qb.Symbol))
+                {
+                    _dataReceived[qb.Symbol] = new List<QuoteBar>();
+                }
+
+                _dataReceived[qb.Symbol].Add(qb);
+            }
+
+            if (_invested || !hasOptionQuoteBars)
+            {
+                return;
+            }
+
+            foreach (var chain in data.OptionChains.Values)
+            {
+                var futureInvested = false;
+                var optionInvested = false;
+
+                foreach (var option in chain.Contracts.Keys)
+                {
+                    if (futureInvested && optionInvested)
+                    {
+                        return;
+                    }
+
+                    var future = option.Underlying;
+
+                    if (!optionInvested && data.ContainsKey(option))
+                    {
+                        MarketOrder(option, 1);
+                        _invested = true;
+                        optionInvested = true;
+                    }
+                    if (!futureInvested && data.ContainsKey(future))
+                    {
+                        MarketOrder(future, 1);
+                        _invested = true;
+                        futureInvested = true;
+                    }
+                }
+            }
+        }
+
+        public override void OnEndOfAlgorithm()
+        {
+            base.OnEndOfAlgorithm();
+
+            if (!_optionFilterRan)
+            {
+                throw new InvalidOperationException("Option chain filter was never ran");
+            }
+            if (!_onDataReached)
+            {
+                throw new Exception("OnData() was never called.");
+            }
+            if (_symbolsReceived.Count != _expectedSymbolsReceived.Count)
+            {
+                throw new AggregateException($"Expected {_expectedSymbolsReceived.Count} option contracts Symbols, found {_symbolsReceived.Count}");
+            }
+
+            var missingSymbols = new List<Symbol>();
+            foreach (var expectedSymbol in _expectedSymbolsReceived)
+            {
+                if (!_symbolsReceived.Contains(expectedSymbol))
+                {
+                    missingSymbols.Add(expectedSymbol);
+                }
+            }
+
+            if (missingSymbols.Count > 0)
+            {
+                throw new Exception($"Symbols: \"{string.Join(", ", missingSymbols)}\" were not found in OnData");
+            }
+
+            foreach (var expectedSymbol in _expectedSymbolsReceived)
+            {
+                var data = _dataReceived[expectedSymbol];
+                var nonDupeDataCount = data.Select(x =>
+                {
+                    x.EndTime = default(DateTime);
+                    return x;
+                }).Distinct().Count();
+
+                if (nonDupeDataCount < 1000)
+                {
+                    throw new Exception($"Received too few data points. Expected >=1000, found {nonDupeDataCount} for {expectedSymbol}");
+                }
+            }
+        }
+
+        /// <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", "2"},
+            {"Average Win", "0%"},
+            {"Average Loss", "0%"},
+            {"Compounding Annual Return", "-15.625%"},
+            {"Drawdown", "0.200%"},
+            {"Expectancy", "0"},
+            {"Net Profit", "-0.093%"},
+            {"Sharpe Ratio", "-11.181"},
+            {"Probabilistic Sharpe Ratio", "0%"},
+            {"Loss Rate", "0%"},
+            {"Win Rate", "0%"},
+            {"Profit-Loss Ratio", "0"},
+            {"Alpha", "0.002"},
+            {"Beta", "-0.016"},
+            {"Annual Standard Deviation", "0.001"},
+            {"Annual Variance", "0"},
+            {"Information Ratio", "-14.343"},
+            {"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"},
+            {"Sortino Ratio", "79228162514264337593543950335"},
+            {"Return Over Maximum Drawdown", "-185.654"},
+            {"Portfolio Turnover", "0.821"},
+            {"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", "9347e3b610cfa21f7cbd968a0135c8af"}
+        };
+    }
+}

Algorithm.CSharp/AddOptionContractExpiresRegressionAlgorithm.cs

@@ -0,0 +1,165 @@
+/*
+ * 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.Linq;
+using QuantConnect.Data;
+using QuantConnect.Interfaces;
+using System.Collections.Generic;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// We add an option contract using <see cref="QCAlgorithm.AddOptionContract"/> and place a trade and wait till it expires
+    /// later will liquidate the resulting equity position and assert both option and underlying get removed
+    /// </summary>
+    public class AddOptionContractExpiresRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private DateTime _expiration = new DateTime(2014, 06, 21);
+        private Symbol _option;
+        private Symbol _twx;
+        private bool _traded;
+
+        public override void Initialize()
+        {
+            SetStartDate(2014, 06, 05);
+            SetEndDate(2014, 06, 30);
+
+            _twx = QuantConnect.Symbol.Create("TWX", SecurityType.Equity, Market.USA);
+
+            AddUniverse("my-daily-universe-name", time => new List<string> { "AAPL" });
+        }
+
+        public override void OnData(Slice data)
+        {
+            if (_option == null)
+            {
+                var option = OptionChainProvider.GetOptionContractList(_twx, Time)
+                    .OrderBy(symbol => symbol.ID.Symbol)
+                    .FirstOrDefault(optionContract => optionContract.ID.Date == _expiration
+                                                      && optionContract.ID.OptionRight == OptionRight.Call
+                                                      && optionContract.ID.OptionStyle == OptionStyle.American);
+                if (option != null)
+                {
+                    _option = AddOptionContract(option).Symbol;
+                }
+            }
+
+            if (_option != null && Securities[_option].Price != 0 && !_traded)
+            {
+                _traded = true;
+                Buy(_option, 1);
+
+                foreach (var symbol in new [] { _option, _option.Underlying })
+                {
+                    var config = SubscriptionManager.SubscriptionDataConfigService.GetSubscriptionDataConfigs(symbol).ToList();
+
+                    if (!config.Any())
+                    {
+                        throw new Exception($"Was expecting configurations for {symbol}");
+                    }
+                    if (config.Any(dataConfig => dataConfig.DataNormalizationMode != DataNormalizationMode.Raw))
+                    {
+                        throw new Exception($"Was expecting DataNormalizationMode.Raw configurations for {symbol}");
+                    }
+                }
+            }
+
+            if (Time.Date > _expiration)
+            {
+                if (SubscriptionManager.SubscriptionDataConfigService.GetSubscriptionDataConfigs(_option).Any())
+                {
+                    throw new Exception($"Unexpected configurations for {_option} after it has been delisted");
+                }
+
+                if (Securities[_twx].Invested)
+                {
+                    if (!SubscriptionManager.SubscriptionDataConfigService.GetSubscriptionDataConfigs(_twx).Any())
+                    {
+                        throw new Exception($"Was expecting configurations for {_twx}");
+                    }
+
+                    // first we liquidate the option exercised position
+                    Liquidate(_twx);
+                }
+            }
+            else if (Time.Date > _expiration && !Securities[_twx].Invested)
+            {
+                if (SubscriptionManager.SubscriptionDataConfigService.GetSubscriptionDataConfigs(_twx).Any())
+                {
+                    throw new Exception($"Unexpected configurations for {_twx} after it has been liquidated");
+                }
+            }
+        }
+
+        /// <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", "3"},
+            {"Average Win", "2.73%"},
+            {"Average Loss", "-2.98%"},
+            {"Compounding Annual Return", "-4.619%"},
+            {"Drawdown", "0.300%"},
+            {"Expectancy", "-0.042"},
+            {"Net Profit", "-0.332%"},
+            {"Sharpe Ratio", "-3.7"},
+            {"Probabilistic Sharpe Ratio", "0.563%"},
+            {"Loss Rate", "50%"},
+            {"Win Rate", "50%"},
+            {"Profit-Loss Ratio", "0.92"},
+            {"Alpha", "-0.021"},
+            {"Beta", "-0.011"},
+            {"Annual Standard Deviation", "0.006"},
+            {"Annual Variance", "0"},
+            {"Information Ratio", "-3.385"},
+            {"Tracking Error", "0.058"},
+            {"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"},
+            {"Sortino Ratio", "-43.418"},
+            {"Return Over Maximum Drawdown", "-14.274"},
+            {"Portfolio Turnover", "0.007"},
+            {"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", "486118a60d78f74811fe8d927c2c6b43"}
+        };
+    }
+}

Algorithm.CSharp/AddOptionContractFromUniverseRegressionAlgorithm.cs

@@ -0,0 +1,217 @@
+/*
+ * 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.Data.UniverseSelection;
+using QuantConnect.Interfaces;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// We add an option contract using <see cref="QCAlgorithm.AddOptionContract"/> and place a trade, the underlying
+    /// gets deselected from the universe selection but should still be present since we manually added the option contract.
+    /// Later we call <see cref="QCAlgorithm.RemoveOptionContract"/> and expect both option and underlying to be removed.
+    /// </summary>
+    public class AddOptionContractFromUniverseRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private DateTime _expiration = new DateTime(2014, 06, 21);
+        private SecurityChanges _securityChanges = SecurityChanges.None;
+        private Symbol _option;
+        private Symbol _aapl;
+        private Symbol _twx;
+        private bool _traded;
+
+        public override void Initialize()
+        {
+            _twx = QuantConnect.Symbol.Create("TWX", SecurityType.Equity, Market.USA);
+            _aapl = QuantConnect.Symbol.Create("AAPL", SecurityType.Equity, Market.USA);
+            UniverseSettings.Resolution = Resolution.Minute;
+            UniverseSettings.DataNormalizationMode = DataNormalizationMode.Raw;
+
+            SetStartDate(2014, 06, 05);
+            SetEndDate(2014, 06, 09);
+
+            AddUniverse(enumerable => new[] { Time.Date <= new DateTime(2014, 6, 5) ? _twx : _aapl },
+                enumerable => new[] { Time.Date <= new DateTime(2014, 6, 5) ? _twx : _aapl });
+        }
+
+        public override void OnData(Slice data)
+        {
+            if (_option != null && Securities[_option].Price != 0 && !_traded)
+            {
+                _traded = true;
+                Buy(_option, 1);
+            }
+
+            if (Time.Date > new DateTime(2014, 6, 5))
+            {
+                if (Time < new DateTime(2014, 6, 6, 14, 0, 0))
+                {
+                    var configs = SubscriptionManager.SubscriptionDataConfigService.GetSubscriptionDataConfigs(_twx);
+                    // assert underlying still there after the universe selection removed it, still used by the manually added option contract
+                    if (!configs.Any())
+                    {
+                        throw new Exception($"Was expecting configurations for {_twx}" +
+                                            $" even after it has been deselected from coarse universe because we still have the option contract.");
+                    }
+                }
+                else if (Time == new DateTime(2014, 6, 6, 14, 0, 0))
+                {
+                    // liquidate & remove the option
+                    RemoveOptionContract(_option);
+                }
+                // assert underlying was finally removed
+                else if(Time > new DateTime(2014, 6, 6, 14, 0, 0))
+                {
+                    foreach (var symbol in new[] { _option, _option.Underlying })
+                    {
+                        var configs = SubscriptionManager.SubscriptionDataConfigService.GetSubscriptionDataConfigs(symbol);
+                        if (configs.Any())
+                        {
+                            throw new Exception($"Unexpected configuration for {symbol} after it has been deselected from coarse universe and option contract is removed.");
+                        }
+                    }
+                }
+            }
+        }
+
+        public override void OnSecuritiesChanged(SecurityChanges changes)
+        {
+            if (_securityChanges.RemovedSecurities.Intersect(changes.RemovedSecurities).Any())
+            {
+                throw new Exception($"SecurityChanges.RemovedSecurities intersect {changes.RemovedSecurities}. We expect no duplicate!");
+            }
+            if (_securityChanges.AddedSecurities.Intersect(changes.AddedSecurities).Any())
+            {
+                throw new Exception($"SecurityChanges.AddedSecurities intersect {changes.RemovedSecurities}. We expect no duplicate!");
+            }
+            // keep track of all removed and added securities
+            _securityChanges += changes;
+
+            if (changes.AddedSecurities.Any(security => security.Symbol.SecurityType == SecurityType.Option))
+            {
+                return;
+            }
+
+            foreach (var addedSecurity in changes.AddedSecurities)
+            {
+                var option = OptionChainProvider.GetOptionContractList(addedSecurity.Symbol, Time)
+                    .OrderBy(symbol => symbol.ID.Symbol)
+                    .First(optionContract => optionContract.ID.Date == _expiration
+                                                      && optionContract.ID.OptionRight == OptionRight.Call
+                                                      && optionContract.ID.OptionStyle == OptionStyle.American);
+                AddOptionContract(option);
+
+                foreach (var symbol in new[] { option, option.Underlying })
+                {
+                    var config = SubscriptionManager.SubscriptionDataConfigService.GetSubscriptionDataConfigs(symbol).ToList();
+
+                    if (!config.Any())
+                    {
+                        throw new Exception($"Was expecting configurations for {symbol}");
+                    }
+                    if (config.Any(dataConfig => dataConfig.DataNormalizationMode != DataNormalizationMode.Raw))
+                    {
+                        throw new Exception($"Was expecting DataNormalizationMode.Raw configurations for {symbol}");
+                    }
+                }
+
+                // just keep the first we got
+                if (_option == null)
+                {
+                    _option = option;
+                }
+            }
+        }
+
+        public override void OnEndOfAlgorithm()
+        {
+            if (SubscriptionManager.Subscriptions.Any(dataConfig => dataConfig.Symbol == _twx || dataConfig.Symbol.Underlying == _twx))
+            {
+                throw new Exception($"Was NOT expecting any configurations for {_twx} or it's options, since we removed the contract");
+            }
+
+            if (SubscriptionManager.Subscriptions.All(dataConfig => dataConfig.Symbol != _aapl))
+            {
+                throw new Exception($"Was expecting configurations for {_aapl}");
+            }
+            if (SubscriptionManager.Subscriptions.All(dataConfig => dataConfig.Symbol.Underlying != _aapl))
+            {
+                throw new Exception($"Was expecting options configurations for {_aapl}");
+            }
+        }
+
+        /// <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", "2"},
+            {"Average Win", "0%"},
+            {"Average Loss", "-0.23%"},
+            {"Compounding Annual Return", "-15.596%"},
+            {"Drawdown", "0.200%"},
+            {"Expectancy", "-1"},
+            {"Net Profit", "-0.232%"},
+            {"Sharpe Ratio", "-7.739"},
+            {"Probabilistic Sharpe Ratio", "1.216%"},
+            {"Loss Rate", "100%"},
+            {"Win Rate", "0%"},
+            {"Profit-Loss Ratio", "0"},
+            {"Alpha", "0.024"},
+            {"Beta", "-0.171"},
+            {"Annual Standard Deviation", "0.006"},
+            {"Annual Variance", "0"},
+            {"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"},
+            {"Sortino Ratio", "-19.883"},
+            {"Return Over Maximum Drawdown", "-67.224"},
+            {"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", "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

@@ -0,0 +1,237 @@
+/*
+ * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
+ * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+*/
+
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using QuantConnect.Brokerages;
+using QuantConnect.Data;
+using QuantConnect.Data.Shortable;
+using QuantConnect.Data.UniverseSelection;
+using QuantConnect.Interfaces;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Tests filtering in coarse selection by shortable quantity
+    /// </summary>
+    public class AllShortableSymbolsCoarseSelectionRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private static readonly DateTime _20140325 = new DateTime(2014, 3, 25);
+        private static readonly DateTime _20140326 = new DateTime(2014, 3, 26);
+        private static readonly DateTime _20140327 = new DateTime(2014, 3, 27);
+        private static readonly DateTime _20140328 = new DateTime(2014, 3, 28);
+        private static readonly DateTime _20140329 = new DateTime(2014, 3, 29);
+
+        private static readonly Symbol _aapl = QuantConnect.Symbol.Create("AAPL", SecurityType.Equity, Market.USA);
+        private static readonly Symbol _bac = QuantConnect.Symbol.Create("BAC", SecurityType.Equity, Market.USA);
+        private static readonly Symbol _gme = QuantConnect.Symbol.Create("GME", SecurityType.Equity, Market.USA);
+        private static readonly Symbol _goog = QuantConnect.Symbol.Create("GOOG", SecurityType.Equity, Market.USA);
+        private static readonly Symbol _qqq = QuantConnect.Symbol.Create("QQQ", SecurityType.Equity, Market.USA);
+        private static readonly Symbol _spy = QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA);
+        private DateTime _lastTradeDate;
+
+        private static readonly Dictionary<DateTime, bool> _coarseSelected = new Dictionary<DateTime, bool>
+        {
+            { _20140325, false },
+            { _20140326, false },
+            { _20140327, false },
+            { _20140328, false },
+        };
+
+        private static readonly Dictionary<DateTime, Symbol[]> _expectedSymbols = new Dictionary<DateTime, Symbol[]>
+        {
+            { _20140325, new[]
+                {
+                    _bac,
+                    _qqq,
+                    _spy
+                }
+            },
+            { _20140326, new[]
+                {
+                    _spy
+                }
+            },
+            { _20140327, new[]
+                {
+                    _aapl,
+                    _bac,
+                    _gme,
+                    _qqq,
+                    _spy,
+                }
+            },
+            { _20140328, new[]
+                {
+                    _goog
+                }
+            },
+            { _20140329, new Symbol[0] }
+        };
+
+        public override void Initialize()
+        {
+            SetStartDate(2014, 3, 25);
+            SetEndDate(2014, 3, 29);
+            SetCash(10000000);
+
+            AddUniverse(CoarseSelection);
+            UniverseSettings.Resolution = Resolution.Daily;
+
+            SetBrokerageModel(new AllShortableSymbolsRegressionAlgorithmBrokerageModel());
+        }
+
+        public override void OnData(Slice data)
+        {
+            if (Time.Date == _lastTradeDate)
+            {
+                return;
+            }
+
+            foreach (var symbol in ActiveSecurities.Keys.OrderBy(symbol => symbol))
+            {
+                if (!Portfolio.ContainsKey(symbol) || !Portfolio[symbol].Invested)
+                {
+                    if (!Shortable(symbol))
+                    {
+                        throw new Exception($"Expected {symbol} to be shortable on {Time:yyyy-MM-dd}");
+                    }
+
+                    // Buy at least once into all Symbols. Since daily data will always use
+                    // MOO orders, it makes the testing of liquidating buying into Symbols difficult.
+                    MarketOrder(symbol, -(decimal)ShortableQuantity(symbol));
+                    _lastTradeDate = Time.Date;
+                }
+            }
+        }
+
+        private IEnumerable<Symbol> CoarseSelection(IEnumerable<CoarseFundamental> coarse)
+        {
+            var shortableSymbols = AllShortableSymbols();
+            var selectedSymbols = coarse
+                .Select(x => x.Symbol)
+                .Where(s => shortableSymbols.ContainsKey(s) && shortableSymbols[s] >= 500)
+                .OrderBy(s => s)
+                .ToList();
+
+            var expectedMissing = 0;
+            if (Time.Date == _20140327)
+            {
+                var gme = QuantConnect.Symbol.Create("GME", SecurityType.Equity, Market.USA);
+                if (!shortableSymbols.ContainsKey(gme))
+                {
+                    throw new Exception("Expected unmapped GME in shortable symbols list on 2014-03-27");
+                }
+                if (!coarse.Select(x => x.Symbol.Value).Contains("GME"))
+                {
+                    throw new Exception("Expected mapped GME in coarse symbols on 2014-03-27");
+                }
+
+                expectedMissing = 1;
+            }
+
+            var missing = _expectedSymbols[Time.Date].Except(selectedSymbols).ToList();
+            if (missing.Count != expectedMissing)
+            {
+                throw new Exception($"Expected Symbols selected on {Time.Date:yyyy-MM-dd} to match expected Symbols, but the following Symbols were missing: {string.Join(", ", missing.Select(s => s.ToString()))}");
+            }
+
+            _coarseSelected[Time.Date] = true;
+            return selectedSymbols;
+        }
+
+        public override void OnEndOfAlgorithm()
+        {
+            if (!_coarseSelected.Values.All(x => x))
+            {
+                throw new AggregateException($"Expected coarse selection on all dates, but didn't run on: {string.Join(", ", _coarseSelected.Where(kvp => !kvp.Value).Select(kvp => kvp.Key.ToStringInvariant("yyyy-MM-dd")))}");
+            }
+        }
+
+        private class AllShortableSymbolsRegressionAlgorithmBrokerageModel : DefaultBrokerageModel
+        {
+            public AllShortableSymbolsRegressionAlgorithmBrokerageModel() : base()
+            {
+                ShortableProvider = new RegressionTestShortableProvider();
+            }
+        }
+
+        private class RegressionTestShortableProvider : LocalDiskShortableProvider
+        {
+            public RegressionTestShortableProvider() : base(SecurityType.Equity, "testbrokerage", Market.USA)
+            {
+            }
+        }
+
+        /// <summary>
+        /// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
+        /// </summary>
+        public bool CanRunLocally { get; } = true;
+
+        /// <summary>
+        /// This is used by the regression test system to indicate which languages this algorithm is written in.
+        /// </summary>
+        public Language[] Languages { get; } = { Language.CSharp, Language.Python };
+
+        /// <summary>
+        /// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
+        /// </summary>
+        public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
+        {
+            {"Total Trades", "5"},
+            {"Average Win", "0%"},
+            {"Average Loss", "0%"},
+            {"Compounding Annual Return", "36.239%"},
+            {"Drawdown", "0%"},
+            {"Expectancy", "0"},
+            {"Net Profit", "0.339%"},
+            {"Sharpe Ratio", "21.173"},
+            {"Probabilistic Sharpe Ratio", "99.997%"},
+            {"Loss Rate", "0%"},
+            {"Win Rate", "0%"},
+            {"Profit-Loss Ratio", "0"},
+            {"Alpha", "0.271"},
+            {"Beta", "0.138"},
+            {"Annual Standard Deviation", "0.011"},
+            {"Annual Variance", "0"},
+            {"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"},
+            {"Sortino Ratio", "79228162514264337593543950335"},
+            {"Return Over Maximum Drawdown", "79228162514264337593543950335"},
+            {"Portfolio Turnover", "0.173"},
+            {"Total Insights Generated", "0"},
+            {"Total Insights Closed", "0"},
+            {"Total Insights Analysis Completed", "0"},
+            {"Long Insight Count", "0"},
+            {"Short Insight Count", "0"},
+            {"Long/Short Ratio", "100%"},
+            {"Estimated Monthly Alpha Value", "$0"},
+            {"Total Accumulated Estimated Alpha Value", "$0"},
+            {"Mean Population Estimated Insight Value", "$0"},
+            {"Mean Population Direction", "0%"},
+            {"Mean Population Magnitude", "0%"},
+            {"Rolling Averaged Population Direction", "0%"},
+            {"Rolling Averaged Population Magnitude", "0%"},
+            {"OrderListHash", "5ce14f87f21733ec686385da7404484c"}
+        };
+    }
+}

Algorithm.CSharp/Alphas/MortgageRateVolatilityAlpha.cs

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

Algorithm.CSharp/AltData/QuiverWallStreetBetsDataAlgorithm.cs

@@ -0,0 +1,62 @@
+/*
+ * 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.Data.Custom.Quiver;
+
+namespace QuantConnect.Algorithm.CSharp.AltData
+{
+    /// <summary>
+    /// Quiver Quantitative is a provider of alternative data.
+    /// This algorithm shows how to consume the <see cref="QuiverWallStreetBets"/>
+    /// </summary>
+    public class QuiverWallStreetBetsDataAlgorithm : QCAlgorithm
+    {
+        /// <summary>
+        /// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
+        /// </summary>
+        public override void Initialize()
+        {
+            SetStartDate(2019, 1, 1);
+            SetEndDate(2020, 6, 1);
+            SetCash(100000);
+
+            var aapl = AddEquity("AAPL", Resolution.Daily).Symbol;
+            var quiverWSBSymbol = AddData<QuiverWallStreetBets>(aapl).Symbol;
+            var history = History<QuiverWallStreetBets>(quiverWSBSymbol, 60, Resolution.Daily);
+
+            Debug($"We got {history.Count()} items from our history request");
+        }
+
+        public override void OnData(Slice data)
+        {
+            var points = data.Get<QuiverWallStreetBets>();
+            foreach (var point in points.Values)
+            {
+                // Go long in the stock if it was mentioned more than 5 times in the WallStreetBets daily discussion
+                if (point.Mentions > 5)
+                {
+                    SetHoldings(point.Symbol.Underlying, 1);
+                }
+                // Go short in the stock if it was mentioned less than 5 times in the WallStreetBets daily discussion
+                if (point.Mentions < 5)
+                {
+                    SetHoldings(point.Symbol.Underlying, -1);
+                }
+            }
+        }
+    }
+}

Algorithm.CSharp/AltData/RobintrackHoldingsAlgorithm.cs

@@ -1,74 +0,0 @@
-/*
- * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
- * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
-*/
-
-using QuantConnect.Data;
-using QuantConnect.Data.Custom.Robintrack;
-
-namespace QuantConnect.Algorithm.CSharp.AltData
-{
-    /// <summary>
-    /// Looks at users holding the stock AAPL at a given point in time
-    /// and keeps track of changes in retail investor sentiment.
-    ///
-    /// We go long if the sentiment increases by 0.5%, and short if it decreases by -0.5%
-    /// </summary>
-    public class RobintrackHoldingsAlgorithm : QCAlgorithm
-    {
-        private Symbol _aapl;
-        private Symbol _aaplHoldings;
-        private decimal _lastValue;
-        private bool _isLong;
-
-        public override void Initialize()
-        {
-            SetStartDate(2018, 5, 1);
-            SetEndDate(2020, 5, 5);
-            SetCash(100000);
-
-            _aapl = AddEquity("AAPL", Resolution.Daily).Symbol;
-            _aaplHoldings = AddData<RobintrackHoldings>(_aapl).Symbol;
-            _isLong = false;
-        }
-
-        public override void OnData(Slice data)
-        {
-            foreach (var kvp in data.Get<RobintrackHoldings>())
-            {
-                var holdings = kvp.Value;
-
-                if (_lastValue != 0)
-                {
-                    var percentChange = (holdings.UsersHolding - _lastValue) / _lastValue;
-                    var holdingInfo = $"There are {holdings.UsersHolding} unique users holding {kvp.Key.Underlying} - users holding % of U.S. equities universe: {holdings.UniverseHoldingPercent * 100m}%";
-
-                    if (percentChange >= 0.005m && !_isLong)
-                    {
-                        Log($"{UtcTime} - Buying AAPL - {holdingInfo}");
-                        SetHoldings(_aapl, 0.5);
-                        _isLong = true;
-                    }
-                    else if (percentChange <= -0.005m && _isLong)
-                    {
-                        Log($"{UtcTime} - Shorting AAPL - {holdingInfo}");
-                        SetHoldings(_aapl, -0.5);
-                        _isLong = false;
-                    }
-                }
-
-                _lastValue = holdings.UsersHolding;
-            }
-        }
-    }
-}

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

@@ -0,0 +1,344 @@
+/*
+ * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
+ * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+*/
+
+using QuantConnect.Data;
+using QuantConnect.Data.Market;
+using QuantConnect.Interfaces;
+using QuantConnect.Orders;
+using QuantConnect.Orders.Fees;
+using QuantConnect.Orders.Fills;
+using QuantConnect.Securities;
+using QuantConnect.Securities.Option;
+using System;
+using System.Collections.Generic;
+using System.Linq;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// This regression algorithm tests the order processing of the backtesting brokerage.
+    /// We open an equity position that should fill in two parts, on two different bars.
+    /// We open a long option position and let it expire so we can exercise the position.
+    /// To check the orders we use OnOrderEvent and throw exceptions if verification fails.
+    /// </summary>
+    /// <meta name="tag" content="backtesting brokerage" />
+    /// <meta name="tag" content="regression test" />
+    /// <meta name="tag" content="options" />
+    class BacktestingBrokerageRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private Security _security;
+        private Symbol _spy;
+        private OrderTicket _equityBuy;
+        private Option _option;
+        private Symbol _optionSymbol;
+        private OrderTicket _optionBuy;
+        private bool _optionBought = false;
+        private bool _equityBought = false;
+        private decimal _optionStrikePrice;
+
+        /// <summary>
+        /// Initialize the algorithm
+        /// </summary>
+        public override void Initialize()
+        {
+            SetCash(100000);
+            SetStartDate(2015, 12, 24);
+            SetEndDate(2015, 12, 28);
+
+            // Get our equity
+            _security = AddEquity("SPY", Resolution.Hour);
+            _security.SetFillModel(new PartialMarketFillModel(2));
+            _spy = _security.Symbol;
+
+            // Get our option
+            _option = AddOption("GOOG");
+            _option.SetFilter(u => u.IncludeWeeklys()
+                                   .Strikes(-2, +2)
+                                   .Expiration(TimeSpan.Zero, TimeSpan.FromDays(10)));
+            _optionSymbol = _option.Symbol;
+        }
+
+        /// <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 (!_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;
+            }
+
+            if (!_optionBought)
+            {
+                // Buy our option
+                OptionChain chain;
+                if (data.OptionChains.TryGetValue(_optionSymbol, out chain))
+                {
+                    // Find the second call strike under market price expiring today
+                    var contracts = (
+                        from optionContract in chain.OrderByDescending(x => x.Strike)
+                        where optionContract.Right == OptionRight.Call
+                        where optionContract.Expiry == Time.Date
+                        where optionContract.Strike < chain.Underlying.Price
+                        select optionContract
+                        ).Take(2);
+
+                    if (contracts.Any())
+                    {
+                        var optionToBuy = contracts.FirstOrDefault();
+                        _optionStrikePrice = optionToBuy.Strike;
+                        _optionBuy = MarketOrder(optionToBuy.Symbol, 1);
+                        _optionBought = true;
+                    }
+                }
+            }
+        }
+
+        /// <summary>
+        /// All order events get pushed through this function
+        /// </summary>
+        /// <param name="orderEvent">OrderEvent object that contains all the information about the event</param>
+        public override void OnOrderEvent(OrderEvent orderEvent)
+        {
+            // Get the order from our transactions
+            var order = Transactions.GetOrderById(orderEvent.OrderId);
+
+            // Based on the type verify the order
+            switch (order.Type)
+            {
+                case OrderType.Market:
+                    VerifyMarketOrder(order, orderEvent);
+                    break;
+
+                case OrderType.OptionExercise:
+                    VerifyOptionExercise(order, orderEvent);
+                    break;
+
+                default:
+                    throw new ArgumentOutOfRangeException();
+            }
+        }
+
+        /// <summary>
+        /// To verify Market orders is process correctly
+        /// </summary>
+        /// <param name="order">Order object to analyze</param>
+        public void VerifyMarketOrder(Order order, OrderEvent orderEvent)
+        {
+            switch (order.Status)
+            {
+                case OrderStatus.Submitted:
+                    break;
+
+                // All PartiallyFilled orders should have a LastFillTime
+                case OrderStatus.PartiallyFilled:
+                    if (order.LastFillTime == null)
+                    {
+                        throw new Exception("LastFillTime should not be null");
+                    }
+
+                    if (order.Quantity / 2 != orderEvent.FillQuantity)
+                    {
+                        throw new Exception("Order size should be half");
+                    }
+                    break;
+
+                // All filled equity orders should have filled after creation because of our fill model!
+                case OrderStatus.Filled:
+                    if (order.SecurityType == SecurityType.Equity && order.CreatedTime == order.LastFillTime)
+                    {
+                        throw new Exception("Order should not finish during the CreatedTime bar");
+                    }
+                    break;
+
+                default:
+                    throw new ArgumentOutOfRangeException();
+            }
+        }
+
+        /// <summary>
+        /// To verify OptionExercise orders is process correctly
+        /// </summary>
+        /// <param name="order">Order object to analyze</param>
+        public void VerifyOptionExercise(Order order, OrderEvent orderEvent)
+        {
+            // If the option price isn't the same as the strike price, its incorrect
+            if (order.Price != _optionStrikePrice)
+            {
+                throw new Exception("OptionExercise order price should be strike price!!");
+            }
+
+            if (orderEvent.Quantity != -1)
+            {
+                throw new Exception("OrderEvent Quantity should be -1");
+            }
+        }
+
+        /// <summary>
+        /// Runs after algorithm, used to check our portfolio and orders
+        /// </summary>
+        public override void OnEndOfAlgorithm()
+        {
+            if (!Portfolio.ContainsKey(_optionBuy.Symbol) || !Portfolio.ContainsKey(_optionBuy.Symbol.Underlying) || !Portfolio.ContainsKey(_equityBuy.Symbol))
+            {
+                throw new Exception("Portfolio does not contain the Symbols we purchased");
+            }
+
+            //Check option holding, should not be invested since it expired, profit should be -400
+            var optionHolding = Portfolio[_optionBuy.Symbol];
+            if (optionHolding.Invested || optionHolding.Profit != -400)
+            {
+                throw new Exception("Options holding does not match expected outcome");
+            }
+
+            //Check the option underlying symbol since we should have bought it at exercise
+            //Quantity should be 100, AveragePrice should be option strike price
+            var optionExerciseHolding = Portfolio[_optionBuy.Symbol.Underlying];
+            if (!optionExerciseHolding.Invested || optionExerciseHolding.Quantity != 100 || optionExerciseHolding.AveragePrice != _optionBuy.Symbol.ID.StrikePrice)
+            {
+                throw new Exception("Equity holding for exercised option does not match expected outcome");
+            }
+
+            //Check equity holding, should be invested, profit should be
+            //Quantity should be 52, AveragePrice should be ticket AverageFillPrice
+            var equityHolding = Portfolio[_equityBuy.Symbol];
+            if (!equityHolding.Invested || equityHolding.Quantity != 52 || equityHolding.AveragePrice != _equityBuy.AverageFillPrice)
+            {
+                throw new Exception("Equity holding does not match expected outcome");
+            }
+        }
+
+        /// <summary>
+        /// PartialMarketFillModel that allows the user to set the number of fills and restricts
+        /// the fill to only one per bar.
+        /// </summary>
+        private class PartialMarketFillModel : ImmediateFillModel
+        {
+            private readonly decimal _percent;
+            private readonly Dictionary<long, decimal> _absoluteRemainingByOrderId = new Dictionary<long, decimal>();
+
+            /// <param name="numberOfFills"></param>
+            public PartialMarketFillModel(int numberOfFills = 1)
+            {
+                _percent = 1m / numberOfFills;
+            }
+
+            /// <summary>
+            /// Performs partial market fills once per time step
+            /// </summary>
+            /// <param name="asset">The security being ordered</param>
+            /// <param name="order">The order</param>
+            /// <returns>The order fill</returns>
+            public override OrderEvent MarketFill(Security asset, MarketOrder order)
+            {
+                var currentUtcTime = asset.LocalTime.ConvertToUtc(asset.Exchange.TimeZone);
+
+                // Only fill once a time slice
+                if (order.LastFillTime != null && currentUtcTime <= order.LastFillTime)
+                {
+                    return new OrderEvent(order, currentUtcTime, OrderFee.Zero);
+                }
+
+                decimal absoluteRemaining;
+                if (!_absoluteRemainingByOrderId.TryGetValue(order.Id, out absoluteRemaining))
+                {
+                    absoluteRemaining = order.AbsoluteQuantity;
+                    _absoluteRemainingByOrderId.Add(order.Id, order.AbsoluteQuantity);
+                }
+
+                var fill = base.MarketFill(asset, order);
+                var absoluteFillQuantity = (int)(Math.Min(absoluteRemaining, (int)(_percent * order.Quantity)));
+                fill.FillQuantity = Math.Sign(order.Quantity) * absoluteFillQuantity;
+
+                if (absoluteRemaining == absoluteFillQuantity)
+                {
+                    fill.Status = OrderStatus.Filled;
+                    _absoluteRemainingByOrderId.Remove(order.Id);
+                }
+                else
+                {
+                    absoluteRemaining = absoluteRemaining - absoluteFillQuantity;
+                    _absoluteRemainingByOrderId[order.Id] = absoluteRemaining;
+                    fill.Status = OrderStatus.PartiallyFilled;
+                }
+
+                return fill;
+            }
+        }
+
+        /// <summary>
+        /// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
+        /// </summary>
+        public bool CanRunLocally { get; } = true;
+
+        /// <summary>
+        /// This is used by the regression test system to indicate which languages this algorithm is written in.
+        /// </summary>
+        public Language[] Languages { get; } = { Language.CSharp };
+
+        /// <summary>
+        /// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
+        /// </summary>
+        public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
+        {
+            {"Total Trades", "3"},
+            {"Average Win", "0%"},
+            {"Average Loss", "-0.40%"},
+            {"Compounding Annual Return", "-22.231%"},
+            {"Drawdown", "0.400%"},
+            {"Expectancy", "-1"},
+            {"Net Profit", "-0.321%"},
+            {"Sharpe Ratio", "-11.083"},
+            {"Probabilistic Sharpe Ratio", "0%"},
+            {"Loss Rate", "100%"},
+            {"Win Rate", "0%"},
+            {"Profit-Loss Ratio", "0"},
+            {"Alpha", "-0.003"},
+            {"Beta", "0.097"},
+            {"Annual Standard Deviation", "0.002"},
+            {"Annual Variance", "0"},
+            {"Information Ratio", "9.742"},
+            {"Tracking Error", "0.021"},
+            {"Treynor Ratio", "-0.26"},
+            {"Total Fees", "$2.00"},
+            {"Estimated Strategy Capacity", "$0"},
+            {"Fitness Score", "0.212"},
+            {"Kelly Criterion Estimate", "0"},
+            {"Kelly Criterion Probability Value", "0"},
+            {"Sortino Ratio", "79228162514264337593543950335"},
+            {"Return Over Maximum Drawdown", "-73.565"},
+            {"Portfolio Turnover", "0.425"},
+            {"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", "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", "498372354"}
+            {"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

@@ -222,12 +222,13 @@ namespace QuantConnect.Algorithm.CSharp
             {"Information Ratio", "0"},
             {"Tracking Error", "0"},
             {"Treynor Ratio", "0"},
-            {"Total Fees", "$85.33"},
+            {"Total Fees", "$85.34"},
+            {"Estimated Strategy Capacity", "$0"},
             {"Fitness Score", "0.5"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
             {"Sortino Ratio", "79228162514264337593543950335"},
-            {"Return Over Maximum Drawdown", "-43.917"},
+            {"Return Over Maximum Drawdown", "-43.943"},
             {"Portfolio Turnover", "1.028"},
             {"Total Insights Generated", "0"},
             {"Total Insights Closed", "0"},
@@ -242,7 +243,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "1073240275"}
+            {"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

@@ -1,4 +1,4 @@
-/*
+/*
  * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
  * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
  *
@@ -15,9 +15,11 @@
 */
 
 using System;
+using System.Collections.Generic;
 using System.Linq;
 using QuantConnect.Data;
 using QuantConnect.Data.Market;
+using QuantConnect.Interfaces;
 using QuantConnect.Orders;
 using QuantConnect.Securities;
 
@@ -31,7 +33,7 @@ namespace QuantConnect.Algorithm.CSharp
     /// <meta name="tag" content="using data" />
     /// <meta name="tag" content="options" />
     /// <meta name="tag" content="filter selection" />
-    public class BasicTemplateOptionsFilterUniverseAlgorithm : QCAlgorithm
+    public class BasicTemplateOptionsFilterUniverseAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
     {
         private const string UnderlyingTicker = "GOOG";
         public Symbol OptionSymbol;
@@ -40,20 +42,17 @@ namespace QuantConnect.Algorithm.CSharp
         {
             SetStartDate(2015, 12, 24);
             SetEndDate(2015, 12, 24);
-            SetCash(10000);
+            SetCash(100000);
 
             var equity = AddEquity(UnderlyingTicker);
             var option = AddOption(UnderlyingTicker);
+            OptionSymbol = option.Symbol;
 
-            // set our custom filter for this option chain
-            option.SetFilter(universe => from symbol in universe
-                                                          .WeeklysOnly()
-                                                           // Expiration method accepts TimeSpan objects or integer for days.
-                                                           // The following statements yield the same filtering criteria
-                                                          .Expiration(0, 10)
-                                                          // .Expiration(TimeSpan.Zero, TimeSpan.FromDays(10))
+            // Set our custom universe filter, Expires today, is a call, and is within 10 dollars of the current price
+            option.SetFilter(universe => from symbol in universe.WeeklysOnly().Expiration(0, 1)
                                          where symbol.ID.OptionRight != OptionRight.Put &&
-                                              universe.Underlying.Price - symbol.ID.StrikePrice < 60
+                                              -10 < universe.Underlying.Price - symbol.ID.StrikePrice &&
+                                              universe.Underlying.Price - symbol.ID.StrikePrice < 10
                                          select symbol);
 
             // use the underlying equity as the benchmark
@@ -67,14 +66,13 @@ namespace QuantConnect.Algorithm.CSharp
                 OptionChain chain;
                 if (slice.OptionChains.TryGetValue(OptionSymbol, out chain))
                 {
-                    // find the second call strike under market price expiring today
+                    // Get the first ITM call expiring today
                     var contract = (
                         from optionContract in chain.OrderByDescending(x => x.Strike)
-                        where optionContract.Right == OptionRight.Call
                         where optionContract.Expiry == Time.Date
                         where optionContract.Strike < chain.Underlying.Price
                         select optionContract
-                        ).Skip(2).FirstOrDefault();
+                        ).FirstOrDefault();
 
                     if (contract != null)
                     {
@@ -88,5 +86,63 @@ namespace QuantConnect.Algorithm.CSharp
         {
             Log(orderEvent.ToString());
         }
+
+        /// <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", "2"},
+            {"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", "$1.00"},
+            {"Estimated Strategy Capacity", "$0"},
+            {"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", "92d8a50efe230524512404dab66b19dd"}
+        };
     }
 }

Algorithm.CSharp/BasicTemplateOptionsFrameworkAlgorithm.cs

@@ -160,7 +160,8 @@ namespace QuantConnect.Algorithm.CSharp
             {"Information Ratio", "0"},
             {"Tracking Error", "0"},
             {"Treynor Ratio", "0"},
-            {"Total Fees", "$4.00"},
+            {"Total Fees", "$3.00"},
+            {"Estimated Strategy Capacity", "$74000.00"},
             {"Fitness Score", "0"},
             {"Kelly Criterion Estimate", "0.327"},
             {"Kelly Criterion Probability Value", "1"},
@@ -180,7 +181,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "50.0482%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "352959406"}
+            {"OrderListHash", "ce06ddfa4b2ffeb666a8910ac8836992"}
         };
     }
 }

Algorithm.CSharp/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

@@ -147,12 +147,13 @@ 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"},
             {"Sortino Ratio", "79228162514264337593543950335"},
-            {"Return Over Maximum Drawdown", "-30.28"},
-            {"Portfolio Turnover", "1.029"},
+            {"Return Over Maximum Drawdown", "-30.158"},
+            {"Portfolio Turnover", "1.033"},
             {"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", "-1708974186"}
+            {"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

@@ -0,0 +1,190 @@
+/*
+ * 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.Data.UniverseSelection;
+using QuantConnect.Interfaces;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Demonstration of how to chain a coarse and fine universe selection with an option chain universe selection model
+    /// that will add and remove an <see cref="OptionChainUniverse"/> for each symbol selected on fine
+    /// </summary>
+    public class CoarseFineOptionUniverseChainRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        // initialize our changes to nothing
+        private SecurityChanges _changes = SecurityChanges.None;
+        private int _optionCount;
+        private Symbol _lastEquityAdded;
+        private Symbol _aapl;
+        private Symbol _twx;
+
+        public override void Initialize()
+        {
+            _twx = QuantConnect.Symbol.Create("TWX", SecurityType.Equity, Market.USA);
+            _aapl = QuantConnect.Symbol.Create("AAPL", SecurityType.Equity, Market.USA);
+            UniverseSettings.Resolution = Resolution.Minute;
+
+            SetStartDate(2014, 06, 05);
+            SetEndDate(2014, 06, 06);
+
+            var selectionUniverse = AddUniverse(enumerable => new[] { Time.Date <= new DateTime(2014, 6, 5) ? _twx : _aapl },
+                enumerable => new[] { Time.Date <= new DateTime(2014, 6, 5) ? _twx : _aapl });
+
+            AddUniverseOptions(selectionUniverse, universe =>
+            {
+                if (universe.Underlying == null)
+                {
+                    throw new Exception("Underlying data point is null! This shouldn't happen, each OptionChainUniverse handles and should provide this");
+                }
+                return universe.IncludeWeeklys()
+                    .FrontMonth()
+                    .Contracts(universe.Take(5));
+            });
+        }
+
+        public override void OnData(Slice data)
+        {
+            // if we have no changes, do nothing
+            if (_changes == SecurityChanges.None ||
+                _changes.AddedSecurities.Any(security => security.Price == 0))
+            {
+                return;
+            }
+
+            // liquidate removed securities
+            foreach (var security in _changes.RemovedSecurities)
+            {
+                if (security.Invested)
+                {
+                    Liquidate(security.Symbol);
+                }
+            }
+
+            foreach (var security in _changes.AddedSecurities)
+            {
+                if (!security.Symbol.HasUnderlying)
+                {
+                    _lastEquityAdded = security.Symbol;
+                }
+                else
+                {
+                    // options added should all match prev added security
+                    if (security.Symbol.Underlying != _lastEquityAdded)
+                    {
+                        throw new Exception($"Unexpected symbol added {security.Symbol}");
+                    }
+
+                    _optionCount++;
+                }
+
+                SetHoldings(security.Symbol, 0.05m);
+
+                var config = SubscriptionManager.SubscriptionDataConfigService.GetSubscriptionDataConfigs(security.Symbol).ToList();
+
+                if (!config.Any())
+                {
+                    throw new Exception($"Was expecting configurations for {security.Symbol}");
+                }
+                if (config.Any(dataConfig => dataConfig.DataNormalizationMode != DataNormalizationMode.Raw))
+                {
+                    throw new Exception($"Was expecting DataNormalizationMode.Raw configurations for {security.Symbol}");
+                }
+            }
+            _changes = SecurityChanges.None;
+        }
+
+        public override void OnSecuritiesChanged(SecurityChanges changes)
+        {
+            _changes += changes;
+        }
+
+        public override void OnEndOfAlgorithm()
+        {
+            var config = SubscriptionManager.Subscriptions.ToList();
+            if (config.Any(dataConfig => dataConfig.Symbol == _twx || dataConfig.Symbol.Underlying == _twx))
+            {
+                throw new Exception($"Was NOT expecting any configurations for {_twx} or it's options, since coarse/fine should have deselected it");
+            }
+
+            if (_optionCount == 0)
+            {
+                throw new Exception("Option universe chain did not add any option!");
+            }
+        }
+
+        /// <summary>
+        /// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
+        /// </summary>
+        public 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", "13"},
+            {"Average Win", "0.65%"},
+            {"Average Loss", "-0.05%"},
+            {"Compounding Annual Return", "3216040423556140000000000%"},
+            {"Drawdown", "0.500%"},
+            {"Expectancy", "1.393"},
+            {"Net Profit", "32.840%"},
+            {"Sharpe Ratio", "7142722224839133"},
+            {"Probabilistic Sharpe Ratio", "0%"},
+            {"Loss Rate", "83%"},
+            {"Win Rate", "17%"},
+            {"Profit-Loss Ratio", "13.36"},
+            {"Alpha", "25946898967164744"},
+            {"Beta", "66.241"},
+            {"Annual Standard Deviation", "3.633"},
+            {"Annual Variance", "13.196"},
+            {"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"},
+            {"Sortino Ratio", "79228162514264337593543950335"},
+            {"Return Over Maximum Drawdown", "79228162514264337593543950335"},
+            {"Portfolio Turnover", "0.232"},
+            {"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", "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/CoarseTiingoNewsUniverseSelectionAlgorithm.cs

@@ -0,0 +1,119 @@
+/*
+ * 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.Interfaces;
+using System.Collections.Generic;
+using System.Linq;
+using QuantConnect.Data;
+using QuantConnect.Data.Custom.Tiingo;
+using QuantConnect.Data.UniverseSelection;
+using QuantConnect.Securities;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Example algorithm of a custom universe selection using coarse data and adding TiingoNews
+    /// If conditions are met will add the underlying and trade it
+    /// </summary>
+    public class CoarseTiingoNewsUniverseSelectionAlgorithm : QCAlgorithm
+    {
+        private const int NumberOfSymbols = 3;
+        private List<Symbol> _symbols;
+
+        public override void Initialize()
+        {
+            SetStartDate(2014, 03, 24);
+            SetEndDate(2014, 04, 07);
+
+            UniverseSettings.FillForward = false;
+
+            AddUniverse(new CustomDataCoarseFundamentalUniverse(UniverseSettings, SecurityInitializer, CoarseSelectionFunction));
+
+            _symbols = new List<Symbol>();
+        }
+
+        // sort the data by daily dollar volume and take the top 'NumberOfSymbols'
+        public IEnumerable<Symbol> CoarseSelectionFunction(IEnumerable<CoarseFundamental> coarse)
+        {
+            // sort descending by daily dollar volume
+            var sortedByDollarVolume = coarse.OrderByDescending(x => x.DollarVolume);
+
+            // take the top entries from our sorted collection
+            var top = sortedByDollarVolume.Take(NumberOfSymbols);
+
+            // we need to return only the symbol objects
+            return top.Select(x => QuantConnect.Symbol.CreateBase(typeof(TiingoNews), x.Symbol, x.Symbol.ID.Market));
+        }
+
+        public override void OnData(Slice data)
+        {
+            var articles = data.Get<TiingoNews>();
+
+            foreach (var kvp in articles)
+            {
+                var news = kvp.Value;
+                if (news.Title.IndexOf("Stocks Drop", 0, StringComparison.CurrentCultureIgnoreCase) != -1)
+                {
+                    if (!Securities.ContainsKey(kvp.Key.Underlying))
+                    {
+                        // add underlying we want to trade
+                        AddSecurity(kvp.Key.Underlying);
+                        _symbols.Add(kvp.Key.Underlying);
+                    }
+                }
+            }
+
+            foreach (var symbol in _symbols)
+            {
+                if (Securities[symbol].HasData)
+                {
+                    SetHoldings(symbol, 1m / _symbols.Count);
+                }
+            }
+        }
+
+        public override void OnSecuritiesChanged(SecurityChanges changes)
+        {
+            changes.FilterCustomSecurities = false;
+            Log($"{Time} {changes}");
+        }
+
+        private class CustomDataCoarseFundamentalUniverse : CoarseFundamentalUniverse
+        {
+            public CustomDataCoarseFundamentalUniverse(UniverseSettings universeSettings, ISecurityInitializer securityInitializer, Func<IEnumerable<CoarseFundamental>, IEnumerable<Symbol>> selector)
+                : base(universeSettings, securityInitializer, selector)
+            { }
+
+            public override IEnumerable<SubscriptionRequest> GetSubscriptionRequests(Security security, DateTime currentTimeUtc, DateTime maximumEndTimeUtc,
+                ISubscriptionDataConfigService subscriptionService)
+            {
+                var config = subscriptionService.Add(
+                    typeof(TiingoNews),
+                    security.Symbol,
+                    UniverseSettings.Resolution,
+                    UniverseSettings.FillForward,
+                    UniverseSettings.ExtendedMarketHours,
+                    dataNormalizationMode: UniverseSettings.DataNormalizationMode);
+                return new[]{new SubscriptionRequest(isUniverseSubscription: false,
+                    universe: this,
+                    security: security,
+                    configuration: config,
+                    startTimeUtc: currentTimeUtc,
+                    endTimeUtc: maximumEndTimeUtc)};
+            }
+        }
+    }
+}

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", "-1.416"},
-            {"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

@@ -38,6 +38,10 @@ namespace QuantConnect.Algorithm.CSharp
             // Find more symbols here: http://quantconnect.com/data
             AddSecurity(SecurityType.Equity, "SPY", Resolution.Second);
 
+            // Disabling the benchmark / setting to a fixed value
+            // SetBenchmark(time => 0);
+
+            // Set the benchmark to AAPL US Equity
             SetBenchmark("AAPL");
         }
 
@@ -78,28 +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"},
@@ -114,7 +119,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "2069976135"}
+            {"OrderListHash", "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

@@ -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;
+using QuantConnect.Interfaces;
+using QuantConnect.Securities;
+using System.Collections.Generic;
+using QuantConnect.Data;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Demonstration of using custom buying power model in backtesting.
+    /// QuantConnect allows you to model all orders as deeply and accurately as you need.
+    /// </summary>
+    /// <meta name="tag" content="trading and orders" />
+    /// <meta name="tag" content="transaction fees and slippage" />
+    /// <meta name="tag" content="custom buying power models" />
+    public class CustomBuyingPowerModelAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private Symbol _spy;
+
+        public override void Initialize()
+        {
+            SetStartDate(2013, 10, 01);
+            SetEndDate(2013, 10, 31);
+            var security = AddEquity("SPY", Resolution.Hour);
+            _spy = security.Symbol;
+
+            // set the buying power model
+            security.SetBuyingPowerModel(new CustomBuyingPowerModel());
+        }
+
+        public override void OnData(Slice slice)
+        {
+            if (Portfolio.Invested)
+            {
+                return;
+            }
+
+            var quantity = CalculateOrderQuantity(_spy, 1m);
+            if (quantity % 100 != 0)
+            {
+                throw new Exception($"CustomBuyingPowerModel only allow quantity that is multiple of 100 and {quantity} was found");
+            }
+
+            // We normally get insufficient buying power model, but the
+            // CustomBuyingPowerModel always says that there is sufficient buying power for the orders
+            MarketOrder(_spy, quantity * 10);
+        }
+
+        public class CustomBuyingPowerModel : BuyingPowerModel
+        {
+            public override GetMaximumOrderQuantityResult GetMaximumOrderQuantityForTargetBuyingPower(
+                GetMaximumOrderQuantityForTargetBuyingPowerParameters parameters)
+            {
+                var quantity = base.GetMaximumOrderQuantityForTargetBuyingPower(parameters).Quantity;
+                quantity = Math.Floor(quantity / 100) * 100;
+                return new GetMaximumOrderQuantityResult(quantity);
+            }
+
+            public override HasSufficientBuyingPowerForOrderResult HasSufficientBuyingPowerForOrder(
+                HasSufficientBuyingPowerForOrderParameters parameters)
+            {
+                // 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();
+            }
+        }
+
+        /// <summary>
+        /// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
+        /// </summary>
+        public bool CanRunLocally { get; } = true;
+
+        /// <summary>
+        /// This is used by the regression test system to indicate which languages this algorithm is written in.
+        /// </summary>
+        public Language[] Languages { get; } = { Language.CSharp, Language.Python };
+
+        /// <summary>
+        /// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
+        /// </summary>
+        public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
+        {
+            {"Total Trades", "1"},
+            {"Average Win", "0%"},
+            {"Average Loss", "0%"},
+            {"Compounding Annual Return", "4775.196%"},
+            {"Drawdown", "21.600%"},
+            {"Expectancy", "0"},
+            {"Net Profit", "38.619%"},
+            {"Sharpe Ratio", "33.779"},
+            {"Probabilistic Sharpe Ratio", "77.029%"},
+            {"Loss Rate", "0%"},
+            {"Win Rate", "0%"},
+            {"Profit-Loss Ratio", "0"},
+            {"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"},
+            {"Estimated Strategy Capacity", "$19000000.00"},
+            {"Fitness Score", "0.395"},
+            {"Kelly Criterion Estimate", "0"},
+            {"Kelly Criterion Probability Value", "0"},
+            {"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"},
+            {"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", "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

@@ -0,0 +1,262 @@
+/*
+ * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
+ * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+*/
+
+using System;
+using System.Collections.Generic;
+using System.Globalization;
+using Newtonsoft.Json;
+using QuantConnect.Data;
+using QuantConnect.Interfaces;
+using QuantConnect.Securities;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Regression test to demonstrate setting custom Symbol Properties and Market Hours for a custom data import
+    /// </summary>
+    /// <meta name="tag" content="using data" />
+    /// <meta name="tag" content="custom data" />
+    /// <meta name="tag" content="crypto" />
+    /// <meta name="tag" content="regression test" />
+    public class CustomDataPropertiesRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private string _ticker = "BTC";
+        private Security _bitcoin;
+
+        /// <summary>
+        /// Initialize the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
+        /// </summary>
+        public override void Initialize()
+        {
+            SetStartDate(2011, 9, 13);
+            SetEndDate(2015, 12, 01);
+
+            //Set the cash for the strategy:
+            SetCash(100000);
+
+            // Define our custom data properties and exchange hours
+            var properties = new SymbolProperties("Bitcoin", "USD", 1, 0.01m, 0.01m, _ticker);
+            var exchangeHours = SecurityExchangeHours.AlwaysOpen(TimeZones.NewYork);
+
+            // Add the custom data to our algorithm with our custom properties and exchange hours
+            _bitcoin = AddData<Bitcoin>(_ticker, properties, exchangeHours);
+
+            //Verify our symbol properties were changed and loaded into this security
+            if (_bitcoin.SymbolProperties != properties)
+            {
+                throw new Exception("Failed to set and retrieve custom SymbolProperties for BTC");
+            }
+
+            //Verify our exchange hours were changed and loaded into this security
+            if (_bitcoin.Exchange.Hours != exchangeHours)
+            {
+                throw new Exception("Failed to set and retrieve custom ExchangeHours for BTC");
+            }
+
+            // For regression purposes on AddData overloads, this call is simply to ensure Lean can accept this
+            // with default params and is not routed to a breaking function.
+            AddData<Bitcoin>("BTCUSD");
+        }
+
+        /// <summary>
+        /// Event Handler for Bitcoin Data Events: These Bitcoin objects are created from our
+        /// "Bitcoin" type below and fired into this event handler.
+        /// </summary>
+        /// <param name="data">One(1) Bitcoin Object, streamed into our algorithm synchronized in time with our other data streams</param>
+        public void OnData(Bitcoin data)
+        {
+            //If we don't have any bitcoin "SHARES" -- invest"
+            if (!Portfolio.Invested)
+            {
+                //Bitcoin used as a tradable asset, like stocks, futures etc.
+                if (data.Close != 0)
+                {
+                    //Access custom data symbols using <ticker>.<custom-type>
+                    Order("BTC.Bitcoin", Portfolio.MarginRemaining / Math.Abs(data.Close + 1));
+                }
+            }
+        }
+
+        public override void OnEndOfAlgorithm()
+        {
+            // Reset our Symbol property value, for testing purposes.
+            SymbolPropertiesDatabase.SetEntry(Market.USA, MarketHoursDatabase.GetDatabaseSymbolKey(_bitcoin.Symbol), SecurityType.Base,
+                SymbolProperties.GetDefault("USD"));
+        }
+
+        /// <summary>
+        /// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
+        /// </summary>
+        public bool CanRunLocally { get; } = true;
+
+        /// <summary>
+        /// This is used by the regression test system to indicate which languages this algorithm is written in.
+        /// </summary>
+        public Language[] Languages { get; } = { Language.CSharp, Language.Python };
+
+        /// <summary>
+        /// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
+        /// </summary>
+        public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
+        {
+            {"Total Trades", "1"},
+            {"Average Win", "0%"},
+            {"Average Loss", "0%"},
+            {"Compounding Annual Return", "157.498%"},
+            {"Drawdown", "84.800%"},
+            {"Expectancy", "0"},
+            {"Net Profit", "5319.081%"},
+            {"Sharpe Ratio", "2.086"},
+            {"Probabilistic Sharpe Ratio", "69.456%"},
+            {"Loss Rate", "0%"},
+            {"Win Rate", "0%"},
+            {"Profit-Loss Ratio", "0"},
+            {"Alpha", "1.736"},
+            {"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", "2.269"},
+            {"Return Over Maximum Drawdown", "1.858"},
+            {"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", "0d80bb47bd16b5bc6989a4c1c7aa8349"}
+        };
+
+        /// <summary>
+        /// Custom Data Type: Bitcoin data from Quandl - http://www.quandl.com/help/api-for-bitcoin-data
+        /// </summary>
+        public class Bitcoin : BaseData
+        {
+            [JsonProperty("timestamp")]
+            public int Timestamp = 0;
+            [JsonProperty("open")]
+            public decimal Open = 0;
+            [JsonProperty("high")]
+            public decimal High = 0;
+            [JsonProperty("low")]
+            public decimal Low = 0;
+            [JsonProperty("last")]
+            public decimal Close = 0;
+            [JsonProperty("bid")]
+            public decimal Bid = 0;
+            [JsonProperty("ask")]
+            public decimal Ask = 0;
+            [JsonProperty("vwap")]
+            public decimal WeightedPrice = 0;
+            [JsonProperty("volume")]
+            public decimal VolumeBTC = 0;
+            public decimal VolumeUSD = 0;
+
+            /// <summary>
+            /// 1. DEFAULT CONSTRUCTOR: Custom data types need a default constructor.
+            /// We search for a default constructor so please provide one here. It won't be used for data, just to generate the "Factory".
+            /// </summary>
+            public Bitcoin()
+            {
+                Symbol = "BTC";
+            }
+
+            /// <summary>
+            /// 2. RETURN THE STRING URL SOURCE LOCATION FOR YOUR DATA:
+            /// This is a powerful and dynamic select source file method. If you have a large dataset, 10+mb we recommend you break it into smaller files. E.g. One zip per year.
+            /// We can accept raw text or ZIP files. We read the file extension to determine if it is a zip file.
+            /// </summary>
+            /// <param name="config">Configuration object</param>
+            /// <param name="date">Date of this source file</param>
+            /// <param name="isLiveMode">true if we're in live mode, false for backtesting mode</param>
+            /// <returns>String URL of source file.</returns>
+            public override SubscriptionDataSource GetSource(SubscriptionDataConfig config, DateTime date, bool isLiveMode)
+            {
+                if (isLiveMode)
+                {
+                    return new SubscriptionDataSource("https://www.bitstamp.net/api/ticker/", SubscriptionTransportMedium.Rest);
+                }
+
+                //return "http://my-ftp-server.com/futures-data-" + date.ToString("Ymd") + ".zip";
+                // OR simply return a fixed small data file. Large files will slow down your backtest
+                return new SubscriptionDataSource("https://www.quantconnect.com/api/v2/proxy/quandl/api/v3/datasets/BCHARTS/BITSTAMPUSD.csv?order=asc&api_key=WyAazVXnq7ATy_fefTqm", SubscriptionTransportMedium.RemoteFile);
+            }
+
+            /// <summary>
+            /// 3. READER METHOD: Read 1 line from data source and convert it into Object.
+            /// Each line of the CSV File is presented in here. The backend downloads your file, loads it into memory and then line by line
+            /// feeds it into your algorithm
+            /// </summary>
+            /// <param name="line">string line from the data source file submitted above</param>
+            /// <param name="config">Subscription data, symbol name, data type</param>
+            /// <param name="date">Current date we're requesting. This allows you to break up the data source into daily files.</param>
+            /// <param name="isLiveMode">true if we're in live mode, false for backtesting mode</param>
+            /// <returns>New Bitcoin Object which extends BaseData.</returns>
+            public override BaseData Reader(SubscriptionDataConfig config, string line, DateTime date, bool isLiveMode)
+            {
+                var coin = new Bitcoin();
+                if (isLiveMode)
+                {
+                    //Example Line Format:
+                    //{"high": "441.00", "last": "421.86", "timestamp": "1411606877", "bid": "421.96", "vwap": "428.58", "volume": "14120.40683975", "low": "418.83", "ask": "421.99"}
+                    try
+                    {
+                        coin = JsonConvert.DeserializeObject<Bitcoin>(line);
+                        coin.EndTime = DateTime.UtcNow.ConvertFromUtc(config.ExchangeTimeZone);
+                        coin.Value = coin.Close;
+                    }
+                    catch { /* Do nothing, possible error in json decoding */ }
+                    return coin;
+                }
+
+                //Example Line Format:
+                //Date      Open   High    Low     Close   Volume (BTC)    Volume (Currency)   Weighted Price
+                //2011-09-13 5.8    6.0     5.65    5.97    58.37138238,    346.0973893944      5.929230648356
+                try
+                {
+                    string[] data = line.Split(',');
+                    coin.Time = DateTime.Parse(data[0], CultureInfo.InvariantCulture);
+                    coin.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);
+                    coin.Close = Convert.ToDecimal(data[4], CultureInfo.InvariantCulture);
+                    coin.VolumeBTC = Convert.ToDecimal(data[5], CultureInfo.InvariantCulture);
+                    coin.VolumeUSD = Convert.ToDecimal(data[6], CultureInfo.InvariantCulture);
+                    coin.WeightedPrice = Convert.ToDecimal(data[7], CultureInfo.InvariantCulture);
+                    coin.Value = coin.Close;
+                }
+                catch { /* Do nothing, skip first title row */ }
+
+                return coin;
+            }
+        }
+    }
+}

Algorithm.CSharp/CustomDataRegressionAlgorithm.cs

@@ -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

@@ -26,11 +26,12 @@ using QuantConnect.Securities;
 namespace QuantConnect.Algorithm.CSharp
 {
     /// <summary>
-    /// Demonstration of using custom fee, slippage and fill models for modelling transactions in backtesting.
+    /// Demonstration of using custom fee, slippage, fill, and buying power models for modelling transactions in backtesting.
     /// QuantConnect allows you to model all orders as deeply and accurately as you need.
     /// </summary>
     /// <meta name="tag" content="trading and orders" />
     /// <meta name="tag" content="transaction fees and slippage" />
+    /// <meta name="tag" content="custom buying power models" />
     /// <meta name="tag" content="custom transaction models" />
     /// <meta name="tag" content="custom slippage models" />
     /// <meta name="tag" content="custom fee models" />
@@ -50,6 +51,7 @@ namespace QuantConnect.Algorithm.CSharp
             _security.SetFeeModel(new CustomFeeModel(this));
             _security.SetFillModel(new CustomFillModel(this));
             _security.SetSlippageModel(new CustomSlippageModel(this));
+            _security.SetBuyingPowerModel(new CustomBuyingPowerModel(this));
         }
 
         public void OnData(TradeBars data)
@@ -60,13 +62,13 @@ namespace QuantConnect.Algorithm.CSharp
             if (Time.Day > 10 && _security.Holdings.Quantity <= 0)
             {
                 var quantity = CalculateOrderQuantity(_spy, .5m);
-                Log("MarketOrder: " + quantity);
+                Log($"MarketOrder: {quantity}");
                 MarketOrder(_spy, quantity, asynchronous: true); // async needed for partial fill market orders
             }
             else if (Time.Day > 20 && _security.Holdings.Quantity >= 0)
             {
                 var quantity = CalculateOrderQuantity(_spy, -.5m);
-                Log("MarketOrder: " + quantity);
+                Log($"MarketOrder: {quantity}");
                 MarketOrder(_spy, quantity, asynchronous: true); // async needed for partial fill market orders
             }
         }
@@ -109,7 +111,7 @@ namespace QuantConnect.Algorithm.CSharp
                     fill.Status = OrderStatus.PartiallyFilled;
                 }
 
-                _algorithm.Log("CustomFillModel: " + fill);
+                _algorithm.Log($"CustomFillModel: {fill}");
 
                 return fill;
             }
@@ -131,7 +133,7 @@ namespace QuantConnect.Algorithm.CSharp
                     1m,
                     parameters.Security.Price*parameters.Order.AbsoluteQuantity*0.00001m);
 
-                _algorithm.Log("CustomFeeModel: " + fee);
+                _algorithm.Log($"CustomFeeModel: {fee}");
                 return new OrderFee(new CashAmount(fee, "USD"));
             }
         }
@@ -150,11 +152,31 @@ namespace QuantConnect.Algorithm.CSharp
                 // custom slippage math
                 var slippage = asset.Price*0.0001m*(decimal) Math.Log10(2*(double) order.AbsoluteQuantity);
 
-                _algorithm.Log("CustomSlippageModel: " + slippage);
+                _algorithm.Log($"CustomSlippageModel: {slippage}");
                 return slippage;
             }
         }
 
+        public class CustomBuyingPowerModel : BuyingPowerModel
+        {
+            private readonly QCAlgorithm _algorithm;
+
+            public CustomBuyingPowerModel(QCAlgorithm algorithm)
+            {
+                _algorithm = algorithm;
+            }
+
+            public override HasSufficientBuyingPowerForOrderResult HasSufficientBuyingPowerForOrder(
+                HasSufficientBuyingPowerForOrderParameters parameters)
+            {
+                // custom behavior: this model will assume that there is always enough buying power
+                var hasSufficientBuyingPowerForOrderResult = new HasSufficientBuyingPowerForOrderResult(true);
+                _algorithm.Log($"CustomBuyingPowerModel: {hasSufficientBuyingPowerForOrderResult.IsSufficient}");
+
+                return hasSufficientBuyingPowerForOrderResult;
+            }
+        }
+
         /// <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>
@@ -171,30 +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"},
@@ -209,7 +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

@@ -0,0 +1,144 @@
+/*
+ * 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.Data.Market;
+using QuantConnect.Interfaces;
+using QuantConnect.Securities;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Algorithm simply fetch one-day history prior current time.
+    /// </summary>
+    public class DailyHistoryForDailyResolutionRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private Symbol[] _symbols = {
+            QuantConnect.Symbol.Create("GBPUSD", SecurityType.Forex, market: Market.FXCM),
+            QuantConnect.Symbol.Create("EURUSD", SecurityType.Forex, market: Market.Oanda),
+            QuantConnect.Symbol.Create("AAPL", SecurityType.Equity, market: Market.USA),
+            QuantConnect.Symbol.Create("BTCUSD", SecurityType.Crypto, market: Market.GDAX),
+            QuantConnect.Symbol.Create("XAUUSD", SecurityType.Cfd, market: Market.Oanda)
+        };
+
+        private HashSet<Symbol> _received = new HashSet<Symbol>();
+
+        public override void Initialize()
+        {
+            SetStartDate(2018, 3, 26);
+            SetEndDate(2018, 4, 10);
+            foreach (var symbol in _symbols)
+            {
+                AddSecurity(symbol, Resolution.Daily);
+            }
+        }
+
+        public override void OnData(Slice data)
+        {
+            using (var enumerator = data.GetEnumerator())
+            {
+                while (enumerator.MoveNext())
+                {
+                    var current = enumerator.Current;
+                    var symbol = current.Key;
+                    _received.Add(symbol);
+
+                    List<BaseData> history;
+
+                    if (current.Value.DataType == MarketDataType.QuoteBar)
+                    {
+                        history = History(1, Resolution.Daily).Get<QuoteBar>(symbol).Cast<BaseData>().ToList();
+                    }
+                    else
+                    {
+                        history = History(1, Resolution.Daily).Get<TradeBar>(symbol).Cast<BaseData>().ToList();
+                    }
+
+                    if (!history.Any()) throw new Exception($"No {symbol} data on the eve of {Time} {Time.DayOfWeek}");
+                }
+            }
+        }
+
+        public override void OnEndOfAlgorithm()
+        {
+            if (_received.Count != _symbols.Length)
+            {
+                throw new Exception($"Data for symbols {string.Join(",", _symbols.Except(_received))} were not received");
+            }
+        }
+
+
+        /// <summary>
+        /// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
+        /// </summary>
+        public bool CanRunLocally { get; } = true;
+
+        /// <summary>
+        /// This is used by the regression test system to indicate which languages this algorithm is written in.
+        /// </summary>
+        public Language[] Languages { get; } = { Language.CSharp };
+
+        /// <summary>
+        /// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
+        /// </summary>
+        public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
+        {
+            {"Total Trades", "0"},
+            {"Average Win", "0%"},
+            {"Average Loss", "0%"},
+            {"Compounding Annual Return", "0%"},
+            {"Drawdown", "0%"},
+            {"Expectancy", "0"},
+            {"Net Profit", "0%"},
+            {"Sharpe Ratio", "0"},
+            {"Probabilistic Sharpe Ratio", "0%"},
+            {"Loss Rate", "0%"},
+            {"Win Rate", "0%"},
+            {"Profit-Loss Ratio", "0"},
+            {"Alpha", "0"},
+            {"Beta", "0"},
+            {"Annual Standard Deviation", "0"},
+            {"Annual Variance", "0"},
+            {"Information Ratio", "-0.098"},
+            {"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"},
+            {"Sortino Ratio", "79228162514264337593543950335"},
+            {"Return Over Maximum Drawdown", "79228162514264337593543950335"},
+            {"Portfolio Turnover", "0"},
+            {"Total Insights Generated", "0"},
+            {"Total Insights Closed", "0"},
+            {"Total Insights Analysis Completed", "0"},
+            {"Long Insight Count", "0"},
+            {"Short Insight Count", "0"},
+            {"Long/Short Ratio", "100%"},
+            {"Estimated Monthly Alpha Value", "$0"},
+            {"Total Accumulated Estimated Alpha Value", "$0"},
+            {"Mean Population Estimated Insight Value", "$0"},
+            {"Mean Population Direction", "0%"},
+            {"Mean Population Magnitude", "0%"},
+            {"Rolling Averaged Population Direction", "0%"},
+            {"Rolling Averaged Population Magnitude", "0%"},
+            {"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
+        };
+    }
+}

Algorithm.CSharp/DailyHistoryForMinuteResolutionRegressionAlgorithm.cs

@@ -0,0 +1,144 @@
+/*
+ * 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.Data.Market;
+using QuantConnect.Interfaces;
+using QuantConnect.Securities;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Algorithm simply fetch one-day history prior current time.
+    /// </summary>
+    public class DailyHistoryForMinuteResolutionRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private Symbol[] _symbols = {
+            QuantConnect.Symbol.Create("GBPUSD", SecurityType.Forex, market: Market.FXCM),
+            QuantConnect.Symbol.Create("EURUSD", SecurityType.Forex, market: Market.Oanda),
+            QuantConnect.Symbol.Create("AAPL", SecurityType.Equity, market: Market.USA),
+            QuantConnect.Symbol.Create("BTCUSD", SecurityType.Crypto, market: Market.GDAX),
+            QuantConnect.Symbol.Create("XAUUSD", SecurityType.Cfd, market: Market.Oanda)
+        };
+
+        private HashSet<Symbol> _received = new HashSet<Symbol>();
+
+        public override void Initialize()
+        {
+            SetStartDate(2018, 3, 26);
+            SetEndDate(2018, 4, 10);
+            foreach (var symbol in _symbols)
+            {
+                AddSecurity(symbol, Resolution.Minute);
+            }
+
+            Schedule.On(DateRules.EveryDay(), TimeRules.Every(TimeSpan.FromHours(1)), MakeHistoryCall);
+        }
+
+        private void MakeHistoryCall()
+        {
+            foreach (var symbol in _symbols)
+            {
+                _received.Add(symbol);
+
+                bool hasHistory = false;
+
+                foreach (var dataType in SubscriptionManager.AvailableDataTypes[symbol.SecurityType])
+                {
+                    if (dataType == TickType.Quote)
+                    {
+                        hasHistory |= History(1, Resolution.Daily).Get<QuoteBar>(symbol).Any();
+                    }
+                    else
+                    {
+                        hasHistory |= History(1, Resolution.Daily).Get<TradeBar>(symbol).Any();
+                    }
+                }
+
+                if (!hasHistory) throw new Exception($"No {symbol} data on the eve of {Time} {Time.DayOfWeek}");
+            }
+        }
+
+        public override void OnEndOfAlgorithm()
+        {
+            if (_received.Count != _symbols.Length)
+            {
+                throw new Exception($"Data for symbols {string.Join(",", _symbols.Except(_received))} were not received");
+            }
+        }
+
+
+        /// <summary>
+        /// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
+        /// </summary>
+        public bool CanRunLocally { get; } = true;
+
+        /// <summary>
+        /// This is used by the regression test system to indicate which languages this algorithm is written in.
+        /// </summary>
+        public Language[] Languages { get; } = { Language.CSharp };
+
+        /// <summary>
+        /// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
+        /// </summary>
+        public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
+        {
+            {"Total Trades", "0"},
+            {"Average Win", "0%"},
+            {"Average Loss", "0%"},
+            {"Compounding Annual Return", "0%"},
+            {"Drawdown", "0%"},
+            {"Expectancy", "0"},
+            {"Net Profit", "0%"},
+            {"Sharpe Ratio", "0"},
+            {"Probabilistic Sharpe Ratio", "0%"},
+            {"Loss Rate", "0%"},
+            {"Win Rate", "0%"},
+            {"Profit-Loss Ratio", "0"},
+            {"Alpha", "0"},
+            {"Beta", "0"},
+            {"Annual Standard Deviation", "0"},
+            {"Annual Variance", "0"},
+            {"Information Ratio", "-0.111"},
+            {"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"},
+            {"Sortino Ratio", "79228162514264337593543950335"},
+            {"Return Over Maximum Drawdown", "79228162514264337593543950335"},
+            {"Portfolio Turnover", "0"},
+            {"Total Insights Generated", "0"},
+            {"Total Insights Closed", "0"},
+            {"Total Insights Analysis Completed", "0"},
+            {"Long Insight Count", "0"},
+            {"Short Insight Count", "0"},
+            {"Long/Short Ratio", "100%"},
+            {"Estimated Monthly Alpha Value", "$0"},
+            {"Total Accumulated Estimated Alpha Value", "$0"},
+            {"Mean Population Estimated Insight Value", "$0"},
+            {"Mean Population Direction", "0%"},
+            {"Mean Population Magnitude", "0%"},
+            {"Rolling Averaged Population Direction", "0%"},
+            {"Rolling Averaged Population Magnitude", "0%"},
+            {"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
+        };
+    }
+}

Algorithm.CSharp/DaylightSavingTimeHistoryRegressionAlgorithm.cs

@@ -0,0 +1,137 @@
+/*
+ * 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.Data.Market;
+using QuantConnect.Interfaces;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Regression test algorithm simply fetch history on boarder of Daylight Saving Time shift
+    /// </summary>
+    public class DaylightSavingTimeHistoryRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private Symbol[] _symbols = new[]
+        {
+            QuantConnect.Symbol.Create("EURUSD", SecurityType.Forex, Market.FXCM),
+            QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA)
+        };
+        /// <summary>
+        /// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
+        /// </summary>
+        public override void Initialize()
+        {
+            SetStartDate(2011, 11, 10);  //Set Start Date
+            SetEndDate(2011, 11, 11);    //Set End Date
+            SetCash(100000);             //Set Strategy Cash
+
+            for (int i = 0; i < _symbols.Length; i++)
+            {
+                var symbol = _symbols[i];
+                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)
+                    .Where(g => g.Count() > 1);
+                if (duplications.Any())
+                {
+                    var time = duplications.First().Key;
+                    throw new Exception($"Duplicated bars were issued for time {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; } = true;
+
+        /// <summary>
+        /// This is used by the regression test system to indicate which languages this algorithm is written in.
+        /// </summary>
+        public Language[] Languages { get; } = { Language.CSharp };
+
+        /// <summary>
+        /// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
+        /// </summary>
+        public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
+        {
+            {"Total Trades", "0"},
+            {"Average Win", "0%"},
+            {"Average Loss", "0%"},
+            {"Compounding Annual Return", "0%"},
+            {"Drawdown", "0%"},
+            {"Expectancy", "0"},
+            {"Net Profit", "0%"},
+            {"Sharpe Ratio", "0"},
+            {"Probabilistic Sharpe Ratio", "0%"},
+            {"Loss Rate", "0%"},
+            {"Win Rate", "0%"},
+            {"Profit-Loss Ratio", "0"},
+            {"Alpha", "0"},
+            {"Beta", "0"},
+            {"Annual Standard Deviation", "0"},
+            {"Annual Variance", "0"},
+            {"Information Ratio", "0"},
+            {"Tracking Error", "0"},
+            {"Treynor Ratio", "0"},
+            {"Total Fees", "$0.00"},
+            {"Estimated Strategy Capacity", "$0"},
+            {"Fitness Score", "0"},
+            {"Kelly Criterion Estimate", "0"},
+            {"Kelly Criterion Probability Value", "0"},
+            {"Sortino Ratio", "79228162514264337593543950335"},
+            {"Return Over Maximum Drawdown", "79228162514264337593543950335"},
+            {"Portfolio Turnover", "0"},
+            {"Total Insights Generated", "0"},
+            {"Total Insights Closed", "0"},
+            {"Total Insights Analysis Completed", "0"},
+            {"Long Insight Count", "0"},
+            {"Short Insight Count", "0"},
+            {"Long/Short Ratio", "100%"},
+            {"Estimated Monthly Alpha Value", "$0"},
+            {"Total Accumulated Estimated Alpha Value", "$0"},
+            {"Mean Population Estimated Insight Value", "$0"},
+            {"Mean Population Direction", "0%"},
+            {"Mean Population Magnitude", "0%"},
+            {"Rolling Averaged Population Direction", "0%"},
+            {"Rolling Averaged Population Magnitude", "0%"},
+            {"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
+        };
+    }
+}

Algorithm.CSharp/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

@@ -0,0 +1,139 @@
+/*
+ * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
+ * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+*/
+
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using QuantConnect.Data;
+using QuantConnect.Interfaces;
+using QuantConnect.Orders;
+using QuantConnect.Securities;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Regression algorithm which reproduces GH issue 4446
+    /// </summary>
+    public class DelistedFutureLiquidateRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private Symbol _contractSymbol;
+
+        /// <summary>
+        /// Initialize your algorithm and add desired assets.
+        /// </summary>
+        public override void Initialize()
+        {
+            SetStartDate(2013, 10, 08);
+            SetEndDate(2013, 12, 30);
+
+            var futureSP500 = AddFuture(Futures.Indices.SP500EMini);
+            futureSP500.SetFilter(0, 182);
+        }
+
+        /// <summary>
+        /// Event - v3.0 DATA EVENT HANDLER: (Pattern) Basic template for user to override for receiving all subscription data in a single event
+        /// </summary>
+        /// <param name="slice">The current slice of data keyed by symbol string</param>
+        public override void OnData(Slice slice)
+        {
+            if (_contractSymbol == null)
+            {
+                foreach (var chain in slice.FutureChains)
+                {
+                    var contract = chain.Value.OrderBy(x => x.Expiry).FirstOrDefault();
+                    // if found, trade it
+                    if (contract != null)
+                    {
+                        _contractSymbol = contract.Symbol;
+                        MarketOrder(_contractSymbol, 1);
+                    }
+                }
+            }
+        }
+
+        public override void OnEndOfAlgorithm()
+        {
+            Log($"{_contractSymbol}: {Securities[_contractSymbol].Invested}");
+            if (Securities[_contractSymbol].Invested)
+            {
+                throw new Exception($"Position should be closed when {_contractSymbol} got delisted {_contractSymbol.ID.Date}");
+            }
+        }
+
+        public override void OnOrderEvent(OrderEvent orderEvent)
+        {
+            Log($"{orderEvent}. Delisting on: {_contractSymbol.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; } = true;
+
+        /// <summary>
+        /// This is used by the regression test system to indicate which languages this algorithm is written in.
+        /// </summary>
+        public Language[] Languages { get; } = { Language.CSharp };
+
+        /// <summary>
+        /// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
+        /// </summary>
+        public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
+        {
+            {"Total Trades", "2"},
+            {"Average Win", "1.63%"},
+            {"Average Loss", "0%"},
+            {"Compounding Annual Return", "7.292%"},
+            {"Drawdown", "1.300%"},
+            {"Expectancy", "0"},
+            {"Net Profit", "1.634%"},
+            {"Sharpe Ratio", "2.495"},
+            {"Probabilistic Sharpe Ratio", "92.298%"},
+            {"Loss Rate", "0%"},
+            {"Win Rate", "100%"},
+            {"Profit-Loss Ratio", "0"},
+            {"Alpha", "0.006"},
+            {"Beta", "0.157"},
+            {"Annual Standard Deviation", "0.033"},
+            {"Annual Variance", "0.001"},
+            {"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"},
+            {"Sortino Ratio", "1.362"},
+            {"Return Over Maximum Drawdown", "9.699"},
+            {"Portfolio Turnover", "0.023"},
+            {"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", "00d6dc8775da38f7f79defad06de240a"}
+        };
+    }
+}

Algorithm.CSharp/DelistingEventsAlgorithm.cs

@@ -45,7 +45,7 @@ namespace QuantConnect.Algorithm.CSharp
             SetEndDate(2007, 05, 25);    //Set End Date
             SetCash(100000);             //Set Strategy Cash
             // Find more symbols here: http://quantconnect.com/data
-            AddSecurity(SecurityType.Equity, "AAA", Resolution.Daily);
+            AddSecurity(SecurityType.Equity, "AAA.1", Resolution.Daily);
             AddSecurity(SecurityType.Equity, "SPY", Resolution.Daily);
         }
 
@@ -58,7 +58,7 @@ namespace QuantConnect.Algorithm.CSharp
             _dataCount += data.Bars.Count;
             if (Transactions.OrdersCount == 0)
             {
-                SetHoldings("AAA", 1);
+                SetHoldings("AAA.1", 1);
                 Debug("Purchased Stock");
             }
 
@@ -71,7 +71,7 @@ namespace QuantConnect.Algorithm.CSharp
 
             // the slice can also contain delisting data: data.Delistings in a dictionary string->Delisting
 
-            var aaa = Securities["AAA"];
+            var aaa = Securities["AAA.1"];
             if (aaa.IsDelisted && aaa.IsTradable)
             {
                 throw new Exception("Delisted security must NOT be tradable");
@@ -160,6 +160,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Tracking Error", "0.155"},
             {"Treynor Ratio", "1.589"},
             {"Total Fees", "$55.05"},
+            {"Estimated Strategy Capacity", "$0"},
             {"Fitness Score", "0.002"},
             {"Kelly Criterion Estimate", "0"},
             {"Kelly Criterion Probability Value", "0"},
@@ -179,7 +180,7 @@ namespace QuantConnect.Algorithm.CSharp
             {"Mean Population Magnitude", "0%"},
             {"Rolling Averaged Population Direction", "0%"},
             {"Rolling Averaged Population Magnitude", "0%"},
-            {"OrderListHash", "-2022527947"}
+            {"OrderListHash", "61f4d3c109fc4b6b9eb14d2e4eec4843"}
         };
     }
 }

Algorithm.CSharp/DelistingFutureOptionRegressionAlgorithm.cs

@@ -0,0 +1,149 @@
+/*
+ * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
+ * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+*/
+
+using System;
+using QuantConnect.Data;
+using QuantConnect.Interfaces;
+using QuantConnect.Securities;
+using System.Collections.Generic;
+using System.Linq;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Regression algorithm reproducing issue #5160 where delisting order would be cancelled because it was placed at the market close on the delisting day
+    /// </summary>
+    public class DelistingFutureOptionRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private bool _traded;
+        private int _lastMonth;
+
+        public override void Initialize()
+        {
+            SetStartDate(2012, 1, 1);
+            SetEndDate(2013, 1, 1);
+            SetCash(10000000);
+
+            var dc = AddFuture(Futures.Dairy.ClassIIIMilk, Resolution.Minute, Market.CME);
+            dc.SetFilter(1, 120);
+
+            AddFutureOption(dc.Symbol, universe => universe.Strikes(-2, 2));
+            _lastMonth = -1;
+        }
+
+        public override void OnData(Slice data)
+        {
+            if (Time.Month != _lastMonth)
+            {
+                _lastMonth = Time.Month;
+                var investedSymbols = Securities.Values
+                    .Where(security => security.Invested)
+                    .Select(security => security.Symbol)
+                    .ToList();
+
+                var delistedSecurity = investedSymbols.Where(symbol => symbol.ID.Date.AddDays(1) < Time).ToList();
+                if (delistedSecurity.Count > 0)
+                {
+                    throw new Exception($"[{UtcTime}] We hold a delisted securities: {string.Join(",", delistedSecurity)}");
+                }
+                Log($"Holdings({Time}): {string.Join(",", investedSymbols)}");
+            }
+
+            if (Portfolio.Invested)
+            {
+                return;
+            }
+
+            foreach (var chain in data.OptionChains.Values)
+            {
+                foreach (var contractsValue in chain.Contracts.Values)
+                {
+                    MarketOrder(contractsValue.Symbol, 1);
+                    _traded = true;
+                }
+            }
+        }
+
+        public override void OnEndOfAlgorithm()
+        {
+            if (!_traded)
+            {
+                throw new Exception("We expected some FOP trading to happen");
+            }
+            if (Portfolio.Invested)
+            {
+                throw new Exception("We shouldn't be invested anymore");
+            }
+        }
+
+        /// <summary>
+        /// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
+        /// </summary>
+        public bool CanRunLocally { get; } = true;
+
+        /// <summary>
+        /// This is used by the regression test system to indicate which languages this algorithm is written in.
+        /// </summary>
+        public Language[] Languages { get; } = { Language.CSharp };
+
+        /// <summary>
+        /// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
+        /// </summary>
+        public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
+        {
+            {"Total Trades", "16"},
+            {"Average Win", "0.01%"},
+            {"Average Loss", "-0.02%"},
+            {"Compounding Annual Return", "-0.111%"},
+            {"Drawdown", "0.100%"},
+            {"Expectancy", "-0.679"},
+            {"Net Profit", "-0.112%"},
+            {"Sharpe Ratio", "-1.052"},
+            {"Probabilistic Sharpe Ratio", "0.000%"},
+            {"Loss Rate", "80%"},
+            {"Win Rate", "20%"},
+            {"Profit-Loss Ratio", "0.61"},
+            {"Alpha", "-0.001"},
+            {"Beta", "-0.001"},
+            {"Annual Standard Deviation", "0.001"},
+            {"Annual Variance", "0"},
+            {"Information Ratio", "-1.182"},
+            {"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"},
+            {"Sortino Ratio", "-0.128"},
+            {"Return Over Maximum Drawdown", "-0.995"},
+            {"Portfolio Turnover", "0"},
+            {"Total Insights Generated", "0"},
+            {"Total Insights Closed", "0"},
+            {"Total Insights Analysis Completed", "0"},
+            {"Long Insight Count", "0"},
+            {"Short Insight Count", "0"},
+            {"Long/Short Ratio", "100%"},
+            {"Estimated Monthly Alpha Value", "$0"},
+            {"Total Accumulated Estimated Alpha Value", "$0"},
+            {"Mean Population Estimated Insight Value", "$0"},
+            {"Mean Population Direction", "0%"},
+            {"Mean Population Magnitude", "0%"},
+            {"Rolling Averaged Population Direction", "0%"},
+            {"Rolling Averaged Population Magnitude", "0%"},
+            {"OrderListHash", "de309ab56d2fcd80ff03df2802d9feda"}
+        };
+    }
+}

Algorithm.CSharp/DisplacedMovingAverageRibbon.cs

@@ -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", "13.284%"},
+            {"Compounding Annual Return", "13.366%"},
             {"Drawdown", "10.700%"},
-            {"Expectancy", "0.061"},
-            {"Net Profit", "13.284%"},
-            {"Sharpe Ratio", "0.96"},
-            {"Probabilistic Sharpe Ratio", "46.111%"},
+            {"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", "0.004"},
+            {"Information Ratio", "-0.021"},
             {"Tracking Error", "0.171"},
-            {"Treynor Ratio", "-1.754"},
-            {"Total Fees", "$8669.33"},
-            {"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", "1.124"},
-            {"Return Over Maximum Drawdown", "1.242"},
-            {"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", "-1120327913"}
+            {"OrderListHash", "a5b4f8473c39a7e9d62659fa9f6a4e2f"}
         };
     }
 }