Compare commits
7 Commits
10855
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feature-op
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2dd94f3687 |
@@ -1,6 +1,4 @@
|
||||
packages/*
|
||||
.git/*
|
||||
.github/*
|
||||
.vs/*
|
||||
.nuget/*
|
||||
Tests/*
|
||||
*/packages/*
|
||||
*/.git/*
|
||||
*/.vs/*
|
||||
*/.nuget/*
|
||||
4
.github/funding.yml
vendored
4
.github/funding.yml
vendored
@@ -1,4 +0,0 @@
|
||||
# These are supported funding model platforms
|
||||
|
||||
github: quantconnect
|
||||
#custom: ['https://github.com/sponsors/QuantConnect']
|
||||
4
.github/pull_request_template.md
vendored
4
.github/pull_request_template.md
vendored
@@ -24,8 +24,6 @@
|
||||
#### Types of changes
|
||||
<!--- What types of changes does your code introduce? Put an `x` in all the boxes that apply: -->
|
||||
- [ ] Bug fix (non-breaking change which fixes an issue)
|
||||
- [ ] Refactor (non-breaking change which improves implementation)
|
||||
- [ ] Performance (non-breaking change which improves performance. Please add associated performance test and results)
|
||||
- [ ] New feature (non-breaking change which adds functionality)
|
||||
- [ ] Breaking change (fix or feature that would cause existing functionality to change)
|
||||
- [ ] Non-functional change (xml comments/documentation/etc)
|
||||
@@ -39,4 +37,4 @@
|
||||
- [ ] All new and existing tests passed.
|
||||
- [ ] My branch follows the naming convention `bug-<issue#>-<description>` or `feature-<issue#>-<description>`
|
||||
|
||||
<!--- Template inspired by https://www.talater.com/open-source-templates/#/page/99 -->
|
||||
<!--- Template inspired by https://www.talater.com/open-source-templates/#/page/99 -->
|
||||
31
.github/workflows/gh-actions.yml
vendored
31
.github/workflows/gh-actions.yml
vendored
@@ -1,31 +0,0 @@
|
||||
name: Build & Test Lean
|
||||
|
||||
on:
|
||||
push:
|
||||
branches: ['*']
|
||||
pull_request:
|
||||
branches: [master]
|
||||
|
||||
jobs:
|
||||
build:
|
||||
runs-on: ubuntu-16.04
|
||||
container:
|
||||
image: quantconnect/lean:foundation
|
||||
steps:
|
||||
- uses: actions/checkout@v2
|
||||
|
||||
- name: Restore nuget dependencies
|
||||
run: |
|
||||
nuget restore QuantConnect.Lean.sln -v quiet
|
||||
nuget install NUnit.Runners -Version 3.11.1 -OutputDirectory testrunner
|
||||
|
||||
- name: Build
|
||||
run: msbuild /p:Configuration=Release /p:VbcToolExe=vbnc.exe /v:quiet /p:WarningLevel=1 QuantConnect.Lean.sln
|
||||
|
||||
- name: Run Tests
|
||||
run: mono ./testrunner/NUnit.ConsoleRunner.3.11.1/tools/nunit3-console.exe ./Tests/bin/Release/QuantConnect.Tests.dll --where "cat != TravisExclude" --labels=Off --params:log-handler=ConsoleErrorLogHandler
|
||||
|
||||
- name: Generate & Publish python stubs
|
||||
run: |
|
||||
chmod +x ci_build_stubs.sh
|
||||
./ci_build_stubs.sh -d -t -g #Ignore Publish as of since credentials are missing on CI
|
||||
13
.gitignore
vendored
13
.gitignore
vendored
@@ -34,9 +34,6 @@
|
||||
|
||||
# QC Cloud Setup Bash Files
|
||||
*.sh
|
||||
# Include docker launch scripts for Mac/Linux
|
||||
!run_docker.sh
|
||||
!research/run_docker_notebook.sh
|
||||
|
||||
# QC Config Files:
|
||||
# config.json
|
||||
@@ -144,7 +141,6 @@ $tf/
|
||||
# ReSharper is a .NET coding add-in
|
||||
_ReSharper*/
|
||||
*.[Rr]e[Ss]harper
|
||||
*.DotSettings
|
||||
*.DotSettings.user
|
||||
|
||||
# JustCode is a .NET coding addin-in
|
||||
@@ -170,9 +166,6 @@ AutoTest.Net/
|
||||
# Installshield output folder
|
||||
[Ee]xpress/
|
||||
|
||||
# JetBrains Rider
|
||||
.idea/
|
||||
|
||||
# DocProject is a documentation generator add-in
|
||||
DocProject/buildhelp/
|
||||
DocProject/Help/*.HxT
|
||||
@@ -269,9 +262,3 @@ Launcher/Plugins/*
|
||||
/ApiPython/quantconnect.egg-info/*
|
||||
|
||||
QuantConnect.Lean.sln.DotSettings*
|
||||
|
||||
#User notebook files
|
||||
Research/Notebooks
|
||||
|
||||
#Docker result files
|
||||
Results/
|
||||
144
.idea/readme.md
generated
144
.idea/readme.md
generated
@@ -1,144 +0,0 @@
|
||||
<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.
|
||||
37
.idea/workspace.xml
generated
37
.idea/workspace.xml
generated
@@ -1,37 +0,0 @@
|
||||
<?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>
|
||||
27
.travis.yml
27
.travis.yml
@@ -1,26 +1,21 @@
|
||||
sudo: required
|
||||
language: csharp
|
||||
dotnet: 5.0
|
||||
mono:
|
||||
- 5.12.0
|
||||
- 5.8.0
|
||||
solution: QuantConnect.Lean.sln
|
||||
before_install:
|
||||
- export PATH="$HOME/miniconda3/bin:$PATH"
|
||||
- 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
|
||||
- wget https://cdn.quantconnect.com/miniconda/Miniconda3-4.3.31-Linux-x86_64.sh
|
||||
- bash Miniconda3-4.3.31-Linux-x86_64.sh -b
|
||||
- rm -rf Miniconda3-4.3.31-Linux-x86_64.sh
|
||||
- sudo ln -s $HOME/miniconda3/lib/libpython3.6m.so /usr/lib/libpython3.6m.so
|
||||
- conda update -y python conda pip
|
||||
- conda install -y python=3.6.8
|
||||
- conda install -y numpy=1.18.1
|
||||
- conda install -y pandas=0.25.3
|
||||
- conda install -y cython=0.29.15
|
||||
- conda install -y scipy=1.4.1
|
||||
- conda install -y wrapt=1.12.1
|
||||
- conda install -y cython pandas scipy
|
||||
install:
|
||||
- nuget install NUnit.Runners -Version 3.11.1 -OutputDirectory testrunner
|
||||
- nuget restore QuantConnect.Lean.sln
|
||||
- nuget install NUnit.Runners -Version 2.6.4 -OutputDirectory testrunner
|
||||
script:
|
||||
- dotnet build /p:Configuration=Release /p:VbcToolExe=vbnc.exe /v:quiet /p:WarningLevel=1 QuantConnect.Lean.sln
|
||||
- mono ./testrunner/NUnit.ConsoleRunner.3.11.1/tools/nunit3-console.exe ./Tests/bin/Release/QuantConnect.Tests.dll --where "cat != TravisExclude" --labels=Off --params:log-handler=ConsoleErrorLogHandler
|
||||
- chmod +x ci_build_stubs.sh
|
||||
- sudo -E ./ci_build_stubs.sh -d -t -g -p
|
||||
- msbuild /p:Configuration=Release /p:VbcToolExe=vbnc.exe QuantConnect.Lean.sln
|
||||
- mono ./testrunner/NUnit.Runners.2.6.4/tools/nunit-console.exe ./Tests/bin/Release/QuantConnect.Tests.dll --exclude:TravisExclude --labels
|
||||
after_success:
|
||||
- coveralls
|
||||
136
.vs/readme.md
136
.vs/readme.md
@@ -1,136 +0,0 @@
|
||||
<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.
|
||||
82
.vscode/launch.json
vendored
82
.vscode/launch.json
vendored
@@ -1,82 +0,0 @@
|
||||
{
|
||||
/*
|
||||
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}"
|
||||
}]
|
||||
}
|
||||
]
|
||||
}
|
||||
206
.vscode/readme.md
vendored
206
.vscode/readme.md
vendored
@@ -1,206 +0,0 @@
|
||||
<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.
|
||||
|
||||
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",
|
||||
|
||||
|
||||
<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.
|
||||
139
.vscode/tasks.json
vendored
139
.vscode/tasks.json
vendored
@@ -1,139 +0,0 @@
|
||||
{
|
||||
/*
|
||||
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/"
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -1,119 +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 System;
|
||||
using System.Collections.Generic;
|
||||
using QuantConnect.Algorithm.Framework.Alphas;
|
||||
using QuantConnect.Algorithm.Framework.Execution;
|
||||
using QuantConnect.Algorithm.Framework.Portfolio;
|
||||
using QuantConnect.Algorithm.Framework.Selection;
|
||||
using QuantConnect.Interfaces;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Test algorithm using <see cref="AccumulativeInsightPortfolioConstructionModel"/> and <see cref="ConstantAlphaModel"/>
|
||||
/// generating a constant <see cref="Insight"/> with a 0.25 confidence
|
||||
/// </summary>
|
||||
public class AccumulativeInsightPortfolioRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
/// <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()
|
||||
{
|
||||
// Set requested data resolution
|
||||
UniverseSettings.Resolution = Resolution.Minute;
|
||||
|
||||
SetStartDate(2013, 10, 07); //Set Start Date
|
||||
SetEndDate(2013, 10, 11); //Set End Date
|
||||
SetCash(100000); //Set Strategy Cash
|
||||
|
||||
// set algorithm framework models
|
||||
SetUniverseSelection(new ManualUniverseSelectionModel(QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA)));
|
||||
SetAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromMinutes(20), 0.025, 0.25));
|
||||
SetPortfolioConstruction(new AccumulativeInsightPortfolioConstructionModel());
|
||||
SetExecution(new ImmediateExecutionModel());
|
||||
}
|
||||
|
||||
public override void OnEndOfAlgorithm()
|
||||
{
|
||||
if (// holdings value should be 0.03 - to avoid price fluctuation issue we compare with 0.06 and 0.01
|
||||
Portfolio.TotalHoldingsValue > Portfolio.TotalPortfolioValue * 0.06m
|
||||
||
|
||||
Portfolio.TotalHoldingsValue < Portfolio.TotalPortfolioValue * 0.01m)
|
||||
{
|
||||
throw new Exception($"Unexpected Total Holdings Value: {Portfolio.TotalHoldingsValue}");
|
||||
}
|
||||
}
|
||||
|
||||
/// <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", "199"},
|
||||
{"Average Win", "0.00%"},
|
||||
{"Average Loss", "0.00%"},
|
||||
{"Compounding Annual Return", "-12.472%"},
|
||||
{"Drawdown", "0.200%"},
|
||||
{"Expectancy", "-0.586"},
|
||||
{"Net Profit", "-0.170%"},
|
||||
{"Sharpe Ratio", "-9.693"},
|
||||
{"Probabilistic Sharpe Ratio", "12.704%"},
|
||||
{"Loss Rate", "79%"},
|
||||
{"Win Rate", "21%"},
|
||||
{"Profit-Loss Ratio", "0.95"},
|
||||
{"Alpha", "-0.149"},
|
||||
{"Beta", "0.037"},
|
||||
{"Annual Standard Deviation", "0.008"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-9.471"},
|
||||
{"Tracking Error", "0.212"},
|
||||
{"Treynor Ratio", "-2.13"},
|
||||
{"Total Fees", "$199.00"},
|
||||
{"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"},
|
||||
{"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", "0a28eedf6304023f5002ef672b489b88"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -1,4 +1,4 @@
|
||||
/*
|
||||
/*
|
||||
* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
|
||||
* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
|
||||
*
|
||||
@@ -110,46 +110,38 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
/// </summary>
|
||||
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "9"},
|
||||
{"Average Win", "0.89%"},
|
||||
{"Average Loss", "-0.27%"},
|
||||
{"Compounding Annual Return", "196.104%"},
|
||||
{"Drawdown", "1.700%"},
|
||||
{"Expectancy", "1.853"},
|
||||
{"Net Profit", "1.498%"},
|
||||
{"Sharpe Ratio", "4.275"},
|
||||
{"Probabilistic Sharpe Ratio", "60.462%"},
|
||||
{"Total Trades", "6"},
|
||||
{"Average Win", "0.91%"},
|
||||
{"Average Loss", "-0.23%"},
|
||||
{"Compounding Annual Return", "214.278%"},
|
||||
{"Drawdown", "1.600%"},
|
||||
{"Expectancy", "2.248"},
|
||||
{"Net Profit", "1.581%"},
|
||||
{"Sharpe Ratio", "2.803"},
|
||||
{"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"},
|
||||
{"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"},
|
||||
{"Portfolio Turnover", "0.411"},
|
||||
{"Profit-Loss Ratio", "3.87"},
|
||||
{"Alpha", "0"},
|
||||
{"Beta", "59.491"},
|
||||
{"Annual Standard Deviation", "0.244"},
|
||||
{"Annual Variance", "0.06"},
|
||||
{"Information Ratio", "2.756"},
|
||||
{"Tracking Error", "0.244"},
|
||||
{"Treynor Ratio", "0.012"},
|
||||
{"Total Fees", "$10.88"},
|
||||
{"Total Insights Generated", "3"},
|
||||
{"Total Insights Closed", "3"},
|
||||
{"Total Insights Analysis Completed", "3"},
|
||||
{"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", "$13262182.1037"},
|
||||
{"Total Accumulated Estimated Alpha Value", "$2284042.4734"},
|
||||
{"Mean Population Estimated Insight Value", "$761347.4911"},
|
||||
{"Mean Population Direction", "100%"},
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "100%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "506e9fe18984ba6e569b2e327030de3a"}
|
||||
{"Rolling Averaged Population Magnitude", "0%"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,210 +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 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"},
|
||||
{"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"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -1,244 +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 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"},
|
||||
{"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"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -1,164 +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 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", "-0.01"},
|
||||
{"Annual Standard Deviation", "0.006"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-3.374"},
|
||||
{"Tracking Error", "0.058"},
|
||||
{"Treynor Ratio", "2.133"},
|
||||
{"Total Fees", "$2.00"},
|
||||
{"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"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -1,216 +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 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", "0.027"},
|
||||
{"Beta", "-0.174"},
|
||||
{"Annual Standard Deviation", "0.006"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-11.586"},
|
||||
{"Tracking Error", "0.042"},
|
||||
{"Treynor Ratio", "0.286"},
|
||||
{"Total Fees", "$2.00"},
|
||||
{"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"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -40,8 +40,8 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
private int _expectedContractIndex;
|
||||
private readonly List<Symbol> _expectedContracts = new List<Symbol>
|
||||
{
|
||||
SymbolRepresentation.ParseOptionTickerOSI("GOOG 151224P00750000"),
|
||||
SymbolRepresentation.ParseOptionTickerOSI("GOOG 151224P00747500"),
|
||||
SymbolRepresentation.ParseOptionTickerOSI("GOOG 151224P00750000"),
|
||||
SymbolRepresentation.ParseOptionTickerOSI("GOOG 151224P00752500")
|
||||
};
|
||||
|
||||
@@ -212,14 +212,13 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
{"Total Trades", "6"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "0%"},
|
||||
{"Drawdown", "0%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "0%"},
|
||||
{"Average Loss", "-0.21%"},
|
||||
{"Compounding Annual Return", "-98.595%"},
|
||||
{"Drawdown", "0.600%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "-0.631%"},
|
||||
{"Sharpe Ratio", "0"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "0"},
|
||||
@@ -229,27 +228,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Information Ratio", "0"},
|
||||
{"Tracking Error", "0"},
|
||||
{"Treynor Ratio", "0"},
|
||||
{"Total Fees", "$6.00"},
|
||||
{"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", "cf8f76fa441c2a5e3b2dbbabcab32cd2"}
|
||||
{"Total Fees", "$6.00"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -74,8 +74,8 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
}
|
||||
else if (Time.DayOfWeek == DayOfWeek.Thursday)
|
||||
{
|
||||
RemoveSecurity(_aig);
|
||||
RemoveSecurity(_bac);
|
||||
RemoveSecurity(_aig);
|
||||
lastAction = Time;
|
||||
}
|
||||
}
|
||||
@@ -112,45 +112,24 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "5"},
|
||||
{"Average Win", "0.47%"},
|
||||
{"Average Win", "0.49%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "293.067%"},
|
||||
{"Compounding Annual Return", "305.340%"},
|
||||
{"Drawdown", "1.400%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "1.765%"},
|
||||
{"Sharpe Ratio", "13.11"},
|
||||
{"Probabilistic Sharpe Ratio", "80.231%"},
|
||||
{"Net Profit", "1.805%"},
|
||||
{"Sharpe Ratio", "6.475"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "100%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "0.705"},
|
||||
{"Beta", "0.7"},
|
||||
{"Annual Standard Deviation", "0.157"},
|
||||
{"Annual Variance", "0.025"},
|
||||
{"Information Ratio", "1.76"},
|
||||
{"Tracking Error", "0.072"},
|
||||
{"Treynor Ratio", "2.933"},
|
||||
{"Total Fees", "$26.39"},
|
||||
{"Fitness Score", "0.374"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "373.973"},
|
||||
{"Portfolio Turnover", "0.374"},
|
||||
{"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", "5f7ba8b5defb310a2eaf98b11abd3b74"}
|
||||
{"Alpha", "0.003"},
|
||||
{"Beta", "82.247"},
|
||||
{"Annual Standard Deviation", "0.141"},
|
||||
{"Annual Variance", "0.02"},
|
||||
{"Information Ratio", "6.401"},
|
||||
{"Tracking Error", "0.141"},
|
||||
{"Treynor Ratio", "0.011"},
|
||||
{"Total Fees", "$26.40"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
/*
|
||||
/*
|
||||
* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
|
||||
* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
|
||||
*
|
||||
@@ -67,43 +67,35 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Total Trades", "3"},
|
||||
{"Average Win", "1.02%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "289.119%"},
|
||||
{"Compounding Annual Return", "289.117%"},
|
||||
{"Drawdown", "2.200%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "1.752%"},
|
||||
{"Sharpe Ratio", "9.235"},
|
||||
{"Probabilistic Sharpe Ratio", "68.013%"},
|
||||
{"Sharpe Ratio", "4.52"},
|
||||
{"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"},
|
||||
{"Tracking Error", "0.006"},
|
||||
{"Treynor Ratio", "2.029"},
|
||||
{"Alpha", "0.007"},
|
||||
{"Beta", "80.237"},
|
||||
{"Annual Standard Deviation", "0.197"},
|
||||
{"Annual Variance", "0.039"},
|
||||
{"Information Ratio", "4.466"},
|
||||
{"Tracking Error", "0.197"},
|
||||
{"Treynor Ratio", "0.011"},
|
||||
{"Total Fees", "$9.77"},
|
||||
{"Fitness Score", "0.747"},
|
||||
{"Kelly Criterion Estimate", "38.64"},
|
||||
{"Kelly Criterion Probability Value", "0.229"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "107.109"},
|
||||
{"Portfolio Turnover", "0.747"},
|
||||
{"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"},
|
||||
{"Estimated Monthly Alpha Value", "$158418.3850"},
|
||||
{"Total Accumulated Estimated Alpha Value", "$25522.9620"},
|
||||
{"Mean Population Estimated Insight Value", "$257.8077"},
|
||||
{"Mean Population Direction", "54.5455%"},
|
||||
{"Mean Population Magnitude", "54.5455%"},
|
||||
{"Rolling Averaged Population Direction", "59.8056%"},
|
||||
{"Rolling Averaged Population Magnitude", "59.8056%"},
|
||||
{"OrderListHash", "0b8cbbafdb77bae2f7abe3cf5e05ac5c"}
|
||||
{"Rolling Averaged Population Magnitude", "59.8056%"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
/*
|
||||
/*
|
||||
* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
|
||||
* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
|
||||
*
|
||||
@@ -86,43 +86,35 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Total Trades", "10"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "-0.01%"},
|
||||
{"Compounding Annual Return", "-14.333%"},
|
||||
{"Compounding Annual Return", "-14.943%"},
|
||||
{"Drawdown", "3.300%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "-0.169%"},
|
||||
{"Sharpe Ratio", "-0.131"},
|
||||
{"Probabilistic Sharpe Ratio", "45.057%"},
|
||||
{"Net Profit", "-0.177%"},
|
||||
{"Sharpe Ratio", "-0.136"},
|
||||
{"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"},
|
||||
{"Alpha", "0"},
|
||||
{"Beta", "-4.084"},
|
||||
{"Annual Standard Deviation", "0.332"},
|
||||
{"Annual Variance", "0.11"},
|
||||
{"Information Ratio", "-0.169"},
|
||||
{"Tracking Error", "0.332"},
|
||||
{"Treynor Ratio", "0.011"},
|
||||
{"Total Fees", "$13.98"},
|
||||
{"Fitness Score", "0.146"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "1"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "-4.398"},
|
||||
{"Portfolio Turnover", "0.268"},
|
||||
{"Total Insights Generated", "15"},
|
||||
{"Total Insights Closed", "12"},
|
||||
{"Total Insights Analysis Completed", "12"},
|
||||
{"Long Insight Count", "15"},
|
||||
{"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", "8971c92ba163cec8526379865d9b9ee4"}
|
||||
{"Estimated Monthly Alpha Value", "$33015795.7342"},
|
||||
{"Total Accumulated Estimated Alpha Value", "$4585527.1853"},
|
||||
{"Mean Population Estimated Insight Value", "$382127.2654"},
|
||||
{"Mean Population Direction", "66.6667%"},
|
||||
{"Mean Population Magnitude", "66.6667%"},
|
||||
{"Rolling Averaged Population Direction", "34.3681%"},
|
||||
{"Rolling Averaged Population Magnitude", "34.3681%"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
/*
|
||||
/*
|
||||
* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
|
||||
* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
|
||||
*
|
||||
@@ -93,43 +93,35 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Total Trades", "23"},
|
||||
{"Average Win", "0.00%"},
|
||||
{"Average Loss", "-0.01%"},
|
||||
{"Compounding Annual Return", "-75.307%"},
|
||||
{"Compounding Annual Return", "-75.360%"},
|
||||
{"Drawdown", "5.800%"},
|
||||
{"Expectancy", "-0.859"},
|
||||
{"Net Profit", "-5.586%"},
|
||||
{"Sharpe Ratio", "-3.257"},
|
||||
{"Probabilistic Sharpe Ratio", "5.931%"},
|
||||
{"Net Profit", "-5.594%"},
|
||||
{"Sharpe Ratio", "-5.582"},
|
||||
{"Loss Rate", "92%"},
|
||||
{"Win Rate", "8%"},
|
||||
{"Profit-Loss Ratio", "0.70"},
|
||||
{"Alpha", "-0.593"},
|
||||
{"Beta", "0.692"},
|
||||
{"Annual Standard Deviation", "0.204"},
|
||||
{"Annual Variance", "0.042"},
|
||||
{"Information Ratio", "-2.884"},
|
||||
{"Tracking Error", "0.194"},
|
||||
{"Treynor Ratio", "-0.962"},
|
||||
{"Alpha", "-1.454"},
|
||||
{"Beta", "15.578"},
|
||||
{"Annual Standard Deviation", "0.212"},
|
||||
{"Annual Variance", "0.045"},
|
||||
{"Information Ratio", "-5.664"},
|
||||
{"Tracking Error", "0.212"},
|
||||
{"Treynor Ratio", "-0.076"},
|
||||
{"Total Fees", "$25.92"},
|
||||
{"Fitness Score", "0.004"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "1"},
|
||||
{"Sortino Ratio", "-4.462"},
|
||||
{"Return Over Maximum Drawdown", "-13.032"},
|
||||
{"Portfolio Turnover", "0.083"},
|
||||
{"Total Insights Generated", "33"},
|
||||
{"Total Insights Closed", "30"},
|
||||
{"Total Insights Analysis Completed", "30"},
|
||||
{"Long Insight Count", "33"},
|
||||
{"Short Insight Count", "0"},
|
||||
{"Long/Short Ratio", "100%"},
|
||||
{"Estimated Monthly Alpha Value", "$0"},
|
||||
{"Total Accumulated Estimated Alpha Value", "$0"},
|
||||
{"Mean Population Estimated Insight Value", "$0"},
|
||||
{"Mean Population Direction", "0%"},
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "ce59e51c8e404b5dbbc02911473aed1c"}
|
||||
{"Estimated Monthly Alpha Value", "$-7788114"},
|
||||
{"Total Accumulated Estimated Alpha Value", "$-3937325"},
|
||||
{"Mean Population Estimated Insight Value", "$-131244.2"},
|
||||
{"Mean Population Direction", "46.6667%"},
|
||||
{"Mean Population Magnitude", "46.6667%"},
|
||||
{"Rolling Averaged Population Direction", "61.4247%"},
|
||||
{"Rolling Averaged Population Magnitude", "61.4247%"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,236 +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 System;
|
||||
using System.Collections.Generic;
|
||||
using System.Linq;
|
||||
using QuantConnect.Brokerages;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Data.Shortable;
|
||||
using QuantConnect.Data.UniverseSelection;
|
||||
using QuantConnect.Interfaces;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Tests filtering in coarse selection by shortable quantity
|
||||
/// </summary>
|
||||
public class AllShortableSymbolsCoarseSelectionRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private static readonly DateTime _20140325 = new DateTime(2014, 3, 25);
|
||||
private static readonly DateTime _20140326 = new DateTime(2014, 3, 26);
|
||||
private static readonly DateTime _20140327 = new DateTime(2014, 3, 27);
|
||||
private static readonly DateTime _20140328 = new DateTime(2014, 3, 28);
|
||||
private static readonly DateTime _20140329 = new DateTime(2014, 3, 29);
|
||||
|
||||
private static readonly Symbol _aapl = QuantConnect.Symbol.Create("AAPL", SecurityType.Equity, Market.USA);
|
||||
private static readonly Symbol _bac = QuantConnect.Symbol.Create("BAC", SecurityType.Equity, Market.USA);
|
||||
private static readonly Symbol _gme = QuantConnect.Symbol.Create("GME", SecurityType.Equity, Market.USA);
|
||||
private static readonly Symbol _goog = QuantConnect.Symbol.Create("GOOG", SecurityType.Equity, Market.USA);
|
||||
private static readonly Symbol _qqq = QuantConnect.Symbol.Create("QQQ", SecurityType.Equity, Market.USA);
|
||||
private static readonly Symbol _spy = QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA);
|
||||
private DateTime _lastTradeDate;
|
||||
|
||||
private static readonly Dictionary<DateTime, bool> _coarseSelected = new Dictionary<DateTime, bool>
|
||||
{
|
||||
{ _20140325, false },
|
||||
{ _20140326, false },
|
||||
{ _20140327, false },
|
||||
{ _20140328, false },
|
||||
};
|
||||
|
||||
private static readonly Dictionary<DateTime, Symbol[]> _expectedSymbols = new Dictionary<DateTime, Symbol[]>
|
||||
{
|
||||
{ _20140325, new[]
|
||||
{
|
||||
_bac,
|
||||
_qqq,
|
||||
_spy
|
||||
}
|
||||
},
|
||||
{ _20140326, new[]
|
||||
{
|
||||
_spy
|
||||
}
|
||||
},
|
||||
{ _20140327, new[]
|
||||
{
|
||||
_aapl,
|
||||
_bac,
|
||||
_gme,
|
||||
_qqq,
|
||||
_spy,
|
||||
}
|
||||
},
|
||||
{ _20140328, new[]
|
||||
{
|
||||
_goog
|
||||
}
|
||||
},
|
||||
{ _20140329, new Symbol[0] }
|
||||
};
|
||||
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2014, 3, 25);
|
||||
SetEndDate(2014, 3, 29);
|
||||
SetCash(10000000);
|
||||
|
||||
AddUniverse(CoarseSelection);
|
||||
UniverseSettings.Resolution = Resolution.Daily;
|
||||
|
||||
SetBrokerageModel(new AllShortableSymbolsRegressionAlgorithmBrokerageModel());
|
||||
}
|
||||
|
||||
public override void OnData(Slice data)
|
||||
{
|
||||
if (Time.Date == _lastTradeDate)
|
||||
{
|
||||
return;
|
||||
}
|
||||
|
||||
foreach (var symbol in ActiveSecurities.Keys)
|
||||
{
|
||||
if (!Portfolio.ContainsKey(symbol) || !Portfolio[symbol].Invested)
|
||||
{
|
||||
if (!Shortable(symbol))
|
||||
{
|
||||
throw new Exception($"Expected {symbol} to be shortable on {Time:yyyy-MM-dd}");
|
||||
}
|
||||
|
||||
// Buy at least once into all Symbols. Since daily data will always use
|
||||
// MOO orders, it makes the testing of liquidating buying into Symbols difficult.
|
||||
MarketOrder(symbol, -(decimal)ShortableQuantity(symbol));
|
||||
_lastTradeDate = Time.Date;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
private IEnumerable<Symbol> CoarseSelection(IEnumerable<CoarseFundamental> coarse)
|
||||
{
|
||||
var shortableSymbols = AllShortableSymbols();
|
||||
var selectedSymbols = coarse
|
||||
.Select(x => x.Symbol)
|
||||
.Where(s => shortableSymbols.ContainsKey(s) && shortableSymbols[s] >= 500)
|
||||
.OrderBy(s => s)
|
||||
.ToList();
|
||||
|
||||
var expectedMissing = 0;
|
||||
if (Time.Date == _20140327)
|
||||
{
|
||||
var gme = QuantConnect.Symbol.Create("GME", SecurityType.Equity, Market.USA);
|
||||
if (!shortableSymbols.ContainsKey(gme))
|
||||
{
|
||||
throw new Exception("Expected unmapped GME in shortable symbols list on 2014-03-27");
|
||||
}
|
||||
if (!coarse.Select(x => x.Symbol.Value).Contains("GME"))
|
||||
{
|
||||
throw new Exception("Expected mapped GME in coarse symbols on 2014-03-27");
|
||||
}
|
||||
|
||||
expectedMissing = 1;
|
||||
}
|
||||
|
||||
var missing = _expectedSymbols[Time.Date].Except(selectedSymbols).ToList();
|
||||
if (missing.Count != expectedMissing)
|
||||
{
|
||||
throw new Exception($"Expected Symbols selected on {Time.Date:yyyy-MM-dd} to match expected Symbols, but the following Symbols were missing: {string.Join(", ", missing.Select(s => s.ToString()))}");
|
||||
}
|
||||
|
||||
_coarseSelected[Time.Date] = true;
|
||||
return selectedSymbols;
|
||||
}
|
||||
|
||||
public override void OnEndOfAlgorithm()
|
||||
{
|
||||
if (!_coarseSelected.Values.All(x => x))
|
||||
{
|
||||
throw new AggregateException($"Expected coarse selection on all dates, but didn't run on: {string.Join(", ", _coarseSelected.Where(kvp => !kvp.Value).Select(kvp => kvp.Key.ToStringInvariant("yyyy-MM-dd")))}");
|
||||
}
|
||||
}
|
||||
|
||||
private class AllShortableSymbolsRegressionAlgorithmBrokerageModel : DefaultBrokerageModel
|
||||
{
|
||||
public AllShortableSymbolsRegressionAlgorithmBrokerageModel() : base()
|
||||
{
|
||||
ShortableProvider = new RegressionTestShortableProvider();
|
||||
}
|
||||
}
|
||||
|
||||
private class RegressionTestShortableProvider : LocalDiskShortableProvider
|
||||
{
|
||||
public RegressionTestShortableProvider() : base(SecurityType.Equity, "testbrokerage", Market.USA)
|
||||
{
|
||||
}
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
|
||||
/// </summary>
|
||||
public bool CanRunLocally { get; } = true;
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate which languages this algorithm is written in.
|
||||
/// </summary>
|
||||
public Language[] Languages { get; } = { Language.CSharp, Language.Python };
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
|
||||
/// </summary>
|
||||
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "5"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "36.294%"},
|
||||
{"Drawdown", "0%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "0.340%"},
|
||||
{"Sharpe Ratio", "21.2"},
|
||||
{"Probabilistic Sharpe Ratio", "99.990%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "0.274"},
|
||||
{"Beta", "0.138"},
|
||||
{"Annual Standard Deviation", "0.011"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "7.202"},
|
||||
{"Tracking Error", "0.068"},
|
||||
{"Treynor Ratio", "1.722"},
|
||||
{"Total Fees", "$307.50"},
|
||||
{"Fitness Score", "0.173"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "79228162514264337593543950335"},
|
||||
{"Portfolio Turnover", "0.173"},
|
||||
{"Total Insights Generated", "0"},
|
||||
{"Total Insights Closed", "0"},
|
||||
{"Total Insights Analysis Completed", "0"},
|
||||
{"Long Insight Count", "0"},
|
||||
{"Short Insight Count", "0"},
|
||||
{"Long/Short Ratio", "100%"},
|
||||
{"Estimated Monthly Alpha Value", "$0"},
|
||||
{"Total Accumulated Estimated Alpha Value", "$0"},
|
||||
{"Mean Population Estimated Insight Value", "$0"},
|
||||
{"Mean Population Direction", "0%"},
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "6b1b205e5a6461ffd5bed645099714cd"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -19,27 +19,28 @@ 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.Alphas
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
///<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 : QCAlgorithm
|
||||
/// <summary>
|
||||
public class MortgageRateVolatilityAlgorithm : QCAlgorithmFramework
|
||||
{
|
||||
public override void Initialize()
|
||||
{
|
||||
@@ -50,8 +51,8 @@ namespace QuantConnect.Algorithm.CSharp.Alphas
|
||||
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));
|
||||
@@ -63,6 +64,8 @@ namespace QuantConnect.Algorithm.CSharp.Alphas
|
||||
SetRiskManagement(new NullRiskManagementModel());
|
||||
|
||||
}
|
||||
|
||||
public void OnData(QuandlMortgagePriceColumns data) { }
|
||||
|
||||
private class MortgageRateVolatilityAlphaModel : AlphaModel
|
||||
{
|
||||
@@ -76,7 +79,7 @@ namespace QuantConnect.Algorithm.CSharp.Alphas
|
||||
private readonly StandardDeviation _mortgageRateStd;
|
||||
|
||||
public MortgageRateVolatilityAlphaModel(
|
||||
QCAlgorithm algorithm,
|
||||
QCAlgorithmFramework algorithm,
|
||||
int indicatorPeriod = 15,
|
||||
double insightMagnitude = 0.0005,
|
||||
int deviations = 2,
|
||||
@@ -99,7 +102,7 @@ namespace QuantConnect.Algorithm.CSharp.Alphas
|
||||
WarmUpIndicators(algorithm);
|
||||
}
|
||||
|
||||
public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice data)
|
||||
public override IEnumerable<Insight> Update(QCAlgorithmFramework algorithm, Slice data)
|
||||
{
|
||||
var insights = new List<Insight>();
|
||||
|
||||
@@ -138,7 +141,7 @@ namespace QuantConnect.Algorithm.CSharp.Alphas
|
||||
return insights;
|
||||
}
|
||||
|
||||
private void WarmUpIndicators(QCAlgorithm algorithm)
|
||||
private void WarmUpIndicators(QCAlgorithmFramework algorithm)
|
||||
{
|
||||
// Make a history call and update the indicators
|
||||
algorithm.History(new[] { _mortgageRate }, _indicatorPeriod, _resolution).PushThrough(bar =>
|
||||
|
||||
@@ -1,117 +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 System;
|
||||
using System.Collections.Generic;
|
||||
using System.Linq;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Data.Custom.Benzinga;
|
||||
using QuantConnect.Data.UniverseSelection;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp.AltData
|
||||
{
|
||||
/// <summary>
|
||||
/// Benzinga is a provider of news data. Their news is made in-house
|
||||
/// and covers stock related news such as corporate events.
|
||||
/// </summary>
|
||||
public class BenzingaNewsAlgorithm : QCAlgorithm
|
||||
{
|
||||
// Predefine a dictionary of words with scores to scan for in the description
|
||||
// of the Benzinga news article
|
||||
private readonly Dictionary<string, double> _words = new Dictionary<string, double>()
|
||||
{
|
||||
{"bad", -0.5}, {"good", 0.5},
|
||||
{"negative", -0.5}, {"great", 0.5},
|
||||
{"growth", 0.5}, {"fail", -0.5},
|
||||
{"failed", -0.5}, {"success", 0.5},
|
||||
{"nailed", 0.5}, {"beat", 0.5},
|
||||
{"missed", -0.5}
|
||||
};
|
||||
|
||||
// Trade only every 5 days
|
||||
private DateTime _lastTrade = DateTime.MinValue;
|
||||
|
||||
/// <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(2018, 6, 5);
|
||||
SetEndDate(2018, 8, 4);
|
||||
SetCash(100000);
|
||||
|
||||
var aapl = AddEquity("AAPL", Resolution.Hour).Symbol;
|
||||
var ibm = AddEquity("IBM", Resolution.Hour).Symbol;
|
||||
|
||||
AddData<BenzingaNews>(aapl);
|
||||
AddData<BenzingaNews>(ibm);
|
||||
}
|
||||
|
||||
public override void OnData(Slice data)
|
||||
{
|
||||
if ((Time - _lastTrade) < TimeSpan.FromDays(5))
|
||||
{
|
||||
return;
|
||||
}
|
||||
|
||||
// Get rid of our holdings after 5 days, and start fresh
|
||||
Liquidate();
|
||||
|
||||
// Get all Benzinga data and loop over it
|
||||
foreach (var article in data.Get<BenzingaNews>().Values)
|
||||
{
|
||||
// Select the same Symbol we're getting a data point for
|
||||
// from the articles list so that we can get the sentiment of the article.
|
||||
// We use the underlying Symbol because the Symbols included in the `Symbols` property
|
||||
// are equity Symbols.
|
||||
var selectedSymbol = article.Symbols.SingleOrDefault(s => s == article.Symbol.Underlying);
|
||||
if (selectedSymbol == null)
|
||||
{
|
||||
throw new Exception($"Could not find current Symbol {article.Symbol.Underlying} even though it should exist");
|
||||
}
|
||||
|
||||
// The intersection of the article contents and the pre-defined words are the words that are included in both collections
|
||||
var intersection = article.Contents.ToLowerInvariant().Split(' ').Intersect(_words.Keys);
|
||||
// Get the words, then get the aggregate sentiment
|
||||
var sentimentSum = intersection.Select(x => _words[x]).Sum();
|
||||
|
||||
if (sentimentSum >= 0.5)
|
||||
{
|
||||
Log($"Longing {article.Symbol.Underlying} with sentiment score of {sentimentSum}");
|
||||
SetHoldings(article.Symbol.Underlying, sentimentSum / 5);
|
||||
|
||||
_lastTrade = Time;
|
||||
}
|
||||
if (sentimentSum <= -0.5)
|
||||
{
|
||||
Log($"Shorting {article.Symbol.Underlying} with sentiment score of {sentimentSum}");
|
||||
SetHoldings(article.Symbol.Underlying, sentimentSum / 5);
|
||||
|
||||
_lastTrade = Time;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnSecuritiesChanged(SecurityChanges changes)
|
||||
{
|
||||
foreach (var r in changes.RemovedSecurities)
|
||||
{
|
||||
// If removed from the universe, liquidate and remove the custom data from the algorithm
|
||||
Liquidate(r.Symbol);
|
||||
RemoveSecurity(QuantConnect.Symbol.CreateBase(typeof(BenzingaNews), r.Symbol, Market.USA));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,66 +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.CBOE;
|
||||
using QuantConnect.Data.Custom.Fred;
|
||||
using QuantConnect.Data.Custom.USEnergy;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp.AltData
|
||||
{
|
||||
public class CachedAlternativeDataAlgorithm : QCAlgorithm
|
||||
{
|
||||
private Symbol _cboeVix;
|
||||
private Symbol _usEnergy;
|
||||
private Symbol _fredPeakToTrough;
|
||||
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2003, 1, 1);
|
||||
SetEndDate(2019, 10, 11);
|
||||
SetCash(100000);
|
||||
|
||||
// QuantConnect caches a small subset of alternative data for easy consumption for the community.
|
||||
// You can use this in your algorithm as demonstrated below:
|
||||
|
||||
_cboeVix = AddData<CBOE>("VIX", Resolution.Daily).Symbol;
|
||||
// United States EIA data: https://eia.gov/
|
||||
_usEnergy = AddData<USEnergy>(USEnergy.Petroleum.UnitedStates.WeeklyGrossInputsIntoRefineries, Resolution.Daily).Symbol;
|
||||
// FRED data
|
||||
_fredPeakToTrough = AddData<Fred>(Fred.OECDRecessionIndicators.UnitedStatesFromPeakThroughTheTrough, Resolution.Daily).Symbol;
|
||||
}
|
||||
|
||||
public override void OnData(Slice data)
|
||||
{
|
||||
if (data.ContainsKey(_cboeVix))
|
||||
{
|
||||
var vix = data.Get<CBOE>(_cboeVix);
|
||||
Log($"VIX: {vix}");
|
||||
}
|
||||
|
||||
if (data.ContainsKey(_usEnergy))
|
||||
{
|
||||
var inputIntoRefineries = data.Get<USEnergy>(_usEnergy);
|
||||
Log($"U.S. Input Into Refineries: {Time}, {inputIntoRefineries.Value}");
|
||||
}
|
||||
|
||||
if (data.ContainsKey(_fredPeakToTrough))
|
||||
{
|
||||
var peakToTrough = data.Get<Fred>(_fredPeakToTrough);
|
||||
Log($"OECD based Recession Indicator for the United States from the Peak through the Trough: {peakToTrough}");
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,62 +0,0 @@
|
||||
/*
|
||||
* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
|
||||
* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
using System.Linq;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Data.Custom.Quiver;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp.AltData
|
||||
{
|
||||
/// <summary>
|
||||
/// Quiver Quantitative is a provider of alternative data.
|
||||
/// This algorithm shows how to consume the <see cref="QuiverWallStreetBets"/>
|
||||
/// </summary>
|
||||
public class QuiverWallStreetBetsDataAlgorithm : QCAlgorithm
|
||||
{
|
||||
/// <summary>
|
||||
/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
|
||||
/// </summary>
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2019, 1, 1);
|
||||
SetEndDate(2020, 6, 1);
|
||||
SetCash(100000);
|
||||
|
||||
var aapl = AddEquity("AAPL", Resolution.Daily).Symbol;
|
||||
var quiverWSBSymbol = AddData<QuiverWallStreetBets>(aapl).Symbol;
|
||||
var history = History<QuiverWallStreetBets>(quiverWSBSymbol, 60, Resolution.Daily);
|
||||
|
||||
Debug($"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);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,99 +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 System.Collections.Generic;
|
||||
using System.Linq;
|
||||
using QuantConnect.Algorithm.Framework.Selection;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Data.Custom.SEC;
|
||||
using QuantConnect.Data.UniverseSelection;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
public class SECReport8KAlgorithm : 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(2019, 8, 21);
|
||||
SetCash(100000);
|
||||
|
||||
UniverseSettings.Resolution = Resolution.Minute;
|
||||
AddUniverseSelection(new CoarseFundamentalUniverseSelectionModel(CoarseSelector));
|
||||
|
||||
// Request underlying equity data.
|
||||
var ibm = AddEquity("IBM", Resolution.Minute).Symbol;
|
||||
// Add SEC report 10-Q data for the underlying IBM asset
|
||||
var earningsFiling = AddData<SECReport10Q>(ibm, Resolution.Daily).Symbol;
|
||||
// Request 120 days of history with the SECReport10Q IBM custom data Symbol.
|
||||
var history = History<SECReport10Q>(earningsFiling, 120, Resolution.Daily);
|
||||
|
||||
// Count the number of items we get from our history request
|
||||
Debug($"We got {history.Count()} items from our history request");
|
||||
}
|
||||
|
||||
public IEnumerable<Symbol> CoarseSelector(IEnumerable<CoarseFundamental> coarse)
|
||||
{
|
||||
// Add SEC data from the filtered coarse selection
|
||||
var symbols = coarse.Where(x => x.HasFundamentalData && x.DollarVolume > 50000000)
|
||||
.Select(x => x.Symbol)
|
||||
.Take(10);
|
||||
|
||||
foreach (var symbol in symbols)
|
||||
{
|
||||
AddData<SECReport8K>(symbol);
|
||||
}
|
||||
|
||||
return symbols;
|
||||
}
|
||||
|
||||
public override void OnData(Slice data)
|
||||
{
|
||||
// Store the symbols we want to long in a list
|
||||
// so that we can have an equal-weighted portfolio
|
||||
var longEquitySymbols = new List<Symbol>();
|
||||
|
||||
// Get all SEC data and loop over it
|
||||
foreach (var report in data.Get<SECReport8K>().Values)
|
||||
{
|
||||
// Get the length of all contents contained within the report
|
||||
var reportTextLength = report.Report.Documents.Select(x => x.Text.Length).Sum();
|
||||
|
||||
if (reportTextLength > 20000)
|
||||
{
|
||||
longEquitySymbols.Add(report.Symbol.Underlying);
|
||||
}
|
||||
}
|
||||
|
||||
foreach (var equitySymbol in longEquitySymbols)
|
||||
{
|
||||
SetHoldings(equitySymbol, 1m / longEquitySymbols.Count);
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnSecuritiesChanged(SecurityChanges changes)
|
||||
{
|
||||
foreach (var r in changes.RemovedSecurities)
|
||||
{
|
||||
// If removed from the universe, liquidate and remove the custom data from the algorithm
|
||||
Liquidate(r.Symbol);
|
||||
RemoveSecurity(QuantConnect.Symbol.CreateBase(typeof(SECReport8K), r.Symbol, Market.USA));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,90 +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 System.Collections.Generic;
|
||||
using System.Linq;
|
||||
using QuantConnect.Algorithm.Framework.Selection;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Data.Custom.SmartInsider;
|
||||
using QuantConnect.Data.UniverseSelection;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
public class SmartInsiderTransactionAlgorithm : QCAlgorithm
|
||||
{
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2019, 3, 1);
|
||||
SetEndDate(2019, 7, 4);
|
||||
SetCash(1000000);
|
||||
|
||||
AddUniverseSelection(new CoarseFundamentalUniverseSelectionModel(CoarseUniverse));
|
||||
|
||||
// Request underlying equity data.
|
||||
var ibm = AddEquity("IBM", Resolution.Minute).Symbol;
|
||||
// Add Smart Insider stock buyback transaction data for the underlying IBM asset
|
||||
var si = AddData<SmartInsiderTransaction>(ibm).Symbol;
|
||||
// Request 60 days of history with the SmartInsiderTransaction IBM Custom Data Symbol.
|
||||
var history = History<SmartInsiderTransaction>(si, 60, Resolution.Daily);
|
||||
|
||||
// Count the number of items we get from our history request
|
||||
Debug($"We got {history.Count()} items from our history request");
|
||||
}
|
||||
|
||||
public IEnumerable<Symbol> CoarseUniverse(IEnumerable<CoarseFundamental> coarse)
|
||||
{
|
||||
var symbols = coarse.Where(x => x.HasFundamentalData && x.DollarVolume > 50000000)
|
||||
.Select(x => x.Symbol)
|
||||
.Take(10);
|
||||
|
||||
foreach (var symbol in symbols)
|
||||
{
|
||||
AddData<SmartInsiderTransaction>(symbol);
|
||||
}
|
||||
|
||||
return symbols;
|
||||
}
|
||||
|
||||
public override void OnData(Slice data)
|
||||
{
|
||||
// Get all SmartInsider data available
|
||||
var transactions = data.Get<SmartInsiderTransaction>();
|
||||
|
||||
foreach (var transaction in transactions.Values)
|
||||
{
|
||||
if (transaction.VolumePercentage == null || transaction.EventType == null)
|
||||
{
|
||||
continue;
|
||||
}
|
||||
|
||||
// Using the Smart Insider transaction information, buy when company does a stock buyback
|
||||
if (transaction.EventType == SmartInsiderEventType.Transaction && transaction.VolumePercentage > 5)
|
||||
{
|
||||
SetHoldings(transaction.Symbol.Underlying, (decimal)transaction.VolumePercentage / 100);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnSecuritiesChanged(SecurityChanges changes)
|
||||
{
|
||||
foreach (var r in changes.RemovedSecurities)
|
||||
{
|
||||
// If removed from the universe, liquidate and remove the custom data from the algorithm
|
||||
Liquidate(r.Symbol);
|
||||
RemoveSecurity(QuantConnect.Symbol.CreateBase(typeof(SmartInsiderTransaction), r.Symbol, Market.USA));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,85 +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 System.Collections.Generic;
|
||||
using System.Linq;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Data.Custom.Tiingo;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Look for positive and negative words in the news article description
|
||||
/// and trade based on the sum of the sentiment
|
||||
/// </summary>
|
||||
public class TiingoNewsAlgorithm : QCAlgorithm
|
||||
{
|
||||
private Symbol _tiingoSymbol;
|
||||
|
||||
// Predefine a dictionary of words with scores to scan for in the description
|
||||
// of the Tiingo news article
|
||||
private readonly Dictionary<string, double> _words = new Dictionary<string, double>()
|
||||
{
|
||||
{"bad", -0.5}, {"good", 0.5},
|
||||
{ "negative", -0.5}, {"great", 0.5},
|
||||
{"growth", 0.5}, {"fail", -0.5},
|
||||
{"failed", -0.5}, {"success", 0.5},
|
||||
{"nailed", 0.5}, {"beat", 0.5},
|
||||
{"missed", -0.5}
|
||||
};
|
||||
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2019, 6, 10);
|
||||
SetEndDate(2019, 10, 3);
|
||||
SetCash(100000);
|
||||
|
||||
var aapl = AddEquity("AAPL", Resolution.Hour).Symbol;
|
||||
_tiingoSymbol = AddData<TiingoNews>(aapl).Symbol;
|
||||
|
||||
// Request underlying equity data
|
||||
var ibm = AddEquity("IBM", Resolution.Minute).Symbol;
|
||||
// Add news data for the underlying IBM asset
|
||||
var news = AddData<TiingoNews>(ibm).Symbol;
|
||||
// Request 60 days of history with the TiingoNews IBM Custom Data Symbol.
|
||||
var history = History<TiingoNews>(news, 60, Resolution.Daily);
|
||||
|
||||
// Count the number of items we get from our history request
|
||||
Debug($"We got {history.Count()} items from our history request");
|
||||
}
|
||||
|
||||
public override void OnData(Slice data)
|
||||
{
|
||||
//Confirm that the data is in the collection
|
||||
if (!data.ContainsKey(_tiingoSymbol)) return;
|
||||
|
||||
// Gets the first piece of data from the Slice
|
||||
var article = data.Get<TiingoNews>(_tiingoSymbol);
|
||||
|
||||
// Article descriptions come in all caps. Lower and split by word
|
||||
var descriptionWords = article.Description.ToLowerInvariant().Split(' ');
|
||||
|
||||
// Take the intersection of predefined words and the words in the
|
||||
// description to get a list of matching words
|
||||
var intersection = _words.Keys.Intersect(descriptionWords);
|
||||
|
||||
// Get the sum of the article's sentiment, and go long or short
|
||||
// depending if it's a positive or negative description
|
||||
var sentiment = intersection.Select(x => _words[x]).Sum();
|
||||
|
||||
SetHoldings(article.Symbol.Underlying, sentiment);
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,80 +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 System.Linq;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Data.Custom.TradingEconomics;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Trades on interest rate announcements from data provided by Trading Economics
|
||||
/// </summary>
|
||||
public class TradingEconomicsAlgorithm : QCAlgorithm
|
||||
{
|
||||
private Symbol _interestRate;
|
||||
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2013, 11, 1);
|
||||
SetEndDate(2019, 10, 3);
|
||||
SetCash(100000);
|
||||
|
||||
AddEquity("AGG", Resolution.Hour);
|
||||
AddEquity("SPY", Resolution.Hour);
|
||||
|
||||
_interestRate = AddData<TradingEconomicsCalendar>(TradingEconomics.Calendar.UnitedStates.InterestRate).Symbol;
|
||||
|
||||
// Request 365 days of interest rate history with the TradingEconomicsCalendar custom data Symbol.
|
||||
// We should expect no historical data because 2013-11-01 is before the absolute first point of data
|
||||
var history = History<TradingEconomicsCalendar>(_interestRate, 365, Resolution.Daily);
|
||||
|
||||
// Count the number of items we get from our history request (should be zero)
|
||||
Debug($"We got {history.Count()} items from our history request");
|
||||
}
|
||||
|
||||
public override void OnData(Slice data)
|
||||
{
|
||||
// Make sure we have an interest rate calendar event
|
||||
if (!data.ContainsKey(_interestRate))
|
||||
{
|
||||
return;
|
||||
}
|
||||
|
||||
var announcement = data.Get<TradingEconomicsCalendar>(_interestRate);
|
||||
|
||||
// Confirm it's a FED Rate Decision
|
||||
if (announcement.Event != TradingEconomics.Event.UnitedStates.FedInterestRateDecision)
|
||||
{
|
||||
return;
|
||||
}
|
||||
|
||||
// In the event of a rate increase, rebalance 50% to Bonds.
|
||||
var interestRateDecreased = announcement.Actual <= announcement.Previous;
|
||||
|
||||
if (interestRateDecreased)
|
||||
{
|
||||
SetHoldings("SPY", 1);
|
||||
SetHoldings("AGG", 0);
|
||||
}
|
||||
else
|
||||
{
|
||||
SetHoldings("SPY", 0.5);
|
||||
SetHoldings("AGG", 0.5);
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,87 +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 System;
|
||||
using System.Linq;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Data.Custom.USTreasury;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
public class USTreasuryYieldCurveRateAlgorithm : QCAlgorithm
|
||||
{
|
||||
private Symbol _yieldCurve;
|
||||
private Symbol _spy;
|
||||
private DateTime _lastInversion = DateTime.MinValue;
|
||||
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2000, 3, 1);
|
||||
SetEndDate(2019, 9, 15);
|
||||
SetCash(100000);
|
||||
|
||||
_spy = AddEquity("SPY", Resolution.Hour).Symbol;
|
||||
_yieldCurve = AddData<USTreasuryYieldCurveRate>("USTYCR", Resolution.Daily).Symbol;
|
||||
|
||||
// Request 60 days of history with the USTreasuryYieldCurveRate custom data Symbol.
|
||||
var history = History<USTreasuryYieldCurveRate>(_yieldCurve, 60, Resolution.Daily);
|
||||
|
||||
// Count the number of items we get from our history request
|
||||
Debug($"We got {history.Count()} items from our history request");
|
||||
}
|
||||
|
||||
public override void OnData(Slice data)
|
||||
{
|
||||
if (!data.ContainsKey(_yieldCurve))
|
||||
{
|
||||
return;
|
||||
}
|
||||
|
||||
// Preserve null values by getting the data with `slice.Get<T>`
|
||||
// Accessing the data using `data[_yieldCurve]` results in null
|
||||
// values becoming `default(decimal)` which is equal to 0
|
||||
var rates = data.Get<USTreasuryYieldCurveRate>().Values.First();
|
||||
|
||||
// Check for null before using the values
|
||||
if (!rates.TenYear.HasValue || !rates.TwoYear.HasValue)
|
||||
{
|
||||
return;
|
||||
}
|
||||
|
||||
// Only advance if a year has gone by
|
||||
if (Time - _lastInversion < TimeSpan.FromDays(365))
|
||||
{
|
||||
return;
|
||||
}
|
||||
|
||||
// if there is a yield curve inversion after not having one for a year, short SPY for two years
|
||||
if (!Portfolio.Invested && rates.TwoYear > rates.TenYear)
|
||||
{
|
||||
Debug($"{Time} - Yield curve inversion! Shorting the market for two years");
|
||||
SetHoldings(_spy, -0.5);
|
||||
|
||||
_lastInversion = Time;
|
||||
|
||||
return;
|
||||
}
|
||||
|
||||
// If two years have passed, liquidate our position in SPY
|
||||
if (Time - _lastInversion >= TimeSpan.FromDays(365 * 2))
|
||||
{
|
||||
Liquidate(_spy);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,123 +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 System;
|
||||
using System.Collections.Generic;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Indicators;
|
||||
using QuantConnect.Interfaces;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Regression algorithm to test the behaviour of ARMA versus AR models at the same order of differencing.
|
||||
/// In particular, an ARIMA(1,1,1) and ARIMA(1,1,0) are instantiated while orders are placed if their difference
|
||||
/// is sufficiently large (which would be due to the inclusion of the MA(1) term).
|
||||
/// </summary>
|
||||
public class AutoRegressiveIntegratedMovingAverageRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private AutoRegressiveIntegratedMovingAverage _arima;
|
||||
private AutoRegressiveIntegratedMovingAverage _ar;
|
||||
private decimal _last;
|
||||
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2013, 1, 07);
|
||||
SetEndDate(2013, 12, 11);
|
||||
|
||||
EnableAutomaticIndicatorWarmUp = true;
|
||||
AddEquity("SPY", Resolution.Daily);
|
||||
_arima = ARIMA("SPY", 1, 1, 1, 50);
|
||||
_ar = ARIMA("SPY", 1, 1, 0, 50);
|
||||
}
|
||||
|
||||
public override void OnData(Slice slice)
|
||||
{
|
||||
if (_arima.IsReady)
|
||||
{
|
||||
if (Math.Abs(_ar.Current.Value - _arima.Current.Value) > 1) // Difference due to MA(1) being included.
|
||||
{
|
||||
if (_arima.Current.Value > _last)
|
||||
{
|
||||
MarketOrder("SPY", 1);
|
||||
}
|
||||
else
|
||||
{
|
||||
MarketOrder("SPY", -1);
|
||||
}
|
||||
}
|
||||
|
||||
_last = _arima.Current.Value;
|
||||
}
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
|
||||
/// </summary>
|
||||
public bool CanRunLocally { get; } = true;
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate which languages this algorithm is written in.
|
||||
/// </summary>
|
||||
public Language[] Languages { get; } = { Language.CSharp, Language.Python };
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
|
||||
/// </summary>
|
||||
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "65"},
|
||||
{"Average Win", "0.00%"},
|
||||
{"Average Loss", "0.00%"},
|
||||
{"Compounding Annual Return", "0.145%"},
|
||||
{"Drawdown", "0.100%"},
|
||||
{"Expectancy", "2.190"},
|
||||
{"Net Profit", "0.134%"},
|
||||
{"Sharpe Ratio", "0.993"},
|
||||
{"Probabilistic Sharpe Ratio", "49.669%"},
|
||||
{"Loss Rate", "29%"},
|
||||
{"Win Rate", "71%"},
|
||||
{"Profit-Loss Ratio", "3.50"},
|
||||
{"Alpha", "0.001"},
|
||||
{"Beta", "0"},
|
||||
{"Annual Standard Deviation", "0.001"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-2.168"},
|
||||
{"Tracking Error", "0.099"},
|
||||
{"Treynor Ratio", "-5.187"},
|
||||
{"Total Fees", "$65.00"},
|
||||
{"Fitness Score", "0"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "1.51"},
|
||||
{"Return Over Maximum Drawdown", "1.819"},
|
||||
{"Portfolio Turnover", "0"},
|
||||
{"Total Insights Generated", "0"},
|
||||
{"Total Insights Closed", "0"},
|
||||
{"Total Insights Analysis Completed", "0"},
|
||||
{"Long Insight Count", "0"},
|
||||
{"Short Insight Count", "0"},
|
||||
{"Long/Short Ratio", "100%"},
|
||||
{"Estimated Monthly Alpha Value", "$0"},
|
||||
{"Total Accumulated Estimated Alpha Value", "$0"},
|
||||
{"Mean Population Estimated Insight Value", "$0"},
|
||||
{"Mean Population Direction", "0%"},
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "c4c9c272037cfd8f6887052b8d739466"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -1,193 +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 System;
|
||||
using System.Collections.Generic;
|
||||
using System.Linq;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Data.Market;
|
||||
using QuantConnect.Indicators;
|
||||
using QuantConnect.Interfaces;
|
||||
using QuantConnect.Securities;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Algorithm which tests indicator warm up using different data types, related to GH issue 4205
|
||||
/// </summary>
|
||||
public class AutomaticIndicatorWarmupDataTypeRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private Symbol _symbol;
|
||||
public override void Initialize()
|
||||
{
|
||||
UniverseSettings.DataNormalizationMode = DataNormalizationMode.Raw;
|
||||
EnableAutomaticIndicatorWarmUp = true;
|
||||
SetStartDate(2013, 10, 08);
|
||||
SetEndDate(2013, 10, 09);
|
||||
|
||||
var SP500 = QuantConnect.Symbol.Create(Futures.Indices.SP500EMini, SecurityType.Future, Market.CME);
|
||||
_symbol = FutureChainProvider.GetFutureContractList(SP500, StartDate).First();
|
||||
|
||||
// Test case: custom IndicatorBase<QuoteBar> indicator using Future unsubscribed symbol
|
||||
var indicator1 = new CustomIndicator();
|
||||
AssertIndicatorState(indicator1, isReady: false);
|
||||
WarmUpIndicator(_symbol, indicator1);
|
||||
AssertIndicatorState(indicator1, isReady: true);
|
||||
|
||||
// Test case: SimpleMovingAverage<IndicatorDataPoint> using Future unsubscribed symbol (should use TradeBar)
|
||||
var sma1 = new SimpleMovingAverage(10);
|
||||
AssertIndicatorState(sma1, isReady: false);
|
||||
WarmUpIndicator(_symbol, sma1);
|
||||
AssertIndicatorState(sma1, isReady: true);
|
||||
|
||||
// Test case: SimpleMovingAverage<IndicatorDataPoint> using Equity unsubscribed symbol
|
||||
var spy = QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA);
|
||||
var sma = new SimpleMovingAverage(10);
|
||||
AssertIndicatorState(sma, isReady: false);
|
||||
WarmUpIndicator(spy, sma);
|
||||
AssertIndicatorState(sma, isReady: true);
|
||||
|
||||
// We add the symbol
|
||||
AddFutureContract(_symbol);
|
||||
AddEquity("SPY");
|
||||
// force spy for use Raw data mode so that it matches the used when unsubscribed which uses the universe settings
|
||||
SubscriptionManager.SubscriptionDataConfigService.GetSubscriptionDataConfigs(spy).SetDataNormalizationMode(DataNormalizationMode.Raw);
|
||||
|
||||
// Test case: custom IndicatorBase<QuoteBar> indicator using Future subscribed symbol
|
||||
var indicator = new CustomIndicator();
|
||||
var consolidator = CreateConsolidator(TimeSpan.FromMinutes(1), typeof(QuoteBar));
|
||||
RegisterIndicator(_symbol, indicator, consolidator);
|
||||
|
||||
AssertIndicatorState(indicator, isReady: false);
|
||||
WarmUpIndicator(_symbol, indicator);
|
||||
AssertIndicatorState(indicator, isReady: true);
|
||||
|
||||
// Test case: SimpleMovingAverage<IndicatorDataPoint> using Future Subscribed symbol (should use TradeBar)
|
||||
var sma11 = new SimpleMovingAverage(10);
|
||||
AssertIndicatorState(sma11, isReady: false);
|
||||
WarmUpIndicator(_symbol, sma11);
|
||||
AssertIndicatorState(sma11, isReady: true);
|
||||
|
||||
if (!sma11.Current.Equals(sma1.Current))
|
||||
{
|
||||
throw new Exception("Expected SMAs warmed up before and after adding the Future to the algorithm to have the same current value. " +
|
||||
"The result of 'WarmUpIndicator' shouldn't change if the symbol is or isn't subscribed");
|
||||
}
|
||||
|
||||
// Test case: SimpleMovingAverage<IndicatorDataPoint> using Equity unsubscribed symbol
|
||||
var smaSpy = new SimpleMovingAverage(10);
|
||||
AssertIndicatorState(smaSpy, isReady: false);
|
||||
WarmUpIndicator(spy, smaSpy);
|
||||
AssertIndicatorState(smaSpy, isReady: true);
|
||||
|
||||
if (!smaSpy.Current.Equals(sma.Current))
|
||||
{
|
||||
throw new Exception("Expected SMAs warmed up before and after adding the Equity to the algorithm to have the same current value. " +
|
||||
"The result of 'WarmUpIndicator' shouldn't change if the symbol is or isn't subscribed");
|
||||
}
|
||||
}
|
||||
|
||||
private void AssertIndicatorState(IIndicator indicator, bool isReady)
|
||||
{
|
||||
if (indicator.IsReady != isReady)
|
||||
{
|
||||
throw new Exception($"Expected indicator state, expected {isReady} but was {indicator.IsReady}");
|
||||
}
|
||||
}
|
||||
|
||||
/// <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 (!Portfolio.Invested)
|
||||
{
|
||||
SetHoldings(_symbol, 0.5);
|
||||
}
|
||||
}
|
||||
|
||||
private class CustomIndicator : IndicatorBase<QuoteBar>, IIndicatorWarmUpPeriodProvider
|
||||
{
|
||||
private bool _isReady;
|
||||
public int WarmUpPeriod => 1;
|
||||
public override bool IsReady => _isReady;
|
||||
public CustomIndicator() : base("Pepe")
|
||||
{ }
|
||||
protected override decimal ComputeNextValue(QuoteBar input)
|
||||
{
|
||||
_isReady = true;
|
||||
return input.Ask.High;
|
||||
}
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
|
||||
/// </summary>
|
||||
public bool CanRunLocally { get; } = true;
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate which languages this algorithm is written in.
|
||||
/// </summary>
|
||||
public Language[] Languages { get; } = { Language.CSharp };
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
|
||||
/// </summary>
|
||||
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "1"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "-99.999%"},
|
||||
{"Drawdown", "16.100%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "-6.366%"},
|
||||
{"Sharpe Ratio", "1.194"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "5.579"},
|
||||
{"Beta", "-63.972"},
|
||||
{"Annual Standard Deviation", "0.434"},
|
||||
{"Annual Variance", "0.188"},
|
||||
{"Information Ratio", "0.996"},
|
||||
{"Tracking Error", "0.441"},
|
||||
{"Treynor Ratio", "-0.008"},
|
||||
{"Total Fees", "$20.35"},
|
||||
{"Fitness Score", "0.138"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "-1.727"},
|
||||
{"Return Over Maximum Drawdown", "-12.061"},
|
||||
{"Portfolio Turnover", "4.916"},
|
||||
{"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", "7c841ca58a4385f42236838e5bf0c382"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -1,155 +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 System;
|
||||
using System.Collections.Generic;
|
||||
using System.Linq;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Indicators;
|
||||
using QuantConnect.Interfaces;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Algorithm which reproduces GH issue 3861, where in some cases 2 consolidators were added when
|
||||
/// using the automatic indicator warmup feature
|
||||
/// </summary>
|
||||
public class AutomaticIndicatorWarmupRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private Symbol _spy;
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2013, 10, 07);
|
||||
SetEndDate(2013, 10, 11);
|
||||
|
||||
EnableAutomaticIndicatorWarmUp = true;
|
||||
|
||||
// Test case 1
|
||||
_spy = AddEquity("SPY").Symbol;
|
||||
var sma = SMA(_spy, 10);
|
||||
if (!sma.IsReady)
|
||||
{
|
||||
throw new Exception("Expected SMA to be warmed up");
|
||||
}
|
||||
|
||||
// Test case 2
|
||||
var indicator = new CustomIndicator(10);
|
||||
RegisterIndicator(_spy, indicator, Resolution.Minute, (Func<IBaseData, decimal>) null);
|
||||
|
||||
if (indicator.IsReady)
|
||||
{
|
||||
throw new Exception("Expected CustomIndicator Not to be warmed up");
|
||||
}
|
||||
WarmUpIndicator(_spy, indicator);
|
||||
if (!indicator.IsReady)
|
||||
{
|
||||
throw new Exception("Expected CustomIndicator to be warmed up");
|
||||
}
|
||||
}
|
||||
|
||||
/// <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 (!Portfolio.Invested)
|
||||
{
|
||||
var subscription = SubscriptionManager.SubscriptionDataConfigService.GetSubscriptionDataConfigs(_spy).First(config => config.TickType == TickType.Trade);
|
||||
|
||||
// we expect 1 consolidator per indicator
|
||||
if (subscription.Consolidators.Count != 2)
|
||||
{
|
||||
throw new Exception($"Unexpected consolidator count for subscription: {subscription.Consolidators.Count}");
|
||||
}
|
||||
SetHoldings(_spy, 1);
|
||||
}
|
||||
}
|
||||
|
||||
private class CustomIndicator : SimpleMovingAverage
|
||||
{
|
||||
private IndicatorDataPoint _previous;
|
||||
public CustomIndicator(int period) : base(period)
|
||||
{
|
||||
}
|
||||
protected override decimal ComputeNextValue(IReadOnlyWindow<IndicatorDataPoint> window, IndicatorDataPoint input)
|
||||
{
|
||||
if (_previous != null && input.EndTime == _previous.EndTime)
|
||||
{
|
||||
throw new Exception($"Unexpected indicator double data point call: {_previous}");
|
||||
}
|
||||
_previous = input;
|
||||
return base.ComputeNextValue(window, input);
|
||||
}
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
|
||||
/// </summary>
|
||||
public bool CanRunLocally { get; } = true;
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate which languages this algorithm is written in.
|
||||
/// </summary>
|
||||
public Language[] Languages { get; } = { Language.CSharp };
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
|
||||
/// </summary>
|
||||
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "1"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "264.819%"},
|
||||
{"Drawdown", "2.200%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "1.668%"},
|
||||
{"Sharpe Ratio", "8.749"},
|
||||
{"Probabilistic Sharpe Ratio", "67.311%"},
|
||||
{"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"},
|
||||
{"Tracking Error", "0.001"},
|
||||
{"Treynor Ratio", "1.922"},
|
||||
{"Total Fees", "$3.26"},
|
||||
{"Fitness Score", "0.248"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "93.761"},
|
||||
{"Portfolio Turnover", "0.248"},
|
||||
{"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", "25885f979ca8c7b44f5d0f7daf00b241"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -1,341 +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.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
|
||||
var quantity = CalculateOrderQuantity(_spy, .1m);
|
||||
_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 50, AveragePrice should be ticket AverageFillPrice
|
||||
var equityHolding = Portfolio[_equityBuy.Symbol];
|
||||
if (!equityHolding.Invested || equityHolding.Quantity != 50 || 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", "-22.335%"},
|
||||
{"Drawdown", "0.400%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "-0.323%"},
|
||||
{"Sharpe Ratio", "-11.098"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.002"},
|
||||
{"Beta", "0.099"},
|
||||
{"Annual Standard Deviation", "0.002"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "9.899"},
|
||||
{"Tracking Error", "0.019"},
|
||||
{"Treynor Ratio", "-0.23"},
|
||||
{"Total Fees", "$2.00"},
|
||||
{"Fitness Score", "0.213"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "-73.456"},
|
||||
{"Portfolio Turnover", "0.426"},
|
||||
{"Total Insights Generated", "0"},
|
||||
{"Total Insights Closed", "0"},
|
||||
{"Total Insights Analysis Completed", "0"},
|
||||
{"Long Insight Count", "0"},
|
||||
{"Short Insight Count", "0"},
|
||||
{"Long/Short Ratio", "100%"},
|
||||
{"Estimated Monthly Alpha Value", "$0"},
|
||||
{"Total Accumulated Estimated Alpha Value", "$0"},
|
||||
{"Mean Population Estimated Insight Value", "$0"},
|
||||
{"Mean Population Direction", "0%"},
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "72a6ced0ed0c2da7136f3be652eb4744"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -1,4 +1,4 @@
|
||||
/*
|
||||
/*
|
||||
* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
|
||||
* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
|
||||
*
|
||||
@@ -71,43 +71,22 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Total Trades", "1"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "0%"},
|
||||
{"Drawdown", "0%"},
|
||||
{"Compounding Annual Return", "-100.000%"},
|
||||
{"Drawdown", "11.000%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "0%"},
|
||||
{"Sharpe Ratio", "0"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Net Profit", "-7.328%"},
|
||||
{"Sharpe Ratio", "-12.15"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "0"},
|
||||
{"Beta", "0"},
|
||||
{"Annual Standard Deviation", "0"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "0"},
|
||||
{"Tracking Error", "0"},
|
||||
{"Treynor Ratio", "0"},
|
||||
{"Total Fees", "$0.00"},
|
||||
{"Fitness Score", "0.506"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "-14.148"},
|
||||
{"Portfolio Turnover", "1.073"},
|
||||
{"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", "18dc611407abec4ea47092e71f33f983"}
|
||||
{"Alpha", "-17.811"},
|
||||
{"Beta", "1239.638"},
|
||||
{"Annual Standard Deviation", "0.762"},
|
||||
{"Annual Variance", "0.581"},
|
||||
{"Information Ratio", "-12.169"},
|
||||
{"Tracking Error", "0.761"},
|
||||
{"Treynor Ratio", "-0.007"},
|
||||
{"Total Fees", "$0.00"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -80,43 +80,22 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Total Trades", "1"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "264.819%"},
|
||||
{"Compounding Annual Return", "263.153%"},
|
||||
{"Drawdown", "2.200%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "1.668%"},
|
||||
{"Sharpe Ratio", "8.749"},
|
||||
{"Probabilistic Sharpe Ratio", "67.311%"},
|
||||
{"Net Profit", "1.663%"},
|
||||
{"Sharpe Ratio", "4.41"},
|
||||
{"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"},
|
||||
{"Tracking Error", "0.001"},
|
||||
{"Treynor Ratio", "1.922"},
|
||||
{"Total Fees", "$3.26"},
|
||||
{"Fitness Score", "0.248"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "93.761"},
|
||||
{"Portfolio Turnover", "0.248"},
|
||||
{"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", "25885f979ca8c7b44f5d0f7daf00b241"}
|
||||
{"Alpha", "0.007"},
|
||||
{"Beta", "76.118"},
|
||||
{"Annual Standard Deviation", "0.192"},
|
||||
{"Annual Variance", "0.037"},
|
||||
{"Information Ratio", "4.354"},
|
||||
{"Tracking Error", "0.192"},
|
||||
{"Treynor Ratio", "0.011"},
|
||||
{"Total Fees", "$3.26"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,53 +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 System;
|
||||
using QuantConnect.Algorithm.Framework.Alphas;
|
||||
using QuantConnect.Algorithm.Framework.Execution;
|
||||
using QuantConnect.Algorithm.Framework.Portfolio;
|
||||
using QuantConnect.Data.UniverseSelection;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Basic template algorithm which showcases <see cref="ConstituentsUniverse"/> simple use case
|
||||
/// </summary>
|
||||
public class BasicTemplateConstituentUniverseAlgorithm : 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(2013, 10, 07);
|
||||
SetEndDate(2013, 10, 11);
|
||||
|
||||
// by default will use algorithms UniverseSettings
|
||||
AddUniverse(Universe.Constituent.Steel());
|
||||
|
||||
// we specify the UniverseSettings it should use
|
||||
AddUniverse(Universe.Constituent.AggressiveGrowth(
|
||||
new UniverseSettings(Resolution.Hour,
|
||||
2,
|
||||
false,
|
||||
false,
|
||||
UniverseSettings.MinimumTimeInUniverse)));
|
||||
|
||||
SetAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromDays(1)));
|
||||
SetExecution(new ImmediateExecutionModel());
|
||||
SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -205,44 +205,23 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
{"Total Trades", "10"},
|
||||
{"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%"},
|
||||
{"Average Loss", "-0.13%"},
|
||||
{"Compounding Annual Return", "-99.979%"},
|
||||
{"Drawdown", "3.500%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "-2.288%"},
|
||||
{"Sharpe Ratio", "-11.335"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"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", "$85.34"},
|
||||
{"Fitness Score", "0.5"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "-43.943"},
|
||||
{"Portfolio Turnover", "1.028"},
|
||||
{"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", "1bf1a6d9dd921982b72a6178f9e50e68"}
|
||||
{"Alpha", "-5.739"},
|
||||
{"Beta", "413.859"},
|
||||
{"Annual Standard Deviation", "0.254"},
|
||||
{"Annual Variance", "0.065"},
|
||||
{"Information Ratio", "-11.39"},
|
||||
{"Tracking Error", "0.254"},
|
||||
{"Treynor Ratio", "-0.007"},
|
||||
{"Total Fees", "$85.34"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -75,39 +75,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "1.100%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "3.459%"},
|
||||
{"Sharpe Ratio", "10.11"},
|
||||
{"Probabilistic Sharpe Ratio", "83.150%"},
|
||||
{"Sharpe Ratio", "6.033"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "1.935"},
|
||||
{"Beta", "-0.119"},
|
||||
{"Alpha", "0.696"},
|
||||
{"Beta", "17.597"},
|
||||
{"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"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "52.533"},
|
||||
{"Return Over Maximum Drawdown", "214.75"},
|
||||
{"Portfolio Turnover", "0.111"},
|
||||
{"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", "82fee25cd17100c53bb173834ab5f0b2"}
|
||||
{"Information Ratio", "5.939"},
|
||||
{"Tracking Error", "0.16"},
|
||||
{"Treynor Ratio", "0.055"},
|
||||
{"Total Fees", "$3.26"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -53,15 +53,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
// set algorithm framework models
|
||||
SetUniverseSelection(new ManualUniverseSelectionModel(QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA)));
|
||||
SetAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromMinutes(20), 0.025, null));
|
||||
|
||||
// We can define who often the EWPCM will rebalance if no new insight is submitted using:
|
||||
// Resolution Enum:
|
||||
SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel(Resolution.Daily));
|
||||
// TimeSpan
|
||||
// SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel(TimeSpan.FromDays(2)));
|
||||
// A Func<DateTime, DateTime>. In this case, we can use the pre-defined func at Expiry helper class
|
||||
// SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel(Expiry.EndOfWeek));
|
||||
|
||||
SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
|
||||
SetExecution(new ImmediateExecutionModel());
|
||||
SetRiskManagement(new MaximumDrawdownPercentPerSecurity(0.01m));
|
||||
}
|
||||
@@ -91,44 +83,37 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
{"Total Trades", "3"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "-1.01%"},
|
||||
{"Compounding Annual Return", "254.782%"},
|
||||
{"Drawdown", "2.200%"},
|
||||
{"Average Loss", "-1.03%"},
|
||||
{"Compounding Annual Return", "245.167%"},
|
||||
{"Drawdown", "2.300%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "1.632%"},
|
||||
{"Sharpe Ratio", "8.371"},
|
||||
{"Probabilistic Sharpe Ratio", "66.555%"},
|
||||
{"Net Profit", "1.597%"},
|
||||
{"Sharpe Ratio", "4.169"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.088"},
|
||||
{"Beta", "1.006"},
|
||||
{"Annual Standard Deviation", "0.221"},
|
||||
{"Annual Variance", "0.049"},
|
||||
{"Information Ratio", "-32.586"},
|
||||
{"Tracking Error", "0.002"},
|
||||
{"Treynor Ratio", "1.839"},
|
||||
{"Alpha", "0.007"},
|
||||
{"Beta", "73.191"},
|
||||
{"Annual Standard Deviation", "0.195"},
|
||||
{"Annual Variance", "0.038"},
|
||||
{"Information Ratio", "4.113"},
|
||||
{"Tracking Error", "0.195"},
|
||||
{"Treynor Ratio", "0.011"},
|
||||
{"Total Fees", "$9.77"},
|
||||
{"Fitness Score", "0.747"},
|
||||
{"Kelly Criterion Estimate", "38.64"},
|
||||
{"Kelly Criterion Probability Value", "0.229"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "85.209"},
|
||||
{"Portfolio Turnover", "0.747"},
|
||||
{"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"},
|
||||
{"Estimated Monthly Alpha Value", "$158418.3850"},
|
||||
{"Total Accumulated Estimated Alpha Value", "$25522.9620"},
|
||||
{"Mean Population Estimated Insight Value", "$257.8077"},
|
||||
{"Mean Population Direction", "54.5455%"},
|
||||
{"Mean Population Magnitude", "54.5455%"},
|
||||
{"Rolling Averaged Population Direction", "59.8056%"},
|
||||
{"Rolling Averaged Population Magnitude", "59.8056%"},
|
||||
{"OrderListHash", "17e29d58e5dabd93569da752c4552c70"}
|
||||
{"Rolling Averaged Population Magnitude", "59.8056%"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
/*
|
||||
/*
|
||||
* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
|
||||
* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
|
||||
*
|
||||
@@ -20,7 +20,6 @@ using System.Linq;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Interfaces;
|
||||
using QuantConnect.Securities;
|
||||
using QuantConnect.Securities.Future;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
@@ -34,15 +33,13 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
/// <meta name="tag" content="futures" />
|
||||
public class BasicTemplateFuturesAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private Symbol _contractSymbol;
|
||||
|
||||
// S&P 500 EMini futures
|
||||
private const string RootSP500 = Futures.Indices.SP500EMini;
|
||||
public Symbol SP500 = QuantConnect.Symbol.Create(RootSP500, SecurityType.Future, Market.CME);
|
||||
public Symbol SP500 = QuantConnect.Symbol.Create(RootSP500, SecurityType.Future, Market.USA);
|
||||
|
||||
// Gold futures
|
||||
private const string RootGold = Futures.Metals.Gold;
|
||||
public Symbol Gold = QuantConnect.Symbol.Create(RootGold, SecurityType.Future, Market.COMEX);
|
||||
public Symbol Gold = QuantConnect.Symbol.Create(RootGold, SecurityType.Future, Market.USA);
|
||||
|
||||
/// <summary>
|
||||
/// Initialize your algorithm and add desired assets.
|
||||
@@ -57,10 +54,8 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
var futureGold = AddFuture(RootGold);
|
||||
|
||||
// set our expiry filter for this futures chain
|
||||
// SetFilter method accepts TimeSpan objects or integer for days.
|
||||
// The following statements yield the same filtering criteria
|
||||
futureSP500.SetFilter(TimeSpan.Zero, TimeSpan.FromDays(182));
|
||||
futureGold.SetFilter(0, 182);
|
||||
futureGold.SetFilter(TimeSpan.Zero, TimeSpan.FromDays(182));
|
||||
|
||||
var benchmark = AddEquity("SPY");
|
||||
SetBenchmark(benchmark.Symbol);
|
||||
@@ -86,8 +81,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
// if found, trade it
|
||||
if (contract != null)
|
||||
{
|
||||
_contractSymbol = contract.Symbol;
|
||||
MarketOrder(_contractSymbol, 1);
|
||||
MarketOrder(contract.Symbol, 1);
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -97,21 +91,6 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnEndOfAlgorithm()
|
||||
{
|
||||
// Get the margin requirements
|
||||
var buyingPowerModel = Securities[_contractSymbol].BuyingPowerModel;
|
||||
var futureMarginModel = buyingPowerModel as FutureMarginModel;
|
||||
if (buyingPowerModel == null)
|
||||
{
|
||||
throw new Exception($"Invalid buying power model. Found: {buyingPowerModel.GetType().Name}. Expected: {nameof(FutureMarginModel)}");
|
||||
}
|
||||
var initialOvernight = futureMarginModel.InitialOvernightMarginRequirement;
|
||||
var maintenanceOvernight = futureMarginModel.MaintenanceOvernightMarginRequirement;
|
||||
var initialIntraday = futureMarginModel.InitialIntradayMarginRequirement;
|
||||
var maintenanceIntraday = futureMarginModel.MaintenanceIntradayMarginRequirement;
|
||||
}
|
||||
|
||||
/// <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>
|
||||
@@ -134,39 +113,19 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "13.500%"},
|
||||
{"Expectancy", "-0.818"},
|
||||
{"Net Profit", "-13.517%"},
|
||||
{"Sharpe Ratio", "-2.678"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Sharpe Ratio", "-29.354"},
|
||||
{"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"},
|
||||
{"Alpha", "-7.746"},
|
||||
{"Beta", "-0.859"},
|
||||
{"Annual Standard Deviation", "0.305"},
|
||||
{"Annual Variance", "0.093"},
|
||||
{"Information Ratio", "-24.985"},
|
||||
{"Tracking Error", "0.414"},
|
||||
{"Treynor Ratio", "10.413"},
|
||||
{"Total Fees", "$15207.00"},
|
||||
{"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"}
|
||||
{"Fitness Score", "0.033"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -33,7 +33,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
public class BasicTemplateFuturesConsolidationAlgorithm : QCAlgorithm
|
||||
{
|
||||
private const string RootSP500 = Futures.Indices.SP500EMini;
|
||||
public Symbol SP500 = QuantConnect.Symbol.Create(RootSP500, SecurityType.Future, Market.CME);
|
||||
public Symbol SP500 = QuantConnect.Symbol.Create(RootSP500, SecurityType.Future, Market.USA);
|
||||
private HashSet<Symbol> _futureContracts = new HashSet<Symbol>();
|
||||
|
||||
public override void Initialize()
|
||||
@@ -43,11 +43,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
SetCash(1000000);
|
||||
|
||||
var futureSP500 = AddFuture(RootSP500);
|
||||
// set our expiry filter for this future chain
|
||||
// SetFilter method accepts TimeSpan objects or integer for days.
|
||||
// The following statements yield the same filtering criteria
|
||||
futureSP500.SetFilter(0, 182);
|
||||
// futureSP500.SetFilter(TimeSpan.Zero, TimeSpan.FromDays(182));
|
||||
futureSP500.SetFilter(TimeSpan.Zero, TimeSpan.FromDays(182));
|
||||
|
||||
SetBenchmark(x => 0);
|
||||
}
|
||||
|
||||
@@ -53,12 +53,12 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
var newYorkTime = utcTime.ConvertFromUtc(TimeZones.NewYork);
|
||||
if (newYorkTime.Date < new DateTime(2013, 10, 09))
|
||||
{
|
||||
yield return QuantConnect.Symbol.Create(Futures.Indices.SP500EMini, SecurityType.Future, Market.CME);
|
||||
yield return QuantConnect.Symbol.Create(Futures.Indices.SP500EMini, SecurityType.Future, Market.USA);
|
||||
}
|
||||
|
||||
if (newYorkTime.Date >= new DateTime(2013, 10, 09))
|
||||
{
|
||||
yield return QuantConnect.Symbol.Create(Futures.Metals.Gold, SecurityType.Future, Market.COMEX);
|
||||
yield return QuantConnect.Symbol.Create(Futures.Metals.Gold, SecurityType.Future, Market.USA);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -142,25 +142,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "5.000%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "-3.312%"},
|
||||
{"Sharpe Ratio", "-7.795"},
|
||||
{"Probabilistic Sharpe Ratio", "0.164%"},
|
||||
{"Sharpe Ratio", "-16.986"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-1.347"},
|
||||
{"Beta", "0.257"},
|
||||
{"Annual Standard Deviation", "0.109"},
|
||||
{"Annual Variance", "0.012"},
|
||||
{"Information Ratio", "-14.763"},
|
||||
{"Tracking Error", "0.188"},
|
||||
{"Treynor Ratio", "-3.318"},
|
||||
{"Alpha", "-0.828"},
|
||||
{"Beta", "-77.873"},
|
||||
{"Annual Standard Deviation", "0.099"},
|
||||
{"Annual Variance", "0.01"},
|
||||
{"Information Ratio", "-17.076"},
|
||||
{"Tracking Error", "0.099"},
|
||||
{"Treynor Ratio", "0.022"},
|
||||
{"Total Fees", "$3.70"},
|
||||
{"Fitness Score", "0.009"},
|
||||
{"Kelly Criterion Estimate", "-112.972"},
|
||||
{"Kelly Criterion Probability Value", "0.671"},
|
||||
{"Sortino Ratio", "-8.425"},
|
||||
{"Return Over Maximum Drawdown", "-35.219"},
|
||||
{"Portfolio Turnover", "0.548"},
|
||||
{"Total Insights Generated", "6"},
|
||||
{"Total Insights Closed", "5"},
|
||||
{"Total Insights Analysis Completed", "5"},
|
||||
@@ -173,8 +166,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Mean Population Direction", "0%"},
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "18ffd3a774c68da83d867e3b09e3e05d"}
|
||||
{"Rolling Averaged Population Magnitude", "0%"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
/*
|
||||
/*
|
||||
* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
|
||||
* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
|
||||
*
|
||||
@@ -20,8 +20,6 @@ using QuantConnect.Data;
|
||||
using QuantConnect.Orders;
|
||||
using QuantConnect.Securities;
|
||||
using QuantConnect.Data.UniverseSelection;
|
||||
using QuantConnect.Interfaces;
|
||||
using System.Collections.Generic;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
@@ -34,7 +32,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
/// <meta name="tag" content="history and warm up" />
|
||||
/// <meta name="tag" content="history" />
|
||||
/// <meta name="tag" content="futures" />
|
||||
public class BasicTemplateFuturesHistoryAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
public class BasicTemplateFuturesHistoryAlgorithm : QCAlgorithm
|
||||
{
|
||||
// S&P 500 EMini futures
|
||||
private string [] roots = new []
|
||||
@@ -43,8 +41,6 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
Futures.Metals.Gold,
|
||||
};
|
||||
|
||||
private int _successCount = 0;
|
||||
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2013, 10, 8);
|
||||
@@ -58,26 +54,6 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
}
|
||||
|
||||
SetBenchmark(d => 1000000);
|
||||
|
||||
Schedule.On(DateRules.EveryDay(), TimeRules.Every(TimeSpan.FromHours(1)), MakeHistoryCall);
|
||||
}
|
||||
|
||||
private void MakeHistoryCall()
|
||||
{
|
||||
var history = History(10, Resolution.Minute);
|
||||
if (history.Count() < 10)
|
||||
{
|
||||
throw new Exception($"Empty history at {Time}");
|
||||
}
|
||||
_successCount++;
|
||||
}
|
||||
|
||||
public override void OnEndOfAlgorithm()
|
||||
{
|
||||
if (_successCount < 49)
|
||||
{
|
||||
throw new Exception($"Scheduled Event did not assert history call as many times as expected: {_successCount}/49");
|
||||
}
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
@@ -92,12 +68,12 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
foreach (var contract in chain.Value)
|
||||
{
|
||||
Log($"{contract.Symbol.Value}," +
|
||||
$"Bid={contract.BidPrice.ToStringInvariant()} " +
|
||||
$"Ask={contract.AskPrice.ToStringInvariant()} " +
|
||||
$"Last={contract.LastPrice.ToStringInvariant()} " +
|
||||
$"OI={contract.OpenInterest.ToStringInvariant()}"
|
||||
);
|
||||
Log(String.Format("{0},Bid={1} Ask={2} Last={3} OI={4}",
|
||||
contract.Symbol.Value,
|
||||
contract.BidPrice,
|
||||
contract.AskPrice,
|
||||
contract.LastPrice,
|
||||
contract.OpenInterest));
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -124,62 +100,5 @@ 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", "0"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "0%"},
|
||||
{"Drawdown", "0%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "0%"},
|
||||
{"Sharpe Ratio", "0"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "0"},
|
||||
{"Beta", "0"},
|
||||
{"Annual Standard Deviation", "0"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "0"},
|
||||
{"Tracking Error", "0"},
|
||||
{"Treynor Ratio", "0"},
|
||||
{"Total Fees", "$0.00"},
|
||||
{"Fitness Score", "0"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "79228162514264337593543950335"},
|
||||
{"Portfolio Turnover", "0"},
|
||||
{"Total Insights Generated", "0"},
|
||||
{"Total Insights Closed", "0"},
|
||||
{"Total Insights Analysis Completed", "0"},
|
||||
{"Long Insight Count", "0"},
|
||||
{"Short Insight Count", "0"},
|
||||
{"Long/Short Ratio", "100%"},
|
||||
{"Estimated Monthly Alpha Value", "$0"},
|
||||
{"Total Accumulated Estimated Alpha Value", "$0"},
|
||||
{"Mean Population Estimated Insight Value", "$0"},
|
||||
{"Mean Population Direction", "0%"},
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
|
||||
};
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -56,12 +56,9 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
// setting up S&P 500 EMini futures
|
||||
var futureSP500 = AddFuture(Futures.Indices.SP500EMini);
|
||||
_futureSymbol = futureSP500.Symbol;
|
||||
|
||||
|
||||
// set our expiry filter for this futures chain
|
||||
// SetFilter method accepts TimeSpan objects or integer for days.
|
||||
// The following statements yield the same filtering criteria
|
||||
futureSP500.SetFilter(10, 182);
|
||||
// futureSP500.SetFilter(TimeSpan.FromDays(10), TimeSpan.FromDays(182));
|
||||
futureSP500.SetFilter(TimeSpan.FromDays(10), TimeSpan.FromDays(182));
|
||||
|
||||
// setting up Dow Jones ETF Options
|
||||
var option = AddOption("DIA");
|
||||
@@ -69,11 +66,8 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
|
||||
option.PriceModel = OptionPriceModels.BinomialCoxRossRubinstein();
|
||||
// option.EnableGreekApproximation = true;
|
||||
// set our strike/expiry filter for this option chain
|
||||
// SetFilter method accepts TimeSpan objects or integer for days.
|
||||
// The following statements yield the same filtering criteria
|
||||
option.SetFilter(-2, +2, 0, 180);
|
||||
// option.SetFilter(-2, +2, TimeSpan.Zero, TimeSpan.FromDays(180));
|
||||
// set our expiry filter for this option chain
|
||||
option.SetFilter(-2, +2, TimeSpan.Zero, TimeSpan.FromDays(180));
|
||||
|
||||
// specifying zero benchmark
|
||||
SetBenchmark(date => 0m);
|
||||
@@ -137,26 +131,26 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
|
||||
if (_barCount % 20 == 1)
|
||||
{
|
||||
Log($"P/L:{Portfolio.TotalUnrealisedProfit.ToStringInvariant("0.00")}, " +
|
||||
$"Fees:{Portfolio.TotalFees.ToStringInvariant("0.00")}, " +
|
||||
$"Profit:{Portfolio.TotalProfit.ToStringInvariant("0.00")}, " +
|
||||
$"Eq:{Portfolio.TotalPortfolioValue.ToStringInvariant("0.00")}, " +
|
||||
$"Holdings:{Portfolio.TotalHoldingsValue.ToStringInvariant("0.00")}, " +
|
||||
$"Vol: {Portfolio.TotalSaleVolume.ToStringInvariant("0.00")}, " +
|
||||
$"Margin: {Portfolio.TotalMarginUsed.ToStringInvariant("0.00")}"
|
||||
);
|
||||
Log(String.Format("P/L:{0:0.00}, Fees:{1:0.00}, Profit:{2:0.00}, Eq:{3:0.00}, Holdings:{4:0.00}, Vol: {5:0.00}, Margin: {6:0.00}",
|
||||
Portfolio.TotalUnrealisedProfit,
|
||||
Portfolio.TotalFees,
|
||||
Portfolio.TotalProfit,
|
||||
Portfolio.TotalPortfolioValue,
|
||||
Portfolio.TotalHoldingsValue,
|
||||
Portfolio.TotalSaleVolume,
|
||||
Portfolio.TotalMarginUsed));
|
||||
|
||||
foreach (var holding in Securities.Values.OrderByDescending(x => x.Holdings.AbsoluteQuantity))
|
||||
{
|
||||
Log($" - {holding.Symbol.Value}, " +
|
||||
$"Avg Prc:{holding.Holdings.AveragePrice.ToStringInvariant("0.00")}, " +
|
||||
$"Qty:{holding.Holdings.Quantity.ToStringInvariant("0.00")}, " +
|
||||
$"Mkt Prc:{holding.Holdings.Price.ToStringInvariant("0.00")}, " +
|
||||
$"Mkt Val:{holding.Holdings.HoldingsValue.ToStringInvariant("0.00")}, " +
|
||||
$"Unreal P/L: {holding.Holdings.UnrealizedProfit.ToStringInvariant("0.00")}, " +
|
||||
$"Fees: {holding.Holdings.TotalFees.ToStringInvariant("0.00")}, " +
|
||||
$"Vol: {holding.Holdings.TotalSaleVolume.ToStringInvariant("0.00")}"
|
||||
);
|
||||
Log(String.Format(" - {0}, Avg Prc:{1:0.00}, Qty:{2:0.00}, Mkt Prc:{3:0.00}, Mkt Val:{4:0.00}, Unreal P/L: {5:0.00}, Fees: {6:0.00}, Vol: {7:0.00}",
|
||||
holding.Symbol.Value,
|
||||
holding.Holdings.AveragePrice,
|
||||
holding.Holdings.Quantity,
|
||||
holding.Holdings.Price,
|
||||
holding.Holdings.HoldingsValue,
|
||||
holding.Holdings.UnrealizedProfit,
|
||||
holding.Holdings.TotalFees,
|
||||
holding.Holdings.TotalSaleVolume));
|
||||
}
|
||||
}
|
||||
|
||||
@@ -167,20 +161,21 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
var underlying = Securities[chain.Key.Underlying];
|
||||
foreach (var contract in chain.Value)
|
||||
{
|
||||
Log($"{Time.ToStringInvariant()} {contract.Symbol.Value}," +
|
||||
$"B={contract.BidPrice.ToStringInvariant()} " +
|
||||
$"A={contract.AskPrice.ToStringInvariant()} " +
|
||||
$"L={contract.LastPrice.ToStringInvariant()} " +
|
||||
$"OI={contract.OpenInterest.ToStringInvariant()} " +
|
||||
$"σ={underlying.VolatilityModel.Volatility:0.00} " +
|
||||
$"NPV={contract.TheoreticalPrice.ToStringInvariant("0.00")} " +
|
||||
$"Δ={contract.Greeks.Delta.ToStringInvariant("0.00")} " +
|
||||
$"Γ={contract.Greeks.Gamma.ToStringInvariant("0.00")} " +
|
||||
$"ν={contract.Greeks.Vega.ToStringInvariant("0.00")} " +
|
||||
$"ρ={contract.Greeks.Rho.ToStringInvariant("0.00")} " +
|
||||
$"Θ={(contract.Greeks.Theta / 365.0m).ToStringInvariant("0.00")} " +
|
||||
$"IV={contract.ImpliedVolatility.ToStringInvariant("0.00")}"
|
||||
);
|
||||
Log(String.Format(@"{0} {1},B={2} A={3} L={4} OI={5} σ={6:0.00} NPV={7:0.00} Δ={8:0.00} Γ={9:0.00} ν={10:0.00} ρ={11:0.00} Θ={12:0.00} IV={13:0.00}",
|
||||
Time.ToString(),
|
||||
contract.Symbol.Value,
|
||||
contract.BidPrice,
|
||||
contract.AskPrice,
|
||||
contract.LastPrice,
|
||||
contract.OpenInterest,
|
||||
underlying.VolatilityModel.Volatility,
|
||||
contract.TheoreticalPrice,
|
||||
contract.Greeks.Delta,
|
||||
contract.Greeks.Gamma,
|
||||
contract.Greeks.Vega,
|
||||
contract.Greeks.Rho,
|
||||
contract.Greeks.Theta / 365.0m,
|
||||
contract.ImpliedVolatility));
|
||||
}
|
||||
}
|
||||
|
||||
@@ -188,12 +183,13 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
foreach (var contract in chain.Value)
|
||||
{
|
||||
Log($"{contract.Symbol.Value}, {Time}, " +
|
||||
$"B={contract.BidPrice} " +
|
||||
$"A={contract.AskPrice} " +
|
||||
$"L={contract.LastPrice} " +
|
||||
$"OI={contract.OpenInterest}"
|
||||
);
|
||||
Log(String.Format("{0}, {1}, B={2} A={3} L={4} OI={5}",
|
||||
contract.Symbol.Value,
|
||||
Time,
|
||||
contract.BidPrice,
|
||||
contract.AskPrice,
|
||||
contract.LastPrice,
|
||||
contract.OpenInterest));
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -205,7 +201,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
|
||||
foreach (var kpv in slice.Bars)
|
||||
{
|
||||
Log($"---> Bar: {Time}, {kpv.Key.Value}, {kpv.Value.Close.ToStringInvariant("0.0000")}");
|
||||
Log($"---> Bar: {Time}, {kpv.Key.Value}, {kpv.Value.Close:0.0000}");
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
/*
|
||||
/*
|
||||
* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
|
||||
* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
|
||||
*
|
||||
@@ -48,10 +48,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
_optionSymbol = option.Symbol;
|
||||
|
||||
// set our strike/expiry filter for this option chain
|
||||
// SetFilter method accepts TimeSpan objects or integer for days.
|
||||
// The following statements yield the same filtering criteria
|
||||
option.SetFilter(-2, +2, 0, 180);
|
||||
// option.SetFilter(-2, +2, TimeSpan.Zero, TimeSpan.FromDays(180));
|
||||
option.SetFilter(-2, +2, TimeSpan.Zero, TimeSpan.FromDays(180));
|
||||
|
||||
// Adding this to reproduce GH issue #2314
|
||||
SetWarmup(TimeSpan.FromMinutes(1));
|
||||
@@ -116,14 +113,13 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
{"Total Trades", "778"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "0%"},
|
||||
{"Drawdown", "0%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "0%"},
|
||||
{"Average Loss", "-0.02%"},
|
||||
{"Compounding Annual Return", "-100%"},
|
||||
{"Drawdown", "6.900%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "-6.860%"},
|
||||
{"Sharpe Ratio", "0"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "0"},
|
||||
@@ -133,27 +129,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Information Ratio", "0"},
|
||||
{"Tracking Error", "0"},
|
||||
{"Treynor Ratio", "0"},
|
||||
{"Total Fees", "$778.00"},
|
||||
{"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", "5484aef1443064c826e0071f757cb0f7"}
|
||||
{"Total Fees", "$778.00"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -41,10 +41,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
_optionSymbol = option.Symbol;
|
||||
|
||||
// set our strike/expiry filter for this option chain
|
||||
// SetFilter method accepts TimeSpan objects or integer for days.
|
||||
// The following statements yields the same filtering criteria
|
||||
option.SetFilter(-2, +2, 0, 10);
|
||||
// option.SetFilter(-2, +2, TimeSpan.Zero, TimeSpan.FromDays(10));
|
||||
option.SetFilter(-2, +2, TimeSpan.Zero, TimeSpan.FromDays(10));
|
||||
|
||||
// use the underlying equity as the benchmark
|
||||
SetBenchmark("GOOG");
|
||||
|
||||
@@ -35,7 +35,8 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
public class BasicTemplateOptionsAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private const string UnderlyingTicker = "GOOG";
|
||||
public Symbol OptionSymbol;
|
||||
public readonly Symbol Underlying = QuantConnect.Symbol.Create(UnderlyingTicker, SecurityType.Equity, Market.USA);
|
||||
public readonly Symbol OptionSymbol = QuantConnect.Symbol.Create(UnderlyingTicker, SecurityType.Option, Market.USA);
|
||||
|
||||
public override void Initialize()
|
||||
{
|
||||
@@ -45,14 +46,10 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
|
||||
var equity = AddEquity(UnderlyingTicker);
|
||||
var option = AddOption(UnderlyingTicker);
|
||||
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));
|
||||
// .Expiration(TimeSpan.Zero, TimeSpan.FromDays(180)));
|
||||
.Expiration(TimeSpan.Zero, TimeSpan.FromDays(180)));
|
||||
|
||||
// use the underlying equity as the benchmark
|
||||
SetBenchmark(equity.Symbol);
|
||||
@@ -113,14 +110,13 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
{"Total Trades", "2"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "0%"},
|
||||
{"Drawdown", "0%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "0%"},
|
||||
{"Average Loss", "-0.28%"},
|
||||
{"Compounding Annual Return", "-78.282%"},
|
||||
{"Drawdown", "0.300%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "-0.282%"},
|
||||
{"Sharpe Ratio", "0"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "0"},
|
||||
@@ -130,27 +126,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Information Ratio", "0"},
|
||||
{"Tracking Error", "0"},
|
||||
{"Treynor Ratio", "0"},
|
||||
{"Total Fees", "$2.00"},
|
||||
{"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", "9d9f9248ee8fe30d87ff0a6f6fea5112"}
|
||||
{"Total Fees", "$2.00"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
/*
|
||||
/*
|
||||
* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
|
||||
* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
|
||||
*
|
||||
@@ -15,11 +15,9 @@
|
||||
*/
|
||||
|
||||
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;
|
||||
|
||||
@@ -33,26 +31,27 @@ 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, IRegressionAlgorithmDefinition
|
||||
public class BasicTemplateOptionsFilterUniverseAlgorithm : QCAlgorithm
|
||||
{
|
||||
private const string UnderlyingTicker = "GOOG";
|
||||
public Symbol OptionSymbol;
|
||||
public readonly Symbol Underlying = QuantConnect.Symbol.Create(UnderlyingTicker, SecurityType.Equity, Market.USA);
|
||||
public readonly Symbol OptionSymbol = QuantConnect.Symbol.Create(UnderlyingTicker, SecurityType.Option, Market.USA);
|
||||
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2015, 12, 24);
|
||||
SetEndDate(2015, 12, 24);
|
||||
SetCash(100000);
|
||||
SetCash(10000);
|
||||
|
||||
var equity = AddEquity(UnderlyingTicker);
|
||||
var option = AddOption(UnderlyingTicker);
|
||||
OptionSymbol = option.Symbol;
|
||||
|
||||
// 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)
|
||||
// set our custom filter for this option chain
|
||||
option.SetFilter(universe => from symbol in universe
|
||||
.WeeklysOnly()
|
||||
.Expiration(TimeSpan.Zero, TimeSpan.FromDays(10))
|
||||
where symbol.ID.OptionRight != OptionRight.Put &&
|
||||
-10 < universe.Underlying.Price - symbol.ID.StrikePrice &&
|
||||
universe.Underlying.Price - symbol.ID.StrikePrice < 10
|
||||
universe.Underlying.Price - symbol.ID.StrikePrice < 60
|
||||
select symbol);
|
||||
|
||||
// use the underlying equity as the benchmark
|
||||
@@ -66,13 +65,14 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
OptionChain chain;
|
||||
if (slice.OptionChains.TryGetValue(OptionSymbol, out chain))
|
||||
{
|
||||
// Get the first ITM call expiring today
|
||||
// find the second call strike under market price 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
|
||||
).FirstOrDefault();
|
||||
).Skip(2).FirstOrDefault();
|
||||
|
||||
if (contract != null)
|
||||
{
|
||||
@@ -86,62 +86,5 @@ 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"},
|
||||
{"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"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -80,10 +80,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
return filter
|
||||
.Strikes(+1, +1)
|
||||
// Expiration method accepts TimeSpan objects or integer for days.
|
||||
// The following statements yield the same filtering criteria
|
||||
.Expiration(0, 7)
|
||||
//.Expiration(TimeSpan.Zero, TimeSpan.FromDays(7))
|
||||
.Expiration(TimeSpan.Zero, TimeSpan.FromDays(7))
|
||||
.WeeklysOnly()
|
||||
.PutsOnly()
|
||||
.OnlyApplyFilterAtMarketOpen();
|
||||
@@ -142,45 +139,37 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "4"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Win", "0.14%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "0%"},
|
||||
{"Drawdown", "0%"},
|
||||
{"Compounding Annual Return", "71.396%"},
|
||||
{"Drawdown", "0.700%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "0%"},
|
||||
{"Sharpe Ratio", "0"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Net Profit", "0.271%"},
|
||||
{"Sharpe Ratio", "9.165"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Win Rate", "100%"},
|
||||
{"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", "$3.00"},
|
||||
{"Fitness Score", "0"},
|
||||
{"Kelly Criterion Estimate", "0.327"},
|
||||
{"Kelly Criterion Probability Value", "1"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "79228162514264337593543950335"},
|
||||
{"Portfolio Turnover", "0"},
|
||||
{"Beta", "24.746"},
|
||||
{"Annual Standard Deviation", "0.025"},
|
||||
{"Annual Variance", "0.001"},
|
||||
{"Information Ratio", "8.883"},
|
||||
{"Tracking Error", "0.025"},
|
||||
{"Treynor Ratio", "0.009"},
|
||||
{"Total Fees", "$4.00"},
|
||||
{"Total Insights Generated", "26"},
|
||||
{"Total Insights Closed", "24"},
|
||||
{"Total Insights Analysis Completed", "24"},
|
||||
{"Long Insight Count", "26"},
|
||||
{"Short Insight Count", "0"},
|
||||
{"Long/Short Ratio", "100%"},
|
||||
{"Estimated Monthly Alpha Value", "$31.01809"},
|
||||
{"Estimated Monthly Alpha Value", "$28.43325"},
|
||||
{"Total Accumulated Estimated Alpha Value", "$1.89555"},
|
||||
{"Mean Population Estimated Insight Value", "$0.07898125"},
|
||||
{"Mean Population Direction", "50%"},
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "50.0482%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "ce06ddfa4b2ffeb666a8910ac8836992"}
|
||||
{"Rolling Averaged Population Magnitude", "0%"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -39,11 +39,8 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
SetCash(1000000);
|
||||
|
||||
var option = AddOption("GOOG");
|
||||
// add the initial contract filter
|
||||
// SetFilter method accepts TimeSpan objects or integer for days.
|
||||
// The following statements yield the same filtering criteria
|
||||
option.SetFilter(-2, +2, 0, 180);
|
||||
// option.SetFilter(-2, +2, TimeSpan.Zero, TimeSpan.FromDays(180));
|
||||
// add the initial contract filter
|
||||
option.SetFilter(-2, +2, TimeSpan.Zero, TimeSpan.FromDays(180));
|
||||
|
||||
// set the pricing model for Greeks and volatility
|
||||
// find more pricing models https://www.quantconnect.com/lean/documentation/topic27704.html
|
||||
@@ -68,20 +65,20 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
var underlying = Securities[chain.Key.Underlying];
|
||||
foreach (var contract in chain.Value)
|
||||
{
|
||||
Log($"{contract.Symbol.Value}," +
|
||||
$"Bid={contract.BidPrice.ToStringInvariant()} " +
|
||||
$"Ask={contract.AskPrice.ToStringInvariant()} " +
|
||||
$"Last={contract.LastPrice.ToStringInvariant()} " +
|
||||
$"OI={contract.OpenInterest.ToStringInvariant()} " +
|
||||
$"σ={underlying.VolatilityModel.Volatility.ToStringInvariant("0.000")} " +
|
||||
$"NPV={contract.TheoreticalPrice.ToStringInvariant("0.000")} " +
|
||||
$"Δ={contract.Greeks.Delta.ToStringInvariant("0.000")} " +
|
||||
$"Γ={contract.Greeks.Gamma.ToStringInvariant("0.000")} " +
|
||||
$"ν={contract.Greeks.Vega.ToStringInvariant("0.000")} " +
|
||||
$"ρ={contract.Greeks.Rho.ToStringInvariant("0.00")} " +
|
||||
$"Θ={(contract.Greeks.Theta / 365.0m).ToStringInvariant("0.00")} " +
|
||||
$"IV={contract.ImpliedVolatility.ToStringInvariant("0.000")}"
|
||||
);
|
||||
Log(String.Format(@"{0},Bid={1} Ask={2} Last={3} OI={4} σ={5:0.000} NPV={6:0.000} Δ={7:0.000} Γ={8:0.000} ν={9:0.000} ρ={10:0.00} Θ={11:0.00} IV={12:0.000}",
|
||||
contract.Symbol.Value,
|
||||
contract.BidPrice,
|
||||
contract.AskPrice,
|
||||
contract.LastPrice,
|
||||
contract.OpenInterest,
|
||||
underlying.VolatilityModel.Volatility,
|
||||
contract.TheoreticalPrice,
|
||||
contract.Greeks.Delta,
|
||||
contract.Greeks.Gamma,
|
||||
contract.Greeks.Vega,
|
||||
contract.Greeks.Rho,
|
||||
contract.Greeks.Theta / 365.0m,
|
||||
contract.ImpliedVolatility));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,63 +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 System.Collections.Generic;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Data.Custom.SEC;
|
||||
using QuantConnect.Securities;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp.Benchmarks
|
||||
{
|
||||
public class SECReportBenchmarkAlgorithm : QCAlgorithm
|
||||
{
|
||||
private List<Security> _securities;
|
||||
/// <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(2018, 1, 1);
|
||||
SetEndDate(2019, 1, 1);
|
||||
|
||||
var tickers = new List<string> {"AAPL", "AMZN", "MSFT", "IBM", "FB", "QQQ",
|
||||
"IWM", "BAC", "BNO", "AIG", "UW", "WM" };
|
||||
_securities = new List<Security>();
|
||||
|
||||
foreach (var ticker in tickers)
|
||||
{
|
||||
var equity = AddEquity(ticker);
|
||||
_securities.Add(equity);
|
||||
|
||||
AddData<SECReport8K>(equity.Symbol, Resolution.Daily);
|
||||
AddData<SECReport10K>(equity.Symbol, Resolution.Daily);
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnData(Slice data)
|
||||
{
|
||||
foreach (var security in _securities)
|
||||
{
|
||||
SECReport8K report8K = security.Data.Get<SECReport8K>();
|
||||
SECReport10K report10K = security.Data.Get<SECReport10K>();
|
||||
|
||||
if (!security.HoldStock && report8K != null && report10K != null)
|
||||
{
|
||||
SetHoldings(security.Symbol, 1d / _securities.Count);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,81 +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 System;
|
||||
using System.Collections.Generic;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Data.Custom.SmartInsider;
|
||||
using QuantConnect.Securities;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp.Benchmarks
|
||||
{
|
||||
public class SmartInsiderEventBenchmarkAlgorithm : QCAlgorithm
|
||||
{
|
||||
private List<Security> _securities;
|
||||
private List<Symbol> _customSymbols;
|
||||
private int _historySymbolCount;
|
||||
|
||||
/// <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(2010, 1, 1);
|
||||
SetEndDate(2019, 1, 1);
|
||||
|
||||
var tickers = new List<string> {"AAPL", "AMZN", "MSFT", "IBM", "FB", "QQQ",
|
||||
"IWM", "BAC", "BNO", "AIG", "UW", "WM" };
|
||||
_securities = new List<Security>();
|
||||
_customSymbols = new List<Symbol>();
|
||||
|
||||
foreach (var ticker in tickers)
|
||||
{
|
||||
var equity = AddEquity(ticker, Resolution.Hour);
|
||||
_securities.Add(equity);
|
||||
|
||||
_customSymbols.Add(
|
||||
AddData<SmartInsiderIntention>(equity.Symbol, Resolution.Daily).Symbol);
|
||||
_customSymbols.Add(
|
||||
AddData<SmartInsiderTransaction>(equity.Symbol, Resolution.Daily).Symbol);
|
||||
}
|
||||
|
||||
Schedule.On(DateRules.EveryDay(), TimeRules.At(16, 0), () =>
|
||||
{
|
||||
foreach (var slice in History(_customSymbols, TimeSpan.FromDays(5)))
|
||||
{
|
||||
_historySymbolCount += slice.Count;
|
||||
}
|
||||
|
||||
foreach (var security in _securities)
|
||||
{
|
||||
SmartInsiderIntention intention = security.Data.Get<SmartInsiderIntention>();
|
||||
SmartInsiderTransaction transaction = security.Data.Get<SmartInsiderTransaction>();
|
||||
|
||||
if (!security.HoldStock && intention != null && transaction != null)
|
||||
{
|
||||
SetHoldings(security.Symbol, 1d / _securities.Count);
|
||||
}
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
public override void OnData(Slice data)
|
||||
{
|
||||
var intentions = data.Get<SmartInsiderIntention>();
|
||||
var transactions = data.Get<SmartInsiderTransaction>();
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -74,46 +74,38 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
/// </summary>
|
||||
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "18"},
|
||||
{"Total Trades", "21"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "-0.16%"},
|
||||
{"Compounding Annual Return", "72.164%"},
|
||||
{"Drawdown", "1.100%"},
|
||||
{"Average Loss", "-0.38%"},
|
||||
{"Compounding Annual Return", "-82.823%"},
|
||||
{"Drawdown", "4.300%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "0.747%"},
|
||||
{"Sharpe Ratio", "4.086"},
|
||||
{"Probabilistic Sharpe Ratio", "61.091%"},
|
||||
{"Net Profit", "-2.384%"},
|
||||
{"Sharpe Ratio", "-6.465"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.305"},
|
||||
{"Beta", "0.564"},
|
||||
{"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"},
|
||||
{"Fitness Score", "0.634"},
|
||||
{"Kelly Criterion Estimate", "13.656"},
|
||||
{"Kelly Criterion Probability Value", "0.228"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "80.05"},
|
||||
{"Portfolio Turnover", "0.634"},
|
||||
{"Total Insights Generated", "17"},
|
||||
{"Total Insights Closed", "14"},
|
||||
{"Total Insights Analysis Completed", "14"},
|
||||
{"Alpha", "0"},
|
||||
{"Beta", "-87.084"},
|
||||
{"Annual Standard Deviation", "0.155"},
|
||||
{"Annual Variance", "0.024"},
|
||||
{"Information Ratio", "-6.536"},
|
||||
{"Tracking Error", "0.155"},
|
||||
{"Treynor Ratio", "0.011"},
|
||||
{"Total Fees", "$72.41"},
|
||||
{"Total Insights Generated", "14"},
|
||||
{"Total Insights Closed", "11"},
|
||||
{"Total Insights Analysis Completed", "11"},
|
||||
{"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", "$891.2215"},
|
||||
{"Mean Population Direction", "50%"},
|
||||
{"Mean Population Magnitude", "50%"},
|
||||
{"Rolling Averaged Population Direction", "12.6429%"},
|
||||
{"Rolling Averaged Population Magnitude", "12.6429%"},
|
||||
{"OrderListHash", "3edd51956c7c97af4863aa6059c11f1a"}
|
||||
{"Short Insight Count", "4"},
|
||||
{"Long/Short Ratio", "150.0%"},
|
||||
{"Estimated Monthly Alpha Value", "$-85612.32"},
|
||||
{"Total Accumulated Estimated Alpha Value", "$-14744.34"},
|
||||
{"Mean Population Estimated Insight Value", "$-1340.395"},
|
||||
{"Mean Population Direction", "27.2727%"},
|
||||
{"Mean Population Magnitude", "27.2727%"},
|
||||
{"Rolling Averaged Population Direction", "5.8237%"},
|
||||
{"Rolling Averaged Population Magnitude", "5.8237%"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -108,9 +108,8 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
// return a message describing why we're not allowing this order
|
||||
message = new BrokerageMessageEvent(BrokerageMessageType.Warning, "InsufficientRemainingCapital",
|
||||
$"Account must maintain a minimum of ${_minimumAccountBalance.ToStringInvariant()} USD at all times. " +
|
||||
$"Order ID: {order.Id.ToStringInvariant()}"
|
||||
);
|
||||
string.Format("Account must maintain a minimum of ${0} USD at all times. Order ID: {1}", _minimumAccountBalance, order.Id)
|
||||
);
|
||||
return false;
|
||||
}
|
||||
return true;
|
||||
|
||||
@@ -1,180 +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 System;
|
||||
using System.Collections.Generic;
|
||||
using System.IO;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Data.Custom.CBOE;
|
||||
using QuantConnect.Indicators;
|
||||
using QuantConnect.Interfaces;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Tests the consolidation of custom data with random data
|
||||
/// </summary>
|
||||
public class CBOECustomDataConsolidationRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private Symbol _vix;
|
||||
private BollingerBands _bb;
|
||||
private bool _invested;
|
||||
|
||||
/// <summary>
|
||||
/// Initializes the algorithm with fake VIX data
|
||||
/// </summary>
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2013, 10, 7);
|
||||
SetEndDate(2013, 10, 11);
|
||||
SetCash(100000);
|
||||
|
||||
_vix = AddData<IncrementallyGeneratedCustomData>("VIX", Resolution.Daily).Symbol;
|
||||
_bb = BB(_vix, 30, 2, MovingAverageType.Simple, Resolution.Daily);
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
|
||||
/// </summary>
|
||||
/// <param name="data">Slice object keyed by symbol containing the stock data</param>
|
||||
public override void OnData(Slice data)
|
||||
{
|
||||
if (_bb.Current.Value == 0)
|
||||
{
|
||||
throw new Exception("Bollinger Band value is zero when we expect non-zero value.");
|
||||
}
|
||||
|
||||
if (!_invested && _bb.Current.Value > 0.05m)
|
||||
{
|
||||
MarketOrder(_vix, 1);
|
||||
_invested = true;
|
||||
}
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Incrementally updating data
|
||||
/// </summary>
|
||||
private class IncrementallyGeneratedCustomData : CBOE
|
||||
{
|
||||
private const decimal _start = 10.01m;
|
||||
private static decimal _step;
|
||||
|
||||
/// <summary>
|
||||
/// Gets the source of the subscription. In this case, we set it to existing
|
||||
/// equity data so that we can pass fake data from Reader
|
||||
/// </summary>
|
||||
/// <param name="config">Subscription configuration</param>
|
||||
/// <param name="date">Date we're making this request</param>
|
||||
/// <param name="isLiveMode">Is live mode</param>
|
||||
/// <returns>Source of subscription</returns>
|
||||
public override SubscriptionDataSource GetSource(SubscriptionDataConfig config, DateTime date, bool isLiveMode)
|
||||
{
|
||||
return new SubscriptionDataSource(Path.Combine(Globals.DataFolder, "equity", "usa", "minute", "spy", $"{date:yyyyMMdd}_trade.zip#{date:yyyyMMdd}_spy_minute_trade.csv"), SubscriptionTransportMedium.LocalFile, FileFormat.Csv);
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Reads the data, which in this case is fake incremental data
|
||||
/// </summary>
|
||||
/// <param name="config">Subscription configuration</param>
|
||||
/// <param name="line">Line of data</param>
|
||||
/// <param name="date">Date of the request</param>
|
||||
/// <param name="isLiveMode">Is live mode</param>
|
||||
/// <returns>Incremental BaseData instance</returns>
|
||||
public override BaseData Reader(SubscriptionDataConfig config, string line, DateTime date, bool isLiveMode)
|
||||
{
|
||||
var vix = new CBOE();
|
||||
_step += 0.10m;
|
||||
var open = _start + _step;
|
||||
var close = _start + _step + 0.02m;
|
||||
var high = close;
|
||||
var low = open;
|
||||
|
||||
return new IncrementallyGeneratedCustomData
|
||||
{
|
||||
Open = open,
|
||||
High = high,
|
||||
Low = low,
|
||||
Close = close,
|
||||
Time = date,
|
||||
Symbol = new Symbol(
|
||||
SecurityIdentifier.GenerateBase(typeof(IncrementallyGeneratedCustomData), "VIX", Market.USA, false),
|
||||
"VIX"),
|
||||
Period = vix.Period,
|
||||
DataType = vix.DataType
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
|
||||
/// </summary>
|
||||
public bool CanRunLocally { get; } = true;
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate which languages this algorithm is written in.
|
||||
/// </summary>
|
||||
/// <remarks>
|
||||
/// Unable to be tested in Python, due to pythonnet not supporting overriding of methods from Python
|
||||
/// </remarks>
|
||||
public Language[] Languages { get; } = { Language.CSharp };
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
|
||||
/// </summary>
|
||||
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "1"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "0.029%"},
|
||||
{"Drawdown", "0%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "0.000%"},
|
||||
{"Sharpe Ratio", "28.4"},
|
||||
{"Probabilistic Sharpe Ratio", "88.597%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "0"},
|
||||
{"Beta", "0"},
|
||||
{"Annual Standard Deviation", "0"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-7.067"},
|
||||
{"Tracking Error", "0.193"},
|
||||
{"Treynor Ratio", "7.887"},
|
||||
{"Total Fees", "$0.00"},
|
||||
{"Fitness Score", "0"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "79228162514264337593543950335"},
|
||||
{"Portfolio Turnover", "0"},
|
||||
{"Total Insights Generated", "0"},
|
||||
{"Total Insights Closed", "0"},
|
||||
{"Total Insights Analysis Completed", "0"},
|
||||
{"Long Insight Count", "0"},
|
||||
{"Short Insight Count", "0"},
|
||||
{"Long/Short Ratio", "100%"},
|
||||
{"Estimated Monthly Alpha Value", "$0"},
|
||||
{"Total Accumulated Estimated Alpha Value", "$0"},
|
||||
{"Mean Population Estimated Insight Value", "$0"},
|
||||
{"Mean Population Direction", "0%"},
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "918912ee4f64cd0290f3d58deca02713"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -130,43 +130,22 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Total Trades", "2"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "0%"},
|
||||
{"Drawdown", "0%"},
|
||||
{"Compounding Annual Return", "-100.000%"},
|
||||
{"Drawdown", "5.600%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "0%"},
|
||||
{"Sharpe Ratio", "0"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Net Profit", "-4.431%"},
|
||||
{"Sharpe Ratio", "-23.129"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "0"},
|
||||
{"Beta", "0"},
|
||||
{"Annual Standard Deviation", "0"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "0"},
|
||||
{"Tracking Error", "0"},
|
||||
{"Treynor Ratio", "0"},
|
||||
{"Total Fees", "$0.00"},
|
||||
{"Fitness Score", "0.501"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "-30.158"},
|
||||
{"Portfolio Turnover", "1.033"},
|
||||
{"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", "aea2e321d17414c1f3c6fa2491f10c88"}
|
||||
{"Alpha", "-8.364"},
|
||||
{"Beta", "395.811"},
|
||||
{"Annual Standard Deviation", "0.243"},
|
||||
{"Annual Variance", "0.059"},
|
||||
{"Information Ratio", "-23.216"},
|
||||
{"Tracking Error", "0.243"},
|
||||
{"Treynor Ratio", "-0.014"},
|
||||
{"Total Fees", "$0.00"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -76,45 +76,24 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "167"},
|
||||
{"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"},
|
||||
{"Average Win", "0.01%"},
|
||||
{"Average Loss", "-0.01%"},
|
||||
{"Compounding Annual Return", "-16.316%"},
|
||||
{"Drawdown", "0.100%"},
|
||||
{"Expectancy", "-0.121"},
|
||||
{"Net Profit", "-0.081%"},
|
||||
{"Sharpe Ratio", "-87.066"},
|
||||
{"Loss Rate", "49%"},
|
||||
{"Win Rate", "51%"},
|
||||
{"Profit-Loss Ratio", "0.74"},
|
||||
{"Alpha", "-0.116"},
|
||||
{"Beta", "-0.092"},
|
||||
{"Annual Standard Deviation", "0.001"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "0"},
|
||||
{"Tracking Error", "0"},
|
||||
{"Treynor Ratio", "0"},
|
||||
{"Total Fees", "$0.00"},
|
||||
{"Fitness Score", "0.5"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "-325.922"},
|
||||
{"Portfolio Turnover", "9.561"},
|
||||
{"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", "6ea6184a2a8d0d69e552ad866933bfb6"}
|
||||
{"Information Ratio", "2.962"},
|
||||
{"Tracking Error", "0.014"},
|
||||
{"Treynor Ratio", "1.116"},
|
||||
{"Total Fees", "$0.00"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -74,14 +74,14 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
};
|
||||
}
|
||||
|
||||
// sort the data by market capitalization and take the top 'NumberOfSymbolsFine'
|
||||
// sort the data by P/E ratio and take the top 'NumberOfSymbolsFine'
|
||||
public IEnumerable<Symbol> FineSelectionFunction(IEnumerable<FineFundamental> fine)
|
||||
{
|
||||
// sort descending by market capitalization
|
||||
var sortedByMarketCap = fine.OrderByDescending(x => x.MarketCap);
|
||||
// sort descending by P/E ratio
|
||||
var sortedByPeRatio = fine.OrderByDescending(x => x.ValuationRatios.PERatio);
|
||||
|
||||
// take the top entries from our sorted collection
|
||||
var topFine = sortedByMarketCap.Take(NumberOfSymbolsFine);
|
||||
var topFine = sortedByPeRatio.Take(NumberOfSymbolsFine);
|
||||
|
||||
// we need to return only the symbol objects
|
||||
return topFine.Select(x => x.Symbol);
|
||||
@@ -162,45 +162,24 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "2"},
|
||||
{"Average Win", "1.16%"},
|
||||
{"Average Win", "1.39%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "32.515%"},
|
||||
{"Compounding Annual Return", "40.038%"},
|
||||
{"Drawdown", "1.400%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "1.164%"},
|
||||
{"Sharpe Ratio", "2.857"},
|
||||
{"Probabilistic Sharpe Ratio", "64.822%"},
|
||||
{"Net Profit", "1.394%"},
|
||||
{"Sharpe Ratio", "3.081"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "100%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "0.237"},
|
||||
{"Beta", "-0.182"},
|
||||
{"Annual Standard Deviation", "0.09"},
|
||||
{"Annual Variance", "0.008"},
|
||||
{"Information Ratio", "2.425"},
|
||||
{"Tracking Error", "0.149"},
|
||||
{"Treynor Ratio", "-1.405"},
|
||||
{"Total Fees", "$2.00"},
|
||||
{"Fitness Score", "0.076"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "27.329"},
|
||||
{"Return Over Maximum Drawdown", "24.003"},
|
||||
{"Portfolio Turnover", "0.076"},
|
||||
{"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", "edd9e9ffc8a1cdfb7a1e6ae601e61b12"}
|
||||
{"Alpha", "-0.033"},
|
||||
{"Beta", "19.023"},
|
||||
{"Annual Standard Deviation", "0.096"},
|
||||
{"Annual Variance", "0.009"},
|
||||
{"Information Ratio", "2.904"},
|
||||
{"Tracking Error", "0.096"},
|
||||
{"Treynor Ratio", "0.016"},
|
||||
{"Total Fees", "$2.00"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,189 +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 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", "7.14272222483913E+15"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Loss Rate", "83%"},
|
||||
{"Win Rate", "17%"},
|
||||
{"Profit-Loss Ratio", "13.36"},
|
||||
{"Alpha", "2.59468989671647E+16"},
|
||||
{"Beta", "67.661"},
|
||||
{"Annual Standard Deviation", "3.633"},
|
||||
{"Annual Variance", "13.196"},
|
||||
{"Information Ratio", "7.24987266907741E+15"},
|
||||
{"Tracking Error", "3.579"},
|
||||
{"Treynor Ratio", "383485597312030"},
|
||||
{"Total Fees", "$13.00"},
|
||||
{"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"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -119,43 +119,22 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Total Trades", "11"},
|
||||
{"Average Win", "0.51%"},
|
||||
{"Average Loss", "-0.33%"},
|
||||
{"Compounding Annual Return", "-31.082%"},
|
||||
{"Compounding Annual Return", "-31.051%"},
|
||||
{"Drawdown", "2.700%"},
|
||||
{"Expectancy", "0.263"},
|
||||
{"Net Profit", "-1.518%"},
|
||||
{"Sharpe Ratio", "-2.118"},
|
||||
{"Probabilistic Sharpe Ratio", "23.259%"},
|
||||
{"Net Profit", "-1.516%"},
|
||||
{"Sharpe Ratio", "-2.526"},
|
||||
{"Loss Rate", "50%"},
|
||||
{"Win Rate", "50%"},
|
||||
{"Profit-Loss Ratio", "1.53"},
|
||||
{"Alpha", "-0.208"},
|
||||
{"Beta", "0.415"},
|
||||
{"Annual Standard Deviation", "0.119"},
|
||||
{"Annual Variance", "0.014"},
|
||||
{"Information Ratio", "-1.167"},
|
||||
{"Tracking Error", "0.126"},
|
||||
{"Treynor Ratio", "-0.607"},
|
||||
{"Total Fees", "$11.63"},
|
||||
{"Fitness Score", "0.013"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "-5.1"},
|
||||
{"Return Over Maximum Drawdown", "-11.717"},
|
||||
{"Portfolio Turnover", "0.282"},
|
||||
{"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", "3d1ae61492b34c39115b76757510c058"}
|
||||
{"Alpha", "-0.556"},
|
||||
{"Beta", "14.058"},
|
||||
{"Annual Standard Deviation", "0.124"},
|
||||
{"Annual Variance", "0.015"},
|
||||
{"Information Ratio", "-2.666"},
|
||||
{"Tracking Error", "0.124"},
|
||||
{"Treynor Ratio", "-0.022"},
|
||||
{"Total Fees", "$11.63"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -102,7 +102,6 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "0%"},
|
||||
{"Sharpe Ratio", "0"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
@@ -110,30 +109,10 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Beta", "0"},
|
||||
{"Annual Standard Deviation", "0"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-1.383"},
|
||||
{"Tracking Error", "0.096"},
|
||||
{"Information Ratio", "0"},
|
||||
{"Tracking Error", "0"},
|
||||
{"Treynor Ratio", "0"},
|
||||
{"Total Fees", "$0.00"},
|
||||
{"Fitness Score", "0"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "79228162514264337593543950335"},
|
||||
{"Portfolio Turnover", "0"},
|
||||
{"Total Insights Generated", "0"},
|
||||
{"Total Insights Closed", "0"},
|
||||
{"Total Insights Analysis Completed", "0"},
|
||||
{"Long Insight Count", "0"},
|
||||
{"Short Insight Count", "0"},
|
||||
{"Long/Short Ratio", "100%"},
|
||||
{"Estimated Monthly Alpha Value", "$0"},
|
||||
{"Total Accumulated Estimated Alpha Value", "$0"},
|
||||
{"Mean Population Estimated Insight Value", "$0"},
|
||||
{"Mean Population Direction", "0%"},
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
|
||||
{"Total Fees", "$0.00"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,145 +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 System;
|
||||
using System.Collections.Generic;
|
||||
using System.Linq;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Data.UniverseSelection;
|
||||
using QuantConnect.Interfaces;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Test algorithm that reproduces GH issues 3410 and 3409.
|
||||
/// Coarse universe selection should start from the algorithm start date.
|
||||
/// Data returned by history requests performed from the selection method should be up to date.
|
||||
/// </summary>
|
||||
public class CoarseSelectionTimeRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private Symbol _spy;
|
||||
private decimal _historyCoarseSpyPrice;
|
||||
|
||||
/// <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(2014, 03, 25);
|
||||
SetEndDate(2014, 04, 01);
|
||||
|
||||
_spy = AddEquity("SPY", Resolution.Daily).Symbol;
|
||||
|
||||
UniverseSettings.Resolution = Resolution.Daily;
|
||||
AddUniverse(CoarseSelectionFunction);
|
||||
}
|
||||
|
||||
public IEnumerable<Symbol> CoarseSelectionFunction(IEnumerable<CoarseFundamental> coarse)
|
||||
{
|
||||
var sortedByDollarVolume = coarse.OrderByDescending(x => x.DollarVolume);
|
||||
var top = sortedByDollarVolume
|
||||
.Where(fundamental => fundamental.Symbol != _spy) // ignore spy
|
||||
.Take(1);
|
||||
|
||||
_historyCoarseSpyPrice = History(_spy, 1).First().Close;
|
||||
|
||||
return top.Select(x => x.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 (data.Count != 2)
|
||||
{
|
||||
throw new Exception($"Unexpected data count: {data.Count}");
|
||||
}
|
||||
if (ActiveSecurities.Count != 2)
|
||||
{
|
||||
throw new Exception($"Unexpected ActiveSecurities count: {ActiveSecurities.Count}");
|
||||
}
|
||||
// the price obtained by the previous coarse selection should be the same as the current price
|
||||
if (_historyCoarseSpyPrice != 0 && _historyCoarseSpyPrice != Securities[_spy].Price)
|
||||
{
|
||||
throw new Exception($"Unexpected SPY price: {_historyCoarseSpyPrice}");
|
||||
}
|
||||
_historyCoarseSpyPrice = 0;
|
||||
if (!Portfolio.Invested)
|
||||
{
|
||||
SetHoldings(_spy, 1);
|
||||
Debug("Purchased Stock");
|
||||
}
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
|
||||
/// </summary>
|
||||
public bool CanRunLocally { get; } = true;
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate which languages this algorithm is written in.
|
||||
/// </summary>
|
||||
public Language[] Languages { get; } = { Language.CSharp };
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
|
||||
/// </summary>
|
||||
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "1"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "58.336%"},
|
||||
{"Drawdown", "0.900%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "1.012%"},
|
||||
{"Sharpe Ratio", "5.09"},
|
||||
{"Probabilistic Sharpe Ratio", "68.472%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "0.322"},
|
||||
{"Beta", "0.265"},
|
||||
{"Annual Standard Deviation", "0.087"},
|
||||
{"Annual Variance", "0.008"},
|
||||
{"Information Ratio", "-0.088"},
|
||||
{"Tracking Error", "0.105"},
|
||||
{"Treynor Ratio", "1.667"},
|
||||
{"Total Fees", "$2.91"},
|
||||
{"Fitness Score", "0.141"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "9.731"},
|
||||
{"Return Over Maximum Drawdown", "61.515"},
|
||||
{"Portfolio Turnover", "0.143"},
|
||||
{"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", "718d73fbddccb63aeacbf4659938b4b8"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -1,119 +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 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)};
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -73,30 +73,23 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
{"Total Trades", "7"},
|
||||
{"Average Win", "0.01%"},
|
||||
{"Average Loss", "-0.40%"},
|
||||
{"Compounding Annual Return", "1114.772%"},
|
||||
{"Drawdown", "1.800%"},
|
||||
{"Expectancy", "-0.319"},
|
||||
{"Net Profit", "3.244%"},
|
||||
{"Sharpe Ratio", "23.478"},
|
||||
{"Probabilistic Sharpe Ratio", "80.383%"},
|
||||
{"Average Loss", "-0.38%"},
|
||||
{"Compounding Annual Return", "1192.794%"},
|
||||
{"Drawdown", "1.700%"},
|
||||
{"Expectancy", "-0.323"},
|
||||
{"Net Profit", "3.326%"},
|
||||
{"Sharpe Ratio", "6.635"},
|
||||
{"Loss Rate", "33%"},
|
||||
{"Win Rate", "67%"},
|
||||
{"Profit-Loss Ratio", "0.02"},
|
||||
{"Alpha", "4.314"},
|
||||
{"Beta", "1.239"},
|
||||
{"Annual Standard Deviation", "0.285"},
|
||||
{"Annual Variance", "0.081"},
|
||||
{"Information Ratio", "47.452"},
|
||||
{"Tracking Error", "0.101"},
|
||||
{"Treynor Ratio", "5.409"},
|
||||
{"Profit-Loss Ratio", "0.01"},
|
||||
{"Alpha", "0"},
|
||||
{"Beta", "152.178"},
|
||||
{"Annual Standard Deviation", "0.253"},
|
||||
{"Annual Variance", "0.064"},
|
||||
{"Information Ratio", "6.594"},
|
||||
{"Tracking Error", "0.253"},
|
||||
{"Treynor Ratio", "0.011"},
|
||||
{"Total Fees", "$67.00"},
|
||||
{"Fitness Score", "0.501"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "148.636"},
|
||||
{"Return Over Maximum Drawdown", "1502.912"},
|
||||
{"Portfolio Turnover", "0.501"},
|
||||
{"Total Insights Generated", "2"},
|
||||
{"Total Insights Closed", "0"},
|
||||
{"Total Insights Analysis Completed", "0"},
|
||||
@@ -109,8 +102,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Mean Population Direction", "0%"},
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "ba44309886ea8ff515ef593a24456c47"}
|
||||
{"Rolling Averaged Population Magnitude", "0%"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -66,45 +66,37 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "7"},
|
||||
{"Average Win", "1.02%"},
|
||||
{"Average Loss", "-1.01%"},
|
||||
{"Compounding Annual Return", "205.606%"},
|
||||
{"Drawdown", "2.200%"},
|
||||
{"Expectancy", "0.339"},
|
||||
{"Net Profit", "1.439%"},
|
||||
{"Sharpe Ratio", "7.166"},
|
||||
{"Probabilistic Sharpe Ratio", "64.794%"},
|
||||
{"Average Win", "1.00%"},
|
||||
{"Average Loss", "-1.03%"},
|
||||
{"Compounding Annual Return", "190.425%"},
|
||||
{"Drawdown", "2.300%"},
|
||||
{"Expectancy", "0.314"},
|
||||
{"Net Profit", "1.372%"},
|
||||
{"Sharpe Ratio", "3.771"},
|
||||
{"Loss Rate", "33%"},
|
||||
{"Win Rate", "67%"},
|
||||
{"Profit-Loss Ratio", "1.01"},
|
||||
{"Alpha", "-0.341"},
|
||||
{"Beta", "0.968"},
|
||||
{"Annual Standard Deviation", "0.213"},
|
||||
{"Annual Variance", "0.045"},
|
||||
{"Information Ratio", "-46.719"},
|
||||
{"Tracking Error", "0.009"},
|
||||
{"Treynor Ratio", "1.575"},
|
||||
{"Profit-Loss Ratio", "0.97"},
|
||||
{"Alpha", "0.007"},
|
||||
{"Beta", "62.953"},
|
||||
{"Annual Standard Deviation", "0.186"},
|
||||
{"Annual Variance", "0.035"},
|
||||
{"Information Ratio", "3.713"},
|
||||
{"Tracking Error", "0.186"},
|
||||
{"Treynor Ratio", "0.011"},
|
||||
{"Total Fees", "$22.77"},
|
||||
{"Fitness Score", "0.999"},
|
||||
{"Kelly Criterion Estimate", "38.64"},
|
||||
{"Kelly Criterion Probability Value", "0.229"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "69.159"},
|
||||
{"Portfolio Turnover", "1.242"},
|
||||
{"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"},
|
||||
{"Estimated Monthly Alpha Value", "$158418.3850"},
|
||||
{"Total Accumulated Estimated Alpha Value", "$25522.9620"},
|
||||
{"Mean Population Estimated Insight Value", "$257.8077"},
|
||||
{"Mean Population Direction", "54.5455%"},
|
||||
{"Mean Population Magnitude", "54.5455%"},
|
||||
{"Rolling Averaged Population Direction", "59.8056%"},
|
||||
{"Rolling Averaged Population Magnitude", "59.8056%"},
|
||||
{"OrderListHash", "e0f388bf9e88b34388c866150b292573"}
|
||||
{"Rolling Averaged Population Magnitude", "59.8056%"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,119 +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 System;
|
||||
using System.Collections.Generic;
|
||||
using QuantConnect.Algorithm.Framework.Alphas;
|
||||
using QuantConnect.Algorithm.Framework.Execution;
|
||||
using QuantConnect.Algorithm.Framework.Portfolio;
|
||||
using QuantConnect.Algorithm.Framework.Selection;
|
||||
using QuantConnect.Interfaces;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Test algorithm using <see cref="ConfidenceWeightedPortfolioConstructionModel"/> and <see cref="ConstantAlphaModel"/>
|
||||
/// generating a constant <see cref="Insight"/> with a 0.25 confidence
|
||||
/// </summary>
|
||||
public class ConfidenceWeightedFrameworkAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
/// <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()
|
||||
{
|
||||
// Set requested data resolution
|
||||
UniverseSettings.Resolution = Resolution.Minute;
|
||||
|
||||
SetStartDate(2013, 10, 07); //Set Start Date
|
||||
SetEndDate(2013, 10, 11); //Set End Date
|
||||
SetCash(100000); //Set Strategy Cash
|
||||
|
||||
// set algorithm framework models
|
||||
SetUniverseSelection(new ManualUniverseSelectionModel(QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA)));
|
||||
SetAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromMinutes(20), 0.025, 0.25));
|
||||
SetPortfolioConstruction(new ConfidenceWeightedPortfolioConstructionModel());
|
||||
SetExecution(new ImmediateExecutionModel());
|
||||
}
|
||||
|
||||
public override void OnEndOfAlgorithm()
|
||||
{
|
||||
if (// holdings value should be 0.25 - to avoid price fluctuation issue we compare with 0.28 and 0.23
|
||||
Portfolio.TotalHoldingsValue > Portfolio.TotalPortfolioValue * 0.28m
|
||||
||
|
||||
Portfolio.TotalHoldingsValue < Portfolio.TotalPortfolioValue * 0.23m)
|
||||
{
|
||||
throw new Exception($"Unexpected Total Holdings Value: {Portfolio.TotalHoldingsValue}");
|
||||
}
|
||||
}
|
||||
|
||||
/// <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", "6"},
|
||||
{"Average Win", "0.00%"},
|
||||
{"Average Loss", "0.00%"},
|
||||
{"Compounding Annual Return", "38.059%"},
|
||||
{"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"},
|
||||
{"Annual Standard Deviation", "0.055"},
|
||||
{"Annual Variance", "0.003"},
|
||||
{"Information Ratio", "-9.844"},
|
||||
{"Tracking Error", "0.165"},
|
||||
{"Treynor Ratio", "1.212"},
|
||||
{"Total Fees", "$6.00"},
|
||||
{"Fitness Score", "0.063"},
|
||||
{"Kelly Criterion Estimate", "38.64"},
|
||||
{"Kelly Criterion Probability Value", "0.229"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "70.188"},
|
||||
{"Portfolio Turnover", "0.063"},
|
||||
{"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", "07eb3e2c199575b547459a534057eb5e"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -1,189 +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 System;
|
||||
using System.Collections.Generic;
|
||||
using System.Linq;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Data.Consolidators;
|
||||
using QuantConnect.Data.Market;
|
||||
using QuantConnect.Indicators;
|
||||
using QuantConnect.Interfaces;
|
||||
using QuantConnect.Securities;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Regression algorithm reproducing data type bugs in the Consolidate API. Related to GH 4205.
|
||||
/// </summary>
|
||||
public class ConsolidateRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private List<int> _consolidationCount;
|
||||
private int _customDataConsolidator;
|
||||
private Symbol _symbol;
|
||||
|
||||
/// <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(2013, 10, 08);
|
||||
SetEndDate(2013, 10, 09);
|
||||
|
||||
var SP500 = QuantConnect.Symbol.Create(Futures.Indices.SP500EMini, SecurityType.Future, Market.CME);
|
||||
_symbol = FutureChainProvider.GetFutureContractList(SP500, StartDate).First();
|
||||
AddFutureContract(_symbol);
|
||||
|
||||
_consolidationCount = new List<int> { 0, 0, 0, 0, 0, 0, 0, 0 };
|
||||
|
||||
var sma = new SimpleMovingAverage(10);
|
||||
Consolidate<QuoteBar>(_symbol, time => new CalendarInfo(time.RoundDown(TimeSpan.FromDays(1)), TimeSpan.FromDays(1)),
|
||||
bar => UpdateQuoteBar(sma, bar, 0));
|
||||
|
||||
var sma2 = new SimpleMovingAverage(10);
|
||||
Consolidate<QuoteBar>(_symbol, TimeSpan.FromDays(1), bar => UpdateQuoteBar(sma2, bar, 1));
|
||||
|
||||
var sma3 = new SimpleMovingAverage(10);
|
||||
Consolidate(_symbol, Resolution.Daily, TickType.Quote, (Action<QuoteBar>)(bar => UpdateQuoteBar(sma3, bar, 2)));
|
||||
|
||||
var sma4 = new SimpleMovingAverage(10);
|
||||
Consolidate(_symbol, TimeSpan.FromDays(1), bar => UpdateTradeBar(sma4, bar, 3));
|
||||
|
||||
var sma5 = new SimpleMovingAverage(10);
|
||||
Consolidate<TradeBar>(_symbol, TimeSpan.FromDays(1), bar => UpdateTradeBar(sma5, bar, 4));
|
||||
|
||||
// custom data
|
||||
var sma6 = new SimpleMovingAverage(10);
|
||||
var symbol = AddData<CustomDataRegressionAlgorithm.Bitcoin>("BTC", Resolution.Minute).Symbol;
|
||||
Consolidate<TradeBar>(symbol, TimeSpan.FromDays(1), bar => _customDataConsolidator++);
|
||||
|
||||
try
|
||||
{
|
||||
Consolidate<QuoteBar>(symbol, TimeSpan.FromDays(1), bar => { UpdateQuoteBar(sma6, bar, -1); });
|
||||
throw new Exception($"Expected {nameof(ArgumentException)} to be thrown");
|
||||
}
|
||||
catch (ArgumentException)
|
||||
{
|
||||
// will try to use BaseDataConsolidator for which input is TradeBars not QuoteBars
|
||||
}
|
||||
|
||||
// Test using abstract T types, through defining a 'BaseData' handler
|
||||
var sma7 = new SimpleMovingAverage(10);
|
||||
Consolidate(_symbol, Resolution.Daily, null, (Action<BaseData>)(bar => UpdateBar(sma7, bar, 5)));
|
||||
|
||||
var sma8 = new SimpleMovingAverage(10);
|
||||
Consolidate(_symbol, TimeSpan.FromDays(1), null, (Action<BaseData>)(bar => UpdateBar(sma8, bar, 6)));
|
||||
|
||||
var sma9 = new SimpleMovingAverage(10);
|
||||
Consolidate(_symbol, TimeSpan.FromDays(1), (Action<BaseData>)(bar => UpdateBar(sma9, bar, 7)));
|
||||
}
|
||||
private void UpdateBar(SimpleMovingAverage sma, BaseData tradeBar, int position)
|
||||
{
|
||||
if (!(tradeBar is TradeBar))
|
||||
{
|
||||
throw new Exception("Expected a TradeBar");
|
||||
}
|
||||
_consolidationCount[position]++;
|
||||
sma.Update(tradeBar.EndTime, tradeBar.Value);
|
||||
}
|
||||
private void UpdateTradeBar(SimpleMovingAverage sma, TradeBar tradeBar, int position)
|
||||
{
|
||||
_consolidationCount[position]++;
|
||||
sma.Update(tradeBar.EndTime, tradeBar.High);
|
||||
}
|
||||
private void UpdateQuoteBar(SimpleMovingAverage sma, QuoteBar quoteBar, int position)
|
||||
{
|
||||
_consolidationCount[position]++;
|
||||
sma.Update(quoteBar.EndTime, quoteBar.High);
|
||||
}
|
||||
|
||||
public override void OnEndOfAlgorithm()
|
||||
{
|
||||
if (_consolidationCount.Any(i => i != 3) || _customDataConsolidator == 0)
|
||||
{
|
||||
throw new Exception("Unexpected consolidation count");
|
||||
}
|
||||
}
|
||||
|
||||
/// <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 (!Portfolio.Invested)
|
||||
{
|
||||
SetHoldings(_symbol, 0.5);
|
||||
}
|
||||
}
|
||||
|
||||
/// <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", "-99.999%"},
|
||||
{"Drawdown", "16.100%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "-6.366%"},
|
||||
{"Sharpe Ratio", "1.194"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "5.579"},
|
||||
{"Beta", "-63.972"},
|
||||
{"Annual Standard Deviation", "0.434"},
|
||||
{"Annual Variance", "0.188"},
|
||||
{"Information Ratio", "0.996"},
|
||||
{"Tracking Error", "0.441"},
|
||||
{"Treynor Ratio", "-0.008"},
|
||||
{"Total Fees", "$20.35"},
|
||||
{"Fitness Score", "0.138"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "-1.727"},
|
||||
{"Return Over Maximum Drawdown", "-12.061"},
|
||||
{"Portfolio Turnover", "4.916"},
|
||||
{"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", "7c841ca58a4385f42236838e5bf0c382"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -13,12 +13,14 @@
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
using QuantConnect.Data.UniverseSelection;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Demonstration of how to estimate constituents of QC500 index based on the company fundamentals
|
||||
/// The algorithm creates a default tradable and liquid universe containing 500 US equities
|
||||
/// which are chosen at the first trading day of each month.
|
||||
/// The algorithm creates a default tradable and liquid universe containing 500 US equities
|
||||
/// which are chosen at the first trading day of each month.
|
||||
/// </summary>
|
||||
/// <meta name="tag" content="using data" />
|
||||
/// <meta name="tag" content="universes" />
|
||||
|
||||
@@ -1,161 +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 System;
|
||||
using System.Collections.Generic;
|
||||
using System.IO;
|
||||
using System.Linq;
|
||||
using QuantConnect.Data.UniverseSelection;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Algorithm used to generate daily data on disk for the different <see cref="ConstituentsUniverse"/>.
|
||||
/// How to use me? search for 'CHANGE ME'
|
||||
/// 1- change the universe name
|
||||
/// 2- change the desired start and end date to generate
|
||||
/// 3- define the universe selection to use
|
||||
/// Data will be generated in:
|
||||
/// 'Globals.DataFolder\SecurityType\Market\universes\Resolution\{UniverseName}\{dates:yyyyMMdd}.csv'
|
||||
/// </summary>
|
||||
/// <remarks>The data produced by this algorithm is the one used by the
|
||||
/// <see cref="ConstituentsUniverseRegressionAlgorithm"/></remarks>
|
||||
/// <remarks>In the cloud, users can implement their own <see cref="ConstituentsUniverseData"/>
|
||||
/// (not using <see cref="SubscriptionTransportMedium.LocalFile"/>) that can fetch the files
|
||||
/// generated from this algorithm</remarks>
|
||||
public class ConstituentsUniverseDataGeneratorAlgorithm : QCAlgorithm
|
||||
{
|
||||
private readonly HashSet<Symbol> _currentSelection = new HashSet<Symbol>();
|
||||
private DateTime _currentDateTime = DateTime.MinValue;
|
||||
private readonly string _rootDataPath = Globals.DataFolder;
|
||||
private string _dataPath;
|
||||
private bool _skippedFirst;
|
||||
|
||||
// Configuration properties: Only these are supported for now (Don't change me)
|
||||
private readonly SecurityType _securityType = SecurityType.Equity;
|
||||
private readonly string _market = Market.USA;
|
||||
private readonly Resolution _resolution = Resolution.Daily;
|
||||
|
||||
// CHANGE ME
|
||||
private readonly string _universeName = "qctest";
|
||||
|
||||
public override void Initialize()
|
||||
{
|
||||
// CHANGE ME
|
||||
SetStartDate(2013, 10, 07); // Set Start Date
|
||||
SetEndDate(2013, 10, 11); // Set End Date
|
||||
SetCash(100000); // Set Strategy Cash
|
||||
|
||||
UniverseSettings.Resolution = Resolution.Daily;
|
||||
_dataPath = Path.Combine(_rootDataPath,
|
||||
_securityType.SecurityTypeToLower(),
|
||||
_market,
|
||||
"universes",
|
||||
_resolution.ResolutionToLower(),
|
||||
_universeName);
|
||||
Directory.CreateDirectory(_dataPath);
|
||||
|
||||
// CHANGE ME
|
||||
int step = 0;
|
||||
AddUniverse(coarse =>
|
||||
{
|
||||
step++;
|
||||
switch (step)
|
||||
{
|
||||
case 1:
|
||||
case 2:
|
||||
return new[]
|
||||
{
|
||||
QuantConnect.Symbol.Create("QQQ", SecurityType.Equity, Market.USA),
|
||||
QuantConnect.Symbol.Create("AAPL", SecurityType.Equity, Market.USA)
|
||||
};
|
||||
case 3:
|
||||
return Enumerable.Empty<Symbol>();
|
||||
case 4:
|
||||
case 5:
|
||||
return new[]
|
||||
{
|
||||
QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA),
|
||||
QuantConnect.Symbol.Create("FB", SecurityType.Equity, Market.USA)
|
||||
};
|
||||
default:
|
||||
throw new Exception("Unexpected step count");
|
||||
}
|
||||
});
|
||||
|
||||
Schedule.On(DateRules.EveryDay(), TimeRules.At(23, 0), SaveConstituentsUniverseDataToDisk);
|
||||
}
|
||||
|
||||
private void SaveConstituentsUniverseDataToDisk()
|
||||
{
|
||||
if (_skippedFirst && Time > _currentDateTime)
|
||||
{
|
||||
if (Time.DayOfWeek == DayOfWeek.Sunday
|
||||
|| Time.DayOfWeek == DayOfWeek.Monday)
|
||||
{
|
||||
// we generate files from Tue to Saturday using current selected securities
|
||||
return;
|
||||
}
|
||||
// files are for Mon to Friday
|
||||
_currentDateTime = Time.Date.AddDays(-1);
|
||||
|
||||
var path = Path.Combine(_dataPath, $"{_currentDateTime:yyyyMMdd}.csv");
|
||||
File.Delete(path);
|
||||
if (_currentSelection.Count == 0)
|
||||
{
|
||||
using (StreamWriter constituentsUniverseFile = new StreamWriter(path, append:true))
|
||||
{
|
||||
constituentsUniverseFile.WriteLine(
|
||||
$"{QuantConnect.Symbol.None.Value},{QuantConnect.Symbol.None.ID.ToString()}");
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
foreach (var symbol in _currentSelection)
|
||||
{
|
||||
using (StreamWriter constituentsUniverseFile = new StreamWriter(path, append: true))
|
||||
{
|
||||
constituentsUniverseFile.WriteLine($"{symbol.Value},{symbol.ID.ToString()}");
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
_skippedFirst = true;
|
||||
}
|
||||
|
||||
public override void OnSecuritiesChanged(SecurityChanges changes)
|
||||
{
|
||||
foreach (var added in changes.AddedSecurities)
|
||||
{
|
||||
Log($"AddedSecurities {added}");
|
||||
if (_currentSelection.Contains(added.Symbol))
|
||||
{
|
||||
throw new Exception("Added symbol already selected");
|
||||
}
|
||||
_currentSelection.Add(added.Symbol);
|
||||
}
|
||||
|
||||
foreach (var removed in changes.RemovedSecurities)
|
||||
{
|
||||
Log($"RemovedSecurities {removed}");
|
||||
if (!_currentSelection.Contains(removed.Symbol))
|
||||
{
|
||||
throw new Exception("Removing symbol already deselected");
|
||||
}
|
||||
_currentSelection.Remove(removed.Symbol);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,215 +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 System;
|
||||
using System.Collections.Generic;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Data.UniverseSelection;
|
||||
using QuantConnect.Interfaces;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Test algorithm using a <see cref="ConstituentsUniverse"/> with test data
|
||||
/// </summary>
|
||||
public class ConstituentsUniverseRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private readonly Symbol _appl = QuantConnect.Symbol.Create("AAPL", SecurityType.Equity, Market.USA);
|
||||
private readonly Symbol _spy = QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA);
|
||||
private readonly Symbol _qqq = QuantConnect.Symbol.Create("QQQ", SecurityType.Equity, Market.USA);
|
||||
private readonly Symbol _fb = QuantConnect.Symbol.Create("FB", SecurityType.Equity, Market.USA);
|
||||
private int _step;
|
||||
|
||||
/// <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(2013, 10, 07); //Set Start Date
|
||||
SetEndDate(2013, 10, 11); //Set End Date
|
||||
SetCash(100000); //Set Strategy Cash
|
||||
|
||||
UniverseSettings.Resolution = Resolution.Daily;
|
||||
|
||||
var customUniverseSymbol = new Symbol(SecurityIdentifier.GenerateConstituentIdentifier(
|
||||
"constituents-universe-qctest",
|
||||
SecurityType.Equity,
|
||||
Market.USA),
|
||||
"constituents-universe-qctest");
|
||||
|
||||
AddUniverse(new ConstituentsUniverse(customUniverseSymbol, UniverseSettings));
|
||||
}
|
||||
|
||||
/// <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)
|
||||
{
|
||||
_step++;
|
||||
if (_step == 1)
|
||||
{
|
||||
if (!data.ContainsKey(_qqq)
|
||||
|| !data.ContainsKey(_appl))
|
||||
{
|
||||
throw new Exception($"Unexpected symbols found, step: {_step}");
|
||||
}
|
||||
if (data.Count != 2)
|
||||
{
|
||||
throw new Exception($"Unexpected data count, step: {_step}");
|
||||
}
|
||||
// AAPL will be deselected by the ConstituentsUniverse
|
||||
// but it shouldn't be removed since we hold it
|
||||
SetHoldings(_appl, 0.5);
|
||||
}
|
||||
else if (_step == 2)
|
||||
{
|
||||
if (!data.ContainsKey(_appl))
|
||||
{
|
||||
throw new Exception($"Unexpected symbols found, step: {_step}");
|
||||
}
|
||||
if (data.Count != 1)
|
||||
{
|
||||
throw new Exception($"Unexpected data count, step: {_step}");
|
||||
}
|
||||
// AAPL should now be released
|
||||
// note: takes one extra loop because the order is executed on market open
|
||||
Liquidate();
|
||||
}
|
||||
else if (_step == 3)
|
||||
{
|
||||
if (!data.ContainsKey(_fb)
|
||||
|| !data.ContainsKey(_spy)
|
||||
|| !data.ContainsKey(_appl))
|
||||
{
|
||||
throw new Exception($"Unexpected symbols found, step: {_step}");
|
||||
}
|
||||
if (data.Count != 3)
|
||||
{
|
||||
throw new Exception($"Unexpected data count, step: {_step}");
|
||||
}
|
||||
}
|
||||
else if (_step == 4)
|
||||
{
|
||||
if (!data.ContainsKey(_fb)
|
||||
|| !data.ContainsKey(_spy))
|
||||
{
|
||||
throw new Exception($"Unexpected symbols found, step: {_step}");
|
||||
}
|
||||
if (data.Count != 2)
|
||||
{
|
||||
throw new Exception($"Unexpected data count, step: {_step}");
|
||||
}
|
||||
}
|
||||
else if (_step == 5)
|
||||
{
|
||||
if (!data.ContainsKey(_fb)
|
||||
|| !data.ContainsKey(_spy))
|
||||
{
|
||||
throw new Exception($"Unexpected symbols found, step: {_step}");
|
||||
}
|
||||
if (data.Count != 2)
|
||||
{
|
||||
throw new Exception($"Unexpected data count, step: {_step}");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnEndOfAlgorithm()
|
||||
{
|
||||
if (_step != 5)
|
||||
{
|
||||
throw new Exception($"Unexpected step count: {_step}");
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnSecuritiesChanged(SecurityChanges changes)
|
||||
{
|
||||
foreach (var added in changes.AddedSecurities)
|
||||
{
|
||||
Log($"AddedSecurities {added}");
|
||||
}
|
||||
|
||||
foreach (var removed in changes.RemovedSecurities)
|
||||
{
|
||||
Log($"RemovedSecurities {removed} {_step}");
|
||||
// we are currently notifying the removal of AAPl twice,
|
||||
// when deselected and when finally removed (since it stayed pending)
|
||||
if (removed.Symbol == _appl && _step != 1 && _step != 2
|
||||
|| removed.Symbol == _qqq && _step != 1)
|
||||
{
|
||||
throw new Exception($"Unexpected removal step count: {_step}");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// <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", "-0.52%"},
|
||||
{"Compounding Annual Return", "-31.636%"},
|
||||
{"Drawdown", "0.900%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "-0.520%"},
|
||||
{"Sharpe Ratio", "-3.097"},
|
||||
{"Probabilistic Sharpe Ratio", "24.675%"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.443"},
|
||||
{"Beta", "0.157"},
|
||||
{"Annual Standard Deviation", "0.074"},
|
||||
{"Annual Variance", "0.005"},
|
||||
{"Information Ratio", "-9.046"},
|
||||
{"Tracking Error", "0.176"},
|
||||
{"Treynor Ratio", "-1.46"},
|
||||
{"Total Fees", "$7.82"},
|
||||
{"Fitness Score", "0.1"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "-35.683"},
|
||||
{"Portfolio Turnover", "0.2"},
|
||||
{"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", "71984e154883ece4aef1d71bafbfccaf"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -129,43 +129,35 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Total Trades", "85"},
|
||||
{"Average Win", "4.85%"},
|
||||
{"Average Loss", "-4.21%"},
|
||||
{"Compounding Annual Return", "-3.100%"},
|
||||
{"Compounding Annual Return", "-3.105%"},
|
||||
{"Drawdown", "52.900%"},
|
||||
{"Expectancy", "-0.052"},
|
||||
{"Net Profit", "-29.298%"},
|
||||
{"Sharpe Ratio", "-0.076"},
|
||||
{"Probabilistic Sharpe Ratio", "0.004%"},
|
||||
{"Expectancy", "-0.053"},
|
||||
{"Net Profit", "-29.335%"},
|
||||
{"Sharpe Ratio", "-0.084"},
|
||||
{"Loss Rate", "56%"},
|
||||
{"Win Rate", "44%"},
|
||||
{"Profit-Loss Ratio", "1.15"},
|
||||
{"Alpha", "-0.013"},
|
||||
{"Beta", "0.009"},
|
||||
{"Annual Standard Deviation", "0.164"},
|
||||
{"Annual Variance", "0.027"},
|
||||
{"Information Ratio", "-0.391"},
|
||||
{"Tracking Error", "0.239"},
|
||||
{"Treynor Ratio", "-1.435"},
|
||||
{"Total Fees", "$755.29"},
|
||||
{"Fitness Score", "0.024"},
|
||||
{"Kelly Criterion Estimate", "-0.84"},
|
||||
{"Kelly Criterion Probability Value", "0.53"},
|
||||
{"Sortino Ratio", "-0.224"},
|
||||
{"Return Over Maximum Drawdown", "-0.058"},
|
||||
{"Portfolio Turnover", "0.05"},
|
||||
{"Alpha", "0.046"},
|
||||
{"Beta", "-3.04"},
|
||||
{"Annual Standard Deviation", "0.181"},
|
||||
{"Annual Variance", "0.033"},
|
||||
{"Information Ratio", "-0.194"},
|
||||
{"Tracking Error", "0.181"},
|
||||
{"Treynor Ratio", "0.005"},
|
||||
{"Total Fees", "$755.20"},
|
||||
{"Total Insights Generated", "85"},
|
||||
{"Total Insights Closed", "85"},
|
||||
{"Total Insights Analysis Completed", "85"},
|
||||
{"Long Insight Count", "42"},
|
||||
{"Short Insight Count", "43"},
|
||||
{"Long/Short Ratio", "97.67%"},
|
||||
{"Estimated Monthly Alpha Value", "$-617339.2"},
|
||||
{"Total Accumulated Estimated Alpha Value", "$-82686580"},
|
||||
{"Mean Population Estimated Insight Value", "$-972783.3"},
|
||||
{"Mean Population Direction", "51.7647%"},
|
||||
{"Estimated Monthly Alpha Value", "$-607698.1"},
|
||||
{"Total Accumulated Estimated Alpha Value", "$-81395260"},
|
||||
{"Mean Population Estimated Insight Value", "$-957591.3"},
|
||||
{"Mean Population Direction", "50.5882%"},
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "48.2217%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "95f34359f25a7a7a2725f0343a75a105"}
|
||||
{"Rolling Averaged Population Direction", "46.5677%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
/*
|
||||
/*
|
||||
* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
|
||||
* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
|
||||
*
|
||||
@@ -13,10 +13,7 @@
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
using System;
|
||||
using System.Collections.Generic;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Interfaces;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
@@ -25,7 +22,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
/// </summary>
|
||||
/// <meta name="tag" content="using data" />
|
||||
/// <meta name="tag" content="benchmarks" />
|
||||
public class CustomBenchmarkAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
public class CustomBenchmarkAlgorithm : QCAlgorithm
|
||||
{
|
||||
/// <summary>
|
||||
/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
|
||||
@@ -38,11 +35,7 @@ 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");
|
||||
SetBenchmark("SPY");
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
@@ -56,69 +49,6 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
SetHoldings("SPY", 1);
|
||||
Debug("Purchased Stock");
|
||||
}
|
||||
|
||||
Symbol symbol;
|
||||
if (SymbolCache.TryGetSymbol("AAPL", out symbol))
|
||||
{
|
||||
throw new Exception("Benchmark Symbol is not expected to be added to the Symbol cache");
|
||||
}
|
||||
}
|
||||
|
||||
/// <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", "264.819%"},
|
||||
{"Drawdown", "2.200%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "1.668%"},
|
||||
{"Sharpe Ratio", "8.749"},
|
||||
{"Probabilistic Sharpe Ratio", "67.311%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "1.119"},
|
||||
{"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"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "9.606"},
|
||||
{"Return Over Maximum Drawdown", "105.85"},
|
||||
{"Portfolio Turnover", "0.249"},
|
||||
{"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", "9cd604d2c1e3c273697e2ff2cc7faef1"}
|
||||
};
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,121 +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 System;
|
||||
using System.Collections.Generic;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Interfaces;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Reproduces Lean GH issue 3572: benchmark _would_ use custom data versus equity
|
||||
/// when both were present
|
||||
/// </summary>
|
||||
public class CustomBenchmarkRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
/// <summary>
|
||||
/// Algorithm initialization
|
||||
/// </summary>
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2013, 10, 07);
|
||||
SetEndDate(2013, 10, 11);
|
||||
|
||||
AddEquity("AAPL", Resolution.Hour);
|
||||
AddData<DummyCustomData>("AAPL");
|
||||
|
||||
// set benchmark will use equity AAPL as benchmark
|
||||
SetBenchmark("AAPL");
|
||||
}
|
||||
|
||||
/// <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 (!Portfolio.Invested)
|
||||
{
|
||||
SetHoldings("AAPL", 1);
|
||||
Debug("Purchased Stock");
|
||||
}
|
||||
}
|
||||
private class DummyCustomData : BaseData
|
||||
{
|
||||
public override SubscriptionDataSource GetSource(SubscriptionDataConfig config, DateTime date, bool isLiveMode)
|
||||
{
|
||||
return new SubscriptionDataSource("NonExistingFile", SubscriptionTransportMedium.LocalFile);
|
||||
}
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
|
||||
/// </summary>
|
||||
public bool CanRunLocally { get; } = true;
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate which languages this algorithm is written in.
|
||||
/// </summary>
|
||||
public Language[] Languages { get; } = { Language.CSharp };
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
|
||||
/// </summary>
|
||||
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "1"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "37.355%"},
|
||||
{"Drawdown", "2.300%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "0.407%"},
|
||||
{"Sharpe Ratio", "5.521"},
|
||||
{"Probabilistic Sharpe Ratio", "60.177%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.001"},
|
||||
{"Beta", "0.997"},
|
||||
{"Annual Standard Deviation", "0.179"},
|
||||
{"Annual Variance", "0.032"},
|
||||
{"Information Ratio", "-7.662"},
|
||||
{"Tracking Error", "0"},
|
||||
{"Treynor Ratio", "0.988"},
|
||||
{"Total Fees", "$7.78"},
|
||||
{"Fitness Score", "0.031"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "-2.145"},
|
||||
{"Return Over Maximum Drawdown", "-8.479"},
|
||||
{"Portfolio Turnover", "0.25"},
|
||||
{"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", "be3334e4aeb9dd7cca4ecc07419d0f95"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -61,7 +61,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
/// <param name="message">Message object</param>
|
||||
public void Handle(BrokerageMessageEvent message)
|
||||
{
|
||||
var toLog = $"{_algo.Time.ToStringInvariant("o")} Event: {message.Message}";
|
||||
var toLog = _algo.Time.ToString("o") + " Event: " + message.Message;
|
||||
_algo.Debug(toLog);
|
||||
_algo.Log(toLog);
|
||||
}
|
||||
|
||||
@@ -1,138 +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 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 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)
|
||||
{
|
||||
return new HasSufficientBuyingPowerForOrderResult(true);
|
||||
}
|
||||
}
|
||||
|
||||
/// <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", "5672.520%"},
|
||||
{"Drawdown", "22.500%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "40.601%"},
|
||||
{"Sharpe Ratio", "40.201"},
|
||||
{"Probabilistic Sharpe Ratio", "77.339%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "41.848"},
|
||||
{"Beta", "9.224"},
|
||||
{"Annual Standard Deviation", "1.164"},
|
||||
{"Annual Variance", "1.355"},
|
||||
{"Information Ratio", "44.459"},
|
||||
{"Tracking Error", "1.04"},
|
||||
{"Treynor Ratio", "5.073"},
|
||||
{"Total Fees", "$30.00"},
|
||||
{"Fitness Score", "0.418"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "113.05"},
|
||||
{"Return Over Maximum Drawdown", "442.81"},
|
||||
{"Portfolio Turnover", "0.418"},
|
||||
{"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", "b88362c462e9ab2942cbcb8dfddc6ce0"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -1,144 +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 System;
|
||||
using System.Collections.Generic;
|
||||
using QuantConnect.Algorithm.Framework.Selection;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Data.Custom.SEC;
|
||||
using QuantConnect.Data.UniverseSelection;
|
||||
using QuantConnect.Interfaces;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Regression algorithm ensures that data added via coarse selection (underlying) is present in ActiveSecurities
|
||||
/// </summary>
|
||||
/// <meta name="tag" content="using data" />
|
||||
/// <meta name="tag" content="custom data" />
|
||||
/// <meta name="tag" content="regression test" />d
|
||||
public class CustomDataAddDataCoarseSelectionRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private List<Symbol> _customSymbols = new List<Symbol>();
|
||||
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2014, 3, 24);
|
||||
SetEndDate(2014, 4, 7);
|
||||
SetCash(100000);
|
||||
|
||||
UniverseSettings.Resolution = Resolution.Daily;
|
||||
|
||||
AddUniverseSelection(new CoarseFundamentalUniverseSelectionModel(CoarseSelector));
|
||||
}
|
||||
|
||||
public IEnumerable<Symbol> CoarseSelector(IEnumerable<CoarseFundamental> coarse)
|
||||
{
|
||||
var symbols = new[]
|
||||
{
|
||||
QuantConnect.Symbol.Create("AAPL", SecurityType.Equity, Market.USA),
|
||||
QuantConnect.Symbol.Create("BAC", SecurityType.Equity, Market.USA),
|
||||
QuantConnect.Symbol.Create("FB", SecurityType.Equity, Market.USA),
|
||||
QuantConnect.Symbol.Create("GOOGL", SecurityType.Equity, Market.USA),
|
||||
QuantConnect.Symbol.Create("GOOG", SecurityType.Equity, Market.USA),
|
||||
QuantConnect.Symbol.Create("IBM", SecurityType.Equity, Market.USA),
|
||||
};
|
||||
|
||||
_customSymbols.Clear();
|
||||
|
||||
foreach (var symbol in symbols)
|
||||
{
|
||||
_customSymbols.Add(AddData<SECReport8K>(symbol, Resolution.Daily).Symbol);
|
||||
}
|
||||
|
||||
return symbols;
|
||||
}
|
||||
|
||||
public override void OnData(Slice data)
|
||||
{
|
||||
if (!Portfolio.Invested && Transactions.GetOpenOrders().Count == 0)
|
||||
{
|
||||
var aapl = QuantConnect.Symbol.Create("AAPL", SecurityType.Equity, Market.USA);
|
||||
SetHoldings(aapl, 0.5);
|
||||
}
|
||||
|
||||
foreach (var customSymbol in _customSymbols)
|
||||
{
|
||||
if (!ActiveSecurities.ContainsKey(customSymbol.Underlying))
|
||||
{
|
||||
throw new Exception($"Custom data underlying ({customSymbol.Underlying}) Symbol was not found in active securities");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// <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", "-33.612%"},
|
||||
{"Drawdown", "2.000%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "-1.669%"},
|
||||
{"Sharpe Ratio", "-4.888"},
|
||||
{"Probabilistic Sharpe Ratio", "5.495%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.273"},
|
||||
{"Beta", "0.045"},
|
||||
{"Annual Standard Deviation", "0.057"},
|
||||
{"Annual Variance", "0.003"},
|
||||
{"Information Ratio", "-1.537"},
|
||||
{"Tracking Error", "0.112"},
|
||||
{"Treynor Ratio", "-6.121"},
|
||||
{"Total Fees", "$3.50"},
|
||||
{"Fitness Score", "0"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "-7.289"},
|
||||
{"Return Over Maximum Drawdown", "-16.95"},
|
||||
{"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", "6b05339bfcb5bd93bfd66e32a1d2181a"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -1,149 +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 System;
|
||||
using System.Collections.Generic;
|
||||
using QuantConnect.Algorithm.Framework.Selection;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Data.Custom.SEC;
|
||||
using QuantConnect.Data.UniverseSelection;
|
||||
using QuantConnect.Interfaces;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Regression algorithm ensures that data added via OnSecuritiesChanged (underlying) is present in ActiveSecurities
|
||||
/// </summary>
|
||||
/// <meta name="tag" content="using data" />
|
||||
/// <meta name="tag" content="custom data" />
|
||||
/// <meta name="tag" content="regression test" />
|
||||
public class CustomDataAddDataOnSecuritiesChangedRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private List<Symbol> _customSymbols = new List<Symbol>();
|
||||
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2014, 3, 24);
|
||||
SetEndDate(2014, 4, 7);
|
||||
SetCash(100000);
|
||||
|
||||
UniverseSettings.Resolution = Resolution.Daily;
|
||||
|
||||
AddUniverseSelection(new CoarseFundamentalUniverseSelectionModel(CoarseSelector));
|
||||
}
|
||||
|
||||
public IEnumerable<Symbol> CoarseSelector(IEnumerable<CoarseFundamental> coarse)
|
||||
{
|
||||
return new[]
|
||||
{
|
||||
QuantConnect.Symbol.Create("AAPL", SecurityType.Equity, Market.USA),
|
||||
QuantConnect.Symbol.Create("BAC", SecurityType.Equity, Market.USA),
|
||||
QuantConnect.Symbol.Create("FB", SecurityType.Equity, Market.USA),
|
||||
QuantConnect.Symbol.Create("GOOGL", SecurityType.Equity, Market.USA),
|
||||
QuantConnect.Symbol.Create("GOOG", SecurityType.Equity, Market.USA),
|
||||
QuantConnect.Symbol.Create("IBM", SecurityType.Equity, Market.USA),
|
||||
};
|
||||
}
|
||||
|
||||
public override void OnData(Slice data)
|
||||
{
|
||||
if (!Portfolio.Invested && Transactions.GetOpenOrders().Count == 0)
|
||||
{
|
||||
var aapl = QuantConnect.Symbol.Create("AAPL", SecurityType.Equity, Market.USA);
|
||||
SetHoldings(aapl, 0.5);
|
||||
}
|
||||
|
||||
foreach (var customSymbol in _customSymbols)
|
||||
{
|
||||
if (!ActiveSecurities.ContainsKey(customSymbol.Underlying))
|
||||
{
|
||||
throw new Exception($"Custom data underlying ({customSymbol.Underlying}) Symbol was not found in active securities");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnSecuritiesChanged(SecurityChanges changes)
|
||||
{
|
||||
bool iterated = false;
|
||||
foreach (var added in changes.AddedSecurities)
|
||||
{
|
||||
if (!iterated)
|
||||
{
|
||||
_customSymbols.Clear();
|
||||
iterated = true;
|
||||
}
|
||||
_customSymbols.Add(AddData<SECReport8K>(added.Symbol, Resolution.Daily).Symbol);
|
||||
}
|
||||
}
|
||||
|
||||
/// <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", "-33.612%"},
|
||||
{"Drawdown", "2.000%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "-1.669%"},
|
||||
{"Sharpe Ratio", "-4.888"},
|
||||
{"Probabilistic Sharpe Ratio", "5.495%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.273"},
|
||||
{"Beta", "0.045"},
|
||||
{"Annual Standard Deviation", "0.057"},
|
||||
{"Annual Variance", "0.003"},
|
||||
{"Information Ratio", "-1.537"},
|
||||
{"Tracking Error", "0.112"},
|
||||
{"Treynor Ratio", "-6.121"},
|
||||
{"Total Fees", "$3.50"},
|
||||
{"Fitness Score", "0"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "-7.289"},
|
||||
{"Return Over Maximum Drawdown", "-16.95"},
|
||||
{"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", "6b05339bfcb5bd93bfd66e32a1d2181a"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -1,156 +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 System;
|
||||
using System.Collections.Generic;
|
||||
using System.Linq;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Data.Custom.SEC;
|
||||
using QuantConnect.Data.Custom.USTreasury;
|
||||
using QuantConnect.Interfaces;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Regression algorithm ensures that added data matches expectations
|
||||
/// </summary>
|
||||
public class CustomDataAddDataRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private Symbol _googlEquity;
|
||||
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2013, 10, 7);
|
||||
SetEndDate(2013, 10, 11);
|
||||
SetCash(100000);
|
||||
|
||||
var twxEquity = AddEquity("TWX", Resolution.Daily).Symbol;
|
||||
var customTwxSymbol = AddData<SECReport8K>(twxEquity, Resolution.Daily).Symbol;
|
||||
|
||||
_googlEquity = AddEquity("GOOGL", Resolution.Daily).Symbol;
|
||||
var customGooglSymbol = AddData<SECReport10K>("GOOGL", Resolution.Daily).Symbol;
|
||||
|
||||
var usTreasury = AddData<USTreasuryYieldCurveRate>("GOOGL", Resolution.Daily).Symbol;
|
||||
var usTreasuryUnderlyingEquity = QuantConnect.Symbol.Create("MSFT", SecurityType.Equity, Market.USA);
|
||||
var usTreasuryUnderlying = AddData<USTreasuryYieldCurveRate>(usTreasuryUnderlyingEquity, Resolution.Daily).Symbol;
|
||||
|
||||
var optionSymbol = AddOption("TWX", Resolution.Minute).Symbol;
|
||||
var customOptionSymbol = AddData<SECReport10K>(optionSymbol, Resolution.Daily).Symbol;
|
||||
|
||||
if (customTwxSymbol.Underlying != twxEquity)
|
||||
{
|
||||
throw new Exception($"Underlying symbol for {customTwxSymbol} is not equal to TWX equity. Expected {twxEquity} got {customTwxSymbol.Underlying}");
|
||||
}
|
||||
if (customGooglSymbol.Underlying != _googlEquity)
|
||||
{
|
||||
throw new Exception($"Underlying symbol for {customGooglSymbol} is not equal to GOOGL equity. Expected {_googlEquity} got {customGooglSymbol.Underlying}");
|
||||
}
|
||||
if (usTreasury.HasUnderlying)
|
||||
{
|
||||
throw new Exception($"US Treasury yield curve (no underlying) has underlying when it shouldn't. Found {usTreasury.Underlying}");
|
||||
}
|
||||
if (!usTreasuryUnderlying.HasUnderlying)
|
||||
{
|
||||
throw new Exception("US Treasury yield curve (with underlying) has no underlying Symbol even though we added with Symbol");
|
||||
}
|
||||
if (usTreasuryUnderlying.Underlying != usTreasuryUnderlyingEquity)
|
||||
{
|
||||
throw new Exception($"US Treasury yield curve underlying does not equal equity Symbol added. Expected {usTreasuryUnderlyingEquity} got {usTreasuryUnderlying.Underlying}");
|
||||
}
|
||||
if (customOptionSymbol.Underlying != optionSymbol)
|
||||
{
|
||||
throw new Exception("Option symbol not equal to custom underlying symbol. Expected {optionSymbol} got {customOptionSymbol.Underlying}");
|
||||
}
|
||||
|
||||
try
|
||||
{
|
||||
var customDataNoCache = AddData<SECReport10Q>("AAPL", Resolution.Daily);
|
||||
throw new Exception("AAPL was found in the SymbolCache, though it should be missing");
|
||||
}
|
||||
catch (InvalidOperationException)
|
||||
{
|
||||
// This is exactly what we wanted. AAPL shouldn't have been found in the SymbolCache, and because
|
||||
// SECReport10Q is a mappable type, we threw
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnData(Slice data)
|
||||
{
|
||||
if (!Portfolio.Invested && !Transactions.GetOpenOrders().Any())
|
||||
{
|
||||
SetHoldings(_googlEquity, 0.5);
|
||||
}
|
||||
}
|
||||
|
||||
/// <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", "31.756%"},
|
||||
{"Drawdown", "0.700%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "0.378%"},
|
||||
{"Sharpe Ratio", "2.708"},
|
||||
{"Probabilistic Sharpe Ratio", "56.960%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "0.079"},
|
||||
{"Beta", "0.099"},
|
||||
{"Annual Standard Deviation", "0.079"},
|
||||
{"Annual Variance", "0.006"},
|
||||
{"Information Ratio", "-6.058"},
|
||||
{"Tracking Error", "0.19"},
|
||||
{"Treynor Ratio", "2.159"},
|
||||
{"Total Fees", "$1.00"},
|
||||
{"Fitness Score", "0.1"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "47.335"},
|
||||
{"Portfolio Turnover", "0.1"},
|
||||
{"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", "214f38f9084bc350c93010aa2fb69822"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -62,7 +62,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
}
|
||||
Console.WriteLine("Buying BTC 'Shares': BTC: " + data.Close);
|
||||
}
|
||||
Console.WriteLine("Time: " + Time.ToStringInvariant("T") + " " + Time.ToStringInvariant("T") + data.Close.ToStringInvariant());
|
||||
Console.WriteLine("Time: " + Time.ToLongDateString() + " " + Time.ToLongTimeString() + data.Close.ToString());
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
|
||||
@@ -1,260 +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 System;
|
||||
using System.Collections.Generic;
|
||||
using System.Globalization;
|
||||
using Newtonsoft.Json;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Interfaces;
|
||||
using QuantConnect.Securities;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Regression test to demonstrate setting custom Symbol Properties and Market Hours for a custom data import
|
||||
/// </summary>
|
||||
/// <meta name="tag" content="using data" />
|
||||
/// <meta name="tag" content="custom data" />
|
||||
/// <meta name="tag" content="crypto" />
|
||||
/// <meta name="tag" content="regression test" />
|
||||
public class CustomDataPropertiesRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private string _ticker = "BTC";
|
||||
private Security _bitcoin;
|
||||
|
||||
/// <summary>
|
||||
/// Initialize the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
|
||||
/// </summary>
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2011, 9, 13);
|
||||
SetEndDate(2015, 12, 01);
|
||||
|
||||
//Set the cash for the strategy:
|
||||
SetCash(100000);
|
||||
|
||||
// Define our custom data properties and exchange hours
|
||||
var properties = new SymbolProperties("Bitcoin", "USD", 1, 0.01m, 0.01m, _ticker);
|
||||
var exchangeHours = SecurityExchangeHours.AlwaysOpen(TimeZones.NewYork);
|
||||
|
||||
// Add the custom data to our algorithm with our custom properties and exchange hours
|
||||
_bitcoin = AddData<Bitcoin>(_ticker, properties, exchangeHours);
|
||||
|
||||
//Verify our symbol properties were changed and loaded into this security
|
||||
if (_bitcoin.SymbolProperties != properties)
|
||||
{
|
||||
throw new Exception("Failed to set and retrieve custom SymbolProperties for BTC");
|
||||
}
|
||||
|
||||
//Verify our exchange hours were changed and loaded into this security
|
||||
if (_bitcoin.Exchange.Hours != exchangeHours)
|
||||
{
|
||||
throw new Exception("Failed to set and retrieve custom ExchangeHours for BTC");
|
||||
}
|
||||
|
||||
// For regression purposes on AddData overloads, this call is simply to ensure Lean can accept this
|
||||
// with default params and is not routed to a breaking function.
|
||||
AddData<Bitcoin>("BTCUSD");
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Event Handler for Bitcoin Data Events: These Bitcoin objects are created from our
|
||||
/// "Bitcoin" type below and fired into this event handler.
|
||||
/// </summary>
|
||||
/// <param name="data">One(1) Bitcoin Object, streamed into our algorithm synchronized in time with our other data streams</param>
|
||||
public void OnData(Bitcoin data)
|
||||
{
|
||||
//If we don't have any bitcoin "SHARES" -- invest"
|
||||
if (!Portfolio.Invested)
|
||||
{
|
||||
//Bitcoin used as a tradable asset, like stocks, futures etc.
|
||||
if (data.Close != 0)
|
||||
{
|
||||
//Access custom data symbols using <ticker>.<custom-type>
|
||||
Order("BTC.Bitcoin", Portfolio.MarginRemaining / Math.Abs(data.Close + 1));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnEndOfAlgorithm()
|
||||
{
|
||||
// Reset our Symbol property value, for testing purposes.
|
||||
SymbolPropertiesDatabase.SetEntry(Market.USA, MarketHoursDatabase.GetDatabaseSymbolKey(_bitcoin.Symbol), SecurityType.Base,
|
||||
SymbolProperties.GetDefault("USD"));
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
|
||||
/// </summary>
|
||||
public bool CanRunLocally { get; } = true;
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate which languages this algorithm is written in.
|
||||
/// </summary>
|
||||
public Language[] Languages { get; } = { Language.CSharp, Language.Python };
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
|
||||
/// </summary>
|
||||
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "1"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "155.262%"},
|
||||
{"Drawdown", "84.800%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "5123.242%"},
|
||||
{"Sharpe Ratio", "2.067"},
|
||||
{"Probabilistic Sharpe Ratio", "68.833%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "1.732"},
|
||||
{"Beta", "0.037"},
|
||||
{"Annual Standard Deviation", "0.841"},
|
||||
{"Annual Variance", "0.707"},
|
||||
{"Information Ratio", "1.902"},
|
||||
{"Tracking Error", "0.848"},
|
||||
{"Treynor Ratio", "46.992"},
|
||||
{"Total Fees", "$0.00"},
|
||||
{"Fitness Score", "0"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "2.238"},
|
||||
{"Return Over Maximum Drawdown", "1.832"},
|
||||
{"Portfolio Turnover", "0"},
|
||||
{"Total Insights Generated", "0"},
|
||||
{"Total Insights Closed", "0"},
|
||||
{"Total Insights Analysis Completed", "0"},
|
||||
{"Long Insight Count", "0"},
|
||||
{"Short Insight Count", "0"},
|
||||
{"Long/Short Ratio", "100%"},
|
||||
{"Estimated Monthly Alpha Value", "$0"},
|
||||
{"Total Accumulated Estimated Alpha Value", "$0"},
|
||||
{"Mean Population Estimated Insight Value", "$0"},
|
||||
{"Mean Population Direction", "0%"},
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "1f54fb75ebcc0daafa5d45bfbaa4fbcb"}
|
||||
};
|
||||
|
||||
/// <summary>
|
||||
/// Custom Data Type: Bitcoin data from Quandl - http://www.quandl.com/help/api-for-bitcoin-data
|
||||
/// </summary>
|
||||
public class Bitcoin : BaseData
|
||||
{
|
||||
[JsonProperty("timestamp")]
|
||||
public int Timestamp = 0;
|
||||
[JsonProperty("open")]
|
||||
public decimal Open = 0;
|
||||
[JsonProperty("high")]
|
||||
public decimal High = 0;
|
||||
[JsonProperty("low")]
|
||||
public decimal Low = 0;
|
||||
[JsonProperty("last")]
|
||||
public decimal Close = 0;
|
||||
[JsonProperty("bid")]
|
||||
public decimal Bid = 0;
|
||||
[JsonProperty("ask")]
|
||||
public decimal Ask = 0;
|
||||
[JsonProperty("vwap")]
|
||||
public decimal WeightedPrice = 0;
|
||||
[JsonProperty("volume")]
|
||||
public decimal VolumeBTC = 0;
|
||||
public decimal VolumeUSD = 0;
|
||||
|
||||
/// <summary>
|
||||
/// 1. DEFAULT CONSTRUCTOR: Custom data types need a default constructor.
|
||||
/// We search for a default constructor so please provide one here. It won't be used for data, just to generate the "Factory".
|
||||
/// </summary>
|
||||
public Bitcoin()
|
||||
{
|
||||
Symbol = "BTC";
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// 2. RETURN THE STRING URL SOURCE LOCATION FOR YOUR DATA:
|
||||
/// This is a powerful and dynamic select source file method. If you have a large dataset, 10+mb we recommend you break it into smaller files. E.g. One zip per year.
|
||||
/// We can accept raw text or ZIP files. We read the file extension to determine if it is a zip file.
|
||||
/// </summary>
|
||||
/// <param name="config">Configuration object</param>
|
||||
/// <param name="date">Date of this source file</param>
|
||||
/// <param name="isLiveMode">true if we're in live mode, false for backtesting mode</param>
|
||||
/// <returns>String URL of source file.</returns>
|
||||
public override SubscriptionDataSource GetSource(SubscriptionDataConfig config, DateTime date, bool isLiveMode)
|
||||
{
|
||||
if (isLiveMode)
|
||||
{
|
||||
return new SubscriptionDataSource("https://www.bitstamp.net/api/ticker/", SubscriptionTransportMedium.Rest);
|
||||
}
|
||||
|
||||
//return "http://my-ftp-server.com/futures-data-" + date.ToString("Ymd") + ".zip";
|
||||
// OR simply return a fixed small data file. Large files will slow down your backtest
|
||||
return new SubscriptionDataSource("https://www.quantconnect.com/api/v2/proxy/quandl/api/v3/datasets/BCHARTS/BITSTAMPUSD.csv?order=asc&api_key=WyAazVXnq7ATy_fefTqm", SubscriptionTransportMedium.RemoteFile);
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// 3. READER METHOD: Read 1 line from data source and convert it into Object.
|
||||
/// Each line of the CSV File is presented in here. The backend downloads your file, loads it into memory and then line by line
|
||||
/// feeds it into your algorithm
|
||||
/// </summary>
|
||||
/// <param name="line">string line from the data source file submitted above</param>
|
||||
/// <param name="config">Subscription data, symbol name, data type</param>
|
||||
/// <param name="date">Current date we're requesting. This allows you to break up the data source into daily files.</param>
|
||||
/// <param name="isLiveMode">true if we're in live mode, false for backtesting mode</param>
|
||||
/// <returns>New Bitcoin Object which extends BaseData.</returns>
|
||||
public override BaseData Reader(SubscriptionDataConfig config, string line, DateTime date, bool isLiveMode)
|
||||
{
|
||||
var coin = new Bitcoin();
|
||||
if (isLiveMode)
|
||||
{
|
||||
//Example Line Format:
|
||||
//{"high": "441.00", "last": "421.86", "timestamp": "1411606877", "bid": "421.96", "vwap": "428.58", "volume": "14120.40683975", "low": "418.83", "ask": "421.99"}
|
||||
try
|
||||
{
|
||||
coin = JsonConvert.DeserializeObject<Bitcoin>(line);
|
||||
coin.EndTime = DateTime.UtcNow.ConvertFromUtc(config.ExchangeTimeZone);
|
||||
coin.Value = coin.Close;
|
||||
}
|
||||
catch { /* Do nothing, possible error in json decoding */ }
|
||||
return coin;
|
||||
}
|
||||
|
||||
//Example Line Format:
|
||||
//Date Open High Low Close Volume (BTC) Volume (Currency) Weighted Price
|
||||
//2011-09-13 5.8 6.0 5.65 5.97 58.37138238, 346.0973893944 5.929230648356
|
||||
try
|
||||
{
|
||||
string[] data = line.Split(',');
|
||||
coin.Time = DateTime.Parse(data[0], CultureInfo.InvariantCulture);
|
||||
coin.Open = Convert.ToDecimal(data[1], CultureInfo.InvariantCulture);
|
||||
coin.High = Convert.ToDecimal(data[2], CultureInfo.InvariantCulture);
|
||||
coin.Low = Convert.ToDecimal(data[3], CultureInfo.InvariantCulture);
|
||||
coin.Close = Convert.ToDecimal(data[4], CultureInfo.InvariantCulture);
|
||||
coin.VolumeBTC = Convert.ToDecimal(data[5], CultureInfo.InvariantCulture);
|
||||
coin.VolumeUSD = Convert.ToDecimal(data[6], CultureInfo.InvariantCulture);
|
||||
coin.WeightedPrice = Convert.ToDecimal(data[7], CultureInfo.InvariantCulture);
|
||||
coin.Value = coin.Close;
|
||||
}
|
||||
catch { /* Do nothing, skip first title row */ }
|
||||
|
||||
return coin;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -60,8 +60,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
//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));
|
||||
Order("BTC", Portfolio.MarginRemaining / Math.Abs(data.Close + 1));
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -84,43 +83,22 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Total Trades", "1"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "155.262%"},
|
||||
{"Compounding Annual Return", "155.365%"},
|
||||
{"Drawdown", "84.800%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "5123.170%"},
|
||||
{"Sharpe Ratio", "2.066"},
|
||||
{"Probabilistic Sharpe Ratio", "68.832%"},
|
||||
{"Sharpe Ratio", "1.2"},
|
||||
{"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"},
|
||||
{"Total Fees", "$0.00"},
|
||||
{"Fitness Score", "0"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "2.238"},
|
||||
{"Return Over Maximum Drawdown", "1.832"},
|
||||
{"Portfolio Turnover", "0"},
|
||||
{"Total Insights Generated", "0"},
|
||||
{"Total Insights Closed", "0"},
|
||||
{"Total Insights Analysis Completed", "0"},
|
||||
{"Long Insight Count", "0"},
|
||||
{"Short Insight Count", "0"},
|
||||
{"Long/Short Ratio", "100%"},
|
||||
{"Estimated Monthly Alpha Value", "$0"},
|
||||
{"Total Accumulated Estimated Alpha Value", "$0"},
|
||||
{"Mean Population Estimated Insight Value", "$0"},
|
||||
{"Mean Population Direction", "0%"},
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "0e7d560d0db2829adb19d3e403c30d97"}
|
||||
{"Alpha", "-0.008"},
|
||||
{"Beta", "73.725"},
|
||||
{"Annual Standard Deviation", "0.84"},
|
||||
{"Annual Variance", "0.706"},
|
||||
{"Information Ratio", "1.183"},
|
||||
{"Tracking Error", "0.84"},
|
||||
{"Treynor Ratio", "0.014"},
|
||||
{"Total Fees", "$0.00"}
|
||||
};
|
||||
|
||||
/// <summary>
|
||||
|
||||
@@ -128,7 +128,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
Time = DateTime.ParseExact(csv[0], "yyyyMMdd", null),
|
||||
Symbol = Symbol.Create(csv[1], SecurityType.Equity, Market.USA),
|
||||
TopGainersRank = Parse.Int(csv[2])
|
||||
TopGainersRank = int.Parse(csv[2])
|
||||
};
|
||||
}
|
||||
|
||||
|
||||
@@ -1,217 +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 System;
|
||||
using System.Collections.Generic;
|
||||
using System.Linq;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Data.Market;
|
||||
using QuantConnect.Interfaces;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Regression algorithm demonstrating use of map files with custom data
|
||||
/// </summary>
|
||||
/// <meta name="tag" content="using data" />
|
||||
/// <meta name="tag" content="custom data" />
|
||||
/// <meta name="tag" content="regression test" />
|
||||
/// <meta name="tag" content="rename event" />
|
||||
/// <meta name="tag" content="map" />
|
||||
/// <meta name="tag" content="mapping" />
|
||||
/// <meta name="tag" content="map files" />
|
||||
public class CustomDataUsingMapFileRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private Symbol _symbol;
|
||||
private bool _initialMapping;
|
||||
private bool _executionMapping;
|
||||
|
||||
/// <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(2013, 06, 27);
|
||||
SetEndDate(2013, 07, 02);
|
||||
|
||||
var foxa = QuantConnect.Symbol.Create("FOXA", SecurityType.Equity, Market.USA);
|
||||
_symbol = AddData<CustomDataUsingMapping>(foxa).Symbol;
|
||||
|
||||
foreach (var config in SubscriptionManager.SubscriptionDataConfigService.GetSubscriptionDataConfigs(_symbol))
|
||||
{
|
||||
if (config.Resolution != Resolution.Minute)
|
||||
{
|
||||
throw new Exception("Expected resolution to be set to Minute");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Checks to see if the stock has been renamed, and places an order once the symbol has changed
|
||||
/// </summary>
|
||||
public override void OnData(Slice slice)
|
||||
{
|
||||
if (slice.SymbolChangedEvents.ContainsKey(_symbol))
|
||||
{
|
||||
var mappingEvent = slice.SymbolChangedEvents.Single(x => x.Key.SecurityType == SecurityType.Base).Value;
|
||||
Log($"{Time} - Ticker changed from: {mappingEvent.OldSymbol} to {mappingEvent.NewSymbol}");
|
||||
if (Time.Date == new DateTime(2013, 06, 27))
|
||||
{
|
||||
// we should Not receive the initial mapping event
|
||||
if (mappingEvent.NewSymbol != "NWSA"
|
||||
|| mappingEvent.OldSymbol != "FOXA")
|
||||
{
|
||||
throw new Exception($"Unexpected mapping event {mappingEvent}");
|
||||
}
|
||||
_initialMapping = true;
|
||||
}
|
||||
else if (Time.Date == new DateTime(2013, 06, 29))
|
||||
{
|
||||
if (mappingEvent.NewSymbol != "FOXA"
|
||||
|| mappingEvent.OldSymbol != "NWSA")
|
||||
{
|
||||
throw new Exception($"Unexpected mapping event {mappingEvent}");
|
||||
}
|
||||
|
||||
_executionMapping = true;
|
||||
SetHoldings(_symbol, 1);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Final step of the algorithm
|
||||
/// </summary>
|
||||
public override void OnEndOfAlgorithm()
|
||||
{
|
||||
if (_initialMapping)
|
||||
{
|
||||
throw new Exception("The ticker generated the initial rename event");
|
||||
}
|
||||
if (!_executionMapping)
|
||||
{
|
||||
throw new Exception("The ticker did not rename throughout the course of its life even though it should have");
|
||||
}
|
||||
}
|
||||
|
||||
/// <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", "-99.920%"},
|
||||
{"Drawdown", "11.100%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "-10.486%"},
|
||||
{"Sharpe Ratio", "-1.534"},
|
||||
{"Probabilistic Sharpe Ratio", "0.001%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.898"},
|
||||
{"Beta", "-7.027"},
|
||||
{"Annual Standard Deviation", "0.651"},
|
||||
{"Annual Variance", "0.424"},
|
||||
{"Information Ratio", "-1.396"},
|
||||
{"Tracking Error", "0.726"},
|
||||
{"Treynor Ratio", "0.142"},
|
||||
{"Total Fees", "$0.00"},
|
||||
{"Fitness Score", "0.127"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "-9.383"},
|
||||
{"Portfolio Turnover", "0.249"},
|
||||
{"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", "1c319ae4b15416184a247bb47b31aabc"}
|
||||
};
|
||||
|
||||
/// <summary>
|
||||
/// Test example custom data showing how to enable the use of mapping.
|
||||
/// Implemented as a wrapper of existing NWSA->FOXA equity
|
||||
/// </summary>
|
||||
private class CustomDataUsingMapping : TradeBar
|
||||
{
|
||||
/// <summary>
|
||||
/// Indicates if there is support for mapping
|
||||
/// </summary>
|
||||
/// <returns>True indicates mapping should be done</returns>
|
||||
public override bool RequiresMapping()
|
||||
{
|
||||
return true;
|
||||
}
|
||||
|
||||
public override SubscriptionDataSource GetSource(SubscriptionDataConfig config, DateTime date, bool isLiveMode)
|
||||
{
|
||||
return base.GetSource(new SubscriptionDataConfig(config,
|
||||
typeof(CustomDataUsingMapping),
|
||||
// create a new symbol as equity so we find the existing data files
|
||||
Symbol.Create(config.MappedSymbol, SecurityType.Equity, config.Market)),
|
||||
date,
|
||||
isLiveMode);
|
||||
}
|
||||
|
||||
public override BaseData Reader(SubscriptionDataConfig config, string line, DateTime date, bool isLiveMode)
|
||||
{
|
||||
return ParseEquity(config, line, date);
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Gets the default resolution for this data and security type
|
||||
/// </summary>
|
||||
/// <remarks>This is a method and not a property so that python
|
||||
/// custom data types can override it</remarks>
|
||||
public override Resolution DefaultResolution()
|
||||
{
|
||||
return Resolution.Minute;
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Gets the supported resolution for this data and security type
|
||||
/// </summary>
|
||||
/// <remarks>This is a method and not a property so that python
|
||||
/// custom data types can override it</remarks>
|
||||
public override List<Resolution> SupportedResolutions()
|
||||
{
|
||||
return new List<Resolution> { Resolution.Minute };
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,4 +1,4 @@
|
||||
/*
|
||||
/*
|
||||
* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
|
||||
* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
|
||||
*
|
||||
@@ -26,12 +26,11 @@ using QuantConnect.Securities;
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Demonstration of using custom fee, slippage, fill, and buying power models for modelling transactions in backtesting.
|
||||
/// Demonstration of using custom fee, slippage and fill 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" />
|
||||
@@ -51,7 +50,6 @@ 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)
|
||||
@@ -62,13 +60,13 @@ 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($"MarketOrder: {quantity}");
|
||||
Log("MarketOrder: " + quantity);
|
||||
MarketOrder(_spy, quantity, asynchronous: true); // async needed for partial fill market orders
|
||||
}
|
||||
}
|
||||
@@ -111,7 +109,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
fill.Status = OrderStatus.PartiallyFilled;
|
||||
}
|
||||
|
||||
_algorithm.Log($"CustomFillModel: {fill}");
|
||||
_algorithm.Log("CustomFillModel: " + fill);
|
||||
|
||||
return fill;
|
||||
}
|
||||
@@ -133,7 +131,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
1m,
|
||||
parameters.Security.Price*parameters.Order.AbsoluteQuantity*0.00001m);
|
||||
|
||||
_algorithm.Log($"CustomFeeModel: {fee}");
|
||||
_algorithm.Log("CustomFeeModel: " + fee);
|
||||
return new OrderFee(new CashAmount(fee, "USD"));
|
||||
}
|
||||
}
|
||||
@@ -152,31 +150,11 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
// custom slippage math
|
||||
var slippage = asset.Price*0.0001m*(decimal) Math.Log10(2*(double) order.AbsoluteQuantity);
|
||||
|
||||
_algorithm.Log($"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>
|
||||
@@ -193,45 +171,24 @@ 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", "-7.582%"},
|
||||
{"Drawdown", "2.400%"},
|
||||
{"Expectancy", "-0.197"},
|
||||
{"Net Profit", "-0.673%"},
|
||||
{"Sharpe Ratio", "-1.565"},
|
||||
{"Probabilistic Sharpe Ratio", "22.763%"},
|
||||
{"Expectancy", "-0.193"},
|
||||
{"Net Profit", "-0.660%"},
|
||||
{"Sharpe Ratio", "-1.563"},
|
||||
{"Loss Rate", "70%"},
|
||||
{"Win Rate", "30%"},
|
||||
{"Profit-Loss Ratio", "1.70"},
|
||||
{"Alpha", "-0.14"},
|
||||
{"Beta", "0.124"},
|
||||
{"Annual Standard Deviation", "0.047"},
|
||||
{"Profit-Loss Ratio", "1.71"},
|
||||
{"Alpha", "-0.174"},
|
||||
{"Beta", "5.695"},
|
||||
{"Annual Standard Deviation", "0.046"},
|
||||
{"Annual Variance", "0.002"},
|
||||
{"Information Ratio", "-5.163"},
|
||||
{"Tracking Error", "0.118"},
|
||||
{"Treynor Ratio", "-0.591"},
|
||||
{"Total Fees", "$62.24"},
|
||||
{"Fitness Score", "0.147"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "-2.792"},
|
||||
{"Return Over Maximum Drawdown", "-3.569"},
|
||||
{"Portfolio Turnover", "2.562"},
|
||||
{"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", "71c17655bd0731eb25433727526e95ba"}
|
||||
{"Information Ratio", "-1.959"},
|
||||
{"Tracking Error", "0.046"},
|
||||
{"Treynor Ratio", "-0.013"},
|
||||
{"Total Fees", "$62.24"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,108 +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.Orders;
|
||||
using QuantConnect.Orders.Fills;
|
||||
using QuantConnect.Securities;
|
||||
using System;
|
||||
using System.Collections.Generic;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Basic template algorithm that implements a fill model with partial fills
|
||||
/// </summary>
|
||||
/// <meta name="tag" content="transaction fees and slippage" />
|
||||
/// <meta name="tag" content="custom fill models" />
|
||||
public class CustomPartialFillModelAlgorithm : QCAlgorithm
|
||||
{
|
||||
private Symbol _spy;
|
||||
private SecurityHolding _holdings;
|
||||
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2019, 1, 1);
|
||||
SetEndDate(2019, 3, 1);
|
||||
|
||||
var equity = AddEquity("SPY", Resolution.Hour);
|
||||
_spy = equity.Symbol;
|
||||
_holdings = equity.Holdings;
|
||||
|
||||
// Set the fill model
|
||||
equity.SetFillModel(new CustomPartialFillModel(this));
|
||||
}
|
||||
|
||||
public override void OnData(Slice data)
|
||||
{
|
||||
var openOrders = Transactions.GetOpenOrders(_spy);
|
||||
if (openOrders.Count != 0) return;
|
||||
|
||||
if (Time.Day > 10 && _holdings.Quantity <= 0)
|
||||
{
|
||||
MarketOrder(_spy, 100, true);
|
||||
}
|
||||
else if (Time.Day > 20 && _holdings.Quantity >= 0)
|
||||
{
|
||||
MarketOrder(_spy, -100, true);
|
||||
}
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Implements a custom fill model that inherit from FillModel. Override the MarketFill method to simulate partially fill orders
|
||||
/// </summary>
|
||||
internal class CustomPartialFillModel : FillModel
|
||||
{
|
||||
private readonly QCAlgorithm _algorithm;
|
||||
private readonly Dictionary<int, decimal> _absoluteRemainingByOrderId;
|
||||
|
||||
public CustomPartialFillModel(QCAlgorithm algorithm)
|
||||
: base()
|
||||
{
|
||||
_algorithm = algorithm;
|
||||
_absoluteRemainingByOrderId = new Dictionary<int, decimal>();
|
||||
}
|
||||
|
||||
public override OrderEvent MarketFill(Security asset, MarketOrder order)
|
||||
{
|
||||
decimal absoluteRemaining;
|
||||
if (!_absoluteRemainingByOrderId.TryGetValue(order.Id, out absoluteRemaining))
|
||||
{
|
||||
absoluteRemaining = order.AbsoluteQuantity;
|
||||
}
|
||||
|
||||
// Create the object
|
||||
var fill = base.MarketFill(asset, order);
|
||||
|
||||
// Set this fill amount
|
||||
fill.FillQuantity = Math.Sign(order.Quantity) * 10;
|
||||
|
||||
if (absoluteRemaining == fill.FillQuantity)
|
||||
{
|
||||
fill.Status = OrderStatus.Filled;
|
||||
_absoluteRemainingByOrderId.Remove(order.Id);
|
||||
}
|
||||
else
|
||||
{
|
||||
fill.Status = OrderStatus.PartiallyFilled;
|
||||
_absoluteRemainingByOrderId[order.Id] = absoluteRemaining - fill.FillQuantity;
|
||||
var price = fill.FillPrice;
|
||||
_algorithm.Debug($"{_algorithm.Time} - Partial Fill - Remaining {absoluteRemaining} Price - {price}");
|
||||
}
|
||||
return fill;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,128 +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 System;
|
||||
using System.Collections.Generic;
|
||||
using System.Linq;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Interfaces;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Test algorithm that verifies that securities added through
|
||||
/// <see cref="QCAlgorithm.AddEquity"/> API and universe selection
|
||||
/// both start sending data at the same time
|
||||
/// </summary>
|
||||
public class CustomUniverseSelectionRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
/// <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(2013, 10, 07);
|
||||
SetEndDate(2013, 10, 11);
|
||||
|
||||
AddEquity("AAPL", Resolution.Daily);
|
||||
|
||||
UniverseSettings.Resolution = Resolution.Daily;
|
||||
AddUniverse(SecurityType.Equity,
|
||||
"SecondUniverse",
|
||||
Resolution.Daily,
|
||||
Market.USA,
|
||||
UniverseSettings,
|
||||
time => new[] { "SPY" });
|
||||
}
|
||||
|
||||
/// <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 (data.Count != 2)
|
||||
{
|
||||
throw new Exception($"Unexpected data count: {data.Count}");
|
||||
}
|
||||
if (ActiveSecurities.Count != 2)
|
||||
{
|
||||
throw new Exception($"Unexpected ActiveSecurities count: {ActiveSecurities.Count}");
|
||||
}
|
||||
if (!Portfolio.Invested)
|
||||
{
|
||||
SetHoldings(Securities.Keys.First(symbol => symbol.Value == "SPY"), 1);
|
||||
Debug("Purchased Stock");
|
||||
}
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
|
||||
/// </summary>
|
||||
public bool CanRunLocally { get; } = true;
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate which languages this algorithm is written in.
|
||||
/// </summary>
|
||||
public Language[] Languages { get; } = { Language.CSharp };
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
|
||||
/// </summary>
|
||||
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "1"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "241.885%"},
|
||||
{"Drawdown", "1.100%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "1.698%"},
|
||||
{"Sharpe Ratio", "7.17"},
|
||||
{"Probabilistic Sharpe Ratio", "68.718%"},
|
||||
{"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"},
|
||||
{"Fitness Score", "0.201"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "211.158"},
|
||||
{"Portfolio Turnover", "0.201"},
|
||||
{"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", "82fee25cd17100c53bb173834ab5f0b2"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -1,218 +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 System;
|
||||
using System.Collections.Generic;
|
||||
using System.Linq;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Interfaces;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Regression algorithm with a custom universe and benchmark, both using the same security.
|
||||
/// </summary>
|
||||
public class CustomUniverseWithBenchmarkRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private const int ExpectedLeverage = 2;
|
||||
private Symbol _spy;
|
||||
private decimal _previousBenchmarkValue;
|
||||
private DateTime _previousTime;
|
||||
private decimal _previousSecurityValue;
|
||||
private bool _universeSelected;
|
||||
private bool _onDataWasCalled;
|
||||
private int _benchmarkPriceDidNotChange;
|
||||
|
||||
/// <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(2013, 10, 4);
|
||||
SetEndDate(2013, 10, 11);
|
||||
|
||||
// Hour resolution
|
||||
_spy = AddEquity("SPY", Resolution.Hour).Symbol;
|
||||
|
||||
// Minute resolution
|
||||
AddUniverse("my-universe", x =>
|
||||
{
|
||||
if(x.Day % 2 == 0)
|
||||
{
|
||||
_universeSelected = true;
|
||||
return new List<string> {"SPY"};
|
||||
}
|
||||
_universeSelected = false;
|
||||
return Enumerable.Empty<string>();
|
||||
}
|
||||
);
|
||||
|
||||
// internal daily resolution
|
||||
SetBenchmark("SPY");
|
||||
|
||||
Symbol symbol;
|
||||
if (!SymbolCache.TryGetSymbol("SPY", out symbol)
|
||||
|| !ReferenceEquals(_spy, symbol))
|
||||
{
|
||||
throw new Exception("We expected 'SPY' to be added to the Symbol cache," +
|
||||
" since the algorithm is also using it");
|
||||
}
|
||||
}
|
||||
|
||||
/// <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)
|
||||
{
|
||||
var security = Securities[_spy];
|
||||
_onDataWasCalled = true;
|
||||
|
||||
var bar = data.Bars.Values.Single();
|
||||
if (_universeSelected)
|
||||
{
|
||||
if (bar.IsFillForward
|
||||
|| bar.Period != TimeSpan.FromMinutes(1))
|
||||
{
|
||||
// bar should always be the Minute resolution one here
|
||||
throw new Exception("Unexpected Bar error");
|
||||
}
|
||||
if (_previousTime.Date == data.Time.Date
|
||||
&& (data.Time - _previousTime) != TimeSpan.FromMinutes(1))
|
||||
{
|
||||
throw new Exception("For the same date expected data updates every 1 minute");
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
if (data.Time.Minute == 0
|
||||
&& _previousSecurityValue == security.Price)
|
||||
{
|
||||
throw new Exception($"Security Price error. Price should change every new hour");
|
||||
}
|
||||
if (data.Time.Minute != 0
|
||||
&& _previousSecurityValue != security.Price)
|
||||
{
|
||||
throw new Exception($"Security Price error. Price should not change every minute");
|
||||
}
|
||||
}
|
||||
_previousSecurityValue = security.Price;
|
||||
|
||||
// assert benchmark updates only on date change
|
||||
var currentValue = Benchmark.Evaluate(data.Time);
|
||||
if (_previousTime.Hour == data.Time.Hour)
|
||||
{
|
||||
if (currentValue != _previousBenchmarkValue)
|
||||
{
|
||||
throw new Exception($"Benchmark value error - expected: {_previousBenchmarkValue} {_previousTime}, actual: {currentValue} {data.Time}. " +
|
||||
"Benchmark value should only change when there is a change in hours");
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
if (data.Time.Minute == 0)
|
||||
{
|
||||
if (currentValue == _previousBenchmarkValue)
|
||||
{
|
||||
_benchmarkPriceDidNotChange++;
|
||||
// there are two consecutive equal data points so we give it some room
|
||||
if (_benchmarkPriceDidNotChange > 1)
|
||||
{
|
||||
throw new Exception($"Benchmark value error - expected a new value, current {currentValue} {data.Time}" +
|
||||
"Benchmark value should change when there is a change in hours");
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
_benchmarkPriceDidNotChange = 0;
|
||||
}
|
||||
}
|
||||
}
|
||||
_previousBenchmarkValue = currentValue;
|
||||
_previousTime = data.Time;
|
||||
|
||||
// assert algorithm security is the correct one - not the internal one
|
||||
if (security.Leverage != ExpectedLeverage)
|
||||
{
|
||||
throw new Exception($"Leverage error - expected: {ExpectedLeverage}, actual: {security.Leverage}");
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnEndOfAlgorithm()
|
||||
{
|
||||
if (!_onDataWasCalled)
|
||||
{
|
||||
throw new Exception("OnData was not called");
|
||||
}
|
||||
}
|
||||
|
||||
/// <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", "-2.53"},
|
||||
{"Tracking Error", "0.211"},
|
||||
{"Treynor Ratio", "0"},
|
||||
{"Total Fees", "$0.00"},
|
||||
{"Fitness Score", "0"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "79228162514264337593543950335"},
|
||||
{"Portfolio Turnover", "0"},
|
||||
{"Total Insights Generated", "0"},
|
||||
{"Total Insights Closed", "0"},
|
||||
{"Total Insights Analysis Completed", "0"},
|
||||
{"Long Insight Count", "0"},
|
||||
{"Short Insight Count", "0"},
|
||||
{"Long/Short Ratio", "100%"},
|
||||
{"Estimated Monthly Alpha Value", "$0"},
|
||||
{"Total Accumulated Estimated Alpha Value", "$0"},
|
||||
{"Mean Population Estimated Insight Value", "$0"},
|
||||
{"Mean Population Direction", "0%"},
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -1,143 +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 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", "-0.084"},
|
||||
{"Tracking Error", "0.183"},
|
||||
{"Treynor Ratio", "0"},
|
||||
{"Total Fees", "$0.00"},
|
||||
{"Fitness Score", "0"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "79228162514264337593543950335"},
|
||||
{"Portfolio Turnover", "0"},
|
||||
{"Total Insights Generated", "0"},
|
||||
{"Total Insights Closed", "0"},
|
||||
{"Total Insights Analysis Completed", "0"},
|
||||
{"Long Insight Count", "0"},
|
||||
{"Short Insight Count", "0"},
|
||||
{"Long/Short Ratio", "100%"},
|
||||
{"Estimated Monthly Alpha Value", "$0"},
|
||||
{"Total Accumulated Estimated Alpha Value", "$0"},
|
||||
{"Mean Population Estimated Insight Value", "$0"},
|
||||
{"Mean Population Direction", "0%"},
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -1,143 +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 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", "-0.096"},
|
||||
{"Tracking Error", "0.212"},
|
||||
{"Treynor Ratio", "0"},
|
||||
{"Total Fees", "$0.00"},
|
||||
{"Fitness Score", "0"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "79228162514264337593543950335"},
|
||||
{"Portfolio Turnover", "0"},
|
||||
{"Total Insights Generated", "0"},
|
||||
{"Total Insights Closed", "0"},
|
||||
{"Total Insights Analysis Completed", "0"},
|
||||
{"Long Insight Count", "0"},
|
||||
{"Short Insight Count", "0"},
|
||||
{"Long/Short Ratio", "100%"},
|
||||
{"Estimated Monthly Alpha Value", "$0"},
|
||||
{"Total Accumulated Estimated Alpha Value", "$0"},
|
||||
{"Mean Population Estimated Insight Value", "$0"},
|
||||
{"Mean Population Direction", "0%"},
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -96,20 +96,12 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
Consolidate("EURUSD", Resolution.Daily, DailyEurUsdBarHandler);
|
||||
|
||||
// API convenience method for easily receiving weekly-consolidated data
|
||||
Consolidate("SPY", Calendar.Weekly, CalendarTradeBarHandler);
|
||||
Consolidate("EURUSD", Calendar.Weekly, CalendarQuoteBarHandler);
|
||||
Consolidate("SPY", CalendarType.Weekly, CalendarTradeBarHandler);
|
||||
Consolidate("EURUSD", CalendarType.Weekly, CalendarQuoteBarHandler);
|
||||
|
||||
// API convenience method for easily receiving monthly-consolidated data
|
||||
Consolidate("SPY", Calendar.Monthly, CalendarTradeBarHandler);
|
||||
Consolidate("EURUSD", Calendar.Monthly, CalendarQuoteBarHandler);
|
||||
|
||||
// API convenience method for easily receiving quarterly-consolidated data
|
||||
Consolidate("SPY", Calendar.Quarterly, CalendarTradeBarHandler);
|
||||
Consolidate("EURUSD", Calendar.Quarterly, CalendarQuoteBarHandler);
|
||||
|
||||
// API convenience method for easily receiving yearly-consolidated data
|
||||
Consolidate("SPY", Calendar.Yearly, CalendarTradeBarHandler);
|
||||
Consolidate("EURUSD", Calendar.Yearly, CalendarQuoteBarHandler);
|
||||
Consolidate("SPY", CalendarType.Monthly, CalendarTradeBarHandler);
|
||||
Consolidate("EURUSD", CalendarType.Monthly, CalendarQuoteBarHandler);
|
||||
|
||||
// requires quote data subscription
|
||||
//Consolidate<QuoteBar>("EURUSD", TimeSpan.FromMinutes(45), FortyFiveMinuteBarHandler);
|
||||
|
||||
@@ -1,118 +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 System;
|
||||
using System.Collections.Generic;
|
||||
using System.Linq;
|
||||
using NodaTime;
|
||||
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];
|
||||
var history = History<QuoteBar>(symbol, 10, Resolution.Daily);
|
||||
|
||||
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"},
|
||||
{"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"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -1,161 +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 System;
|
||||
using System.Collections.Generic;
|
||||
using System.Linq;
|
||||
using QuantConnect.Data.Custom;
|
||||
using QuantConnect.Data.Custom.CBOE;
|
||||
using QuantConnect.Data.Custom.Fred;
|
||||
using QuantConnect.Data.Custom.SEC;
|
||||
using QuantConnect.Data.Custom.Tiingo;
|
||||
using QuantConnect.Data.Custom.USEnergy;
|
||||
using QuantConnect.Data.Custom.USTreasury;
|
||||
using QuantConnect.Interfaces;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// This regression algorithm tests the performance related GH issue 3772
|
||||
/// </summary>
|
||||
public class DefaultResolutionRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2013, 10, 11);
|
||||
SetEndDate(2013, 10, 12);
|
||||
var spy = AddEquity("SPY").Symbol;
|
||||
|
||||
var types = new[]
|
||||
{
|
||||
typeof(SECReport8K),
|
||||
typeof(SECReport10K),
|
||||
typeof(SECReport10Q),
|
||||
typeof(USTreasuryYieldCurveRate),
|
||||
typeof(USEnergy),
|
||||
typeof(CBOE),
|
||||
typeof(TiingoPrice),
|
||||
typeof(Fred)
|
||||
};
|
||||
|
||||
foreach (var type in types)
|
||||
{
|
||||
var custom = AddData(type, spy);
|
||||
|
||||
if (SubscriptionManager.SubscriptionDataConfigService
|
||||
.GetSubscriptionDataConfigs(custom.Symbol)
|
||||
.Any(config => config.Resolution != Resolution.Daily))
|
||||
{
|
||||
throw new Exception("Was expecting resolution to be set to Daily");
|
||||
}
|
||||
|
||||
try
|
||||
{
|
||||
AddData(type, spy, Resolution.Tick);
|
||||
throw new Exception("Was expecting an ArgumentException to be thrown");
|
||||
}
|
||||
catch (ArgumentException)
|
||||
{
|
||||
// expected, these custom types don't support tick resolution
|
||||
}
|
||||
}
|
||||
|
||||
var security = AddData<USEnergyAPI>(spy);
|
||||
if (SubscriptionManager.SubscriptionDataConfigService.GetSubscriptionDataConfigs(security.Symbol)
|
||||
.Any(config => config.Resolution != Resolution.Hour))
|
||||
{
|
||||
throw new Exception("Was expecting resolution to be set to Hour");
|
||||
}
|
||||
|
||||
try
|
||||
{
|
||||
|
||||
AddOption("AAPL", Resolution.Daily);
|
||||
throw new Exception("Was expecting an ArgumentException to be thrown");
|
||||
}
|
||||
catch (ArgumentException)
|
||||
{
|
||||
// expected, options only support minute resolution
|
||||
}
|
||||
|
||||
var option = AddOption("AAPL");
|
||||
if (SubscriptionManager.SubscriptionDataConfigService.GetSubscriptionDataConfigs(option.Symbol)
|
||||
.Any(config => config.Resolution != Resolution.Minute))
|
||||
{
|
||||
throw new Exception("Was expecting resolution to be set to Minute");
|
||||
}
|
||||
|
||||
Quit();
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
|
||||
/// </summary>
|
||||
public bool CanRunLocally { get; } = true;
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate which languages this algorithm is written in.
|
||||
/// </summary>
|
||||
public Language[] Languages { get; } = { Language.CSharp };
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
|
||||
/// </summary>
|
||||
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "0"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "0%"},
|
||||
{"Drawdown", "0%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "0%"},
|
||||
{"Sharpe Ratio", "0"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "0"},
|
||||
{"Beta", "0"},
|
||||
{"Annual Standard Deviation", "0"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "0"},
|
||||
{"Tracking Error", "0"},
|
||||
{"Treynor Ratio", "0"},
|
||||
{"Total Fees", "$0.00"},
|
||||
{"Fitness Score", "0"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "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", "d41d8cd98f00b204e9800998ecf8427e"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -1,138 +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 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", "0.158"},
|
||||
{"Annual Standard Deviation", "0.033"},
|
||||
{"Annual Variance", "0.001"},
|
||||
{"Information Ratio", "-4.942"},
|
||||
{"Tracking Error", "0.08"},
|
||||
{"Treynor Ratio", "0.517"},
|
||||
{"Total Fees", "$3.70"},
|
||||
{"Fitness Score", "0.019"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "1.362"},
|
||||
{"Return Over Maximum Drawdown", "9.699"},
|
||||
{"Portfolio Turnover", "0.023"},
|
||||
{"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", "$0"},
|
||||
{"Mean Population Direction", "0%"},
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "00d6dc8775da38f7f79defad06de240a"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -45,7 +45,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
SetEndDate(2007, 05, 25); //Set End Date
|
||||
SetCash(100000); //Set Strategy Cash
|
||||
// Find more symbols here: http://quantconnect.com/data
|
||||
AddSecurity(SecurityType.Equity, "AAA.1", Resolution.Daily);
|
||||
AddSecurity(SecurityType.Equity, "AAA", Resolution.Daily);
|
||||
AddSecurity(SecurityType.Equity, "SPY", Resolution.Daily);
|
||||
}
|
||||
|
||||
@@ -58,7 +58,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
_dataCount += data.Bars.Count;
|
||||
if (Transactions.OrdersCount == 0)
|
||||
{
|
||||
SetHoldings("AAA.1", 1);
|
||||
SetHoldings("AAA", 1);
|
||||
Debug("Purchased Stock");
|
||||
}
|
||||
|
||||
@@ -66,12 +66,12 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
var symbol = kvp.Key;
|
||||
var tradeBar = kvp.Value;
|
||||
Debug($"OnData(Slice): {Time}: {symbol}: {tradeBar.Close.ToStringInvariant("0.00")}");
|
||||
Debug($"OnData(Slice): {Time}: {symbol}: {tradeBar.Close.ToString("0.00")}");
|
||||
}
|
||||
|
||||
// the slice can also contain delisting data: data.Delistings in a dictionary string->Delisting
|
||||
|
||||
var aaa = Securities["AAA.1"];
|
||||
var aaa = Securities["AAA"];
|
||||
if (aaa.IsDelisted && aaa.IsTradable)
|
||||
{
|
||||
throw new Exception("Delisted security must NOT be tradable");
|
||||
@@ -80,8 +80,11 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
throw new Exception("Securities must be marked as tradable until they're delisted or removed from the universe");
|
||||
}
|
||||
}
|
||||
|
||||
foreach (var kvp in data.Delistings)
|
||||
public void OnData(Delistings data)
|
||||
{
|
||||
foreach (var kvp in data)
|
||||
{
|
||||
var symbol = kvp.Key;
|
||||
var delisting = kvp.Value;
|
||||
@@ -140,46 +143,25 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
/// </summary>
|
||||
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "3"},
|
||||
{"Total Trades", "2"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "-3.23%"},
|
||||
{"Compounding Annual Return", "-79.990%"},
|
||||
{"Drawdown", "4.300%"},
|
||||
{"Average Loss", "-5.58%"},
|
||||
{"Compounding Annual Return", "-87.694%"},
|
||||
{"Drawdown", "5.600%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "-4.312%"},
|
||||
{"Sharpe Ratio", "-5.958"},
|
||||
{"Probabilistic Sharpe Ratio", "0.000%"},
|
||||
{"Net Profit", "-5.578%"},
|
||||
{"Sharpe Ratio", "-10.227"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.685"},
|
||||
{"Beta", "-0.445"},
|
||||
{"Annual Standard Deviation", "0.119"},
|
||||
{"Annual Variance", "0.014"},
|
||||
{"Information Ratio", "-4.887"},
|
||||
{"Tracking Error", "0.155"},
|
||||
{"Treynor Ratio", "1.589"},
|
||||
{"Total Fees", "$55.05"},
|
||||
{"Fitness Score", "0.002"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "-15.687"},
|
||||
{"Return Over Maximum Drawdown", "-18.549"},
|
||||
{"Portfolio Turnover", "0.334"},
|
||||
{"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", "61f4d3c109fc4b6b9eb14d2e4eec4843"}
|
||||
{"Alpha", "-1.953"},
|
||||
{"Beta", "23.587"},
|
||||
{"Annual Standard Deviation", "0.156"},
|
||||
{"Annual Variance", "0.024"},
|
||||
{"Information Ratio", "-10.33"},
|
||||
{"Tracking Error", "0.156"},
|
||||
{"Treynor Ratio", "-0.067"},
|
||||
{"Total Fees", "$36.70"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,148 +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 System;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Interfaces;
|
||||
using QuantConnect.Securities;
|
||||
using System.Collections.Generic;
|
||||
using System.Linq;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Regression algorithm reproducing issue #5160 where delisting order would be cancelled because it was placed at the market close on the delisting day
|
||||
/// </summary>
|
||||
public class DelistingFutureOptionRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private bool _traded;
|
||||
private int _lastMonth;
|
||||
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2012, 1, 1);
|
||||
SetEndDate(2013, 1, 1);
|
||||
SetCash(10000000);
|
||||
|
||||
var dc = AddFuture(Futures.Dairy.ClassIIIMilk, Resolution.Minute, Market.CME);
|
||||
dc.SetFilter(1, 120);
|
||||
|
||||
AddFutureOption(dc.Symbol, universe => universe.Strikes(-2, 2));
|
||||
_lastMonth = -1;
|
||||
}
|
||||
|
||||
public override void OnData(Slice data)
|
||||
{
|
||||
if (Time.Month != _lastMonth)
|
||||
{
|
||||
_lastMonth = Time.Month;
|
||||
var investedSymbols = Securities.Values
|
||||
.Where(security => security.Invested)
|
||||
.Select(security => security.Symbol)
|
||||
.ToList();
|
||||
|
||||
var delistedSecurity = investedSymbols.Where(symbol => symbol.ID.Date.AddDays(1) < Time).ToList();
|
||||
if (delistedSecurity.Count > 0)
|
||||
{
|
||||
throw new Exception($"[{UtcTime}] We hold a delisted securities: {string.Join(",", delistedSecurity)}");
|
||||
}
|
||||
Log($"Holdings({Time}): {string.Join(",", investedSymbols)}");
|
||||
}
|
||||
|
||||
if (Portfolio.Invested)
|
||||
{
|
||||
return;
|
||||
}
|
||||
|
||||
foreach (var chain in data.OptionChains.Values)
|
||||
{
|
||||
foreach (var contractsValue in chain.Contracts.Values)
|
||||
{
|
||||
MarketOrder(contractsValue.Symbol, 1);
|
||||
_traded = true;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnEndOfAlgorithm()
|
||||
{
|
||||
if (!_traded)
|
||||
{
|
||||
throw new Exception("We expected some FOP trading to happen");
|
||||
}
|
||||
if (Portfolio.Invested)
|
||||
{
|
||||
throw new Exception("We shouldn't be invested anymore");
|
||||
}
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
|
||||
/// </summary>
|
||||
public bool CanRunLocally { get; } = true;
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate which languages this algorithm is written in.
|
||||
/// </summary>
|
||||
public Language[] Languages { get; } = { Language.CSharp };
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
|
||||
/// </summary>
|
||||
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "16"},
|
||||
{"Average Win", "0.01%"},
|
||||
{"Average Loss", "-0.02%"},
|
||||
{"Compounding Annual Return", "-0.111%"},
|
||||
{"Drawdown", "0.100%"},
|
||||
{"Expectancy", "-0.679"},
|
||||
{"Net Profit", "-0.112%"},
|
||||
{"Sharpe Ratio", "-1.052"},
|
||||
{"Probabilistic Sharpe Ratio", "0.000%"},
|
||||
{"Loss Rate", "80%"},
|
||||
{"Win Rate", "20%"},
|
||||
{"Profit-Loss Ratio", "0.61"},
|
||||
{"Alpha", "-0.001"},
|
||||
{"Beta", "-0.001"},
|
||||
{"Annual Standard Deviation", "0.001"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-1.187"},
|
||||
{"Tracking Error", "0.115"},
|
||||
{"Treynor Ratio", "1.545"},
|
||||
{"Total Fees", "$37.00"},
|
||||
{"Fitness Score", "0"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "-0.128"},
|
||||
{"Return Over Maximum Drawdown", "-0.995"},
|
||||
{"Portfolio Turnover", "0"},
|
||||
{"Total Insights Generated", "0"},
|
||||
{"Total Insights Closed", "0"},
|
||||
{"Total Insights Analysis Completed", "0"},
|
||||
{"Long Insight Count", "0"},
|
||||
{"Short Insight Count", "0"},
|
||||
{"Long/Short Ratio", "100%"},
|
||||
{"Estimated Monthly Alpha Value", "$0"},
|
||||
{"Total Accumulated Estimated Alpha Value", "$0"},
|
||||
{"Mean Population Estimated Insight Value", "$0"},
|
||||
{"Mean Population Direction", "0%"},
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "de309ab56d2fcd80ff03df2802d9feda"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -167,45 +167,24 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "7"},
|
||||
{"Average Win", "19.16%"},
|
||||
{"Average Win", "19.15%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "16.727%"},
|
||||
{"Drawdown", "12.200%"},
|
||||
{"Compounding Annual Return", "16.720%"},
|
||||
{"Drawdown", "12.500%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "153.058%"},
|
||||
{"Sharpe Ratio", "1.239"},
|
||||
{"Probabilistic Sharpe Ratio", "66.414%"},
|
||||
{"Net Profit", "152.966%"},
|
||||
{"Sharpe Ratio", "1.275"},
|
||||
{"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"},
|
||||
{"Tracking Error", "0.204"},
|
||||
{"Treynor Ratio", "-8.165"},
|
||||
{"Total Fees", "$46.75"},
|
||||
{"Fitness Score", "0.002"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "1.607"},
|
||||
{"Return Over Maximum Drawdown", "1.366"},
|
||||
{"Portfolio Turnover", "0.003"},
|
||||
{"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", "7c4fcd79dd817a9cd3bf44525eaed96c"}
|
||||
{"Alpha", "0.079"},
|
||||
{"Beta", "4.194"},
|
||||
{"Annual Standard Deviation", "0.128"},
|
||||
{"Annual Variance", "0.016"},
|
||||
{"Information Ratio", "1.119"},
|
||||
{"Tracking Error", "0.128"},
|
||||
{"Treynor Ratio", "0.039"},
|
||||
{"Total Fees", "$46.73"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user