Lean CLI

You can run the Research Environment with your current configuration or an old configuration from QuantConnect.

Current Configuration

The default Research Environment configuration is the latest master branch of LEAN. If you set a different research image, the image you set is your current configuration. Follow these steps to start a local research environment with your current configuration:

1. Open a terminal in one of your organization workspaces.
2. Run lean research "<projectName>" to start a local research environment for the project in . / <projectName> on port 8888.

   $ lean research "My Project"
   Starting JupyterLab, access in your browser at localhost:8888

   You can run the environment on a different port by providing the --port <port> option.
3. In the browser window that opens, open a research notebook.

If your configuration is set to the master branch of LEAN, the CLI automatically checks if your image is behind master every seven days. If your image falls behind master, the CLI automatically updates your image to the lastest version. To force an update before the automatic check, add the --update option. To avoid updates, add the --no-update option.

Old Configurations from QuantConnect

Follow these steps to start a local Research Environment with an old research configuration from QuantConnect:

1. View the available versions on the quantconnect/research Docker Hub tags page.
2. Copy the name of the tag you want to run.
3. Open a terminal in one of your organization workspaces.
4. Run lean research "<projectName>" --image quantconnect/research:<tagFromStep2> to start a local Research Environment for the project in . / <projectName>.

   $ lean research "My Project" --image quantconnect/research:11154
   Pulling quantconnect/research:11154...
   20210322 17:27:46.658 TRACE:: Engine.Main(): LEAN ALGORITHMIC TRADING ENGINE v2.5.0.0 Mode: DEBUG (64bit)
   20210322 17:27:46.664 TRACE:: Engine.Main(): Started 5:27 PM

Follow these steps to open a research notebook in PyCharm:

1. Start a local research environment for the project containing the notebook.
2. Open the project containing the notebook in PyCharm.
3. Open PyCharm's settings and go to Build, Execution, Deployment > Jupyter > Jupyter Servers.
4. Tick the radio box in front of Configured Server and enter http://localhost:8888/?token= as the Jupyter Server URL.
5. Click Apply in the bottom-right to save the changes and OK to exit settings window.
6. Open the notebook file you want to work in PyCharm's file tree.

Follow these steps to open a research notebook in VS Code:

1. Start a local research environment for the project containing the notebook.
2. Open the project containing the notebook in VS Code.
3. Open the notebook file you want to work in VS Code's file tree.
4. Open the Kernel Picker button on the top right-hand side of the notebook (or run the Notebook: Select Notebook Kernel command from the Command Palette by pressing Ctrl+Shift+P).
5. Pick Select Another Kernel in the list of choices that pops up.
6. Select Existing Jupyter Server... in the list of choices that pops up.
7. Enter http://localhost:8888/ when asked for the URI of the running Jupyter server.
8. Select Foundation-Py-Default in the list of choices that pops up. This choice gives you access to a kernel for Python notebooks. For C# notebooks, select Foundation-C#-Default.

Sometimes it might be useful to retrieve the results of a previously ran local backtest in the research environment. By default, backtests are saved in the <projectName> / backtests / <timestamp> directory, which is also available in the research environment. You can use the following code snippet to read the contents of a backtest's result file into a local variable and to print its statistics (make sure to replace the path to the backtest with your own):

using Newtonsoft.Json;
using QuantConnect.Packets;

var backtestPath = "backtests/2021-03-03_23-46-43/CSharpProject.json";

var json = File.ReadAllText(backtestPath);
var data = JsonConvert.DeserializeObject<BacktestResultParameters>(json);

foreach (var item in data.Statistics) {
    Console.WriteLine($"{item.Key}: {item.Value}");
}

import json

backtest_path = "backtests/2021-03-03_01-57-38/main.json"

with open(backtest_path) as file:
    data = json.load(file)

for key, value in data["Statistics"].items():
    print(f"{key}: {value}")

If you are logged in using lean login you can also retrieve cloud backtest results in your local research environment. If you know the name of the project and the backtest you can use the following code snippet to retrieve the backtest's results and to print its statistics:

var projectName = "Python Template";
var backtestName = "Adaptable Tan Frog";

var project = api.ListProjects().Projects.First(p => p.Name == projectName);
var partialBacktest = api.ListBacktests(project.ProjectId).Backtests.First(b => b.Name == backtestName);
var backtest = api.ReadBacktest(project.ProjectId, partialBacktest.BacktestId);

foreach (var item in backtest.Statistics) {
    Console.WriteLine($"{item.Key}: {item.Value}");
}

project_name = "Python Template"
backtest_name = "Adaptable Tan Frog"

project = next(p for p in api.list_projects().projects if p.name == project_name)
partial_backtest = next(b for b in api.list_backtests(project.project_id).backtests if b.name == backtest_name)
backtest = api.read_backtest(project.project_id, partial_backtest.backtest_id)

for key in backtest.statistics.keys:
    print(f"{key}: {backtest.statistics[key]}")