Research

The Research Environment is a Jupyter notebook-based, interactive commandline environment where you can access your local data or our cloud data through the QuantBook class. The environment supports both Python and C#. If you use Python, you can import code from the code files in your project into the Research Environment to aid development.

Before you run backtests, we recommend testing your hypothesis in the Research Environment. It's easier to perform data analysis and produce plots in the Research Environment than in a backtest.

Before backtesting or live trading with machine learning models, you may find it beneficial to train them in the Research Environment, save them in the Object Store, and then load them from the Object Store into the backtesting and live trading environment

In the Research Environment, you can also use the QuantConnect API to import your backtest results for further analysis.

The following snippet demonstrates how to use the Research Environment to plot the price and Bollinger Bands of the S&P 500 index ETF, SPY:

The following snippet demonstrates how to use the Research Environment to print the price of the S&P 500 index ETF, SPY:

// Load the required assembly files and data types in a separate cell.
#load "../Initialize.csx"

# Create a QuantBook
qb = QuantBook()

# Add an asset.
symbol = qb.add_equity("SPY").symbol

# Request some historical data.
history = qb.history(symbol, 360, Resolution.DAILY)

# Calculate the Bollinger Bands.
bbdf = qb.indicator(BollingerBands(30, 2), symbol, 360, Resolution.DAILY)

# Plot the data
bbdf[['price', 'lowerband', 'middleband', 'upperband']].plot();

#load "../QuantConnect.csx"
using QuantConnect;
using QuantConnect.Data;
using QuantConnect.Algorithm;
using QuantConnect.Research;

// Create a QuantBook
var qb = new QuantBook();

// Create a security subscription
var symbol = qb.AddEquity("SPY").Symbol;

// Request some historical data
var history = qb.History(symbol, 70, Resolution.Daily);

foreach (var tradeBar in history)
{
    Console.WriteLine($"{tradeBar.EndTime} :: {tradeBar.ToString()}");
}

Local Platform provides multiple deployment targets to enable you to open notebooks on-premise and in QuantConnect Cloud. Each new project you create contains a notebook file by default. Follow these steps to open it:

1. Open the project.
2. In the left navigation menu, click the Explorer icon.
3. In the Explorer panel, expand the QC (Workspace) section.
4. Click the Research.ipynbresearch.ipynb file.

Notebooks are a collection of cells where you can write code snippets or MarkDown. To execute a cell, press Shift+Enter.

The following describes some helpful keyboard shortcuts to speed up your research:

Follow these steps to stop a research node:

1. If you opened the research notebook on Local Platform, close the notebook file.
2. In the Resources panel, click the stop button next to the research node you want to stop.
3. In the Visual Studio Code window, click Yes.

Follow these steps to add notebook files to a project:

1. Open the project.
2. In the right navigation menu, click the Explorer icon.
3. In the Explorer panel, expand the QC (Workspace) section.
4. Click the New File icon.
5. Enter fileName.ipynb.
6. Press Enter.

Follow these steps to rename a notebook in a project:

1. Open the project.
2. In the left navigation menu, click the Explorer icon.
3. In the Explorer panel, right-click the notebook you want to rename and then click Rename.
4. Enter the new name and then press Enter.

Follow these steps to delete a notebook in a project:

1. Open the project.
2. In the left navigation menu, click the Explorer icon.
3. In the Explorer panel, right-click the notebook you want to delete and then click Delete Permanently.
4. Click Delete.

The following table lists some helpful resources to learn Jupyter: