Research

Overview

Introduction

QuantConnect provides an environment for you to perform exploratory research and brainstorm new ideas for algorithm. We simply call this the research environment. Here you can run Python CLI commands and explore the data directly from a Jupyter notebook installed in QuantConnect.

Inside a notebook you can perform virtually any command inside a safe disposable environment. We automatically save your notebook for you. The notebooks are running a Python 3.6 shell.

You can share a notebook by clicking the icon in the bottom right of the page. You can also share a notebook directly to the forums when you create a new discussion or comment.

The foundational class for access QuantConnect data in Jupyter is the QuantBook. The research environment is officially in public-beta while we build out its features.

Importing Dependencies

The foundation class for accessing QuantConnect data through Jupyter is the QuantBook. This is a wrapper on the QCAlgorithm class and has many of the same methods. To use this class you need to add a few references and dependencies at the start of your notebook:

from clr import AddReference
AddReference("System")
AddReference("QuantConnect.Common")
AddReference("QuantConnect.Jupyter")
AddReference("QuantConnect.Indicators")
from System import *
from QuantConnect import *
from QuantConnect.Data.Market import TradeBar, QuoteBar
from QuantConnect.Jupyter import *
from QuantConnect.Indicators import *
from datetime import datetime, timedelta
import matplotlib.pyplot as plt
import pandas as pd

# Create an instance
qb = QuantBook()

You can also see our Tutorials and Videos. You can also get in touch with us via Chat.

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