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What is QuantRocket?

QuantRocket is a versatile platform that utilizes Python for the research, backtesting, and execution of quantitative trading strategies. Designed with Docker, it can be conveniently deployed on local machines or cloud environments, showcasing an open architecture that allows for significant customization and expansion. The platform features a JupyterLab interface and includes a comprehensive set of data integrations, along with support for various backtesting frameworks, such as Zipline—originally the backbone of Quantopian; Alphalens for alpha factor analysis; Moonshot, a backtester leveraging pandas; and MoonshotML, which focuses on walk-forward machine learning backtesting. Additionally, users can benefit from its flexibility to adapt to diverse trading needs and strategies as they evolve.

What is JupyterHub?

JupyterHub is a powerful tool that enables the creation of a multi-user environment, allowing for the spawning, management, and proxying of multiple instances of Jupyter notebook servers. Created by Project Jupyter, it is specifically tailored to support numerous users at once. This platform serves a wide array of functions, making it suitable for educational settings, corporate data science teams, collaborative scientific research endeavors, or groups that utilize high-performance computing resources. However, it's essential to highlight that JupyterHub does not officially support Windows operating systems. While some users may attempt to run JupyterHub on Windows using compatible Spawners and Authenticators, the default settings are not optimized for such an environment. Additionally, any issues encountered on Windows will not receive support, and the testing framework is not designed to work on Windows platforms. Minor patches that could potentially address basic compatibility issues on Windows are infrequent and not guaranteed. Consequently, for those using Windows, it is recommended to operate JupyterHub within a Docker container or a Linux virtual machine, as this ensures better performance and compatibility. This strategy not only improves functionality but also streamlines the installation process, making it easier for Windows users to access the benefits of JupyterHub. Ultimately, adopting this method can lead to a more seamless user experience.

Media

Media

Integrations Supported

Alpaca
Azure Marketplace
Cleanlab
Coiled
Docker
JetBrains DataSpell
JupyterLab
Microsoft Azure
Moonshot
NeevCloud
OpenHexa
PostgreSQL
Quantinuum Nexus
SQLite
Timbr.ai
Train in Data
Vast.ai
Wizata
Zipline

Integrations Supported

Alpaca
Azure Marketplace
Cleanlab
Coiled
Docker
JetBrains DataSpell
JupyterLab
Microsoft Azure
Moonshot
NeevCloud
OpenHexa
PostgreSQL
Quantinuum Nexus
SQLite
Timbr.ai
Train in Data
Vast.ai
Wizata
Zipline

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

QuantRocket

Date Founded

2018

Company Location

United States

Company Website

www.quantrocket.com

Company Facts

Organization Name

JupyterHub

Date Founded

2014

Company Website

github.com/jupyterhub/jupyterhub

Categories and Features

Algorithmic Trading

2-Click Trading
Analyst on Demand
Auto Fibs
Auto Trend Lines
Backtesting
Basket Trading
Drawing Tools
Pattern Tools
Stock Screener
Trading From Charts

Categories and Features

Popular Alternatives

Popular Alternatives

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