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QuantRocket

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Software Status:Active

About QuantRocket

QuantRocket is a trading software platform from QuantRocket that supports quantitative finance and algorithmic trading. It provides data management, backtesting, and live trading capabilities so users can develop and deploy trading strategies efficiently. The platform includes a comprehensive data library, integration with popular trading brokers, and reliable backtesting tools. Users can access historical market data and implement their strategies in real-time, making it suitable for both individual traders and institutional investors. Key capabilities: data management backtesting live trading broker integration historical market data Best for: quantitative analysts and traders that need to develop and test trading strategies.

QuantRocket Details

Vendor
QuantRocket
Year Launched
2018
Location
20 Battery Park Avenue Asheville, NC 28801, US
Deployment
cloud
Training Options
documentation
Countries Served
All Countries
Languages
English, French, Spanish, German, Italian, Portuguese
Users
Quantitative researchers, algo traders, data scientists, independent retail quants, small funds and trading teams
Industries Served
Finance, retail quant traders, hedge funds, quantitative researchers, trading firms, systematic managers.
Tags
QuantRocket, Financial Services, algorithmic trading, backtesting, Zipline, Moonshot, JupyterLab, Python, quantitative research, live trading, data library, timescaledb, global markets.

QuantRocket's In-App Market Place

Does QuantRocket have an in-app market place?

Yes

How many Mini-Apps in the marketplace?

1

Mini Apps

N/A

Pricing Options

Free trial
Free version
Request a quote
Promo Offer

Accepted Payment Currencies

USD ($), EUR (€), GBP (£), AUD (A$), CAD (C$), JPY (¥), CHF (CHF), NZD (NZ$), SGD (S$), HKD (HK$)

Pros & Cons

  • Provides rich global market data, reducing time spent on data cleaning and accelerating research.
  • Enables fully automated or semi-manual live trading across multiple brokers and accounts.
  • Docker deployment ensures consistent performance and easy replication across machines or cloud servers.
  • JupyterLab integration empowers Python users with a familiar, powerful research environment.
  • TimescaleDB-powered real-time data storage supports high-speed analysis for intraday or algorithmic strategies.
  • Requires strong technical and Python skills, making it challenging for non-technical or beginner traders.
  • Self-hosted deployment means users must manage their own servers, updates, and system maintenance.
  • Setting up Docker, brokers, and data connections can be time-consuming for first-time users.
  • Complex workflows may require command-line operations, increasing the learning curve.
  • Some premium datasets require separate purchases, increasing total cost for data-intensive traders.

QuantRocket's Support Options

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