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 is a robust software platform designed for data-driven traders to research and trade quantitative strategies using Python. Its strength lies in supporting multiple open-source Python backtesters like Zipline and Moonshot, ensuring the right tool is available for various strategy styles, from intraday to factor-based. The platform speeds up research by providing easy access to a wealth of global market data and boasts high performance for data-intensive backtests. Live trading features include multi-strategy, multi-account management with options for automated or semi-manual execution, powered by a built-in scheduler. A key advantage is its flexible Docker-based deployment, allowing users to run the platform anywhere—local machine, private VPN, or in the cloud, including the Oracle Cloud free tier—giving users complete control over their hardware and security. Built on open source with JupyterLab as its interface, it offers extensive customization for unique trading requirements. QuantRocket provides a generous free tier for learning. Specific subscription pricing is not provided in this overview, so for definitive software or multi-user license pricing, please Contact QuantRocket LLC at 20 Battery Park Avenue, Asheville, NC 28801.
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.
Features
Key features
Supports Zipline, Moonshot, Alphalens, and Pipeline to match different quantitative research styles.
Provides equities, futures, fundamentals, and minute-level US stock data for comprehensive research.
Runs on Docker across Windows, macOS, Linux, cloud servers, or local machines.
Enables automated or semi-manual multi-strategy and multi-account trading with supported brokers.
Captures and stores live data in TimescaleDB for low-latency analysis and execution.
Provides an interactive Python workspace with autocomplete, API tools, and IDE features.
Allows custom Python scripts, custom indicators, maintenance tasks, and cron scheduling.
Additional features
Integrates Zipline, Moonshot, MoonshotML, Pipeline, and Alphalens for diverse quantitative workflows.
Supports Sharadar, IBKR, EDI, and additional global datasets accessible through the Data Library.
Enables notebook-driven research, parameter scans, machine learning pipelines, and strategy analysis.
Manages order file creation and execution workflows for multi-strategy and multi-account setups.
Compatible with Interactive Brokers and Alpaca for equities and derivatives trading.
Tracks P&L, slippage, drawdowns, and live vs. backtest comparison with visual dashboards.
Collects live price feeds and saves aggregated or tick-level data into TimescaleDB.
Delivers fully containerized architecture for consistent, reproducible infrastructure across environments.
Schedules trading tasks, data collection, and custom script execution automatically.
Permits adding third-party backtesters, custom databases, and filesystem-level access for full customization.
Offers usage guides, installation manuals, API reference, code examples, and inline hover help.
Gives users control over computational resources and security without reliance on third-party hosting.
Pricing
Countries & Languages
Interface languages
Billing currencies
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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
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
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
Email Address
brian@quantrocket.comDocumentation
https://www.quantrocket.com/docs/QuantRocket's Alternatives
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