Roboquant is an algorithmic trading platform that is very fast and flexible while at the same time strives to be easy to use. It is fully open source, written in Kotlin, and can be used in Jupyter Notebooks as well as standalone applications.
It is designed to be used by anyone serious about algo trading, from beginning retail traders to established trading firms. You can find out more at roboquant.org. Please consider giving this repository a star ⭐ if you like the project.
To demonstrate how easy it is to get started, the following code snippet shows the key ingredients required to back-test a trading strategy:
val strategy = EMAStrategy() // (1)
val metric = AccountMetric() // (2)
val roboquant = Roboquant(strategy, metric) // (3)
val feed = CSVFeed("data/US") // (4)
roboquant.run(feed) // (5)-
Create the strategy that you want to validate
-
Define the metrics you want to monitor during the back-test
-
Create a Roboquant instance, the engine of the platform
-
What data should be used, in this case CSV files
-
Run the back test
You can find out more at the online tutorial.
You can devevelop our own trading application in your favourite IDE using roboquant as a library.
Just add roboquant and optional any of the additional modules as a dependency to your build tool, like Maven or Gradle.
Add the following dependency to your pom.xml file:
<dependency>
<groupId>org.roboquant</groupId>
<artifactId>roboquant</artifactId>
<version>VERSION</version>
</dependency>Add the following line to your gradle script:
implementation group: 'org.roboquant', name: 'roboquant', version: 'VERSION'See also installation guide for more ways to install and use roboquant.
Latest available versions:
-
Regular version:
-
Snapshot version:
You can interactively develop your own stategies using a Jupyter Notebook.
If you just want to try it out without any installation, go to
If you already have Docker installed, all it takes is a single command to have a fully functional Jupyter Notebook environment available on your local machine. This image also comes with several example notebooks included.
docker run --rm -p 8888:8888 roboquant/jupyterSome key features of roboquant are:
-
✓ Very fast back-testing, even on large volumes of historical data
-
✓ Easy to develop your own strategies and integrate with third party brokers and data providers
-
✓ Trade in multiple asset classes at the same time
-
✓ Run anything from a technical indicator to complex machine learning based strategies
-
✓ Easy to transition from back-testing to live trading
-
✓ Trade on multi-markets with multi-currencies
-
✓ Developed under open source with a permissive Apache license
-
✓ Use Jupyter Notebooks with insightful charts if you prefer interactive development
-
✓ Batteries included, for example 150+ technical indicators and ready to use datasets
-
✓ Out of the box integration with Alpaca, Interactive Brokers, OANDA, Yahoo Finance, Alpha Vantage, Polygon, Binance and most other crypto exchanges
See also Features for a more extensive feature list and how roboquant compares to some other platforms.
Roboquant is distributed under the Apache 2.0 License. The Apache 2.0 license is a permissive license, meaning there are few restrictions on the use of the code.
Besides all the great open source software that is powering roboquant, also special thanks to JetBrains for making a license available of IntelliJ IDEA.
Roboquant also comes with live trading capabilities. Using this is at your own risk and there are NO GUARANTEES about the correct functioning of the software.
PR are more than welcome, see also the Contribution Guide document. If you’re missing some features, just open an issue on GitHub. See also the todo documentation for already identified backlog items if you look for something to work on.