Roboquant is an algorithmic trading platform that is lightning fast and flexible while at the same time strives to be developer friendly. 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.

Code sample

To demonstrate how easy it is to get started, the following code snippet shows all the 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)

1. Create the strategy that you want to validate

2. Define the metrics you want to monitor during the back-test

3. Create a Roboquant instance, the engine of the platform

4. What data should be used, in this case, CSV files

5. Run the back test

You can find out more at the online tutorial.

Installation

Library

You can develop 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.

Maven

Add the following dependency to your pom.xml file:

<dependency>
    <groupId>org.roboquant</groupId>
    <artifactId>roboquant</artifactId>
    <version>VERSION</version>
</dependency>

Or if you want to create your own new algo-trading project from scratch, you can run the Maven Archetype:

mvn archetype:generate                          \
-DarchetypeGroupId=org.roboquant                \
-DarchetypeArtifactId=roboquant-quickstart      \
-DarchetypeVersion=2.0.0               \
-DgroupId=org.mydomain                          \
-DartifactId=myapp                              \
-Dversion=1.0-SNAPSHOT

Gradle

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:

Jupyter Notebooks

You can interactively develop your own strategies 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 -p 8888:8888 roboquant/jupyter

Features

Some key features of roboquant are:

• ✓ Blazingly 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

• ✓ Transition from back-testing to live trading with minimal changes

• ✓ 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, Alpha Vantage, Polygon, ECB, Binance and most other crypto exchanges

See also Features for a more extensive feature list and how roboquant compares to some other platforms.

License

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.

Related projects

The following list includes projects that extend the behavior of roboquant in some ways:

• Support for trading using the Bybit broker and feeds: https://github.com/alleyway/roboquant-bybit

• Support for a Maven archetype: https://github.com/neurallayer/roboquant-quickstart

• Several sample notebooks and a docker container: https://github.com/neurallayer/roboquant-notebook

Thanks

Besides all the great opensource software that is powering roboquant, also special thanks to JetBrains for making a license available for IntelliJ IDEA.

Disclaimer

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, you can also open an issue on GitHub. See also the todo documentation for already identified backlog items if you look for something to work on.