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Installation

There are two ways to use roboquant:

  1. Interactively in a Jupyter notebook. If you want to get up and running quickly, and want to experiment with many strategies, this is the best approach. Additionally, you get many charts out-of-the-box.

  2. As a library in your own Kotlin or Java application. If you plan to develop large and complex trading strategies, this is the good approach since you have the full power of an IDE like IntelliJ IDEA at your disposal.

Jupyter Notebook

If you have already Docker installed, it only takes a single command to have a fully functional Jupyter Lab environment available:

docker run -p 8888:8888 roboquant/jupyter

This will pull the latest image from DockerHub and run it on your local machine. The image comes with several notebooks included that demonstrate how to develop and run your own strategies.

If you don’t have Docker yet installed on your computer, check out Docker get started and download Docker Desktop from there. If you are running Linux, then your distribution likely already has Docker included. If you don’t have Docker yet installed on your computer, check out Docker get started and download Docker Desktop from there. If you are running Linux, then your distribution likely already has Docker included.

If you don’t want to install anything locally, you can:

  1. Try some same notebooks right now in your browser by clicking: Binder

  2. Go to JetBrains Datalore and create an account there. It supports Kotlin Notebooks and has a free tier available if you just want to try it out.

Standalone Application

Just add roboquant as a dependency to your build tool, like Maven or Gradle.

Maven

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

Gradle

implementation group: 'org.roboquant', name: 'roboquant', version: 'VERSION'

Next to roboquant, the following additional artefacts are available for inclusion in your application:

  • roboquant-crypto Adds support for many of today’s most popular crypto exchanges

  • roboquant-extra Adds out-of-the-box integrations with third party brokers and market data providers.

  • roboquant-ta Adds support for technical analysis indicators and strategies

  • roboquant-jupyter Adds support for running roboquant inside Jupyter Notebooks

  • roboquant-ibkr Adds support for Interactive Brokers

Building from source

First start with cloning the roboquant GitHub repository to your local disk. The quickest way to be up and running is then to install IntelliJ IDEA (either the free community edition or the paid Ultimate version) and open the directory you just cloned. IntelliJ IDEA will recognize it as a Kotlin/Maven project, and you can build it and run test directly from the IDE.

Roboquant uses a directory setup that is similar to most other Kotlin projects:

root
    submodule1
        src/main/kotlin
        src/test/kotlin
    submodule2
        src/main/kotlin
        src/test/kotlin

All source code is written in Kotlin, so there are no Java or other language source files.

Roboquant uses Maven wrapper for the build process, so building and installing the roboquant libraries locally, is as easy as:

./mvnw clean install

The build and install is tested using JDK 17 runtime.

Tip
 If you plan to make many changes and updates to the source code, checkout the Maven Daemon project that provides faster builds. It is an almost 100% drop-in replacement of the regular maven and is much faster.

If you want to deploy a regular release or snapshot, use the -P release option. This will include the required plugins and also generate source- and documentation-jar files. Additionally, it will also build and deploy the roboquant-ibkr module, so you’ll need the locally installed IBKR Java client library.

./mvnw clean deploy -P release

Of course, this requires the having the right credentials for deploying to the Maven Central repo. Also note that autoReleaseAfterClose of the nexus-staging-maven-plugin is set to false, meaning that if the deployment was sucessfull, you still need to (manually) release the software from staging to production.

Interactive Brokers

If you don’t require integration with Interactive Brokers for your trading, you can skip this step.

Unfortunately, it is not allowed to redistribute the Interactive Brokers Java client. So you’ll have to download the TwsApi.jar file yourself. You can download the stable version 10.19 from here: https://interactivebrokers.github.io and within the downloaded archive file you’ll find the required TwsApi.jar.

Then install the jar file in the local Maven repository on your machine using the following command:

mvn install:install-file -Dfile=TwsApi.jar -DgroupId=com.interactivebrokers -DartifactId=tws-api -Dversion=10.19 -Dpackaging=jar

After this step, you can compile and install the modules including the roboquant-ibkr module

./mvnw install -P ibkr
Warning
 If the artefact cannot be found in your local Maven repository during a build, the ibkr profile with the module roboquant-ibkr will be skipped.

Documentation

There is a special doc profile to generate documentation and see if there is something missing. You can run the following command to find missing documentation:

./mvnw dokka:dokka -P doc | grep WARNING

Please note the release profile has its own dokka task to generate a javadoc jar file.

Performance Test

To run the built-in performance tests, you can use the following maven commands:

./mvnw install
./mvnw exec:java -pl roboquant-perf

The output should look something like this:

             _______
            | $   $ |             roboquant
            |   o   |             version: 2.0.0
            |_[___]_|             build: 2023-09-17T09:09:45Z
        ___ ___|_|___ ___         os: Mac OS X 13.5
       ()___)       ()___)        home: /Users/john/.roboquant
      /  / |         | \  \       jvm: OpenJDK 64-Bit Server VM 19.0.2
     (___) |_________| (___)      kotlin: 1.9.10
      | |   __/___\__   | |       memory: 4096MB
      /_\  |_________|  /_\       cpu cores: 8
     // \\  |||   |||  // \\
     \\ //  |||   |||  \\ //
           ()__) ()__)
           ///     \\\
        __///_     _\\\__
       |______|   |______|

 CANDLES ASSETS EVENTS RUNS    FEED    FULL SEQUENTIAL PARALLEL TRADES CANDLES/S
 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
     1M      10   1000  100     4ms    10ms     101ms      23ms     1K       43M
     5M      50   1000  100     1ms     7ms     136ms      18ms     5K      277M
    10M      50   2000  100     3ms    11ms     263ms      34ms    10K      294M
    50M     100   5000  100     5ms    55ms     987ms     130ms    50K      384M
   100M     200   5000  100     9ms   109ms    1593ms     245ms   100K      408M
   500M     500  10000  100    37ms   445ms    8994ms    1614ms   500K      309M
  1000M     500  20000  100    73ms   883ms   19120ms    3325ms  1000K      300M
Note
 The main purpose is to test the performance and stability of the back-test engine itself, not any specific feed, strategy or metric. So the overhead of those components is kept to a minimum, while still running full back tests.

On a high-end server machine (c7g metal instance on AWS), you can expect even higher performance. In a best case scenario, the engine can process over 1.2 billion candle-sticks per second:

             _______
            | $   $ |             roboquant
            |   o   |             version: 2.0.0
            |_[___]_|             build: 2023-09-17T12:03:17Z
        ___ ___|_|___ ___         os: Linux 5.19.0-1025-aws
       ()___)       ()___)        home: /home/ubuntu/.roboquant
      /  / |         | \  \       jvm: Java HotSpot(TM) 64-Bit Server VM 20.0.2
     (___) |_________| (___)      kotlin: 1.9.10
      | |   __/___\__   | |       memory: 30208MB
      /_\  |_________|  /_\       cpu cores: 64
     // \\  |||   |||  // \\
     \\ //  |||   |||  \\ //
           ()__) ()__)
           ///     \\\
        __///_     _\\\__
       |______|   |______|

 CANDLES ASSETS EVENTS RUNS    FEED    FULL SEQUENTIAL PARALLEL TRADES CANDLES/S
 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
     1M      10   1000  100     5ms    22ms     140ms      10ms     1K      100M
     5M      50   1000  100     2ms    13ms     276ms      12ms     5K      416M
    10M      50   2000  100     5ms    25ms     565ms      15ms    10K      666M
    50M     100   5000  100     6ms   106ms    2369ms      47ms    50K     1063M
   100M     200   5000  100    11ms   191ms    3988ms      83ms   100K     1204M
   500M     500  10000  100    53ms   755ms   21449ms     458ms   500K     1091M
  1000M     500  20000  100   130ms  1686ms   42971ms     958ms  1000K     1043M