Setting up Quantiacs

Note: this article points to the Legacy Version of Quantiacs. Please check more recent material on the new version of Quantiacs: get started, simple bitcoin algorithm, machine learning example and optimizer.

Get Started with Quantiacs: In this short article, you will learn how to setup the Quantiacs backtesting environment for Python 3 or Matlab, you will also have an overview of the supported libraries.

Quantiacs supports offline development of trading systems in Python 3 and Matlab which requires downloading the toolbox. Running your trading system and submitting it to win our competition has never been easier.

Python

Step 1: Download and install Python 3.7 on your machine. We recommend using Anaconda as it will reliably install all the necessary dependencies.

To download and install Anaconda, please follow the instructions on the installation page for Windows, MacOS and Linux.

Step 2 : Create a virtual environment for Quantiacs to install the toolbox and manage dependencies:

conda create --name quantiacsbox
conda activate quantiacsbox
conda install -c quantiacspkg quantiacstoolbox

(You can also download the toolbox from github and install it using setuptools.)

Step 3: Launch Quantiacs by simply activating the Quantiacs environment using:

conda activate quantiacsbox

Leave the environment using:

conda deactivate

Step 4: Run a trading system.

We provide several public examples on github as a starting point. If you have downloaded the file trendFollowing.py in your directory, for example, then you can just go to that directory and type:

python trendFollowing.py

This command will evaluate the sample system on the defined data set. After evaluation the toolbox will launch a dashboard with important performance indicators: the equity curve, the long vs short exposure, market-wise performance and the Sharpe Ratio:

To take part in a Quantiacs competition (winners receive $2.25M USD in allocations), submit your strategy to our server. Simply login to your account, press the ‘Upload’ button to upload your file:

Your system will be backtested and its performance updated on a daily basis with fresh new data.

The Python toolbox supports several libraries:

• numpy
• pandas
• keras
• lightgbm
• matplotlib
• scikit-learn
• scipy
• seaborn
• statsmodels
• TA-lib
• tensorflow
• torch
• xgboost

Matlab

To get the Matlab toolbox, download it from our page or from github. Add the opened zip-file to your Matlab search path. This will make the toolbox functions available from the command window.

With the toolbox in your Matlab search path, you can now backtest your trading algorithms. You can get started with sample algorithm examples on github. To evaluate the trend-following sample algorithm defined in the file trendfollowing.m you can simply type:

runts(‘trendfollowing.m’)

This runs the evaluation of the sample system on the defined market list. Once the system is evaluated it opens a window with important performance indicators like the equity curve, the long vs short exposure, market-wise performance and the Sharpe Ratio:

Submitting the system can be made by logging in to your account, pressing the upload button and uploading your file as shown for Python files.

We support the following Matlab toolboxes:

• Curve Fitting
• Deep Learning
• Econometrics
• Financial
• Global Optimization
• Optimization
• Signal Processing
• Statistics and Machine Learning

Submitting systems before end of 2020 will allow you to take part to the live simulation period until end of April 2021. The top 3 performing algorithms, ranked according to Sharpe Ratio, will get allocations of $1M, $750k, and $500k USD.

Questions? Contact us at: info@quantiacs.com