In this article we describe the implementation of a new tool we released for the Quantiacs Python toolbox: a fast optimizer for testing the robustness of a trading system.

As Donald Knuth pointed out, “Premature optimization is the root of all evil.” This is a famous saying among software developers and it is true also in the context of trading system development.

Photo by Jefferson Santos on Unsplash

It is very tempting to write an algorithm, a simple trend-following strategy or a more refined machine-learning based system, and then to search for optimal parameters or hyperparameters which are going to maximize a given score function, normally…


The new Quantiacs platform allows quants to download financial data for free. Predictions for markets can be performed offline, downloading locally the Quantiacs backtester, or online, using our cloud for free. In this article, we describe a supervised learning example based on Ridge regression.

When we built Quantiacs we focused on a platform that allows quants to perform realistic trading simulations without getting lost in more technical details. We believe that high-quality trading systems for global financial markets can be developed by talented research scientists, software developers and students who are not part of the quant hedge fund industry.

Among…


In this article we describe the implementation of a simple quantitative strategy on the CME traded Bitcoin Futures contract with Quantiacs.

This month we released a new version of the Quantiacs platform with several major improvements. In addition to providing a new open-source backtesting tool and the possibility to download for free financial data, we created a cloud environment for quants. Here it is possible to code trading algorithms in Python using Jupyter Notebook or JupyterLab and to run them directly online using our resources for free.

The competition model has also changed: the 15th edition of the Futures contest…


Starting today you have access to a new version of the Quantiacs platform. This article summarizes the new competitions, lists the main new features and provides a simple example for getting started with the new version. The submission window for Futures systems and for Bitcoin Futures systems is open. Winners will get 4M USD in guaranteed allocations.

Quantiacs started Futures contests in 2014 and has allocated over 30M USD to top performing systems. The live evaluation phase of the current Q14 contest has started on January 1st, 2021 and will end on April 30th, 2021.

We are proud to announce…


Note 16 March 2020: 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.

In previous articles we have shown how to implement simple trading strategies in Quantiacs. Here we describe the implementation of an algorithm which uses machine learning methods.

The Python Quantiacs toolbox allows you to use machine learning methods for coding trading algorithms. We describe an example based on keras and use a Long Short-Term Memory (LSTM) model for time series forecasting.

All Quantiacs system files should…


Note 16 March 2020: 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.

A unique feature of the Quantiacs toolbox is the use of asset exposure rather than discrete trades. In this short article we show a simple example which will help you in getting started.

Most trading backtester are event- or order-based. The developer of the trading system has normally to define the set of assets which should be bought or sold and the number of contracts for…


Note 16 March 2020: 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.

In this short note we summarize how Quantiacs contests work and which payouts are distributed to the winners.

The first Quantiacs contest was held in 2014. Since then, competitions took place on a regular basis and more than 30 M USD have been allocated to winning algorithms.

Users can take part to contests writing a trading strategy on futures and submitting it to the Quantiacs platform…


Note 16 March 2020: 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.

This short tutorial describes how to run your systems over an array of different parameters. This step is important for exploring the stability of your system, and finding optimal parameters.

In a previous article, we describe how to code a simple trend-following strategy in Python using the Quantiacs toolbox. …


Note 16 March 2020: 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.

Trend-Following is a simple trading idea that determines an asset should be bought when the price trend moves up, and sold when the price trend moves down. This short article shows how to implement a trend-following strategy using the Quantiacs toolbox with Python.

In the previous article, it shows how to install the Quantiacs toolbox.

Here, we describe the implementation of a trading strategy.

The Trading…


Note 16 March 2020: 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

Quantiacs

Quantiacs is building a crowdsourced quant fund and provides quants worldwide with free data, software and computational resources.

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store