Quantiacs

This is the current Quantiacs toolbox which includes the backtester for developing and testing trading algorithms, quantiacs. Python 45 This repository contains the documentation for the current Quantiacs project. Stylus 2 1.

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Quantiacs

This is the current Quantiacs toolbox which includes the backtester for developing and testing trading algorithms. This library is designed for both beginners and seasoned traders, enabling the development and testing of trading algorithms. Quantiacs hosts a variety of quant competitions, catering to different asset classes and investment styles:. Since , Quantiacs has hosted numerous quantitative trading contests, allocating over 38 million USD to winning algorithms in futures markets. Since , the platform has expanded to include contests for predicting futures, cryptocurrencies, and stocks. The Quantiacs library QNT is optimized for local strategy development. We recommend using Conda for its stability and ease of managing dependencies. Install Anaconda : Download and install Anaconda from Anaconda's official site. Retrieve your API key from your Quantiacs profile. In step two, run the command. You can see the library updates here.

SinceQuantiacs has hosted numerous quantitative trading contests, allocating over 38 million USD to quantiacs algorithms in futures markets, quantiacs. This example shows how to use neural networks for writing a trading system on stocks.

Quantiacs is a crowd-sourced quant platform hosting algorithmic trading contests and a marketplace serving investors and quants. Quantiacs was founded in The company has grown from a base of users of 6, quants in April [2] to over 10, quants in January The company invests some of its own money in the competition winners and aims to become a marketplace for automated trading systems. The performance of the algorithms can be controlled on the Quantiacs website as their charts are publicly displayed. The company focuses on quantitative strategies with long term performance horizons, highly scalable and with multiple years of backtested data.

This is the current Quantiacs toolbox which includes the backtester for developing and testing trading algorithms. Python 45 This repository contains the documentation for the current Quantiacs project. Stylus 2 1. This template shows how to make a submission to the Nasdaq contest and contains some useful code snippets. Jupyter Notebook 1 1. This template shows how the implemented backtester allows for a walking retraining of your model. Jupyter Notebook 1. This example shows how to use supervised learning for writing a trading system on stocks. Jupyter Notebook 4.

Quantiacs

Quick Start. Working with Data. User Guide. Api Reference. Quantiacs hosts quantitative trading contests since and has allocated more than 30M USD to winning algorithms on futures markets. We are expanding the universe of assets you can use and adding new tools. Participate to our competitions and take one of the top spots. Open the strategy development tab ;. Create a strategy from scratch or clone one of the provided templates; after cloning you will be able to edit your strategy. Submit strategies and monitor their live performance in your private area.

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Install Anaconda : Download and install Anaconda from Anaconda's official site. Please introduce links to this page from related articles ; try the Find link tool for suggestions. Firm Ownership -. This library is designed for both beginners and seasoned traders, enabling the development and testing of trading algorithms. Predicting stocks using the SPX index. Quantiacs was founded in Folders and files Name Name Last commit message. Hedge Funds. MIT license. Asset Class No. View all files. This repository contains the documentation for the current Quantiacs project.

This is the current Quantiacs toolbox which includes the backtester for developing and testing trading algorithms. This library is designed for both beginners and seasoned traders, enabling the development and testing of trading algorithms.

There are 2 options:. United States. Yes No. We recommend using Conda for its stability and ease of managing dependencies. This example shows how to use supervised learning for writing a trading system on stocks. Private Equity. This one-liner combines the installation of Python, creation of a virtual environment, and installation of necessary libraries. Retrieve your API key from your Quantiacs profile. Current Opportunity. This example showcases a trading strategy based on fundamental data on the Quantiacs platform.