TRADE LIKE A HEDGE FUND

pip install arbitragelab

ArbitrageLab is a collection of algorithms from the best academic journals and graduate-level textbooks, which focuses on the branch of statistical arbitrage known as pairs trading. We have extended the implementations to include the latest methods that trade a portfolio of n-assets (mean-reverting portfolios).

PURCHASE

ONE SOLUTION, MULTIPLE BENEFITS

Arbitragelab is built for teams who want to hit the ground running.

PRODUCTION-READY

IMPLEMENTATIONS

DOCUMENTATION

EXAMPLE

NOTEBOOKS

STRATEGIES

15+

Based on landmark papers

HOURS

8040

In research and development

COVERAGE

100%

Production-ready code

Arbitragelab Version 0.5.0

Distance Approach

Popularized by Gatev in 2006, this approach holds the position of the most cited pairs trading strategy. The simplicity and transparency of this method make it the first choice when it comes to large empirical research.

Cointegration Approach

Another extremely popular approach outlined by Vidyamurthy 2004, is the cointegration approach. Pairs selected in this method possess econometrically more reliable equilibrium relationships.

Time Series Approach

To improve the trading rules of a given strategy, we turn to time series modeling of a mean-reverting process, other than cointegration.

Stochastic Control Approach

By using stochastic processes we can determine the optimal trading rules for a given mean-reverting strategy, without the need to forecast how the spread will move in the next period or needing a formation period.

Copula Approach

The Copula approach allows you to study the deeper relationship between the different legs of the trade and enables you to analyze the dependency structure from multiple random variables.

PCA Approach

The approach proposed by Avellaneda and Lee (2010) uses PCA to create a seemingly mean-reverted spread which in turn is modeled by an OU-process.

Machine Learning Approach

The machine learning approach is characterized by a collection of techniques from the various approaches in combination with machine learning algorithms, to enhance the strategy creation process.

Other Approaches

The approaches that don’t fall into the scope of previously mentioned ones.

“Rigorous research and efficient algorithms are the cornerstones of modern algorithmic trading. For quants and traders seeking a competitive edge, the ArbitrageLab can empower them to design the optimal mean-reversion trading strategies.”

PROFESSOR TIM LEUNGBoeing Professor of Applied Math & Director of Computational Finance & Risk Management (CFRM) Program at the University of Washington.

“Rigorous research and efficient algorithms are the cornerstones of modern algorithmic trading. For quants and traders seeking a competitive edge, the ArbitrageLab can empower them to design the optimal mean-reversion trading strategies.”

PROFESSOR TIM LEUNGBoeing Professor of Applied Math & Director of Computational Finance & Risk Management (CFRM) Program at the University of Washington.

CHOOSE YOUR TIER