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Toronto - Our research approach utilizes conventional statistical methods in conjunction with a unique focus on adaptive algorithms. This highly proprietary technology combines the power of artificial intelligence together with the principles of robust statistics to generate strategies that can learn from the ever-changing market environment.


Posted on July 29th, 2014

The addition of many small details can make a big difference in seemingly simple strategies. I often like to use cooking analogies, and so I like to think of tomato sauce as a classic example: it contains few ingredients and is simple to make but difficult to master without understanding the interaction between components. Trend-following […]

Factors that Drive the Performance of Momentum

Posted on July 3rd, 2014

In part 1 of the series we introduced a three-factor model that decomposes momentum profitability and how that can be translated into a momentum score for an asset universe. In this post we will show how momentum strategies can be profitable even under the conditions where the market is efficient and time series performance is […]

Momentum Matrices

Posted on June 26th, 2014

In the previous post we introduced the momentum score as a measure of the potential for momentum profits for a given investment universe. Before proceeding to part 2 of the series, I thought it would be interesting for readers to see a pairwise matrix of momentum scores to get a better feel for how they […]

What Factors Drive Momentum?

Posted on June 25th, 2014

Momentum strategies generate a lot of hype and deservedly so- it is the “premier market anomaly”- a praise heaped by no less a skeptic than Eugene Fama himself. For those who do not know Fama, he happens to be both a founder and ardent proponent of the so-called “Efficient Markets Hypothesis.” The belief in momentum […]

Universe Selection

Posted on June 4th, 2014

It is well established that the momentum effect is robust across individual stocks and broad asset classes. However, one of the biggest issues for implementation at the strategy level is to choose a universe for trading. For example, one might choose a broad index such as the S&P500 for an individual stock momentum strategy, but […]

Interview with David Aronson

Posted on May 12th, 2014

                    David Aronson is considered by many serious quants to be one of the first authors to seriously address the subject of data-mining bias in trading system development. His popular book “Evidence-Based Technical Analysis” is a must read for system developers. One of the interesting things […]

Probabilistic Absolute Momentum

Posted on March 3rd, 2014

In the last post on Probabilistic Momentum we introduced a simple method to transform a standard momentum strategy to a  probability distribution to create confidence thresholds for trading. The spreadsheet used to replicate this method can be found here. This framework is intellectually superior to a binary comparison  between two assets because the tracking error […]

Probabilistic Momentum XL

Posted on February 12th, 2014

In the last post, I introduced the concept of viewing momentum as  a probability of one asset outperforming the other versus a binary decision driven by whichever return is greater between a pair of assets. This method incorporates the joint distribution between two assets that factors in their variance and covariance. The difference in the […]

Simple Momentum Strategies Too Dumb?

Posted on January 28th, 2014

Momentum remains the most cherished and frequently used strategy for tactical investors and quantitative systems. Empirical support for momentum as a ubiqutous anomaly across global financial markets is virtually iron-clad– supported by even the most skeptical high priests of academic finance. Simple momentum strategies seek to buy the best performers by comparing the average or […]

FTCA Improved

Posted on December 5th, 2013

The strength of FTCA is both speed and simplicity. One of the weaknesses that FTCA has however, is that cluster membership is determined by a threshold to one asset only at each step (either MC or LC). Asset relationships can be complex, and there is no assurance that all members of a cluster have a […]

RSO vs Standard MVO Backtest Comparison

Posted on October 10th, 2013

In a previous post I introduced Random Subspace Optimization as a method to reduce dimensionality and improve performance versus standard optimization methods. The concept is theoretically sound and is  traditionally applied in machine learning to improve classification accuracy.  It makes sense that it would be useful for portfolio optimization.  To test this method, I used […]

MVO & Statistical Theory

Posted on October 3rd, 2013

Mean-variance optimization (MVO) was introduced by Markowitz as a means of compressing forecasts into an expression of portfolio weights for asset allocation. The theory and mathematical concepts have become central to modern finance. Views on MVO are highly polarized- some feel that it is worthless while others think it is the holy grail. Somewhere in […]

Social Learning Algorithms: Particle Swarm Optimization

Posted on September 6th, 2013

The picture above is not a Sharknado. It is a school of fish that are travelling together in a swarm demonstrating the properties of intelligent social behavior. This observation along with other examples in nature helped to inspire the creation of Particle Swarm Optimization (PSO). PSO is a robust stochastic optimization method based upon the […]

The Mighty Micro-Genetic Algorithm

Posted on August 30th, 2013

A Gentle Introduction to Optimization The field of optimization has evolved significantly over the past few decades. Several new theoretical, algorithmic, and computational methods for optimization have been proposed to solve complex problems in diverse fields such as finance, engineering and molecular biology. In finance, optimization is required to solve portfolio problems, model/predict time series […]

Filtering White Noise

Posted on April 23rd, 2013

Most asset return processes can be characterized as containing a primary trend, along with mean-reversion around that trend, as well as a certain amount of random noise.  Econometricians classify these elements using a Hurst Exponent as either : 1)black noise (trending/positive autocorrelations- Hurst>.5) 2) pink noise (mean-reverting/negative autocorrelations- Hurst<.5) or 3)  white noise ( no trend/mean-reversion, low/insignificant autocorrelations- Hurst=.5). [...]

Minimum Variance Algorithm Comparison

Posted on April 19th, 2013

The Minimum Variance Algorithm was compared to several standard optimization methods and algorithms in a recent set of tests done by Michael Kapler of Systematic Investor.  Michael created a webpage for MVA to review some details of these tests and also to summarize some of the background information.  We plan to release a whitepaper on [...]

Minimum Variance Algorithm Test Drive

Posted on April 4th, 2013

The Minimum Variance Algorithm (MVA) follows much of the same logic as the Minimum Correlation Algorithm (MCA) and differs primarily in the objective function which is to minimize portfolio variance versus correlations. Both are “heuristic” algorithms that seek to approximate the results of more complex methods that require employing quadratic optimization. In a recent whitepaper, [...]

Minimum Variance Algo

Posted on April 1st, 2013

Often readers ask about methods for approximating minimum variance portfolios. In practice the minimum variance portfolio can be calculated in closed form only for long-short portfolios, and requires a quadratic optimizer to solve for long-only portfolios. Source code and examples for  long-only minimum variance can be found at Systematic Investor - a very good blog that [...]