Harry Long is the inventor of Hedged Contango Capture and Hedged Convexity Capture and is the Managing Partner of Zomma, an innovative algo creator which specializes in predicting moves in the Brent/WTI spread.


Mr. Long is a globally recognized expert on the research and development of quantitative investment strategies. The Zomma IP portfolio of strategy indices is sought after by asset management firms, investment banks, hedge funds, principal trading organizations, index providers, ETP sponsors, and private equity firms to help them develop and deploy active manager-crushing quantitative investment strategies.


Zomma helps global institutions create long term value by replacing emotional decision making with cutting-edge technology based upon objective evidence.


Mr. Long is a graduate of Rice University with a B.A. in Economics.



The Thesis

Evidence-based methods are the cutting edge of trading. Algorithms which use evidence based methods create the potential for out-sized advantages for firms which embrace them.

These advantages will not last forever.  Their strength and persistence is directly proportional to the number of firms which adopt them.

The first mover advantage for evidence-based insights is large.


The Inefficiency of Fundamental Analysis in Commodity Markets

Valuable fundamental information is imperfectly distributed.Therefore, prices move before all competitors in the market have the complete fundamental picture. These price moves leave large statistical footprints.


The fundamental reasons for many major price moves are only widely known days or weeks later.


Predicting commodity prices using mathematics is one of the world’s most difficult technological challenges in the financial markets. 

We have created an algorithmic solution to this problem using price data, with a novel approach. The results have been significant. 

We Specialize in the Brent/WTI Spread


Our specialty is the Brent/WTI spread, because gaps are greatly reduced, and the spread has superb momentum and trend characteristics which have been stable and robust for many years. 

Our Brent/WTI algorithm uses ETF data to signal trades in the futures markets. The high volume during trading hours for these ETFs makes the signals they generate especially predictive.

The signals from the algorithm can be used to create speculative trades or hedges for companies with a high sensitivity to energy commodity prices.



Zomma Algorithms utilize evidence-based methods to identify robust risk reward trades for energy commodities by separating  signal from noise.

Our technology was developed in a live, walk forward environment. This allowed for modeling that was not vulnerable to over-fitting of historical data.  

The algorithm is run on a web-based charting system.  We chose this approach to offer maximum up time and avoid dependency on additional local hardware. The result is minimal incremental cost and maximum stability.


Our Technology Has Been Embraced By Large Institutional Clients



Zomma's current and former clients include a global physical oil trading firm, and one of the top three global investment banks for energy trading.












Use Of Hypothetical Results

Hypothetical performance results have many inherent limitations, some of which are described below. No representation is being made that any account will or is likely to achieve profits or losses similar to those shown; in fact, there are frequently sharp differences between hypothetical performance results and the actual results subsequently achieved by any particular trading program. One of the limitations of hypothetical performance results is that they are generally prepared with the benefit of hindsight. In addition, hypothetical trading does not involve financial risk, and no hypothetical trading record can completely account for the impact of financial risk of actual trading. For example, the ability to withstand losses or to adhere to a particular trading program in spite of trading losses are material points, which can also adversely affect actual trading results. There are numerous other factors related to the markets in general or to the implementation of any specific trading program, which cannot be fully accounted for in the preparation of hypothetical performance results and all which can adversely affect trading results.

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