Launched on January 8, 2010, the Key Trends Program is the flagship strategy of KeyQuant. Key Trends culminates the research of Robert Baguenault de Vieville and Raphael Gelrubin, co-founders of KeyQuant. The Key Trends Program is 100% systematic, and trades fifty of the most liquid futures markets across equity indices, bonds, rates, currencies, and commodities. With a unique approach to risk, and innovative tools, the Key Trends Program has outperformed its peers with an annualized net return since inception of +10% (thru Dec. 31 2013).
On a daily basis, the Key Trends trading program estimates the optimal portfolio. The portfolio construction process can be broken down into three steps: Step 1 - Creating the optimal position by market by sampling prices and creating four separate trade signals; Step 2 - Adjusting positions to maximize diversification; and Step 3 - Increasing/decreasing absolute leverage at the portfolio level. The first step can be split in two parts: a) Regression approach: Calculates the trend over varying time-periods as well as its associated perception of risk; b) Non-temporal approach: Simulates the behavior of a predefined number of hypothetical investors, each with their own utility function in regards to risk and return of underlying markets. The calculated output provides an estimate of future price movements (trends), as well as its associated perception of risk. Both models run on a daily (mid-term) and a weekly (long-term) basis. All information is aggregated in a single distribution function for each contract. The distribution function provides a complete statistical description for each market. In the final step, Key Trends offers a unique portfolio allocation mechanism known as the Global Economic Factor (GEF). In the case of an uncertain economic scenario, the GEF systematically adjusts the volatility target downwards to account for uncertainty in trends. The opposite is true, such that during periods of established trends, the GEF will systematically increase the volatility target to maximize profit. Finally, the portfolio allocation does not depend on past trading.