Based on the identification of market dynamics, capital allocation in long positions can be dynamically controlled by means of interrupting an otherwise strictly-long investment strategy allowing for an overall improved risk profile and faster response times during periods of persistent negative market returns. Herein, a portfolio selection methodology updating a reasonably diversified selection of competing S&P 500 constituents within and across various predefined industry groups and which produced above average long-term returns with minimized downside-risk, is proposed. Within the various predefined groups of stocks, Simugram methods are used to model and optimize on the distribution of returns up to and including a horizon of interest. Improvements to previous methods are focused toward calibrating the sampling distribution based on an empirical dataset within the various groups comprising the investor's portfolio, optionally allowing for a varying sampling frequency as dictated by the various group dynamics. By combining within-group optimization alongside with the capability of exiting aggressive long-strategies at seemingly riskier times, focus is on providing more frequent updates on a list of constituents with improved performance in both terms of risk and return.