Consumer stock markets have long been a target of modeling efforts for the economic gains anticipatorily enabled by well-performing models. Aimed at identifying strategies capable of achieving desired returns, many modeling approaches have attempted to capture the innumerable and intricate complexities present within these adaptive socio-technical systems. Decreasingly constrained by available computation power, contemporary models have grown in sophistication to include several of the features present in de facto market systems. However, these models require extensive effort to dictate the variety of states, behaviors, and adaptations that entities of the system may exhibit. Mandating the development of complex formulas and an incredible number of situational considerations, traditional approaches to stock market modeling are intensive to architect and applicable to a limited range of scenarios. Further, these models commonly fail to incorporate external influences on the actions of investing parties. Employing an agent-based approach, independent and externally influenced entities are modeled to simulate market activity. Under the jurisdiction of assigned simple rules, agents of the system interact in complex and emergent ways without requiring macroscopic guiding equations. Successive trails are conducted using varying initialization values, enabling the determination of robust investment strategies performing well across a range of market scenarios.