Recreating market oscillations to study the markets often makes use of induced activity reversal via finite share or auction thresholds, strategically replacing agents via bankruptcy or genetic algorithm rules, heavily data specific network parameterization, or stochastic randomness. However, such techniques do not shed any additional light on how and why intelligent individual scale agents may spontaneously and rationally decide to endogenously change from a buying to a selling posture within a population. This paper introduces Social Netmap, an agent based population of general purpose, parameter-free, adaptive agents adjusting their behavior in real time to the directly observed aggregate and individual behaviors of their neighbors much like real intelligent actors might in a population. Without relying on random processes, validated parameters, turning-point thresholds, or agent replacement, Social Netmap was able to endogenously create typical market oscillations in 21 out of 30 cases of real Dow Jones Industrial Average data. Social Netmap points towards future work in more realistic group behavior of intelligent, rational agents.