Biomedical alloys have long been used for medical and prosthetic applications. With an increase in medical science over recent decades and an ever-increasing proportion of the elderly, implanted devices are growing in popularity. During service, these alloys are subject to harsh environments. They need to be resistive to corrosion, not have any undesirable effects on the surrounding tissue, demonstrate low thermal expansion and be highly predictable in their properties across their service life. Applications where the implant is used to aid bone structure, such as in hip prostheses, also need to have moduli similar to bone in order to protect the patient from harmful stress-shielding effects. Due to the toxicity of commonly used biomedically alloys, such as the Ti-6Al-4V alloy, suitable alternatives are currently a strong point of research. This study investigates three variations of a β-Ti-Nb-Zr-Mo-Sn alloy and aimed to model the pseudoelastic effect under cyclic tensile loading in order to further understand the mechanical properties of these alloys. As well as this, a Python based data processing application has been developed in order to efficiently analyse cyclic data from the tensile testing and for subsequent testing in further investigations of similar loading styles. The investigation concluded that pseudoelasticity was evident in all alloys, with the reduction of pseudoelasticity being proportionate to the amount of unrecoverable strain imposed by each cycle. The proportional reduction was related to the deformation mechanism in the material, with Alloys 1 and 3 being significantly less than Alloy 4. The moduli were reduced significantly after the initial cycle, with negligible reduction in subsequent cycles, concluding the reduction in moduli was proportionate to the magnitude of strain. Finally, the python processing code was able to efficiently and accurately analyse the data obtained from the Instron Universal Tensile Testing Machine. Due to uncertain validity of the results due to significant surface flaws and small dataset, a series of recommendations have also been made to validate and expand on the results presented in this paper.