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This thesis proposes new methods and tools for helping human traders to compete in a high-frequency trading environment. Human traders have difficulty trading against predatory algorithms and the thesis proposes methods that support the creation of assistive tools that can help human traders to compete profitably. It also develops further understanding of classical decision-making theory in a realtime trading context demonstrating that human traders improve decision-making biases when linked together in groups or with an assistive machine. As described in the thesis human traders are monitored, and their data is captured, in realtime and in situ. The trading performance and behavioural characteristics of the traders are studied in this context in order to determine if they can be positively modified. The thesis presents a new model for studying human trading behaviour in realtime and in situ using unique software. It also describes the basis for the development of a range of interventionist and assistive tools that are designed to augment trading performance. The approach put forward is unique in its application. It also provides evidence that human traders are willing to allow machines to augment their trading decisions. The contributions of this thesis are that it overcomes the problem of assessing human trader risk-taking behaviour in realtime and in situ, it makes sense of human trading behaviour at realtime speeds and then it shows that, with new approaches to human-machine collaboration, trading performance improves and classic decision-making biases are reduced. |
