This research paper focuses on the integration of Artificial Intelligence (AI) into the currency trading landscape, positing the development of personalized AI models, essentially functioning as intelligent personal assistants tailored to the idiosyncrasies of individual traders. The paper posits that AI models are capable of identifying nuanced patterns within the trader's historical data, facilitating a more accurate and insightful assessment of psychological risk dynamics in currency trading. The PRI is a dynamic metric that experiences fluctuations in response to market conditions that foster psychological fragility among traders. By employing sophisticated techniques, a classifying decision tree is crafted, enabling clearer decision-making boundaries within the tree structure. By incorporating the user's chronological trade entries, the model becomes adept at identifying critical junctures when psychological risks are heightened. The real-time nature of the calculations enhances the model's utility as a proactive tool, offering timely alerts to traders about impending moments of psychological risks. The implications of this research extend beyond the confines of currency trading, reaching into the realms of other industries where the judicious application of personalized modeling emerges as an efficient and strategic approach. This paper positions itself at the intersection of cutting-edge technology and the intricate nuances of human psychology, offering a transformative paradigm for decision making support in dynamic and high-pressure environments.

翻译：本研究聚焦于人工智能在货币交易领域的融合应用，提出开发个性化人工智能模型——本质上是根据个体交易者特质量身定制的智能个人助理。论文指出，人工智能模型能够识别交易者历史数据中的细微模式，从而对货币交易中的心理风险动态进行更准确、更具洞察力的评估。心理风险指数是一个动态指标，会随引发交易者心理脆弱的市场条件而波动。通过运用先进技术，本文构建了一个分类决策树，在树结构中实现了更清晰的决策边界。将用户按时间顺序的交易记录纳入模型后，该模型能够有效识别心理风险升高的关键节点。计算过程的实时性增强了模型作为主动工具的价值，可为交易者及时预警即将发生的心理风险时刻。本研究的应用意义超越货币交易领域，延伸至其他同样可将个性化建模的高效策略性方法加以运用的行业。本文定位于前沿技术与人类心理复杂性的交叉点，为动态高压环境下的决策支持提供了变革性范式。