Yosy Rahmawati

Papers

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About

Yosy Rahmawati is a researcher at the forefront of integrating financial technology with accessible data science tools. Her work primarily focuses on demystifying algorithmic trading and investment strategies for non-experts, leveraging Python-based simulations and interactive visualization platforms. Her most-cited paper, "Trading Simulation Using Python and Visualization on Streamlit with Machine Learning Decision Tree" (2022), addresses a critical contemporary issue: the proliferation of fraudulent investment schemes and illegal trading robots that exploit public interest in stock and cryptocurrency markets. By combining machine learning decision trees with user-friendly Streamlit interfaces, Rahmawati provides a transparent, educational framework that empowers individuals to make informed trading decisions, reducing vulnerability to scams. This work has garnered 5 citations, reflecting its practical relevance in an era of rising retail investment. Her contributions bridge the gap between complex quantitative finance and everyday users, emphasizing ethical, data-driven approaches to trading. Rahmawati’s research is particularly valuable for students and budding data scientists seeking hands-on tools to explore financial markets safely, positioning her as a key voice in the intersection of fintech, machine learning, and public financial literacy.