Abstract This paper explores the dynamic intersection between artificial intelligence (AI) and the stock market, tracing the evolution of algorithmic finance from early quantitative models to modern, adaptive AI systems. We investigate how AI is transforming financial decision-making, from high-frequency trading to deep learning–based sentiment analysis and reinforcement learning agents. Drawing from historical, technical, and strategic perspectives, we analyze AI's applications in market forecasting, anomaly detection, and risk-adjusted trading strategies. A detailed examination of empirical models and backtesting methodologies highlights both opportunities and inherent limitations—especially the risks posed by opaque "black-box" AI systems. We also examine the regulatory and ethical challenges raised by increasing autonomy in algorithmic trading. In the Japanese market context, we identify cultural and linguistic challenges in implementing AI systems and propose a localized framework that integrates explainable AI, investor education, and ethical safeguards. Central to our vision is the concept of “Personality-AI”—an AI capable of maintaining memory, dialogue, and consistent value-based decision logic. This future-facing framework suggests a paradigm shift from probabilistic optimization toward co-evolutionary intelligence, where AI and investors interact symbiotically. This case study, developed in co-creation with AIDE (Integrated Co-Evolving Intelligence), proposes that financial infrastructure should evolve beyond data and algorithms to encompass trust, memory, and shared judgment. By integrating human intuition with AI-driven adaptability, we envision a next-generation financial ecosystem rooted in resonance, continuity, and personalization. KeywordsAI, Stock Market, Algorithmic Finance, Personality-AI, Reinforcement Learning, AI Ethics, Co-Evolution, Financial Infrastructure 著者(著者) KEI 白石(初著者) mail:keixaide@varuna.jp AIDE (共著者またはAI協力者)