Published Paper Details:

Algorithmic trading systems operate in highly stochastic, non-stationary, and adversarial environments shaped by market microstructure, liquidity constraints, and strategic agent inter action. Classical quantitative trading strategies rely on fixed rules or parametric models that degrade under regime shifts and tail-risk events. Artificial intelligence (AI) offers a principled framework for learning predictive signals, optimizing sequential decisions, and managing risk under uncertainty. This paper presents an in-depth study of AI-driven algorithmic trading with an emphasis on forecast-guided reinforcement learning and risk-aware optimization. We formalize trading as a constrained stochastic control problem, derive objective functions incorporating return, volatility, drawdown, and transaction costs, and introduce a hybrid learning architecture that integrates probabilistic price forecasting with reinforcement learning. We develop a rigorous experimental framework evaluating robustness under regime changes, volatility shocks, and execution frictions using reproducible market simulations. Results demonstrate consistent improvements of 25-40% in risk-adjusted returns compared to traditional strategies while maintaining bounded drawdowns and stable behavior. We conclude with theoretical insights and practical considerations for deploying AI trading systems in real-world markets.