*Result*: Design and Implementation of an AI-Driven Algorithmic Trading Simulation Platform for Strategy Backtesting and Forecasting.

*This paper presents an artificial intelligence-based algorithmic trading simulation platform designed for the Vietnamese market. First, we gather and merge a wide range of financial information, including stock market indicators, gold price movements, corporate earnings reports, and financial news. Apache Airflow facilitates the scheduling and automation of the extract, transform, load (ETL) tasks for data collection and transport. Using the processed dataset, the platform employs several learning models including random forests and long short-term memory networks to generate short-term forecasts. The predictive outputs are then combined with rule-based mechanisms such as momentum rules and pairs-trading logic to produce trading signals. In addition to making predictions, the system acts as a practical educational platform. This enables students and researchers to design, test, and compare trading strategies. The system reports detailed performance statistics, including risk-adjusted returns, maximum drawdown, portfolio turnover, and hit ratios, thereby supporting transparent performance evaluation. [ABSTRACT FROM AUTHOR]

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