International Journal of Advanced Research in Computer and Communication Engineering

Abstract: The financial markets are characterized by high volatility, where retail traders often suffer significant capital losses due to emotional decision-making, cognitive bias, and inadequate risk management strategies. This paper presents TradeAi Pro, a comprehensive web-based algorithmic trading support system designed to democratize institutional-grade market analysis. By combining Computer Vision (OCR) for automated asset recognition with a Hybrid Machine Learning architecture (integrating XGBoost classifiers and Long Short-Term Memory neural networks), the system bridges the gap between raw market data and actionable trading insights. A distinguishing feature of the platform is its automated "Risk Logic Engine," which strictly enforces a 1:2 Risk-to-Reward ratio by dynamically calculating Stop Loss and Take Profit levels based on the asset's Average True Range (ATR). Furthermore, the application includes an interactive AI Trading Coach and an automated journaling module. Ultimately, this framework ensures that trading decisions are data-driven, mathematically sound, and minimized for psychological bias.

Keywords: Algorithmic Trading, Hybrid AI, XGBoost, LSTM, Computer Vision, Risk Management, FinTech.

Downloads: | DOI: 10.17148/IJARCCE.2026.15186