Abstract: TradeLens AI is a risk-aware and explainable trade recommendation system developed to improve transparency and reduce unnecessary exposure in algorithmic trading. The framework follows a multi-layer architecture consisting of a data ingestion module, a predictive modeling unit, and a decision engine designed to evaluate risk before execution.
The predictive component uses an XGBoost model trained on high-frequency financial data obtained from sources such as yfinance and Finnhub. Instead of directly acting on model outputs, predictions are passed through a Risk-Aware Decision Engine (RADET), which applies a confidence threshold and evaluates volatility conditions before approving trade signals.
Simulation results indicate that this layered approach significantly reduces low-quality trade entries while maintaining high prediction reliability. Additionally, the system provides interpretable outputs, allowing users to understand the factors influencing each decision. By combining predictive performance with transparent risk control, TradeLens AI contributes toward more reliable and accountable automated trading systems.

Keywords: Risk-Aware Trading, Decision Support System, Machine Learning, Decision Tree, XGBoost, Explainable AI, Financial Risk Management, Trade Recommendation.

Downloads: | DOI: 10.17148/IARJSET.2026.13470