Intelligent Trading Systems: An Integrated Framework for Adaptive Execution and Machine Learning-Driven Market Simulation

The exponential growth of retail investors in Indianequity markets has created a critical need for accessible, datadrivendecision support systems. This paper presents a novelhybrid machine learning platform that combines Random Forestclassification, technical analysis, and sentiment analysis togenerate actionable trading signals for National Stock Exchange(NSE) listed securities. The system addresses the limitations ofpure machine learning approaches, which are prone to overfittingin financial time-series prediction, by integrating domain-specifictechnical indicators and news sentiment into a weighted ensemblemodel.The proposed methodology employs Random Forest classifierstrained on 25 technical indicators including RSI, MACD,Bollinger Bands, Stochastic Oscillator, and volume-based metrics,calculated from five years of historical OHLCV data for 15NSE large-cap stocks. A hybrid signal generation algorithmcombines ML predictions (40% weight), technical analysis scores(40% weight), and sentiment analysis (20% weight) to produceBUY/SELL/HOLD recommendations with confidence levels. Thesystem achieves 62.3% average prediction accuracy on test data,outperforming pure ML models (58.1%) and pure technicalanalysis (54.7%) by 4–8 percentage points.The full-stack implementation integrates the ML pipeline intoa production-ready web application featuring paper trading simulationwith 100,000 virtual capital, real-time portfolio analyticsincluding Sharpe ratio and maximum drawdown calculations,interactive candlestick charts with technical indicator overlays,and community-driven features for collaborative learning. Theplatform demonstrates practical deployment of machine learningmodels in financial applications while maintaining sub-500 msinference latency for real-time predictions.Experimental results on historical data (2020–2025) demonstratethat portfolios following the hybrid signals achieve anaverage Sharpe ratio of 1.47, significantly higher than randomtrading (0.23) and buy-and-hold strategies (0.89). The systemsuccessfully handles 100+ concurrent users with 95% of APIrequests completing within 2 seconds, validating its scalabilityfor educational and research applications. This work contributesa novel hybrid architecture for financial prediction, demonstratesend-to-end ML deployment patterns, and provides an opensourceeducational platform addressing the accessibility gap inIndian financial technology.