Generating Alpha: A Hybrid AI-Driven Trading System Integrating Technical Analysis, Machine Learning and Financial Sentiment for Regime-Adaptive Equity Strategies

TL;DR

This paper introduces a hybrid AI-driven trading system that combines technical analysis, sentiment analysis, and machine learning to adapt to market regimes, achieving superior returns and lower risks over two years.

Contribution

It presents a novel integrated approach combining multiple AI and technical methods for adaptive equity trading, outperforming benchmarks.

Findings

Achieved a 135.49% return over 24 months.

Outperformed S&P 500 and NASDAQ-100 indexes.

Demonstrated robustness across different market regimes.

Abstract

The intricate behavior patterns of financial markets are influenced by fundamental, technical, and psychological factors. During times of high volatility and regime shifts causes many traditional strategies like trend-following or mean-reversion to fail. This paper proposes a hybrid AI-based trading strategy that combines (1) trend-following and directional momentum capture via EMA and MACD, (2) detection of price normalization through mean-reversion using RSI and Bollinger Bands, (3) market psychological interpretation through sentiment analysis using FinBERT, (4) signal generation through machine learning using XGBoost and (5)dynamically adjusting exposure with market regime filtering based on volatility and return environments. The system achieved a final portfolio value of $235,492.83, yielding a return of 135.49% on initial investment over a period of 24 months. The hybrid model outperformed major benchmark indexes like S&P 500 and NASDAQ-100 over the same period showing strong flexibility and lower downside risk with superior profits validating the use of multi-modal AI in algorithmic trading.