Generating Alpha: A Hybrid AI-Driven Trading System Integrating Technical Analysis, Machine Learning and Financial Sentiment for Regime-Adaptive Equity Strategies
Varun Narayan Kannan Pillai, Akshay Ajith, Sumesh K J
TL;DR
The paper tackles the problem that regime shifts can render traditional trend-following and mean-reversion strategies ineffective. It proposes a unified hybrid AI-driven trading architecture that fuses classical technical indicators (e.g., EMA, MACD, RSI, Bollinger Bands), supervised machine learning (XGBoost), sentiment processing (FinBERT), and market-regime filtering to adapt exposure dynamically. Evaluated on a two-year backtest across 100 S&P 500 tickers using Backtrader, the system achieved a final value of $235,492.83 and a return of $135.49\%$, outperforming the S&P 500, NASDAQ-100, and Dow Jones with a Sharpe ratio of $1.68$ and a maximum drawdown of $-15.6\%$. The results support the viability of multi-modal AI in algorithmic trading, demonstrating robustness across regimes and highlighting practical deployment via cloud infrastructure and Alpaca integration for live or paper trading.
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.