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

Battle Count: 242

Highly relevant to quantitative trading. The paper presents a complete end-to-end algorithmic trading system combining multiple alpha sources (technical, ML, sentiment, regime). It addresses practical concerns like position sizing (ATR-based), risk management, regime adaptation, and live deployment (AWS EC2 + Alpaca API). The hybrid multi-modal approach is directly applicable to systematic equity strategies. The backtest results (135.49% return, Sharpe 1.68) demonstrate practical viability, though the 63% ML accuracy and long-only constraint limit institutional applicability.

Moderate to high complexity. Requires integration of multiple components: technical indicator computation (TA-Lib), XGBoost model training/inference, FinBERT NLP pipeline, regime detection logic, Backtrader simulation framework, and cloud deployment (AWS EC2 + Alpaca API). The modular architecture aids development but the multi-source data pipeline (OHLCV + news + sentiment) and real-time execution add operational complexity. No code is provided, requiring full implementation from scratch.

The paper describes the architecture, modules, and parameters (XGBoost: 200 estimators, depth 6, learning rate 0.05; 70-30 split; FinBERT threshold -0.70; ATR-based sizing). Uses public APIs (yfinance, Alpha Vantage) and open-source tools (Backtrader, HuggingFace transformers, Alpaca API). However, no code repository is provided, and specific feature engineering details and training hyperparameters are partially described. The 100-ticker S&P 500 universe and exact date ranges are specified.