Systematic Alpha Modeling And Portfolio Optimization Using Multi-Factor Quant Strategies

This paper presents a systematic quantitative investment framework that integrates technical factors, fundamental screening, probabilistic return forecasting, and risk-aware portfolio construction within a walk-forward evaluation architecture. The proposed approach combines momentum, volatility, trend structure, fractal persistence, and firm-level financial quality signals to rank assets cross-sectionally and allocate capital using inverse-volatility weighting. Monte Carlo–based simulations provide forward-looking expectations, while strict historical data alignment prevents look-ahead bias and ensures realistic deployability. The strategy is evaluated across multiple decades of market data spanning diverse economic regimes, including expansions, crises, and high-volatility periods. Empirical results demonstrate consistent out-of-sample performance, controlled drawdowns, and stable compounding, indicating robustness across changing market conditions. Rather than relying on a single predictive source, the framework benefits from the aggregation of diversified and complementary signals, leading to improved risk-adjusted returns. The modular design supports extensibility to additional factors, machine learning models, and multi-asset universes, providing a scalable foundation for next-generation quantitative and AI-driven portfolio management systems.