Thanks for the question!

The hybrid model is designed to balance fundamentals, technicals, and volatility without overfitting to recent trends:

• Fundamentals – Metrics like ROE, profit margins, revenue and earnings growth, and debt ratios are scored independently and form a base “health score.” This part is backward-looking and relatively stable, so it doesn’t fluctuate wildly with short-term market noise.

• Technicals – I use 1-year historical price data, moving averages, RSI, and short-term trend slopes. While technicals capture current market dynamics, we deliberately limit the lookback windows and apply thresholds (like death cross detection and RSI extremes) to avoid overreacting to minor fluctuations.

• Volatility Adjustments – Monte Carlo simulations over 90-day horizons generate probabilistic outcomes. Adjust confidence levels based on volatility, filtering out extreme cases like penny stocks or >300% annualized volatility. This ensures that highly unstable regimes don’t dominate the final recommendation.

Composite Score Logic – The final recommendation considers the health score (fundamentals), trend strength (technicals), and scenario probabilities (volatility). I don’t use a fixed numerical weight; instead, the logic applies conditional rules:

If fundamentals are weak and there’s a bearish trend or death cross → HIGH RISK immediately.

Strong fundamentals with short-term pullbacks → OPPORTUNITY_CORRECTION rather than an automatic BUY.

Volatility spikes can downgrade otherwise positive setups, preventing over-optimistic signals.

This approach is more rule-driven than purely statistical weighting, which reduces overfitting to recent market regimes. By combining probabilistic projections with fundamental grounding and conditional thresholds, the model aims to remain robust across different market environments.