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PROBLEM STATEMENT

While refining directional momentum systems, we encountered a major limitation: the rule did not work consistently across all stocks. Certain trigger points seemed to influence momentum shifts, but without a clear framework to identify them, execution remained inconsistent. The challenge was to develop a universal, probability-driven rule that could adapt to different market conditions and assets.

Proposed Solution

To address this, we:
✅ Created timeframe-based momentum charts to identify trigger points that drive momentum shifts.
✅ Integrated probability models into different timeframes to enhance accuracy.
✅ Systematically tested and optimized the model to ensure consistent application across all stocks and market conditions.

Findings & Results

📊 Breakthrough Insights:

• The addition of trigger points improved execution precision.
• Probability-based momentum segmentation provided adaptability across different assets.
• A universal rule was finally achieved, applicable across stocks, timeframes, and market structures.

Conclusion & Next Steps

This research bridged the gap between momentum-based trading and universal market adaptability, proving that integrating time-sensitive triggers and probability models creates a reliable mechanical system. Future enhancements will focus on further refining execution efficiency and capital allocation strategies.

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