Advancing the frontiers of computational finance through rigorous scientific inquiry
This thesis presents the Self-Emergent Processor (SEP), a revolutionary framework for understanding and predicting market dynamics through the lens of thermodynamic principles and information theory. By quantifying the coherence and stability of data patterns in real-time, SEP provides unprecedented insights into market behavior and enables predictive capabilities that outperform traditional quantitative methods.
The framework introduces novel concepts including pattern entropy measurement, coherence field mapping, and stability gradient analysis. Through rigorous mathematical proofs and empirical validation across 5 distinct market scenarios, we demonstrate SEP's ability to identify regime changes, predict volatility clusters, and optimize trading strategies with statistical significance.
See patent documentation for supporting details (all patents pending).
Comprehensive technical documentation of the SEP Engine's architecture, including system design, algorithm specifications, and implementation details.
Detailed mathematical proofs and derivations underlying the SEP framework, including connections to statistical mechanics and information theory.
Results from extensive backtesting and real-world validation of SEP algorithms across multiple asset classes and market conditions.
Analysis of the video "The Law of Large Numbers" and how probability theory informs the SEP Engine's development.
Whitepaper connecting SEP's verification process to the P vs NP problem and presenting backtested alpha generation.
Groundbreaking research connecting the SEP Engine's coherence metrics to P vs NP. Demonstrates alpha generation and verification theory.
Detailed report on SEP Engine backtesting using a confidence-based trading strategy.
Interested in academic collaboration or accessing our research? We welcome inquiries from researchers, institutions, and industry partners.
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