An extension of Shannon entropy to include phase-related information for risk and direction analysis of complex financial products
Authors/Creators
Description
We present a new method for modeling derivatives based on a complex-valued extension of Shannon entropy. Instead of classical real probabilities, we use an imaginary entropy that introduces an additional phase dimension.
This allows risk to be interpreted not only as uncertainty, but as directed uncertainty. This creates a new strategic fictitious decision axis.
The framework is deterministic, interpretable, and programmed entirely in Python.
DISCLAIMER (Research Only)
This repository contains a research prototype. It is provided for educational and research
purposes only. It does NOT constitute financial, investment, legal, medical, or any other
professional advice. No warranty is given. Use at your own risk. Before using any outputs to
inform real-world decisions, obtain advice from qualified professionals and perform
independent verification.