An explainable modeling system that analyzes cryptocurrency prices as equilibrium outcomes shaped by market forces. This simulator computes force decompositions, equilibrium bands, tension scores, and scenario-based what-if simulations to reveal how demand, supply, volatility, liquidity, and speculation negotiate price.

A deep research study introducing the Gene Drift Hypothesis: a framework explaining how tokenomics mutate across market cycles. Analyzes evolutionary forces, selective pressures, behavioral traits, and economic genes that rise, fall, or mutate through bull/bear phases, shaping token species over time.

This article reframes pricing as a negotiation rather than a prediction, showing how price emerges from tensions between product reality, market dynamics, and buyer behavior. It introduces negotiation-aware ML, value decomposition, and equilibrium modeling to build transparent, human-aligned pricing systems.

Python replication of Calvano et al. (2020)

Python implementation of advanced financial network analysis toolkit for creating multi-layered Digital Twins of market dynamics. Implements information-theoretic Transfer Entropy and stochastic Kramers-Moyal methods to map non-linear, directed relationships between assets during normal and crisis periods.

Q-learning oligopoly simulation (Calvano et al., 2020) — high-fidelity Python reproduction with ≥ 95 % accuracy

Dynamic Financial Markets is a data-driven project analyzing stock interactions in the Indian cement sector. Using models like GARCH, Johansen Cointegration, and VAR, it explores volatility, long-term relationships, and dynamic dependencies among Shree Cement, UltraTech, and Ambuja Cement.