众人的因子回测框架 stock factor test

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.

A research-grade exploration of the Tokenomics Ecological Framework, analyzing how tokens behave as predator, prey, parasite, and symbiotic species. Examines ecosystem interactions, evolutionary pressures, species population cycles, and the dynamics of economic predation, mutation, drift, and long-term survival across market cycles.

Replicate Barberis, Jin, and Wang (2021)

Replication of tables and figures of (BGLS, 2020) in Stata, R and Python. Not 100% perfectly the same as the original paper due to probable data misusage, ambiguous descriptions of some datasets in the paper or other reasons.

An end-to-end Python implementation of Cao et al.'s (2025) HLPPL methodology for the identification of financial (asset price) bubbles. Implements 7-parameter Log-Periodic Power Law model fitting, confidence-weighted sentiment analysis, regime-dependent 'BubbleScore' fusion, and Transformer-based forecasting with a backtesting framework.

This repo contains the most important snippets of my masters thesis on the predictive power of Twitter emotions in the early months of the Covid-19 global health emergency

Research study exploring how behavioral biases like overconfidence, loss aversion, and herding influence investors’ decisions in the Indian securities market. It reveals why even smart investors make irrational choices, and how financial literacy transforms emotions into rational, informed investing.

End-to-End Python implementation of Dávila-Fernández & Sordi's (2025) methodology for FX-constrained growth modeling (in emerging markets). Features Bayesian state-space estimation via Gibbs sampling with FFBS algorithm, heterogeneous agent simulation (fundamentalists/chartists), and nonlinear dynamics analysis.

AI-driven behavioral coaching system for rational investment decisions

This repo contains a compiled dataset of Ethereum prices and R code for the detection of speculative bubbles using backward supremum augmented Dickey-Fuller procedure.

University of Pennsylvania Senior Thesis (2021)

🌱 Explore tokenomics through an ecological lens, analyzing predator-prey dynamics and interactions among token species for better understanding and strategy.

🧬 Explore the Gene Drift Hypothesis to understand how tokenomics evolve and adapt through market cycles and behavioral changes in on-chain assets.