Quantitative. Systematic. Collaborative.
Quant Finance Collective
Texas State University
Exploring quantitative thinking across finance, data, and decision-making. We apply mathematics, statistics, and computation to real-world markets through workshops, simulations, and research collaboration.
Core focus areas
Our Quant Exploration
We study markets, uncertainty, and decision-making through mathematics, computation, and structured analysis. We prioritize clarity, depth, and implementation over hype.
Mathematics of Uncertainty
Probability, expected value, and stochastic reasoning.
Market Microstructure & Price Formation
How liquidity, spreads, and information shape prices.
Volatility & Regime Analysis
Modeling risk and interpreting volatility as a dynamic signal.
Simulation & Model Development
Build simulations and decision frameworks that evolve through iteration.
Research Pods & Independent Builds
Member-led projects that form a shared research library.
Practical Implementation & Reproducibility
Reproducible code, notebooks, and practices for maintainable research.
Projects
Active Research Initiatives
QFC acts as a platform for member-built research and simulations. Projects may be developed independently or collaboratively; strong contributions are reviewed and showcased to the collective.
Options Greeks Dashboard
ActiveAn interactive Black-Scholes options pricing tool with live Greeks visualization across strikes and expiries. Computes Delta, Gamma, Theta, Vega, and Rho with payoff diagrams and volatility smile visualization.
HALO
CompletedA market-making system built on the Avellaneda-Stoikov model with a dynamic regime classifier using lag-1 autocorrelation and EWMA volatility, deployed on Kraken targeting SOL/USD. Achieved a Sharpe ratio of 2.4 at the optimal poll cadence.
BTC/ETH Statistical Arbitrage
CompletedA pairs trading system using Engle-Granger cointegration to exploit the mean-reverting spread between BTC and ETH. Spread modeled as an Ornstein-Uhlenbeck process with a 28-minute half-life. Backtested Sharpe ratio of 1.48 with a max drawdown of 8.3%.
Reddit Sentiment Analysis
CompletedA sentiment signal system scraping r/algorand and r/CryptoCurrency to predict short-term ALGO returns. Found statistically significant correlations at lag-2 post volume and lag-3 sentiment ratio. Timing strategy achieved a Sharpe ratio of 1.48 and +22% return vs -16.7% buy-and-hold.
Join the Collective
Attend, Connect, Contribute
Attend weekly meetings, join our Discord, or reach out by email to contribute projects and simulations. We welcome students at all levels who are interested in building and learning.
If you have materials to contribute, please share them via the Discord or email; contributions are curated for clarity and rigor.
First meeting prep
- • Bring a laptop if possible
- • Expect a short primer followed by discussion
- • No finance background required; curiosity is enough