Brandon Pennington

Quantitative Finance Researcher

Imperial College London | London, UK

About

A quantitative finance researcher at Imperial College London focusing on the application of reinforcement learning to real estate markets and derivative pricing.

I am interested in how RL agents can learn optimal strategies in illiquid markets with complex dynamics, bridging financial modeling with machine learning techniques. My research builds on foundational work in stochastic control for finance and recent advances in deep reinforcement learning, with particular emphasis on partially observable environments, long-horizon credit assignment, and robust policy learning under model misspecification.

Research Interests

Selected Reading & Influences

Current Project

re_gym

A Gymnasium-compatible reinforcement learning environment for training agents on real estate option trading and derivative strategies with realistic market dynamics.

The library models real estate market characteristics including illiquidity, transaction costs, bid-ask spreads, and mean-reverting property values with regime-switching volatility.

Environments

  • LeaseOption-v1 – Single property with exercise decisions
  • PropertyPortfolio-v1 – Multi-property acquisition and disposition
  • MortgageHedging-v1 – Interest rate hedging strategies
  • REITTrading-v1 – REIT allocation with property simulation

Features

  • Stochastic interest rate models (Hull-White, Vasicek, CIR)
  • Compatible with Stable-Baselines3 and FinRL
  • UK Land Registry and FRED data integration

Contact

Email: BrandPennington@protonmail.com

Git: github.com/BrandPennington

Bio: brandpennington.github.io