Corey Pritchard

Junior Quantitative Trader

Options & Volatility

Applied AI

Corey Pritchard

Junior Quantitative Trader

Options & Volatility

Applied AI

Download CV (PDF)
Junior Quantitative Trader in London: pricing, skew and PnL risk on a front-office desk.

I'm a junior quantitative trader on the front-office desk at Exinity, a CFD broker in London. I set skew in the desk's pricing sheet, analyse the PnL impact of pricing and liquidity changes, and maintain the desk's ranking of spread performance across 25–30 liquidity providers, which is used to route flow.

 

I also drove adoption of Claude across the desk and wider teams, and I'm building a Power BI tool that masks sensitive data so it's ready for AI use. Outside work I build open-source projects in rough volatility, deep hedging and SPX volatility surfaces.

 

First Class MMORSE (Mathematics, Operational Research, Statistics and Economics), University of Southampton, 2021–2025. First place, AmplifyMe trading challenge.

SKILLS
  • QUANTITATIVE

    Probability, stochastic processes, statistics, time-series analysis (GARCH), Monte Carlo simulation, optimisation
  • MARKETS

    Options theory and volatility modelling (Black-Scholes, Heston, Greeks), volatility surfaces, skew and PnL risk, execution quality and liquidity-provider analysis; FX, CFDs, futures, crypto
  • PROGRAMMING & TOOLS

    Python (NumPy, SciPy, pandas, scikit-learn, statsmodels, PyTorch), SQL, C++, VBA, Excel, Power BI; Linux, Git; Claude, MCP and agentic coding tools
Projects
Python
Rough Volatility

Estimated the roughness of S&P 500 volatility at H = 0.148 (standard error 0.013), against 0.5 for Brownian volatility; the implied-skew route gives 0.167. A rough Bergomi simulation reproduces the ATM skew slope: −0.41 against −0.40 in theory.

View on GitHub: github.com/coreyjpritchard/rough-volatility

Python, PyTorch
Deep Hedging

A neural hedge of a short call in a Black-Scholes market with transaction costs: mean P&L −0.83 against −1.37 for delta hedging, and entropic risk 1.05 against 1.54.

View on GitHub: github.com/coreyjpritchard/deep-hedging

Python
SPX Volatility Surface

SPX ATM skew follows 0.305·T−0.333; the best Heston fit has ρ = −0.53; the arbitrage-free SANOS surface has 0 static-arbitrage violations, against 2,244 for raw SVI.

View on GitHub: github.com/coreyjpritchard/volsurface

All three are personal projects. Code and results are public; every figure here comes from the linked repositories.

Experience
Oct 2025 - Present
Exinity
Junior Quantitative Trader | Front office, London
  • Set skew manually in the desk's Excel pricing sheet, and analyse how changes to pricing, skew, hedging and liquidity-provider setup affect PnL, using tick, order-flow and execution data (Python, SQL).
  • Built and maintain the desk's ranking of spread performance across 25–30 liquidity providers, used to route flow and choose providers.
  • Made the case for AI and drove adoption of Claude across the desk and wider teams; building a Power BI tool that masks sensitive data so it's safe to use with AI.
Summer 2024
Clearing
Product Development Intern

Moved from product development into automation in the first week: Python browser and Excel automation (Selenium, BeautifulSoup) for non-technical teams, saving 10+ hours a week.

Education
Sep 2021 - Jun 2025
University of Southampton
MMath (MMORSE) | First Class Honours | Dean's List ×5

Mathematics, Operational Research, Statistics and Economics. First place, AmplifyMe Quantitative Statistical Arbitrage Challenge with Morgan Stanley (Oct 2025).

Sep 2019 - Jun 2021
Bristol Grammar School
A-levels

Mathematics A*, Further Maths (AS) A, Economics A, Physics A

CONTACT

    © 2026 Corey Pritchard

    This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.