Quantitative Researcher | Risk Magazine Quant of the Year 2024

Quantitative researcher focused on systematic strategies, portfolio optimization, and stochastic volatility modeling. Currently Global Head of Quantitative Analytics at LGT Private Banking. Co-originator of the Robust Optimisation of Strategic and Active Asset Allocation (ROSAA) framework and the log-normal beta stochastic volatility model.

For publications, speaking, and full background → artursepp.com

Quantitative Investment Strategies (QIS) package implements Python analytics for visualisation of financial data, performance reporting, analysis of quantitative strategies.

Features:

• Financial data visualization
• Performance reporting and analytics
• Quantitative strategy analysis
• Portfolio construction tools

Implementation of optimization analytics for constructing and backtesting optimal portfolios in Python.

Features:

• Portfolio optimization algorithms
• Risk budgeting implementation
• Backtesting frameworks
• Performance attribution

Python implementation of pricing analytics and Monte Carlo simulations for stochastic volatility models including Karasinski-Sepp log-normal stochastic volatility model and Heston volatility model.

Features:

• Karasinski-Sepp log-normal stochastic volatility model
• Heston model
• Monte Carlo simulations
• Analytical valuation of European call and put options

Python functionality for getting different data from Bloomberg: prices, implied vols, fundamentals.

Features:

• Bloomberg data fetching wrapper
• Price data retrieval
• Implied volatility data
• Fundamental data access
• Built on xbbg package integration

Python implementation of vectorised pricers for vanilla options

Features:

• Black-Scholes log-normal option pricing
• Bachelier normal option pricing

Package	Stars	Forks	Total Downloads	Monthly
QuantInvestStrats
OptimalPortfolios
StochVolModels
BloombergFetch
VanillaOptionPricers