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 Investment Solutions Quant Group 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