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E380
ArturSepp/README.md

Artur Sepp πŸ‘‹

"Connecting financial applications with science and technology"

PhD in Statistics | Quantitative Researcher | Author

Email

About Me

Global Head of Investment Services Quant Group @ LGT Private Banking

I'm a statistician and quant researcher passionate about connecting financial applications with science and technology. With 20+ years of experience in financial markets, my professional journey spans quantitative research, portfolio management, and trading of quantitative investment strategies across investment and private banks, hedge funds, and family offices.

Expertise

  • Quantitative investing and asset allocation
  • Modeling of financial markets and instruments
  • Statistical and Machine Learning methods
  • Modern computational and programming tools
  • Stochastic volatility models

πŸ† Recognition

Quant of the Year – Risk Awards 2024
Profile on Risk.net

For a complete list of publications and blog posts, visit artursepp.com

🀝 Connect With Me

πŸ“¦ Key Python Packages

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

OptimalPortfolios (optimalportfolios)

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

Features:

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

StochVolModels (stochvolmodels)

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

BloombergFetch (bbg-fetch)

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

VanillaOptionPricers (vanilla-option-pricers)

Python implementation of vectorised pricers for vanilla options

Features:

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

Package Download Statistics

Package GitHub Stars GitHub Forks Total Downloads Monthly Downloads Weekly Downloads
QuantInvestStrats (qis) GitHub stars GitHub forks Downloads Downloads Downloads
OptimalPortfolios (optimalportfolios) GitHub stars GitHub forks Downloads Downloads Downloads
StochVolModels (stochvolmodels) GitHub stars GitHub forks Downloads Downloads Downloads
BloombergFetch (bbg-fetch) GitHub stars GitHub forks Downloads Downloads Downloads
VanillaOptionPricers (vanilla-option-pricers) GitHub stars GitHub forks Downloads Downloads Downloads

Pinned Loading

  1. StochVolModels StochVolModels Public

    Python implementation of pricing analytics and Monte Carlo simulations for stochastic volatility models including log-normal SV model, Heston

    Python 177 37

  2. QuantInvestStrats QuantInvestStrats Public

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

    Python 454 51

  3. OptimalPortfolios OptimalPortfolios Public

    Implementation of optimisation analytics for constructing and backtesting optimal portfolios in Python

    Python 58 22

  4. BloombergFetch BloombergFetch Public

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

    Python 12 6

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