Quantwork - Quantitative Solutions
Optimal Combination of AI/ML with Analytical Methods and Web3 Solutions
Do you need support in the development and implementation
of quantitative models, web3 applications or mathematical issues?
Then you have come to the right place!
Dr. Ulrich Moosbrugger, CQF
Consulting
As a freelance data scientist and quant, I focus on issues related to data analysis and preparation, model development, and result evaluation. In addition to conceptual challenges, I am also happy to help with concrete implementation. Among other things, I see one of my strengths as being a bridge builder between business/research and concrete technology implementation - in traditional finance as well as in the web3 space.
Working with Quantwork
An experienced eye is valuable in the (further) development of models!
In addition to extensive methodological competence in the field of quantitative methods and models - whether conventional mathematical/statistical or machine learning approaches - I also offer many years of experience in the financial sector, including topics such as investment and trading models, as well as the valuation of complex derivatives. I am also familiar with newer developments such as liquidity strategies for decentralized market makers (DEX) and other topics in the crypto space.
In general, I cover topics such as convex and global optimization, time series analysis/prediction, as well as supervised/unsupervised and reinforcement learning.
I primarily implement my solutions in Python, C++/Rust and Solidity. In doing so, I draw on many years of experience using common frameworks that are used in quantitative applications:
This includes powerful ML libraries such as PyTorch, Scikit-Learn, and Tensorflow/Keras, as well as specialized mathematical libraries such as QuantLib, CVXOPT, and SciPy.
Experience
Investment model development for large banks and asset managers
- Portfolio optimization (multi-asset, commodities)
- Hedging strategies
- Robo-advisor models
- Crypto strategies / DeFi
- Valuation of complex derivatives using Monte Carlo simulations and finite differences
- Optimization of strategic/tactical asset allocations
- ALM models
- High-frequency trading (optimal control/reinforcement learning)
Evaluation of employee stock option plans
Topic analysis, e.g. risk reports (NLP/LDA)
Data extraction from alternative data sources such unstructured PDF documents and quantification of diagrams in image format
Implementation of PowerBI visuals (Typescript) for real-time display of simulations using importance sampling MC methods
Creation of web scraping tools (Selenium) for automated aggregation of public data on websites
Development of an inventory management system with Django
