01
Market analytics platform
ARGUS
A Python market-data system growing from an FX utility into a layered analytics and monitoring platform.
Independent project - active development
- Python
- pandas
- NumPy
- yfinance
- pytest
Informatics · Data · Systems
I turn messy data
into useful systems.
I’m Lev, an Informatics student in Leipzig building practical projects at the intersection of data engineering, analytics, automation and AI-enabled workflows.
↓Selected work
Projects with a data story — not just a dependency list.02
Quant engine architecture
Q-Bet
A modular Python quant engine for matched betting, sports arbitrage, strategy simulation and controlled execution planning.
Independent project - core domain stage
- Python
- Pydantic
- pytest
- Quant modeling
- Data pipelines
03
AI-assisted test infrastructure
S.M.A.R.T.
A team project with CHECK24 combining natural-language Playwright generation, deterministic mock data and execution feedback.
Student team - frontend, workflow & validation
- TypeScript
- Go
- S3
- Parquet
- Redis
- Docker
04
Workflow automation
Notion Sync
A desktop automation that mirrors records from multiple Notion databases into one central operational database.
Independent project - working prototype
- Electron
- Node.js
- Notion API
- JSON
- Automation
How I frame projects
Less dependency list. More system understanding.Useful products need a readable system story.
The goal is to make every project understandable at three levels: the user problem, the data or automation flow, and the next practical step.
- 01Start with the workflow
What is manual, fragmented, slow or hard to trust?
- 02Show the data path
Where does input enter, how is it normalized, and what result reaches the user?
- 03State the product direction
Roadmaps matter when they explain how a prototype becomes more useful.