Eugen Geist
Seasoned Software & Data Engineering Professional with extensive experience in high-frequency trading systems, data warehousing, and cloud solutions. Expert in optimizing mission-critical systems and implementing engineering best practices. Specialized in Python, SQL and cloud technologies.
Currently working as a Freelance Developer focusing on software and data engineering.
Skilled in developing distributed systems, data pipelines, and performance optimization, consistently delivering solutions that maximize business value.
Session
In this talk, we'll explore how you can implement your own distributed system for algorithmic trading leveraging the power of open source without being dependent on trading bot providers.
We will discuss different challenges occurring in HFT inter alia processing massive amounts of data with low latency and reliable risk control and how to solve them. Furthermore we will touch on the topic of regulatory requirements in trading.
These challenges will be addressed through a distributed system implemented in Python, utilizing Kafka for real-time data streaming, PostgreSQL for persistent storage and DuckDB for high-performance analysis. We will examine approaches to decouple the components to re-use and scale them across different markets.
Cryptocurrency markets are used as a proving ground for the PoC due to easy availability for everyone.