Software freelancer
in computational finance


From August 2011 until February 2013 I was working as a software freelancer
in the computation finance domain.
The main technical topics were:

  • Evaluating papers at that time to gauge trends in the quant trading world for a client
  • Extensive timeseries analysis on specific markets with tools like R, Matlab, Python etc.
  • Implementing trading algorithms and tools in C++/Kotlin/Python

More Details

Since I signed a formal obligation of confidence, I cannot be too specific here.
Overall I can say that I was very lucky to get in touch with a client who
had specific needs for trading tools.
I took the opportunity for a temporary contract and became a freelancer.
The client was so happy with my implementation for the needed tools, that he
asked me to implement also a live trading algorithm.

Amongst others I researched the following topics

  • Optimal stopping
  • Cointegration
  • Mean reversion
  • ML and AI
  • Backtesting
  • Data collection and preparation

Implementing a trading algorithm connects many aspects of software design.
Not only is the logic itself important, but special awareness has to be put on

  • High-throughput on financial data
  • Model scalability to incorporate other markets and opportunities on the fly
  • Plugin architecture design for hot-swapping models
  • High-availability which means handling of errors in a graceful way

For evaluating ideas and present them visually, I mainly used R which has
excellent stastical and plotting support.
When it comes to implement the ideas, I used the popular libraries in Python
like NumPy, Pandas, SciPy etc.
The final products were then implemented in C++ and Kotlin which make use
of the forementioned libraries at runtime.




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