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Welcome to OpenStockMachines

OpenStockMachines is a multi-disciplinary project that combines:

  • Stock Trading
  • Multi-agent Modeling
  • and Machine Learning

the primary purpose of the project is to better understand the financial world that we are all living in. Each finding is reviewed and published in the associated blog: https://goo.gl/LfFUSr


Contribute

Currently, there are 5 contributors.

Please contact Dmitry Burlyaev if you want to contribute: surname.name@gmail.com (replace with the name in lower case)

Collaboration Path:

We start with an idea, e.g. real estate bubble detection. If we find it relevant for the mankind, we start coding.

From your side :

  • You can either come up with a nice idea to investigate
  • Help to implement an on-going research direction
  • Theoretically contribute to our knowledge base in the next areas:
  1. Trading
  2. macro-economics
  3. AI
  4. Multi-agent modeling and emergent behavior

Check on-going activities in On-going activity section).


On-going activity

Direction: Remittance Game - find currency exchange strategy for expats.

Synopsis: There are more than 50 mln expats worldwide these days. But almost everyone asks the next questions:

  • I am paid in a currency different from one in my homeland. I will come back home after few years abroad, when should I exchange money?
  • My pension fond is in a country where I don't live anymore - is it risky?
  • I still have some savings at home. Should I transfer all now if I am not planning to return to my homeland?

How to answer on these questions without losing your money... in the world of Brexit, modern central banks' QE policies .... we will know soon...

Publications

The results of each research direction end up here.


Code Examples

Coming soon


Installation and Tests

Just check-out and run main.

Tip: Will be updated soon.


Topic-relevant information

  1. Data Science Grenoble MeetUp, "Machine Learning Applications for Stock Trading": https://goo.gl/vK26Ge
  2. "A Guided Tour of Machine Learning for Traders": https://goo.gl/fUqEGP

License

MIT