Unhackathon #4 december 10th

Here is our next event coming up on December 10th
This time on top of the usual “coding day” where people propose their project and form teams to work on it, we added 2 features :
– a beginner’s corner, for the ones starting off with Python, R or datascience itself.
– a talks corner to share during 30′ some thoughts, an experience, or introduce your project in depths. 3 talks are already planned for December 10th. If you feel like bringing one, just let us know !
All details including the location and the list of talks is on the eventbrite ticket.
See you on the 10th !

November Unhackathon

Our 3rd event !

Once again a small crowd of Data Scientists has been courageous enough to fight their impulse for just chilling out in the wonderful sunday’s weather in HongKong and instead came to hone their skills on 2 topics :

  • An exploration of HKEX data and its links to HK financial markets
  • A study of the very hyped cryptocurrencies

Crypto-currencies correlation

This topic stemmed from the follow-up of the previous “Coindex” subject.
The study of correlation should give an idea of how much diversification would be important in a portfolio or index of crypto-currencies, in other words, how much an index would provide a sense of the true performance of the currencies in the crypto world.

Here the focus has been given to a classical-flavored study of correlation among the currencies available on Poloniex Exchange on sep 16th, 2017.
First of all a joyplot retrieved the shapes of return distributions for many currencies :
ridge_plot.jpegSome currencies such as OMG (OmiseGo) and CVC (Civic) are too new and then have a short historics that meks them not at all normally distributed, and are then considered as outliers and removed from the scope.

Then we came up with proper correlation calculations


And we can get a 36% global average correlation (average of all 1 to 1 correlations), hinting that diversification could be an important driver of portfolio efficiency.

If we graph this measure along time, we see that the correlation tends to increase along time, suggesting that there is some re-correlation of crypto markets.

Next step might be to understand why this re-correlation happens.

The complete analysis, including the used data, can be found on github.