September un-Hackathon


Our second event!

Following the success of our first event, we again met up at the MakerHive in Kennedy Town for our un-hackathon. This is our term for a hackathon where the agenda would be set by participants and people would have fun coding together, instead of being a competition. It’s a way to improve your skills and share projects you are passionate about with the community.

Some projects from our previous event were pitched again while a number of new projects were also started. After teams were formed, the coding quickly got under way.Attendees gathered for the presentation as the teams showed off their results.

Web scraping

A initiative to scrape public data with Python and R, Scrapy was used to pull HKEX data.

Visualisation of the block chain

On 12th May, computers worldwide were hit by the WannaCry ransomware attack. The attackers asked ransom payments to be made to a number of bitcoin wallets. Blockchain data about these wallets from the period of the attack was sourced and visualised using D3.

Horse racing prediction

“Anomalies” in betting market for horse racing mean that the outcome of a horse race could be predicted. RapidMiner and Python was used to scrape the data and create a predictive model.

horse racing team

The team were well organised and even produced a presentation of their results!

Traffic analysis

This team scraped data on traffic incidents using Scrapy (Python) and then visualised using R.




Crypto-currencies investment strategies

This project is a follow-up of the previous unhackathon, at the end of which we remained puzzled by some unexplainable moves in certain currencies.
This time we had better grasp at it and we went for analysing correlations and properties of simple indices made of a basket of currencies.

The global correlation among 20 first currencies amounted to 36% since 2017


this is low enough to hope for some diversification effect to take place.

Building an index where each currency has the same weight is indeed providing a real overperformance if we consider BTCUSD as the benchmark.
Moreover scaling down the index so that volatility, or risk, is equivalent to the one of Bitcoin vs USD then produces significant gain of 15% over BTC.

On top of this the skew while negative for Bitcoin becomes positive for the index : this means that frequent small losses encountered by the index are compensated by less frequent big much bigger gains !

This is encouraging to build up some other indices and strategies, and this project could yield to promising applications :

  • Trading strategies, either short or medium term, dynamic or static, including machine learning algorithms for the discovery of alpha in this market
  • The development of an algorithmic trading tools following these strategies
  • Also some online analytics on single currencies or portfolio of them
  • Potentially some advisory for portfolio construction