Stockmarket and investments always seem like an interesting and relatively easy way of earning big amounts of money. The downside of course is the high level of financial risk involved. Our team wanted to apply financial analytics for eliminating or a least reducing risk levels involved in trading. This is how we came up with the idea of FairFinance.

By gathering data on stocks and bond market the algorithms is able to determine the parameter we call 'conference level'. User interacts only with end results of algorithm presented in a form of highly visualized data. Our choice to build web app allowed us to cover maximum potential audience.

While working on the project we faced a number of issues we had to overcome. From the gigantic amount of poorly organized data to limitations of web technology - it was necessary to use elegant and creative solutions to keep moving towards completion of the project.

The heart of FairFinance is the algorithm of conference level calculation. We have spent more than 20 hours achieving optimal level of accuracy and performance, but the final result was definitely worthed time spent.

The combination of mathematical and financial expertise is the most valuable outcome of Penn Apps Winter 2015.

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