If you are participating in the Academia Datathon 2018, or just wanna be a crypto superhero forecasting cryptocurrency prices, this article was made just for you! It is a quick reference to tons of materials to get you started on the topic.
As asked by the organizers of the Datathon here are some suggestions about possible tech stack I found useful for time series analysis, which can be applied for the Cryptocurrency Challenge like libraries/articles/blogs about Moving Average and its variations, Kalman Filter, Fourier and Hilbert Decomposition, LSTM Recurrent Neural Network and others.
Originally authored by Neven Pičuljan, Freelance Software Engineer at Toptal, this article also features an illustrative case study on how hedge funds can use such systems, presented through experiments. We will also consider how deep learning systems can be improved and how hedge funds can go about hiring talent to build those systems, i.e., what sort of background does deep learning talent need to have.
As you may know, the Foreign Exchange (Forex) market is used for trading between currency pairs. But you might not be aware that it’s the most liquid market in the world.
I revealed solutions you can’t find in any popular textbook that I have applied for problems faced while developing credit risk models. Credit risk models predict whether a borrower will pay their loan back, and people like me in effect decide if you will be approved for a credit card or a mortgage loan. Statistical classification algorithms work best for such problems with a binary outcome. In particular, the logistic regression is preferred in practice due to its quantification of a probability of default between 0 and 1.
In his presentation, Peter revealed how important are news and their interpretation for making money from trading on the stock exchange. Traders try to buy low after any market overreaction to bad news and sell high when the price moves back to the level before the news. The amount of information released every day about the companies, the economy and the markets themselves is enormous and difficult to process and interpret by a trader.
