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NLP Tag

04 Aug

Posted at 17:25h in Uncategorized

We are going behind the scenes to discover how our in-house data scientist Alexander Pieper uses News Sentiment data to create Buy/Sell Signals for stocks. Transforming unstructured news data into structured signals! A Step-by-Step Guide to transform raw data into an AAPL Trading Signal in Python: https://medium.com/yukkalab/efficient-float-array-storage-on-elasticsearch-a098f937bbc2 Summary and Outlook What did we do in this article? We went from having raw News Sentiment data to having a daily executable investment signal for the Apple stock in only 14 lines of code (excluding backtesting (16 lines) and plots (45 lines)). A portfolio using this...

02 Aug

Posted at 09:26h in News & Trend Lab insights, Product, YUKKA

A forecast based on current sentiments in the YUKKA Lab News Lab. It has been a difficult last week for tech and software sectors with both Facebook (FB) and Twitter (TWR) loosing over 20% of their market cap even though EPS (earnings per share) have met (Twitter) or even excelled (Facebook) analyst estimates. The main reason for this loss in share value is similar in both cases but of different origin: user growth has heavily declined in Q2 of 2018. Twitter is finally going after all those fake accounts and accounts...

24 Jul

Posted at 08:18h in NLP, Product, YUKKA

Have you ever heard of knowledge engineering? Automotive engineers test and optimize prototypes of automobiles, civil engineers develop technical solutions for construction projects and knowledge engineers construct knowledge bases, which enable machines algorithm to think. Let's have a closer look on knowledge engineer's key task. So instead of starting your day in a factory, slipping into a blue overall, knowledge engineers put themselves into their personal construction site, called the office. As in many engineering fields, a great solution is derived from a challenging problem. More specific in our case: customers data...

18 May

Posted at 15:37h in NLP, Product, YUKKA

Mistakes, misunderstandings and missteps. Most of us will do anything to avoid them. In many cases humans find it uncomfortable, not to say humiliating, to even admit to own fallibility. Nevertheless, mistakes are inevitable, not only to humans but also to algorithms. In the fast-paced world we now live in, a big source of such mistakes could be to easily incline toward optimizing what does not really matter! Given that in the financial domain the weight of influential parameters in the market can vary across time in our world of continuous...

28 Feb

Posted at 15:44h in NLP, YUKKA

Once upon a time, there was a researcher, an NLP one. Day by day our hero was writing programs, collecting data, processing it, delivering various analysis and extracting information. What? Doesn't sound like a real story full of magic, dragons, and princesses? Then let me pose a few questions: How often do you ask princess Siri to show you the way to the nearest coffee shop? Have you ever used the knight of translate button on Facebook? Do you use autocorrect feature to fight the error dragons on your mobile? Add countless applications related to machine...