Quantitative Trading and Data Science in the News August 7 2017

August 7, 2017

Citadel’s Flagship Funds Gain Almost 7% This Year

…Citadel’s Tactical Trading Fund, which uses equity and quantitative strategies, rose 3 percent last month, bringing year-to-date performance through July to 4.9 percent. …

Hedge funds lose more than half a billion on wrong-way bet against Tesla

It’s not just hedge funds that bet the wrong way on Tesla. Wall Street analysts, normally a very bullish crowd, were largely negative on the stock heading into the earnings report. They reiterated their bearishness in reports on Thursday, despite the stock pop.

“We were surprised by the after hours move in TSLA shares and continue to be cautious on the stock, especially as the risk profile shifts from the hype of the Model 3 to execution, or ‘production hell’ as Elon Musk refers to it,” Cowen analyst Jeffrey Osborne wrote in a note.

CloudQuant note — We wonder what the Algos were choosing? Were they different than the analysts?

Watson Machine Learning is now Generally Available

IBM announced the general availability of the IBM Watson Machine Learning service. Over the past 12 months feedback from hundreds of users of the Watson Machine Learning (WML) service led to this announcement.

CloudQuant note — We love seeing more people able to advance the cause of Data Science and Machine Learning

Google chief funds new machine-learning effort at Princeton’s IAS

A $2 million donation will launch new research at the Institute for Advanced Study (IAS) in Princeton to forge an understanding of how machine learning evolves.

Machine learning — sometimes called the leading edge of artificial intelligence — is the rapidly developing computer technology behind self-driving cars, complex web searches, medical and science applications, and face and speech recognition. Machine-learning programs synthesize knowledge in a way that’s analogous to how children learn. The programs take examples, generalize, and then develop rules and understanding about the world without being taught directly. With time, the programs become better at particular tasks.

CloudQuant note — We love seeing academic chances to advance the cause of Data Science and Machine Learning

10 hot data analytics trends — and 5 going cold

CloudQuant note — We are definitely in agreement on the topics of Scikit-learn, TensorFlow, and Jupyter Notebooks