摘要： Improving High-Frequency Trading: Using Artificial Intelligence, Machine Learning and Software Defined Radios to Break Down Technological Barriers.
摘要： One step back, two steps forward. That was the path for automation, algorithms and artificial intelligence applications in fixed income trading over the past few months.
摘要： Algorithmic trading has proliferated across global FX markets over the past decade. Today, roughly 20% of the institutional foreign exchange trading volume is now executed through algos. FX algo usage is following that of the equities market - where algos currently account for more than half of all equity trading volume. So what’s behind all this algo growth?
摘要： Executing trades in the financial market has been made extremely accessible. With a few hundred $ and an internet connection you have the whole world under your thumb. This makes it seem that trading is a simple way of making big bucks. Being profitable in the market however demands a lot more than just entering trades, even if you happen to obtain accurate signals.
摘要： I hesitated using the word “tick” in the title of this post, lest potential readers think I am writing yet another post on tick sizes.[1] But I assure you, this post has absolutely nothing to do with tick size.
摘要： Given the commodification and decline of high frequency trading, I was a bit surprised to see that Michael Lewis wrote a book on the topic. Not only that, but based on the reviews (I haven't read the actual book), it sounds like a scary "tell-all" book revealing how HFT rips off "the little guy".
摘要： Right around the time you get your first basic regression or classification model going, it will at least cross your mind. The vast piles of time series data, coupled with the possibility of retiring young has the irresistible pull of finding an old treasure map in your grandfather’s attic. How can you NOT think about it? Can you use machine learning to predict the market?
摘要： We sat down with an algorithmic trader to learn more about how algorithms are remaking the industry, and why it matters. We talked about what algorithmic finance actually looks like, who the winners and losers are likely to be in the new big data gold rush, and why we may be entering an era of irrational cyborg exuberance.
