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. 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.
摘要： While volatility and illiquidity in the U.S. equities markets have created obstacles to low-cost transacting, trading innovations being put in place in 2019 are now making liquidity more efficient and accessible to buy-side firms that continue to invest in technology, according to new research, IET 2019, Liquidity: Blocks, Algos, Analytics and Impact, the first of a six-part US equities trading interview-based benchmark study that has been published annually by TABB Group for 15 years.
摘要： When first introduced, algorithms were designed primarily for automation to mimic a trader executing orders in pursuit of specific benchmarks. In the second phase, brokers stressed qualitative analysis by leveraging real-time data from the order book to model their assertions, and tailor how model behavior would respond to changing market conditions. In the most recent phase, leading providers on the sell-side have begun to use quantitative measures into their execution strategies, most notably integrating machine learning principles.
摘要： Fundamentally, trading is about analyzing the supply and demand of a security (asset which can be traded), such as stocks, commodities, or Forex pairs. A trader then makes decisions to purchase or sell these securities, ideally for a profit. When entering a trade, there are numerous factors to take into consideration, such key price levels, liquidity, and momentum.
摘要： Artificial intelligence is upending the financial management industry in spectacular ways. The majority of machine learning and deep learning solutions have focused on fundamental analysis of securities. However, deep learning and other artificial intelligence technologies will also change the future of technical analysis as well.