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.
摘要： Automated trading systems, also referred to as mechanical trading systems, algorithmic trading, automated trading or system trading, allow traders to establish specific rules for both trade entries and exits that, once programmed, can be automatically executed via a computer.