Professional traders are anticipating artificial intelligence and machine learning to be the most influential technology over the next three years. JP Morgan's flagship survey reveals more than half of professional and institutional traders anticipate machine learning to lead technology. Currently a third of client traders predict mobile trading applications to be the most influential this year. Certainly the Reddit Gamestop rally powered by low cost trading platform is already testament to just how quickly the environment has changed. Read more: 'Robots will take our jobs': Eigen boss Lewis Liu on the future of the City worker Electronic trading picked up last year and all surveyed expect to increase electronic volumes this year.
Henri Waelbroeck seems to fit the popular image of the scientist transplanted into the world of high finance and hedge fund trading, the sort of stereotype found in books like "The Fear Index" by Robert Harris. Waelbroeck, director of research at machine learning-enhanced trade execution system Portware, was previously a professor at the Institute of Nuclear Sciences at the National University of Mexico (UNAM). His areas of expertise include: complex systems science, quantum gravity theories, genetic algorithms, artificial neural networks, chaos theory. The impression Waelbroeck conveys is one of precision. He explains that algorithms have grown in complexity since being introduced to the world of trading around 2000.
October's flash crash in sterling was caused by several factors - including the time of day - according to a report by the international banking body, the Bank for International Settlements. In the early hours of 7 October, the pound fell by about 9% against the dollar - an abnormally large swing in two such widely traded currencies - before then largely recovering. The BIS says there were no significant losses suffered by traders. The BIS report, which drew on detailed analysis by the Bank of England, says the conditions for such a move were created by the lack of sterling dealers in the market at the time of day. The trade took place in Asian markets, at a time of day when key sterling counter traders in London and other important Western markets are not operating.
Advances in Reinforcement Learning (RL) span a wide variety of applications which motivate development in this area. While application tasks serve as suitable benchmarks for real world problems, RL is seldomly used in practical scenarios consisting of abrupt dynamics. This allows one to rethink the problem setup in light of practical challenges. We present Trade Execution using Reinforcement Learning (TradeR) which aims to address two such practical challenges of catastrophy and surprise minimization by formulating trading as a real-world hierarchical RL problem. Through this lens, TradeR makes use of hierarchical RL to execute trade bids on high frequency real market experiences comprising of abrupt price variations during the 2019 fiscal year COVID19 stock market crash. The framework utilizes an energy-based scheme in conjunction with surprise value function for estimating and minimizing surprise. In a large-scale study of 35 stock symbols from the S&P500 index, TradeR demonstrates robustness to abrupt price changes and catastrophic losses while maintaining profitable outcomes. We hope that our work serves as a motivating example for application of RL to practical problems.
We investigate the limiting behavior of trader wealth and prices in a simple prediction market with a finite set of participants having heterogeneous beliefs. Traders bet repeatedly on the outcome of a binary event with fixed Bernoulli success probability. A class of strategies, including (fractional) Kelly betting and constant relative risk aversion (CRRA) are considered. We show that when traders are willing to risk only a small fraction of their wealth in any period, belief heterogeneity can persist indefinitely; if bets are large in proportion to wealth then only the most accurate belief type survives. The market price is more accurate in the long run when traders with less accurate beliefs also survive. That is, the survival of traders with heterogeneous beliefs, some less accurate than others, allows the market price to better reflect the objective probability of the event in the long run.