AI-powered trading hasn\u2019t yet reached an \u201ciPhone moment,\u201d when everyone is carrying around an algorithmic, reinforcement learning portfolio manager in their pocket, but something like that is coming, experts say.<\/p>\n
In fact, the power of AI meets its match when faced with the dynamic, adversarial arena of trading markets. Unlike an AI agent informed by endless circuits of self-driving cars learning to accurately recognize traffic signals, no amount of data and modeling will ever be able to tell the future.<\/p>\n
This makes refining AI trading models a complex, demanding process. The measure of success has typically been gauging profit and loss (P&L). But advancements in how to customize algorithms are engendering agents that continually learn to balance risk and reward when faced with a multitude of market conditions.<\/p>\n
Allowing risk-adjusted metrics such as the Sharpe Ratio<\/a> to inform the learning process multiplies the sophistication of a test, said Michael Sena, chief marketing officer at Recall Labs<\/a>, a firm that has run 20 or so AI trading arenas, where a community submits AI trading agents, and those agents compete over a four or five day period.<\/p>\n \u201cWhen it comes to scanning the market for alpha, the next generation of builders are exploring algo customization and specialization, taking user preferences into account,\u201d Sena said in an interview. \u201cBeing optimized for a particular ratio and not just raw P&L is more like the way leading financial institutions work in traditional markets. So, looking at things like, what is your max drawdown, how much was your value at risk to make this P&L?\u201d<\/p>\n