Alessio Farhadi posted “A.I. Trading – A Question of Ethics” on LinkedIn. His main point is that machine learning and algos do not have ethics. … Fairness to the industry requires that one should review the steps that have been taken by innovators, regulators, broker-dealers, and exchanges to mitigate any potential dangers of using computers and algorithms to trade.
The impact of machine learning and open source resources on quant trading could be described as explosive. At FIA Expo in Chicago, CloudQuant’s CEO Morgan Slade will be discussing how that’s translating into opportunity for a wider variety of participants.
Quantitative Trading and Data Science in the News August 28, 2017, covering crowdsourced quantitative investment, artificial intelligence and more
Trevor Trinkino, a quantitative analysts and trader at Kershner Trading Group recently put together an introduction to Machine Learning utilizing CloudQuant and Jupyter Notebooks. In this video he walks you through a high-level process for implementing machine learning into a trading algorithm, …
World Economic Forum published that Artificial Intelligence (AI) is a rapidly growing discussion point in corporations and governments. This is driven by: 1. Everything is now becoming a connected device. 2. Computing is becoming free. 3. Data is becoming the new oil. 4. Machine learning is becoming the new combustion engine.
Crowdsourcing in fund management and trading is the move to utilize anyone with an internet connection to participate in the research with the goal of finding new and better ways of trading. During the discussion the differing approaches being taken with the business models, and the technology, and the challenges each are facing.
Join us at the NY MarketsWiki Education to hear Morgan Slade’s thoughts on the The Algorithmic Trading Tesseract brings cloud computing, alternative data, machine learning, and crowd researchers together forming a revolutionary crowd in the financial industry.
… Maybe the experts can beat the monkeys after all. That is, if the experts are software engineers writing sophisticated algorithms for computer-generated trading. …
Bloomberg recently wrote that “It’s no secret that hedge fund managers are always looking for new sources of data that will help them in their never-ending quest to beat the market.” (1) One of the most interesting new sources of data is social sentiment.
… the model of hedge funds charging “2 and 20” — a 2 per cent management fee and 20 per cent performance fee — for investing in large-cap stocks rising and falling “doesn’t work any more” and is ripe for disruption.