Machine Learning News & Topics for Quantitative Trading and Algorithmic Development

Machine Learning (ML) is the evolution of artificial intelligence where the computer (program) works with data to discover patterns (also called features) that can be used later to evaluate other data.  ML is typically broken down into three categories: supervised ML, unsupervised ML, and reinforcement learning.

CloudQuant does enable Machine Learning. To learn more check out “An Intro to Machine Learning with CloudQuant and Jupyter Notebooks” by Trevor Trinkino. This post and video cover one quantitative trader’s approach to supervised ML.

Posts

Morgan Slade, Python Data Scientist and Trader

Quant Trading and Superpowers: Morgan Slade speaks on Opportunity

,
"You have a chance to try and change an industry" said Slade, CEO of CloudQuant at the MarketsWiki Education’s World of Opportunity event in New York.
New York

New York, midtown - Experienced Quantitative Portfolio Manager or Strategist

CloudQuant’s Global Systematic Trading unit is seeking experienced Quantitative Portfolio Managers and Strategists for the U.S. equity market. Ideal candidates will have an MS or Ph.D. in an Engineering or Pure Science discipline from a top school with formal academic coursework in Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing, Digital Signal Processing,
chicago cityscape and sears tower

Chicago - Experienced Quantitative Portfolio Manager or Strategist

CloudQuant’s Global Systematic Trading unit is seeking experienced Quantitative Portfolio Managers and Strategists for the U.S. equity market. Ideal candidates will have an MS or Ph.D. in an Engineering or Pure Science discipline from a top school with formal academic coursework in Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing, Digital Signal Processing,
San Francisco

San Francisco - Experienced Quantitative Portfolio Manager or Strategist

CloudQuant’s Global Systematic Trading unit is seeking experienced Quantitative Portfolio Managers and Strategists for the U.S. equity market. Ideal candidates will have an MS or Ph.D. in an Engineering or Pure Science discipline from a top school with formal academic coursework in Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing, Digital Signal Processing,
Quantitative Strategies and Capital for Trading

Quantitative Trading and Data Science in the News August 14 2017

Topics include: GeoLocation Alternative Data, robotic revolution, buy side, sell side, hot jobs, financial crime, ...
Quantitative Strategies and Capital for Trading

Quantitative Trading and Data Science in the News August 7 2017

Citadel fund up 7%, Hedge funds betting against Tesla (and losing), Data Analytic trends, ...
SPY Benchmark Trader

Machines Poised to Take Over 30% of Work at Banks, McKinsey Says

New technologies are poised to sweep through investment banks, relieving many rank-and-file employees of roughly a third of their current workload, according to McKinsey & Co. The shift, already stoking angst on Wall Street, may take only a few years. CloudQuant sees opportunities.
World Market Access

2017 - The Year of Artificial Intelligence

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.
Battle of the Quants June 2017

Battle of The Quants - Discusses Crowd Researching in NY

,
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.
Python based Trading Strategies by Machine Learning

Trading Strategy development—Powered by Machine Learning

,
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.