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

Quantitative Strategy, Trading, and Algo Development Industry News

Quantitative Trading and Artificial Intelligence News Recap: September 11, 2017

Quantitative Trading and Data Science in the News August 28, 2017, covering crowdsourced quantitative investment, artificial intelligence and more
Algo developer getting paid

Intro to Machine Learning with CloudQuant and Jupyter Notebooks

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, ...
Morgan Slade, Python Data Scientist and Trader

Quant Trading and Superpowers: Morgan Slade speaks on Opportunity

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"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.