Data Science

Data Science is the application of scientific method, processes, systems, and tools to discover insights and knowledge or insights from all forms of data.  Modern Data Scientists use tools and skills including pythonJupyter Notebooks, linear algebra, statistics, machine learning, and more in their quest to develop those insights.  Some in the business and trading consider Data Science to be synonymous with business analysis or quantitive research.

Posts

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.
Algo developer getting paid

Why the top jobs in finance will go to gig workers

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“We’re tapping into the new skills coming out of educational institutions and students’ and graduates’ new ways of looking at things, but there are also opportunities for experienced people to connect the dots related to the ontological relationships between the data and the stock markets and other assets,” Slade said. “There are huge untapped resources out there, and we try to engage with the researchers as if they were employees and support them as such.”
Algorithmic Trading with other peoples money

Skills to Become a Quantitative Trader

Your Proprietary Trading Algorithm is always your property on CloudQuant. Any trading strategy that you develop is yours. Not ours. You do not transfer ownership of the algo to CloudQuant. You do not transfer any copyrights to CloudQuant. This is fundamental to the operations and success of CloudQuant.
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

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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.
Quantitative Strategy, Trading, and Algo Development Industry News

The Patient Chart Pattern Trader

“Chart pattern trading is a style that is more suitable for recreational trading rather than professional. This is one reason it was never considered seriously by the majority of hedge funds. In addition to requiring patience, slow chart pattern formations offer enough time for detection and competition is high at diminishing returns.”
Algo Trading powered by Alternative Data Sets

MarketsWiki Education World of Opportunity July 2017 NY

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.
Quantitative Strategy, Trading, and Algo Development Industry News

An Index-Fund Evangelist Is Straying From His Gospel

... Maybe the experts can beat the monkeys after all. That is, if the experts are software engineers writing sophisticated algorithms for computer-generated trading. ...
Sample python code from the CloudQuant trading strategy backtesting and trade simulation platform

Code-Dependent: Pros and Cons of the Algorithm Age

Algorithms are aimed at optimizing everything. They can save lives, make things easier and conquer chaos. Still, experts worry they can also put too much control in the hands of corporations and governments, perpetuate bias, create filter bubbles, cut choices, creativity and serendipity, and could result in greater unemployment.