Quantitative Finance

Quantitative Finance or Mathematical Finance is applied mathematics with a strong focus on the financial markets. In Quant Finance, especially quant finance for trading firms, the quant seeks to use numerical models from market prices, company or asset fundamental data, and alternative data to develop new trading or investment models. The goal is to provide a new trading model.

CloudQuant provides quantitive financial enthusiast and professionals with free tools to develop new trading models. These crowd researchers develop the trading models and work with quantitative traders to execute the trading strategy. The quant is paid based on a license agreement where the trading strategy receives an allocation and they retain their own intellectual property (the algo.)

Posts

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.”
Your algo is always yours, not ours.

Your Algos are Your Private Property on CloudQuant

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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.
Sample python code from the CloudQuant trading strategy backtesting and trade simulation platform

Python Algorithm Trading – The 4 Basic Elements

Creating a python algorithm for trading means that one must cover four basic building elements. Market data, order processing, tracking/analysis, and backtesting. These four elements are all required to build a successful trading strategy.
Innovation in Trading

The next wave of broker innovation will be Crowdsourced algos

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The next wave of broker innovation likely will be geared toward democratizing quantitative trading, according to Kershner Trading Group Founder and CEO Andy Kershner. That would vastly expand the universe of high-level quant traders globally, which Kershner roughly estimated stands at perhaps 5,000 today.
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.
Python based Trading Strategies by Machine Learning

Trading Strategy development—Powered by Machine Learning

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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.
Sharpe Ratio distribution

Four Problems with the Sharpe Ratio

If you are an algorithmic trader, developer, or data scientists they you have already heard of the Sharpe Ratio. Many of you use this measurement as your score card for how well your algo performs.
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. ...
News for Machine Learning and Algo Trading

Recommend Investment Blogs

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Wesley R. Gray (@alphaarchitect), the CEO and CIO of Alpha Architect, a quantitative asset manager published a list of “high-quality research produced by financial professionals in the blogosphere” on the Wall Street Journal
Quantitative Strategy, Trading, and Algo Development Industry News

Rise of Robots: Inside the World's Fastest Growing Hedge Funds

Believe the hype. Quants have never been more popular. After doubling over the past decade, assets run by so-called systematic funds have hit a record $500 billion this year, according to estimates from Barclays Plc.