Hedge Funds

A hedge fund is an alternative investment fund available only to sophisticated investors, such as institutions and individuals with significant assets. These funds typically utilize a variety of investing techniques and asset classes to achieve their return on assets for the fund investors.

Artificial Intelligence, Machine learning, and crowdsourced algorithmic trading platforms are challenging professional fund managers as the tools, data, and data science techniques powerful enough to forecast market moves better are becoming more readily available to anyone with an internet connection.


Machine Learning, Quantitative Investing News

Industry News: Machine Learning and Artificial Intelligence News 10/30/2017

AI and ML for CloudQuant, ArcaEx, Corporate earnings reports, Hedge Funds, Microsoft, Alexa, Saturday Night Live, the apocalypse, Elon Musk, and more …
Quantitative Strategy, Trading, and Algo Development Industry News

Industry News: Machine Learning and Artificial Intelligence News for the week ending October 2, 2017

Machine Learning and Artificial Intelligence News for the week ending Oct 2, 2017 that we found interesting from our FINTECH and CrowdSourcing perspective
John "Morgan" Slade

FintekNews: 3 Questions with John “Morgan” Slade of CloudQuant

FintekNews recently asked 3 Questions of our CEO Morgan Slade. This is in response to our recently announced launch with a $15M allocation to a crowd based trading strategy algo creator.
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, …
Quantitative Strategy, Trading, and Algo Development Industry News

Quantitative Trading and Data Science in the News August 28, 2017

Quantitative Trading and Data Science in the News August 28 2017: CloudQuant opens door to crowd algo traders, RBC AI pilot, RBC pilots AI-based financial insight tools, AI focussed Chip, Momentum trading guide…
Quantitative Strategies and Capital for Trading

Quantitative Trading and Data Science in the News August 7 2017

August 7, 2017

Citadel’s Flagship Funds Gain Almost 7% This Year

…Citadel’s Tactical Trading Fund, which uses equity and quantitative strategies, rose 3 percent last month, bringing year-to-date performance through July to 4.9 percent. …  

Hedge funds lose more than half a billion on wrong-way bet against Tesla

It’s not just hedge funds that bet the wrong way on Tesla. Wall Street analysts, normally a very bullish crowd, were largely negative on the stock heading into the earnings report. They reiterated their bearishness in reports on Thursday, despite the stock pop. “We were surprised by the after hours move in TSLA shares and continue to be cautious on the stock, especially as the risk profile shifts from the hype of the Model 3 to execution, or ‘production hell’ as Elon Musk refers to it,” Cowen analyst Jeffrey Osborne wrote in a note. CloudQuant note — We wonder what the Algos were choosing? Were they different than the analysts?  

Watson Machine Learning is now Generally Available

IBM announced the general availability of the IBM Watson Machine Learning service. Over the past 12 months feedback from hundreds of users of the Watson Machine Learning (WML) service led to this announcement. CloudQuant note — We love seeing more people able to advance the cause of Data Science and Machine Learning  

Google chief funds new machine-learning effort at Princeton’s IAS

A $2 million donation will launch new research at the Institute for Advanced Study (IAS) in Princeton to forge an understanding of how machine learning evolves. Machine learning — sometimes called the leading edge of artificial intelligence — is the rapidly developing computer technology behind self-driving cars, complex web searches, medical and science applications, and face and speech recognition. Machine-learning programs synthesize knowledge in a way that’s analogous to how children learn. The programs take examples, generalize, and then develop rules and understanding about the world without being taught directly. With time, the programs become better at particular tasks. CloudQuant note — We love seeing academic chances to advance the cause of Data Science and Machine Learning  

10 hot data analytics trends — and 5 going cold

Big data, machine learning, data science — the data analytics revolution is evolving rapidly. Keep your BA/BI pros and data scientists ahead of the curve with the latest technologies and strategies for data analysis.
CloudQuant note — We are definitely in agreement on the topics of Scikit-learn, TensorFlow, and Jupyter Notebooks
Quantitative Strategy, Trading, and Algo Development Industry News


The Patient Chart Pattern Trader

“Although HFT and algo trading dominate market activity nowadays to the tune of about 80% of transaction volume, there are still a number of old school chart pattern traders around. This is evident from social media messages where these traders post charts with patterns, such as head and shoulders, triangles, trendlines, double tops and bottoms, just to name a few. Although some of those chart traders aim to only teach their “art” to new traders, some are obviously patient enough to trade with it.” 
“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.”
“The conclusion is that chart trading was a style for patient traders during times when everything was slow, from data collection, to chart drawing, to analysis and to executive trades. Nowadays the word is faster by several orders of magnitude. Good chart traders could obviously survive the new dynamics but the expectation should be low given dominance of algos. At the same time, learning that old style of trading is more interesting in the context of studying the reasons it is no longer applicable to the markets.”
See what Michael Harris has to say about it on Medium.com
Note: CloudQuant has seen many chart pattern traders bring their discipline of trading and analysis to their python based algorithms.
Python based Trading Strategies by Machine Learning

Trading Strategy development—Powered by Machine Learning

Morgan Slade, the CEO of CloudQuant spent some time with discussing the world of crowd research, technology, and data science with ChatWithTraders.com Topics covered:
  • How large funds and institutions put on $100-million positions; how they work orders into the market, structure the trade and handle market impact etc.
  • Morgan explains why he feels as though the common approach to strategy development is counter intuitive, and shares an alternative 3-step formula.
  • A simple description of how machine learning and data science is being used by traders, and an example of how ML has been used to improve existing strategies.
This interview is the follow on interview that Chat With Traders had Andy Kershner. Towards the end of that episode, Andy briefly mentioned a cloud-based algo development platform and fund, CloudQuant, which is a subsidiary of Kershner Trading Group.

About Morgan Slade

Learn more about Morgan, his 20 years of experience as a trader, portfolio manager, researcher, technologist, executive and entrepreneur, and the team at CloudQuant.
Quantitative Strategy, Trading, and Algo Development Industry News

An Index-Fund Evangelist Is Straying From His Gospel

In his classic 1973 book “A Random Walk Down Wall Street,” Burton Malkiel, a Princeton economics professor, made an assertion that was startling at the time: that “a blindfolded monkey throwing darts at the stock listings could select a portfolio that would do just as well as one selected by the experts.”

Three years later, Vanguard, the asset manager where Mr. Malkiel served on the board for 27 years, started the first passive index fund, an innovation that has swept the financial world.

Now, at age 84, Mr. Malkiel has had a remarkable change of heart: Maybe the experts can beat the monkeys after all. That is, if the experts are software engineers writing sophisticated algorithms for computer-generated trading.

A large and growing body of academic research suggests there are market anomalies that can be exploited to beat a strict index approach. Some of that research has been recognized with Nobels in economic science — William F. Sharpe in 1990 and Eugene F. Fama in 2013. One of these findings is that value outperforms growth, rewarding those who identify stocks with lower price-earnings ratios and other metrics that suggest they’re undervalued. Another factor is momentum, in which stocks that are already outperforming market averages continue to do so.

There’s a lot of statistical and, perhaps more important, behavioral support for these strategies,” Mr. Hougan said. “You’ll find plenty of two- or three-year stretches where this will underperform, but if you buy and hold, it’s going to add value. We’ve seen value outperform for over 80 years. And Wealthfront is blending five factors that should smooth out and reduce those periods of underperformance.”

Read the full article on the New York Times

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. In some ways, their meteoric rise is due to the same technological advances that are disrupting most industries. Faster computers and better data has enabled asset managers to automate skills that once were limited to market legends. The diversity of quant strategies, however, makes it hard to generalize about the group. Categories include factor investing, risk parity and managed futures — not to mention secretive black-box funds, like Renaissance Technologies LLC. Even fundamental traders now arm themselves with quantitative techniques, accounting for $55 billion of systematic assets, according to Barclays. Read the full article on Bloomberg