Quantitative Trading

Quantitative trading utilizes trading strategies based on quantitative analysis to systematically identify trading opportunities and to execute trades as identified. A Quant trader may work for the buy side or sell side of the trading industry. Buy side quants are looking to trade for investment purposes. These trades typically seek to make a profit from either short-term price movements (alpha) or from longer-term investment returns (beta.) Sell side quants provide quantitative trading in their brokerage activities. The sell side algos are typically accumulation type strategies (i.e. volume weighted average pricing – VWAP) that help investors get the best price for their overall order.

Quantitative trading techniques include high-frequency trading, sentiment analysis trading, and statistical arbitrage.

Quantitative trading is not synonymous with High-Frequency Trading (HFT) even though all HFT firms employ some form of algorithmic trading.

CloudQuant utilizes crowd researchers to provide the quantitative analysis that is then used by our quantitive traders.

Posts

Quantitative Strategy, Trading, and Algo Development Industry News

Finding Novel Ways to Trade on Sentiment Data

It’s no secret that hedge fund managers are always looking for new sources of data that will help them in their never-ending quest to beat the market. Quantitative researchers at Bloomberg have been developing innovative methods to help reveal embedded signals in one of the more popular sources of unconventional financial data: sentiment analysis of news stories and social posts. “Everyone is looking into alternative data sets, sometimes without really understanding their value,” says Dr. Arun Verma, Ph.D., a researcher who leads the Quant solutions team within Bloomberg’s Quantitative Research group, which is headed by Bruno Dupire. “They are looking at data like sentiment, supply chain relationships, and even things like satellite imagery. Often Machine Learning methods are applied to optimize alpha from such data, but a lack of scientific rigor can lead to poor out of sample performance. We avoid the trap of extreme data mining by using robust statistics.” Read the full story on Tech at Bloomberg, June 14 2017 Alternative Data sets from Bloomberg for social sentiment making its way into algorithmic trading. At Cloudquant have been using it in our backtesting with the intention of improving quantitative trading strategies.     
Quantitative Strategy, Trading, and Algo Development Industry News

Discretionary Managers Seek Alpha in Alternative Data

Alternative data providers see huge potential in providing their data to discretionary asset managers who are losing assets to quantitative and systematic funds.

As active managers trail the performance of passive index funds and exchange-traded funds (ETFs), discretionary fund managers are scrambling to consume big data analytics into their decision making process.
While early movers in the big data analytics industry have mainly been quant hedge funds and systematic fund managers, the next wave is going to be discretionary fund managers, according to panelists at an event sponsored by Wall Street Horizon, EstimizeOTAS Technologies and FlexTrade Systems.
Read the full story on Traders Magazine Online
Quantitative Strategies and Capital for Trading

Citigroup taps quant trader Thomas Chippas

According to MarketWatch: By  Published: June 2, 2017 1:13 p.m. ET
Citigroup Inc. has tapped a quantitative trading veteran to help the bank vault into that hot category, as part of a broader build-up of its equities unit.
Thomas Chippas will join as global head of quantitative execution, the bank said Friday. Mr. Chippas previously led quant-trading units at Barclays PLC and Deutsche Bank AG, before leaving for jobs beyond Wall Street, most recently as chief operating officer of blockchain-technology startup Axoni Inc. Read the full article on MarketWatch
Quantitative Strategy, Trading, and Algo Development Industry News

Quants Run Wall St. Now?

From LinkedIn by  May 30, 2017
We were sort of confused when we noticed the Wall Street Journal is doing an entire series on what they’re calling “the quants,” including the lead article title “The Quants Run Wall Street Now.” Now? Are they not familiar with Scott Patterson’s 2010 book “The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It.” Mr. Patterson was, after all, a staff reporter of… wait for it… the Wall Street Journal. We get it, algorithms and quants and machine learning and roboadvisors and all the rest are the latest buzz words in a financial world increasingly comfortable with the concept of computers running things, from self-driving cars to algorithm-driven shopping recommendations to self-adjusting thermostats. There’s also the fact that “Quant hedge funds” are responsible for 27% of all U.S. stocks traded these days, just slightly behind individual investors at 29%, and now comfortably ahead of such trading by “other hedge funds” and traditional asset managers. Read the full post on LinkedIn
Stock Market, Quantitative Strategy, Trading, and Algo Development Industry News

Machine learning set to shake up equity hedge funds – Financial Times

AI seen becoming powerful enough to forecast market moves better than humans

Financial Times May 25, 2017 by: Lindsay Fortado and Robin Wigglesworth
Machine learning poses a threat to equity hedge funds within the next decade as the technique becomes powerful enough to forecast market moves better than humans, one of the earliest investors in the industry is forecasting. Jeff Tarrant, the founder of Protégé Partners, says that the model of hedge funds charging “2 and 20” — a 2 per cent management fee and 20 per cent performance fee — for investing in large-cap stocks rising and falling “doesn’t work any more” and is ripe for disruption. He pointed to the overhaul of other industries in the past decade at the hands of engineers and scientists. “Jeff Bezos picked off the bookstore business. Apple totally picked off the music business and Netflix totally changed television. Now [machine learning] is going to pick off the hedge funds.”
Read the full story on Financial Times
quantitative algo trading conceptual dashboard

Hedge funds and VCs are throwing money at ex-Bridgewater data scientists’ startup

If you aren’t being recruited because you didn’t work at Bridgewater, maybe it is time to create your own hedge fund operation by creating an algo on CloudQuant. EFinancialCareers is reporting that hedge funds and VCs are throwing money at ex-Bridgewater data scientists’ startup. “Banks and hedge funds used to want to hire quantitative analysts, frequently PhDs in engineering, math, statistics or a hard science such as physics or chemistry. But the main problem financial services firms are facing right now is the data itself. It’s messy, unstructured and still difficult to use for alpha opportunities.” Read the full article on EFinancialCareers.com  
Morgan Slade at Benzing with FintechTV

Benzinga Awards Interview of Morgan Slade

CloudQuant’s own Morgan Slade spoke with Fintech TV on our incubator for trading strategies during the Benzinga Global Fintech Awards. http://bit.ly/2rLqp6W
Quantitative Strategies and Capital for Trading

Funds Face ‘Alt’ Data Challenge

MarketsMedia by By Rob Daly on 5/18/2017
Although alternative data sets are helping funds with systematic investment strategies, those funds that employ discretionary strategies are finding it harder to separate the new trading signals from the noise.

Much of it comes from the structure of the discretionary funds, which have separated their data science/quant research teams away from their portfolio managers, according to Leigh Drogen, CEO of Estimize and who participated in an alternative data panel hosted by Wall Street Horizon.

“They are left sending reports and Excel spreadsheets to the portfolio managers and asking them to buy in with P&L,” he said. “It’s almost like they were a sell-side shop.”

Even if a managing partner and head quant are convinced that new data sets can capture further alpha, the portfolio managers have to buy into it.

Read the full story on MarketsMedia
 
quantitative algo trading conceptual dashboard

An Incubator for Trading Strategies

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GLG Partners has hired a head of machine learning. Financial News reports that this is a new role entirely. The article points out that the volume of data that GLG is utilizing is growing. A similar article reports that BlackRock is putting a greater emphasis on computer models.
Kershner Trading, parent of CloudQuant

Kershner Trading Announces the Formation of CloudQuant

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November 1, 2016

Chicago, Illinois

For Immediate Release

Kershner Trading Group, LLC announces the formation of CloudQuant®, a wholly owned subsidiary.

CloudQuant is:

  • an educator,
  • a facilitator for traders, technologists, and data scientists
  • a technology provider,
In short, CloudQuant is the Trading Strategy Incubator which will allow the trader, software engineer, data scientist or individual with an great trading idea to receive funding for an algorithm that has been tested and proven in backtesting.

CloudQuant began as an internal trading tool for traders with extraordinary trading ideas to be able to explore quantitative trading with the evolution of data science. The technology behind our innovative approach to incubating trading strategies began as an internal system and has been proven through daily use within the Kershner Trading Group. Kershner’s experience as a trading firm focused every day on the work of operating and executing profitable quantitative trading strategies provided the trading and technology expertise to design a platform that enables researchers to go from idea to production implementation in a matter of hours.

CloudQuant’s mission is to provide you with tools to develop and prove your trading strategies. We then license your strategy from you. Once licensed we assign capital from our funds to your strategy. Our professional traders, risk managers, and technologists oversee the running of the trading strategy.

When a client’s trading strategy makes money the client will be paid.  Loses are the responsibility of CloudQuant.