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

Scorecard

Backtest Visualization on CloudQuant

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The Quantitative Strategy Backtest ScoreCard is saving time for crowd researchers who are able to visualize the results of multi-day backtests quickly, even as the backtest is running.
chicago cityscape and sears tower

Built in Chicago: Wanna try your hand at high-frequency trading? There's an app for that

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Built in Chicago discusses CloudQuant, a Chicago-based algorithmic trading startup, lets anyone try their hand at devising their own strategies.
John "Morgan" Slade

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

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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.
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,
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, ...
Daily ROIC Prior to improvements

Improving A Trading Strategy

TD Sequential is a technical indicator for stock trading developed by Thomas R. DeMark in the 1990s. It uses bar plot of stocks to generate trading signals. ... Several elements could be modified in this strategy. Whether to include the countdown stage, the choice of the number of bars in the setup stage and countdown stage, the parameters that help to decide when to exit and the size of the trade will affect strategy performance. In addition, we could use information other than price to decide whether the signal should be traded.
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, ...
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.