CloudQuant algo trading blog covers topics for quantitative traders

SMB Quant Video 1 - Developing a Model

Jeff Holden develops algo trading models at SMB Capital in New York. He utilizes CloudQuant extensively during his research and development process. In this video he details his process for developing an idea into a model.

SMB Quant Video 2 - Democratization of Trading - CloudQuant

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In video two of SMB's new Quant series Paul Tunney discusses why you should use CloudQuant to develop an algorithmic trading model.

Algo Trading powered by Alternative Data Sets

Conversations: Effects of Alternative Data Sets on Trading Algorithms

Blog, Press

What effect can alternative data sets have on trading algorithms? We asked a few of our teammates and systematic traders what the effect of alternative data sets is on trading algos. We thought we could spread some insight as to why our alternative…

Roles Played by Bayesian Networks in Machine Learning

Blog

Bayesian Network is a probabilistic graphical model which comprises variables and its relationships. It uses Bayesian inference and learning to develop the algorithm.

Conversations: Recommendations to Someone Starting out at CloudQuant

Blog, CloudQuant Press, Press

The CloudQuant team discusses their helpful thoughts for beginners on CloudQuant. We want to boost everyone starting out on our platform in their algo development and backtesting. Everyone in our company uses the CloudQuant website and coding platform in one way or another. We all use our own application, just like the crowd researchers. When we say that our free backtesting tools are "institutional grade" we really mean it. Every algo we run in our trading and investment strategies is proven in the same backtesting engine as the crowd uses. We rely on the scorecards, the reports, and the simulated trades to ensure that our trading is successful.

Conversations: Learning Python within CloudQuant

Blog

What was your experience like learning Python within CloudQuant? We asked our portfolio managers and product management teammates who code in Python to explain their starting experiences in programming with Python with CloudQuant. We wanted to share with everyone what encouraged them to keep learning throughout the years. Everyone here codes as part of their job. This includes the CEO all the way down to the interns. We rely on our Backtesting Engine to ensure that trading algorithms work well before committing money to the automated trading strategies. But we also use JupyterLab in our daily work. We generate our reports, monitor our systems, and do all sorts of tasks in Python. Python has overtaken the spreadsheet in CloudQuant.

Conversations: What we wish we knew when we started AlgoTrading

Blog, CloudQuant Press, Press

CloudQuant's portfolio managers and quantitative algo traders look back on their starts in Algorithmic Trading. This candid overview allows everyone to see the "Things We Wish We Knew When We Started AlgoTrading". This is a short collection of the interviews with some of our amazing coders here in the office

$FB Decline 19%- What did the Social and technical analysis show? - July 26 2018

Blog

$FB's 19% drop was preceded by TA-LIB and Social Market Analytics indicators to sell. The $120 Billion drop in market cap could have been an opportunity to short sell before the market close the previous night.

Google Wants to Dominate AI in 2018. Here's Why

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