News covering: Social Market Analytics Sentiment Data for Forex; The Mosaic Effect with Big Data and AltData; Success as a Data Scientist; Data Ops Workbench; Turkish Volatility Showed Value of Alt Data
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 data is so valuable the also developers. We all start using the […]
Bayesian Network is a probabilistic graphical model which comprises variables and its relationships. It uses Bayesian inference and learning to develop the algorithm.
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.
Sonal Gupta is an MBA graduate student at Case Western Reserve University in Ohio. She has five years experience in leading software development teams, product development and consulting engagements. She has the ability to analyze large volumes of data and generating actionable insights. We asked her what her experience has been like as a female in data science and invite you to read her response below.
The financial sector is making a massive shift towards machine intelligence in capital markets. This panel shares their experience in using data science and domain expertise in understanding data context.
“It’s exciting to see the growing number of women in Science, Technology, Engineering and Math (STEM); my advice is to not be afraid to jump in headfirst,” said Sarah Leonard, graduate student at the University of Chicago. “It is a difficult field but also lucrative and rapidly growing.”
Leonard sat down with CloudQuant to talk about her experiences in data science, her insight as a female in a male dominated world, and the intensive process it took to find her dream job.
Moderated by Jessica Titlebaum Darmoni from the Title Connection, Slade was joined by Brian Peterson, Algorithmic Trading Lead at DV Trading, Inderdeep Singh from CME Group’s Innovation Lab and Matthew Dixon, Assistant Professor of Finance and Statistics at Illinois Institute of Technology.
If you knew your trading strategy would work 50% of the time, would you commit your scarce savings to trade it? What if it worked 75% of the time? Backtesting gives one the confidence to know that your trading strategy will work.
CloudQuant Thoughts: 80% of trading is being handled by robots? We know that 100% of trades are touched by automation these days. If it isn’t in the actual order processing then it is in the clearing process, the risk process, or the account management process. When someone using a retail broker’s website sees their portfolio there is added information that is presented. Almost all of that is touched by automation with some form of a “robot” or AI process.