Alternative Data is growing as a necessary weapon for traders and quantitative investors. Yet there are many barriers to alternative data success. (Includes python code)
A few of our power users reported that long-running backtests would sometimes run out of memory. These power-users are the people who often find new trading strategies and so we wanted to work with them to improve the performance of our backtesting tools. Over the past couple of weeks, our senior engineer found that the problem wasn’t in our code, but in one of the popular Python libraries that we use.
We found the problem in numpy and numba.
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 […]
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.
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.
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
On May 15th Trevor Trinkino presented part two of a three-part Machine Learning webinar with FXCM. Part one is here. Part 2 – Preprocess data for Random Forest. PnL and prediciton improvements… In part two Trevor goes over how to clean and pre-process data from CloudQuant to use in a Random Forest Classifier. He then looks at the […]
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On February 8th Trevor Trinkino presented Machine Learning with FXCM in a webinar.
During this presentation, he promised to make available his machine learning Python Notebook and the supporting data file. These are available on our Google drive at:
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.