CloudQuant algo trading blog covers topics for quantitative traders

Python Plotly Candlestick Chart with annonations

Candlestick Charts in Python with Plotly

Some traders are visually oriented. They need charts. As data scientists, we need to be able to present information in a way that others can understand. Presenting traders a candlestick chart is one of the best ways to transfer useful data. Blog Purpose: ✅ Demonstrate how to create a basic candlestick chart in Python 3 ✅ Demonstrate how to highlight/annotate points on the chart Topics covered in this post: Python, Plotly, OHLC, Candlestick Charts, Jupyter, Pandas, Traders

Web Scraping a Stock Symbol's URL using Yahoo Finance with Python for Alternative Data Links

Blog

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)

Fixing Python Memory Leaks

Blog

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.

Jessica Darmoni's Review of the 34th Futures Industry Association Expo

Blog, Industry News

The Futures Industry Association (FIA) held their 34th Annual FIA Expo conference at the Hilton Chicago last week bringing together capital markets influencers, regulators and innovators to discuss some of the most pressing matters facing the industry.

SMB Quant Video 1 - Developing a Model

Blog

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

Blog

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.

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