atoti

California wildfires and solar irradiance

California wildfires are increasingly intense. Understanding how these wildfires impact everything from air quality to solar irradiance is crucial. In January 2020, California mandated solar panel usage in new construction. Even before 2020, and certainly since, solar panel adoption increased year after year. But at the same time, California experienced...

Is ClickHouse really that fast? a friendly comparison with atoti

A Value-at-Risk benchmark where atoti appears consistently five times faster than ClickHouse for this use case. As data volumes and market complexity have increased, the financial services industry is ever more reliant on technologies that can quickly and seamlessly help them perform data analytics. Performing calculations on hundreds of millions...

Using Twitter to forecast cryptocurrency returns #3 – A time series analysis using VAR

Find out which features can forecast the returns with Granger Causality Test If you have followed my first article, it’s tough scraping Twitter for the sentiment analysis. Here are my initial thoughts:  A Tweet can be super positive but has no impact if there are no followers on the TweetA...

Using Twitter to forecast cryptocurrency returns #1 – How to scrape Twitter for sentiment analysis

80/20 data science dilemma: tough working on Twitter sentiment When my manager brought up the idea of forecasting Cryptocurrencies’ returns with Twitter sentiment, I immediately performed a Google search on how I can lay my hands on the tweets and the cryptocurrencies. Information seems to be abundant and readily available...

Market risk analytics in python: Interactive rolling VaR

The Recipe for Stressed VaR calibration In this post, I want to illustrate how to create an analytical application with atoti and Python that can help to visualize and interactively slice-and-dice the impact of increasing volatility on the Value-at-Risk (VaR) metrics of an investment portfolio. This might be particularly interesting...

Automated data sampling for fast application modeling

Having a snappy, interactive experience is very important when modeling an atoti application. In order to provide such an experience, atoti can automatically sample your data during the modeling phase, and seamlessly load the full data set when publishing the application to its users. Photo by Guillaume Jaillet on Unsplash...