Resources for developers using Python for scientific computing and quantitative analysis
In this article, we’ll use some basic machine learning methods to train a bot to play cards against me. The card game that I’m interested in is called Literature, a game similar to Go Fish.
The version of Literature that we implemented is roughly similar to the rules I linked above. Literature is played in two teams, and the teams compete to collect “sets.” A set is a collection of either A – 6 of a suit or 8 – K of a suit (7’s are not included in the game).
The purpose of this article is to introduce the reader to some of the tools used to spot stock market trends.
We will utilize a data set consisting of five years of daily stock market data for Analog Devices. The time period we consider starts on January 1, 2013 and ends on December 31, 2017. We will start analyzing the data using line plots, then introduce candlestick charts. Patterns that can be seen in the candlestick chart will be introduced which can be used to spot changes in the market. We add another of level analysis by overlaying moving averages and discussing how these can help confirm trend changes. Finally, we construct a figure that concisely summarizes the stock price data for any company.
An introduction to running parallel tasks with Celery, plus how and why we built an API on top of Celery’s Canvas task primitives.
One of the technology goals of Zymergen is to empower biologists to explore genetic edits of microbes in a high throughput and highly automated manner. The Computational Biology team at Zymergen is responsible for building software to help scientists design and execute these genetic edits. (For a brief overview, see our Zymergen 101 tutorial).
In this tutorial you will learn how to use OpenCV to stream video from a webcam to a web browser/HTML page using Flask and Python.
