Articles

Real-Time Stock Data Using Marketplace’s API

,

In this time of global economic turmoil, it’s more important than it’s ever been that your financial decisions are based on accurate, up-to-date, market information.

In a world where stock price is a key confidence marker, the businesses that attract attention, secure investment, and grow, are the ones that can demonstrate their value in a wider market.

Up to now, displaying accurate market pricing has been prohibitively expensive, needing direct access to a huge dataset, and the code to mine it. So we’re delighted to introduce marketstack, a real-time market data API that’s reliable, simple to integrate with your site or app, is lightning fast, and includes a free-forever plan.

What is marketstack?

marketstack is a REST API that allows you to access stock data for public companies at 72 global exchanges including the New York Stock Exchange, the Nasdaq, the Tokyo Stock Exchange, and the London Stock Exchange.

marketstack delivers real-time market data, accurate to a single minute, ensuring that the information you base your decisions on, and the information you pass on to your customers, is always up to date.

There are more than 125,000 stock tickers, from over 50 different countries; you can query stocks, or over 75 different market indices; intraday market data is included, meaning you can monitor trades that close at the end of the day; you can even retrieve data about time-zones and international currencies.

Why Choose marketstack

marketstack uses cutting-edge technology to deliver market data in an easy-to-integrate JSON format, which is lightweight and incredibly easy to dig into.

Requests are made via a simple HTTP GET call, and all requests are run through bank-quality 256-bit HTTPS encryption. Whatever code stack you’re using, whether it’s PHP, Python, Node, or plain old JavaScript, marketstack provides comprehensive documentation to get your team up to speed in mere minutes.

The highly reliable cloud infrastructure can handle anything from a few dozen requests per year, all the way up to millions of requests per day. Regardless of the scale of your project, marketstack is robust and flexible enough to handle it.

It’s Not Just About Money

marketstack isn’t just about the bulls and bears of markets, in the tech sector specifically, stock price is an indicator of wider business trends and performance.

When Apple became the first US company to reach a $2 trillion valuation, not only was its stock price central to the story, but it indicated a trend in the tech giant’s dominance that went beyond cold hard cash.

When an eccentric billionaire makes outrageous, and ill-judged comments, and tanks his company’s valuation, the fluctuations in stock price are a big part of the story.

Beyond the spin of politicians, the market index of different exchanges is an indication of what analysts with in-depth knowledge really think during an election campaign.

With over 30 years of historical data, marketstack is a history of business, particularly the burgeoning tech sector, and makes that history available with a simple to use API.

marketstack’s Rock Solid API

marketstack’s API is built on top of apilayer technology, one of the most respected, and trusted API providers in the world, with a huge amount of experience delivering data reliably. Millions of API requests can be run through the API hourly, and it still has almost 100% uptime.

Any API is only as good as the data it supplies, and marketstack’s data is supplied by numerous high-authority providers around the globe, resulting in unprecedented accuracy.

As a result, marketstack is trusted by over 30,000 companies — including Microsoft, Amazon, Uber, and Credit Suisse — and 80+ universities.

Getting Started with marketstack

marketstack is entirely free for up to 1000 requests per month, with access to 1 year of historical data, as well as end-of-day data. No credit card is required to get started, and you’ll never be billed. This is the perfect option for simple integrations, or developers working on proof-of-concept builds. What we really like about marketstack is that the free package is genuinely usable. It’s not just a trial version that presses you into upgrading.

For anyone who needs more comprehensive data, packages that include market indices, technical support, and commercial use permissions, start at $9.99 per month, or just $7.99 per month when billed annually.

Head over the marketstack today to claim your free API key, and get started.

 

[– This is a sponsored post on behalf of marketstack –]

Source


Source de l’article sur Webdesignerdepot

/par

Decision Tree Classifier Python Code Example

,

In this post, you will learn about how to train a decision tree classifier machine learning model using Python. The following points will be covered in this post:

  • What is decision tree?
  • Decision tree python code sample

What Is a Decision Tree?

Simply speaking, the decision tree algorithm breaks the data points into decision nodes resulting in a tree structure. The decision nodes represent the question based on which the data is split further into two or more child nodes. The tree is created until the data points at a specific child node is pure (all data belongs to one class). The criteria for creating the most optimal decision questions is the information gain. The diagram below represents a sample decision tree.

Source de l’article sur DZONE

/par

Deploying a Django Application to AWS EC2 Instance With Docker

,

In AWS, we have several ways to deploy Django (and not Django applications) with Docker. We can use ECS or EKS clusters. If we don’t have one ECS or Kubernetes cluster up and running, maybe it can be complex. Today, I want to show how deploy a Django application in production mode within a EC2 host. Let’s start.

The idea is create one EC2 instance (one simple Amazon Linux AMI AWS-supported image). This host doesn’t initially have Docker installed. We need to install it. When we launch one instance, when we’re configuring the instance, we can specify user data to configure an instance or run a configuration script during launch.

Source de l’article sur DZONE

/par

Python 101 – Creating Multiple Processes

,

Most CPU manufacturers are creating multi-core CPUs now. Even cell phones come with multiple cores! Python threads can’t use those cores because of the Global Interpreter Lock. Starting in Python 2.6, the multiprocessing module was added which lets you take full advantage of all the cores on your machine.

In this article, you will learn about the following topics:

Source de l’article sur DZONE

/par

Fake News’ Foe: Machine Learning and Twilio

,

Fake news has become a huge issue in our digitally-connected world and it is no longer limited to little squabbles — fake news spreads like wildfire and is impacting millions of people every day.

How do you deal with such a sensitive issue? Countless articles are being churned out every day on the internet — how do you tell real from fake? It’s not as easy as turning to a simple fact-checker which is typically built on a story-by-story basis. As developers, can we turn to machine learning?

Source de l’article sur DZONE

/par

Parallel Grid Search in H2O

,

H2O is, at its core, a platform for distributed, in-memory computing. On top of the distributed computation platform, machine learning algorithms are implemented. At H2O, we design every operation, be it data transformation, training of machine learning models, or even parsing to utilize the distributed computation model. In order to work with big data fast, it’s necessary.

However, a single operation usually can not utilize clusters’ computational resources to the very maximum. Data needs to be distributed across the cluster, and many operations require sequential execution of tasks, which, even if implemented in a distributed manner, follow after each other and require data exchange. These and many other smaller factors, if summed up together, may introduce a significant overhead.

Source de l’article sur DZONE

/par

Building a Recommendation System Using Deep Learning Models

,

In this article, I am going to explain how we integrate some deep learning models, in order to make an outfit recommendation system. We want to build an outfit recommendation system. We used four deep learning models to get some important characteristics of the clothing used by the user.

The recommendation systems can be classified into 4 groups:

Source de l’article sur DZONE

/par

La Création De Valeur À Partir Des Données De L’Automatisation

,

Selon l’ 2019 Gartner quatrième rapport annuel de Chief Data Officer (CDO) de l’Enquête, la mise en œuvre d’une base de données et d’analyse de la stratégie a été classé comme le troisième plus important facteur de succès quand il s’agit d’un CDO de l’organisation.

Quand il s’agit de données, nous sommes tous conscients des quatre « Vs » — variété, de la vitesse, de la véracité et de volume – pourtant, pour de nombreuses organisations, leur entreposage de données de l’infrastructure n’est plus équipés pour y faire face. En outre, la valeur, la cinquième « V », est encore plus insaisissable. Donc, en tenant compte de l’ampleur de données que de nombreuses entreprises modernes ont signifie que répondre à ces défis exige une nouvelle approche de l’automatisation en cours de la fondation.

Source de l’article sur DZONE

/par

Python 3.8 New Features — Code Name Walrus

,
He chonk

A brief and practical guide to the most important/interesting new features/changes coming with Python 3.8 (code name – walrus – joking :) ). So, first let’s announce the news:

Python 3.8 was released on October 14th, 2019.

Source de l’article sur DZONE

/par

Book review – Supercharged Python, by Brian Overland and John Bennet

,

If you have been following beginner or even intermediate guides on Python and are starting to feel the need for more advanced learning; this book may be the one you have been looking for.

According to the authors, this book was written for those who already know the basics of Python but want to deepen their knowledge and skills. While being targeted to people who already know the fundamentals of Python, it still includes a quick review in the first chapter. It briefly discusses fundamentals like variables, operators, data types, basic I/O, if/else, while, for, function definitions and arguments, lists, tuples, dictionaries, sets, and the distinction between global and local variables. This initial chapter is presented as being an optional reading, as its contents are pretty basic, but the authors recommend that the reader takes a minute or so on the last to pages, which cover the global statement.

Source de l’article sur DZONE

/par