How Do We Make A Simple Python Implementation That Will Help You Draw And Draw Financial Data (All Important Metrics And Ratios You Can Think Of)?

Stock market analysis and good investment (for long-term growth) require careful scrutiny of financial data. Various metrics and ratios are often used in this analysis, ie it is necessary to evaluate the investability or current situation of an Investment Product.
For example, Price Above Earnings or ratio of PE. It is the annual earnings / share ratio of the stock price.
Or, book value per share. It is obtained by dividing the ratio of the common equity of a company by its outstanding shares. When a share is low in value, it will have a higher book value per share relative to the current stock price in the market.
Capturing and Analyzing Financial Data with Python

Usually such data can be obtained from websites such as Yahoo Finance. However, you cannot download or scrape data programmatically unless you are using some kind of paid, registered service like Finage Stock API.
However, there are many microservices that provide this type of data through a simple API call. To take advantage of this, in this article, we'll show you how to write a simple Python class script to interface with a financial data microservice.
With this Python class, you can pull data with almost all important financial metrics and ratios and build a Pandas DataFrame by calling a number of simple methods.
We also offer simple graph methods (bar graph and scatter charts) to analyze data graphically. Note that you need to get your own secret API key (free) from the website and save after instantiating the class object. However, let's examine the Python package / class and the various methods that come with it.
Python class and various built-in methods
Core Python class in my Github repository. Feel free to star and fork and improve the repo. You can clone the repository and start using the script in your own Notebook.
To keep the code clean, in this article, we demonstrate the use of the class in a test Jupyter notebook.
We start by importing the normal libraries and class object.
Let's create a data dictionary now:
For all methods in this class, we have to pass the company's ticker symbol (in the US financial market). For Apple Inc this is 'AAPL'.
If we examine this glossary, we will note that a large amount of data is received from the API endpoint. Below is a partial screenshot.
Creating a DataFrame with data from multiple companies
Working with Python dictionaries is fine, but for large-scale data analysis we should consider building a Pandas DataFrame. We provide a built-in method to do this. Creating a DataFrame is as easy as transmitting a list of simpler symbols, and the code does all the data scraping and configuration work for you.
Let's say we want to download all financial data for the companies below,
Sales team
A beautifully formatted DataFrame is ready to use!
What types of data are already available?
We can easily examine the data types taken from the API service. Remember, we pass the 'profile', 'metrics' or 'ration' argument and retrieve the corresponding list of data items.
Drawing - visual analysis
We added code for simple visual analysis with data in the package.
It is often useful to examine various metrics and financial ratios in simple bar charts. To do this, simply type in the name of the variable you want to draw. You can also add the usual Matplotlib keyword arguments such as color and transparency (alpha).
You can also draw simple scatter charts to visually analyze the relationships between financial metrics.
You can also pass a third variable that will be used to scale the size of the markers in the scatter chart. This helps, indirectly, to visualize more than two variables in a 2D graph. For example, we pass the share price parameter as the third variable in the code below.
Custom analysis with Finage Stock API
Often times, investors may want to create their own filters and investment logic with available data.
For example, considering only companies with a market capitalization of over $ 200 billion, and then corporate value over EBIDTA in the bar chart.

We can access the underlying DataFrame, create a custom DataFrame, and then assign this custom DataFrame to a new object to take advantage of the financeAPI () ready graphics methods.
That way, we won't have to request data from the API again. Avoid reading data as much as possible due to the limitation of the number of data read with the free API key.


Stock Market News API

News API Get breaking news headlines and search for articles from news sources and blogs all over the web with our News API, it is a simple and easy to use API that currently returns JSON metadata for headlines and articles live all over the web. Everything is cached asynchronously for super fast response. If you're in the Free Development stage for development, start a direct trial.

Get JSON results with simple HTTP GET requests. Finage News API has become an integral element that allows us to deliver relevant and timely political news to our users and allow them to instantly act to communicate with their representatives using call scripts created based on the articles they read.

Historical and Real-Time Data Access with Finage

Its strong backend and high performance make Finage a recognized financial data provider. Finage WS (FWS) provides high performance by keeping the engine alive.

Finage provides VPS (Virtual Private Server) for subscribers to any of the WebSocket packages. This means that you have the chance to make the changes you want on your own server and you can control it as you wish. For example, you can check total connections, server CPU, and more.


After your WebSocket subscription, Finage generates two keys for you. These;

ADDRESS_KEY: This key is used to create your own personal address. For example, if your ADDRESS_KEY is "abcd1234", you will have a WebSocket address such as to create a connection.


SOCKET_KEY = This key is like an API_KEY. You must use this key to establish a connection on your server. In addition, the FWS engine checks the IP address of the incoming connection if it is not whitelisted and then rejects the connection. You can create a link with your application by creating an address with these two keys.

After creating the link, you can create the symbols you want thanks to the command codes that will be provided to you with Finage. You can also subscribe to multiple symbols at the same time and easily unsubscribe.

One of the privileges you will get with Finage is that it has open, high, low, closed (OHLC) and volume values, so it can be added to your response and your response can be tailored to your needs.


Finage packages have 15 years of historical US Stock data as standard. However, you can access 30 years of US stock data by customizing your package according to your professional needs. By defining Finage's historical API to your account after your subscription, you can see it on the control panel and use it easily.

With Finage, you have the freedom to customize, edit and create your own rules on the system without any coding or extra charge. You can always get help with the expert technical team.

You can start to use Finage Stock Market News API today.