Automating Data Extraction using Pandas, yfinance and FRED

Unlocking Financial Insights with Automation: Extracting Data using yfinance and FRED

In today's data-driven world, accountants and financial professionals are no strangers to the vast amounts of information available on the internet. From stock prices to economic indicators, staying informed about financial markets and trends is crucial for making informed decisions. However, manually collecting and processing this data can be time-consuming and prone to errors. This is where the power of automation comes into play, revolutionizing the way accountants work and leverage financial information.

One of the tools that have gained popularity among financial professionals is the yfinance library. yfinance provides a seamless way to extract historical financial data from sources like Yahoo Finance. With just a few lines of code, accountants and financial analysts can access critical financial information and gain valuable insights.

Imagine a scenario where an accountant needs to analyze the historical stock prices of a portfolio of companies. Instead of tediously searching and entering data, yfinance automates the process. 

In the google colab below, the accountant or financial analyst defines the list of ticker symbols and the desired date range. The yf.download function does the heavy lifting by fetching historical stock data for the specified companies. The extracted data can then be used for analysis, visualization, and reporting.

The implications of this automation extend beyond just efficiency. With the rise of robotic process automation (RPA), accountants are embracing technology to streamline tasks, reduce errors, and free up time for strategic decision-making. Automation tools like yfinance align with the trend towards data-driven insights that guide financial strategies.

In a landscape where information is currency, accountants armed with automation tools are better equipped to navigate complex financial markets. By harnessing the power of libraries like yfinance, they can effortlessly gather, process, and analyze data, all while focusing on the high-value tasks that require human expertise. This convergence of accounting and automation opens the door to new opportunities and empowers professionals to make well-informed decisions in a rapidly changing financial world.

As the digital era continues to shape the financial landscape, accountants embracing automation tools like yfinance are not only streamlining their workflows but also positioning themselves as strategic partners in the financial decision-making process.

Yahoo finance API

Yahoo finance API can be exploited to retrieve information about stocks. Stock Ticker Lookup is extremely practical for obtaining stock tickers (abbreviations for stocks or other traded assets) here https://finance.yahoo.com/lookup/

Stock tickers can be used to expedite the retrieval of data. The following steps can be applied. Go to Yahoo Finance. Enter a quote into the search field. Then select a quote in the search results to view it. Then copy ticker which can be used in the python environment. A few  packages can be set set to run this in python: 1 pandas_datareader and 2. yfinance. Also, it may be important to update datareader:

pip install --upgrade pandas-datareader

Also the Federal Reserve of St Louis also provides a large number of macroeconomic Time Series - these can be retrieved using 

pdr.get_data_fred

import pandas_datareader as pdr

Robotic Process Automation (RPA) and Financial Data Visualization

Robotic Process Automation (RPA) and data visualization are two powerful techniques that can significantly enhance financial data analysis. Combining these techniques with the pandas_datareader library, which accesses the FRED (Federal Reserve Economic Data) database, can lead to improved insights and streamlined workflows in finance.

Robotic Process Automation (RPA)

RPA involves automating repetitive tasks using software robots, freeing up human resources for higher-level activities. When applied to financial data, RPA can be executed with the pandas_datareader library to automatically retrieve and process economic indicators from the FRED database. RPA accelerates decision-making and reduces errors by processing data automatically.

Data Visualization

Data visualization is vital for conveying insights from complex financial data. The pandas_datareader library, in combination with visualization tools like matplotlib, can create powerful visualizations. These visualizations provide clear comparisons of economic indicators over time, aiding stakeholder understanding.

Combining RPA, data visualization, and the pandas_datareader library empowers financial professionals to automate tasks, make informed decisions, and communicate insights effectively. This integration enhances financial operations in a dynamic landscape.

Open a QuantEcon Github Python notebook directly in Google Colab

Python facilitates the querying of online databases programmatically. An important database for Economists and Financial Analysts  is FRED Federal Reserve Economic Data  — that has in excess of 800,000 economic time series. They cover banking, business/fiscal, consumer price indexes, employment and population, exchange rates, and many economic time series. These are compiled by the Federal Reserve and many are collected from US government agencies such as the U.S. Census and the Bureau of Labor Statistics. 

An example, we consider below in google colab is the  unemployment rate. Using FRED, the entire series for the US civilian unemployment rate can be downloaded directly by URL link (note that this requires an internet connection). Here, we use pandas python code from QuantEcon.

https://python-programming.quantecon.org/pandas.html

QuantEcon develops  open source software for economic/financial modeling critical to building open, collaborative platforms for sharing. Development is largely based on open source scientific computing environments such as Python, R and Julia. Here we show how to open a  QuantEcon Github Python notebook directly in Google Colab.

https://github.com/QuantEcon/quantecon-notebooks-python

https://github.com/QuantEcon/quantecon-notebooks-python/blob/master/pandas.ipynb