Scrape Financial Data
Web scraping for financial statements with Python — 1
ProcessingHere is a simple trick you can flexibly adjust the stock symbol and plug it into the URL link. It will come in handy later if you want to extract hundreds of company’s financial statements. # Enter a stock symbolindex= ‘MSFT’# URL link url_is = ‘’ + index + ‘/financials? p=’ + indexurl_bs = ‘’ + index +’/balance-sheet? p=’ + indexurl_cf = ‘’ + index + ‘/cash-flow? p=’+ indexNow we have the URL link saved. If you manually open them on a Web browser, it will look like the URLNext, we just need to open the link and read it into a proper format called lxml. Simple ad_data = ur. urlopen(url_is)() soup_is= BeautifulSoup(read_data, ’lxml’)Well, if you open soup_is, it will look like a mess because the elements were originally in HTML format. All elements are systemically arranged in how do know which classes the relevant data are stored in? After a few searches, we know that they are stored in“div”, we can create an empty list and use a for loop to find all elements and append them to the [] # Create empty listfor l in nd_all(‘div’): #Find all data structure that is ‘div’ () # add each element one by one to the list ls = [e for e in ls if e not in (‘Operating Expenses’, ’Non-recurring Events’)] # Exclude those columnsYou will find that there are a lot of “none” elements in ls because not all “div” has an element. We just need to filter those w_ls = list(filter(None, ls))And now it looks like we take a step further and start reading the list starting in the 12th w_ls = new_ls[12:]Well, now we have a list. But how do we turn it into a data frame? First, we need to iterate 6 items at a time and store them in tuples. However, we want a list so that the pandas library can read it into a data _data = list(zip(*[iter(new_ls)]*6))Perfect, that is exactly what we want. Now, we just have to read it into a data come_st = Frame(is_data[0:])Sweet. It is almost done. We just need to read the first row as the column and the first column as the row index. Here are some lumns = [0] # Name columns to first row of dataframeIncome_st = [1:, ] # start to read 1st rowIncome_st = Income_st. T # transpose lumns = [0] #Name columns to first row of ([0], inplace=True) #Drop first index = ‘’ # Remove the index (index={‘ttm’: ‘12/31/2019’}, inplace=True) #Rename ttm in index columns to end of the yearIncome_st = Income_st[lumns[:-5]] # remove last 5 irrelevant columnsAfter using the same techniques for the Income statement, balance sheet and cash flow, your Data Frames should look like the StatementBalance SheetCash flow statementAfter doing a transpose on the DataFrames, DateTime is turned into a row index and the features become column are some afterthought questions:How are the features correlated to the stock price of a company? How do you find out if they are correlated? If so, which time period of stock price are related to the features from our financial statements? What else can you do with the extracted data on developing algorithmic trading model? Feel free to leave your answers and comments below at the response to check if you can come up with some unique you for reading this article, feel free to share it if you find it useful. Here is another article on utilizing the new DataFrame and do further financial accounting analysis with Python.
3 Ways to Scrape Financial Data WITHOUT Python | Octoparse
Financial market is a place of risks and instability. It’s hard to predict how the curve will go and sometimes, for investors, one decision could be a make-or-break move. That’s why experienced practitioners never lose track of the financial data.
We human beings are wired to see in short term. Unless we have a database with data in well structure, we are not able to get a handle on voluminous information. Data scraping is the solution that gets complete data at your fingertip.
Table of Contents
What We Are Scraping When We Scrape Financial Data?
Why Scrape Financial Data?
How to Scrape Financial Data without Python
Let’s get started!
When it comes to scraping financial data, stock market data is in the spotlight of attention. But there’s more, trading prices and changes of securities, mutual funds, futures, cryptocurrencies, etc. Financial statements, press releases and other business-related news are also sources of financial data that people will scrape.
Financial data, when extracted and analyzed in real time, can provide wealthy information for investments and trading. And people in different positions scrape financial data for varied purposes.
Stock market prediction
Stock trading organizations leverage data from online trading portals like Yahoo Finance to keep records of stock prices. This financial data help companies to predict the market trends and buy/sell stocks for the highest profits. Same for trades in futures, currencies and other financial products. With complete data at hand, cross-comparison becomes easier and a bigger picture manifests.
Equity research
“Don’t put all the eggs in one basket. ” Portfolio managers do equity research to predict the performance of multiple stocks. Data is used to identify the pattern of their changes and further develop an algorithmic trading model. Before getting to this end, a vast amount of financial data will involve in the quantitative analysis.
Sentiment analysis of financial market
Scraping financial data is not merely about numbers. Things can go qualitatively. We may find that the presupposition raised by Adam Smith is untenable – people are not always economic, or say, rational. Behavioral economics reveals that our decisions are susceptible to all kinds of cognitive biases, plainly, emotions.
Using the data from financial news, blogs, relevant social media posts and reviews, financial organizations can perform sentiment analysis to grab people’s attitude towards the market, which can be an indicator of the market trend.
If you are a non-coder, stay tuned, let me explain how you can scrape financial data with the help of Octoparse. Yahoo Finance is a nice source to get comprehensive and real-time financial data. I will show you below how to scrape from the site.
Besides, there are lots of financial data sources with up-to-date and valuable information you can scrape from, such as Google Finance, Bloomberg, CNNMoney, Morningstar, TMXMoney, etc. All these sites are HTML codes in nature, which means that all the tables, news articles, and other texts/URLs can be extracted in bulk by a web scraping tool.
To know more about what web scraping is and what it is used for, you can check out this article.
There are 3 ways to get the data:
Use a web scraping template
Build your web crawlers
Turn to data scraping services
In order to help newbies get an easy start on web scraping, Octoparse offer an array of web scraping templates. These templates are preformatted crawlers ready-to-use. Users can pick one of them to pull data from respective pages instantly.
The Yahoo Finance template offered by Octoparse is designed to scrape the Cryptocurrency data. No more configuration is required. Simply click “try it” and you will get the table data in minutes.
In addition to Cryptocurrency data, you can also build a crawler from scratch in 2 steps to scrape world indices from Yahoo Finance. A customized crawler is highly flexible in terms of data extraction. This method is also workable to scrape other pages from Yahoo Finance.
Step 1: Enter the web address to build a crawler
The bot will load the website in the built-in browser, and one click on the Tips Panel can trigger the auto-detection process and get the table data fields done.
Step 2: Execute the crawler to get data
When your desired data are all highlighted in red, save the settings and run the crawler. As you can see in the pop-up, all the data are scraped down successfully. Now, you can export the data into Excel, JSON, CSV, or to your database via APIs.
3. Financial data scraping services
If you are scraping financial data from time to time in a rather small amount, help yourself with handy web scraping tools. You may find joy in building your own crawlers. However, if you are in need of voluminous data for a profound analysis, say millions of records, and have a high standard of accuracy, it is better to hand your scraping needs to a group of reliable web scraping professionals.
Why data scraping services deserve?
Time and energy-saving
The only thing you would bother is to convey clearly to the data service provider what data you want. Once this is done, the data service team will deal with the rest of all, no hassle. You can plunge into your core business and do what you good at. Let professionals get the scraping job done for you.
Zero learning curve & tech issues
Even the easiest scraping tool takes time to master. The ever-changing environment in different websites may be hard to deal with. And when you are scraping on a large scale, you may encounter issues such as IP ban, low speed, duplicate data, etc. Data scraping service can free you from these troubles.
No legal violations
If you are not paying enough attentions to the terms of service of the data sources you are scraping from, you may get yourself into trouble. With the support of experienced legal counsel, a professional web scraping service provider works in accordance with laws and the whole scraping process will be implemented in a legitimate manner.
Read more:
Cryptocurrency Market Analysis with Web Scraping
Scrape information from Yahoo Finance
Scrape Stock Info from Bloomberg
Video: Web Scraping | Cryptocurrency Market
Author: Milly
Edited by Cici
Scraping – Financial Dictionary
Dr Fiona Mattatall, an obstetrician gynaecologist in Calgary, Canada, tweeted saying she “would not recommend a vaginal cleanse that includes scraping of the walls (even to rid traces of an ex). “Evaluation of corneal scraping smear examination methods in the diagnosis of bacterial and fungal keratitis: a survey of eight years of laboratory scraping involves capturing data from a legacy application in order to display it using a more modern user interface, but it could be used to gather business intelligence or to steal data. “Web scraping is growing at an alarming rate and if left unchecked, it is just a matter of time until all sites with useful content will be targeted by competitors harvesting data, ” said Robert Kane, CEO of erefore, it is particularly suitable for scraping quality inspection what this paper cording to Wainwright, they are also found in some common reef fish that feed by scraping trogen can be generated on the spot, stored, transported to our site and used for the scraping and mothballing process, all in the same up your wallpaper scrapings as you go along because once dry, they set hard and can stick like glue to floors and other ‘s when other countries stopped bowing and scraping to unelected heads of state: USA 1776, France 1792, Portugal 1910, China 1912, Russia low-cost airline Ryanair (LSE:RYA) has reported that the Hamburg Courts have upheld a previous decision, made by the Regional Court of Hamburg, to grant the carrier an injunction preventing Vtours from “scraping” Ryanair’s website to resell its flights. I continued scraping, but all of a sudden, I slipped forward again, and I knew I was going to fall this time.
Frequently Asked Questions about scrape financial data
Where can I scrape financial data?
Besides, there are lots of financial data sources with up-to-date and valuable information you can scrape from, such as Google Finance, Bloomberg, CNNMoney, Morningstar, TMXMoney, etc.Jan 25, 2021
What is scrape in finance?
scrape. A slang expression for a property with improvements that should be bulldozed because they detract from the value of the land. Example: “That old shopping center is a scrape.” Flashcards & Bookmarks ?
Is it OK to scrape data?
You can use scraped data for anything It is perfectly legal if you scrape data from websites for public consumption and use it for analysis. However, it is not legal if you scrape confidential information for profit.Aug 16, 2021
