Web Scrapping with Python
Budget: $30 – $250 USD
I am an entrepreneur researching AI methods to identify trading patterns in stocks, creating a user friendly app built in Django that will produce daily summaries based on information collected from the web.
As one of my experiments I am trying to analyze the daily stock analysts upgrades and downgrades:
The task is as follows:
- Create a python procedure inside a django app that can run daily
- This routine should access the following URL: https://www.marketbeat.com/ratings/us/
- It should not be discovered and/or blocked so it should simulate a real browser.
- It has to scrap the Analyst rating table and produce a pandas dataframe with the following information:
- Date
- Company Ticker or Symbol
- Company Name
- Action
- Brokerage
- Analyst
- Current Price (not including the % value shown in the website)
- Original Price Target
- New Price Target
- Rating
It should provide the previously mentioned pandas dataframe and a status code indicating if it was successful or if it failed, if failed it should add the message received by the original sender.
As one of my experiments I am trying to analyze the daily stock analysts upgrades and downgrades:
The task is as follows:
- Create a python procedure inside a django app that can run daily
- This routine should access the following URL: https://www.marketbeat.com/ratings/us/
- It should not be discovered and/or blocked so it should simulate a real browser.
- It has to scrap the Analyst rating table and produce a pandas dataframe with the following information:
- Date
- Company Ticker or Symbol
- Company Name
- Action
- Brokerage
- Analyst
- Current Price (not including the % value shown in the website)
- Original Price Target
- New Price Target
- Rating
It should provide the previously mentioned pandas dataframe and a status code indicating if it was successful or if it failed, if failed it should add the message received by the original sender.